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This Week

- Animal research is not always king: researchers should explore the alternatives

- Structure peer review to make it more robust

- Severe turbulence ahead — how scientists can keep air travellers safe in a warming world

EDITORIAL 16 July 2024

Animal research is not always king: researchers should explore the alternatives

Technological advances can reduce the numbers of laboratory animals used in studies — but they need to be carefully validated.

Key differences between the immune responses of humans and mice make mice poor models for treating diseases such as sepsis in humans.Credit: Philippe Merle/AFP/Getty

It’s been more than a year since US President Joe Biden signed a law allowing researchers to test drug candidates on human tissue or computer models before moving to trials in people. This challenged a regulatory dogma, accepted since the 1930s, that vaccines and drugs need to be tested on one rodent species and one non-rodent, such as a primate.

Researchers such as Ofer Levy, a vaccinologist at Boston Children’s Hospital in Massachusetts, rejoiced. Levy has long advocated that studies in human tissue can, in some cases, be more informative than those done on animals. But the number of researchers who don’t know about the law, which was signed in December 2022, stuns him: “I mention it, and they look at me like I fell from the sky,” he says.

Animal studies are still important and often necessary, especially in the latter stages of vaccine and drug development. Moreover, researchers and funders need to be cautious and establish a way to carefully validate disease models based on human tissue. The changed mandate for the US Food and Drug Administration (FDA) is a reminder for researchers to reduce their reliance on animals, particularly in early-stage research.

Global data on animals in research are incomplete, but there are signs that the number of scientific procedures performed on live animals is falling in some countries. According to UK government data for example, 2.76 million such procedures were performed in the country in 2022. This is the lowest number since 2002, and markedly lower than the 4.14-million peak in 2015.

What, then, are the alternatives? Induced pluripotent stem cells — which have been ‘reprogrammed’ so that they can turn into any cell type — offer a way to generate human cells from skin or blood samples. Organoids, 3D cultures that can comprise multiple cell types, take such Petri-dish systems to the next level. They allow researchers to evaluate the interplay between cell types and the importance of their spatial organization on drug responses. Researchers are innovating in other ways, too. Small tissue samples can yield a wealth of data, which means human biopsies taken using very fine needles, which minimize the risk of harm, can be used for such studies.

Such approaches will never completely replace animal research. An organoid is not a full substitute for a mouse when evaluating, for example, how an experimental drug is absorbed and metabolized throughout the body. But these techniques allow for more initial hypotheses to be based on human-tissue responses to the environment or a disease, rather than beginning with observations of the animal model and looking for parallels in humans.

An important application has been in immunology. Although much has been learnt about the human immune system by studying mice, there are key differences between biological processes in the two species[1], so a particular mouse immune response might not be replicated in a human. For example, differences in how mice respond to infection have generally made them poor models of sepsis, a life-threatening condition in which the body’s reaction to infection can damage organs. As a result, treatments for sepsis developed in rodents have translated poorly to humans[2].

Even among humans, responses to vaccines can vary by age, sex and geographical region in ways that are often difficult to model using animals[3]. And it is difficult to discover the molecular basis of longevity in a human centenarian while rifling through the genome of a rodent that reaches old age within two years. Around 90% of therapies tested in early-stage clinical trials fail to make it to market — a failure rate thought to result partially from a heavy reliance on animal models in preclinical research. Cancer cures in mice rarely translate to approved drugs in humans.

And then there are animal-rights concerns. Researchers must have a strong justification for using animals in their studies, and even then, there are rules limiting their use. For example, according to NIH guidance, it is inhumane to keep a mouse alive in a lab after some of the painful and debilitating consequences of ageing begin to take hold.

Experiments involving animals can also take an emotional toll on the researchers who carry out the work. And studies on captive primates raise extra ethical concerns, particularly when the work is outsourced to countries with relatively few restrictions on primate research.

Replacing animals in studies with human tissue brings its own challenges, which need to be mitigated if the practice is to become widespread. Correlations found in human-tissue biopsies, for example, might require subsequent experiments in animals to establish causality. Conducting such studies in humans could be complex, expensive and ethically fraught.

And just because a technique is based on human cells, that doesn’t make it necessarily superior. Funders and researchers will need to invest in the validation of these methods, by comparing results of computer predictions or tests using organoids with the results of clinical trials of the same experimental drug. Even Levy emphasizes that animals are also crucial for parts of his team’s research. “We still make use of animal models — just 70–80% less than the average research group,” he says.

The FDA policy allowing non-animal data to be used to support drug approvals needs to be much better communicated and amplified by the agency, funders and research institutions. And more researchers need to embrace the change. The outcome will be more-rigorous evaluation of experimental medications, and more-meaningful results.

Nature 631, 481 (2024)

doi: https://doi.org/10.1038/d41586-024-02314-8

References

1. Mestas, J. & Hughes, C. C. W. J. Immunol. 172, 2731–2738 (2004).

2. Fink, M. P. Virulence 5, 143–153 (2014).

3. van Dorst, M. M. A. R. et al. Nature Rev. Immunol. 24, 250–263 (2024).

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WORLD VIEW 16 April 2024

Structure peer review to make it more robust

Everyone who reviews a manuscript should answer a transparent set of questions, to ensure that scientific literature is subject to reliable quality control.

By

Mario Malički

In February, I received two peer-review reports for a manuscript I’d submitted to a journal. One report contained 3 comments, the other 11. Apart from one point, all the feedback was different. It focused on expanding the discussion and some methodological details — there were no remarks about the study’s objectives, analyses or limitations.

My co-authors and I duly replied, working under two assumptions that are common in scholarly publishing: first, that anything the reviewers didn’t comment on they had found acceptable for publication; second, that they had the expertise to assess all aspects of our manuscript. But, as history has shown, those assumptions are not always accurate (see Lancet 396, 1056; 2020). And through the cracks, inaccurate, sloppy and falsified research can slip.

As co-editor-in-chief of the journal Research Integrity and Peer Review (an open-access journal published by BMC, which is part of Springer Nature), I’m invested in ensuring that the scholarly peer-review system is as trustworthy as possible. And I think that to be robust, peer review needs to be more structured. By that, I mean that journals should provide reviewers with a transparent set of questions to answer that focus on methodological, analytical and interpretative aspects of a paper.

For example, editors might ask peer reviewers to consider whether the methods are described in sufficient detail to allow another researcher to reproduce the work, whether extra statistical analyses are needed, and whether the authors’ interpretation of the results is supported by the data and the study methods. Should a reviewer find anything unsatisfactory, they should provide constructive criticism to the authors. And if reviewers lack the expertise to assess any part of the manuscript, they should be asked to declare this.

Other aspects of a study, such as novelty, potential impact, language and formatting, should be handled by editors, journal staff or even machines, reducing the workload for reviewers.

The list of questions reviewers will be asked should be published on the journal’s website, allowing authors to prepare their manuscripts with this process in mind. And, as others have argued before, review reports should be published in full. This would allow readers to judge for themselves how a paper was assessed, and would enable researchers to study peer-review practices.

To see how this works in practice, since 2022 I’ve been working with the publisher Elsevier on a pilot study of structured peer review in 23 of its journals, covering the health, life, physical and social sciences. The preliminary results indicate that, when guided by the same questions, reviewers made the same initial recommendation about whether to accept, revise or reject a paper 41% of the time, compared with 31% before these journals implemented structured peer review. Moreover, reviewers’ comments were in agreement about specific parts of a manuscript up to 72% of the time (M. Malički and B. Mehmani Preprint at bioRxiv https://doi.org/mrdv; 2024). In my opinion, reaching such agreement is important for science, which proceeds mainly through consensus.

I invite editors and publishers to follow in our footsteps and experiment with structured peer reviews. Anyone can trial our template questions (see go.nature.com/4ab2ppc), or tailor them to suit specific fields or study types. For instance, mathematics journals might also ask whether referees agree with the logic or completeness of a proof. Some journals might ask reviewers if they have checked the raw data or the study code. Publications that employ editors who are less embedded in the research they handle than are academics might need to include questions about a paper’s novelty or impact.

Scientists can also use these questions, either as a checklist when writing papers or when they are reviewing for journals that don’t apply structured peer review.

Some journals — including Proceedings of the National Academy of Sciences, the PLOS family of journals, F1000 journals and some Springer Nature journals — already have their own sets of structured questions for peer reviewers. But, in general, these journals do not disclose the questions they ask, and do not make their questions consistent. This means that core peer-review checks are still not standardized, and reviewers are tasked with different questions when working for different journals.

Some might argue that, because different journals have different thresholds for publication, they should adhere to different standards of quality control. I disagree. Not every study is groundbreaking, but scientists should view quality control of the scientific literature in the same way as quality control in other sectors: as a way to ensure that a product is safe for use by the public. People should be able to see what types of check were done, and when, before an aeroplane was approved as safe for flying. We should apply the same rigour to scientific research.

Ultimately, I hope for a future in which all journals use the same core set of questions for specific study types and make all of their review reports public. I fear that a lack of standard practice in this area is delaying the progress of science.

Nature 631, 483 (2024)

doi: https://doi.org/10.1038/d41586-024-01101-9

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Competing Interests

M.M. is co-editor-in-chief of the Research Integrity and Peer Review journal that publishes signed peer review reports alongside published articles. He is also the chair of the European Association of Science Editors Peer Review Committee.

WORLD VIEW 16 July 2024

Severe turbulence ahead — how scientists can keep air travellers safe in a warming world

From weakening jet streams to causing bumpier flights, climate change is altering atmospheric behaviour. Researchers need to find out how.

By

Haoxuan Yu

Climate change is not just leading to more heatwaves and hurricanes — it’s making the atmosphere more turbulent, too. As I found out while flying from Kuala Lumpur to London a few weeks ago, sudden jolts from bumpy air can send your coffee flying. Riding jet streams can feel like being on a roller coaster.

Air turbulence has been in the news because dozens of passengers on a Singapore Airlines flight in May were injured and one person died when the aeroplane lost 54 metres of altitude within seconds over Myanmar. Those passengers were not just unlucky; the frequency of such events is rising, because global warming is making the air stormier.

As a researcher in fluid dynamics and climate change, I can assure you that flying remains one of the safest modes of travel. But I also know that the changing patterns of air turbulence need much more research. Scientists need to understand how climate change is altering air pressure, buoyancy and currents. Pilots need tools that can predict and pinpoint areas of extreme turbulence, which is often invisible and can hit out of the blue.

Whereas storm clouds are obvious and can be flown around, ‘clear-air turbulence’ is invisible to the eye and to conventional radar. Climate change is increasing the severity and frequency of such atmospheric disturbances. For example, between 1979 and 2020, the frequency of severe clear-air turbulence rose by 55% over the United States and North Atlantic Ocean (M. C. Prosser et al. Geophys. Res. Lett. 50, e2023GL103814; 2023).

Global warming is altering horizontal and vertical temperature gradients in the atmosphere, affecting atmospheric stability and patterns of air movement (S. H. Lee et al. Nature 572, 639–642; 2019). For example, air turbulence often occurs around jet streams — fast air currents driven by temperature differences between the Equator and poles. Sudden changes in wind speed and direction, known as wind shear, cause bumpiness. Because polar regions are warming faster than tropical ones, climate change is reducing the temperature gradients that cause jet streams, which alters their behaviour.

Vertical temperature gradients are also affected by climate change. Greenhouse gases trap heat in the lower layers of the atmosphere and the stratosphere cools. Larger temperature gradients might lead to more frequent and intense convection, resulting in stronger storms. Planes near areas with strong convection might encounter more severe turbulence than elsewhere, making it feel as if your flight is navigating through a stormy sea.

Climate change is also causing gravity, buoyancy, pressure, inertia and spin to interact in the atmosphere in increasingly complex ways. For example, as Earth warms, atmospheric oscillations known as gravity waves become more frequent and intense. These waves can create turbulence by generating strong vertical wind shear. They also interact with the jet stream and other atmospheric forces, leading to more unpredictable weather patterns. Understanding such dynamic changes is crucial for developing predictive models and strategies to ensure aviation safety.

Action in three areas is needed. First, scientists can use computer simulations of Earth’s atmosphere to gain a deeper theoretical understanding of the mechanisms of air turbulence and how they are affected by climate change. The increased frequency of turbulence-related incidents provides case studies and data to refine the models.

Second, researchers need to develop technologies for detecting and forecasting air turbulence. For instance, a technology called lidar — radar using lasers instead of radio waves — is able to detect clear-air turbulence that radar cannot. However, current systems are large and heavy. More-compact and cost-effective lidar systems are urgently needed by the aviation industry. Satellites and meteorological technologies can also provide real-time data on atmospheric conditions over wide areas. These data can be fed into simulations to model turbulence along an aircraft’s path in real time. By integrating these tools and technologies, researchers can develop robust and reliable predictive models.

Third, artificial intelligence (AI) can be used to optimize predictive models. Training AI algorithms on vast data sets of air turbulence, including meteorological data, aircraft sensor readings, historical flight records, pilot reports, weather radar and lidar data, would enable them to spot subtle changes and complex patterns in turbulence, enhancing the accuracy of predictions. Using AI systems would also speed up computations, potentially making real-time turbulence predictions possible. To incorporate AI, researchers need to develop algorithms that can handle the complexity of atmospheric data, improve the accuracy and speed of simulations, and create real-time predictive systems that can be applied to enhance safety and efficiency in aviation.

Like the rest of the planet, the skies are telling a story of change: the invisible hand of global warming is stirring the atmosphere in unexpected ways. The Singapore Airlines incident must be a wake-up call. The skies might be stormier, but with effort and ingenuity, researchers can help flyers to find smoother and safer air ahead.

Nature 631, 484 (2024)

doi: https://doi.org/10.1038/d41586-024-02315-7

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Competing Interests

The author declares no competing interests.

News in Focus

- Serious errors plague DNA tool that’s a workhorse of biology

- ITER delay: what it means for nuclear fusion

- Iran elects heart surgeon as president: scientists are hopeful

- If bird flu sparks a human pandemic, your past immunity could help

- Stars hint at an unusual black hole lurking in our Galaxy

- How anti-obesity drugs cause nausea: finding offers hope for better drugs

- This lab-grown meat probably tastes like real beef

- How PhD students and other academics are fighting the mental-health crisis in science

NEWS 10 July 2024

Serious errors plague DNA tool that’s a workhorse of biology

Researchers analysed thousands of laboratory-made plasmids and discovered that nearly half of them had defects, raising questions of experimental reproducibility.

By

Katherine Bourzac

Plasmids (shown here in a coloured transmission electron micrograph with various genes highlighted) are circular DNA structures used in biology laboratories.Credit: Dr Gopal Murti/SPL

Laboratory-made plasmids, a workhorse of modern biology, have problems. Researchers performed a systematic assessment of the circular DNA structures by analysing more than 2,500 plasmids produced in labs and sent to a company that provides services such as packaging the structures inside viruses so they can be used as gene therapies. The team found that nearly half of the plasmids had design flaws, including errors in sequences crucial to expressing a therapeutic gene. The researchers posted their findings to the preprint server bioRxiv last month ahead of peer review[1].

The study shines a light on “a lack of knowledge” about how to do proper quality control on plasmids in the lab, says Hiroyuki Nakai, a geneticist at Oregon Health & Science University in Portland who was not involved in the work. He was already aware of problems with lab-made plasmids, but was surprised by the frequency of errors uncovered by the study. There are probably many scientific papers that have been published for which the results are not reproducible owing to errors in plasmid design, he adds.

Wasted time

Plasmids are popular tools in biology labs because bacteria, including the widely used model organism Escherichia coli, use the structures to store and exchange genes. This means that biologists can make designer plasmids containing various genes of interest, and then coax E. coli to take them up and make lots of copies.

Bruce Lahn, chief scientist at VectorBuilder, a company based in Chicago, Illinois, that provides gene-delivery tools, says that he and other biologists have been noticing problems with plasmid quality for years. For example, when Lahn was a professor at the University of Chicago, a graduate student in his lab spent six months trying to reproduce two plasmids that had been reported in the scientific literature. “We didn’t think twice about the quality of the plasmids, but then the experiment wouldn’t work” because the plasmids contained errors, he says.

Now at VectorBuilder, Lahn says he’s faced with the issue all the time — so he decided to evaluate it systematically. When customers submit error-laden plasmids, “it ends up wasting a lot of time”, and the extra steps involved in doing quality control add to the cost of producing the plasmids and packaging them into viruses, he says.

The VectorBuilder team’s analysis found a hodgepodge of errors in the more than 2,500 plasmids it evaluated. Some contained genes that coded for proteins toxic to E. coli, which means that they could slow or stop the growth of the organisms biologists rely on to replicate their plasmids. Others, destined for packaging into viruses, encoded proteins toxic to those viruses. And some contained repetitive DNA sequences that can accumulate mutations inside plasmids.

Checking for errors

The most rampant errors Lahn and his colleagues found were related to a key gene-therapy tool. Therapies are often packaged into adeno-associated viruses (AAVs), which are mostly harmless and can ferry treatments to cells. When making the plasmids for these AAVs, researchers sandwich a therapeutic gene between sequences called ITRs, which play a crucial part in ensuring that the gene gets packaged into the virus for delivery. In essence, these sequences send a biological signal to cells that says “I belong in this virus”. But the team found that about 40% of the AAV plasmids in the study had mutations in the ITR regions that could garble this important message. If researchers were to use these misdesigned plasmids, their gene therapy might not work — and it could take the scientists a long time to find out why.

Mark Kay, a paediatrics and genetics specialist at the Stanford School of Medicine in California, has also seen at first hand that plasmid errors can delay lab projects. But he’s confident that scientists can spot and fix these errors. He says that gene-therapy researchers are familiar with potential ITR issues, and that errors are unlikely to lead to problems in clinical settings. That’s because regulatory agencies such as the US Food and Drug Administration have stringent standards that require researchers to carefully analyse their plasmids before using them in the clinic.

Nakai says checking plasmids for errors by sequencing them could alert researchers to the problems highlighted in the study. A few companies, including Plasmidsaurus in Eugene, Oregon, and Elim Biopharmaceuticals in Hayward, California, offer plasmid sequencing for about US$15.00 per sample, says Nakai, who has no financial interest in either company. He also recommends that new lab members spend time learning from experienced plasmid constructors; it’s a tedious, artisanal process, he says, but if you get it wrong, it can waste a tremendous amount of time and money.

Another way for labs to avert issues is by publicly sharing their plasmid sequences in open-access repositories, says Melina Fan, chief scientific officer of the non-profit organization Addgene in Watertown, Massachusetts. Addgene provides one such repository, Fan says, and it “sequences the deposited plasmids and shares the sequence data via its website for community use”. Verification of plasmids is important, she adds.

Lahn hopes that his team’s analysis will draw researchers’ attention to the fact that these workhorse lab tools are often taken for granted. “The health of the tool is not something people question,” he says, even though they should.

Nature 631, 487-488 (2024)

doi: https://doi.org/10.1038/d41586-024-02280-1

References

1. Bai, X. et al. Preprint at bioRxiv https://doi.org/10.1101/2024.06.17.596931 (2024).

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NEWS 08 July 2024

ITER delay: what it means for nuclear fusion

The world’s biggest fusion-energy experiment is likely to be beaten to its goals by other projects — but the massive reactor still has value, say scientists.

By

Elizabeth Gibney

A module being assembled at the international nuclear fusion project ITER in Saint-Paul-lez-Durance in southern France. Credit: Nicolas Tucat/AFP via Getty

The world’s flagship project to prove the viability of fusion energy has announced a four-year delay to its major experiments, pushing them back to 2039, at a cost of more than US$5 billion. The ITER experiment, which is sponsored by governments around the globe, now looks unlikely to be the first facility of its kind to achieve the milestone known as net gain — the creation of more energy from a reaction than is directly put into it. But physicists say that the project remains essential to building the foundations of a future fusion industry.

“The delay sounds dramatic, but within the physics communities I don’t think it’s going to have too much of an impact,” says Rachael McDermott, a plasma physicist at the Max Planck Institute for Plasma Physics in Garching, Germany. “ITER is still going to be extremely relevant and extremely important, no matter when it comes.”

Convincing funders of that could prove challenging. The project, which began construction near Saint-Paul-Lez-Durance in France in 2010, originally aimed to start up by 2016 and perform the first experiments validating fusion power in 2020. Including contributions of goods and services, funders have already stumped up around $22 billion for the project. Now private fusion firms say they expect to reach ITER’s goals before the public experiment is even switched on.

Part of ITER’s value lies in the project’s potential to share its experiences with private firms. One silver lining of the delay might be that it prompts ITER to engage more with industry, says Melanie Windridge, a plasma physicist and the chief executive of Fusion Energy Insights, a company in London that tracks developments in fusion energy. “It’s kind of forcing ITER to say, we can’t hold on to all this knowledge until the end of the project, because there are private companies coming up that need this now,” she says.

ITER also has an important role in creating supply chains and fusion industries in its member countries, she adds. “It shouldn’t be seen as us and them,” she says. “Everyone is aiming for the same goals.”

Viable energy source

Fusion experiments such as ITER aim to harness the phenomenon that powers the Sun, whose energy comes from the fusion of hydrogen atoms. Replicating this process on Earth could provide an almost inexhaustible source of clean energy, but it is challenging to create the conditions for fusion and to harvest its output.

In 2022, scientists at the US National Ignition Facility in Livermore, California, created a ‘burning plasma’, in which fusion was sustained by heat from the reaction rather than external sources. In doing so, it became the first to achieve net gain, creating more energy from fusion than was used to initiate the reaction. The facility did this using lasers, a different technique from ITER’s. But no one has yet achieved one of ITER’s main goals — creating a long-lived, burning plasma that delivers ten times as much heat as is directly put in — widely seen as a demonstration that fusion can become a viable energy source.

Delays to the project — a collaboration between China, the European Union, India, Japan, Russia, South Korea and the United States — have been no secret. Over the decades, it has been plagued by a string of hold ups, cost overruns and management issues. In 2014, the project’s outgoing director-general, Osamu Motojima, told Nature that ITER would not survive if its start-up date slipped to even 2025, let alone 2034.

The COVID-19 pandemic hampered collaboration, and corroding parts and inconsistencies between components have necessitated considerable repairs to equipment. “I don’t think anybody expected it all to go together perfectly, but I’m not sure we anticipated the inconsistencies,” says McDermott.

Fast track to research

ITER’s current director-general, Pietro Barabaschi, who was appointed in 2022, presented details of the updated timeline at a meeting of the project’s decision-making council on 20 June and briefed journalists on 3 July. Barabaschi painted the delay as a chance to rejig ITER’s plans in light of recent developments in fusion energy. The project’s initial switch-on will be pushed back by nine years, from 2025 to 2034. But the plan is now to skip a “rather symbolic” initial phase and get “as fast as possible to real research”, he said.

This means using a more complete machine from the start, with ITER’s ‘tokamak’ — which uses magnets to squeeze super-heated plasmas of hydrogen isotopes into a doughnut shape — reaching full strength in 2036, just three years behind the previous schedule. Full operation of the reactor has been delayed by four years, from 2035 to 2039, when it will use the heavy forms of hydrogen, deuterium and radioactive tritium, as fuel. The new plans include using a new material — tungsten — for the fusion-facing wall, because it erodes less readily than the beryllium originally planned.

But the revised schedule will cost around an extra €5 billion (US$5.4 billion), funding that is yet to be confirmed by member states. Asked about how funders are likely to react, Barabaschi said, “We will have to wait and see.” He added, “My personal impression is that there is still very strong support from the members on this project.”

The United States, at least, will probably be able to honour its obligations to ITER, thanks to the Department of Energy adding a 50% contingency to its budget in 2018, says Carlos Paz-Soldan, a plasma physicist at Columbia University in New York.

Private push

Private fusion efforts are now likely to achieve many of the technical milestones ITER was intended to reach first, thanks to investment and advances in physics and materials science, says Brandon Sorbom, a researcher in superconducting magnets and co-founder of Commonwealth Fusion Systems (CFS), a spin-off firm that emerged from the Massachusetts Institute of Technology (MIT) in Cambridge.

Private firms, which drew $1.4 billion in investment worldwide in 2023, are bullish about their plans. In a 2023 survey, 65% of companies predicted that a fusion plant will be delivering electricity to the grid by 2035. But Barabaschi is sceptical about this. Even if the viability of fusion was proved today, “I don’t believe we would be in a position to have it commercially deployed by 2040”, he said. “There is a big gap between having, say, a process proven and then to deploy it and make it commercially viable.”

McDermott thinks that the SPARC reactor — a compact version of the tokamak technology being built by MIT and CFS in Devens, Massachusetts — is likely to be the project that beats ITER to net gain. But fusion scientists also argue that ITER, as an experiment, is designed to do things that commercial firms are not. Many facets of fusion physics depend on size, and ITER’s massive dimensions make it a unique test bed for plant-scale physics, McDermott says. ITER will also provide physicists’ first chance to study how large numbers of fast-moving helium nuclei, produced by fusion reactions, interact over long timescales to create a burning plasma.

Future problems

ITER’s research also aims to solve problems that fusion power plants will one day face, but that many private firms are not yet taking seriously enough, says McDermott. These include testing ways to use the neutrons that emerge from fusion to ‘breed’ more tritium fuel, a scarce resource, and studying how materials get damaged in the harsh conditions inside the reactor. Eventually, the aim is for ITER to create ten times more power during fusion than goes into heating the plasma, but there is no plan for ITER to use this power to generate electricity, and calculations for net gain include only direct heat, not other sources of energy that go into the experiment.

Windridge says that ITER is becoming increasingly open to sharing the knowledge gained from publicly funded research with companies. The project’s first public–private workshop in May was busier than organizers had anticipated. “That’s a testament to how important the work of ITER has been,” she says.

If a private company achieved a sustained burning plasma at a fraction of ITER’s size, cost and complexity, that could change funders’ dedication to ITER, says Paz-Soldan. “I do think the value proposition for ITER would require re-evaluation if this were to occur,” he says. “But I do not think now is the appropriate time to have this conversation.”

Nature 631, 488-489 (2024)

doi: https://doi.org/10.1038/d41586-024-02247-2

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NEWS 09 July 2024

Iran elects heart surgeon as president: scientists are hopeful

Researchers say unexpected victory for Masoud Pezeshkian could spell improvements to human rights, investment in science and academic freedom — if Iran’s top leaders give him space to act.

By

Michele Catanzaro

Masoud Pezeshkian: "Protest is the university's right. A society that does not protest is dead.” Credit: Majid Saeedi/Getty

Iran has elected a former heart surgeon as its next president. Scientists expect Masoud Pezeshkian, who ran the nation’s health ministry in an earlier administration, to revive universities and reconnect the country’s isolated scientists with their international counterparts. His term could also usher in improvements to human rights, investment in science, greater academic freedom and the revival of talks on the country’s nuclear programme. But not everyone agrees that change will come or be lasting.

“He is a son of the higher-education system of Iran [and] likely to be a good advocate of science,” says Moneef Zou’bi, former director-general of the Islamic World Academy of Sciences, based in Amman. “His mere presence there will be quite inspirational for university presidents and top researchers,” adds Zou’bi, who studies Middle East science policy.

Others, however, note that lasting reform in Iran will be hard to achieve in the present geopolitical climate and while Iran remains a theocracy headed by a clerical supreme leader, Ali Khamenei, who wields both religious and political authority. The government also appoints university leaders and vets professors.

The presidential elections took place over two rounds, after Iran’s former president, Ebrahim Raisi, died in a helicopter crash in May. The early elections came two years after students and academics at the country’s universities were part of nationwide protests by the Woman, Life, Freedom movement. The demonstrations were sparked by the death of 22-year-old Mahsa Amini in police custody, who had been arrested for allegedly violating Iran’s compulsory headscarf laws.

Pezeshkian won in the second round of voting on 5 July on a platform to kick-start the country’s ailing economy and take a softer line on students and academics involved in political dissent. He also says he wants Iran to enter new negotiations with the international community, in hopes of relieving sanctions against the country and restarting talks on its nuclear programme.

International sanctions have contributed to dire economic conditions and a cost-of-living crisis, says Hossein Akhani, a systematic-botany researcher at the University of Tehran. The country has systemic inflation, which peaked at more than 45% earlier this year but is now starting to fall. “The elections bring hope to the [scientific] community. This is a first step towards a change,” he says.

Pezeshkian trained as a heart surgeon and worked as a battlefield medic before becoming president of Tabriz University of Medical Sciences in 1994. Between 2001 and 2005, he ran the country’s health ministry under a previous reformist administration, that of president Mohammad Khatami.

Ali Gorji, a neuroscientist at the University of Münster in Germany, who is originally from Iran, is hopeful about his election win. “Given his background, he may potentially spur initiatives aimed at increasing scientific funding, upgrading research infrastructure, and fostering international collaborations in scientific disciplines.”

‘Protest is the right of students’

At an elections hustings event at Tehran University on 27 June, Pezeshkian emphasized that headscarves should not be imposed by force, and he acknowledged that students and academics should have the right to protest without facing violent repression.

“We make women and girls hate our beliefs, and this is a disaster,” he is quoted as saying in a report from the event, published by the Islamic Republic News Agency, based in Tehran. “Protest is the right of students and employees. Protest is the university’s right. A society that does not protest is dead,” he added.

“I will change the disciplinary regulations and the way of dealing with students and professors. Someone who is a scientist should not be [mistreated] because of his thinking.”

But cosmologist Encieh Erfani, who left her position in Iran in 2022 in protest of state violence against students, is not optimistic. “The total power is in the hand of the dictator [a reference to Khamenei]. I hope the West does not make the mistake of believing in reforms in Iran. No dictator can be reformed,” she says.

Sanctions dilemma

Pezeshkian has also vowed to restart talks on Iran’s nuclear programme. It is a key factor in the international sanctions imposed by Western countries, which Pezeshkian wants to see lifted.

“No government in history has been able to achieve growth and prosperity within a cage,” he said in a 2 July televised debate against his opponent Saeed Jalili. “We cannot think about development in this country and not have a logical relationship with the world. We cannot show claws and teeth to others. We must interact, and tolerate the world.”

He campaigned alongside Iran’s former foreign minister and academic Javad Zarif, an architect of the nuclear deal brokered in 2015 with John Kerry, who was then the US secretary of state. Sanctions on Iran were eased in exchange for a verifiable commitment to drastically reduce its stockpile of weapons-grade uranium. The deal collapsed in 2018, when former US president Donald Trump unilaterally withdrew from the agreement, and sanctions were reimposed.

Under the sanctions, international research collaborations are difficult. The country’s researchers face barriers in purchasing some types of equipment, applying for grants and even travelling to conferences. “The process for attending a foreign conference is very hard, and foreign scientists do not accept invitations to attend our conferences,” says Hamid Gourabi, a geneticist at the Royan Institute in Tehran, who hopes for sanctions relief, a widespread desire among Iranian scientists.

But a resuscitation of the agreement will not be easy, says Matthew Bunn, who studies nuclear security and is based at Harvard University’s Kennedy School of Government in Cambridge, Massachusetts. “With the distrust and hostility on all sides, it will be extremely difficult to convince Iran to take down all the nuclear infrastructure it has built up since president Trump’s disastrous pull-out from the deal. A nuclear deal without any broader progress on Iran’s conflicts with the United States, Israel and the Arab states is not politically sustainable.”

Nature 631, 490-491 (2024)

doi: https://doi.org/10.1038/d41586-024-02262-3

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NEWS 09 July 2024

If bird flu sparks a human pandemic, your past immunity could help

Older populations might be more protected than younger ones because of exposure to ‘matched’ strains during childhood, but an H5N1 pandemic is likely to take a major toll all the same.

By

Max Kozlov

The bird-flu virus H5N1 (purple; artificially coloured) infects a human cell.Credit: Steve Gschmeissner/Science Photo Library

As the H5N1 bird-flu virus spreads relentlessly in animals around the world, researchers seeking to understand how a human H5N1 pandemic might unfold have turned to a rich source of clues: data on the immune system’s response to influenza.

Such information provides hints about who could be most vulnerable in an H5N1 pandemic. Previous research also suggests that, in a confrontation with the virus, our immune systems would not be starting from scratch — thanks to previous infections with, and vaccinations against, other forms of flu. But this immunity is unlikely to prevent H5N1 from inflicting serious damage to global health, if a pandemic were to begin.

From feathers to fur

The H5N1 strain now running rampant began as a bird pathogen before branching out to mammals. Classified as a ‘highly pathogenic’ virus for its lethality in birds, it has killed millions of domestic and wild birds around the world since it first emerged in 1996.[1] It has also spread to a growing list of mammal species, including seals and foxes, and has caused more than 460 human deaths since 2003.

So far, the virus has not gained the ability to spread effectively between people, which has kept the potential for a pandemic at bay. But its repeated jumps from birds to mammals and evidence of transmission among mammals, such as elephant seals (Mirounga leonina)[2], have alarmed researchers, who warn that the virus is gaining opportunities to become adept at spreading easily between people.

These worries were magnified when H5N1 was detected in March for the first time in US cattle — animals that interact frequently with humans. As of 8 July, US health officials have confirmed bird-flu infections in nearly 140 dairy herds in 12 states and in 4 dairy farm workers.

All of the workers had mild symptoms, but scientists caution that the virus is still a threat. It’s possible that the workers escaped severe illness because they might have caught H5N1 through exposure to milk from infected cows rather than the usual airborne particles, says Seema Lakdawala, an influenza virologist at Emory University School of Medicine in Atlanta, Georgia. Or perhaps it’s because the workers might have been infected through the eye rather than the typical route of the mouth or nose.

Malik Peiris, a virologist at the University of Hong Kong, says he is not surprised by these infections, “nor reassured that mildness of these cases means that this virus is inherently mild”.

Immune preparedness

The virus’s inherent virulence isn’t the only factor that would shape a pandemic, Peiris says. Another is the immune system’s state of readiness.

Through a combination of past infection and immunization, by the time people reach adulthood, they have generally had considerable exposure to flu. Some estimates[3] suggest that up to half of younger populations are infected annually with ‘seasonal’ flu viruses, which cause regular waves of infections.

But exposure to seasonal flu offers limited protection against the new flu strains that could cause pandemics. These strains are genetically distinct from circulating seasonal strains, meaning that they face less built-up immunity in humans and can thus be more dangerous.

For now, H5N1 does not spread easily between people. But scientists worry that if it gains that ability, it could spark a pandemic, given that it is genetically different from seasonal flu viruses now in circulation. Tests of people across the United States found that few have antibodies against today’s strain of H5N1. This implies that “most of the population would be susceptible to infection from this virus if it were to start infecting people easily”, according to the US Centers for Disease Control and Prevention, which ran the tests.

Good news, bad news

That doesn’t leave people completely unprotected, because exposure to an older pandemic flu strain can defend against a newer one, says Michael Worobey, an evolutionary biologist at the University of Arizona in Tucson. For example, in a 2009 pandemic caused by the swine-flu virus H1N1, 80% of deaths were in people younger than 65[4]. Older generations were spared owing to immunity stemming from exposure to different H1N1 strains when they were younger.

Exposure to H1N1 during the 2009 pandemic and at other times might, in turn, provide some protection against the H5N1 strain on the rise today. Both the H5N1 and H1N1 viruses have a surface protein designated N1, and an immune system that responds to H1N1 might also respond to H5N1. Peiris and his colleagues found that the near-universal exposure to H1N1 in 2009 and subsequent years produces antibodies that respond to H5N1 in nearly 97% of the samples they collected[5]. He is now running animal experiments to determine whether this antibody response confers protection against infection and serious illness.

All-important first flu case

There’s yet another complicating factor to the immune response to H5N1: a person’s first bout of flu might have an outsized effect on their future immunity. In a 2016 paper[6], Worobey and his colleagues analysed almost two decades’ worth of severe infections caused by two subtypes of bird flu, H5N1 and H7N9. They found that people are generally unscathed by the flu strain that best ‘matches’ the one that had caused their first childhood flu infection — whereas they are more vulnerable to mismatched strains.

Thus, people born before 1968 have tended to escape H5N1’s ravages, because they probably had their first flu infection at a time when the dominant flu virus in circulation matched H5N1. But people born after 1968 eluded the worst of H7N9, because their first encounter with flu was probably with a virus that matched it rather than H5N1. Immunity from a first infection provided 75% protection against severe disease and 80% protection against death with a matching bird-flu virus, the authors found.

If an H5N1 outbreak were to occur, this first-bout effect predicts that older people could once again be largely spared whereas younger people could be more vulnerable, Worobey says. “We should have that somewhere between the back and front of our minds,” he says.

Nature 631, 491-492 (2024)

doi: https://doi.org/10.1038/d41586-024-02170-6

References

1. Xu, X., Subbarao, K., Cox, N. & Guo, Y. Virology 261, 15–19 (1999).

2. Uhart, M. et al. Preprint at bioRxiv https://doi.org/10.1101/2024.05.31.596774 (2024).

3. Cohen, C. et al. Lancet Glob. Health 9, e863–e874 (2021).

4. Dawood, F. S. et al. Lancet Infect. Dis. 12, 687–695 (2012).

5. Daulagala, P. et al. Emerg. Infect Dis. 30, 168–171 (2024).

6. Gostic, K. M., Ambrose, M., Worobey, M. & Lloyd-Smith, J. O. Science 354, 722–726 (2016).

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NEWS 10 July 2024

Stars hint at an unusual black hole lurking in our Galaxy

Images taken by the Hubble Space Telescope point to an elusive intermediate-size black hole in the star cluster ω Centauri.

By

Davide Castelvecchi

Astronomers can use the movements of stars to determine the location of a black hole (illustration).Credit: Victor de Schwanberg/Science Photo Library

By perusing two decades’ worth of pictures from the Hubble Space Telescope archives, astrophysicists have found what could be evidence of a nearby black hole at least 8,200 times as massive as the Sun.

The object would be the second-largest black hole to be found in our Galaxy, if further studies can confirm the findings, which are described today in Nature[1]. It could also be the strongest candidate yet for an intermediate-mass black hole — an object in the enigmatic no man’s land between the ‘supermassive’ black holes thought to lie at the centre of most galaxies, and much smaller ones that weigh about as much as a single large star.

Fast-moving stars

Astrophysicist Maximilian Häberle at the Max Planck Institute for Astronomy in Heidelberg, Germany, and his collaborators examined more than 500 images of ω Centauri, a dense cluster of 10 million stars roughly 18,000 light years (5.43 kiloparsecs) from the Solar System. The images had mostly been taken to help calibrate Hubble’s instruments over the years.

The team patched the images together to reconstruct the movement of more than 150,000 stars in the cluster. Most stars moved as theoretical models predicted, Häberle says. “But then, there were some that were moving faster.” Seven stars, all close to the centre of ω Centauri, were moving too fast to be held by the gravity of the cluster alone.

This suggested that the stars had been accelerated by the gravitational pull of a massive object, such as a black hole. From the stars’ velocities, the object would need to be at least 8,200 solar masses, but it could weigh as much as 50,000 Suns. “We did not know before whether we would find it or not,” says Häberle. “It was a little bit of a risk, and we might have found nothing.”

Images taken by the Hubble Space Telescope suggest that an intermediate-size black hole could be lurking in the star cluster ω Centauri.Credit: ESA/Hubble & NASA, M. Häberle (MPIA)

“It’s a hard experiment,” and the evidence for the presence of a black hole is “very far from conclusive”, says Gerry Gilmore, an astrophysicist at the University of Cambridge, UK. In particular, the data show no evidence yet that the trajectories are curving, which would be expected of stars orbiting a massive object. In the case of Sagittarius A*, the black hole of 4.3 million solar masses at the centre of the Milky Way, years of observations found incontrovertible proof of such curved orbits — for which two lead researchers won a Nobel prize in 2020. The Gaia space telescope has also spotted some dormant, star-sized black holes, from the motion of a single companion star[2].

Most black holes have been found over the past half century, by detecting radiation such as X-rays or radio waves[3] produced by superheated gas that’s spiralling into the hole. The first hint of the presence of Sagittarius A* was indeed a radio source — albeit not a very bright one. But no such emissions have been found in ω Centauri.

Mysterious middleweights

The mass of the candidate object in ω Centauri would put it squarely in the intermediate-mass black hole range, generally considered to span between around 100 and 100,000 solar masses. So far, the only solid evidence for black holes in this range comes from detecting gravitational waves produced by two merging black holes. One such event, seen in 2019, is thought to have produced an object of around 150 solar masses.

The search for midsize black holes has a long history of claims that are later disproved. Astrophysicists long suspected that some sources of ‘ultraluminous’ X-rays could be black holes in this size range. But most of those candidates have now been shown to be neutron stars that shine unusually brightly by overheating matter from a companion star. “These are most likely associated with ‘normal’ young binary systems,” says Giuseppina Fabbiano, an astrophysicist at the Harvard–Smithsonian Center for Astrophysics in Cambridge, Massachusetts.

Big questions remain — including how some black holes come to be supermassive, and whether they are the result of multiple mergers, starting from stellar-mass black holes and going through intermediate masses such as that of the ω Centauri candidate.

The team is now planning follow-up spectroscopic observations with the James Webb Space Telescope, Häberle says. Although Hubble data show only how the stars move across the field of view, the stars’ spectra will reveal how they move along the line of sight, enabling the astronomers to fully reconstruct their velocities in 3D.

Nature 631, 492-493 (2024)

doi: https://doi.org/10.1038/d41586-024-02277-w

Read the related News & Views: ‘Speedy stars blow the cover of hidden black hole’.

References

1. Häberle, M. et al. Nature 631, 285–288 (2024).

2. Panuzzo, P. et al. Astron. Astrophys. 686, L2 (2024).

3. Schmidt, M. Nature 197, 1040 (1963).

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NEWS 10 July 2024

How anti-obesity drugs cause nausea: finding offers hope for better drugs

The neurons that produce a sick feeling and food aversion are distinct from those that induce a feeling of fullness.

By

Mariana Lenharo

An injection pen for the diabetes drug Ozempic (semaglutide), which leads to substantial weight loss but also causes nausea bad enough to make people stop taking it.Credit: Jaap Arriens/NurPhoto/Getty

Next-generation anti-obesity drugs such as Wegovy can melt away weight — but they can also cause intolerable nausea. Now scientists have pinpointed a brain pathway that is involved in this common side effect, raising the prospect of effective weight-loss drugs that don’t make people sick[1].

The scientists found that the nausea-inducing brain circuit, which also triggers aversion to food, is separate from the circuit that helps the drugs to produce satiety, the feeling of fullness that prevents people from eating more.

“The implication is that if we can now selectively target the satiety circuits without targeting the aversion circuits, we could potentially develop better drugs with fewer side effects,” says Amber Alhadeff, a neuroscientist at the Monell Chemical Senses Center in Philadelphia, Pennsylvania, and a co-author of the study, published today in Nature.

Sickness and health

Drugs such as Wegovy mimic a hormone called glucagon-like peptide 1 (GLP-1), which controls blood sugar levels and acts on the brain to reduce appetite. (Wegovy and the diabetes drug Ozempic are brand names for semaglutide, which is made by Novo Nordisk, based in Bagsværd, Denmark.) Multiple regions of the brain have GLP-1 receptors, but which specific receptor populations are involved in the drugs’ effects is not fully understood.

To resolve that uncertainty, Alhadeff and her colleagues first killed neurons with GLP-1 receptors in specific regions of the brains of mice. The researchers then gave the mice a GLP-1-mimicking drug: either semaglutide or exenatide, which also has weight-loss effects.

Mice lacking the GLP-1 neurons in a region called the hindbrain ate normally. This showed that the medications’ effect had been completely blocked. But the medications still worked after the researchers killed the GLP-1 neurons in the brain region called the hypothalamus, which is known to play an important part in appetite regulation and was thought to be important for the action of GLP-1 drugs[2]. “We were shocked by this,” says Alhadeff. “The takeaway is that the hindbrain is really the primary site of action for the drugs.”

The researchers then investigated two subregions of the hindbrain, called the area postrema (AP) and the nucleus of the solitary tract (NTS). When the scientists turned on the animals’ AP neurons, the mice exhibited nausea and food aversion and reduced their food intake. By contrast, when they turned on NTS neurons in mice, the animals cut back on their food — but showed no signs of nausea.

That means that nausea is not necessary for GLP-1 drugs to suppress food intake. This is one of the key points of the study, says Martin Myers, a neuroscientist at the University of Michigan in Ann Arbor. “If there were a way to turn on just the NTS GLP-1 receptor neurons, or all other GLP-1 receptor neurons, avoiding just those in the AP, that would certainly be a much better drug,” he says. “The trick, of course, is how to do that.”

Nature 631, 493-494 (2024)

doi: https://doi.org/10.1038/d41586-024-02254-3

References

1. Huang, K.-P. et al. Nature https://doi.org/10.1038/s41586-024-07685-6 (2024).

2. Kim, K. S. et al. Science https://doi.org/10.1126/science.adj2537 (2024).

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NEWS 10 July 2024

This lab-grown meat probably tastes like real beef

Cultured meat that tastes and smells more appetizing could enhance public perception of artificial steaks.

By

Helena Kudiabor

Beef stem cells are added to a hydrogel that contains flavour compounds.Credit: Yonsei University

Meat grown in the lab has been hailed as an environmentally friendly, cruelty-free alternative to conventional chops and steaks — but struggles to replicate the taste of the real thing. Now, researchers have engineered cultured meat that releases beefy flavours at high temperatures, a development that could improve its appeal to diners.

In a study published on 9 July in Nature Communications[1], the team showed that enhancing in vitro animal cells with compounds associated with the Maillard reaction — the process that gives cooked food its darkened colour and appetizing flavours — helps to replicate the aroma, and therefore the flavour, of conventional meat.

“To our knowledge, it’s the first approach to regulate the flavour properties of cultured meat,” says study co-author Milae Lee, a biomolecular engineer at Yonsei University in Seoul.

“There’s not enough research like this, that focuses on this kind of end-product analysis,” says Seren Kell, head of science and technology at the Good Food Institute Europe in Forest, Belgium. It is “really important to make sure that cultivated meat can match consumer expectation”, she adds.

Flavour focus

Cultured meat, which is produced by growing animal muscle cells in the lab, has numerous advantages compared to standard meat. There’s no need to slaughter animals to produce the stem cells the process requires, and manufacturing the meat at scale could eventually have a lower carbon footprint than rearing livestock. “A lot of greenhouse-gas emission is involved to produce conventional meat,” says Lee. “For cultured meat, these kinds of environmental-pollution factors can be minimized.”

Although previous research has explored ways to advance cultured-meat production, mimicking the structures of familiar products such as meatballs, these studies often “don’t really focus on the ‘organoleptic’ — sense — properties, especially flavour and taste”, says Lee.

When conventional meat is cooked at high temperatures, it undergoes the Maillard reaction — its amino acids and sugars react with each other, giving the meat its recognizable aroma and taste. But because cultured meat has a different amino-acid profile to conventional meat, it doesn’t react to the same extent.

To try to rectify this, Lee and her colleagues developed a compound that could be added to cultured meat, containing furfuryl mercaptan — a product of the Maillard reaction known to contribute to a savoury flavour profile — along with substances that would help it to bind to the meat and keep it from breaking down. They engineered the compound to be ‘switchable’, meaning that the flavour would be released when the meat was heated to 150 °C.

They were also keen to ensure that the compound was compatible with the cell-culturing process. They incorporated it into a hydrogel: a jellylike material that can be used as a scaffold for stem cells as they grow into muscle tissue and become more meat-like.

The researchers used an electronic nose — a device that analyses the chemical make-up of smells — to test how well the hydrogel and cell cultures held the flavour. At room temperature, the meat they grew didn’t have much of a flavour. But when heated to 150 °C, it produced compounds associated with savoury, fruity and meaty flavours. This shows that the hydrogel works well with the flavour component to produce a controlled release of meaty aromas.

Further research revealed that a combination of three different Maillard-reaction products provided a flavour even closer to that of conventional beef, with floral and creamy notes alongside more savoury aromas. The team plans to trial other mixtures in future, says Lee. The group is also hoping to scale up the technology — at the moment, the process of making meat in the lab is slow and labour-intensive, and only small quantities are produced.

Kell is keen for the team to explore the potential of different meaty flavours, by “looking at all the dominant flavour compounds in other species and product types, beyond just beef”.

Nature 631, 494 (2024)

doi: https://doi.org/10.1038/d41586-024-02273-0

References

1. Lee, M. et al. Nature Commun. 15, 5450 (2024).

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NEWS FEATURE 09 July 2024 Correction 12 July 2024

How PhD students and other academics are fighting the mental-health crisis in science

Universities and institutions across the globe are exploring unique initiatives to help their students and staff cope with the stress of research.

By

Shannon Hall

Illustration: Piotr Kowalczyk

On the first day of her class, Annika Martin asks the assembled researchers at the University of Zurich in Switzerland to roll out their yoga mats and stand with their feet spread wide apart. They place their hands on their hips before swinging their torsos down towards the mat and back up again. The pose, called ‘wild goose drinking water’ is from Lu Jong, a foundational practice in Tantrayana Buddhism.

Martin, a health psychologist, can sense that some students are sceptical. They are academics at heart, many of whom have never tried yoga, and registered for Martin’s course to learn how to deal with the stress associated with academic research. Over the course of a semester, she teaches her students about stress and its impact on the body before giving them the tools to help cope with it — from yoga, meditation and progressive muscle relaxation to journalling.

It is one of many initiatives designed to combat the mental-health crisis that is gripping science and academia more broadly. The problems are particularly acute for students and early-career researchers, who are often paid meagre wages, have to uproot their lives every few years and have few long-term job prospects. But senior researchers face immense pressure as well. Many academics also experience harassment, discrimination, bullying and even sexual assault. The end result is that students and academics are much more likely to experience depression and anxiety than is the general population.

But some universities and institutions are starting to fight back in creative ways.

The beginning of a movement

The University of Zurich now offers academics several popular courses on mental health. Beyond Martin’s class, called ‘Mindfulness and Meditation’, one helps students learn how to build resilience and another provides senior researchers with the tools they need to supervise PhD candidates.

The courses are in high demand. “We have way more registrations than we have actual course spots,” says Eric Alms, a programme manager who is responsible for many of the mental-health courses at the University of Zurich. “I’m happy that my courses are so successful. On the other hand, it’s a sign of troubling times when these are the most popular courses.”

Several studies over the past few years have collectively surveyed tens of thousands of researchers and have documented the scope and consequences of science’s mental-health crisis.

In 2020, the biomedical research funder Wellcome in London, surveyed more than 4,000 researchers (mostly in the United Kingdom) and found that 70% felt stressed on the average work day. Specifically, survey respondents said that they felt intense pressure to publish — so much so that they work 50–60 hours per week, or more. And they do so for little pay, without a sense of a secure future. Only 41% of mid-career and 31% of early-career researchers said that they were satisfied with their career prospects in research.

The International Max Planck Research School for Intelligent Systems run bootcamps involving activities such as painting.Credit: Alejandro Posada

A survey designed by Cactus Communications, a science-communication and technology company headquartered in Mumbai, India, analysed the opinions of 13,000 researchers in more than 160 countries in 2020 and found that 37% of scientists experienced discrimination, harassment or bullying in their work environment. This was especially true for researchers from under-represented groups and was the case for 42% of female researchers, 45% of homosexual researchers and 60% of multiracial researchers.

Yet some experts are hopeful that there is change afoot. As well as the University of Zurich, several other institutions have started to offer courses on mental health. Imperial College London, for example, conducts more than two dozen courses, workshops and short webinars on topics as diverse as menstrual health and seasonal depression. Most of these have been running for at least five years, but several were developed in response to the COVID-19 pandemic. “At that time, the true dimension of the mental-health crisis in science was unveiled and potentially exacerbated by the lockdowns,” says Ines Perpetuo, a research-development consultant for postdocs and fellows at Imperial College London.

Desiree Dickerson, a clinical psychologist with a PhD in neuroscience who leads workshops at the University of Zurich, Imperial College London and other institutes around the world, says she has a heavier workload than ever before. “Before COVID, this kind of stuff wasn’t really in the spotlight,” she says. “Now it feels like it is gaining a solid foothold — that we are moving in the right direction.”

Some of this change has been initiated by graduate students and postdocs. When Yaniv Yacoby was a graduate student in computer science at Harvard University in Cambridge, Massachusetts, for example, he designed a course to teach the “hidden curriculum of the PhD”. The goal was to help students to learn how to succeed in science (often by breaking down preconceived ideas), while creating an inclusive and supportive community. An adapted form of that course is now offered by both Cornell University in Ithaca, New York, and the University of Washington in Seattle. And Yacoby has worked with other universities to develop single-session workshops to jump-start mental-health advocacy and normalize conversations about it in academia.

Similarly, Jessica Noviello, a planetary scientist at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, built a workshop series designed to target a key stressor for academics’ mental health: job insecurity, or specifically, the ability to find a job that aligns with career plans and life goals. She argues that most advisers lack experience outside academia, “making it hard for them to advise students about other career options”, and most institutes don’t have the resources to bring in outside speakers. Yet it is a key issue. The 2020 Wellcome survey found that nearly half of the respondents who had left research reported difficulty in finding a job.

So Noviello established the Professional Advancement Workshop Series (PAWS) in August 2021. The programme has run workshops and panel discussions about careers at national laboratories and in science journalism and media communications, science policy, data science, NASA management and more. And it has hosted two sessions on mental-health topics. “PAWS isn’t a programme that specifically set out to improve mental health in the sciences, but by building a community and having conversations with each other, the experts, and ourselves, I think we are giving ourselves tools to make choices that benefit us, and that is where mental health begins,” Noviello says.

Beyond the classroom

Although these courses and workshops mark a welcome change, say researchers, many wonder whether they are enough.

Melanie Anne-Atkins, a clinical psychologist and the associate director of student experience at the University of Guelph in Canada, who gives talks on mental health at various universities, says that she rarely sees universities follow through after her workshops. “People are moved to tears,” she says. “But priorities happen afterward. And even though they made a plan, it never rises to that. Because dollars will always come first.”

David Trang, a planetary geologist based in Honolulu, Hawaii, at the Space Science Institute, is currently working towards a licence in mental-health counselling to promote a healthier work environment in the sciences. He agrees with Anne-Atkins — arguing that even individual researchers have little incentive to make broad changes. “Caring about mental health, caring about diversity, equity and inclusion is not going to help scientists with their progress in science,” he says. Although they might worry about these matters tremendously, Trang argues, mental-health efforts won’t help scientists to win a grant or receive tenure. “At the end of the day, they have to care about their own survival in science.”

Still, others argue that these workshops are a natural and crucial first step — that people need to de-stigmatize these topics before moving forward. “It is quite a big challenge,” Perpetuo says. “But you have to understand what’s under your control. You can control your well-being, your reactions to things and you can influence what’s around you.”

PhD students compete in a team-building relay race at a bootcamp run by the International Max Planck Research School for Intelligent Systems.Credit: Alejandro Posada

That is especially pertinent to the typical scientist who tends to see their work as a calling and not just a job, argues Nina Effenberger, who is studying computer science at the University of Tübingen in Germany. The Wellcome survey found that scientists are often driven by their own passion — making failure deeply personal. But a solid mental-health toolkit (one that includes the skills taught in many of the new workshops) will help them to separate their work from their identity and understand that a grant denial or a paper rejection is not the end of their career. Nor should it have any bearing on their self-worth, Effenberger argues. It is simply a part of a career in science.

Moreover, Dickerson argues that although systemic change is necessary, individuals will drive much of that change. “My sense is that if I can empower the individual, then that individual can also push back,” she says.

Many researchers are starting to do just that through efforts aimed at improving working conditions for early-career researchers, an area of widespread concern. The Cactus survey found that 38% of researchers were dissatisfied with their financial situation. And another survey of 3,500 graduate students by the US National Science Foundation in 2020 (see go.nature.com/3xbokbk) found that more than one-quarter of the respondents experienced food insecurity, housing insecurity or both.

In the United States, efforts to organize unions have won salary increases and other benefits, such as childcare assistance, at the University of California in 2022, Columbia University in New York City in 2023 and the University of Washington in 2023. These wins are part of a surge in union formation. Last year alone, 26 unions representing nearly 50,000 graduate students, postdocs and researchers, formed in the United States.

There has also been collective action in other countries. In 2022, for example, graduate students ran a survey on their finances, and ultimately won an increase in pay at the International Max Planck Research School for Intelligent Systems (IMPRS-IS), an interdisciplinary doctoral programme within the Max Planck Society in Munich, Germany.

Union drives are only part of the changes that are happening beyond the classroom. In the past few years, Imperial College London has revamped its common rooms, lecture halls and other spaces to create more places in which students can congregate. “If they have a space where they can go and chat, it is more conducive to research conversations and even just personal connection, which is one of the key aspects of fostering mental health,” Perpetuo says. Imperial also introduced both one-day and three-day voluntary retreats for postdocs and fellows to build personal relationships.

The IMPRS-IS similarly runs ‘bootcamps’ or retreats for many of its doctoral students and faculty members. Dickerson spoke at the one last year. The programme also mandates annual check-ins at which students can discuss group dynamics and raise any issues with staff. It has initiated thesis advisory committees so that no single academic supervisor has too much power over a student. And it plans to survey its students’ mental health twice a year for the next three years to probe the mental health of the institute. The institute has even set various mental-health goals, such as high job satisfaction among PhD students regardless of gender.

Dickerson applauds this change. “One of the biggest problems that I see is a fear of measuring the problem,” she says. “Many don’t want to ask the questions and I think those that do should be championed because I think without measuring it, we can’t show that we are actually changing anything.”

She hopes that other universities will follow suit and provide researchers with the resources that they need to improve conditions. Last year, for example, Trang surveyed the planetary-science community and found that imposter syndrome and feeling unappreciated were large issues — giving him a focus for many future workshops. “We’re moving slowly to make changes,” he says. “But I’m glad we are finally turning the corner from ‘if there is a problem’ to ‘let’s start solving the problem.’”

Nature 631, 496-498 (2024)

doi: https://doi.org/10.1038/d41586-024-02225-8

Updates & Corrections

- Correction 12 July 2024: An earlier version of this story incorrectly said that Nina Effenberger was involved in a survey on graduate-student finances that won an increase in pay.

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Books & Arts

- Kyoto review: ‘thrilling’ play shows fight for landmark climate treaty

- The Napoleonic tweets, Books in brief

ARTS REVIEW 05 July 2024

Kyoto review: ‘thrilling’ play shows fight for landmark climate treaty

Drama behind the scenes at the Kyoto Protocol negotiations is laid bare in a major theatrical production.

By

Peter Stott

The Alliance of Small Island States, represented by Kiribati (Andrea Gatchalian, centre). Credit: Manuel Harlan/Royal Shakespeare Company

Kyoto Dir . Stephen Daldry and Justin Martin Swan Theatre, Stratford-upon-Avon, UK 18 June to 13 Jul 2024

Climate change poses a wicked problem to humanity. We are all complicit, at least in part, in an ongoing devastation that threatens everyone, but for whom the least well-off are also the most vulnerable. The warnings of climate scientists have been ignored for much too long. Something in the telling needs to change.

A play, Kyoto — which had its world premiere last month at the Royal Shakespeare Company’s Swan Theatre in Stratford-upon-Avon, UK — attempts to cast a fresh light on climate narratives by focusing on the negotiations of the Kyoto Protocol. This international treaty, signed on 11 December 1997 in Kyoto, Japan, was the first to commit countries to reduce their greenhouse-gas emissions to mitigate climate change.

These momentous events, as recounted in Joe Murphy and Joe Robertson’s gripping drama, are set in the round, with actors joined by audience members surrounding a circular wooden conference table. It all adds up to a fabulously engaging evening of theatre.

In the shoes of the opposition

Making a fossil-fuel lobbyist and supporting lawyer, Don Pearlman (brilliantly played by Stephen Kunken), the narrator and protagonist of the play is a master stroke. Showing us the unfolding events through his eyes avoids the preachiness that often besets accounts of environmental degradation. Doing it this way — nuanced, complicated and human — and interrogating the difficult nature of achieving a consensus feels like the fresh approach needed.

Early in the play, climate scientist Ben Santer (played by Dale Ripley) explains to Pearlman how changes in atmospheric temperatures are a distinctive fingerprint of human activities. Santer’s findings, later published in Nature (B. D. Santer Nature 382, 39–46; 1996), gave rise to the landmark statement by the Intergovernmental Panel on Climate Change (IPCC) that there was a “discernible human influence on global climate”.

The audience surrounded the stage, making for an immersive experience.Credit: Manuel Harlan/Royal Shakespeare Company

Despite vicious backlash from climate-change deniers, such as atmospheric physicist Fred Singer (another key character played by Vincent Franklin), this scientific evidence eventually forced governments to take action to reduce emissions at the highly charged Kyoto summit.

Haggling over the intricacies of language, a process in which a single word can be argued over for hours and a misplaced comma can lead to diplomatic disaster, might seem a rather dry subject for an audience not immersed in the nuances of climate science and policy as much as I am. But judging by the rapt attention of the audience, engaged interval chatter and rapturous final applause, that is far from the case. Such deftly handled material cuts through to the heart. And that, given the pressing need for collective action on climate, including by our politicians, is immensely heartening.

Reaching the summit

As a junior climate researcher, I attended the 1997 Kyoto summit alongside another colleague at the Met Office Hadley Centre in Exeter, UK. There were very few scientists there back then, and our job was to explain to delegates our predictions of rising temperatures, inundated coastlines and damaged crops from a table in the lobby just outside the main congress chamber.

I also presented the latest evidence, hot off the press, that climate had already changed as a result of human-made emissions. It was just that evidence that the real Singer, when he confronted me at our lobby table, was so keen to denounce. And it was just that evidence that was the core reason delegates kept going, forgoing sleep hour after hour, beyond the closed doors a few steps away from our table. Only curbing emissions would protect planetary health, even if curbing those emissions would also threaten the economic status quo.

German representative Angela Merkel (played by Ingrid Oliver).Credit: Manuel Harlan/Royal Shakespeare Company

The denouement was as thrilling in Kyoto the play as it was in Kyoto the city. I remember well the fevered activity in the lobbies, and UK minister and negotiator John Prescott (portrayed in knockabout style by Ferdy Roberts) failing to make it to our table three times because he kept being waylaid by frantic delegates. In the theatre, I particularly enjoyed the moment when the interpreters have gone home and total mayhem ensues as delegates exchange punctuation marks in multiple languages, ever more raucously and hilariously.

Life imitates art

Most thrillingly of all, Kyoto feels as relevant today as the summit did in 1997. In the 27 years since, many similar dramas have played out behind the scenes of other climate meetings. In searching, as Santer did, for detectable effects of human-induced emissions, climate scientists such as myself have uncovered a galloping rise in the frequency of damaging heatwaves, floods and droughts.

The IPCC reports that I’ve worked on have involved the same sorts of tortuous negotiation over words so vividly described in Murphy and Robertson’s drama. I have seen how oil-industry-funded climate deniers continue to operate, shifting their approach from denying global warming to attacking the necessity of reducing emissions fast enough to avoid catastrophic consequences that no society, no matter how well adapted, could possibly survive untraumatized.

At times I have felt despair at the lack of political will to tackle climate change. At others, I have felt encouraged that the transition towards a more sustainable future is already under way. Whatever state we’re in now, as Kyoto so entertainingly demonstrates, there is no substitute for continuing to put in the work to protect the planet that is our only home. Fighting for agreement across entrenched divides is the only way, just as those exhausted, sleep-deprived delegates showed, all those years ago in Japan’s old imperial capital.

Nature 631, 499-500 (2024)

doi: https://doi.org/10.1038/d41586-024-02251-6

Competing Interests

The author declares no competing interests.

BOOK REVIEW 12 July 2024

The Napoleonic tweets, Books in brief

Andrew Robinson reviews five of the best science picks.

By

Andrew Robinson

Accidental

Tim James Robinson (2024)

Science often progresses through step-by-step discoveries. But major breakthroughs still happen. Science writer Tim James smartly retells stories of such “spasms of serendipity” that happened through moments of accidental “clumsiness”, “misfortunes and failures”, “surprises” and “eurekas”. For example, in 1903, chemist Edouard Benedictus dropped a dirty glass flask and noticed it did not shatter because of its cellulose nitrate coating. In 1909, after reading of dangerous shattered glass during car crashes, he invented laminated glass.

Facing the Unseen

Damon Tweedy St. Martin’s (2024)

Over the past two decades, the US suicide rate has increased by 30%. The US National Institute of Medical Health estimates that mental illness affects about 20% of US adults, yet many physicians are averse to psychiatrists. This leads critics to question psychiatry’s “status as a legitimate medical discipline”, writes US professor of psychiatry and staff physician Damon Tweedy, who formerly shared their aversion. His compelling analysis of this desperate situation draws on vivid encounters in outpatient clinics, emergency rooms and hospitals.

The Importance of Being Educable

Leslie Valiant Princeton Univ. Press (2024)

Intelligence lacks a clear and agreed definition in science, leading to confusion about IQ and the potential of artificial intelligence. Leslie Valiant, a pioneer of machine learning, prefers to define human intellectual uniqueness as educability instead of intelligence. “Educability is the capability to learn and acquire belief systems from one’s own experience and from others, and to apply these to new situations”. Perhaps inevitably, Valiant links educability to computation, exploring this connection in his complex but jargon-free book.

Alien Earths

Lisa Kaltenegger St. Martin’s (2024)

The Milky Way contains about 200 billion stars. The number of potential extrasolar planets is mind-boggling. Surely other Earth-like planets must exist? Astronomer Lisa Kaltenegger agrees, noting: “So far, despite wild claims to the contrary, we have not found any definitive proof of life on other planets.” She launched the Carl Sagan Institute at Cornell University in Ithaca, New York, to search for life in the cosmos, alongside experts in fields ranging from astronomy to the performing arts. Her book is an authoritative and enjoyable read.

The Afterlife of Data

Carl Öhman Univ. Chicago Press (2024)

Printed books can immortalize the dead. But what should happen to posthumous online presence, asks political scientist Carl Öhman in his stimulating, sometimes spooky book. Imagine if we could access French general Napoleon Bonaparte’s Facebook messages or the data patterns of people in 1930s Germany, he remarks. “The lessons learned would be endless.” But if we simply leave it to businesses to manage “our collective digital past”, he argues that it will surely be used to make money.

Nature 631, 500 (2024)

doi: https://doi.org/10.1038/d41586-024-02295-8

Competing Interests

The author declares no competing interests.

Opinion

- Edward C. Stone obituary: physicist who guided Voyager probes to interstellar space

- Make gene therapies more available by manufacturing them in lower-income nations

OBITUARY 10 July 2024

Edward C. Stone obituary: physicist who guided Voyager probes to interstellar space

Space scientist who took humanity on a tour of the Solar System and beyond, in a journey of discovery like no other.

By

Alan C. Cummings

Credit: NASA/Carla Cioffi

Edward Stone was a pre-eminent space scientist with an exceptional record of leading space missions and building ground-based astronomical facilities. The public face of NASA’s Voyager missions for the launch of the two probes in 1977, he served as project scientist for 50 years. Stone introduced the world to the wonders of the gas-giant planets (Jupiter, Saturn, Uranus and Neptune) in multiple press conferences, from Voyager 1’s encounter with Jupiter in 1979 to Voyager 2’s fly-by of Neptune in 1989. He also oversaw the probes entering interstellar space — the first in 2012 and the second in 2018. They continue to transmit data to Earth today.

At the helm of the California Association for Research and Astronomy in the 1990s, Stone was involved in the development of the twin 10-metre Keck telescopes on Maunakea in Hawaii, two of the most productive ground-based astronomical facilities ever built. And as the executive director of the Thirty Meter Telescope, for eight years he oversaw the huge international collaboration that plans to build one of the world’s largest optical–infrared telescopes on the same mountain — land that is sacred to Native Hawaiians, leading to years-long protests and controversy. The telescope aims to capture light from the earliest galaxies in the Universe and search for habitable planets. Stone’s involvement was a service to the astronomy community, because his observational interests were focused on cosmic rays.

Stone was born in Knoxville and grew up in Burlington, both in Iowa. His father was a construction supervisor who communicated his curiosity about how things worked to his son. Stone studied physics at the University of Chicago, Illinois, for a master’s degree and PhD. Soon after he started studying there, in 1957, the Soviet Union launched Sputnik 1 — the first artificial Earth satellite — starting the space race. Stone took notice, and was at a good place to get involved. His thesis adviser, cosmic-ray-research pioneer John Simpson, was deploying instruments attached to high-altitude balloons as well as using ground-based neutron monitors. Instruments on rockets soon followed. Stone’s thesis experiment was on a now-declassified US spy satellite, Discoverer 31, flown in 1961.

After earning his doctorate in 1964, Stone joined Rochus ‘Robbie’ Vogt, whom he studied alongside at Chicago, at the California Institute of Technology (Caltech) in Pasadena, as a research fellow. Together, they formed the Space Radiation Laboratory, where I work. They focused on observations of cosmic rays, with instruments first carried on balloons and later launched into space. Stone moved up the academic ranks at Caltech, becoming a professor of physics in 1976. In 1972, he was appointed project scientist for NASA’s MJS77 mission, later renamed Voyager. Stone was also directly involved in developing an instrument for use aboard the Voyagers — the Cosmic Ray Subsystem — helping to design its cosmic-ray telescopes and draw up the calibration procedures.

The Voyager missions capitalized on a rare alignment of the four gas-giant planets — a once-in-176-years opportunity that allowed a single spacecraft to visit all four. It required a launch between 1976 and 1980 to succeed. For budget reasons, only a two-spacecraft mission to Jupiter and Saturn was initially approved. Voyager 2 was later re-programmed to visit Uranus and Neptune as well.

Eleven experiments were planned. Stone’s role included developing a planetary fly-by programme that would be agreeable to all the participating principal investigators. To accomplish this, he learnt about the science of each experiment — earning the respect of all involved. One of the researchers, Tom Krimigis, recalled: “He was always knowledgeable, insightful and fair in his decisions, with the principal focus on the best science; he never deviated from that.”

The Voyager planetary fly-bys resulted in many discoveries, including moons, rings, a moon with volcanoes, moons with more water than there is on Earth and Triton — a moon of Neptune that is one of the coldest places in the Solar System and yet has geysers. Textbooks on the outer planets of the Solar System were rewritten. But the culmination of Stone’s research career came when Voyager 1 crossed the heliopause — the boundary between interplanetary and interstellar space, at 18.2 billion kilometres from the Sun. The Cosmic Ray Subsystem was at last able to measure something that cannot be quantified inside the heliopause because of the Sun’s outflowing solar wind: the intensity of low-energy cosmic rays in the Milky Way galaxy.

For his work on the Voyagers, Stone was awarded the National Medal of Science by then US president George Bush in 1991, and in 2019 he received the Shaw Prize in astronomy. A prolific administrator and multitasker, Stone chaired the physics, mathematics and astronomy division at Caltech for five years in the 1980s and was the director of NASA’s Jet Propulsion Laboratory from 1991 to 2001. During his tenure there, he oversaw the first landing of a robot on another planet — the Mars rover Sojourner. His work ethic was extraordinary. In total, he held a major role on 14 NASA missions and 2 US Department of Defense missions — most of the time while running the Space Radiation Laboratory at Caltech.

In 2022, owing to declining health, Stone retired as Voyager project scientist and became emeritus professor at Caltech. He was always even-tempered in his dealing with colleagues and sought to reach a consensus on whatever debate was going on. He will be greatly missed in both the space-science and astronomical communities.

Nature 631, 501 (2024)

doi: https://doi.org/10.1038/d41586-024-02285-w

Competing Interests

The author declares no competing interests.

COMMENT 17 July 2024

Make gene therapies more available by manufacturing them in lower-income nations

Gene therapies will become more affordable everywhere only if they are developed and manufactured in the regions of the world most in need of them.

By

Evelyn Mwesigwa Harlow &

Jennifer E. Adair

A pharmacist at a health-care centre in India dispenses free medicine to a person with sickle-cell disease.Credit: Rafiq Maqbool/AP Photo/Alamy

Last November, the United Kingdom became the first country to authorize the use of a therapy called Casgevy, based on CRISPR gene-editing technology, for the treatment of sickle-cell disease. Within a few weeks, three other countries — the United States, Bahrain and Saudi Arabia — had done the same. In December 2023, the United States also approved the use of Lyfgenia, another gene therapy, to treat the disease.

Sickle-cell disease kills nearly 400,000 people each year globally. It causes red blood cells to become sickle-shaped and clog blood vessels, which can result in severe pain and tissue damage, among other problems[1]. More than 75% of people with the disease — one of the most common inherited disorders in the world — are born in sub-Saharan Africa and India. The 2021 Global Burden of Disease Study, a regional and global effort to assess mortality and disability resulting from major diseases, found it to be the 12th leading cause of death globally in children under five[1]. In Africa, more than 50% of infants or children diagnosed with sickle-cell disease die before the age of five[2].

Until the advent of gene therapies, the only cure was a bone-marrow transplant. This requires finding a healthy donor, followed by invasive treatment over weeks[3].

Given all this, obtaining regulatory approval for two gene therapies is a phenomenal achievement. Yet at prices of US$2.2 million and $3.1 million per treatment, respectively, for Casgevy (exaxamglogene autotemcel) and Lyfgenia (lovotibeglogene autotemcel), the risk is that both will be withdrawn from the market because too few people or health-care systems can afford them.

This has already happened for four other gene therapies, including a related treatment called Zynteglo (betibeglogene autotemcel), for β-thalassaemia. Another inherited blood disorder, β-thalassaemia causes anaemia, tiredness and weakness in about 3% of the global population, but its incidence can reach 20% in regions including Africa, the Middle East and southeast Asia[4]. In 2021, Zynteglo was withdrawn from the European market after its developer, the pharmaceutical company Bluebird Bio, based in Somerville, Massachusetts, failed to persuade public bodies such as the United Kingdom’s National Institute for Health and Care Excellence (NICE) to approve it. Zynteglo costs $1.8 million per treatment.

Researchers, drug developers and drug manufacturers are squandering the chance to make gene therapies viable even in wealthier countries, let alone transformative for the world. To seize the opportunity, they must take into account the populations most in need, as well as the global market for treatments — both when developing and when valuing the drugs. This means reconsidering who is enrolled in clinical trials and where those trials are conducted. It also means partnering with low- and middle-income countries (LMICs), and facilitating the development and production of gene therapies in the nations that need them most.

Safe and effective for whom?

Casgevy and Lyfgenia are among the latest examples of treatments (see ‘Forging ahead’) that involve making alterations to people’s genomes through the replacement, deletion or insertion of genetic material. Since 2004, regulators worldwide have authorized 30 other gene therapies for genetic disorders, including immune and red-blood-cell disorders, retinal degeneration and leukodystrophy — a set of conditions affecting the central nervous system — as well as for severe cases of some cancers[5]. Of the 12 genetic disorders that can now be treated using gene therapies, 9 were previously incurable.

Source: Ref. 5

For Casgevy and Lyfgenia, the challenges didn’t start with costs. In both cases, participants in the clinical trials did not represent the populations most affected by the disease they are meant to treat.

For Casgevy, trial participants were from the United States, Canada or Europe. For Lyfgenia, the pilot trial included three people from France. All participants in the main trial were from the United States, and the drug was administered only in US clinics. But in North America and Europe, around 1 in 2,000 to 1 in 3,300 people have sickle-cell disease, compared with around 1 in 1,300 in South America and the Caribbean, 1 in 1,000 in India, 1 in 500 in the Middle East, and 1 in 100 in Africa[6].

What’s more, because US and European regulators urge drug developers to focus first on those who are made sickest by the disease (which, in the United States and Europe, includes adults), all trial participants for both drugs were older than 12 and most were older than 21 — despite the preponderance of sickle-cell disease among children globally. Casgevy is now being evaluated in a phase III clinical trial in people aged 2–11, but only in the United States, the United Kingdom, Germany and Italy.

This lack of representation in drug trials of those who are most affected by the disease is a violation of article 27 of the Universal Declaration of Human Rights, which states that “everyone has the right … to share in scientific advancements and its benefits”. It is especially problematic for gene therapies for diseases that are prevalent in Africa, where human populations are the most genetically ancient and diverse in the world. Treatments might not work in genetic contexts different from those in which they were tested[7], and to produce gene therapies that are as effective as possible globally, drug developers should be testing them in populations that are ancient in evolutionary terms.

Unfair pricing

Failure to consider the global population during trials could affect the effectiveness and safety of drugs in diverse populations. It is certainly affecting the pricing of gene therapies.

The multimillion-dollar price tags — which exclude the costs of clinical care — do not actually reflect how much the drugs cost to manufacture. Besides regulatory and research-and-development costs, these prices reflect the perceived maximum value that these therapies add to the person being treated and to society — for example, through avoiding medical costs that would otherwise be incurred during someone’s life under standard clinical care. (People with sickle-cell disease might receive pain medication or transfusions of red blood cells during a pain crisis, say.) Such ‘value-based pricing’ assessments are generally made by the drug developers themselves, or by a lengthy government review process. Typically, developers will evaluate their own products and governments and independent researchers conduct independent evaluations.

A person with sickle-cell disease receives a blood transfusion at a hospital in Kansas City, Missouri.Credit: Tammy Ljungblad/The Kansas City Star/Tribune News Service via Getty

Yet the way in which gene therapies are currently valued is riddled with difficulties.

Most value-based pricing models do not consider need, affordability or disease prevalence. Moreover, the perception of value varies for different populations and can change over time. In Europe and the United States, for example, sickle-cell disease is classed as a rare genetic disease (even though worldwide it is one of the most common inherited disorders). And this categorization changes how a drug to treat the disease is valued; societies are thought to be more willing to shoulder the costs of an expensive treatment if only a relatively small number of people are affected by the disease[8].

Estimates of ‘value added’ assume that recipients of treatments are cured for life. But the data presented by drug companies to authorities, such as the United Kingdom’s National Health Service, often come from only two years of follow-up.

Those evaluating the drugs do not consider that lower-priced treatments might become available in the future — say, if some part of the manufacturing process becomes automated. Evaluators also assume that manufacturing will only ever happen in high-income countries, even though drugs could be produced more cheaply in nations such as India, where the costs of labour and locally produced components, including gene-editing reagents, are much lower than in Europe or the United States.

Perhaps most crucially, drug evaluators use clinical data, for instance on the number of times people report a pain crisis to health-care providers, collected only from those in the countries where they expect the drug to be marketed[9]. In other words, estimates of the value of gene therapies are based on clinical data that are not collected from most of the people with the disease that the therapies are designed to treat.

All this means that gene therapies are perceived to be less cost-effective and more expensive to produce than they would be if their development, production, marketing and uptake shifted to countries where the relevant diseases are most prevalent. For sickle-cell disease, this includes Nigeria, India, the Democratic Republic of the Congo, Tanzania and Uganda[10].

Home grown

The capacity of LMICs to pursue research and development is much greater than many people in wealthier nations might assume. The Drugs for Neglected Diseases Initiative, an international non-profit organization, has brought 13 new drugs to market over the past 20 years, in part by including clinicians, researchers and trial participants from countries such as Ethiopia, Kenya, Malaysia, India and Brazil in the research-and-development cycle (see go.nature.com/3vj59yc).

Global inequities in COVID-19 vaccination have been a powerful reminder of the importance of local manufacturing of both vaccines and treatments. And governments across the world have been pushing hard for this.

A woman in Kano, Nigeria, massages the legs of her daughter, who has sickle-cell disease.Credit: KC Nwakalor/New York Times/Redux/eyevine

Through initiatives such as the New Partnership for Africa’s Development, the African Union is increasingly supporting the local manufacturing of treatments, vaccines and interventions such as mosquito nets, for example. Similar investment is happening in India. In October last year, India’s equivalent of the US Food and Drug Administration, the Central Drugs Standard Control Organization, approved the use of a home-grown chimeric antigen receptor T-cell therapy, called NexCAR19, to treat blood cancers.

The same government buy-in, national entities and infrastructure must support the local manufacturing of approved gene therapies, to give local populations a chance to access them. The cost savings made by shifting the manufacturing of some of these therapies to Africa or Asia could improve people’s access to them in certain high-income countries too. This is particularly true in Europe, which has more regulatory flexibility than does the United States.

Once it became clear, in 2022 and 2023, that regulators would be reviewing both Casgevy and Lyfgenia and probably approving their use for sickle-cell disease, other biotech companies, including Aruvant Biosciences, based in New York City; Sangamo, based in Richmond, California; and Bioverativ, part of the multinational drug-maker Sanofi; abandoned the development of three gene-therapy products for the disease. Although these pipelines were lagging behind those for Casgevy and Lyfgenia, clinical trials had been indicating that these other drugs would also be effective and safe.

Agreements around intellectual property would need to be negotiated. But, in principle, abandoned drug-development pipelines could be transferred to LMICs immediately. This would jump-start research and development for gene therapies locally, without imposing high-income-country requirements on low-resource settings. In countries such as India, for example, the rules around what clinical treatments (if any) people should have received before undertaking gene therapy will differ from those derived from clinical data collected in the United States[11].

Most of the intellectual property in cell and gene therapy is owned by academic research centres. But in cases where a commercial developer owns the intellectual property, tax incentives, expanded government funding or public–private partnerships could all support the transfer of technology to regions where the disease of interest is more prevalent.

To help to achieve this kind of technology transfer, governments of LMICs need to build the trust of US and European biotech corporations and academic research centres — not just by increasing their own investment in research and training, but also by conducting transparent assessments of their countries’ scientific, infrastructural and funding capacity.

There is considerable political will to address diseases that hit local populations the hardest. This year, Tanzania committed 3.6 billion shillings (around US$1,400,000) to support the use of bone-marrow transplants to treat children with sickle-cell disease. And depending on how much their costs can be brought down, there could be a strong global market for gene therapies.

No matter their income level, parents will do anything they can to save their child’s life. Since 2011, clinics in Nigeria have been providing bone-marrow transplants to Africans to treat sickle-cell disease. In India, hundreds of people have received a bone-marrow transplant to treat sickle-cell disease over the same period. In most cases, recipients and their relatives have crowdsourced the $25,000–50,000 needed per treatment.

Find a way

The Global Gene Therapy Initiative (GGTI), of which we are both founding members, aims to enable people anywhere in the world to find out more about the development of gene therapies. Last year, one of our colleagues, Elizabeth Merab, an award-winning Kenyan journalist in health and science, and a member of the GGTI’s international advisory board, died from complications of sickle-cell disease at the age of 31.

In 2022, Merab addressed various stakeholders, including one of us (J.E.A.), at a meeting about the role of gene therapies in treating sickle-cell disease and HIV at the Sunnylands Estate in Rancho Mirage, California. It had been three years since news outlets around the world had reported how, in a much-anticipated experiment, clinicians had used a CRISPR-based gene therapy to try to treat sickle-cell disease in a woman called Victoria Gray.

At the Sunnylands Estate, Merab, who was diagnosed aged ten, told us how she had overheard the doctor tell her parents that her options were limited. Hydroxyurea, a drug that reduces symptoms for some people, became available in the 1980s, but didn’t work for her. Later, she learnt about bone-marrow transplants, but her doctors told her that she would have to receive the treatment outside Africa and pay for it herself. Then, at the meeting, she learnt about gene therapy for sickle-cell disease.

“The only thing more difficult than hearing that you have a disease for which there are no cures, is to hear that you have a disease for which cures are available, but they are not available to you,” she told us.

Everyone with a devastating disease should have access to a cure when one exists. True progress will come only when low-income countries are included in the development of gene therapies.

Nature 631, 502-504 (2024)

doi: https://doi.org/10.1038/d41586-024-02310-y

References

1. GBD 2021 Sickle Cell Disease Collaborators. Lancet Haematol. 10, e585–e599 (2023).

2. Oron, A. P. et al. BMC Med. 18, 92 (2020).

3. Niederwieser, D. et al. Haematologica 107, 1045–1053 (2022).

4. Rao, E., Chandraker, S. K., Singh, M. M. & Kumar, R. Gene 896, 148022 (2024).

5. American Society of Gene & Cell Therapy and Citeline. Gene, Cell + RNA Therapy Landscape Report: Q1 2024 Quarterly Data Report (2024). Available at go.nature.com/3w4fbsk

6. Colombatti, R., Hegemann, I., Medici, M. & Birkegård, C. J. Clin. Med. 12, 5538 (2023).

7. Cancellieri, S. et al. Nature Genet. 55, 34–43 (2023).

8. Henrard, S. & Arickx, F. Bull. World Health Organ. 94, 779–781 (2016).

9. Ataga, K. I. Blood 142, 2047–2054 (2023).

10. Adigwe, O. P., Onoja, S. O. & Onavbavba, G. J. Blood Med. 14, 367–376 (2023).

11. Barros, L. R. C. et al. Cancers 14, 2667 (2022).

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Competing Interests

The authors declare no competing interests.

Work

- Science on the edge: how extreme outdoor skills enhanced our fieldwork

- Your reagent is past its use-by date. Should you bin it?

- Serpents of the people: how a religious festival helps me to monitor snake behaviour

CAREER FEATURE 15 July 2024

Science on the edge: how extreme outdoor skills enhanced our fieldwork

Researchers describe how they developed extraordinary physical skills for their research and how to stay calm in the face of risks.

By

Shihab Jamal

Find a new job

Jason Gulley learnt ice climbing to study cave formations in glaciers such as the Ngozumpa glacier in Nepal. Credit: Jason Gulley

One summer evening in 2004, around the campfire during a rock climbing trip to the Red River Gorge in Kentucky, Doug Benn, a glaciologist at the University of St Andrews, UK, shared a photo with Jason Gulley. It was a picture of a 6-metre-deep hole, shaped like an upside down ‘L’ in the walls of the Khumbu Glacier, the highest glacier in the world at an altitude of 7,600 m on the southwestern slopes of Mount Everest in Nepal. Then a final-year undergraduate geology student at Eastern Kentucky University in Richmond, Gulley thought that the hole’s shape looked like the result of meltwater drainage, which hinted that long, intricate caves can form and melt in glaciers. But, it would be impossible to work out exactly how those processes happen without getting inside these voids.

So, the two dreamt up what some might consider to be an outrageous plan. Gulley, now a geologist at the University of South Florida in Tampa, decided to learn to ice climb so that he could explore the insides of the world’s highest glaciers. Gulley already had experience caving and underground diving, but that wasn’t enough for expeditions that would require specialized technical skills for the extreme environmental conditions. So, he started taking regular trips, driving about 15 hours to Batchawana Bay, Canada, to learn ice climbing.

A year later, Gulley, Benn and their colleagues climbed and crawled along narrow, dark passages with only a headlamp, to explore and map the tunnels inside glaciers that sit at an altitude of around 5,000 m in the Himalayas. “I realized that I might be able to combine exploration and research into a career,” says Gulley. “Our research showed that glacier caves in the Everest region are literally rotting glaciers from the inside out and they form as a direct result of a warming climate.”

Indeed, many research projects or data-collection tasks require scientists to learn unusual, extreme or very technical physical skills that go beyond the typical field-work duties of setting up a tent or driving an all-terrain vehicle. Nature spoke to enterprising scientists who were willing to learn such skills. They describe how they did it, both mentally and physically, why safety is paramount and how their new talents opened up research avenues.

Scientists applying high-stakes skills for research fieldwork can face injury, equipment failure, extreme weather or vast and harsh environments. Researchers, therefore, must prioritize safety, enrol in professional training courses when available and not push themselves past their limits. But with the right training and precautions, they can be flexible and quick thinkers when the unexpected happens.

Steep learning curves

It’s exciting when a challenging adventure can also be the key to answering your research questions, says Krzysztof Gaidzik, a palaeoseismologist at the University of Silesia in Katowice, Poland, who studies ancient and active earthquakes. He learnt to rock climb to explore granite formations during his PhD fieldwork near the border of Poland and the Czech Republic.

“Research and adventure goals must always go together in balance,” says Gaidzik, who has learnt several extreme adventure skills to support his research. He says it’s important for researchers to constantly practise and hone their skills to be ready for surprises on expeditions. At the same time, researchers should respect the adrenaline rush and recognize when it could push them to take risks that might distract them and jeopardize their research goals or safety.

In 2008, Gaidzik began visiting Colca Canyon in southern Peru, one of the world’s deepest canyons at around 3,400 m, to study ancient earthquakes and active tectonic structures. While collecting data, he realized that the ruggedness of the landscape sometimes limited his access to sites of interest. So, he learnt to snorkel to reach rocks submerged in deep river water. “I’m currently planning to learn to paraglide to land in difficult-to-reach places,” he says.

Elise Imbeau, a biologist and co-founder of Viventem, a scientific support agency for Arctic research in Cambridge Bay, Canada, uses canoes to guide research teams across the Canadian Arctic Archipelago’s rivers and lakes. These teams might study marine and freshwater fish, kelp-forest biodiversity or freshwater invertebrates. She also guides teams in fields outside her specialty, such as polar chemistry and geology.

Elise Imbeau with dog Shila, who looks out for polar bears on canoe field trips in the Arctic.Credit: Dani Nowosad

And when it’s too difficult for teams to collect samples, Imbeau and her partner, who have run the agency since 2020, take up the mission. Some groups of researchers use helicopters to access hard-to-reach sites in the region. But not everyone has the budget. “We thought, why not try it by canoeing, and the idea proved successful,” says Imbeau.

Imbeau already had experience kayaking and white-water rafting, but those skills weren’t cut out for the Arctic expeditions. Using an inflatable canoe, Imbeau began by practising canoeing through ice patches near the coast to reach the middle of half-frozen lakes and seas, where the ice cover is thick enough to walk on. She had to learn to make perfect forward strokes in the unwieldy inflatable — positioning the paddle blade in such a way that it pulled water efficiently for maximum momentum. She also learnt how to do capsizing self-rescue techniques to get back into the canoe if help is not available. “When guiding teams,” says Imbeau, “we teach them the basics of canoeing, and stay close to make sure they are paddling safely.”

Claudio Sillero, a biologist at the University of Oxford, UK, has been leading the Ethiopian Wolf Conservation Programme (EWCP) since the late 1980s to monitor these endangered animals. Growing up on a farm in Argentina, Sillero spent time around horses, but was not an expert rider. When he saw the nomadic Oromo people in Ethiopia riding horses while herding livestock, it struck him that riding would help him to cover the roughly 2,000-square-kilometre range of mostly rugged terrain, where wolf packs roam. Each trek covering the range takes a week of travel on horseback.

Claudio Sillero (left) and his colleague track wolves on horseback in Ethiopia.Credit: Claudio Sillero

Sillero says that riding took him to places that he couldn’t reach otherwise because of cliffs and the lack of roads. His desire to track the wolves effectively drove him to develop his riding skills so he could canter and gallop on steep terrain. EWCP owns a collection of horses and Sillero’s team of research associates and students learn riding basics. “We can cover not only a larger range but also achieve the closest distances,” to the wolves, says Sillero. Unlike when researchers are on foot, he explains, the “wolves don’t react with fear or flee when we approach on horses, but rather ignore us,” he says. This is because no one hunts them on horseback, he notes.

Safety first

Wild animals can pose a significant threat, however. “I boat up on the Beaufort Sea, north of Alaska, and constantly look out for polar bears, they are a real threat,” says Noelle Helder, a marine scientist at the University of Alaska Fairbanks. Imbeau notes that her teams “take trained dogs to alert us by barking when any polar bears come close”.

Helder wasn’t that into marine sports before she began her career studying marine biology at the University of South Florida in Tampa, where she learnt to scuba dive and sail, and began working on a variety of marine research projects across the Caribbean. Helder and her colleague once ended a research dive to map coral reefs when an intense thunderstorm began to form. They immediately returned to the boat to find its batteries dead. The situation became tense as the waves, wind and thunder increased, until the researchers finally flagged down a passing boat. Although they returned safely to shore after abandoning their own boat, “it was a reminder that things can change very quickly when you’re working on the water”, says Helder. Currently, Helder maps the effects of climate change on Alaska’s coasts and coral-reef ecosystems.

Working on water brings not only rapidly fluctuating weather, but also strong winds, shifting tides and currents. “Before going out to work in the field, we list the potential risks for a range of emergency scenarios, including rapid-response plans for accidents and sudden natural conditions,” says Imbeau.

Both Imbeau and Helder usually work on water during the milder months. But even in warmer seasons, the water temperature remains dangerously cold, around 0–7 °C, and so exposure can be life threatening, with loss of consciousness occurring within 15–30 minutes of immersion. Imbeau says that researchers can reduce risks by wearing drysuits, checking the detailed weather forecast and learning how to determine whether the weather is changing. “There are safety procedures that we adhere to, such as communication devices, first aid and an emergency numbers list,” she says. Importantly, she notes, “every time we go to the field, there is someone in town who knows where we are”.

Climbers also must check the weather carefully to avoid windy or wet conditions that can result in falls or falling rocks. Along with a helmet, food, water, a headlamp and a flashlight, Gaidzik makes sure that his bag contains a first-aid kit, a knife, map, compass, a mobile phone and a GPS system for emergencies. “It’s also very important to check and clean ropes regularly,” he says.

Palaeoseismologist Krysztof Gaidzik trains to keep his rock-climbing skills sharp for fieldwork on hard-to-reach active tectonic structures.Credit: Krzysztof Gaidzik

Rock and ice climbers must also constantly assess changing terrain conditions as they climb. Gulley has been in precarious situations that required quick thinking. Once, inside a Himalayan ice cave, Gulley and his colleagues walked onto a false floor of ice, which suddenly collapsed and plunged Gulley into darkness. Fortunately, he only fell about two metres and was unhurt. “I felt like my heart stopped for a moment,” he says.

Even minor injuries or broken equipment can end expeditions prematurely. “It’s important to try not to get into dangerous situations while climbing or diving in an unforgiving environment,” Gulley says, “If something goes wrong, risks would have resulted in real costs,” or worse, the loss of life.

Gaidzik climbs remote rock formations, during which days can go by without seeing locals who could help in an emergency. On one expedition in Colca Canyon for a project on landscape evolution, he was climbing down a steep gorge when suddenly rocks started falling around him. Although the rocks he was tied into above had become loose, the fallen rocks had also made the rock wall beneath him unstable. There was no chance to get down safely: “I realized how dangerous it was and how far away I was from any possible medical help,” says Gaidzik. So, he untied, moved across to a more stable path and climbed back up to the surface.

Riding horses can also be risky, says Sillero. They can be fearful and refuse to pass through narrow paths or walk on steep slopes. If forced, they can rear and throw the rider. Sillero has learnt to calm and reassure horses in such situations. “Developing trust and mutual respect with a horse is essential for safe and successful riding over rough terrain,” says Sillero.

Mental and physical preparation

When applying extreme skills to fieldwork, these researchers recommend regular exercise, adequate sleep and a balanced diet to maintain energy and mental clarity, prevent injury and improve strength and flexibility. Gaidzik brings protein-rich snacks and plenty of water to maintain hydration and body temperature in harsh conditions.

He says that part of the mental preparation starts with setting scientific goals as a priority and avoiding distractions that don’t lead to those goals. Furthermore, he says, it’s good to leave your comfort zone and identify fears before heading out on your research expedition. Helder thinks it’s important to ensure there is a culture of safety among the research team. People should feel comfortable speaking out if they feel something is unsafe and know that they will be heard and respected for that decision. “You’re not going to be creative in an environment where people can’t express their concerns or talk to you about safety,” she says.

When Helder began her master’s research in 2019, she already had a certified scientific diving licence, which requires specialized training and qualifications that vary according to the scientific specialization. Scientific divers must attend to all the safety factors required when scuba diving, but also be able to multitask effectively. She also had a captain’s licence to operate diving and trawling boats, which allowed her to participate in research projects at hard-to-reach sites. She says that her previous experience has allowed her to make plans for research, evaluate risks and to make key, quick decisions in the field. “This has definitely opened many doors in my career to new opportunities to work with new people and explore new places,” says Helder.

Her success encouraged Helder to acquire another extreme skill — in 2021 she began training as a long-distance ocean rower. Helder and three other female marine scientists teamed up to win the women’s division of the World’s Toughest Row in 2024, a race in which they crossed the Atlantic ocean in 38 days. “Rowing wasn’t in our skill set when we started,” she says. “But all of our collective experience working in different areas of diving research, boating and being offshore in remote places contributed to us winning.”

Nature 631, 695-697 (2024)

doi: https://doi.org/10.1038/d41586-024-02311-x

TECHNOLOGY FEATURE 15 July 2024 Correction 17 July 2024

Your reagent is past its use-by date. Should you bin it?

Some products are perfectly fine to use after their expiration date — if quality-control tests check out.

By

Diana Kwon

Even if their best-before dates have passed, some materials can still be used — and doing so is often a cost-conscious choice.Credit: Getty

Everyone has at one time or another grabbed something out of the fridge or store cupboard, only to find that its use-by date is long past. It still looks good, and it smells OK. Should you throw it out? For most of us, the item in question might be milk, eggs or cereal. For Giorgio Cattoretti, it was antibodies.

Cattoretti has travelled the world with his antibodies. He started collecting them in the early 1990s at the National Cancer Institute of Milan in Italy for his studies in immunology and oncology, and brought them with him when he moved to the United States in 1994. “They stayed in the fridge for 13 years in the US, and then I shipped them back to Italy,” says Cattoretti, a now-retired pathologist whose most recent appointment was at the University of Milano-Bicocca in Italy. “And they kept working.”

Cattoretti held on to those antibodies for both sentimental and practical reasons: some were unique clones that were no longer available, others were simply too costly to replace. But, according to their best-before dates, many had been expired for more than a decade.

Whether a reagent is still usable after its expiration date is a question that researchers often ask. Social-media websites, such as Reddit and ResearchGate, are full of discussions sparked by posts such as: “How seriously do you take chemical expiry dates?” and “Do PCR reagents really expire?”

The answer is: sometimes. Certain reagents will still work just fine, so using them can be both financially savvy and environmentally friendly. But scientists must rely on intuition, experience and the right tests to make sure that outdated materials are working properly.

Unexpectedly long life

The laboratory in which Sofia Kinton earned her doctorate at Northeastern University in Boston, Massachusetts, had a large collection of restriction enzymes — proteins used to cleave DNA at specific sites — many of which were old and seldom used. “My [principal investigator] was a classic hoarder,” Kinton jokes. “He never threw anything out.”

One day, a student from a neighbouring lab asked to borrow one. “We discovered that the enzyme was from the 1990s, making it one year older than she was,” says Kinton, who is now a research scientist at the pharmaceutical company Sanofi in Boston. The enzyme had been expired for decades. “She tried it anyway, and it still worked.”

A handful of studies have explored reagent longevity. About a decade ago, for example, Cattoretti and his colleagues put some of the oldest antibodies in their lab fridges to the test[1]. They assessed three dozen antibodies that were 12–26 years past their expiration date and found that 34 still worked correctly in immunohistochemistry experiments, in which antibodies are used to highlight specific markers in thin slices of tissue. Other more recent studies have similarly found that antibodies can be used long beyond the manufacturers’ best-before dates — some as many as 21 years later[2].

For Stephen Bustin, a molecular biologist at Anglia Ruskin University in Cambridge, UK, a similar study of PCR master mixes, which are premixed blends of reagents used to amplify nucleic acids, was motivated by the funding drought that UK researchers experienced in the wake of the country’s exit from the European Union in 2020. Bustin and his team came to two conclusions: the master mixes worked long after their expiry date — more than seven years, in one case — and they could be used at lower concentrations than recommended, to stretch the supply[3].

Some reagents still work well for decades after they have expired.Credit: Stephen Barnes/Science/Alamy

Academics often decide to keep — and use — expired reagents for both financial and practical reasons, Bustin says. For instance, because many researchers buy materials in bulk near the end of the fiscal year to spend the remainder of their lab budgets, many reagents sit for long periods before they are used up. “I’m using master mixes that are from around 2007,” he says.

Fabian List, who in 2023 completed his PhD in entomology at Texas A&M University in College Station, says that he used a PCR master mix that had expired five years earlier to complete his dissertation research. “In my experience, it was quite common to use expired reagents,” says List, who now works at the pest-control company APC in Hamburg, Germany. If materials are stored correctly — that is, according to the manufacturer’s recommendations — and remain undisturbed, “they normally last much longer than you would expect”, he adds.

Setting a date

To establish a product’s best-before date, manufacturers typically perform a battery of tests. This includes placing the material under various conditions — different temperatures, for example — and assessing how its physical parameters, such as pH and conductivity, and performance in a typical experiment change over time, explains Markus Sprenger-Haussels, vice-president of product development at Qiagen, a scientific-reagent manufacturer with headquarters in Venlo, the Netherlands. Companies also often stress-test their products — for instance, under very high temperatures — to determine their shelf life.

Other methods, such as examining historical data and ongoing monitoring of reagent performance, are used, too, says Lívia Guadaim, a communications business partner for global science and lab solutions at Merck, a life-sciences reagent manufacturer headquartered in Rahway, New Jersey.

If a reagent is expired, that doesn’t necessarily mean that it will stop working immediately, but its performance will usually decline over time, Sprenger-Haussels says. “With the shelf life we indicate on our products, we are guaranteeing that they are good, even in worst-case scenarios, as long as you use the product as described,” he says. “If people use them longer than this, then it’s an off-label use and we simply cannot guarantee the performance.”

Storage conditions such as temperature, humidity and light exposure can affect reagent lifespan. Certain chemicals, such as salts, can last almost indefinitely if stored properly, says Mikael Kubista, a biochemist at the Institute of Biotechnology of the Czech Academy of Sciences in Vestec. “When I was a trainee, we had chemicals that had been ordered 20, 30 years earlier by previous professors, and they were often working fine.”

The nature of the study can also determine how efficient a reagent needs to be. If a scientist is simply isolating genomic DNA from blood, a high PCR yield might not be crucial — so a lower-performing reagent could suffice. However, if a researcher is trying to nail down the culprit behind a rare infection, with only a few copies of viral DNA present in the sample, it’s much more important to use reagents at peak performance.

Kinton says that the case of the student borrowing an expired enzyme was a good example of when to use such a reagent — testing the enzyme’s validity took only an hour or two, and the study had low stakes. “There’s really not a downside to attempting it,” Kinton says. “If it’s a situation where it’s a more precious sample or there’s a more urgent need, we might be more cautious.”

To use or not to use?

To extend a the lifespan of a reagent, researchers should consider not only storage factors such as light, temperature and humidity, but also preparation methods. Buffers, for instance, can influence the stability of PCR primers and probes, Kubista says. It’s also important to avoid freezing and thawing reagents several times, he adds. “Typically, we freeze them in aliquots, so we only have to thaw them once, because freeze–thawing degrades material.”

Also consider the containers in which materials are stored. Some reagents come in plastic containers that can become brittle if handled infrequently, says Ann Ehrenhofer-Murray, a molecular biologist at the Humboldt University of Berlin, who, like Cattoretti, has held onto antibodies that are decades old. “That’s a reason to throw it away — not because the due date is out.”

There are, however, some settings, such as clinical diagnostics and clinical trials, in which rules around expiration dates are much stricter. Many researchers say that, outside these situations, it’s perfectly fine to use your old reagent — even if it’s more than two decades past expiry — as long as tests show that it can still do the job that it was designed for.

Kubista’s advice: test the reagent on a previously analysed sample or compare it with a newly purchased, non-expired version, to see whether it still works as intended. And repeat that experiment several times, he adds — that’s just good science. “If the reproducibility is satisfactory with the expired reagent, it’s usually fine to use.”

Nature 631, 698-699 (2024)

doi: https://doi.org/10.1038/d41586-024-02312-w

Updates & Corrections

- Correction 17 July 2024: An earlier version of this story misspelt Markus Sprenger-Haussels’s name.

References

1. Argentieri, M. C. et al. Histopathology 63, 869–876 (2013).

2. Henwood, A. F. Biotech. Histochem. 98, 86–93 (2023).

3. Bustin, S. et al. Int. J. Mol. Sci. 23, 8486 (2022).

WHERE I WORK 15 July 2024

Serpents of the people: how a religious festival helps me to monitor snake behaviour

Ernesto Filippi tracks the health of captured specimens thanks to a citizen-science project inspired by an ancient ritual.

By

Francesco Martinelli

Ernesto Filippi is a biological technical officer at the Italian Ministry of Environment and Energy Security. Credit: Elisabetta Zavoli for Nature

“In this photo, I’m holding a snake known in Italy as a cervone (Elaphe quatuorlineata). It’s one of 189 that we’ve inspected so far in 2024 in Cocullo, a small town in the Abruzzo region of central Italy.

In Cocullo, snakes feature in a spring ritual that dates back at least 2,000 years. At the start of April, snake catchers (known as serpari) capture dozens of snakes in preparation for what’s become Christianized as the procession of St Dominic of Sora. A statue of the saint — who lived in the region and, according to legend, cured many people from snake bites — is then adorned with live snakes and carried through the streets of Cocullo.

A project to monitor the health and growth of the snakes has been in place for more than 15 years. I work with Gianpaolo Montinaro, who conceived the project in 2007, to supervise the monitoring on behalf of Italy’s environment ministry. My team, which includes two veterinarians at the University of Bari, acquires biological data and samples and places a microchip in each snake so that it can identified in future. At the end of the ritual, the snakes are released at the exact spot where they were captured.

The information collected contributes to scientific publications and strategies on snake conservation — and, from this year, to a project at the Max Planck Institute of Animal Behavior to study animals’ responses to natural disasters. We’d never have been able to collect data on snake populations for so long had it not been for the Cocullo festival. Since 2010, we’ve gathered data on more than 1,500 snakes. This is a genuine example of citizen science, and is all the more impressive given that snakes are widely feared.

Outside the snake-festival season, I deal with invasive exotic species, biodiversity-protection policies and endangered species.”

Nature 631, 702 (2024)

doi: https://doi.org/10.1038/d41586-024-02313-9

This interview has been edited for length and clarity.

Research

- Mechanical release of homogenous proteins from supramolecular gels

- Chemical reservoir computation in a self-organizing reaction network

- Human degradation of tropical moist forests is greater than previously estimated

- A deep catalogue of protein-coding variation in 983,578 individuals

- Transposase-assisted target-site integration for efficient plant genome engineering

- Brainstem Dbh+ neurons control allergen-induced airway hyperreactivity

- Semantic encoding during language comprehension at single-cell resolution

- Spike deep mutational scanning helps predict success of SARS-CoV-2 clades

- Plasmacytoid dendritic cells control homeostasis of megakaryopoiesis

- Emergence of large-scale cell death through ferroptotic trigger waves

- NBS1 lactylation is required for efficient DNA repair and chemotherapy resistance

Article Open access Published: 17 July 2024

Mechanical release of homogenous proteins from supramolecular gels

Simona Bianco,

Muhammad Hasan,

Ashfaq Ahmad,

Sarah-Jane Richards,

Bart Dietrich,

Matthew Wallace,

Qiao Tang,

Andrew J. Smith,

Matthew I. Gibson &

…

Dave J. Adams

Nature volume 631, pages 544–548 (2024)

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Abstract

A long-standing challenge is how to formulate proteins and vaccines to retain function during storage and transport and to remove the burdens of cold-chain management. Any solution must be practical to use, with the protein being released or applied using clinically relevant triggers. Advanced biologic therapies are distributed cold, using substantial energy, limiting equitable distribution in low-resource countries and placing responsibility on the user for correct storage and handling. Cold-chain management is the best solution at present for protein transport but requires substantial infrastructure and energy. For example, in research laboratories, a single freezer at −80 °C consumes as much energy per day as a small household[1]. Of biological (protein or cell) therapies and all vaccines, 75% require cold-chain management; the cost of cold-chain management in clinical trials has increased by about 20% since 2015, reflecting this complexity. Bespoke formulations and excipients are now required, with trehalose[2], sucrose or polymers[3] widely used, which stabilize proteins by replacing surface water molecules and thereby make denaturation thermodynamically less likely; this has enabled both freeze-dried proteins and frozen proteins. For example, the human papilloma virus vaccine requires aluminium salt adjuvants to function, but these render it unstable against freeze–thaw[4], leading to a very complex and expensive supply chain. Other ideas involve ensilication[5] and chemical modification of proteins[6]. In short, protein stabilization is a challenge with no universal solution7,8. Here we designed a stiff hydrogel that stabilizes proteins against thermal denaturation even at 50 °C, and that can, unlike present technologies, deliver pure, excipient-free protein by mechanically releasing it from a syringe. Macromolecules can be loaded at up to 10 wt% without affecting the mechanism of release. This unique stabilization and excipient-free release synergy offers a practical, scalable and versatile solution to enable the low-cost, cold-chain-free and equitable delivery of therapies worldwide.

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Main

Consideration of the pathways of thermal protein deactivation shows that irreversible aggregation, rather than chemical degradation or unfolding, is the primary mechanism for loss of activity. For example, simple mechanical stimulation (shaking) of insulin leads to aggregation into amyloid-type fibres and loss of efficacy and bioavailability, and the gene therapy Zolgensma has just a 14-day shelf life, cannot be agitated and must be retained at 2–8 °C. Clearly, this is a barrier to wider use. Aggregation of recombinant human interferon beta leads to immunogenicity for patients with multiple sclerosis[9]. Emerging antibody-based therapies have a problem with aggregation at all stages of their life cycle, necessitating complex formulation processes.

Others have developed synthetic polymers with trehalose side-chains for protein stabilization, enabling both freeze-drying and heating stabilization, but the conjugation reduces activity[10]. However, for many therapeutic applications, covalent conjugation of a polymer using multistep synthetic chemistry and purification is not practical and has the requirement to seek regulatory approvals for each material, substantially increasing the barrier to clinical translation. This is a broad issue that means any stored protein is eventually delivered mixed with its stabilization agents, rather than the desirable solution of protein in buffer. Fully reversible hydrogels based on polyethylene glycol (PEG) have emerged to protect diverse proteins against thermal stress11,12 and messenger RNA formulations have been stabilized in a dissolvable matrix[13]. Similarly, frozen formulations to prevent ice crystal growth suppress protein aggregation[14]. However, in each case these gels are based on new chemical entities (and hence a substantial barrier to human use), may require a chemical stimulus (pH, sugars) to release the protein, which in practice must be dosed by the user, and take more than 1 h to release the protein. While potent, these strategies result in protein mixed with gel-forming components, which require separate approval and rigorous evaluation of their safety profile before being suitable for human use. Even materials based on PEG have immunological concerns for frequent exposure11,12,15.

An ideal biologic storage and transport solution would (1) remove or reduce cold-chain requirements; (2) be broadly applicable; (3) have zero or minimal excipients in the delivered protein solution; (4) not require complex chemical triggers to release; and (5) tolerate high loadings up to 100 mg ml−1. Here we solve each of these points using a simple, low-resource strategy. We show that low-molecular-weight supramolecular gel networks can physically entrap proteins, thereby preventing irreversible aggregation, and hence retain function at temperatures as high as 50 °C for at least 4 weeks. Upon applying pressure to push the gelated solution through a syringe filter, pure non-aggregated homogeneous and functional protein is released with all excipients trapped in the filter, ensuring only protein and buffer are delivered. This differs fundamentally from previous approaches[11] which require a chemical trigger or dilution[16] and do not therefore provide pure protein.

Gels can be formed by the self-assembly of low-molecular-weight gelators (LMWG)17,18. These are small molecules that self-assemble to give long fibrous structures which entangle to form a three-dimensional network. These gels tend to be very stiff but break at low strain. This breaking at low strain has been widely described as a drawback of these systems19,20. However, here we use this perceived failing as a unique benefit to enable a mechanical trigger for homogeneous protein delivery that is not possible with conventional gels.

We formed gels from a range of LMWG (Supplementary Fig. 1). On the basis of gelation at physiological conditions, we focused on one system (Fig. 1a). The advantage of this system is that gelation can be induced by the addition of a TRIS buffer solution to a concentrated solution of the LMWG at pH 8 to give a final self-supporting material at pH 6.8. The mechanical properties can be tuned by the addition of a calcium salt (Fig. 1b). In the absence of the calcium salt, the system has relatively low storage (G′) and loss (G″) moduli, with the material breaking at low strain (less than 1%). The materials are not true gels in that there is some frequency dependence on G′ and G″, and at low frequency G′ and G″ are essentially the same (Supplementary Fig. 3e). In the presence of the calcium salt, the moduli are improved and the samples are less frequency dependent (Supplementary Fig. 3e) but, crucially, the material still breaks at low strain (Fig. 1c and Supplementary Figs. 2, 3 and 4). Using small-angle X-ray scattering (SAXS; Supplementary Fig. 5 and Supplementary Table 1) to probe the sample without salt, the scattering data can be fit simply to a power law (Fig. 1d, red), suggesting the presence of large heterogeneities in the network or irregular aggregates in the gel. The data for the gel prepared in the presence of CaCl_2 could be fit to an elliptical cylinder model combined with a power law (Fig. 1d, black, Supplementary Fig. 6 and Supplementary Table 2). The data further present a peak at q = 0.154 Å (d = 40.8 Å), which suggests that the gelator fibres are part of a periodic arrangement in the structure with a crystalline character. Circular dichroism indicates a different arrangement in the secondary structures of the systems. The circular dichroism spectrum of the sample without any salt exhibits a negative band with a shoulder in the range 200–240 nm (Fig. 1e, red, and Supplementary Fig. 7). The sample with CaCl_2 shows a negative band around 235 nm and a positive band occurring near 190 nm, which could be indicative of a β-sheet-like arrangement (Fig. 1e, black, and Supplementary Fig. 7).

Fig. 1: Initial gel studies.

a, Chemical structure of the gelator used here. b, Photographs of example gels formed at pH 6.8 (left) and at pH 6.8 in the presence of a calcium salt (right). c, Typical strain sweep for gels formed in the absence (red) and presence (black) of a calcium salt, showing a high stiffness (G′, filled circles) but a low breakage strain. Measurements were performed in triplicates and plotted data are presented as mean ± s.d. d, SAXS patterns of gels made in presence of calcium chloride (black) and without CaCl_2 (red). The fits obtained through model fitting are overlayed on each spectrum (details in Supplementary Information). The plotted data show the averaged scattering pattern obtained from five measurements across the sample. The error bars are generated during data processing by calculating scattering signal uncertainty in the detected data according to previously published methods29,30. e, Circular dichroism spectra of gels in presence of calcium chloride (black) and without CaCl_2 (red). Circular dichroism spectra were collected in duplicates for each sample and averaged. f, Cartoon showing the concept of this work. g, Images exemplifying the syringe filter release protocol for gels. The gel is first loaded in a syringe fitted with a 0.22 μm filter. The gel is passed through the filter by gentle extrusion, releasing a clear solution. a.u., arbitrary units; CD, circular dichroism.

Encapsulating additives, including proteins, in gels formed by LMWG is trivial21,22. The proteins are trapped in the space between the fibres; if the gel network is sufficiently permanent and the pores between fibres sufficiently small, the protein is unable to easily diffuse in the gel and hence aggregate[23]. Indeed, restricted diffusion has been shown to occur in LWMG by means of controlled release experiments24,25. Combining this concept with the potential for these gels to break at low strain means we can form gels within a syringe for long-term room temperature storage, followed by release through a syringe filter; the essentially insoluble extended supramolecular network will be trapped in the syringe filter, meaning that only the pure protein is released (Fig. 1f,g).

A model macromolecular cargo was loaded into gels to probe the effects on gel structure, function and release. Dextran (relative molecular mass Mr 6,000, hydrodynamic radius 1.6 nm)[26] was chosen as a non-interacting additive with a similar size to insulin[27]. Dextran was added at concentrations up to 10 wt% (Fig. 2a, a value far above the loadings required for real-world drug storage and in line with recent moves towards higher-concentration biologic formulations; Supplementary Fig. 8), while keeping all other parameters constant. Our gelation approach is almost instantaneous: in comparison, chemically triggered hydrogel storage solutions require the addition of dosed release agents, such as sugars and require up to an hour to gel[11]. The gel properties were retained over all loadings, which is essential for a broadly applicable storage solution (Fig. 2b). Extended aging experiments showed that these properties did not change significantly over at least 136 days (Fig. 2c). The gels can be formed in situ inside a syringe. The added dextran does not affect the gelator’s packing (Supplementary Fig. 8 and Supplementary Table 2). As initial proof-of-concept, we entrapped a fluorescent dextran within a gel and then extruded it through a syringe filter. Pure dextran was collected directly (Fig. 2d), with 92 ± 2% of the expected amount collected, showing that there is little entrapped permanently in the network (Supplementary Fig. 9). No gelator could be detected in the extruded liquid by 1H NMR spectroscopy (Fig. 2e), showing that the gel network is trapped in the syringe filter and our simple approach leads to excipient-free homogeneous protein on demand. The gelator is trapped in the filter as shown by Fourier-transform infrared spectroscopy (FTIR) data (Supplementary Fig. 10). To add further quantification, we used LMWG which incorporate an inherently fluorescent group. After extrusion, very low fluorescence could be detected in the extrudates (less than 5 ppm; Supplementary Fig. 11), showing the power of this approach (Supplementary Figs. 12–16).

Fig. 2: Dextran is encapsulated in, and released from, CaCl_2-crosslinked LMWGs.

a, Gels formed using CaCl_2 encapsulating dextran (left, 1 wt%; right, 10 wt%). b, Rheological data for gels formed encapsulating dextran. Measurements were performed in triplicate and plotted data are presented as mean ± s.d. c, Rheological data for gels formed encapsulating 1 wt% dextran over time. Measurements were performed in triplicate and plotted data are presented as mean ± s.d. d, Extrusion through a syringe filter releases the entrapped material. e, NMR evidence showing that no gelator is released during extrusion. The peaks at around 5.0 and 7.6 ppm are from the buffer.

With confirmation that macromolecules can be encapsulated and selectively released by mechanical trigger, protein storage was undertaken to demonstrate recovery and the mode of action (preventing aggregation). As a first challenge, we chose to encapsulate (bovine) insulin as a model therapeutic protein (Fig. 3a). Insulin is one of the most widely used protein drugs in the world but must be stored cool or freeze-dried, requiring the user to self-prepare the solution before injection[28]. The instructions for insulin are very specifically not to shake, as it is prone to aggregate into extended amyloid fibres, losing efficacy and bioavailability. After initial screening using dynamic light scattering, insulin was loaded into gels (with no Ca2+ in this case), which were then incubated at 25 °C and shaken at 600 rpm: this is very aggressive agitation, far beyond a real-world stress. After this, pure excipient-free protein was recovered by action of the syringe and any aggregation to amyloid was determined by the thioflavin T assay (Fig. 3a). All samples were treated identically, and the solution-only samples were passed through the syringe filter, to exclude effects of the filtering process on aggregation or the possibility that the filter could exclude aggregatess. Limited aggregation was detected in the gel-loaded samples, but extensive aggregation occurred in the absence of the gel. Saturation transfer difference (STD) nuclear magnetic resonance (NMR) spectroscopy strongly implies that that the insulin is not adsorbed onto the fibres of the gel while encapsulated (Supplementary Fig. 17). The rheological properties of the gels are somewhat affected by the addition of insulin (Supplementary Fig. 18), but the overall process is still effective; in no case was gelator detected in the solutions extruded through the syringe filter (Supplementary Figs. 19 and 20), although overlapping of peaks makes the analysis more difficult than in the dextran case. Complete recovery of the expected volume of insulin (100% taking into account the volume of the syringe filter used to collect the gelator) was achieved in the extruded sample (Supplementary Fig. 21). Mass spectrometry before and after the encapsulation and release demonstrate the chemical stability of the insulin under these conditions (Supplementary Fig. 22). Additionally, SAXS shows that the scattering from a solution of insulin before encapsulation and from a solution after encapsulation and release is very similar, demonstrating a lack of aggregation (Supplementary Fig. 23). Hence, our approach provides dual protection against aggregation and allows protein release in a pure form. Indeed, we can encapsulate insulin at a concentration of 3.2 mg ml−1, a concentration around that of commercial formulations (U100), with complete release of excipient-free insulin (Supplementary Figs. 19 and 20). We also tested two structurally related LMWG (Supplementary Fig. 24), which protected insulin against aggregation, confirming that our mode of action is linked to physical immobilization, not a specific interaction. Finally, a cell-based assay confirmed that released insulin retained CD220 binding, equal to fresh, demonstrating that it is correct folded and retains biological activity (Fig. 3d).

Fig. 3: Retention of protein function in hydrogels.

a, General preparation process for gel. b, Probe for aggregation inhibition using insulin with thioflavin T assay. c, Quantification of thioflavin T assay (n = 5); error bars, mean ± s.d. d, Cell-based assay for CD220 recognition by insulin following recovery from gel with shaking at 600 rpm for 24 h, compared to fresh (n = 2); error bars, mean ± s.e.m. e, The β-galactosidase assay for post-recovery function. f, Activity recovery after 7 days at 50 °C for independent repeats (n = 3); error bars, mean ± s.d. g, Comparison of 1 and 4 weeks storage and recovery of β-galactosidase (solution for 1 and 4 weeks, n = 3; gel for 1 week, n = 5; gel for 4 weeks, n = 4); error bars, mean ± s.d.

As a further test, β-galactosidase was encapsulated (with added Ca2+). The β-galactosidase catalyses the decomposition of o-nitrophenyl-galactoside allowing colorimetric evaluation of activity. The gels were placed in an incubator at 50 °C (a stringent test beyond feasible transport conditions) for 7 days (Fig. 3e). The rheological properties of samples at this temperature are still gel-like as long as the calcium salt is present (Supplementary Fig. 25), but calcium does reduce the recovered protein yield. After this time, protein was recovered by simple syringe action. Compared to fresh, β-galactosidase in buffer alone retained just 26.1 ± 0.4% of its function but across 15 repeats (3 technical, 5 biological) the gel-stored protein recovered 84.4–108.1% of function (per protein mass), with a mean of 97.1 ± 2.8% (Fig. 3f). This is equal performance to a multi-armed boronic acid function PEG hydrogel[11] but our technology functions at biomedically relevant volumes (0.5 ml versus 50 μl), does not require addition of any triggers, such as sugars or acid, and the resulting protein was excipient-free. As an even more challenging test, gels were stored at 50 °C but for 4 weeks. This removed all activity from the solution-stored β-galactosidase but the gel-stored protein retained up to 20% of its function (Fig. 3g): this is a remarkable level of recovery from a harsh storage condition at conditions that even the hottest locations on earth rarely face. To show that the gels are robust, samples were mailed through the UK postal service to experience a realistic distribution challenge (shaking, dropping, temperature range; Supplementary Fig. 26). Over the 2 day delivery, the gels were recovered intact and about 100% activity was retained, showing that this method could conceptually be used for the postal delivery of active proteins for healthcare (Supplementary Fig. 27).

We have demonstrated that protein function is retained in stiff low-molecular-weight gels when stored at temperatures as high as 50 °C for up to 4 weeks by preventing irreversible aggregation. Owing to the unique mechanical properties of the low-molecular-weight gels, they break when passed through an in-line syringe filter, thereby releasing the fully functional protein cargo with no need for any chemical triggers, and the effect is instantaneous. Owing to the extended supramolecular network formed by the gelators, all the gel components (for example, excipients) are retained in the filter, meaning that only protein and buffer are released. This means only pure protein in buffer is delivered, unlike all competing technologies which lead to protein containing new, often untested, excipients which would then be delivered to a patient and hence face regulatory and safety barriers to use. For example, others have described a system encapsulating a therapeutic to maintain function. In their case, release occurs on dilution, meaning that the polymer gel components are still present[16]. Similarly, encapsulation and stability have been shown in a multiarm PEG gel[11]. A release solution is required to break the gel, meaning that delivery would contain the gel components and release solution. Hence, our excipient-free approach is not just an iteration but a major step forward. This is also a benefit over freeze-dried or frozen formulations, as it requires neither on-use reconstitution nor energy-intensive ultra-low freezers, respectively. We have therefore provided a new scalable means of storing, transporting and safely using biologics, affording a simple and effective strategy to deliver biologic therapies to low-resource settings for which they are needed most.

Methods

All chemicals were purchased from Sigma-Aldrich and used as received. Deionized water was used throughout. The gelator used here was synthesized following the protocols of ref. [31]. Full details are provided in the Supplementary Information (p. 25). The other gelators tested (Supplementary Fig. 1) were prepared as described elsewhere[32].

pH measurements

The pH of solutions and gels was measured using an FC200 pH probe (Hanna instruments) calibrated using pH 4.01, 7.01 and 10.01 buffer solutions. The probe was rinsed with deionized water between measurements.

Rheology

Rheological measurements were carried out using Anton Paar Physica MCR301 and M101 Rheometers. A cup and vane (ST10-4V-8.8/97.5-SN42404) system was used for all frequency and strain sweeps, with a measuring gap of 1.35 mm. Gels were prepared directly in 7 ml Sterilin vials, which were loaded on to the rheometer and measured in situ to ensure that no damage was carried out to the gels by transfer from vials. Strain sweeps were performed from 0.08% to 1,000% at a frequency of 10 rad s−1. Frequency sweeps were performed from 1 to 100 rad s−1 at a constant strain of 0.1% (within the linear viscoelastic region for all gels). To test the behaviour of the materials at low frequency, frequency sweeps were further collected from 0.01 to 100 rad s−1 at a constant strain of 0.1% (within the linear viscoelastic region for all gels). To measure the yield stress of the gels, strain sweeps were performed at a frequency of 1 rad s−1 from 0.1% and 1,000%. The yield stress value was obtained by plotting the elastic stress (G′ γ_0) against the strain amplitude, according to previously published methods33,34,35,36,37.

To obtain frequency sweeps of the samples at higher temperatures, gels were prepared directly in 7 ml aluminium cups and loaded on to the rheometer. The temperature was raised linearly from 25 to 60 °C with a heating rate of 2 °C min−1. Then, frequency sweeps were collected from 1 to 100 rad s−1, while keeping a constant strain of 0.1% and temperature of 60 °C.

Small-angle X-ray scattering

SAXS experiments were performed at Diamond Light Source, at the I22 beamline[38]. The beamline operates at an energy of 12.4 keV and the camera length was set to 4.275 m to give a q range of 0.002–0.30 Å−1. The gels were prepared as described in the next section and immediately loaded in glass capillaries using a 1 ml syringe with a 21 G needle. The raw data were processed using the DAWN Science software (v.2.27)[39], according to a standard I22 pipeline[30]. As part of the processing, the backgrounds were subtracted from the raw two-dimensional SAXS data and a full azimuthal integration was performed to reduce the data to an I versus q plot. The plots were then fitted to structural models using the SasView software (v.5.0.4).

BioSAXS experiments were performed at the B21 beamline at Diamond Light Source. The beamline operates at a fixed energy of 13 keV and a camera length of 3.600 m to obtain a q range of 0.0031–0.38 Å−1.

A total 50 μl of each sample was loaded into a 96-well plate and measured using the BioSAXS EMBL Arinax sample-handling robot. For each sample, 30 × 1 s frames were collected at 20 °C. The two-dimensional raw data were processed in the DAWN Science software (v.2.27)[39] to yield the I versus q plots. The data were then averaged and the buffer background was manually subtracted using the ScÅtter software (https://bl1231.als.lbl.gov/scatter/, R. P. Rambo).

Formation of gels for CD-005

CD-005 gels were prepared either with or without the addition of CaCl_2. For both, 50 mg of CD-005 was added to 20 mg of K_2CO_3 and dissolved in 5 ml of deionized water under overnight stirring. For the samples without CaCl_2, 0.4 ml of the gelator solution was transferred to a vial, to which 1 ml of 1.5 M Tris HCl (pH 6.8) was added. For the samples formed with the calcium trigger, 5.5 µl of a 200 mg ml−1 CaCl_2 solution was first added to 1 ml of 1.5 M Tris HCl (pH 6.8) in a separate vial. This was then added in one aliquot to 0.4 ml of gelator buffer. For rheological measurements, the gels were prepared in vials and left undisturbed overnight on the bench. To prepare the samples in syringes, the gels were prepared as above and immediately transferred (less than 1 min) to a syringe through a 21 G needle. All the samples needed to be prepared at 19 °C to ensure optimal gelation.

Preparation of other tested gels by reduction in pH using GdL

Micellar solutions were prepared as previously described at concentrations of 5 mg ml−1 (ref. [32]). Gel samples were prepared in 7 ml Sterilin vials by addition of 2 ml of stock solution (adjusted to pH 10.5) to 16 mg of solid glucono-δ-lactone (GdL) for 5 mg ml−1 of solutions. The vials were swirled briefly by hand to ensure complete dissolution of GdL then left to stand overnight undisturbed.

Encapsulation of dextran, insulin and β-galactosidase

For the CD-005 gels, dextran was encapsulated in the gel by dissolution of dextran powder in 1.5 M Tris HCl, pH 6.8 at a range of concentrations (0.14–14 wt%). A total 1 ml of this solution was transferred to 0.4 ml of the gelator solution described above to achieve the desired final concentrations of dextran (0.1–10 wt%). For the gels obtained by reduction in pH, the dextran was dissolved at the various required concentrations in deionized H_2O under stirring. These solutions were then used to prepare the micellar solutions of the gelators at 5 mg ml−1.

Release protocols through syringe filter

To release the gel with dextran inclusion, the gel prepared in the syringe was passed through a 2.7 μm filter. Figure 1g exemplifies the methodology of release through the filter: the sample is first allowed to gel overnight in a syringe, then the gel is gently passed through the 2.7 μm syringe filter, which releases a clear solution. Generally, about 80% of the liquid is recovered from the procedure.

Release protocols for UV–Vis and fluorescence

For the quantification of released insulin and dextran, samples were prepared as follows. Insulin was dissolved in 1.5 M Tris HCl (pH 6.8) at concentrations of 0.28 and 4.48 mg ml−1. To avoid aggregation, the samples were left overnight on a roller at 76 rpm to ensure complete dissolution. To form gels in the syringes, 0.4 ml of the CD-005 gelator solution prepared as described above was transferred to a 12 ml syringe. Then, 1 ml of the insulin solution in Tris was transferred using a 5 ml syringe and a 21 G needle directly in the 12 ml syringe. This resulted in quick formation of the gels at final concentrations of 0.2 and 3.2 mg ml−1. The gels were left overnight to stabilize and then gently passed through a 2.7 μm filter, releasing a clear solution. Ultraviolet–visible (UV–Vis) light was measured directly on the obtained solution. To quantify the amount of insulin released, insulin solutions were prepared at a range of concentrations (0.2–4.48 mg ml−1) in a similar way in 1.5 M Tris HCl.

For the quantification of released dextran, 1.4 wt% (14 mg ml−1) solution of fluorescein isothiocyanate dextran was prepared in 1.5 M Tris HCl buffer by dissolving 14 mg of the dextran in 1 ml of buffer. A total of 5.5 μl of 200 mg ml−1 of CaCl_2 solution was then added to 1 ml of this solution and swirled briefly. To form gels in syringes, a similar method was used as described above, achieving a final concentration of 1 wt% (10 mg ml−1). The gels were left overnight to stabilize and then gently passed through a 2.7 μm filter. The resulting dextran solution was too concentrated to study by means of UV–Vis and fluorescence. For UV–Vis, a dilution of a factor of 10 was carried out using buffer, reaching a final concentration of 1 mg ml−1. For fluorescence, a factor of 100 was needed (0.1 mg ml−1) to avoid self-quenching at higher concentrations. To quantify the amount of released dextran, fluorescein isothiocyanate dextran was dissolved in 1.5 M Tris HCl buffer at desired concentrations (1–0.05 mg ml−1), ensuring the same pH throughout.

Circular dichroism

Circular dichroism data were acquired on a Chirascan VX spectrometer (Applied Photophysics) using a quartz cuvette with a 0.01 mm path length. The spectra were collected in the range 180–400 nm with a scanning step size of 1.0 nm and scanning rate of 0.25 s at room temperature. The samples were prepared in Sterilin vials as described, keeping the same volumes of the components. Small amounts of the gels were then transferred to the cuvette before measurement.

FTIR spectroscopy

Data were recorded using an Agilent Cary 630 FTIR spectrometer (with ATR attachment). The filter paper from the 2.7 μm filter was removed by carefully opening the syringe filters. Then, the background of the empty ATR crystal was taken. Small amounts of a clean filter and one after extrusion were deposited on the ATR crystal to record the spectra.

UV–Vis

Absorption spectra were recorded on an Agilent Cary 60 UV–Vis spectrophotometer using a quartz cuvette with 0.1 mm path length. Samples were prepared as above and 300 μl of the solution was transferred to the cuvette using a 200 μl pipette.

Fluorescence

Fluorescence data were collected using an Agilent Technologies Cary Eclipse fluorescence spectrometer. Samples were prepared as described above at a 2 ml volume and transferred to a quartz cuvette with a 1 cm path length. For the fluorescein isothiocyanate dextran, the excitation wavelength was 470 nm. For the 2NapIF release studies, the excitation length was 320 nm. In all cases, the excitation and emission slit widths were 5 nm and 5 nm. To quantify the amount of 2NapIF in the extrudate, a known volume of the extruded sample was freeze-dried (1 ml) and then fully redissolved in DMSO.

NMR spectroscopy

Water suppression and STD experiments (Supplementary Fig. 17) were recorded on a Bruker spectrometer operating at 499.31 MHz and equipped with a Neo console and Bruker 5 mm SmartProbe. The 1H experiments were recorded using the perfect echo WATERGATE sequence of ref. [40] incorporating the double echo W5 sequence of ref. [41]. The delay between successive pulses in the selective pulse train was set at 333 μs, corresponding to 3,000 Hz between the null points. The 1H spectra were acquired in four dummy scans and 128 scans with a relaxation delay of 1 s and signal acquisition time of 4.2 s. STD spectra were obtained using the same sequence but with an overall relaxation delay of 5 s. Presaturation was applied during the final 4 s of the relaxation delay using a train of 100 Gaussian pulses (40 ms) with peak powers of 243 Hz at 100 ppm (off resonance) and −3.8 ppm (on resonance) in separate experiments, which were recorded with 16 dummy scans and 16 scans. Spectra were processed with an exponential line broadening factor of 1 Hz and referenced to the CH_3 triplet of ethanol (1.2 ppm) present as an impurity in our commercial insulin sample[42].

The 3.2 mg ml−1 of insulin solution and the CD-005 gel were prepared and aged in 5 mm NMR tubes (Wilmad 528-PP) for 20 h at 22 °C.

High-resolution mass spectrometry

High-resolution mass spectrometry was recorded using a ThermoScientific Exactive Plus Orbi-Trap with ESI ionization at the University of Strathclyde, Glasgow. For the analysis, the sample is directly injected by means of the UHPLC of 10 μl of sample into the solvent flow (0.1% formic acid in methanol). The sample is injected for 1.8 min at a flow rate of 0.1 ml min−1. The mass spectrometer uses positive/negative polarity switching to obtain both the positive and negative mass spectrum co-currently, with a scan range of 400–6,000 Da at a resolution of 70,000. On the basis of the predicted mass, the interest molecular peak is mass matched in the spectrum with 4 decimal point accuracy (isotopic discrimination levels). For the insulin-containing samples, the mass was best detected at 3+ charge.

Insulin-loaded gel preparations

NapIF

First, 1.1 ml of 0.1 M NaOH was added to 8.9 ml of deionized H_2O. A total of 50 mg of NapIF was then added to this solution and left to dissolve overnight on a rotary shaker. Before gelation, insulin is added to a final concentration of 0.2 mg ml−1. Gelation is initiated by adding 80 mg of GdL to this solution. The sample is then pipetted into 5 × 2 ml syringes before being sealed with parafilm and left overnight.

NapFF

First, 1 ml of 0.1 M NaOH was added to 9 ml of deionized H_2O. A total of 50 mg of NapFF was then added to this solution and left to dissolve overnight on a rotary shaker. Before gelation, insulin is added to a final concentration of 0.2 mg ml−1. Gelation is initiated by adding 20 mg of GdL to this solution. The sample is then pipetted into 5 × 2 ml syringes before being sealed with parafilm and left overnight.

CD-005

A total of 20 mg of potassium carbonate was dissolved in 5 ml of deionized H_2O. A total of 50 mg of CD-005 was added to this solution and left to dissolve overnight on a rotary shaker. A total of 4 ml of this solution was added to 10 ml of 1.5 M Tris HCl at pH 6.8. Immediately after, insulin was added to a final concentration of 0.2 mg ml−1. The sample was then pipetted into 5 × 2 ml syringes before being sealed with parafilm and left overnight.

Insulin storage and assay

Insulin in solution, along with insulin in gel (in syringe), were agitated on an Eppendorf Smartblock at 600 rpm, 25 °C for 6 h. Before analysis, the samples in syringes was passed through a 0.22 μm filter to separate protein from the gelators and then used in the assays.

Dynamic light scattering

Dynamic light scattering was used to measure the hydrodynamic radius on a Zetasizer ZS (Malvern Panalytical). Measurements were carried out using a 4 mW He-Ne 633 nm laser module operating at 25 °C at an angle of 173° (back scattering) and results were analysed using Malvern DTS 7.03 software. There were ten replications for each of the samples with at least 12 measurements recorded for each run.

Thioflavin T assay

A 1 mM stock solution of thioflavin T was prepared in H_2O. This thioflavin T was diluted in PBS (pH 7.4) so that the final thioflavin T concentration in each well was 25 μM in 100 μl. Another 100 μl of insulin solution from each of the gelators after passing through 0.22 μm filter was added to the wells. Thioflavin T fluorescence was measured using a fluorescence microplate reader (excitation 450 nm, emission 485 nm).

Cell-based assay for insulin function

Insulin in solution and in gel were prepared as described above. In brief, 50 mg of CD-005 (gelator) was dissolved in 5 ml of K_2CO_3 (20 mg) solution overnight the day before experiment. Insulin was dissolved in 1.5 M Tris HCl buffer (pH 6.8) at protein concentration of 0.2 mg ml−1 and then added into 0.4 ml of gelator solution. The mixture was immediately transferred into a 3 ml syringe. All the syringes, including insulin solution only, gelator solution only, Tris HCl buffer only and insulin in gel, were agitated on Eppendorf Smartblock at 600 rpm, 25 °C for 6 h. Before insulin activity cell assay, all the samples were passed through a 0.22 µm filter to separate protein from gelator.

iLite Insulin Assay Ready Cells (purchased from Svar Life Sciences) was used as received to test insulin activity and assay was performed according to manufacturer’s instructions. Cells were quickly thawed in 37 °C water bath, 250 µl of which was diluted to 6 ml using full culture media (RPMI supplied with 10% FBS and 1% PSA). A total of 40 µl of cell diluent was then mixed with insulin solution at equal volume and incubated in white tissue culture plate at 37 °C for a further 5 h. Firefly luciferase substrate of 80 µl was then added into cells and incubated at room temperature for 15 min. The whole plate was read on plate reader for firefly luciferase luminescence intensity. Specifically, fresh insulin solution was prepared to make a calibration curve with insulin stock concentration ranging from 2,000 to 0 ng ml−1, where 1,000 ng ml−1 was selected as insulin activity test concentration. Concentration of separated insulin sample was determined by A280 on NanoDrop and calculated on the basis of protein sequence. Protein was diluted to indicated concentrations for activity test. Dilution of gel only and buffer only was identical to protein dilution.

β-galactosidase storage and assay

Stock solutions of the gelator and protein were freshly prepared before mixing. A total of 20 mg of KCO_3 was dissolved in 5 ml of distilled water, to which 50 mg of CD-005 (gelator) was added. This mixture was stirred overnight to dissolution. In a separate vial, 5.5 µl of 200 mg ml−1 of CaCl_2 solution was added to 1 ml of 1.5 M Tris HCl, pH 6.8. The β-galactosidase was added to give a 10 mg ml−1 solution. A total of 0.4 ml of the gelator in buffer was transfered to a vial, to which 1 ml of the protein solution was added. These were agitated and rapidly (less than 1 min) transferred into the syringe.

Gel-loaded syringes were stored in a thermostated incubator for the indicated amount of time, before being retrieved and assayed for function. Protein was recovered from the syringe by passing through a syringe filter (0.22 mm). A total of 50 µl of 100 µg ml−1 of β-galactosidase solution (this was adjusted using PBS as TRIS can inhibit the activity of this protein) was added into the wells of a 96-well plate, containing 100 µl of 16 mM oNPG (4.82 mg ml−1). The absorbance was then measured at 420 nm each minute for 10 min. Activity was normalized to total protein mass using a standard BCA assay, as the non-optimized calcium stabilized gels did not give 100% recovery from a single extrusion.

Postage protocols

Gels containing β-galactosidase were prepared as described above. Five gels and one solution of β-galactosidase (that is, in the absence of gel) were triple-bagged and put in a parcel. They were posted using Royal Mail signed delivery. A temperature logger was included in the parcel. In total, the package was in delivery for 3 days. A readout of the temperature logger is shown in Supplementary Fig. 12 (the range was 17.6–24.2 °C). On receipt, samples were visually inspected for leaks (none seen) and to confirm that the gels were intact. Samples were recovered and assayed as above.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All data are available in the main text or the Supplementary Information or available on reasonable request.

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Acknowledgements

This work has been supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 814236, European Research council grant no. 866056, Royal Society for an Industry Fellowship (M.I.G.) 191037, Engineering and Physical Sciences Research Council (EP/L021978/2) and the University of Glasgow. M.W. thanks UKRI for a Future Leaders Fellowship (MR/T044020/1). This work was carried out with the support of Diamond Light Source, instrument I22 (proposal SM27906). We thank N. Cowieson (Diamond Light Source) for help collecting the SAXS data at B21 (SM37100). This work benefitted from the SasView software, originally developed by the DANSE project under NSF award DMR-0520547. We would like to thank J. R. Bame and G. J. Anderson (University of Strathclyde) for collecting the high-resolution mass spectrometry data. For the purpose of open access, the author(s) has applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising from this submission.

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1. These authors contributed equally: Simona Bianco, Muhammad Hasan

Authors and Affiliations

1. Department of Chemistry, University of Glasgow, Glasgow, UK

Simona Bianco, Bart Dietrich & Dave J. Adams

2. Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, UK

Muhammad Hasan, Ashfaq Ahmad & Matthew I. Gibson

3. Department of Chemistry, University of Warwick, Coventry, UK

Ashfaq Ahmad, Sarah-Jane Richards, Qiao Tang & Matthew I. Gibson

4. Department of Chemistry, University of Manchester, Manchester, UK

Sarah-Jane Richards & Matthew I. Gibson

5. School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich, UK

Matthew Wallace

6. Diamond Light Source Ltd, Diamond House, Harwell Science and Innovation Campus, Didcot, UK

Andrew J. Smith

7. Manchester Institute of Biotechnology, University of Manchester, Manchester, UK

Matthew I. Gibson

Contributions

M.I.G. and D.J.A. conceived this work. S.B., Q.T., S.-J.R., B.D., M.H., A.A., A.J.S. and M.W. undertook data curation. S.B., M.H., M.I.G. and D.J.A. were responsible for formal analysis. S.B., Q.T., M.H., S.-J.R., B.D., A.A., M.W. and A.J.S. carried out the investigations. S.B., M.H., B.D., A.J.S., M.W., M.I.G. and D.J.A. developed the methodology. S.B., M.H. and M.I.G. produced the visualizations. M.I.G. and D.J.A. acquired funding. M.I.G. and D.J.A. carried out project administration. M.I.G. and D.J.A. supervised the work. M.I.G. and D.J.A. wrote the original draft. S.B., M.H., M.I.G., D.J.A., S.-J.R., A.A., M.W. and Q.T. were involved in reviewing and editing the final manuscript.

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Correspondence to Matthew I. Gibson or Dave J. Adams.

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S.B., M.H., A.A., S.-J.R., M.I.G. and D.J.A. are named inventors on a patent application relating to this work. The remaining authors declare no competing interests.

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Bianco, S., Hasan, M., Ahmad, A. et al. Mechanical release of homogenous proteins from supramolecular gels. Nature 631, 544–548 (2024). https://doi.org/10.1038/s41586-024-07580-0

- Received: 14 September 2023

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- DOI: https://doi.org/10.1038/s41586-024-07580-0

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Chemical reservoir computation in a self-organizing reaction network

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Quentin Duez,

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Abstract

Chemical reaction networks, such as those found in metabolism and signalling pathways, enable cells to process information from their environment1,2. Current approaches to molecular information processing and computation typically pursue digital computation models and require extensive molecular-level engineering[3]. Despite considerable advances, these approaches have not reached the level of information processing capabilities seen in living systems. Here we report on the discovery and implementation of a chemical reservoir computer based on the formose reaction[4]. We demonstrate how this complex, self-organizing chemical reaction network can perform several nonlinear classification tasks in parallel, predict the dynamics of other complex systems and achieve time-series forecasting. This in chemico information processing system provides proof of principle for the emergent computational capabilities of complex chemical reaction networks, paving the way for a new class of biomimetic information processing systems.

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Main

Complex chemical reaction networks are involved in all key processes of life. Of these processes, information processing sits at an important nexus between a cell and its surrounding environment. Signalling pathways process environmental information to coordinate cellular responses, while metabolic networks work to maintain homeostasis in response to the ever-changing surroundings1,2. Since the dawn of computer science, researchers have speculated about harnessing the inherent potential of physical and biological systems for computation, a function that can also be formulated as information processing5,6,7. Substantial progress has been made in constructing chemical systems that use Boolean logic8,9, digital computation10,11,12,13, neural networks14,15,16,17,18, pattern recognition augmented by in silico deep learning[19] and sequence recognition[20]. Self-learning chemical systems have been theorized for abstract chemical reactions21,22. These approaches demonstrate how molecular systems may perform computation, but do not achieve the information processing capabilities of living systems. Unlocking the full potential of molecular systems requires (1) moving beyond a strict adherence to reproducing digital computation principles and (2) finding an approach that overcomes the laborious nature of bottom-up ‘molecule-by-molecule’ design patterns.

Our recent work on dynamic self-organization of chemical reaction pathways in the formose reaction4,23 inspired us to consider its propensity for information processing. This complex reaction network produces a rich diversity of possible chemical compositions that are nonlinearly dependent on a small number of input reactants and catalysts. Under flow conditions, the distribution of these compounds can be modulated using changes in reactor input concentrations, allowing a range of complex self-organized reaction responses to be controlled with a relatively simple set of input parameters. These properties, and the experimental tractability of the formose reaction, make it an excellent candidate system for exploring chemical information processing using the model of physical reservoir computation. Physical reservoir computing is part of a family of so-called neuromorphic approaches, which use the analogue and dynamic nature of physical systems to process information and perform computations24,25,26,27. A range of computational tasks, such as classification[28] and simulation29,30, have been demonstrated in a variety of materials, such as photonic devices[31], spintronic oscillators[32] and nanowire networks[33].

Here we report on the experimental realization of in chemico computing by establishing that the formose reaction has emergent computing properties, obviating the need for complex bottom-up design and creating new opportunities for scalable molecular computing. We demonstrate that the formose reaction is capable of performing several parallel, nonlinear classification tasks, how it can model the behaviour of complex dynamical systems and how it can perform time series forecasting. Our work shows how chemical reaction networks process information on the basis of self-organization, and, much like biological systems, can achieve a variety of powerful computational tasks using information from their environment.

A chemical reservoir computer

Our chemical reservoir computer is built around the formose reaction (Fig. 1a) in a continuous stirred tank reactor (CSTR) (Extended Data Fig. 1 and Methods). Following the reservoir computation model[34], we can approximate any target (dynamic) transformation (f) under the influence of a set of input variables (u) (Fig. 1b,e) by feeding the input variables as a sequence of chemical concentrations into the reservoir (Fig. 1c). We investigated three kinds of target transformations: analytical expressions in the form of classification tasks, integral solutions of differential equations and chaotic maps in the form of time series forecasts of the Lorenz system. The first type of task uses only the steady-state features of the reservoir to approximate a static function, whereas the other types use the full dynamics of the reservoir to approximate different kinds of dynamic systems. Input concentrations to the reservoir are controlled by changing the flow rates of formaldehyde, dihydroxyacetone (DHA), sodium hydroxide (NaOH) and calcium chloride (CaCl_2), making a total of four possible reactor inputs in our set-up. Reservoir outputs are measured by an ion mobility mass spectrometer, from which we extract the relative abundance of up to 106 different ions, characterized by unique mass-to-charge (m/z) ratios and inverse mobilities, with a time resolution of 500 ms (Fig. 1d, Methods and Supplementary Information section 1). The nonlinear response of the chemical reservoir computer to the input u, a collection of ion species denoted by x, is recorded and converted to a ‘computational’ output by training a single linear read-out layer, which multiplies every ion signal with a weight (denoted by W) and sums the resulting weighted signals (Fig. 1f). These weights are trained to replicate the target function using a simple linear regression algorithm specific to the computation task (background explanation in Supplementary Information section 2). This single-layer training step is an essential feature of reservoir computation; it allows us to translate the reservoir response into the desired computation result. By using different sets of weights, the same experimental data can be used to solve several computation tasks. Depending on the computation task, the inputs, reservoir states and outputs may be time dependent or remain constant.

Fig. 1: A schematic overview of the formose reservoir computer.

a, The formose reaction and its information processing abilities. Left, a schematic view of the formose reaction network. Arrows indicate chemical transformations between compounds in the network. Dihydroxyacetone and formaldehyde are used as initial reactants and indicated with purple arrows. Right, graphical summary of the information processing tasks of which the formose reaction is capable. b–f, A schematic overview of the experimental set-up and reservoir training process. A set of input variables u used to obtain a target (dynamic) transformation f(t, u) (b). These input variables are also used as flow inputs into the reservoir. Syringe pumps containing the formose reagents (formaldehyde, dihydroxyacetone, sodium hydroxide and calcium chloride) are connected to the inlets of a CSTR and are used to feed the input into the reservoir (c). The reservoir outlet is connected to an ion mobility mass spectrometer for online detection of the reservoir composition (d). The state of the reactor x is measured over time in response to changing inputs. The target (dynamic) transformation f(t, u) obtained from the input (e). Weights W are trained on the states of the reservoir to obtain an approximation to the target function, which can then be used for further predictions (f). a.u., arbitrary units.

Nonlinear classification

We first demonstrate how the formose reservoir can chemically process information from its environment (for instance, the concentrations of species flowing into the reactor) and produce a well-defined classification response (for example, 0 or 1). We created a two-dimensional input space consisting of 132 randomly sampled concentrations of formaldehyde and NaOH (10–150 mM and 10–50 mM, respectively), normalized between 0 and 1 (Fig. 2a) while keeping the DHA and CaCl_2 inputs constant. This parameter space was chosen based on previous work[4] that demonstrates a complex, hierarchical compositional landscape in this concentration range. For each unique input point in this space, the reactor was allowed to reach a steady-state composition over a 30-minute equilibration period. The final output of the formose reservoir was obtained as the averaged ion intensities over the final 10-minute sample period of the steady-state output, or 1,200 data points per unique input for each of the 106 ion signals (Methods, Supplementary Information section 3.1 and Supplementary Figs. 5 and 6). These data establish a large dataset that is deterministic (Supplementary Information section 3.2 and Supplementary Fig. 7) and robust against outliers and overfitting. We next trained weights on the 106-dimensional output for every input combination using a linear support vector classifier (LSVC) algorithm (Fig. 2d), resulting in a classification for every unique input (Fig. 2c). This training procedure was performed for a variety of nonlinear classification tasks (Fig. 2d) and validated by calculating the average Φ accuracy (also known as the phi coefficient, or Matthews correlation coefficient) for 520 different leave-five-out train-test splits (Methods). The reported Φ accuracies are the averages over all test sets and can be found in Extended Data Table 1.

Fig. 2: Nonlinear classification.

a–c, Schematic showing the classification of an input combination. a, A scatter plot showing combinations of formaldehyde and NaOH concentrations used to create an input space for nonlinear classification problems. b, A bar chart representing the steady-state averaged reservoir response (not all compounds shown), associated with selected formaldehyde–NaOH input combinations. Weights are trained on a target classification and applied to the reservoir response. c, The response is classified as either a 0 or a 1 on the basis of trained weights. d, Results of reservoir classifications for various classification tasks. Dot locations indicate the corresponding input from a, with the colour of every point indicating the test-set accuracy of that point for 520 different leave-five-out train-test splits (20 repeats of 26 splits), where +1 corresponds to perfect predictions, and 0 to total failure (see Methods). Shaded areas indicate the different classes of the classification function. The bar chart below every classification plot shows a comparison between the average test-set Φ accuracy for the formose reservoir (FRC), the training layer without reservoir (LSVC) and various other machine learning classifiers. The dashed line indicates the score achieved by the formose reservoir. acc., accuracy; Form., formaldehyde; GP, Gaussian process classifier.

Notably, the reservoir can emulate all Boolean logic gates and various, more advanced, nonlinear classification tasks, such as sine and (concentric) circle classifications (Fig. 2d). Such tasks have previously been possible only in molecular systems specifically designed to perform the function of a single logic gate[35]. The formose reservoir is capable of performing any of these nonlinear classification tasks without requiring a redesign of the chemical network. It can therefore perform a broader range of computational tasks than previous molecular systems, enabling considerable computational generalizability. Comparing the formose reservoir to standard nonlinear classification algorithms allows us to further demonstrate the flexibility in information processing available with the chemical reservoir, compared to standard in silico classification methods. For the linear tasks (AND, OR, linear and triangle tasks), the reservoir performs similarly to the in silico algorithms. For the nonlinear tasks (XOR, checkers, circle, sine, concentric circles and dots), the formose reservoir outperforms Gaussian process classification. It scores comparably to support vector classifiers (SVCs), multilayer perceptrons (MLPs) and extreme learning machines (ELMs) for the XOR, checkers, sine, concentric circles and dots tasks, and is only outscored significantly for the circle classification task. Notably, SVCs and MLPs are specifically designed algorithms for such nonlinear classification tasks as shown here, and are therefore expected to perform well. Crucially, the formose reservoir produces similar results to these specialized classifiers solely by tuning a single linear regression head.

Predicting complex dynamical systems

Living systems can detect, exploit and predict changes in their environment over time. Encouraged by the formose reservoir’s flexibility for performing classification tasks in time-invariant settings, we modified our approach towards using it to predict the dynamic behaviour of complex systems in a fluctuating environment. We investigated the ability of the formose reservoir to predict ordinary differential equations typically encountered in fields such as ecology, systems biology, chemistry and engineering. These types of dynamical system are challenging to simulate and predict, especially when mathematical functions are not available to describe them. These systems are often exposed to random fluctuations from their environments, affecting their behaviour in complex manners.

We showcase the formose reservoir’s ability to simulate a dynamical model system by fitting it to a carbon-metabolism model of Escherichia coli, a large, partially recursive, nonlinear metabolic network with 87 substrates and 92 reactions, adapted from refs. [36] and [37] to include extra inflow and outflow terms. An overview of the training and prediction procedure is shown in Fig. 3a, and a schematic of this network is shown in Fig. 3b (full details in Methods). We perturbed the system using a fluctuating DHA input (u(t)) and solved the differential equations of the system to obtain the response in substrate concentrations (y(t)). Likewise, we exposed the formose reaction to u(t) (by means of DHA inflow; Extended Data Fig. 2) and used a training period of 30 min (approximately 3,600 data points per ion signal for 106 ion signals) to find the linear mapping that allows the formose reaction to reconstruct the behaviour of the metabolic network (Fig. 3c). Performing the mapping in this manner is equivalent to learning which linear combinations of compounds produced in the formose system best recreate the behaviour of the dynamical system under investigation. By continuing the fluctuating input pattern after the training period, the learned linear mapping allowed us to use the formose reservoir as an emulator of the dynamic system.

Fig. 3: Modelling of complex dynamics.

a, Schematic showing the modelling of a metabolic network. Left, metabolic model with a time-dependent input (purple) is converted to a set of rate equations, which are used to provide an in silico target function. Right, the chemical reservoir uses the same time-dependent input (purple) to produce a complex output over time. Middle, weights are trained to map this output onto the target, which are then used for further predictions. b, A schematic representation of the carbon metabolism found in E. coli. The fluctuating DHA input is indicated in purple, and the location of the three substrates shown in c are indicated with blue rectangles. Arrows indicate irreversible reactions; diamonds indicate reversible reactions. c, Three example predictions for the behaviour of pyruvate (Pyr.), 3-phosphoglyceric acid (3PG) and AMP. The red lines indicate the ‘true’ simulated response of the model. The yellow lines show part of the train set, and the blue lines show the predictions of the formose reservoir after training. Indicated times on the x axes are both the physical reservoir time and the model time. The absolute scaled error (ASE) (see Methods) over time is shown below the predictions. d, Comparison plots between true and predicted concentrations of various substrates in the carbon metabolism under a fluctuating DHA input. Plots with decreasing concentration ranges are shown left to right, with the dashed region indicating the concentration range of the next plot. Different colours indicate different substrates in the metabolic network. Conc., concentration.

In Fig. 3c, time trace comparisons between true and predicted concentrations are shown for pyruvate, 3-phosphoglyceric acid and the co-factor adenosine monophosphate (AMP), showing how the formose reservoir can closely predict the behaviour of the network, for a training time of 30 min and a prediction time of 60 min. Extended prediction times of up to 90 min are shown in Extended Data Fig. 3 and are shown per substrate in Supplementary Figs. 17 and 18. In the metabolic simulation, the effects of environmental fluctuations vary across the network: they can be linear for substrates close to the environmental inputs, or highly nonlinear for more downstream substrates. The formose reservoir can capture both types of behaviour, using its dynamic properties to correctly incorporate nonlinear and delayed responses. Comparison plots between reservoir prediction and the true in silico behaviour for all substrates are shown in Fig. 3d for different concentration regimes (each colour represents a substrate), showing that most predictions closely match the true behaviour across several orders of magnitude. It is not a perfect predictor, as it inaccurately captures the behaviour of some species that accumulate or break down over very long timescales without being influenced by environmental fluctuations, primarily substrates involved in the glyoxylate (GLX) and acetyl coenzyme A (ACCOA) cycles (Supplementary Figs. 17 and 18). However, this inaccuracy is anticipated, as the formose reservoir does not contain such long reaction timescales. Nevertheless, these results show that the internal nonlinear dynamics of the formose reaction network make it a promising reservoir system for computing the dynamic behaviour of complex (bio)chemical systems.

Forecasting and mutual information

Autonomous systems, such as bacteria[38], the human brain[39] and self-learning artificial intelligence (AI)[40], can anticipate changes in their environment based on learned experiences to navigate, maintain stability and make decisions. Inspired by the prospect of performing such operations using chemical information processing, we wanted to explore how the formose reservoir’s short-term information storage capabilities, or memory, can be harnessed to forecast future environmental dynamics. For environmental dynamics with a temporal structure (for example, deterministic dynamics and/or periodicity), we can attempt to forecast changes by learning a linear mapping between the reservoir state x(t) and a future input as u(t + δt) = Wx(t) (shown in Fig. 4a). Here, W denotes the linear mapping in the form of static weights learned during a short training phase, and u(t + δt) represents the environmental dynamics a time δt into the future. We demonstrate this mapping using a chaotic three-dimensional input, based on the Lorenz attractor, using orthogonal projections of its trajectory to generate three time-dependent inputs into the reactor (DHA, NaOH and formaldehyde), for which we recorded the reservoir response to forecast the inputs 120 s into the future (Methods and Extended Data Fig. 4). A 20 min training period was used; the forecasts for each of the fluctuating inputs are shown in Fig. 4b. For two of the input dimensions (DHA and NaOH), the formose reservoir can accurately and reliably forecast their dynamics for several hours (Supplementary Fig. 20), even while the reactor contents are continuously refreshed. The formaldehyde forecast is less accurate, although the overall dynamics are still correctly predicted. This is probably because the time-dependent response of the formose reaction to increasing formaldehyde concentration is slower than the input dynamics.

Fig. 4: Memory and prediction in the formose reservoir computer.

a, Schematic of the prediction procedure to forecast inputs using the reservoir. A time-varying input is fed into the reservoir and the reservoir response recorded. Weights are trained on the reservoir state and the input at a time interval δt into the future. These trained weights are then used to forecast as-yet unseen future inputs. b, Time traces, error plots and comparison plots for forecasts of simultaneously varying DHA, NaOH and formaldehyde inputs that resemble the behaviour of a Lorenz attractor. True inputs are shown as purple, orange and red lines, and the forecasts (δt = 120 s) as blue lines. The ASEs (see Methods) over time are shown below the predictions. c, A schematic showing how a time-dependent input propagates through the formose network, with different compounds responding in distinct ways. Only the DHA input is shown (left). The response over time of four ion signals is shown, as well as comparison plots between the DHA input and each output. Below every plot, the direct mutual information between DHA input and ion signal is shown (I(u; x)). d, A plot of the mutual information between ion signals x(t + δt) and the formaldehyde, NaOH and DHA input patterns u(t) as a function of the lag parameter δt. Four traces corresponding to the ion signals in c are indicated.

These results show that the formose reservoir assimilates information from its input over time, which can be used to leverage correlations in environmental dynamics to predict future environmental changes. To further evaluate the memory properties of the reservoir, we calculated the mutual information, sometimes referred to as predictive information[41], between every compound in the formose reservoir x(t) and the three inputs at varying time delays u(t + δt), quantifying the propagation of environmental information through the formose network over time. The relationship between input, ion signals and mutual information is shown schematically in Fig. 4c. We use δt as a time-lag parameter, allowing us to calculate the mutual information between reservoir state and past or future inputs according to equation (1) (Methods). The calculated mutual information is shown for a range of lag times into both the past and the future in Fig. 4d for all ion signals, with several compounds highlighted (mutual information per ion for δt = 0 is shown in Supplementary Fig. 21). These compounds show varying degrees of mutual information with the three inputs, where several compounds, such as [C_6H_12O_6Ca]2+ and [C_7H_14O_7Na]+ highlighted in the figure, show increased mutual information with both past and future input (corresponding to negative and positive values of δt), confirming that some compounds exhibit a short-term memory. We propose that these species are involved in reactions at longer timescales and can thus function as a type of memory. Formaldehyde shows much lower overall mutual information than the other two inputs. This may, again, be due to the low sensitivity of the formose reaction to changes of formaldehyde concentration on the timescale of the applied dynamics.

The heterogeneous network memory effects we observe are crucial to the operation of fully autonomous systems, which can anticipate and understand changes in their surrounding environment and respond accordingly. The chemical interface demonstrated here shows how we can intimately tap into complex molecular information from the environment, integrating and consolidating it into a well-defined response.

Conclusion

We have demonstrated in chemico reservoir computing on the basis of information processing by a self-organized chemical reaction network. This system can perform several classification tasks in parallel, emulate the behaviour of biochemically important reaction networks and forecast changes in chaotic dynamical environments. These capabilities are reminiscent of how biological systems process and respond to environmental information, thus providing an interface through which autonomous systems, such as artificial cells or electronic devices, may receive and learn from the chemical environment.

Our approach circumvents some potential limitations in designed (bio)molecular computers, such as the need for explicit engineering of individual reactions and limited generalizability, giving rise to new opportunities in the development of chemical computers. The simple scalability and extensibility of self-organizing chemical reaction networks shows potential for rapid improvements, especially in large-scale computation and simulation of multiscale dynamical systems. In the future, the inclusion of different initiators, such as glycolaldehyde or erythrulose, could increase the number of available inputs into the system[4], and extension of the reaction with phosphorylated and cyanide-based compounds may further diversify complex reaction outputs42,43.

A key challenge for in chemico computation is to replace the current electronic ‘read-out layer’ with a fully chemical read-out capable of autonomous learning. We provide an extra proof-of-concept experiment demonstrating how simple colorimetric read-outs of information processing may be implemented for the formose reaction (Methods and Extended Data Fig. 5). By combining the reservoir output with selected reagents, a colorimetric response is produced that depends on both mixture composition and reagent, resulting in a specific hue or colour per input. The reagents thus function as a fixed read-out layer to the reaction mixture, chemically setting the read-out weights.

The information processing abilities of the formose reaction, and, potentially, of other self-organizing chemical networks, may offer a powerful interface with biological systems. For example, the formose reaction has been used to stimulate bioluminescent responses through interaction with a quorum-sensing pathway in the marine bacterium Vibrio harveyi[44]. Such an interface would allow us to establish a new class of intelligent matter, driven directly by the flux of information through chemical reaction networks. In a broader context, our work shows that complex chemical networks have inherent computational capabilities. By focusing on self-organization, complex molecular mixtures and nonlinearity, the full information processing capabilities of complex chemical systems may finally come to fruition.

Methods

Materials

CaCl_2, paraformaldehyde and tri-sodium citrate dihydrate were obtained from Sigma Aldrich. NaOH, thymol and copper(II) sulfate pentahydrate were obtained from Fisher Scientific. 1,3-Dihydroxyacetone dimer was obtained from Fluorochem EU. Resorcinol was obtained from TCI Europe NV. Formaldehyde solutions were prepared by depolymerization of paraformaldehyde at 60 °C; the final formaldehyde concentration was determined by titration with sodium sulfite and phenolphthalein[45]. For all aqueous solutions, ultrapure water, obtained from an Elga Purelab Chorus 1, was used. Before use, water was degassed by stirring under vacuum for 10–15 min. Ion mobility mass spectrometry experiments were performed with a timsToF instrument (Bruker Daltonics) equipped with an electrospray ionization source operating in positive mode.

Flow reactions

A CSTR (volume 435 μl) with five inlets and an outlet was fabricated from poly(methyl methacrylate) by the Radboud TechnoCentre. LabM8 syringe pumps with BD Plastipak syringes were used to control input flow rates (a schematic drawing and a photograph of the set-up are provided in Extended Data Fig. 1). Syringes were loaded with the specified solutions and connected to the reactor with filled tubing. When the flow of water was divided between two syringes, the flows were fused using a Y connector before reaching the reactor. The reactor and a small outlet tubing were filled according to the initial conditions of the experiment. Once filled, the outlet tubing was capped with a one-way flow check valve and the system was allowed to build up pressure to overcome the crack pressure of the valve. Subsequently, the reactor output was diluted with a water flow (0.8 ml min−1) controlled by a Bruker Elute HPG 1300 high-performance liquid chromatography system. The dilution flow was merged with the reactor outlet with a Y connector. With a subsequent Y connector, the flow was diverted between the instrument and a Restek RT-25020 backpressure regulator connected to a waste line. The backpressure regulator provided a constant pressure of 2 bars in the reservoir.

Inputs to the reactor were controlled by changing the flow rates of selected syringes. For a desired input concentration Cin, the flow rate can be calculated as F = Ftot Cin/Csyr, with Ftot the total flow rate of the system (217.5 μl min−1 in all experiments, corresponding to a residence time of 2 min) and Csyr the concentration of the selected syringe.

Flow inputs

Experimental conditions were selected based on previously published research[4], to create high compositional diversity over the used concentration range. The general workflow consisted of first generating a desired input function u(t), and then scaling the generated function to a suitable input profile. To do so, the function was first mean-centred, scaled with a manually chosen factor, which was chosen to maximize amplitude without generating negative flows. After scaling, a baseline value of the corresponding syringe was added, so the profile fluctuated around the initial input concentration. The flow rate of water was used to counterbalance changes in flow rate, to ensure a constant total flow rate of 217.5 μl min−1, corresponding to a residence time of 2 min. All reactions were allowed to equilibrate at steady state for at least 30 min before starting flow profiles, which included another initial 30 min of steady state. Details of the parameters used for generating the various flow profiles are provided in the respective Methods sections.

Mass spectrometry

Trapped ion mobility spectrometry (TIMS) experiments were performed using an N_2 carrier gas by scanning inverse ion mobilities from 0.4 Vs cm−2 to 0.84 Vs cm−2. The ramp time was set to 500 ms and the accumulation time to 20 ms to minimize ion activation in the TIMS region. The mass range scanned by the time-of-flight (ToF) analyser was set to m/z 50–650. A complete description of the instrumental parameters is available in Supplementary Information section 1.1.

Ion intensity extraction

A list of ions with reference m/z and inverse mobilities was established based on the most intense signals observed (Supplementary Information section 1.2). Ion chromatograms were then extracted for mass- and mobility-selected ions based on the reference list of ions using the TimsPy library[46]. Ion chromatograms were extracted with a mass width of 0.02 Da and a mobility width of 0.006 Vs cm−2.

Nonlinear classification

Varying input concentrations of formaldehyde and NaOH were applied with constant concentrations of DHA (50 mM) and CaCl_2 (15 mM). For every input in the nonlinear classification dataset, ion signals were collected for 30 min (Supplementary Information section 3.1 and Supplementary Figs. 5 and 6). The output in the last 10 min of this period were averaged to reduce noise and used as steady-state data, resulting in 106-dimensional vectors for all 132 inputs. These vectors were subsequently normalized to remove the mean and scaled to unit variance across features. For the selected nonlinear classification tasks, a linear support vector classifier was trained to obtain classifications of the inputs. For every task, a stratified leave-five-out cross-validation was performed, with 520 repeats in total, with every input as part of the test set 20 times (20 repeats of 26 random splits, five inputs per split), and the Φ score was calculated over the test set for every repeat as

$$\varPhi =\frac{{\rm{TP}}\times {\rm{TN}}-{\rm{FP}}\times {\rm{FN}}}{\sqrt{\left({\rm{TP}}+{\rm{FP}}\right)\left({\rm{TP}}+{\rm{FN}}\right)\left({\rm{TN}}+{\rm{FP}}\right)\left({\rm{TN}}+{\rm{FN}}\right)}}$$

where TP denotes the number of true positives, TN denotes true negatives, FP denotes false positives and FN denotes false negatives. This score returns +1 for perfect predictions, and −1 for completely wrong predictions. The reported Φ accuracy was then obtained as (Φ + 1)/2, and averaged over all 520 repeats. More information is available in Supplementary Information sections 3.2–3.6, and code is provided in the analysis/classification.ipynb notebook.

Complex dynamics prediction

A fluctuating DHA flow profile was sampled from a normal distribution with a mean of 36.25 μl min−1 and a standard deviation of 10.36 μl min−1, corresponding to a mean input concentration of 50 mM, with a standard deviation of approximately 14.268 mM. Each flow rate was held constant for 60 s before switching, with an inversely fluctuating water input to ensure the total flow rate remained constant. The formaldehyde, NaOH and CaCl_2 inputs were held constant at 50 mM, 30 mM and 15 mM, respectively.

For the in silico simulation of the carbon metabolism of E. coli, a Systems Biology Markup Language (SBML) model from ref. [36] was adapted to include inflow and outflow terms for every substrate of the form \(\varnothing \to X\left({k}_{{\rm{f}}}{X}_{{\rm{in}}}\right)\) and \(X\to \varnothing \left({k}_{{\rm{f}}}X\right)\). The flow constant (residence time) was set to \({k}_{{\rm{f}}}=0.5\,{\min }^{-1}\), and the inflow concentrations \({X}_{{\rm{in}}}\) were set to the initial concentrations of the model. This modified SBML file was subsequently compiled into a C++ module by the AMICI computational package[47] and loaded as a Python module.

To generate the training and test sets, the model was first run for 1,000 min until a steady state was reached. Then, for every step in the fluctuating input pattern, the DHA input flow concentration was set to the corresponding value of the physical fluctuating flow profile. The model was simulated with this input flow for the duration of the physical flow profile (1 min) before the new DHA input flow was set. For every step, the simulation was initialized at the final state of the previous step. By appending the results of all simulation steps, a complete record of the behaviour of the network under fluctuating conditions was obtained.

Next, the same DHA input flow was used as input into the formose reservoir. The response of the formose reservoir was collected every 500 ms, after which the output was averaged over bins of 10 s to reduce noise. The recorded formose reservoir response was trained on the individual substrate time series of the model for 30 min, using a ridge regression algorithm with the regularization strength set to α = 5 × 10−5. The trained weights were then used to predict the substrate time series directly from the reservoir output for the remainder of the measurement time (code available in the analysis/dynamics.ipynb notebook).

Absolute scaled error

To compare predictions for the dynamic tasks to the true values over time, we calculated the absolute scaled error (ASE), which is the absolute error between predictions and true values, divided by the mean absolute error of a naive mean forecast based on the training data. This error measure produces scale-invariant values that can be used to compare predictions across different data scales.

$${\rm{ASE}}(t)=\frac{| \,\widehat{y}(t)-y(t)| }{\frac{1}{{T}_{{\rm{train}}}}\mathop{\sum }\limits_{t}^{{T}_{{\rm{train}}}}| \,y(t)-{\bar{y}}_{{\rm{train}}}| }$$

where ŷ(t) is the prediction at time t, y(t) is the true value at time t and \({\bar{y}}_{{\rm{train}}}\) is the mean value of the train set.

Forecasting

DHA, NaOH and formaldehyde inputs were simultaneously varied according to the dynamics of a Lorenz attractor (ρ = 28, σ = 10, β = 8/3) over the duration of 8 h, with a constant concentration of CaCl_2 (15 mM). The x, y and z axes were scaled to the NaOH, DHA and formaldehyde inputs by 1.4, 1.0 and 1.3, respectively. For a more detailed description of the flow profile used, see Supplementary Information section 5.1. The reservoir response was measured every 500 ms, after which the output was averaged over bins of 10 s to reduce noise. Next, a ridge regression algorithm was used with the regularization strength set to α = 5 × 10−5 to train the formose response on the input flows 2 min (120 s) into the future for a duration of 30 min. The trained weights were then used to forecast the input flows 2 min into the future directly from the reservoir output for the remainder of the measurement time (code available in the analysis/forecast.ipynb notebook).

Mutual information

Mutual information is defined for a pair of random variables X and Y as

$$I(X{\rm{;}}Y)=\sum _{Y}\sum _{X}{P}_{(X,Y)}(x,y)\log \,\left(\frac{{P}_{(X,Y)}(x,y)}{{P}_{X}(x){P}_{Y}(y)}\right)$$

with PX and PY the marginal distributions, and P(X,Y) the joint distribution of the random variables. This formula was adapted to calculate the mutual information between a time-dependent input signal u(t) and a single ion output signal at a different time x(t + δt) as

$${I}_{U{\rm{;}}X}\left({\rm{\delta }}t\right)={\sum }_{t}{P}_{U,X}\left({x}_{t+{\rm{\delta }}t},{u}_{t}\right)\mathrm{ln}\frac{{P}_{U,X}\left({x}_{t+{\rm{\delta }}t},{u}_{t}\right)}{{P}_{X}\left({x}_{t+{\rm{\delta }}t}\right){P}_{U}\left({u}_{t}\right)}$$

(1)

where u(t) is a time-dependent input flow and x(t) is a single ion trace over time. An implementation from the Scikit-learn computational package48,49 was used to perform the calculations (code available in the analysis/mutual_information.ipynb notebook).

Chemical read-out for the formose reaction

Benedict’s reagent was prepared by dissolving sodium citrate (8.65 g) and Na_2CO_3 (5.0 g) in 40 ml of water. CuSO_4·5H_2O was dissolved in 5 ml of water and slowly mixed with the sodium citrate, sodium carbonate solution. The solution was further diluted to 50 ml total volume. Seliwanoff’s reagent was prepared by dissolving resorcinol (25 mg) or thymol (25 mg) in HCl (3 M, 50 ml). Polytetrafluoroethylene (PTFE) tubing, 1/16” outside diameter × 0.032” inside diameter, was used as a plug-flow reactor, with a total volume of 480 μl. Unless otherwise specified, concentrations were 50 mM for DHA, 30 mM for NaOH, 15 mM for CaCl_2 and 100 mM for formaldehyde, with a 4 min residence time. Output was sampled by connecting the plug-flow reactor to a Bio-Rad drop former. Droplets were collected in a liquid nitrogen-cooled microplate, with two droplets (70 μl) per well. To each well, 150 μl of colorimetric reagent (Benedict’s, Seliwanoff’s resorcinol or Seliwanoff’s thymol) was added. The microplates were heated to 100 °C using a Grant-bio PHMP-100, and pictures were taken at regular intervals, using a Nikon Z5 with a Laowa 100-mm F2.8 CA-Dreamer Macro 2X. Images were adapted to plots using OpenCV-Python[50].

To achieve a direct read-out of the formose reservoir, we added reagents to the reaction mixture. The overall sum of the concentration of compounds in the mixture ‘multiplied’ by the reaction with the added reagent results in a specific colorimetric response depending on both mixture composition and reagent. As Extended Data Fig. 5 shows, each combination results in a specific hue or colour. The reagents thus function as a fixed read-out layer to the reaction mixture, chemically setting the read-out weights. The reagents tested here have different mechanisms for their colorimetric response: Benedict’s reagent functions under basic conditions, through the oxidation of Cu(II) to Cu(I), and the associated colour changes from blue to red; Seliwanoff’s reagent (both for resorcinol and thymol) functions under acidic conditions, where compounds are first dehydrated towards furfural derivates, followed by a condensation reaction with resorcinol or thymol, forming a coloured dye. Seliwanoff’s reagent classically uses resorcinol, but, in principle, other phenolic compounds can be used. We demonstrate colorimetric responses for a grid consisting of varying DHA and NaOH inputs. For Benedict’s reagent, we observe increasing sensitivity with decreasing NaOH:DHA, compared to Seliwanoff’s reagent, for which we observe increasing sensitivity with increasing NaOH:DHA for both the resorcinol- and the thymol-based reagents.

The chemical read-out allows for the identification of different environmental inputs, especially if combinations of different reagents or reaction times are used. Potentially, this approach may be further extended to incorporate a feedback mechanism that can change the amount and type of reagents and modify other system hyperparameters to perform a specific computational task, either through the inclusion of an in-the-loop computer or directly through physical learning[51].

Data availability

Data are available at Zenodo (https://doi.org/10.5281/zenodo.10136537)[52].

Code availability

Python code for working with the datasets, as described in the Methods and Supplementary Information, is available at Zenodo (https://doi.org/10.5281/zenodo.10136537)[52].

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Acknowledgements

We thank B. van Sluijs and O. Maguire for helpful discussions and suggestions. We thank M. Derks (Labm8) for his help and work in the design of the syringe pumps and flow set-up. We thank the Radboud TechnoCentre, specifically A. de Kleine, for the design and manufacturing of the reactors. This project has received funding from the European Union’s Horizon 2020 research and innovation programmes under grant agreement no. 833466 (ERC Advanced Grant Life-Inspired), the Dutch Ministry of Education, Culture and Science (Functional Molecular Systems, Gravitation programme 024.001.035), the Dutch Research Council (grant OCENW.KLEIN.348) and the Simons Collaboration on the Origins of Life (SCOL; award 477123), and was also supported by the Radboud–Glasgow Collaboration Fund, and the European Union and the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract numbers 22.00017 and 22.00034 (Horizon Europe Research and Innovation Project CORENET).

Author information

Authors and Affiliations

1. Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands

Mathieu G. Baltussen, Thijs J. de Jong, Quentin Duez, William E. Robinson & Wilhelm T. S. Huck

Contributions

M.G.B. and W.T.S.H. conceived the research. T.J.d.J., Q.D. and W.E.R. developed the experimental methodology. M.G.B., T.J.d.J. and W.E.R. designed the experiments, and T.J.d.J. and Q.D. performed the experiments and preprocessed the data. M.G.B., T.J.d.J., Q.D. and W.E.R. wrote the data analysis software. M.G.B. performed the analysis and trained the models. W.T.S.H. and W.E.R. supervised the project. M.G.B. and W.T.S.H. wrote the original draft, and all authors discussed results and contributed to writing the manuscript.

Corresponding author

Correspondence to Wilhelm T. S. Huck.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Experimental setup.

a) Schematic overview of the flow reactor setup. Syringes are mounted inside syringe pumps and fed into the CSTR. One way flow from the reactor is ensured by a check valve. Flow is subsequently diluted and split to waste and timsToF using a back-pressure regulator. b) Photograph of the experimental setup, syringe pumps are in the bottom left, the CSTR is on top of the white blue stirring plate.

Extended Data Fig. 2 Fluctuating input flow and reservoir response.

a) Fluctuating input flow profiles used in the prediction of metabolic network behaviour. CaCl_2, formaldehyde, and NaOH were kept constant at a flow rate of 36.25 µL/min, while DHA was varied. b) Ion signals observed in response to the changing flow inputs. Colours indicate different ion signals.

Extended Data Fig. 3 Prediction results for substrates in the metabolic network.

Prediction results for substrates in the metabolic network. True (simulated) time series are shown as solid lines, predictions of the trained formose reservoir as dashed lines. Four different substrate concentration regimes are shown.

Extended Data Fig. 4 Lorenz attractor input flow and reservoir response.

a-c) Orthogonal projections of the Lorenz attractor on the input space, represented by the flow rates of three reaction inputs (DHA, NaOH and formaldehyde) d) Dynamic flow profiles for DHA, NaOH, formaldehyde, and water for the Lorenz attractor experiment. The flow of CaCl_2 was kept constant at a rate of 30.2083 µL/min. e) Ion signals observed in response to the changing flow inputs. Colours indicate different ion signals.

Extended Data Fig. 5 Schematic overview and results of classification using colorimetric readout.

a) A scatter plot showing the sampled DHA and NaOH b) each sample consists of a unique composition of compounds, depending on the conditions each compound has a specific reactivity with the reagent, this can also be viewed as a set of c) In isolation each compound would produce a different color, depending on its concentration and reactivity with the reagent. These isolated contributions cannot be observed, instead we observe one final output colour that can be considered a sum of the individual effects. d) A potential final readout. e) Result of visual readout using Benedict’s reagent after 5 min. f) Result of colorimetric test using Seliwanoff’s resorcinol reagent after 1 h and 42 min. e) Result of colorimetric test using Seliwanoff’s thymol reagent after 1 h and 55 min.

Extended Data Table 1 Classification accuracies

Supplementary information

Supplementary Information

This file contains Supplementary Figs. 1–21, extended mass spectrometry methods, further discussion of chemical reservoir computation and a further explanation of the various types of analysis used in this work.

Peer Review File

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Baltussen, M.G., de Jong, T.J., Duez, Q. et al. Chemical reservoir computation in a self-organizing reaction network. Nature 631, 549–555 (2024). https://doi.org/10.1038/s41586-024-07567-x

- Received: 24 October 2023

- Accepted: 14 May 2024

- Published: 26 June 2024

- Issue Date: 18 July 2024

- DOI: https://doi.org/10.1038/s41586-024-07567-x

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Human degradation of tropical moist forests is greater than previously estimated

C. Bourgoin,

G. Ceccherini,

M. Girardello,

C. Vancutsem,

V. Avitabile,

P. S. A. Beck,

R. Beuchle,

L. Blanc,

G. Duveiller,

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A. Cescatti &

…

F. Achard

Nature volume 631, pages 570–576 (2024)

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Abstract

Tropical forest degradation from selective logging, fire and edge effects is a major driver of carbon and biodiversity loss1,2,3, with annual rates comparable to those of deforestation[4]. However, its actual extent and long-term impacts remain uncertain at global tropical scale[5]. Here we quantify the magnitude and persistence of multiple types of degradation on forest structure by combining satellite remote sensing data on pantropical moist forest cover changes[4] with estimates of canopy height and biomass from spaceborne[6] light detection and ranging (LiDAR). We estimate that forest height decreases owing to selective logging and fire by 15% and 50%, respectively, with low rates of recovery even after 20 years. Agriculture and road expansion trigger a 20% to 30% reduction in canopy height and biomass at the forest edge, with persistent effects being measurable up to 1.5 km inside the forest. Edge effects encroach on 18% (approximately 206 Mha) of the remaining tropical moist forests, an area more than 200% larger than previously estimated[7]. Finally, degraded forests with more than 50% canopy loss are significantly more vulnerable to subsequent deforestation. Collectively, our findings call for greater efforts to prevent degradation and protect already degraded forests to meet the conservation pledges made at recent United Nations Climate Change and Biodiversity conferences.

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Tropical moist forests (TMFs) have a major role in the provision of global ecosystem services, including climate and water cycle regulation, carbon sequestration and biodiversity conservation[8]. Despite their importance, TMFs are disappearing at an alarming rate[4]. In addition, degradation from selective logging, fires, edge effects or a combination of these disturbances is affecting forests and their capacity to provide ecosystem services at a rate comparable to—and in some years larger than—deforestation4,9,10. Here we define edge effects as changes in forest structure and functionality that occur at forest edges, driven by habitat fragmentation[7]. Furthermore, degraded forests are more vulnerable to additional disturbances such as climate extremes, reducing their potential resilience and threatening their long-term future11,12,13,14—for instance, Vancutsem et al.[4] showed that nearly half of TMFs are ultimately deforested.

Reducing forest degradation has great potential to reduce carbon emissions and increase carbon sequestration[15]. Yet, large uncertainties remain in quantifying the contribution of forest degradation to the global carbon fluxes (25–69% of overall carbon losses1,2). More accurate estimates would directly support Reducing Emissions from Deforestation and Forest Degradation (REDD+) activities under the United Nations Framework Convention on Climate Change (UNFCCC[16]). Despite the advancements in remote sensing capabilities for assessing carbon fluxes associated with each type of disturbance17,18,19,20,21, a pantropical assessment of forest degradation on forest structure is still lacking. Furthermore, the depth of edge effect penetration within forest interiors is likely to be underestimated, mainly owing to the scarcity of forest structure data across the tropics[22].

The deployment of the Global Ecosystem Dynamics Investigation[6] (GEDI) instrument on the International Space Station in late 2018, which specifically targets forest structure, offers a unique opportunity to shed light on forest degradation at pantropical scale. Here we provide an assessment of the impacts of human-induced degradation on global TMF structure and of the forest’s ability to recover to its pre-disturbance condition. Specifically, building on a previous work[23], we quantify in a consistent manner at pantropical scale: (1) the extent of forest degradation in 2022 taking into account edge effects; (2) the impact of different types of disturbance on forest structural characteristics and their persistence over time (over the period 1990 to 2022); (3) the rates of forest structure recovery after each type of degradation (selective logging, fire or edge effect) and forest regrowth after deforestation; and (4) the vulnerability of degraded forests to subsequent deforestation.

Within this scope, we combine a wall-to-wall dataset on forest degradation, deforestation and regrowth dynamics derived from Landsat imagery at 30 m spatial resolution[4] with spatially discontinuous estimates of forest canopy structure from GEDI. We jointly analyse the canopy height (RH98), that is, the top of the canopy or the nearest tallest vegetation in the footprint; the height of median energy (RH50), which describes the vertical distribution of canopy elements and gaps[24]; and the aboveground biomass density (AGBD), which represents the aboveground woody biomass per unit area[25].

Spatial patterns of forest canopy heights

The analysis of more than 23 million sample footprints of GEDI data over intact TMF—that is, areas with no signs of human activity detected during the past three decades and located at least 3 km from forest/non-forest edge (see Methods)—reveals regional and continental variability in forest structure, with the tallest canopies found in Insular Asia, western Africa and western and eastern Amazonia (Fig. 1a and Extended Data Table 1). Overall, canopy heights are higher in Asia (34.4 ± 10.7 m) than in Africa (29.3 ± 8.6 m) and in Americas (28.6± 7.4 m) (mean ± s.d.). Similar results are found for AGBD (370.8 ± 205.2 Mg ha−1 in Asia, 225.5 ± 110.9 Mg ha−1 in Africa and 239.5 ± 129.9 Mg ha−1 in Americas; Extended Data Fig. 1). These results support previous observations[26] showing that intact tropical forests in Asia, which are typically dominated by hardwood wind-dispersed species, show a higher frequency of large and tall trees (those with RH98 greater than 30 m) compared with Africa and South America (Supplementary Fig. 1).

Fig. 1: Canopy heights of intact, degraded edge forests and regrowths.

a–d, Canopy height (RH98) of intact forests (a), degraded forests from logging, fire or natural disturbances (b), forest/non-forest edges (120 m width) (c) and forest regrowths (d) in 1.5° (each side is approximately 167 km) hexagon grid cells between 30° N and 30° S. Canopy heights are mean values calculated over the period 2019–2022. Note that all the forest regrowth areas in c, regardless of their age, are shown here (an in-depth analysis by age class is shown in Fig. 3). Grid cells with fewer than 600 GEDI samples or with no statistically significant differences in canopy height between intact and non-intact forests were masked (Welch two-sided t-test. P < 0.05). 100%, 99% and 99% of the hexagons for degraded, edge and regrowth forests, respectively, were statistically significant. n represents the total number of GEDI sample plots for each forest cover type (Supplementary Fig. 2). Summary statistics of RH98 and AGBD are shown in Extended Data Table 1.

We found lower RH98 and AGBD values for the three types of disturbed forests considered in our study compared with intact forests. We find that the minimum difference between intact and degraded forests is 10 m for mean RH98 and 122 Mg ha−1 for mean AGBD (Fig. 1b). Forests within 120 m to forest/non-forest edge show, on average, a 11 m lower RH98 and 150 Mg ha−1 lower AGBD compared to intact forests with an absence of emergent trees and a canopy height distribution dominated by smaller trees (Fig. 1c and Supplementary Fig. 1). Regrowing forests on deforested land have, on average, a 16 m lower RH98 than intact forests, and an average AGBD of 80.4 ± 87.3 Mg ha−1 (Fig. 1d).

Magnitude and scale of edge effects

Potapov et al.[27] showed that fragmentation of intact forest landscapes by agricultural or road expansion initiates an edge effect (here referred to as forest/non-forest edge effect) and a cascade of changes that lead to landscape transformation and loss of conservation values. To a lesser extent, smaller canopy openings following selective logging and fire could also be a source of important and yet unaccounted edge effects[28] (here referred to as forest/degraded forest edge effect). Here we show the impacts of these two anthropogenic edge effects on the vertical structure of the forest, which is caused by microclimatic alterations[22] and leads to large-tree mortality[29].

We first assessed the scale and magnitude of forest/non-forest edge effect on forest structure metrics in the vicinity of deforested lands at varying distances to the forest edge (Extended Data Fig. 2). We used two indicators: the first and more conservative is the distance at which the RH98 of the edge forest reaches 95% of the RH98 of the intact forest; and the second is the distance at which the differences in RH98 between edge and intact forest are not any more significant based on an ANOVA test (Methods).

Forests, classified as undisturbed in the Tropical Moist Forest dataset from the Joint Research Centre (JRC-TMF), showed a decrease in RH98 from the edge up to 350, 400 and 1,500 m into the forest interiors in America, Africa and Asia, respectively (red dotted lines in Fig. 2a corresponding to first criteria). Edges have a significant effect on canopy height for each continent, with maximum distance of effect being largest in the Americas (second criteria). In fact we found significant differences in the RH98 until 7 km distance to the forest edge in the Americas (Tukey’s honest significant difference test, F = 2,141, P < 0.0001) and 1.7 km in Africa (F = 544, P < 0.0001) and Asia (F = 582, P < 0.0001). On average, we estimated a 20% reduction in RH98 within the first approximately 200 m of the forest edge relative to the intact forest interior, associated with a change in structure and loss of tallest trees (Fig. 2b). The largest extent of edge effects were detectable along active and consolidated deforestation fronts of the Amazon (Brazilian arc of deforestation, Peru and Colombia active fronts), in Borneo and Sumatra coasts marked by high fragmentation levels, and on the borders of the Congo basin (Fig. 2c). The variability in the magnitude of change and extent of the edge effect within each continent is likely to be linked to forest structure composition and environmental conditions18,22,30 and to different landscape configurations[29] (for example, forest patch size and connectivity) and composition (for example, proportion and type of agricultural land) that can increase forest sensitivity to edge effects[31].

Fig. 2: Spatial scale and magnitude of edge effects caused by deforestation.

a, Canopy height (RH98) of undisturbed forests (located at more than 120 m from degraded forests) and all forests shown in grey (including undisturbed and degraded forests) at various distances to the forest edge that separates forest cover from agricultural land and other land covers. Inset, degradation area due to fire and selective logging calculated at various distances to the edge. The red dotted vertical line corresponds to the distance between the forest edge and the point at which 95% of intact forest RH98 is reached (red horizontal dotted line): to 350, 400 and 1,500 m for America, Africa and Asia, respectively. Vertical bars indicate the spatial s.d. The number of GEDI sample footprints for each distance to the forest edge is reported in Supplementary Fig. 3. F value from one-sided ANOVA; ****P ≤ 0.0001; NS, not significant. Tukey post hoc tests are presented in Supplementary Data. b, Average distribution of canopy heights of undisturbed forest (located at more than 120 m from degraded forests) at various distances to the forest edge. c, Scale of the edge effect, represented as the distance from forest edge at which RH98 reaches 95% of the value of RH98 for intact forest. Colours for the undisturbed forest at indicated distances from the forest edge in a correspond to those in b,c. Grey cells in c represent areas where the accumulated deforestation (from 1991 to 2022) is less than 2% of the forest area in 1990.

We detected larger scales of edge effects on AGBD in the Americas (1,000 m) and Africa (750 m) and lower scales in Asia (1,020 m) compared with RH98 (Extended Data Fig. 3). These results show that the edge effect on biomass is far exceeding the previously measured17,18,32,33 120 m. In total, the area with edge effects represented 18% (approximately 206 Mha) of the total forest area in 2022. This represents an increase in area of 221% compared with the total area of forest edge zones defined using the 120 m distance to the edge identified in previous studies.

We detect the cumulative impacts of selective logging and forest fire up to around 1.5 km into forest interiors and we quantify an additional 10% decrease in RH98 on average compared with the distribution of forest affected by edge-desiccation effects only (grey distribution of all forests in Fig. 2a). The increased tree mortality due to edge effects triggers positive feedback loops with fires, which can penetrate up to 1 km into forest interiors (red curve in Fig. 2a, inset). At the same time, fragmentation makes the interior of the forest more accessible, which leads to increased hunting and resource extraction[33]. As a result, we found evidence of selective logging within 500 m from the forest edge, except in Asia, where logging operations often occurred deeper within the forest interior (purple curve in Fig. 2a, inset). In addition, the increased frequency of extreme droughts may directly increase tree mortality and fire incidence at the edges34,35, making the first kilometre of the forest edge highly vulnerable to land use and climate change impacts.

We also assessed the change in forest height in the vicinity of degraded forests (forest-degraded forest edge effects). We observed that canopy heights in undisturbed forests near logged or burned forests (that is, within a 120 m radius) were on average significantly lower than in intact forests by 15% and 22% for RH98, respectively (Extended Data Fig. 4, 22% and 32% lower on average for RH50). These results are due not only to localized edge effects from degradation, but also to the omission in the Landsat-based forest cover change datasets of small-scale (<0.09 ha) and low-intensity disturbances at the interface between undisturbed and degraded forests[36], highlighting the added value of LiDAR-based assessment to compensate for the intrinsic limitations of optical sensors in detecting these phenomena.

Our findings on the spatial extent of the two edge effects (from both deforested land and degraded forests) were used to map the remaining intact TMF landscapes in 2020 (Methods). This resulted in smaller extents (−14%) compared with the 2020 assessment by Potapov et al.[27] (502 Mha versus 426 Mha with our approach). Around 48% of our assessment of intact forest landscape falls within the World Database on Protected Areas (versus 53% estimated by Potapov et al.[27]). Conversely, 57% (approximately 60% in Americas and Africa, and 28% in Asia) of protected TMFs are mapped as intact forest, reinforcing their importance for maintaining forest structure and functioning[37]. The main differences between the two maps (Extended Data Fig. 5) concern mosaics of disturbed forest and deforested land in the JRC-TMF dataset (excluded from our approach but present in Potapov et al.[27]). In Gabon, we have larger estimates of forest cover compared with Potapov et al[27], mainly because undisturbed forest blocks are excluded in their analysis. This discrepancy is attributed to limited Landsat imagery before 2005 in the western part of central Africa, resulting in an underestimation of historical disturbances such as selective logging, particularly in the 2000s[4].

These results demonstrate the relevance of the effects of forest edge creation following tree cover loss at pantropical scale. These effects are especially important in forests with high conservation value and will contribute significantly to carbon emissions from tropical deforestation and degradation through induced edge effects[38].

Persistence of forest degradation effects

Assessing the persistence of forest/non-forest edge effects over time is critical to understanding the long-term consequences of deforestation and fragmentation on the structure of forest remnants. Here we show that there is no significant recovery in RH98 (Fig. 3a), RH50 and AGBD (Extended Data Fig. 6) during the 30 years following the creation of the forest edge. Undisturbed forests within the first 120 m of the forest edge exhibit on average a 15% lower RH98 compared with intact forests from the first year after edge creation. Even larger decreases of 25% and 30% on average for RH50 and AGBD, respectively, show that the sensitivity of RH50 and AGBD to edge-related desiccation is higher than that of RH98. Degradation of forest edges from logging or fire triggered an additional 30% decrease in RH98 on average (50% and 40% decrease in RH50 and AGBD, respectively), with no evidence of recovery over time, which is corroborated by airborne laser scanning studies at local scales17,18,31. This pattern is likely to be due to the long-term persistence of the edge effects driven by changes in the growing conditions and the exposure to additional anthropogenic disturbances17,39.

Fig. 3: Impacts of forest degradation from selective logging, fire and edge effects.

a–c, Long-term effects on canopy height (RH98) from edge-desiccation effects (a), degradation (fire or logging) of edge forest (a), selective logging (‘logged 1×’ indicates an area has been logged once over the past 30 years), fire (b) and secondary forests regrowing on abandoned deforested lands (c). Results are reported as the percentage of intact forest canopy height (solid line) after normalizing the difference in canopy height (RH98) within each grid cell between intact forest and each forest type (degraded, edge forest and regrowth) and age. Data are mean RH98 ± spatial s.d. GEDI samples for each disturbance type and related time since disturbance are reported in Supplementary Fig. 5. F value from one-sided ANOVA; ****P ≤ 0.0001. Tukey post hoc tests are presented in Supplementary Data.

Beyond 120 m from the forest edge, where effects of edge proximity are reduced (see Fig. 2), we observed immediate effects and post-disturbance recovery dynamics that differ considerably between continents, disturbance types and forest structure metrics (Fig. 3b and Extended Data Fig. 6). Selective logging effects on RH98 and AGBD are higher in Asia (decreases of 20% and 50%, respectively) compared with Americas and Africa (combined decreases of 10% and 30%, respectively), which can be explained by higher selective logging intensity in Asia (30–40 m^3 ha−1 for the Amazon, 50 m^3 ha−1 for Africa and 270 m^3 ha−1 in Asia40,41). Within 20 years since the last disturbance, we found that logged forests recovered on average 25%, 15% and 27% of RH50 in America, Africa and Asia, respectively, with slower recovery for AGBD (average of 11% recovery across the three continents). The absence of recovery trends in RH98 can be explained by the slow regrowth of late successional, large and emergent trees[19], whereas forest understory dynamics, including tree removals, collateral damages from selective logging (such as dead fallen trees) and the fast regrowth of pioneer and understory species, affect the average vertical distribution of plant material, captured by RH50, making this metric a robust indicator of the long-term effects from degradation and subsequent recovery[42] (Extended Data Fig. 7). Recovery rates for AGBD are lower compared with those reported by Rappaport et al.[21] and Philipson et al.[43] showing an average of 25–32% recovery of AGBD 20 years after logging. The difference in the average annual rates of recovery across continents is likely to be influenced by logging intensity[40], forest composition and climate conditions[44].

The immediate impacts from forest fires are much higher than those from selective logging, with decreases of 35%, 40% and 60% in RH98 for the Americas, Africa and Asia, respectively, and decreases of 60% in AGBD for the Americas and Africa and 80% for Asia. These results are consistent with short-term changes in AGBD from logging and fire reported in the literature21,45. No recovery trend in RH98 or AGBD was detected even ten years after the last disturbance, confirming the long-lasting effects of fire on tree mortality and losses of AGBD46,47. Manipulative studies of post-fire degradation in the Amazon showed strong understory vegetation regrowth under the remaining dominant and taller trees within 5 years after the disturbance, resulting in partial canopy closure[48] (70–80%). This vegetation dynamic is better captured by changes in RH50 than by changes in RH98. The high variability in recovery rates is probably due to different fire frequencies, intensity, climate and forest-type-specific responses[49].

In comparison with forest degradation, trends of forest regrowing on deforested land could be observed and quantified across continents and forest structure metrics (Fig. 3c and Extended Data Fig. 6). After 10–15 years, the regrowth plateaued at 60% of intact forest RH98 with low growth rates (0.5% yr−1, 0.7% yr−1, and 0.9% yr−1 for the Americas, Africa and Asia, respectively). The regrowth for AGBD was on average half that of RH50, reaching 43% (40%, 33% and 57% Americas, Africa and Asia, respectively) of intact forest AGBD after 20 years of regrowing rates, which are similar to those reported by Poorter et al.[50] (33%) using field inventories, or by Heinrich et al.[15] (36–49%) using remote sensing data. However, the slowdown in regrowth rates of AGBD after ten years of regeneration may indicate that several drivers are affecting forest growth and are not captured by Poorter et al.[50] (Supplementary Data). We found that land use intensity through repeated deforestation events and fire occurrences before forest regrowth may have negative effects on regrowth after 5–10 years (Supplementary Fig. 4; and corroborated by previous studies[51]), whereby fire legacies could decrease regrowth rates by 20 to 75%, particularly in drier and water-deficient regions.

The fate of degraded forests

The stage of forest degradation is linked to the type, intensity and recurrence of past disturbances, as well as to the time since the previous disturbance. Here we show that degradation also has a crucial role in predicting future deforestation, whereby the likelihood of total deforestation and land use change increases with the degree of forest degradation. Our results indicate that degraded forests followed by recent deforestation (2020–2022) had significantly lower canopy heights and AGBD compared to those not subjected to deforestation (Extended Data Fig. 8 and Supplementary Fig. 6). On average, degraded forests followed by deforestation experienced severe impacts, with average reductions in RH50, RH98 and AGBD of 60%, 45% and 65%, respectively. These impacts are probably due to unsustainable logging and/or fire, as shown in Fig. 3. Moreover, these structural parameters have a large spatial variability (±12.8%, ±13.3% and ±14.6% for RH50, RH98 and AGBD, respectively), reflecting the complexity of the degradation processes and underlying factors driving deforestation in the tropics[52].

We found that forest relative heights (RH50 and RH98) and distance to the edge were strong predictors of the probability of deforestation (Extended Data Fig. 9 and Supplementary Fig. 7). Degraded forests in America showed, on average, a higher deforestation risk than in Africa or Asia, as 50% of deforestation probability was reached when forests lost 50% of their initial heights (60% in Africa and Asia). Furthermore, proximity to the forest edge, recognized in previous research as a key factor in assessing deforestation risk[53], showed complex interactions with canopy height in degraded forests. This observation highlights the interplay between different factors such as degradation, exposure to human activities and edge-desiccation effects within the first kilometre from the forest edge, contributing to an increased likelihood of subsequent deforestation. However, within 120 m of the forest edge, degradation had a role in enhancing subsequent deforestation only in the Americas, and no statistical differences in RH50, RH98 or AGBD were found for the other continents (Extended Data Fig. 8 and Supplementary Fig. 6).

Conclusions

Our study demonstrates that the integration of recent and spatially sparse spaceborne LiDAR observations (GEDI), with long-term and spatially continuous spaceborne optical datasets (JRC-TMF) provides a novel approach to assess forest degradation and recovery at the pantropical scale. We show that the magnitude of degradation effects on canopy structure are greater than previously reported, with a 20–80% decrease in canopy height and AGBD. The effects of edges on forest vertical structure were previously assumed[7] to extend no more than about 100 m. Our results show that this is a significant underestimate, and we measure edge effects up to around 1.5 km into the forest interior, implying that the overall spatial impact of fragmentation across the pantropical belt is severely overlooked by at least 200%. We show that the cumulative impacts of selective logging, fires and edge effects have significant long-term effects on the structure of global TMFs, but as the 30-year period of our study is too short to observe a full recovery of the forest structure for most types of forest disturbances and regions, future studies should further address this question. Although the current areas of fast-regrowing forests allow offsetting of around 25% of carbon loss from deforestation[15], we found here that the full recovery of forest structure after deforestation or degradation would take a centennial timescale and may be slowed down by anthropogenic factors. Finally, this study provides new insights for identifying the forests that are most vulnerable to agricultural expansion. Forest canopy structure, combined with disturbance history, is a significant indicator of deforestation risk and should be used to target forest monitoring and prioritize conservation in highly degraded areas. This type of spatially explicit information on tropical forest degradation is crucial for implementing more effective forest-based mitigation policies[54] and conservation activities agreed under the UNFCCC and the UN Convention on Biological Diversity[55] (https://www.cbd.int/meetings/COP-15).

Methods

In this study, we use the spaceborne GEDI[6] from the National Aeronautics and Space Administration (NASA) to analyse the extent of forest degradation on canopy structure at pantropical scale, but its short lifetime limits long-term monitoring. To overcome this limitation, we combine GEDI data with long-term information on forest dynamics from Landsat using a space-time substitution strategy. While this approach has been used in previous studies[15], it assumes that differences in neighbouring land characteristics can be used as a proxy for changes over time and that climate and vegetation remain relatively constant over the 20- to 30-year analysis period. For example, when studying forest recovery, we assume that different height metrics from GEDI represent different ages since the last disturbance.

Preparation of input datasets

TMF datasets

We use JRC-TMF, which provides information on changes in humid forest cover from 1990 to 2022 derived from the Landsat archive collection (more details on the methodology and accuracy assessment in Vancutsem et al.[4]). Mangrove forests were excluded from the analysis as periodical tidal floods affect the consistent estimation of canopy height over time. Bamboo-dominated forests were also excluded, as the dynamics of forest structure are related to seasonal or occasional defoliation rather than anthropogenic disturbances. We used the JRC Transition Map and the Annual Change Collection that capture the TMF extent and the related disturbances on an annual basis to derive the following classes.

Intact forests

Undisturbed forest (forest without any disturbance observed over the Landsat time series) located at more than 120 m from degraded forests and more than 3,000 m from the forest/non-forest edge.

Degraded forests

Closed evergreen or semi-evergreen forests that have been temporarily disturbed for a period of a maximum of 2.5 years by selective logging, fire, or unusual weather events. We derived the year since the last degradation from the JRC-TMF dataset used as a proxy for forest recovery. To attribute forest degradation to its direct driver, we first used the global forest cover loss due to fire dataset (GFC-Fire) from 2001 to 2021 from The Global Land Analysis and Discovery (GLAD) laboratory[56]. All certainties of forest fires were considered. Regarding degradation due to selective logging, we performed an extensive visual interpretation and delineation of selective logging operations based on their specific spatial features visible on the JRC-TMF Transition Map. The selected degraded forest pixels correspond to temporary logging roads, logging felling gaps, decks, and skid trails. This dataset covers Brazil, French Guiana, Guyana, Cameroon, Central African Republic, Gabon, Congo, the Democratic Republic of Congo, Indonesia, Malaysia and Papua New Guinea. The managed forest concessions dataset from the World Resource Institute was used to guide the collection of polygons in central Africa and southeast Asia while the delineation in the Amazon was generated from previous scientific experience57,58. An independent visual interpretation of selective logging was performed in order to analyse how the delineation influenced our results. This sensitivity analysis showed small differences in the magnitude and trends of logging impacts on forest structure without altering the subsequent analysis and conclusions (Supplementary Fig. 8). It also proved to be unbiased and robust when comparing changes in forest height in the vicinity of forest degraded by selective logging (Supplementary Fig. 9). We created a buffer of 300 m radius (10 Landsat pixels) around fire pixels to avoid an overlap between the two causes when analysing impacts from selective logging alone. When looking at forest degradation alone, we excluded pixels within the edge forest defined with a conservative value of 120 m from the edge.

Forest edge

To compute forest edges, we considered undisturbed or degraded forest pixels from the JRC-TMF Annual Change Collection dataset for years spanning from 1989 to 2022. We applied a 5 × 5 pixel moving window for all annual forest maps to remove isolated pixels for both forest and non-forest classes using the sieve algorithm and replace them with the value of the most frequent class within the moving window. For the analysis of forest edge effect penetration, we used the extent of forest cover in 2022 to derive undisturbed forest edges using edge widths varying from 60 m to 10,200 m at different intervals (0–60 m, 60–120 m, 120–240 m, 240–420 m, 420–720 m, 720–1,020 m, 1,020–1,500 m, 1,500–2,040 m, 2,040–2,580 m, 2,580–3,120 m, 3,120–4,020 m, 4,020–5,100 m, 5,100–6,000 m, 6,000–7,200 m, 7,200–8,100 m, 8,100–9,000 m, 9,000–10,200 m) using the Euclidean distance calculated from the non-forest class. These distances were selected based on previous studies reporting on the scale at which edge effects operate and affect microclimate[59] (up to 400 m), canopy moisture levels[29] (up to 2.7 km), phenology[60] (up to 5–10km) and forest biomass[22] (up to 1.5 km). The first 6 intervals of distances are centred on the most recent and accurate evaluation of the extent of the edge effect17,18,32,61 (~100–200 m). To focus on the scale of edge effects due to deforestation, we discarded grid cells of 1.5° showing a value of accumulated deforestation (1991–2022) compared to forest area in 1990 of less or equal than 2% (estimates derived from JRC-TMF). To mitigate the effects of canopy disturbance interactions between degraded and undisturbed forests, we eliminated areas of transition using a buffer of 120 m around degraded forests. This distance corresponds to the area initially affected by the felling of individual trees in selective logging operations[62] where localized edge effects are the highest[28]. To calculate the age of forest edges, we adopted a 120 m edge width, which constitutes the threshold of significant AGBD changes observed in the tropics7,17,18. We produced forest edges from 1989 to 2022, masked natural edges (transitions forest/water and forest/savannah), derived the year of forest edge creation and computed the age of all edges classified as forest in 2022. We separated forest edges into undisturbed forest edges, burned forest edges (where a fire from the GLAD dataset occurred after the year of forest edge creation) and logged forest edges (all other types of degradation occurring after the year of forest edge creation).

Forest regrowth or secondary forest

Forest regrowth or secondary forest refers to a two-phase transition from moist forests to deforested land to vegetative regrowth. A minimum duration of 3 years (2020–2022) of permanent presence of moist forest cover is needed to classify a pixel as forest regrowth to avoid confusion with other land uses. Using the JRC-TMF Annual Change Collection, we calculated the age of secondary forests (from 1 to 32 years old), which may have an uncertainty of 1 year, depending on whether a deforestation event was detected at the end of a year or at the beginning of the next year. In case of late detection, the area will be classified as regrowing one year later (if it does not show signs of permanent deforestation).

GEDI dataset

The GEDI mission uses a LiDAR deployed on the International Space Station from April 2019 until March 2023. One of its primary scientific objectives is to map forest structural properties and understand the effects of vegetation structure on biodiversity. It provides sparse measurements (hereafter sample plots or shots) of vegetation structure, including forest canopy height[6] with a vertical accuracy of about 50 cm, over an area defined by a sampling footprint of ~25 m width. For our analysis, we used GEDI L2A[63] Elevation and Height Metrics (version 2) and GEDI L4A[64] Above Ground Biomass (version 2.1) which represent returned laser energy metrics on canopy height and estimated AGBD for each 25 m diameter GEDI footprint. The footprint data are geolocated and have an expected positional error[6] (that is, horizontal geolocation accuracy) of 11 m. For each footprint, we extracted a set of relative height (RH) metrics, the AGBD and the associated prediction standard error (AGBD_SE). AGBD are reported as weighted averages, using the AGBD_SE as weight. Note that the estimation of AGBD based on RH metrics from GEDI L2A varied considerably in performance across the TMF domain, having a determination coefficient (R^2) of 0.66 (mean residual error (MRE) of 10.4 Mg ha−1), 0.64 (MRE of 15.32 Mg ha−1), 0.36 (MRE of 121.15 Mg ha−1) and 0.61 (MRE of 8.17 Mg ha−1) for South America, Africa, Asia and Oceania, respectively (further details on the validation of the GEDI L4A are in ref. [25]).

RH metrics represent the height (in metres) at which a percentile of the laser energy is returned relative to the ground. RH98 corresponds to the maximum canopy height (hereafter ‘canopy height’), which is a more stable height metric than RH100. RH50 (also known as ‘height of median energy’ (HOME)[24]) is the median height at which the 50th percentile of the cumulative waveform energy returned relative to the ground and has been identified as one of the LiDAR metrics with the greatest potential for estimating structural characteristics in tropical forests[24]. When validated against ground-based data, RH50 generally exhibits a strong correlation with key structural variables, including AGBD, stem diameter, and basal area[65]. Due to its strong dependence on the vertical distribution of canopy elements and gaps within the canopies and canopy cover, RH50 serves as a highly complementary metric to RH98 for characterizing changes in canopy structure from degradation[66] (see also Extended Data Fig. 7).

We selected GEDI data acquired from 1 January 2019 to 31 December 2022. To select the highest quality data, we filtered the GEDI data (both GEDI L2A and L4A) by selecting only the observations collected in power beam mode and labelled them as good quality (quality flag equals 1), thus avoiding risks of having degraded geolocation under suboptimal operating conditions (degrade flag equals 0). Additionally, we filtered GEDI 2A data using only night acquisitions to limit the background noise effects of reflected solar radiation. We used the Shuttle Radar Topography Mission (SRTM) information to exclude GEDI footprints above 20° slopes to avoid errors in vegetation height. Steep slopes might lead to erroneous relative height metrics (especially over sparsely vegetated areas), so applying our threshold of 20° is a conservative approach[67]. Additionally, we filtered out GEDI footprints classified as water in the Global Land Analysis and Discovery Landsat Analysis Ready Data quality layer (ARD; https://glad.umd.edu/ard/home) or when a GEDI footprint was located within an urban area defined by the Global Urban Dataset of Florczyk et al.[68]. Finally, we excluded GEDI footprints with RH98 values below 5 m to be compliant with the Food and Agriculture Organization (FAO) definition of forest.

Further, we used the beam sensitivity information from GEDI L2A as a proxy for signal-to-noise ratio and the ability of GEDI to penetrate the highest canopy cover. For the intact and undisturbed forest classes, we considered only shots with a beam sensitivity greater than 0.98, while for the other classes (for example, degraded, edge and regrowth forests), we used a beam sensitivity greater than 0.95, as previously recommended67,69.

Combining datasets

On the temporal scale, we used separately yearly GEDI data to estimate as accurately as possible the year since the last disturbance (that is, degradation, forest edge creation or deforestation). All degraded and edge forests were masked out if the date of disturbance or the year of edge creation occurred during the GEDI acquisition period. A similar step was performed for secondary forests when the year of regrowth overlapped with the GEDI acquisition period. On the spatial scale, to reduce the noise caused by GEDI geolocation errors, we applied a morphological (circular shape) filter of 35 m to the forest cover change class of interest (intact, degraded, edge or regrowth), which resulted in the removal of single- small-patches of pixels. We thus ensured that GEDI samples fell within the class of interest and avoided any partial overlap. The extent of mapped forest change areas in the JRC-TMF dataset was used to target the sampling of GEDI footprints and quantify forest edge effects or canopy disturbance contagiousness between degraded and undisturbed forests on forest structure still classified as ‘undisturbed forest’.

To ensure robust and comparable observations of forest structure metrics across the multiple classes of forest cover change, we considered a minimum of 600 GEDI samples for each 1.5° grid cell (~167 km at the Equator; around a given point) and a minimum of 7 grid cells per continent to derive continent-level statistics of forest RHs and AGBD. When analysing the time series (Fig. 3 and Supplementary Fig. 5), a minimum threshold of 30 GEDI samples for each time step of the trajectory—and a minimum of 600 GEDI samples for the sum of all the time steps—within each grid cell was required. Note that the time step does not refer to the GEDI date but to the JRC-TMF dataset where the timing of degradation, regrowth etc. is assessed. Similarly, for edge effect penetration, a minimum of 30 GEDI samples for each distance to the edge within each grid cell—and a minimum of 600 GEDI samples for the sum of all the distances—was required (see Fig. 2 and Supplementary Fig. 3). Metadata on the number of GEDI samples for aggregated classes of forest cover change is provided in Supplementary Fig. 2. Wall-to-wall information of relative heights with high spatial resolution on large scales, such as those produced by Lang[70] for canopy height only, will increase in the future the quantity of data, thus improving the quality and the robustness of the analysis.

The computation of canopy heights for intact, degraded, edge, and regrowing TMFs at the 1.5° grid cell level may vary due to local environmental and anthropogenic factors (for example, soil and forest types), leading to potential high variability in the reported canopy height statistics. In order to reduce sampling bias in the structural variable dataset, we randomly resampled GEDI observations 500 times within each 1.5° × 1.5° grid cell. We then summarized the random samples by calculating the mean and standard deviation of each structural variable, for each grid cell. Using this random sampling procedure based on the iteration (500 times) of sampling 300 GEDI observations for each grid cell, we found that the intra-grid variability of canopy heights was not significant. The results of the random sampling procedure show the low standard deviation for each class of RH98 distribution and forest considered (that is, intact, degraded, edge and regrowth) (Supplementary Fig. 10).

Intact forest landscape assessment and comparison with Potapov’s data product

We selected undisturbed forests in 2020 free from any disturbances located at: (1) a distance higher than the scale of the forest/non-forest edge effect identified at the grid cell level; and (2) more than 120 m distance from degraded forests from the JRC-TMF dataset (identified scale of the forest/degraded forest edge effect). Potapov’s map of 2020[27] was constrained to the extent of TMFs (excluding mangroves and bamboo-dominated forests). We resampled our JRC-TMF-derived intact forest landscape (IFL) map from 30 m to 1 km. We computed the number of connected pixels (where each pixel contains the number of 4-connected neighbours) and then restricted them to values greater or equal to 500 to obtain an approximation of forest patch area greater than 500 km^2 (to match the definition of IFL of Potapov, with a minimum area of 500 km^2). Other criteria in Potapov on minimum IFL patch width (10 km) or minimum corridor width (2 km) were not implemented in our approach.

Statistical tests

We performed a series of one-way ANOVAs to test for differences in the impacts of edge effects at different distances and times on the long-term recovery of the relative heights and biomass variables. ANOVAs were performed separately for each continent. For the height variables (RH50 and RH98), a series of standard one-way ANOVAs were used. In the analyses involving AGBD, we used a modified approach to propagate the prediction standard error associated with the AGBD dataset values which involved using a Monte Carlo approach (n = 500). In brief, we generated random noise that was added to the AGBD data. For each iteration i we generated a noise term, noiseij, by drawing a random value from a normal distribution with mean μ of 0 and s.d. equal to the prediction standard error of the AGBD (σj) for each GEDI footprint. The noise can be represented as: \({{\rm{n}}{\rm{o}}{\rm{i}}{\rm{s}}{\rm{e}}}_{ij}\sim N(\mu ,{\sigma }_{j}^{2})\). We then perturbed the AGBD values by adding the generated noise to the original dataset (biomassoriginal,j) for the ith iteration (biomassperturbed,i,j). We then performed an ANOVA for each iteration using the perturbed dataset and recorded the results. We subsequently examined the distribution density of the F values. The results showed minimal variability suggesting that observed differences are robust to uncertainty associated with the AGBD values (Supplementary Fig. 11). For each ANOVA, we conducted a series of Tukey honest significant difference post hoc tests to assess significant differences between distance classes or time steps. The significance level was set to P < 0.05.

Modelling deforestation risk

We assessed whether changes in RH50, RH98 and AGBD due to the occurrence of forest degradation and the distance to the edge represent an early warning signal of future deforestation. We retrieved GEDI footprints of 2019 sampled in forest degraded before 2018, followed or not by deforestation (2020–2022), together with GEDI footprints of 2020 sampled in forest degraded before 2019, followed or not by deforestation (2021–2022) and, footprints of 2021 sampled in forest degraded before 2020 followed or not by deforestation (2022). We then separated all samples based on their location within the first 120 m to the edge or beyond. The probability of deforestation in degraded forests was modelled using a generalized linear modelling approach. We fitted two models. One included only a single predictor, so that the percentage of intact forest height was the only predictor (Supplementary Fig. 12). The second model included two predictors—that is, the percentage of intact forest height and the distance to the edge. The error structure associated with the models was assumed to be binomial with a logit link function. A given model takes the general form:

$${Y}_{i} \sim B({\pi }_{i},{n}_{i})$$

(1)

$$E({Y}_{i}) \sim {n}_{i}\times {\pi }_{i}\,\,{\rm{and}}\;{\rm{var}}({Y}_{i}) \sim {n}_{i}\times {\pi }_{i}\times (1{-\pi }_{i})$$

(2)

$${\rm{logit}}({\pi }_{i})={\eta }_{i}$$

(3)

$${\eta }_{i}=\alpha +\beta {X}_{i}$$

(4)

where Yi is the ith observation corresponding to the occurrence of a deforestation event and βXi is a matrix of regression coefficients.

Models were fitted within a Bayesian framework. We fitted the models using the programming language Stan via the brms package in the R software for statistical computing[71]. Models were run using 4 chains of 4,000 iterations each, with a warm-up of 1,000. We used the brms default priors for our model parameters. Convergence was visually assessed using trace plots (Supplementary Fig. 13) and the Rhat values (that is, the ratio of the effective sample size to the overall number of iterations, with values close to one indicating convergence). Markov chain Monte Carlo diagnostics showed a good convergence of the four chains, while the posterior distributions are centred around one peak value. The discriminatory ability of the models—that is, their ability to successfully predict a deforestation event—was assessed using the receiver operating characteristic (ROC) curve. We calculated the area under the curve (AUC) and compared the values with the guidelines provided by Swets[72].

Cloud computing platform

All data extraction for this study was performed in Google Earth Engine[73], which provides the ability to compute GEDI footprint statistics and analyse the entire data records with high computational efficiency. The GEE data catalogue contains processed L2A and L4A GEDI data products—that is, the rasterized versions of the original GEDI products, with each GEDI shot footprint represented by a 25 m pixel.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All data used in this study are from publicly available sources. GEDI data are archived on NASA Distributed Active Archive Centers (DAACs). GEDI’s footprint-level Relative Height data were taken from the GEDI02_A height and elevation product, available at LPDAAC: https://doi.org/10.5067/GEDI/GEDI02_A.002. GEDI’s biomass data (AGBD) was taken from the GEDI04_A product also available at LPDAAC: https://doi.org/10.3334/ORNLDAAC/2056. The JRC-TMF dataset can be accessed at https://forobs.jrc.ec.europa.eu/TMF/data.php#gee. The slope is processed using SRTM data downloaded from https://developers.google.com/earth-engine/datasets/catalog/SRTMGL1_003. The Intact Forest Landscape dataset for 2020 can be downloaded at https://intactforests.org/data.ifl.html. Managed Forest Concessions dataset (accessed in February 2022) can be downloaded at https://data.globalforestwatch.org/documents/gfw::managed-forest-concessions-downloadable/about. The World Database on Protected Areas (accessed in October 2023) can be downloaded at www.protectedplanet.net. To ensure the full reproducibility and transparency of our research, we provide all of the data analysed during the current study. Pre-processed data, post-processed data, drivers of forest degradation, maps, codes and final figures developed in this study are made publicly available and briefly described to facilitate reproducibility and applicability. These data are permanently and publicly available on a Zenodo repository (https://doi.org/10.5281/zenodo.11235618)[74].

Code availability

To ensure full reproducibility and transparency of our research, we provide all of the scripts used in our analysis. Codes used for this study (GEE and R scripts) are permanently and publicly available in a Zenodo repository: https://doi.org/10.5281/zenodo.11235618[74].

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Acknowledgements

The views expressed are purely those of the writers and may not in any circumstances be regarded as stating an official position of the European Commission. The authors thank S. Carboni for her help in the visual interpretation of selective logging. This study has been partly financed through the European Union’s Amazonia+ programme and by the Directorate General for Climate Action of the European Commission (DG-CLIMA) through Lot 2 (TroFoMo (Tropical moist Forest Monitoring)) of the ForMonPol (Forest Monitoring for Policies) Administrative Arrangement.

Author information

Author notes

1. These authors contributed equally: C. Bourgoin, G. Ceccherini

Authors and Affiliations

1. European Commission, Joint Research Centre, Ispra, Italy

C. Bourgoin, G. Ceccherini, M. Girardello, C. Vancutsem, V. Avitabile, P. S. A. Beck, R. Beuchle, M. Migliavacca, A. Cescatti & F. Achard

2. CIRAD, Forêts et Sociétés, Montpellier, France

L. Blanc

3. Forêts et Sociétés, Univ Montpellier, CIRAD, Montpellier, France

L. Blanc

4. Max Planck Institute for Biogeochemistry, Jena, Germany

G. Duveiller

5. CIRAD, UMR AMAP, Montpellier, France

G. Vieilledent

6. AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France

G. Vieilledent

Contributions

C.B., G.C., C.V., M.G., A.C. and F.A. conceived the idea and designed the methodology. C.B., G.C., A.C., M.G. and G.V. analysed the data and wrote the Google Earth Engine and R scripts. R.B. provided data on selective logging in Brazil. C.B. and G.C. wrote the manuscript with contributions from the other authors. All authors contributed critically to the interpretation of the results and gave final approval for publication.

Corresponding author

Correspondence to C. Bourgoin.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Mathew Williams and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.

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Extended data figures and tables

Extended Data Fig. 1 AGBD of intact, degraded, edge forests and regrowths.

Above Ground Biomass Density (AGBD) of intact forests (a) degraded forests (b), forest edges (c) and regrowths (d) in 1.5° (~ 167 km) grid cells between 30°N and 30°S. Grid cells with less than 600 GEDI samples or with no statistically significant differences in canopy height between intact and non-intact forests were masked (Welch two-sided t-test p < 0.05).

Extended Data Fig. 2 Transects of GEDI derived canopy heights.

Transects of GEDI relative heights (RH50 and RH98 from year 2020) in the Brazilian Amazon, Congo basin and West Sumatra crossing deforested land, edge forest (edge width of 350 m, 800 m and 1000 m, respectively) and intact forest. The two black lines represent the height of RH98 and RH50. Background data: Google (08/2019, 12/2023 and 03/2022 for panels a-c, respectively), © 2024 Maxar Technologies.

Extended Data Fig. 3 Spatial scale and magnitude of edge effects caused by deforestation on AGBD and RH50.

Average distribution of RH50 (panel a) and AGBD (panel b) of undisturbed forests (located at more than 120 metres from degraded forests) and all forests (including undisturbed and degraded forests) at various distances to the forest edge (agricultural and other land covers). The inset caption represents the degradation area due to fire (red curve) and selective logging (purple curve) calculated at various distances to the edge. The red dotted vertical line is placed at a distance equal to 350, 400, and 1500 m for America, Africa, and Asia, respectively, and corresponds to the distance between the forest edge and the point at which 95% of intact forest RH98 is reached (red horizontal dotted line). Vertical bars indicate the spatial standard deviation. F represents the F-Value in one-sided ANOVA and asterisks indicate the level of statistical significance for ANOVA: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, **** p ≤ 0.0001, ns stands for not significant. Tukey post-hoc tests are available in supplementary data. The number of GEDI sample footprints for each distance to the forest edge is reported in Supplementary Fig. 3.

Extended Data Fig. 4 Edge effects caused by forest degradation from selective logging and fire.

Difference in RH98 (panel a) and RH50 (panel b) for forest classified as undisturbed in the JRC-TMF dataset located within and outside a buffer area (120 m radius) around logged or burned forest. Asterisks indicate the level of statistical significance of these comparisons: Adjusted p values are determined by two-tailed unpaired T test. The number at the bottom of each boxplot corresponds to the number of GEDI samples. Boxplot shows data from the 25th–75th percentile, the median (line) and whiskers extending to the minimum and maximum within 1.5× interquartile range.

Extended Data Fig. 5 Intact Forest Landscape mapping.

Intact forest landscape (IFL) mapping of the year 2020 in 1.5-degree grid cells between 30°N and 30°S. (a) Area of TMF-based IFL (here referred to as Bourgoin - the main author of this study - approach). Dark grey grid cells present no IFL area. (b) Area of IFL derived from Potapov’s 2020. The extent was restricted to the tropical moist forest domain. We further excluded mangrove, forest conversion to water detected in the JRC-TMF dataset and bamboo-dominated forest areas to allow comparison with our approach. (c) Difference in area between our approach (i.e. JRC-derived/Bourgoin) and Potapov’s.

Extended Data Fig. 6 Impacts of forest degradation from selective logging, fire and edge effects on AGBD and RH50.

Long-term impacts on RH50 and AGBD from edge-desiccation effect (6a, 6d), degradation (fire or logging) of edge forest (6a, 6d), selective logging (logged 1x corresponds to logged once over the last 3 decades), fire (6b, 6e) and secondary forests regrowing on abandoned deforested lands (6c, 6 f). Results are reported as the percentage of intact forest canopy height (solid line) after normalising the difference in RH50 and AGBD within each grid cell between intact forest and each forest type (degraded, edge forest, regrowth) and age. Dots represent the average value of RH50/AGBD and vertical bars indicate the spatial standard deviation. F represents the F-Value in one-sided ANOVA and asterisks indicate the level of statistical significance for ANOVA: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, **** p ≤ 0.0001, ns stands for not significant. Tukey post-hoc tests are available in supplementary data. GEDI samples for each disturbance type and related time since disturbance are reported in Supplementary Fig. 5.

Extended Data Fig. 7 Cumulative Return Energy from GEDI for different forest types.

Cumulative Return Energy from GEDI L2A for different forest types (30 GEDI footprints for each forest type were sampled in a grid cell located in the northeast of the Brazilian Amazon). The vertical black lines refer to RH98 and RH50. The horizontal grey lines refer to the height of RH98 and RH50 for Intact Forest. The error band indicates the standard deviation.

Extended Data Fig. 8 Canopy heights of degraded forests before being deforested.

Differences in canopy heights of different forest types before deforestation, compared to canopy heights of similar and contemporary forests that are not deforested. The canopy heights (RH98) are all retrieved during the period 2019-2021, while the deforestation events occurred 1-3 years after GEDI measurements (2020-2022). The different forest types are degraded forest (located beyond the edge), degraded edge forest (width 120 m), and undisturbed edge forests. For degraded forests, degradation occurred before 2019, and no disturbance was observed during the year of GEDI data acquisition. Big circles represent the averages, and the small dots are individual GEDI samples. Adjusted p values are determined by two-tailed unpaired T test. The number at the top of each distribution corresponds to the number of GEDI samples.

Extended Data Fig. 9 Modelling of deforestation risk based on canopy heights and distance to forest edge.

a) Marginal model plot of the binary logistic regression model of RH98 predicting whether deforestation was reported. The plot shows the expected influence of degraded forest structure (canopy height, expressed as a percentage of intact forest RH98) on the probability of deforestation. The grey shaded areas indicate the 95% credibility intervals of the predicted values. OR is the odds ratio. Stacked dots represent the GEDI samples deforested/not deforested. The marginal effect at the mean (MEM) quantifies for a one-point increase in RH98 (i.e. the x-axis) the associated percentage point variation in the probability of deforestation. AUC is the area under the ROC curve101. Marginal effects are partial derivatives of the regression equation for each variable in the model for each unit in the data. b) Plot of the marginal effects showing the probability of deforestation based on degraded forest structure (canopy height, expressed as a percentage of intact forest height) and the distance to the forest edge.

Extended Data Table 1 Summary statistics of canopy heights and AGBD for intact, degraded, edge forests and regrowths

Supplementary information

Supplementary Information

Supplementary Table and figures supporting the results and methodology

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Supplementary Data

Results in the form of a multi-tab Excel sheet of the Tukey post hoc tests related to ANOVA statistics presented in Figs. 2 and 3 and Extended Data Figs. 3 and 6.

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Bourgoin, C., Ceccherini, G., Girardello, M. et al. Human degradation of tropical moist forests is greater than previously estimated. Nature 631, 570–576 (2024). https://doi.org/10.1038/s41586-024-07629-0

- Received: 31 January 2023

- Accepted: 30 May 2024

- Published: 03 July 2024

- Issue Date: 18 July 2024

- DOI: https://doi.org/10.1038/s41586-024-07629-0

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A deep catalogue of protein-coding variation in 983,578 individuals

Kathie Y. Sun,

Xiaodong Bai,

Siying Chen,

Suying Bao,

Chuanyi Zhang,

Manav Kapoor,

Joshua Backman,

Tyler Joseph,

Evan Maxwell,

George Mitra,

Alexander Gorovits,

Adam Mansfield,

Boris Boutkov,

Sujit Gokhale,

Lukas Habegger,

Anthony Marcketta,

Adam E. Locke,

Liron Ganel,

Alicia Hawes,

Michael D. Kessler,

Deepika Sharma,

Jeffrey Staples,

Jonas Bovijn,

Sahar Gelfman,

Alessandro Di Gioia,

Veera M. Rajagopal,

Alexander Lopez,

Jennifer Rico Varela,

Jesús Alegre-Díaz,

Jaime Berumen,

Roberto Tapia-Conyer,

Pablo Kuri-Morales,

Jason Torres,

Jonathan Emberson,

Rory Collins,

Regeneron Genetics Center,

RGC-ME Cohort Partners,

Michael Cantor,

Timothy Thornton,

Hyun Min Kang,

John D. Overton,

Alan R. Shuldiner,

M. Laura Cremona,

Mona Nafde,

Aris Baras,

Gonçalo Abecasis,

Jonathan Marchini,

Jeffrey G. Reid,

William Salerno &

…

Suganthi Balasubramanian

Nature volume 631, pages 583–592 (2024)

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Abstract

Rare coding variants that substantially affect function provide insights into the biology of a gene1,2,3. However, ascertaining the frequency of such variants requires large sample sizes4,5,6,7,8. Here we present a catalogue of human protein-coding variation, derived from exome sequencing of 983,578 individuals across diverse populations. In total, 23% of the Regeneron Genetics Center Million Exome (RGC-ME) data come from individuals of African, East Asian, Indigenous American, Middle Eastern and South Asian ancestry. The catalogue includes more than 10.4 million missense and 1.1 million predicted loss-of-function (pLOF) variants. We identify individuals with rare biallelic pLOF variants in 4,848 genes, 1,751 of which have not been previously reported. From precise quantitative estimates of selection against heterozygous loss of function (LOF), we identify 3,988 LOF-intolerant genes, including 86 that were previously assessed as tolerant and 1,153 that lack established disease annotation. We also define regions of missense depletion at high resolution. Notably, 1,482 genes have regions that are depleted of missense variants despite being tolerant of pLOF variants. Finally, we estimate that 3% of individuals have a clinically actionable genetic variant, and that 11,773 variants reported in ClinVar with unknown significance are likely to be deleterious cryptic splice sites. To facilitate variant interpretation and genetics-informed precision medicine, we make this resource of coding variation from the RGC-ME dataset publicly accessible through a variant allele frequency browser.

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Main

Exome sequencing has enabled the discovery of rare coding variants, and has thus provided insights into gene function that have accelerated the pace of disease-associated gene discovery across Mendelian and common disorders1,2,3,6,9,10,11,12. Furthermore, exome sequencing has identified protective alleles that highlight drug targets that could be amenable to pharmacological intervention2,13,14,15,16,17. For example, anti-PCSK9 drug therapy is based on the observation that a loss of PCSK9 function is associated with reduced levels of cholesterol[18].

Cataloguing rare coding variation can help with the implementation of precision medicine19,20. Large datasets of genetic variation that are representative of the human population are essential for the comprehensive discovery and interpretation of rare variants. Roadmaps for numerous large-scale sequencing studies have been proposed, and several efforts are now underway21,22,23,24. The Genome Aggregation Database[4] (gnomAD) and Trans-Omics for Precision Medicine[8] (TOPMed) initiatives have developed large public databases of genetic variation derived from approximately 200,000 individuals and 132,000 individuals, respectively. Here, we describe a harmonized collection of exonic data derived from 983,578 individuals who represent a diverse array of ancestries. We calculate continental and fine-scale ancestry-based allele frequencies across this dataset and make the data publicly available through the RGC research browser: https://rgc-research.regeneron.com/me.

Survey of variation in the RGC-ME dataset

The Regeneron Genetics Center Million Exome (RGC-ME) dataset contains the genetic variation observed in 983,578 individuals. These data span dozens of collaborations, including large biobanks and health systems. All data were generated by Regeneron Genetics Center using a single harmonized sequencing and informatics protocol. Previously published datasets, such as the UK Biobank and the Mexico City Prospective Study, were reprocessed9,25. The RGC-ME dataset comprises both outbred and founder populations spanning African (AFR), European (EUR), East Asian (EAS), Indigenous American (IAM), Middle Eastern (MEA) and South Asian (SAS) continental ancestries, and includes cohorts with relatively high rates of consanguinity. More than 190,000 of the unrelated participants (23%) are of non-EUR ancestry in the RGC-ME dataset, as compared with 35,000 in gnomAD genomes (v.3.1.2), 53,000 in gnomAD exomes (v.2.1.1), and 91,000 in TOPMed Freeze 8, indicating that RGC-ME represents a large increase in the number of individuals of non-EUR ancestry in datasets of genetic variation4,8 (Fig. 1a and Supplementary Table 1a).

Fig. 1: Variant survey and population counts in the RGC-ME dataset.

a, Summed proportional ancestry (sum of weighted ancestry probabilities) at continental, sub-continental and regional levels for 821,979 unrelated samples (Supplementary Table 1b). All subsequent variant counts and surveys have been performed in the unrelated analysis set. UNK, unknown. b, Count of variants unique to RGC-ME (that is, variants not in gnomAD v.3.1.2 genomes, gnomAD v.2.1.1 exomes and TOPMed Freeze 8), broken down by singletons and variant functional category. M, million. c, Variant counts in different functional categories, proportion of singletons and per-individual median values. All counts were based on variants in the canonical transcript. pLOF includes frameshift, essential splice donor and acceptor (excluding splice sites in UTRs) and stop-gained variants.

We performed a comprehensive survey of genetic variation, encompassing single-nucleotide variants (SNV) and insertion–deletion (indel) variants. To estimate population allele frequencies, we focused on 821,979 unrelated samples (referred to hereafter as the 822K unrelated set; Supplementary Table 1a). We identified 16,425,629 unique mutated genomic positions (that is, sites) in autosomal and X-chromosomal coding regions, with one unique reference–alternate allele change (that is, variant) every two bases on average. In canonical transcripts within sequencing target regions, mutations at 35.6%, 32.2% and 9.5% of all possible genomic positions that can lead to synonymous, missense and stop-gained variants, respectively, were observed. In highly methylated CpG sites, we observed 95.0% of all possible synonymous, 92.2% of missense and 78.6% of stop-gained variants. Across all mutational contexts, 21.4% and 8.4% of all possible synonymous variants and stop-gained variants, respectively, were observed (Extended Data Fig. 1). Thus, RGC-ME represents a major advance towards the comprehensive discovery of rare variants.

Among coding variation in canonical transcripts, 1,115,116 pLOF variants were identified, which include those causing a premature stop, affecting essential splice donor and acceptor sites or causing frameshifts (Fig. 1c). Of these pLOF variants, 53.3% were observed as singletons; that is, only observed in one individual. In addition, 4,645,092 synonymous (35.7% as singletons) and 10,444,562 missense (40.0% as singletons) variants in canonical transcripts were detected. A total of 48% of coding variants in canonical transcripts were unique to RGC-ME and absent in other large-scale datasets4,8 (Fig. 1b). Each sample had a median of 137 pLOF, 8,652 missense and 10,184 synonymous variants (Fig. 1c). AFR individuals had, on average, 18.6% more variants across all functional categories compared with individuals of other ancestries (Extended Data Fig. 2), as expected on the basis of the ‘Out of Africa’ model of human population history[26].

Constrained genes

Population-scale sequencing allows the quantification of pLOF variation in genes, which is key to understanding the relationship between genes and diseases. Several gene constraint metrics have been developed to estimate the pLOF tolerance of genes[27]. Here, we estimated pLOF depletion using the cumulative frequency of pLOF variants in a gene to derive a selection coefficient, shet, that quantifies fitness loss due to heterozygous pLOF variation[28]. We estimated the indispensability of 16,710 protein-coding genes on the basis of the observed number of rare pLOF variants per gene with a cumulative alternate allele frequency (AAF) of less than 0.1% compared with the expected number based on gene-specific mutation rates (Supplementary Table 2).

The mean shet value in the RGC-ME dataset for canonical transcripts was 0.073 (95% highest posterior density (HPD)): [0.043, 0.12] (median shet = 0.021) (Fig. 2a), which suggests that, on average, a pLOF would result in 7.3% lower evolutionary fitness relative to the reference allele. This estimate is comparable with the mean shet value of 0.073 [0.029, 0.18] (median = 0.028) computed using the same method on the ExAC dataset[29] (n ≈ 60,000). Our sample size (n ≈ 822,000) helped to accurately quantify rare pLOF variants and compute more precise constraint scores than the ExAC values. This finding is best illustrated in known haploinsufficient genes, which are expected to be more constrained and thus have larger shet values relative to all genes (Extended Data Fig. 3a). Compared with values that were computed with ExAC data, shet values for haploinsufficient genes in the RGC-ME dataset were significantly higher (\({\Delta \bar{s}}_{\text{het}}=0.045\), P = 0.002) and had smaller 95% HPD ranges despite those larger means (∆Var(shet) = −0.026, P = 4.5 × 10−21). Estimates for all genes were more precise in 822K samples compared with a randomly downsampled set of 60,000 samples from the RGC-ME dataset (Extended Data Fig. 3b), in which mean and median 95% HPD ranges were 6.2- and 4.0-fold larger, respectively.

Fig. 2: Estimates of gene-level constraint, representing shet, from the RGC-ME dataset.

a, Mean shet probability density for 16,710 canonical transcripts with 95% CIs calculated with 10,000 bootstrapped samples from the means of individual genes. b, Odds ratios (points) and 95% CIs (short horizontal lines; computed using standard error) for genes with shet cut-off > 0.073 (deemed highly constrained genes) to be included in each gene category listed on the y axis compared with genes below the cut-off. Genes defined as ‘human knockouts’ are those with carriers of rare, biallelic pLOF variants observed in the RGC-ME dataset. A total of 16,710 canonical transcripts were included in each category, which contained at minimum 234 ‘true’ genes (that category being haploinsufficient genes). HGMD, Human Gene Mutation Database.

The shet value is higher in genes that are associated with Mendelian diseases28,30 (Extended Data Fig. 3a), and can differentiate groups of genes under varying degrees of selection (Fig. 2b). We used shet to identify constrained genes by comparing the shet scores of known high-constraint genes (haploinsufficient, autosomal dominant and developmental-specific autosomal dominant) with those of low-constraint genes (haplosufficient and genes with rare biallelic pLOF variants from the RGC-ME dataset) (Extended Data Fig. 4a). Among 1,476 genes in the ‘high-constraint’ and 3,893 genes in the ‘low-constraint’ groups, 89.1% of genes with a shet score greater than the mean (0.073) and 66.6% of genes with a shet score greater than the median (0.021) belonged to the high group (Supplementary Table 2). These thresholds served as cut-offs for mean and lower bound (2.5% HPD), respectively, to identify highly constrained genes with fitness deficits on a par with dominant disease-causing genes that also reflect uncertainty in the mean.

We compared shet to other published LOF constraint measures, such as LOEUF[4], and an alternate method for estimating shet based on approximate Bayesian computing[31], which we refer to as shet-ABC (Supplementary Figs. 2–4). Spearman rank correlations between shet from RGC-ME and these estimates were high (−0.768 with LOEUF; 0.778 with shet-ABC). However, shet derived from RGC-ME had higher sensitivity and specificity in differentiating between constrained and unconstrained genes, compared with LOEUF and shet-ABC (Extended Data Fig. 4a).

Improving shet estimates is most valuable for genes with few expected pLOF mutations[4], particularly shorter genes. The RGC-ME dataset allowed shet to be estimated more precisely for the smallest quantiles of gene coding sequence (CDS) length (Extended Data Fig. 3c), and derived more informative constraint metrics using an allele-frequency-based approach and a larger sample. We derived constraint scores for 923 genes that had 5 or fewer expected pLOF variants, deemed underpowered for similar analyses with LOEUF[32]. These 923 underpowered genes were significantly shorter, with a mean CDS length of 573 base pairs compared to 1,797 base pairs for genes with more than 5 expected pLOF variants. Eighty-six genes were highly constrained, with a mean shet value greater than 0.073 and a lower bound greater than 0.021 (Extended Data Fig. 4b), and are promising candidates for efforts to discover new disease-associated genes. Thirty per cent (26 of 86) have been linked to human diseases or shown to be essential in mice or cell lines (Supplementary Table 2). These include well-studied genes with known importance in cellular function, such as the transcription factor TWIST1 (ref. [33]), DNA- and RNA-binding protein BANF1 (refs. 34,35) and transactivator CITED2 (ref. [36]).

Overall, 3,988 highly constrained genes had shet values greater than 0.073 and a lower bound greater than 0.021. Although 1,153 of these lack known associations with human diseases or lethal mouse knockout phenotypes, they are likely to have high functional importance. These constrained genes might lack disease associations because the loss of even a single copy is incompatible with life or causes reduced reproductive success without clinical disease[37].

Constrained coding regions

Identifying sub-genic regions that are intolerant of mutations can reveal functionally important regions that would otherwise be missed when constraint scores are aggregated at the gene level. Models of local coding constraint are powerful tools for identifying protein domains with crucial functions and for variant prioritization38,39,40. In addition to gene constraint derived from pLOF variation, we also identified regions depleted of missense variation using the missense tolerance ratio (MTR)40,41, defined as the ratio of the observed to the expected proportion of missense variants adjusted by synonymous variation in a defined codon window. Using the 822K unrelated samples, we calculated the MTR for each amino acid along the CDS within sliding windows of 21 and 31 amino acids (MTR scores available on figshare; see ‘Data availability’) and characterized continuous segments of missense constrained regions (Supplementary Table 3).

Compared with benign missense variants, ClinVar pathogenic missense (two stars or more) variants were highly enriched in the top percentile of exome-wide MTR scores (odds ratio = 100.0 and 89.8, computed with 21- and 31-codon windows, respectively; Fig. 3a). Our sample size, which is nearly four times larger than that used in previous MTR estimates[41], resulted in improved discrimination between pathogenic and benign variants for top-10-percentile MTR scores in which we observed significant enrichment (Fig. 3a). This larger sample size enabled us to identify 24% more missense variants in the top-1-percentile constrained MTR scores (512,499 versus 413,147) compared with a subsampled set of 225,000, after adjusting for a false discovery rate (FDR) lower than 0.1. In addition, the increased power derived from 822K samples resulted in higher resolution for distinguishing pathogenic from benign variants for MTR computed with 21-codon windows, albeit at the expense of having fewer scored missense variants overall (295,958 constrained missense variants).

Fig. 3: Missense regional constraint captured by MTR.

a, Odds ratio (OR) (points) and 95% CIs (error bars, two-sided Fisher’s exact test) of ClinVar pathogenic versus benign variants in MTR ranking regions across the whole exome. Comparisons include MTR calculated using the 822K unrelated samples from the RGC-ME dataset on 31-codon (blue) and 21-codon (pink) windows, and MTR calculated using a random subset of 225,000 samples from the larger 822K samples using a 31-amino-acid sliding window (yellow). MTR values include variants for which the FDR is less than 0.1. A total of 21,047 benign ClinVar variants and 12,872 pathogenic ClinVar variants (two stars or more (CV2+)) were included. b, MTR ranking distribution of different protein functional regions and variant groups (in order: DNA-binding sites (n = 2,787), ClinVar pathogenic sites (n = 10,673), active sites (n = 2,787), transmembrane region (n = 2,787), localized to extracellular (n = 25,665), localized to cytoplasm (n = 32,994) and ClinVar benign sites (n = 20,739)). Box plot shows median and 25–75% interquartile range. The whisker minima and maxima represent the smallest and largest data points within 1.5× the interquartile range from the lower quartile and upper quartile, respectively. Every functional category was significantly more constrained than the category to its right with two-sided Wilcoxon rank-sum test (least significant p-value = 2 × 10−4, Bonferroni correction). c, Distribution of the proportion of genes located in exome-wide top-15-percentile MTR regions against the heterozygous selection coefficient, shet. Genes with a significant proportion in the most constrained 15-percentile MTR region are coloured in pink and yellow (P < 0.05, Bonferroni-corrected, one-sided binomial tests with π_0 = 0.15), stratified by LOF constraint (shet = 0.073). Pink dots highlight genes that are tolerant of LOF, but have some regions depleted of missense variation. Blue lines indicate the density of genes with shet scores from 0 to 1 (right margin) and genes with a proportion of MTR in the top 15 percentile exome wide (above plot). d, MTR track of an oncogene, KRAS, a missense-specific constrained gene, along with the domain structure of the protein. The blue MTR-constrained region is defined by top-15-percentile exome-wide MTR rank. The N-terminal region containing amino acids 1–80 is depleted of missense variation, even though KRAS is tolerant of heterozygous LOF variation (shet = 0.002).

Deleterious variants are expected to have lower allele frequencies than neutral variants, owing to negative selection. We can infer the functional importance of different classes of variation by comparing the proportion of singletons in each class. We computed the deleteriousness of variants using an updated mutability-adjusted proportion of singletons (MAPS) metric5,32 and derived an MTR score threshold at which their MAPS score corresponds to that of missense variants that were predicted to be deleterious by five out of five prediction algorithms in dbNSFP (v.3.2; see Supplementary Information); that is, 5/5 missense variants. Variants with MTR values in the top-15-percentile exome-wide threshold (MTR < 0.841) were predicted to be as deleterious as 5/5 missense variants (Extended Data Fig. 5a). For 31-codon windows, 1.24% (129,990) of all missense variants (excluding known ClinVar pathogenic variants) observed in the RGC-ME dataset had significant MTR scores in the top 15 percentile. These missense variants in the top 15 percentile of exome-wide MTR are potentially deleterious and could be suitable for prioritization in projects aiming to discover disease-associated genes.

MTR is a useful metric of regional constraint that may capture functionally important segments within genes. We defined MTR-constrained regions as continuous regions within a protein that have variants with MTR values in the top-15-percentile threshold (Supplementary Information and Extended Data Fig. 6a). We identified 41,114 missense constrained regions in 12,349 genes (Supplementary Table 3). Our findings overlap with results from a previous study[38] that estimated the regional observed-to-expected missense ratio (ɣ) from around 60,000 ExAC samples (Extended Data Fig. 6b) to derive a composite missense deleterious score called MPC. We refer to the MPC-derived constrained regions as MPC segments, and compared these with MTR-constrained regions. MTR-constrained regions had a median length of 22 residues [14–35, quartile 1–quartile 3], compared with 358 [208, 579] in MPC segments. Overall, we identified 8.59 times more MTR-constrained regions than MPC segments (ɣ ≤ 0.612, top 15 percentile) across 2,832 transcripts with data from both methods (Extended Data Fig. 6c).

We examined the distribution of de novo missense variants in MTR-constrained regions and observed a significant enrichment (P = 2.61 × 10−10) of variants identified in individuals with neurodevelopmental disorders (Extended Data Fig. 7a,b). Case variants were 1.85 times [1.50, 2.31 (95% CI)] more likely to occur in constrained regions, compared with controls. As expected, well-supported (two stars or more) ClinVar pathogenic missense variants were also highly enriched (P ≈ 0) in MTR-constrained regions. Pathogenic variants were 8.82 times [8.17, 9.53 (95% CI)] more likely to occur in missense constrained regions than were benign variants.

Missense constrained sites were found in key functional regions, such as DNA-binding regions and active sites (Fig. 3b). Among membrane proteins, transmembrane regions ranked higher in MTR-constrained regions than did cytoplasmic and extracellular domains. We also compared the overlap of MTR-constrained and functional regions by computing Jaccard indices. Ubiquitin-conjugating (UBC) core domains and DNA-binding regions had the highest overlap with constrained regions (Jaccard index = 0.52 and 0.18, respectively), suggesting that, among UBC enzymes, more than half of the union set between MTR-constrained regions and core domains overlapped. Other enriched functional regions included protein kinases and nuclear receptor ligand-binding domains (Supplementary Table 4).

A total of 4,064 genes contained regions depleted in missense variation with a significant proportion of their coding sequence in the top 15 percentile of MTR (binomial test with π_0 = 0.15, P < 0.05 after multiple testing correction; Supplementary Table 5). To identify genes with signatures of missense-only constraint, we assessed the LOF-constraint metric, shet, of these highly missense constrained genes (Fig. 3c). Among the 4,064 genes, 1,482 either were not LOF constrained or lacked shet estimates. These genes had significantly shorter CDS lengths than those of the 1,424 LOF-specific constrained genes (P = 2.9 × 10−40, Wilcoxon test; Extended Data Fig. 7c). Estimating region-level LOF constraint is difficult owing to strong selection against pLOF variants, which leads to a paucity of pLOF variation. MTR serves as a complementary lens for identifying, first, functionally important regions at a higher resolution than gene-level LOF constraint, and, second, regions within genes that are depleted of missense variation but tolerant of LOF variation. For example, KRAS, a well-known oncogene, is LOF tolerant (shet = 0.002, LOEUF = 1.24); however, the first 80 amino acids (42%) of the protein sequence were ranked in the top 1 percentile of exome-wide MTR (Fig. 3d). This region includes the P-loop, switch 1 and switch 2 functional domains, which form crucial binding interfaces for effector proteins[42], and these results therefore highlight the importance of regional constraint metrics.

Understanding ‘human knockouts’

Identifying genes with biallelic pLOF variants provides an opportunity to understand gene function directly through the phenotypic characterization of individuals who have such variants—effectively, naturally occurring ‘human knockouts’. The RGC-ME dataset includes founder populations and cohorts with high rates of consanguinity, contributing to a comprehensive collection of homozygous loss-of-function variation25,43,44,45. Overall, we identified 4,686 genes comprising 8,576 rare (AAF < 1%) homozygous pLOF variants in 64,852 individuals (Supplementary Table 6). Furthermore, we identified 1,205 genes with carriers of rare (AAF < 1%) heterozygous pLOF variants in trans; that is, compound heterozygotes, 162 of which lacked homozygous pLOFs. In total, 4,848 genes were discovered with carriers of biallelic pLOF variants in which both alleles of a gene were affected by pLOF variation and could be described as putative gene knockouts (pKOs). Of these, 1,751 (1,650 from homozygous pLOFs only) have not to our knowledge been previously reported. Biallelic pLOF variants in RGC-ME are rare; 64.3% of homozygous pLOF variants and 37.4% of pKOs were detected in one participant (Fig. 4a). As expected, cohorts with higher rates of consanguinity were enriched in homozygous pLOF variants, compared with outbred populations, despite smaller sample sizes (Fig. 4b,c and Extended Data Table 1).

Fig. 4: Rare biallelic pLOF variants and ‘human knockouts’ in the RGC-ME dataset.

a, Distribution of the number of individuals per pKO on the log_10 scale. Carriers of homozygous pLOFs and compound heterozygous variants were included in this analysis. b, Breakdown of the number of unique pKO genes observed in the RGC-ME dataset by ancestry. Both sets of rare biallelic variants—homozygous pLOFs and compound heterozygous—were included in this analysis. See Extended Data Table 1a for a breakdown by ancestry of each type. c, Projected accrual of pKO genes using homozygous pLOF variant data at hypothetical cohort sizes for each ancestry in 983,578 related individuals. Curves reflect the accrual of the expected number of genes with at least one, at least five and at least ten carriers, respectively, of a rare, homozygous pLOF. Asterisk denotes the inclusion of cohorts with a high rate of consanguinity.

pKOs were significantly less constrained, with a lower shet (on average −0.074 [−0.077, −0.071 (95% CI)], t-test) relative to all other genes. Only 2.67% of pKOs had an shet value greater than 0.073, as compared with 21.6% of all human genes, and 47.2% of pKOs were in the lowest quintile of shet scores exome-wide (shet < 7.07 × 10−3). A caveat is that shet, like most gene-specific measures of constraint, is designed to capture the effect of heterozygous LOF[46]. Although genes containing biallelic pLOF variants are under less heterozygous selective pressure, existing sample sizes are inadequate[47] to directly compute selection on homozygous variation. pKOs are overrepresented in drug and xenobiotic metabolism pathways (Supplementary Fig. 6).

Among very rare doubleton variants for which we observed exactly two copies of the alternate allele, we observed a clear excess of homozygotes that is likely to be explained by population structure and background inbreeding. For example, among missense and synonymous variants, we observed 5,857 and 2,490 homozygotes among 1,580,917 and 679,335 doubleton variants, respectively, compared with a Hardy–Weinberg equilibrium (HWE) expectation of fewer than one homozygote in each case. These estimates corresponded to a background inbreeding coefficient of 0.37%. Among pLOF variants, we observed only 406 homozygotes among 129,405 doubleton variants (Supplementary Table 7). Although this number is much larger than HWE expectations, it is around 15% less than the expected 479 homozygotes calculated using an inbreeding coefficient of 0.37% (P = 0.0095, Fisher’s exact test). This suggests that a notable proportion of these homozygotes were never observed in our sample population.

Genes with biallelic inactivating mutations could reveal potential drug targets that can be disrupted with minimal side effects[43]. Drug targets with homozygous pLOF variants in humans are more likely to progress from phase I trials to approval[44]. Of 997 inhibitory preclinical targets listed in the Drug Repurposing Hub, 182 (18.3%) had at least one individual with a rare biallelic pLOF variant in the RGC-ME dataset[48]. In-depth phenotyping of human knockouts can help researchers to better understand the efficacy and side-effect profiles of these potential drug targets. Human knockouts provide a way to understand the consequences of lifelong deficiency of a gene[49].

Annotation of splice-affecting variants

Several prediction tools50,51,52,53 have been developed to understand the effects of genetic variants on alternative splicing. Although these tools mainly assess whether a variant affects splicing, some also provide a pathogenicity metric or score threshold as a measure of deleteriousness. Predicted cryptic splice sites with SpliceAI scores greater than 0.8 have been validated at high rates using RNA sequencing and are as depleted at common allele frequencies as pLOF variants[50]. Here, we used human genetic data to optimize splice prediction score thresholds enriched for deleterious variants that affect splicing. We systematically quantified the deleteriousness of variants at various splice prediction score thresholds using the MAPS metric. As previously demonstrated2,4, pLOF variants had the highest MAPS scores, followed by missense, synonymous and noncoding variants, respectively (Fig. 5a).

Fig. 5: Identification of deleterious variants that are predicted to affect splicing.

a, MAPS across different functional categories. Error bars show standard deviation around the mean proportion of singletons (points). The yellow dashed line represents the SpliceAI and MMSplice score threshold for variants that have a MAPS score equal to that of 5/5 missense variants (predicted deleterious by five algorithms). Variants with a SpliceAI score ≥ 0.35 or a MMSplice score ≥ 0.97 are predicted deleterious SAVs. Noncoding variants refers to intronic, downstream (variant located 5′ of a gene), upstream (variant located 3′ of a gene) and 5′ and 3′ UTR variants captured by exome sequencing. Coding variants are inclusive of canonical splice sites, splice region and UTR splice sites. All variants that passed quality control and were observed in unrelated individuals in the RGC-ME dataset were included in this analysis (n = 34,512,842 variants). b, Enrichment of ClinVar pathogenic variants (two stars or more) in predicted SAVs compared with corresponding variant sets filtered by either LOFTEE, 5/5 missense deleteriousness models or CADD. Points represent odds ratios and bars depict 95% CIs (two-sided Fisher’s exact test, no multiple testing correction). ‘All variants’ include 313,390 coding and noncoding variants, and ‘splice sites’ include essential and UTR splice sites; counts of variants included in each calculation are provided in the table. HC, high-confidence. c, Empirical validation of MAPS-predicted deleterious SAVs (intersection set): enrichment of predicted deleterious SAVs in experimentally validated SDVs compared with non-SDVs. Points represent odds ratios and bars depict 95% CIs (two-sided Fisher’s exact test, no multiple testing correction). A total of n = 36,636 variants, of which 346 SAVs are validated SDVs, are included.

We used splice predictions from SpliceAI[50] and MMSplice[51] to group variants into predicted splice score bins, and identified the minimum threshold at which the MAPS score of the variants is equal to that of 5/5 missense variants (variants predicted to be deleterious by five out of five prediction methods). The proposed prediction score thresholds of 0.35 for SpliceAI and 0.97 for MMSplice pathogenicity (Fig. 5a and Extended Data Fig. 5b) identify predicted deleterious splice-affecting variants (SAVs).

A total of 296,696 predicted deleterious coding SAVs (inclusive of canonical splice sites, splice region and untranslated region (UTR) splice sites) in the RGC-ME dataset had scores that exceeded the MAPS-derived splicing thresholds for both SpliceAI and MMSplice (referred to as the intersection set; Extended Data Fig. 8a). Of these, 43.5% (129,118) were cryptic splice sites (that is, non-canonical splice sites). Unsurprisingly, canonical splice sites and variants within the splice region comprised the largest category of predicted deleterious SAVs. Both SpliceAI and MMSplice identified around 80% of LOFTEE (loss of function transcript effect estimator; ref. [4]) high-confidence splice sites and around 10% of variants within splice regions as predicted deleterious SAVs (Extended Data Fig. 8a,b). In addition, around 68% of LOFTEE low-confidence splice sites were predicted to be deleterious SAVs (94% of low-confidence splice sites were in the UTR). The impact of non-canonical splice variants on alternative splicing is often underestimated; we found that missense variants accounted for 11.3% of all predicted deleterious SAVs identified by both SpliceAI and MMSplice in the RGC-ME dataset (Extended Data Fig. 8a,b).

Predicted deleterious SAVs were enriched in well-supported ClinVar pathogenic variants (two stars or more) compared with other metrics of variant deleteriousness (Fig. 5b); for example, compared with combined annotation dependent depletion (CADD)54,55 score ≥ 20 (odds ratio = 4.5, P = 0). Missense SAVs were significantly enriched for pathogenic variants compared with 5/5 missense variants (odds ratio = 1.8, P = 3.3 × 10−8) and missense variants with CADD ≥ 20 (odds ratio = 3.6, P = 1.01 × 10−26), respectively. Notably, splice sites in the intersection set were also significantly enriched for pathogenic variants compared to LOFTEE high-confidence splice sites, indicating that the MAPS-derived metric identifies deleterious splice sites. Similar results were obtained when we evaluated the enrichment of pathogenic variants compared with benign ones (Supplementary Table 8a).

We next assessed the MAPS-derived splice prediction thresholds for variants that have been experimentally assessed for splicing effects56,57,58 (Supplementary Table 9). Predicted deleterious SAVs identified in the intersection set were significantly enriched in experimentally validated large-effect splice-disrupting variants (SDVs) compared with non-SDVs in all functional categories except the splice site category, although the odds ratio was greater than one for splice sites (Fig. 5c). Variants of unknown significance (VUSs) in ClinVar that were predicted as deleterious SAVs were also significantly enriched in experimentally validated SDVs (Fig. 5c). Of the 563 predicted deleterious SAVs assayed in the experimental data, 346 (61.5%) were SDVs and more than half were cryptic splice sites, including 13 ClinVar VUSs (Extended Data Fig. 8d).

We also derived stringent thresholds to identify SAVs by removing canonical splice sites and calibrating exclusively coding non-splice-site (nonSS) variants to a MAPS score comparable with 5/5 missense variants. These thresholds corresponded to a SpliceAI score of 0.43 and an MMSplice score of 0.97 (Extended Data Fig. 5c). Pathogenic enrichment was consistent when comparing deleterious coding nonSS and missense SAVs with corresponding variant categories filtered by CADD ≥ 20 (Supplementary Table 8b,c). Consistent results were also obtained when comparing the enrichment of deleterious SAVs in SDVs to non-SDVs after applying thresholds for coding nonSS variants (Extended Data Fig. 8c,e).

Clinical utility of rare variants

To understand the prevalence of disease-associated alleles in the general population, we identified well-supported ClinVar[59] pathogenic variants (two stars or more) across 2,042 genes in 822K unrelated RGC-ME samples. We found that 40.7% of pathogenic variants (20,343/49,990) were observed in the RGC-ME dataset, of which 99.6% (20,262) had an AAF of less than 0.1% and 17.8% (3,619) were observed once. In comparison, 20% (9,821) and 29% (14,700) of pathogenic variants were observed in ExAC exomes (n ≈ 60,000) and gnomAD v.2.1.1 exomes (n ≈ 126,000), respectively (Extended Data Fig. 9a). This highlights the importance of the RGC-ME dataset’s larger sample size in identifying rare pathogenic variants. On average, individuals carry 1.58 pathogenic variants, with the majority of these individuals being heterozygous carriers of these variants. Specifically, 61.4% of the 822K unrelated individuals were heterozygous carriers of pathogenic recessive alleles in 1,143 of 2,659 known autosomal recessive genes (mean, 0.98 pathogenic alleles per person); 0.21% of the samples were homozygotes of pathogenic variants in 167 autosomal recessive genes; and 3.64% were heterozygous carriers of 353 of 1,629 total autosomal dominant genes. Pathogenic variant annotations should be interpreted cautiously owing to the incomplete penetrance of disease alleles[60].

The American College of Medical Genetics identified a set of genes (ACMG SF v.3.1) with clinically actionable variants that predispose individuals to diseases and for which medical interventions are available to reduce mortality and morbidity[61]. Among the 822K unrelated individuals, 22,846 (2.77%) had at least one ClinVar-reported (two stars or more) pathogenic missense or pLOF variant for 72 out of 76 autosomal genes on the ACMG list (Supplementary Table 10). As expected, two of the most prevalent pathogenic variations were the HFE Cys282Tyr allele (enriched in EUR, nEUR-homozygotes = 3,220 and AAFEUR = 13.8%) and the TTR Val142Ile allele (enriched in AFR, nAFR = 1,670 and AAFAFR = 3.4%).

We also tallied carriers of likely pathogenic pLOF variants (novel variants not yet reported as pathogenic in ClinVar) in 44 genes in which truncation is known to lead to disease. A total of 2,357 (0.3%) individuals in the RGC-ME dataset carried 1,407 likely pathogenic variants across 40 of these genes. In total, 3.06% of the individuals in the RGC-ME dataset were carriers of pathogenic or likely pathogenic variants. Excluding individuals with high-frequency pathogenic variants in the HFE (Cys282Tyr) and TTR (Val142Ile) genes, 2.38% of the individuals in the RGC-ME dataset carried an actionable variant (Supplementary Table 10). This number is comparable with those from other reports6,7,62 of actionable variants, which range from 2% to 4.1% for gene sets that include ACMG v.2.0 and v.3.0. As expected, pathogenic variants are rare in large-scale studies of the general population. We found that 39% and 79% of pathogenic and likely pathogenic variants, respectively, were singletons. Focusing on non-ACMG genes, we found that 1.27% of individuals were heterozygous carriers of pathogenic variants in autosomal dominant genes, and 0.21% were homozygotes of pathogenic variants in autosomal recessive genes.

Because the RGC-ME dataset includes uniformly processed exome data from a relatively large proportion of individuals from continental ancestries other than EUR, we assessed the range of allele frequencies of variants present in ClinVar across four continental populations: AFR, EUR, IAM and SAS. Approximately 34% of unique pathogenic coding variants in equalized subsamples were observed only in individuals of non-EUR ancestry, which indicates that sampling diverse populations is necessary for the comprehensive identification of rare variation. Across all unrelated individuals, on average, those of EUR ancestry had 63% more pathogenic variants that were well characterized (rated two stars or more) per sample than did individuals of AFR ancestry. Conversely, individuals of EUR ancestry had, per sample, 25.6% fewer VUSs (Extended Data Fig. 9b,c) and 18.6% fewer variants across all functional types (Extended Data Fig. 2). In individuals of AFR ancestry, a consistent pattern of significantly fewer high-confidence (two stars or more) pathogenic variants (−0.576 [−0.567, −0.585 (95% CI)], t-test) to a surplus of VUSs (42.13 [421.97, 42.28]), compared with individuals of EUR ancestry, suggests that the most well-characterized pathogenic variants were depleted in this population (Extended Data Fig. 9b). Recruiting diverse individuals to enable the identification and characterization of novel pathogenic variants might help to address this ascertainment bias. Further analyses of pathogenic coding variants and differentiated alleles between ancestries are included in Supplementary Fig. 7 and Supplementary Table 11.

Understanding VUSs is currently a bottleneck in the interpretation of variation in clinically relevant genes and a challenge in clinical management[19]. Although VUSs have less empirical evidence for pathogenicity, they comprise the bulk of ClinVar, with more than one million variants. Notably, VUSs in regions of low MTR may be deleterious, comprising 5,079 (0.68%) VUSs in the top 1 percentile of MTR-constrained regions and 17,500 VUSs (2%) in the top 15 percentile (Supplementary Table 12). Using the MAPS-derived splicing score thresholds, we identified more than 11,000 candidate deleterious cryptic splice sites among VUSs (1,366 synonymous variants in 822 genes and 10,407 missense variants in 3,501 genes), offering potential insights into their functional consequences for clinical prioritization and interpretation efforts.

Discussion

The RGC-ME dataset, derived from 983,578 exomes, provides a harmonized catalogue of around 20 million coding variants in individuals from a diverse array of ancestries and is publicly accessible at https://rgc-research.regeneron.com/me/home. Cataloguing variation at scale provides an opportunity to accurately estimate the frequency of rare variants—allowing us to precisely compute gene and regional constraint metrics, expand the compendium of rare human knockouts, annotate deleterious cryptic splice sites, characterize variant frequencies across different ancestries and assess the population prevalence of pathogenic variation. RGC-ME will be an invaluable resource for interpreting rare variants and is a step towards the realization of precision medicine.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Genetic variation data for 821,979 unrelated individuals are made publicly available through the RGC-ME browser (https://rgc-research.regeneron.com/me/home). Features include genomic locations, alleles, fine-scale ancestry assignments, population-specific allele frequencies and functional annotations for the genetic variants. In addition, vcf files can be downloaded from the web portal. Exome-wide MTR scores are available for download from figshare: https://doi.org/10.6084/m9.figshare.24587328 (ref. [63]). The human reference genome GRCh38 can be obtained from ftp://ftp-trace.ncbi.nlm.nih.gov/1000genomes/ftp/technical/reference/GRCh38_reference_genome/GRCh38_full_analysis_set_plus_decoy_hla.fa. Ensembl Release 100 gene and transcript builds can be accessed from https://ftp.ensembl.org/pub/release-100/gtf/homo_sapiens/ and corresponding gene and transcript reference nucleotide and protein sequence data from https://ftp.ensembl.org/pub/release-100/fasta/homo_sapiens/. Individual-level sequence data have been deposited with the UK Biobank and are freely available to approved researchers. Instructions for access to UK Biobank data are available at https://www.ukbiobank.ac.uk/enable-your-research. Information about the data access policy for researchers interested in the Mexico City Prospective Study data can be found at https://www.ctsu.ox.ac.uk/research/prospective-blood-based-study-of-150-000-individuals-in-mexico. Geisinger Health System individual-level data are available to qualified academic, non-commercial researchers through an information transfer agreement by contacting Lance J. Adams (ljadams1@geisinger.edu). Information about the data access policy, procedures and contact details for the cohorts included in this dataset can be obtained through the URLs given in the RGC-ME browser at https://rgc-research.regeneron.com/me/data-contributors. This information is also provided in Supplementary Table 1c, with relevant references if available.

Code availability

Publicly available software and packages used in this study are described in the Supplementary Information. In summary, sequencing reads were generated using bcl2fastq v.2.20 and were mapped to references using BWA-MEM v.0.7.17. Variants were identified using DeepVariant v.0.10, aggregated with GLnexus v.1.4.3 and converted to bed, bim or fam format using PLINK v.1.9. Variants were annotated with VEP (Ensembl, v.100.4) and pLOF variants were further classified with the VEP LOFTEE plug-in. Array variants were phased using Eagle v.2.4 and imputed using Minimac4. PLINK v.2 was used for principal component analysis and to compute Fst, a measure of genetic differentiation among population groups. The csq function in BCFtools v.1.18 was used to annotate in-frame indels resulting from a combination of frameshift indels on the same haplotype, and bedtools v.2.30.0 was used to determine the genetic context and neighbouring nucleotides of variants. Picard LiftoverVcf v.3.0.0 was used to transform sequence coordinates to GRCh38. Relatedness was determined with PRIMUS: https://primus.gs.washington.edu/primusweb/res/documentation.html. We adapted scripts from https://github.com/pjshort/dddMAPS to compute updated MAPS metrics. For identifying compound heterozygous variants, exome variants were merged with a well-imputed common variant backbone and phased using SHAPEIT5 (https://github.com/odelaneau/shapeit5). Large-scale data manipulation used Scala v.2.12 on a 10.4 LTS runtime (Apache Spark v.3.2.1) with standard Spark functions. Beyond standard R packages, visualization tools and data-processing libraries (for example, dplyr, ggplot2 and data.table), we used rstan (v.2.33) to build Bayesian hierarchical models for calculating heterozygous selection coefficients, rmutil (v.4.1.2) to project LOF accrual and boot (v.4.1.1) for bootstrapping. Python code used standard packages (for example, scipy, numpy and pandas) for analysis, scikit-learn (v.1.0) to model variant quality (see Supplementary Tables 13 and 14) and sqlalchemy (v.2.0.23) to store and query tables. Custom code to generate LOF projection curves is available at https://github.com/rgcgithub/rgc_me_analysis.

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Acknowledgements

This work was supported in part by the Intramural Research Program of the National Institute of Mental Health (ZIA-MH002843) and a grant from R01 NCI R01 CA157823. Ethical approval for the UK Biobank was previously obtained from the North West Centre for Research Ethics Committee (11/ NW/0382). The work described herein was approved by the UK Biobank under application number 26041. Informed consent was obtained for all study participants. Approval for Geisinger Health System MyCode analyses was provided by the Geisinger Health System Institutional Review Board under project number 2006-0258. Informed consent was obtained for all study participants. Appropriate consent for the Penn Medicine BioBank was obtained from each participant regarding the storage of biological specimens, genetic sequencing and genotyping and access to all available EHR data. This study was approved by the Institutional Review Board of the University of Pennsylvania and complied with the principles set out in the Declaration of Helsinki. All individuals participating in the Mayo–RGC project generation provided informed consent for the use of specimens and data in genetic and health research and ethical approval for project generation was provided by the Mayo Clinic Institutional Review Board (09-007763). All research performed in this study used de-identified data (without any Protected Health Information data) with no possibility of re-identifying any of the participants. Approval for the Indiana Biobank was provided by the Indiana University Institutional Review Board under project number 1105005445. For participants in the Mexico City Prospective Study, approval for the study was given by the Mexican Ministry of Health, the Mexican National Council of Science and Technology (0595 P-M) and the Central Oxford Research Ethics Committee (C99.260) and the Ethics and Research commissions from the Medicine Faculty at the National Autonomous University of Mexico (UNAM) (FMED/CI/SPLR/067/2015). All study participants provided written informed consent. Study participants were recruited from the BioMe Biobank Program of the Charles Bronfman Institute for Personalized Medicine at Mount Sinai Medical Center from 2007 onward. The BioMe Biobank Program (Institutional Review Board 07-0529) operates under a Mount Sinai Institutional Review Board-approved research protocol. All study participants provided written informed consent. The authors thank the RGC-ME Cohort Partners for contributions to this initiative. A list of cohorts (with links to the projects where available) and the data contributors can be accessed from the RGC-ME browser (https://rgc-research.regeneron.com/me/data-contributors). The authors thank everyone who made this work possible, the professionals from the member institutions who contributed to and supported this work and, most especially, all of the participants, without whom this research would not be possible. This study is funded by Regeneron Genetics Center and Regeneron Pharmaceuticals.

Author information

Author notes

1. These authors contributed equally: Kathie Y. Sun, Xiaodong Bai

Authors and Affiliations

1. Regeneron Genetics Center, Tarrytown, NY, USA

Kathie Y. Sun, Xiaodong Bai, Siying Chen, Suying Bao, Chuanyi Zhang, Manav Kapoor, Joshua Backman, Tyler Joseph, Evan Maxwell, George Mitra, Alexander Gorovits, Adam Mansfield, Boris Boutkov, Sujit Gokhale, Lukas Habegger, Anthony Marcketta, Adam E. Locke, Liron Ganel, Alicia Hawes, Michael D. Kessler, Deepika Sharma, Jeffrey Staples, Jonas Bovijn, Sahar Gelfman, Alessandro Di Gioia, Veera M. Rajagopal, Alexander Lopez, Jennifer Rico Varela, Gonçalo Abecasis, Giovanni Coppola, Andrew Deubler, Aris Economides, Adolfo Ferrando, Luca A. Lotta, Alan Shuldiner, Katherine Siminovitch, Christina Beechert, Erin D. Brian, Laura M. Cremona, Hang Du, Caitlin Forsythe, Zhenhua Gu, Kristy Guevara, Michael Lattari, Kia Manoochehri, Prathyusha Challa, Manasi Pradhan, Raymond Reynoso, Ricardo Schiavo, Maria Sotiropoulos Padilla, Chenggu Wang, Sarah E. Wolf, Amelia Averitt, Nilanjana Banerjee, Dadong Li, Sameer Malhotra, Justin Mower, Mudasar Sarwar, Jeffrey C. Staples, Sean Yu, Aaron Zhang, Andrew Bunyea, Krishna Pawan Punuru, Sanjay Sreeram, Gisu Eom, Benjamin Sultan, Rouel Lanche, Vrushali Mahajan, Eliot Austin, Sean O’Keeffe, Razvan Panea, Tommy Polanco, Ayesha Rasool, Lance Zhang, Evan Edelstein, Ju Guan, Olga Krasheninina, Samantha Zarate, Adam J. Mansfield, Evan K. Maxwell, Kathie Sun, Manuel Allen Revez Ferreira, Kathy Burch, Adrian Campos, Lei Chen, Sam Choi, Amy Damask, Sheila Gaynor, Benjamin Geraghty, Arkopravo Ghosh, Salvador Romero Martinez, Christopher Gillies, Lauren Gurski, Joseph Herman, Eric Jorgenson, Michael Kessler, Jack Kosmicki, Nan Lin, Adam Locke, Priyanka Nakka, Karl Landheer, Olivier Delaneau, Maya Ghoussaini, Joelle Mbatchou, Arden Moscati, Aditeya Pandey, Anita Pandit, Charles Paulding, Jonathan Ross, Carlo Sidore, Eli Stahl, Maria Suciu, Peter VandeHaar, Sailaja Vedantam, Scott Vrieze, Jingning Zhang, Rujin Wang, Kuan-Han Wu, Bin Ye, Blair Zhang, Andrey Ziyatdinov, Yuxin Zou, Kyoko Watanabe, Mira Tang, Brian Hobbs, Jon Silver, William Palmer, Rita Guerreiro, Amit Joshi, Antoine Baldassari, Cristen Willer, Sarah Graham, Ernst Mayerhofer, Mary Haas, Niek Verweij, George Hindy, Tanima De, Parsa Akbari, Luanluan Sun, Olukayode Sosina, Arthur Gilly, Peter Dornbos, Juan Rodriguez-Flores, Moeen Riaz, Gannie Tzoneva, Momodou W. Jallow, Anna Alkelai, Ariane Ayer, Veera Rajagopal, Vijay Kumar, Jacqueline Otto, Neelroop Parikshak, Aysegul Guvenek, Jose Bras, Silvia Alvarez, Jessie Brown, Jing He, Hossein Khiabanian, Joana Revez, Kimberly Skead, Valentina Zavala, Lyndon J. Mitnaul, Marcus B. Jones, Esteban Chen, Michelle G. LeBlanc, Jason Mighty, Nirupama Nishtala, Nadia Rana, Jennifer Rico-Varela, Jaimee Hernandez, Alison Fenney, Randi Schwartz, Jody Hankins, Samuel Hart, Ann Perez-Beals, Gina Solari, Johannie Rivera-Picart, Michelle Pagan, Sunilbe Siceron, Michael Cantor, Timothy Thornton, Hyun Min Kang, John D. Overton, Alan R. Shuldiner, M. Laura Cremona, Mona Nafde, Aris Baras, Gonçalo Abecasis, Jonathan Marchini, Jeffrey G. Reid, William Salerno & Suganthi Balasubramanian

2. Faculty of Medicine, National Autonomous University of Mexico (UNAM), Mexico City, Mexico

Jesús Alegre-Díaz, Jaime Berumen, Roberto Tapia-Conyer & Pablo Kuri-Morales

3. Instituto Tecnológico y de Estudios Superiores de Monterrey, Monterrey, Mexico

Pablo Kuri-Morales

4. Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK

Jason Torres, Jonathan Emberson & Rory Collins

5. Accelerated Cures, Waltham, MA, USA

David Gwynne

6. Lundquist Institute, Torrance, CA, USA

Jerome I. Rotter & Robert Weinreb

7. Case Western Reserve University, Cleveland, OH, USA

Jonathan L. Haines

8. University of Miami, Miami, FL, USA

Margaret A. Pericak-Vance

9. University of Pennsylvania, Philadelphia, PA, USA

Dwight Stambolian, Yuki Bradford, Scott Damrauer, Stephanie DerOhannessian, Theodore Drivas, Scott Dudek, Joseph Dunn, Ned Haubein, Renae Judy, Yi-An Ko, Colleen Morse Kripke, Meghan Livingstone, Nawar Naseer, Kyle P. Nerz, Afiya Poindexter, Marjorie Risman, Salma Santos, Giorgio Sirugo, Julia Stephanowski, Teo Tran, Fred Vadivieso, Anurag Verma, Shefali S. Verma, JoEllen Weaver, Colin Wollack, Daniel J. Rader, Marylyn Ritchie, Joan O’Brien & Brendan Keating

10. Albert Einstein College of Medicine, Bronx, NY, USA

Nir Barzilai, Yousin Suh & Zhengdong Zhang

11. University of Maryland School of Medicine, Baltimore, MD, USA

Elliot Hong & Braxton Mitchell

12. Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia

Nicholas B. Blackburn, Bennet J. McComish & Bruce V. Taylor

13. Griffith University, Gold Coast, Queensland, Australia

Simon Broadley

14. Murdoch University, Perth, Western Australia, Australia

Marzena J. Fabis-Pedrini

15. Perron Institute for Neurological and Translational Science, Nedlands, Western Australia, Australia

Marzena J. Fabis-Pedrini & Allan G. Kermode

16. Monash University, Melbourne, Victoria, Australia

Vilija G. Jokubaitis

17. Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia

Trevor J. Kilpatrick & Justin P. Rubio

18. University of Newcastle, Newcastle, New South Wales, Australia

Jeanette Lechner-Scott & Rodney J. Scott

19. Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia

Stephen Leslie & Allan Motyer

20. University of Sydney, Sydney, New South Wales, Australia

Grant P. Parnell

21. Center for Non-Communicable Diseases, Karachi, Pakistan

Danish Saleheen

22. Cincinnati Children’s Hospital, Cincinnati, OH, USA

Ken Kaufman, Leah Kottyan, Lisa Martin & Marc E. Rothenberg

23. Columbia University, New York City, NY, USA

Abdullah Ali & Azra Raza

24. UT Southwestern Medical Center, Dallas, TX, USA

Jonathan Cohen

25. University of Pittsburgh, Pittsburgh, PA, USA

Adam Glassman, Erin E. Kershaw, Georgios Papachristou & David C. Whitcomb

26. Duke University, Durham, NC, USA

William E. Kraus, Christopher B. Newgard & Svati H. Shah

27. Flinders University of South Australia, Adelaide, South Australia, Australia

Jamie Craig & Alex Hewitt

28. Indiana University School of Medicine, Indianapolis, IN, USA

Naga Chalasani, Tatiana Foroud, Suthat Liangpunsakul & Lois B. Travis

29. Vanderbilt University Medical Center, Nashville, TN, USA

Nancy J. Cox, Eric Gamazon & Eric D. Austin

30. University of Chicago, Chicago, IL, USA

Eileen Dolan & Omar El-Charif

31. Loyola University, Chicago, IL, USA

Heather Wheeler

32. Kaiser Permanente, Oakland, CA, USA

Lori Sakoda & John Witte

33. Mayo Clinic, Rochester, MN, USA

Kostantinos Lazaridis

34. Geisinger Health System, Danville, PA, USA

Adam Buchanan, David J. Carey, Christa L. Martin, Michelle N. Meyer, Kyle Retterer & David Rolston

35. National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA

Nirmala Akula, Emily Besançon, Sevilla D. Detera-Wadleigh, Layla Kassem, Francis J. McMahon & Thomas G. Schulze

36. Northwestern University, Chicago, IL, USA

Adam Gordon, Maureen Smith & John Varga

37. Mount Sinai School of Medicine, New York City, NY, USA

Erwin Bottinger & Judy Cho

38. University of Alabama at Birmingham, Birmingham, AL, USA

S. Louis Bridges & Robert Kimberly

39. University of California, Los Angeles, Los Angeles, CA, USA

Marlena Fejzo

40. University of Colorado School of Medicine, Aurora, CO, USA

Richard A. Spritz

41. University of Michigan Medical School, Ann Arbor, MI, USA

James T. Elder, Rajan P. Nair, Philip Stuart & Lam C. Tsoi

42. Ann Arbor Veterans Affairs Hospital, Ann Arbor, MI, USA

James T. Elder

43. University of Ottawa, Ottawa, Ontario, Canada

Robert Dent & Ruth McPherson

44. University of Texas Health Science Center at Houston, Houston, TX, USA

Shervin Assassi & Maureen D. Mayes

Consortia

Regeneron Genetics Center

- RGC Management and Leadership Team

- Gonçalo Abecasis

- , Aris Baras

- , Michael Cantor

- , Giovanni Coppola

- , Andrew Deubler

- , Aris Economides

- , Adolfo Ferrando

- , Luca A. Lotta

- , John D. Overton

- , Jeffrey G. Reid

- , Alan Shuldiner

- & Katherine Siminovitch

- Sequencing and Lab Operations

- John D. Overton

- , Christina Beechert

- , Erin D. Brian

- , Laura M. Cremona

- , Hang Du

- , Caitlin Forsythe

- , Zhenhua Gu

- , Kristy Guevara

- , Michael Lattari

- , Alexander Lopez

- , Kia Manoochehri

- , Prathyusha Challa

- , Manasi Pradhan

- , Raymond Reynoso

- , Ricardo Schiavo

- , Maria Sotiropoulos Padilla

- , Chenggu Wang

- & Sarah E. Wolf

- Clinical Informatics

- Michael Cantor

- , Amelia Averitt

- , Nilanjana Banerjee

- , Dadong Li

- , Sameer Malhotra

- , Justin Mower

- , Mudasar Sarwar

- , Deepika Sharma

- , Jeffrey C. Staples

- , Sean Yu

- & Aaron Zhang

- Genome Informatics and Data Engineering

- Jeffrey G. Reid

- , Mona Nafde

- , George Mitra

- , Sujit Gokhale

- , Andrew Bunyea

- , Krishna Pawan Punuru

- , Sanjay Sreeram

- , Gisu Eom

- , Sujit Gokhale

- , Benjamin Sultan

- , Rouel Lanche

- , Vrushali Mahajan

- , Eliot Austin

- , Sean O’Keeffe

- , Razvan Panea

- , Tommy Polanco

- , Ayesha Rasool

- , William Salerno

- , Xiaodong Bai

- , Lance Zhang

- , Boris Boutkov

- , Evan Edelstein

- , Alexander Gorovits

- , Ju Guan

- , Lukas Habegger

- , Alicia Hawes

- , Olga Krasheninina

- , Samantha Zarate

- , Adam J. Mansfield

- , Evan K. Maxwell

- , Suganthi Balasubramanian

- , Suying Bao

- , Kathie Sun

- & Chuanyi Zhang

- Analytical Genetics and Data Science

- Gonçalo Abecasis

- , Manuel Allen Revez Ferreira

- , Joshua Backman

- , Kathy Burch

- , Adrian Campos

- , Lei Chen

- , Sam Choi

- , Amy Damask

- , Liron Ganel

- , Sheila Gaynor

- , Benjamin Geraghty

- , Arkopravo Ghosh

- , Salvador Romero Martinez

- , Christopher Gillies

- , Lauren Gurski

- , Joseph Herman

- , Eric Jorgenson

- , Tyler Joseph

- , Michael Kessler

- , Jack Kosmicki

- , Nan Lin

- , Adam Locke

- , Priyanka Nakka

- , Jonathan Marchini

- , Karl Landheer

- , Olivier Delaneau

- , Maya Ghoussaini

- , Anthony Marcketta

- , Joelle Mbatchou

- , Arden Moscati

- , Aditeya Pandey

- , Anita Pandit

- , Charles Paulding

- , Jonathan Ross

- , Carlo Sidore

- , Eli Stahl

- , Maria Suciu

- , Timothy Thornton

- , Peter VandeHaar

- , Sailaja Vedantam

- , Scott Vrieze

- , Jingning Zhang

- , Rujin Wang

- , Kuan-Han Wu

- , Bin Ye

- , Blair Zhang

- , Andrey Ziyatdinov

- , Yuxin Zou

- , Olivier Delaneau

- , Maya Ghoussaini

- , Jingning Zhang

- , Kyoko Watanabe

- & Mira Tang

- Therapeutic Area Genetics

- Adolfo Ferrando

- , Giovanni Coppola

- , Luca A. Lotta

- , Alan Shuldiner

- , Katherine Siminovitch

- , Brian Hobbs

- , Jon Silver

- , William Palmer

- , Rita Guerreiro

- , Amit Joshi

- , Antoine Baldassari

- , Cristen Willer

- , Sarah Graham

- , Ernst Mayerhofer

- , Mary Haas

- , Niek Verweij

- , George Hindy

- , Jonas Bovijn

- , Tanima De

- , Parsa Akbari

- , Luanluan Sun

- , Olukayode Sosina

- , Arthur Gilly

- , Peter Dornbos

- , Juan Rodriguez-Flores

- , Moeen Riaz

- , Manav Kapoor

- , Gannie Tzoneva

- , Momodou W. Jallow

- , Anna Alkelai

- , Giovanni Coppola

- , Ariane Ayer

- , Veera Rajagopal

- , Sahar Gelfman

- , Vijay Kumar

- , Jacqueline Otto

- , Neelroop Parikshak

- , Aysegul Guvenek

- , Jose Bras

- , Silvia Alvarez

- , Jessie Brown

- , Jing He

- , Hossein Khiabanian

- , Joana Revez

- , Kimberly Skead

- & Valentina Zavala

- Research Program Management and Strategic Initiatives

- Lyndon J. Mitnaul

- , Marcus B. Jones

- , Esteban Chen

- , Michelle G. LeBlanc

- , Jason Mighty

- , Nirupama Nishtala

- , Nadia Rana

- , Jennifer Rico-Varela

- & Jaimee Hernandez

- Senior Partnerships and Business Operations

- Alison Fenney

- , Randi Schwartz

- , Jody Hankins

- & Samuel Hart

- Business Operations and Administrative Coordinators

- Ann Perez-Beals

- , Gina Solari

- , Jaimee Hernandez

- , Johannie Rivera-Picart

- , Michelle Pagan

- & Sunilbe Siceron

RGC-ME Cohort Partners

- Accelerated Cures

- David Gwynne

- African Descent and Glaucoma Evaluation Study (ADAGES) III

- Jerome I. Rotter

- & Robert Weinreb

- Age-related macular degeneration in the Amish

- Jonathan L. Haines

- , Margaret A. Pericak-Vance

- & Dwight Stambolian

- Albert Einstein College of Medicine

- Nir Barzilai

- , Yousin Suh

- & Zhengdong Zhang

- Amish Connectome Project

- Elliot Hong

- Amish Research Clinic

- Braxton Mitchell

- The Australia and New Zealand MS Genetics Consortium

- Nicholas B. Blackburn

- , Simon Broadley

- , Marzena J. Fabis-Pedrini

- , Vilija G. Jokubaitis

- , Allan G. Kermode

- , Trevor J. Kilpatrick

- , Jeanette Lechner-Scott

- , Stephen Leslie

- , Bennet J. McComish

- , Allan Motyer

- , Grant P. Parnell

- , Rodney J. Scott

- , Bruce V. Taylor

- & Justin P. Rubio

- Center for Non-Communicable Diseases (CNCD)

- Danish Saleheen

- Cincinnati Children’s Hospital

- Ken Kaufman

- , Leah Kottyan

- , Lisa Martin

- & Marc E. Rothenberg

- Columbia University

- Abdullah Ali

- & Azra Raza

- Dallas Heart Study

- Jonathan Cohen

- Diabetic Retinopathy Clinical Research (DRCR) Retina Network

- Adam Glassman

- Duke University

- William E. Kraus

- , Christopher B. Newgard

- & Svati H. Shah

- Flinders University of South Australia

- Jamie Craig

- & Alex Hewitt

- Indiana Biobank

- Naga Chalasani

- , Tatiana Foroud

- & Suthat Liangpunsakul

- Indiana University School of Medicine

- Nancy J. Cox

- , Eileen Dolan

- , Omar El-Charif

- , Lois B. Travis

- , Heather Wheeler

- & Eric Gamazon

- Kaiser Permanente

- Lori Sakoda

- & John Witte

- Mayo Clinic

- Kostantinos Lazaridis

- Mexico City Prospective Study (MCPS)

- Jesús Alegre-Díaz

- , Jaime Berumen

- , Rory Collins

- , Jonathan Emberson

- , Pablo Kuri-Morales

- , Roberto Tapia-Conyer

- & Jason Torres

- MyCode-DiscovEHR Geisinger Health System Biobank

- Adam Buchanan

- , David J. Carey

- , Christa L. Martin

- , Michelle N. Meyer

- , Kyle Retterer

- & David Rolston

- National Institute of Mental Health

- Nirmala Akula

- , Emily Besançon

- , Sevilla D. Detera-Wadleigh

- , Layla Kassem

- , Francis J. McMahon

- & Thomas G. Schulze

- Northwestern University

- Adam Gordon

- , Maureen Smith

- & John Varga

- Penn Medicine Biobank

- Yuki Bradford

- , Scott Damrauer

- , Stephanie DerOhannessian

- , Theodore Drivas

- , Scott Dudek

- , Joseph Dunn

- , Ned Haubein

- , Renae Judy

- , Yi-An Ko

- , Colleen Morse Kripke

- , Meghan Livingstone

- , Nawar Naseer

- , Kyle P. Nerz

- , Afiya Poindexter

- , Marjorie Risman

- , Salma Santos

- , Giorgio Sirugo

- , Julia Stephanowski

- , Teo Tran

- , Fred Vadivieso

- , Anurag Verma

- , Shefali S. Verma

- , JoEllen Weaver

- , Colin Wollack

- , Daniel J. Rader

- & Marylyn Ritchie

- Primary Open-Angle African American Glaucoma Genetics (POAAG) study

- Joan O’Brien

- Regeneron–Mt. Sinai BioMe Biobank

- Erwin Bottinger

- & Judy Cho

- UAB GWAS in African Americans with rheumatoid arthritis

- S. Louis Bridges

- UAB Whole exome sequencing of systemic lupus erythematosus patients

- Robert Kimberly

- University of California, Los Angeles

- Marlena Fejzo

- University of Colorado School of Medicine

- Richard A. Spritz

- University of Michigan Medical School

- James T. Elder

- , Rajan P. Nair

- , Philip Stuart

- & Lam C. Tsoi

- University of Ottawa

- Robert Dent

- & Ruth McPherson

- University of Pennsylvania

- Brendan Keating

- University of Pittsburgh

- Erin E. Kershaw

- , Georgios Papachristou

- & David C. Whitcomb

- University of Texas Health Science Center at Houston

- Shervin Assassi

- & Maureen D. Mayes

- Vanderbilt University Medical Center

- Eric D. Austin

Contributions

Conceptualization: S. Balasubramanian, W.S., J.G.R., J.M., G.A., A.B. and H.M.K. Data generation: X.B., W.S., J.G.R., J.D.O, M.C., E.M., A.G., A. Mansfield, B.B., L.H., A. Marcketta, A.E.L. and A.L. Formal analysis: K.Y.S., S.C., S. Bao, C.Z., M.K., J. Backman, T.J., L.G. and A.H. Browser application: E.M., A.G., B.B., G.M., S.G. and M.N. Supervision: S. Balasubramanian and W.S. Writing (original draft): K.Y.S., X.B., S.C., S. Bao, M.K. and S. Balasubramanian. Writing (review and editing): K.Y.S., S. Balasubramanian, S. Bao, J.G.R., A.R.S., G.A., J.M., M.L.C., M.D.K., D.S., J.S., J. Backman, S.G., A.D.G., V.M.R., J.A., J. Bovijn, R.T.-C., P.K.-M., J.T., J.E., R.C., T.T. and C.Z. Project management: J.R.V.

Corresponding authors

Correspondence to William Salerno or Suganthi Balasubramanian.

Ethics declarations

Competing interests

K.Y.S., X.B., S.C., S. Bao, C.Z., M.K., J. Backman, T.J., E.M., G.M., A.G., A. Mansfield, B.B., S.G., L.H., A. Marcketta, A.E.L., L.G., A.H., M.D.K., D.S., J.S., J. Bovijn, S.G., A.D.G., V.M.R., A.L., J.R.V., M.C., T.T., J.D.O., A.R.S., M.L.C., M.N., A.B., G.A., J.M., J.G.R., W.S. and S. Balasubramanian are current employees and/or stockholders of Regeneron Genetics Center or Regeneron Pharmaceuticals. H.M.K. was a former employee of Regeneron Genetics Center.

Peer review

Peer review information

Nature thanks Luke O’Connor and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Mutation saturation survey in RGC-ME data.

Counts are based on variant-transcript pairs. *Methylation level: mean methylation values across tissues of >0.65, 0.2–0.65, <0.2 correspond to methylation level of 2, 1, 0, respectively. CpG sites with methylation level of 2 are highly methylated, sites with methylation level 0 or 1 are grouped as lowly methylated sites. Variants are subset to target exome regions. Only variants that passed QC are included in the number of all possible variants.

Extended Data Fig. 2 Box plots summarizing counts of variants observed in each unrelated sample in the RGC-ME dataset.

The lower bound, centre and upper bound of each box plot represents the 25, 50, and 75 percentiles of the distributions of counts. Points represent outliers, and whisker minima and maxima represent the smallest and largest points 1.5-times beyond the interquartile range. Individuals were assigned discrete ancestries based on overall fine-scale ancestry probabilities >50% and a total of 755,261 individuals were included. See Supplementary Table 1 for sample size breakdowns.

Extended Data Fig. 3 shet distribution across gene essentiality and disease categories, and evidence that a larger sample improves the precision of shet estimates.

a, Proportion of genes in each disease/essentiality annotation list (gathered from published literature and databases) that correspond to each shet decile. b, Ratio of variances from MCMC sampling for shet calculated on full dataset (824k) and randomly downsampled set of 60,000 individuals. The mean ratio around 0.05 suggests that gene-level variance from the full dataset is 20x smaller than variance for the same gene using the downsampled set. This is the case despite similar, or even higher, shet mean estimates as shown by the colour bar. c, Mean (points) and 95% HPD (green bars) from MCMC sampling for shet estimates of genes in each CDS length quantile for downsampled 60k (left) and full 822K (right) samples. HPD and variance in panels b,c were derived from MCMC sampling for 8,000 final iterations.

Extended Data Fig. 4 Comparisons between shet computed with RGC-ME and other gene constraint metrics.

a, Receiver operator curves showing the discrimination between the “high” and “low” constraint genes using different constraint metrics: shet-RGCME, shet-ABC, and LOEUF for transcripts with values from all three methods. The highly constrained category comprised 1,476 haploinsufficient and autosomal dominant (including developmental-specific) genes and the comparison group was represented by 3,893 haplosufficient and genes with rare, biallelic pLOFs in RGC-ME. Dotted lines indicate specificity and sensitivity for shet = 0.073. Sensitivity = TP / (TP+FN), Specificity = TN / (TN+FP). Spearman rank correlations between shet from RGC-ME with these estimates are high (-0.768 with LOEUF, 0.778 with shet-ABC). b, shet vs LOEUF results for 973 genes with ≤5 expected LOFs. 5 genes are highlighted for short coding sequence length (CDS), that are constrained according to shet (both mean>0.073 and lower bound>0.021) and unconstrained according to LOEUF. LOEUF is an alternate measure of pLOF-based gene constraint and ranges from 0 to around 2; a value < 0.35 is considered constrained. Error bars (grey lines) around shet denote 95% highest posterior density.

Extended Data Fig. 5 Determination of the MAPS score threshold to define constrained regions and SAVs.

a–c, To systematically evaluate the deleteriousness of missense variants at various MTR scores, we compared MAPS scores across MTR (a) and splice score (b,c) thresholds. a, MTR was divided from 5% to 100% with step size of 5% (lower MTR percentile is more constrained). For each MTR percentile threshold, we divided variants into two sets: variants that pass the percentile threshold (red dots), and variants greater than the percentile (blue dots). The y axis represents MAPS score for each set of variants; the x-axis shows the tested MTR percentile threshold. Error bars represent standard deviation around the mean proportion of singletons per bin. A total of 68,636,473 variants were included in this analysis. b,c, Schematic of MAPS-derived filters for SpliceAI and MMSplice for all variants (b) and coding nonSS variants (c), respectively. A list of prediction score thresholds were set up for both SpliceAI and MMSplice, ranging from 0.1 to 0.99, with step size of 0.02. For each threshold, we divided variants into two sets: the set of variants that passed the threshold, represented as red dots, and the set of variants that failed the filter, represented as blue dots. The y axis represents MAPS score for each set of variants; the x-axis shows the tested score threshold. All MAPS scores were calculated based on the set of variants that pass QC metrics and have splice prediction scores in RGC-ME unrelated samples. Error bars represent standard deviation around the mean proportion of singletons per bin. For all variants (b), the total number of variants were n = 12,886,467 and 20,604,651 for SpliceAI and MMSplice, respectively. For coding nonSS variants (c), n = 5,468,779 and 4,013,820 for SpliceAI and MMSplice, respectively.

Extended Data Fig. 6 MTR-based segmentation and comparison of constrained regions in 2,832 genes that have constrained regions in both MTR and MPC (matched on transcript IDs).

a, Dashed line indicates the top-15-percentile exome-wide MTR threshold used for MTR-based segmentation (0.841). Blue and red regions represent MTR-constrained and unconstrained regions, respectively. b, Constrained MTR regions that overlap with MPC segments. 80% of MTR-constrained regions (represented by the combined area of red and yellow) overlap with MPC-constrained segments (yellow), whereas 40% of constrained MPC segments (represented by the combined area of green and yellow) are included in the intersection (yellow). The numbers indicated on the Venn diagram represent number of amino acids. Aside from 2,832 genes that had both MTR- and MPC-constrained regions, an additional 297 genes had only MPC-constrained segments, and 9,514 genes had only MTR-constrained regions (not included in the Venn diagram). c, Distribution of the fold change of the number of MTR-constrained regions and constrained MPC segments (\(\gamma \le 0.6\)) per gene. Dashed line indicates the median fold change of 3. Data shown is for the 2,832 genes that have constrained region annotations both in MTR and MPC segments.

Extended Data Fig. 7 Constrained MTR regions are enriched in ClinVar pathogenic variants and shorter than LOF-specific constrained genes.

a, Odds ratios (points) comparing enrichment of pathogenic versus benign ClinVar variants (solid line) and de novo variants in cases versus controls (dotted line) in MTR-constrained regions. Error bars represent 95% CIs (Fisher’s exact test). The total number of variants in each comparison are shown in b for the MTR-percentile cut-offs highlighted in yellow. In total, 5,818 case and 553 control variants were used in the de novo analysis and 7,944 pathogenic and 11,993 benign variants were used in the ClinVar analysis. b, Table of case and control variants in constrained and unconstrained regions to compute statistical tests for ClinVar (“CV”) and de novo (“DN”) variants across 5 different MTR-percentile thresholds (13-17%, yellow boxed region in a). Statistics include hypergeometric tests (p-value for enrichment of case and control variants in constrained regions) and odds ratios comparing enrichment of case vs control in constrained regions. The background rate of constrained regions among variants in the comparison set represented by “% constrained background”. c, CDS length comparison between 1,482 missense-specific constrained genes (defined where >15% of gene is in the top 15 percentile of MTR, based on one-sided binomial tests with π_0 = 0.15, p < 0.05, Bonferroni corrected) and 1,424 LOF-specific constrained genes with shet score <0.073. Log10 CDS length for all 19,644 genes (canonical transcripts) shown in the grey curve. The missense-specific constrained genes had significantly shorter CDS length than LOF-specific constrained genes (p = 2.9 × 10−40, two-sided Wilcoxon test).

Extended Data Fig. 8 Characteristics of predicted deleterious SAVs.

a, Summary of unique variant-transcript pairs of predicted deleterious SAVs in different functional categories using MAPS-defined prediction score thresholds for SpliceAI and MMSplice. Percents are computed out of total variants in each effect class. b, Distribution of different functional categories of predicted deleterious SAVs. (HC: High confidence, LC: Low confidence. These annotation tags are derived from LOFTEE). c, Empirical validation of MAPS-predicted deleterious coding nonSS SAVs: enrichment of predicted coding nonSS deleterious SAVs in experimentally validated SDVs compared to non-SDVs. Odds ratios (points) were derived using two-sided Fisher’s exact test and error bars show 95% CIs. A total of n = 17,395 variants, of which 147 SAVs were validated SDVs, were included. d,e, Fraction of predicted deleterious SAVs (d) and coding nonSS SAVs (e) that were validated as SDVs by any of the three splice reporter assays.

Extended Data Fig. 9 ClinVar variant counts.

a, Counts of autosomal pathogenic ClinVar high-confidence variants (two stars or more) observed in large-scale exome sequencing studies, including RGC-ME, gnomAD (exomes, v2.1.1), and ExAC, are indicated on the top of each bar. The left axis indicates the total coverage of ClinVar pathogenic variants represented in each dataset. b, Bars and points both depict the mean per cent difference of per-individual counts in ClinVar categories (pathogenic 0+, 1+, 2+, VUS+CI) across continental ancestries using all unrelated samples, with respect to EUR (e.g. [countAFR – countEUR]/countAFR × 100). CV0, 1, and 2 refer to ClinVar pathogenic star rating 0+, 1+, and 2+ categories, respectively. VUS/CI combines variants annotated as “variants of unknown significance” and “conflicting interpretations”. All per-individual counts in non-EUR were significantly different compared to counts in EUR (e.g. per-individual counts of ClinVar 2+ variants in AFR were 0.576 [0.567, 0.585] lower than those in EUR) except for ClinVar 0+ counts in IAM compared with EUR (t-tests with Bonferroni correction). Error bars show 95% CI of mean per cent difference from t-test. c, Per-individual count of VUSs and conflicting information (CI, left); and pathogenic variants (right) in RGC-ME for all unrelated samples. The lower bound, centre and upper bound of each box plot represents the 25, 50, and 75 percentiles of the distributions of counts. Points represent outliers, and whisker minima and maxima represent the smallest and largest points 1.5-times beyond the interquartile range. For b,c, a total of 749,584 unrelated individuals were included across 4 ancestries and individuals were assigned discrete ancestries based on overall FSA probabilities >50%; see Supplementary Table 1a for sample size breakdowns. The x-axis indicates ClinVar star rating which is a measure of the confidence of the annotation (pathogenic/benign).

Extended Data Table 1 Sample breakdown of individuals with rare biallelic pLOF variants by ancestry

Supplementary information

Supplementary Information

This Supplementary information file contains the following. Description of Supplementary Tables 1–14. Supplementary Tables 1–6 and 11 are provided as separate data Excel tables. Supplementary Tables 7–10 and 12–14 are embedded within the Supplementary Information document. Supplementary Methods and descriptions of Supplementary Analyses. Supplementary Figures 1–7. Supplementary References.

Reporting Summary

Supplementary Table 1

This table includes: sample subsets of RGC-ME used in different analyses; full breakdown of sample counts in fine-scale ancestry groups used in Fig. 1 and for the browser; and sample sizes and collaborator details for each dataset in RGC-ME. See main Supplementary Information PDF for full legend.

Supplementary Table 2

shet values for 16,710 genes and other annotations, including additional annotations, LOEUF scores from gnomAD, minor allele frequency and coding sequence length. See main Supplementary Information PDF for full legend.

Supplementary Table 3

List of continuous segments of missense constrained regions found in 12,349 genes (canonical transcripts), based on the top-15-percentile threshold of MTR values. See main Supplementary Information PDF for full legend.

Supplementary Table 4

Jaccard index analysis between the MTR-constrained regions and features from UniProt (release 2022_05). See main Supplementary Information PDF for full legend.

Supplementary Table 5

List of genes with significant proportion of CDS in the top 1, 5, 10, 15 and 20 percentile of exome-wide MTR missense constraint scores based on one-sided binomial tests. See main Supplementary Information PDF for full legend.

Supplementary Table 6

List of 4,848 genes with rare (alternate allele frequency < 1%) biallelic pLOF variants (homozygous alternate and compound heterozygous) reported for the entire RGC-ME dataset including related individuals. See main Supplementary Information PDF for full legend.

Supplementary Table 11

List of highly differentiated variants (FST > 0.15).

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Sun, K.Y., Bai, X., Chen, S. et al. A deep catalogue of protein-coding variation in 983,578 individuals. Nature 631, 583–592 (2024). https://doi.org/10.1038/s41586-024-07556-0

- Received: 19 June 2023

- Accepted: 10 May 2024

- Published: 20 May 2024

- Issue Date: 18 July 2024

- DOI: https://doi.org/10.1038/s41586-024-07556-0

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Transposase-assisted target-site integration for efficient plant genome engineering

Peng Liu,

Kaushik Panda,

Seth A. Edwards,

Ryan Swanson,

Hochul Yi,

Pratheek Pandesha,

Yu-Hung Hung,

Gerald Klaas,

Xudong Ye,

Megan V. Collins,

Kaili N. Renken,

Larry A. Gilbertson,

Veena Veena,

C. Nathan Hancock &

…

R. Keith Slotkin

Nature volume 631, pages 593–600 (2024)

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Abstract

The current technologies to place new DNA into specific locations in plant genomes are low frequency and error-prone, and this inefficiency hampers genome-editing approaches to develop improved crops1,2. Often considered to be genome ‘parasites’, transposable elements (TEs) evolved to insert their DNA seamlessly into genomes3,4,5. Eukaryotic TEs select their site of insertion based on preferences for chromatin contexts, which differ for each TE type6,7,8,9. Here we developed a genome engineering tool that controls the TE insertion site and cargo delivered, taking advantage of the natural ability of the TE to precisely excise and insert into the genome. Inspired by CRISPR-associated transposases that target transposition in a programmable manner in bacteria10,11,12, we fused the rice Pong transposase protein to the Cas9 or Cas12a programmable nucleases. We demonstrated sequence-specific targeted insertion (guided by the CRISPR gRNA) of enhancer elements, an open reading frame and a gene expression cassette into the genome of the model plant Arabidopsis. We then translated this system into soybean—a major global crop in need of targeted insertion technology. We have engineered a TE ‘parasite’ into a usable and accessible toolkit that enables the sequence-specific targeting of custom DNA into plant genomes.

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Main

Transgenesis in plants is widely used to both generate research tools and engineer crop improvement[13]. Traditional transgene integration occurs at random locations in the plant genome[14], which can generate unintended mutations and subject the expression of the transgene to undesired position effects15,16. Sequence-specific target-site integration of transgenes is highly desired and has been attempted in plants for over a decade (reviewed previously[1]). Although there has been progress17,18, the low efficiency and quality of current target-site integration technologies hampers crop genome engineering[2].

Several approaches have been taken in plants to insert transgenic sequences into specific genomic locations17,18,19. Homologous recombination (HR) in plants has been demonstrated, but the rate is extremely low and it is therefore not used[20]. Programmable nuclease systems that create sequence-specific DNA breaks, such as CRISPR–Cas9, have improved targeted integration[1]. Prime editing techniques in plants enable the insertion of small sequences up to 34 base pairs (bp) at a targeted site[21], but larger insertions are required to encode new extrinsic traits[22]. Homology-directed repair (HDR) can integrate new DNA through the resolution of induced double-stranded DNA breaks by recombination with a supplied DNA sequence that matches both flanks of the cleaved DNA[23]. HDR occurs at a low frequency in plants as the repair of the DNA breaks occurs primarily by non-homologous end joining (NHEJ), which can include the integration of random fragments of DNA[24]. The preference for NHEJ repair in plants has been taken advantage of to knock-in supplied extrachromosomal DNA during the repair of a CRISPR-induced double-stranded break25,26. HDR and NHEJ knock-ins are frequently subject to deletions of the flanking target-site DNA and/or the sequence that was delivered (the cargo)17,27. Owing to the low integration frequency and high deletion rate, these approaches involve the expensive production of many transgenic plants followed by laborious screening for a rare intact targeted integration event.

TEs, and more specifically class II DNA transposons, can ‘cut and paste’ their DNA into new genomic locations. TE-encoded transposase proteins excise their corresponding TE DNA from a donor position, protect this extrachromosomal DNA from nucleases3,4 and accurately insert the TE DNA into the genome at a new integration site3,4,5 (Extended Data Fig. 1). The selection of the integration site is highly variable among different TE types, and can target accessible chromatin[6], a favoured chromatin state[7], sites of double-stranded breaks[8] or a short sequence such as TAA[9]. Bacterial genomes contain natural CRISPR-associated transposases that have been engineered using different CRISPR guide RNAs (gRNAs) to transpose the TE to specific sites in bacterial genomes10,11,12. These systems lack Cas cleavage activity and are dependent on the transposase to perform integration. Moreover, other DNA transposons have been synthetically combined with either an active[28] or catalytically inactive29,30 programmable nuclease to target TE transposition in animal cell and tissue cultures. Here we generated a transposase-assisted target-site integration (TATSI) system by co-expressing catalytically active programmable nucleases with the rice Pong DNA transposon system[31]. We accomplished transposase-mediated targeted insertion in a model plant (Arabidopsis) and translated this technology into a crop plant (soybean). We demonstrate a higher frequency and accuracy of targeted integration compared with currently used methodologies, as well as the delivery of enhancers and gene cargos, in individual plants rather than cultured cells.

Combining a transposase and Cas protein

We aimed to combine the function of CRISPR–Cas targeting and TE transposition into a single system by fusing the rice Pong transposase proteins with programmable nucleases such as Cas9. We began with a previously established Arabidopsis vector system in which hyperactive versions of the rice Pong proteins (named ORF1 and ORF2) have been removed from the TE and are ubiquitously expressed by genic promoters. Both Pong ORF1 and ORF2 are necessary for the excision and insertion of the non-autonomous rice TE mPing[9] (Extended Data Fig. 1). The Pong ORF1 protein binds to 15 bp terminal inverted repeats on the ends of the 430 bp mPing element, which itself is flanked by TTA or TAA repeats that are necessary for excision[32] (Extended Data Fig. 1). We synthetically fused Cas9, the Cas9(D10A) nickase (cleaves only one strand of DNA) or dCas9 (catalytically dead version) to the N- and C-terminal ends of Pong ORF1 and ORF2, resulting in 12 fusion protein transgene configurations (Extended Data Fig. 2a). We used a two-step transformation strategy, in which mPing is located within a GFP expression cassette that was previously integrated into the Arabidopsis genome (donor element)[9]. This line was then germinally transformed with our Pong-Cas9 fusion proteins and tested for the excision of mPing. This includes a transgene version in which ORF1, ORF2 and Cas9 are all present but unfused. mPing excision, demonstrated by GFP fluorescence and verified by PCR, was detected for all 12 fusion proteins and controls in which ORF1 and ORF2 are present with or without Cas9 (Fig. 1a,b and Extended Data Fig. 2b–d), demonstrating that ORF1 and ORF2 are capable of transposase function with fusions on either end.

Fig. 1: The combined activities of a transposase and programmable nuclease result in targeted insertion.

a, Excision of the mPing TE from a GFP reporter restores fluorescence. Arabidopsis seedlings were imaged; the cotyledons are outlined with a white dashed line. ‘ORF2–Cas9’ represents a translational fusion of these proteins. Scale bars, 500 μm. b, Excision of mPing assayed by PCR in pooled seedlings. The top band represents mPing within GFP (donor position), and the smaller band is generated after mPing excision. c, PCR primer design for detecting targeted insertions of mPing at the PDS3 locus. U and D are PDS3 primers that surround the CRISPR target site. R and L are mPing primers. TIR, terminal inverted repeat. d, PCR amplification analysis of targeted insertions of mPing at the PDS3 locus in pooled seedlings. AtADH1 was the PCR control. e, Sanger sequencing of the insertion junctions generated after mPing insertion into PDS3. The light grey bars behind the DNA-sequencing peaks represent quality scores for each base call. Bases highlighted in red are mismatches compared with the reference sequence. The flanking TTA sequence that comes with mPing from the donor site is annotated. f, Model of targeted insertion of mPing at the PDS3 locus. A functional ORF2–Cas9 fusion protein excises mPing out of the 35S–GFP donor site, cuts the PDS3 gene guided by the gRNA and mPing is inserted into the PDS3 target site. The diagram in f was created using BioRender.

Each transgene used a CRISPR gRNA that targets the Arabidopsis PHYTOENE DESATURASE3 (PDS3) gene. We verified that that our CRISPR–Cas9 system was functional through a mutation-detection assay at PDS3 (Extended Data Fig. 3a) and the observation of pds3-mutant white seedlings and plant sectors with the catalytically active Cas9 protein (Extended Data Fig. 3b,c). We observed a decrease in the rate of mutations induced by Cas9 when fused to ORF1 or ORF2, and determined that the N-terminal fusions to Cas9 have higher activity than C-terminal fusions (Extended Data Fig. 3c).

We used a combination of four PCR reactions to assay for targeted insertion of mPing at the PDS3 gRNA target site (Fig. 1c). Of the 12 tested protein fusions and 4 controls, 2 provided reproducible and high-rate targeted insertion of mPing (Fig. 1d and Extended Data Fig. 4a). These two are the fusion of Cas9 to the C terminus of ORF2, and unfused Cas9 (Fig. 1d). Targeted insertion occurred only in plants that possessed both a fully catalytically active Cas9 and the ORF1 + ORF2 proteins. We verified that the ORF2–Cas9 fusion protein was intact and not generating separate versions of the two proteins (Extended Data Fig. 4b). Targeted insertions were verified by Sanger sequencing of the insertion junctions (Fig. 1e and Extended Data Fig. 4c).

To determine whether mPing targeted insertion occurs specifically with the Cas9 nuclease, or whether other programmable nucleases could be used, we combined Pong ORF1 and ORF2 with Lachnospiraceae bacterium LbCas12a, a distinct CRISPR-guided nuclease[33]. Three configurations of Cas12a were tested for mPing targeted insertion: Cas12a–ORF2, ORF2–Cas12a and unfused Cas12a. As with Cas9, the ORF2–Cas12a fusion protein and unfused Cas12a resulted in mPing excision and targeted insertion (Extended Data Fig. 5), demonstrating that a range of nucleases can be used in combination with Pong transposase proteins to perform targeted integration. These findings demonstrate that, after excision from the donor site, the mPing TE inserts into double-stranded breaks generated by a programmable nuclease system (Fig. 1f).

Precision at targeted insertion sites

To determine the precision of junction sequences between mPing and the PDS3 target site, we performed amplicon deep sequencing of pooled PDS3 insertion events. We analysed more than 1,703 distinct targeted insertion junctions at the PDS3 locus and found that the majority of the insertions occur at the CRISPR cleavage site or within 4 bp (Fig. 2a). Second, the majority (66.8%) of mPing inserted elements have intact ends and are full length (over the region assayed) (Fig. 2b), demonstrating that, when mPing inserts, the complete or near-complete element is delivered. This full or near-full insertion of the cargo TE is probably due to the binding of the TE by the transposase proteins while extrachromosomal[5], protecting the DNA ends from nucleases and deletions (Fig. 2c). Third, a three-nucleotide target-site duplication with the sequence TTA/TAA is a feature produced after mPing free transposition[31] (Extended Data Fig. 1). The TTA/TAA sequence does not need to be present at the target site for TATSI targeted integration, but a new flanking TTA/TAA sequence is often observed after TE integration at the TATSI target site (Extended Data Fig. 4c). This new TTA/TAA flanking sequence is generated from the donor site after a staggered cut by the Pong transposase. The presence of the flanking TTA/TAA bases demonstrates that the targeted insertion event only occurs after (1) ORF1 and ORF2 excise mPing out of the donor site, and (2) cleavage of the target site by the programmable nuclease (Fig. 2c).

Fig. 2: Precision of targeted insertion events.

a, The dashed line marks the Cas9 cleavage site on the PDS3 target sequence before TE integration. Insertion sites are assayed at the 5′ (relative to PDS3) (left) or 3′ junction (right) of mPing insertions. The ‘0’ site marks insertion at the exact Cas9-cleavage site. PAM, protospacer adjacent motif. b, Sequencing analysis of targeted insertion junction points mapped to mPing indicates how much of the mPing element was delivered to the targeted insertion site. The x axis shows the nucleotide position along the mPing element. The break in the x axis represents the interior of mPing that was not assayed. c, Model mPing excision by an ORF2 transposase-generated staggered break, blunt cleavage of the target site by Cas9, then integration, repair and resolution of mPing at the target site by NHEJ. The diagram was created using BioRender. d, Nucleotide (nt) variation at the junction of mPing insertions into PDS3. The precision of each nucleotide at the insertion site was determined on the 5′ junction (left) or 3′ junction (right). The size of the circle represents the percentage of reads in which that nucleotide is as expected (y = 0), has an insertion (y ≥ 1) or deletion (y ≤ −1). The number of SNPs at the insertion site is shown at the top of the y axis. Pearson’s χ^2 tests were used to test the statistical significance of the difference in polymorphism between the two protein configurations. e, mPing insertion sites in pooled seedlings. The Arabidopsis nuclear genome is displayed on the x axis. The PDS3 target site is shown with an arrow and red datapoint. The scale of each y axis was determined by the maximum datapoint. A dashed line at 10,000 reads per million (RPM) is shown for each sample. Chr., chromosome; Rep., distinct biological replicates; WT, wild type. f, Quantitative analysis of the number and read support of free-transposition sites in pooled replicates for each genotype.

We observed examples of both perfectly accurate insertions of mPing at the target site as well as sequence polymorphism generated upon insertion. The majority of these polymorphisms were located at the junction nucleotides between the flanking TTA/TAA bases and target-site DNA (Fig. 2d). A 1 bp single-nucleotide polymorphism (SNP) or insertion was frequently observed on either the 5′ or 3′ end of the insertions (relative to the PDS3 gene direction), while deletions up to 7 bp can occur in the flanking TTA/TAA bases or into the PDS3 gene (Fig. 2d). The most common variation is a short 1–3 bp deletion at the junction between the flanking TTA/TAA bases and PDS3 (Fig. 2d and Extended Data Fig. 4c). We compared the level of polymorphism generated at the target site between the fused and unfused configurations and found that the difference was statistically significant (P < 0.01), with the unfused configuration generating marginally more ‘base for base’ perfect insertions compared with the fused configuration (Fig. 2d). Together, these data demonstrate that the mPing cargo is intact after targeted insertion, with small deletions occurring at the bases at the junction of mPing and the target-site DNA. These small deletions are probably caused by NHEJ during the repair of the integration junction that is necessary to resolve the mPing extrachromosomal DNA that has three-nucleotide overhangs with the blunt ends of the Cas9 nuclease-cleaved insertion site (Fig. 2c).

Off-target rate

To investigate whether mPing insertions occur at other regions of the genome besides the CRISPR–Cas on-target site, we performed insertion-seq to identify all of the rice mPing insertion sites in the Arabidopsis genome. We captured the sequences flanking the mPing terminal inverted repeats by generating a genomic library in a plasmid vector, then performing PCR between a vector primer and an mPing internal primer, followed by deep sequencing (Methods). This technology is more sensitive for the detection of insertions that may occur in only one or few cells compared to commonly used whole-genome sequencing. We used three control lines, including wild-type (no ORF1, ORF2 or CRISPR–Cas system), a line with only mPing (without ORF1/ORF2 and Cas9) and two biological replicates of pooled seedlings that have an active mPing transposition system (+ORF1, +ORF2) but lack the CRISPR–Cas system. The +ORF1, +ORF2 samples represent unfettered free transposition of mPing into the Arabidopsis genome (a qualitative analysis is shown in Fig. 2e (left)), and have hundreds of mPing insertion sites throughout the genome (a quantitative analysis is shown in Fig. 2f (left) and Extended Data Fig. 6a). When the CRISPR–Cas system is added in +ORF1, +ORF2, +Cas9 (unfused) plants, the number of mPing insertion sites is reduced in this pool of seedlings (Fig. 2f (middle) and Extended Data Fig. 6a), and the targeted site of PDS3 represents one of the sites of mPing insertion (Fig. 2e (middle)). In fused +ORF1, +ORF2–Cas9 pooled seedlings, the primary location of mPing insertion is the targeted PDS3 site (Fig. 2e (right)), although other sites of insertion can also be detected (Fig. 2f (right) and Extended Data Fig. 6a). The non-PDS3 mPing insertion sites are not directed by CRISPR–Cas9 cleavage, as none of the off-target sites are in common between replicates with Cas9 (Extended Data Fig. 6a), and mPing does not insert at predicted gRNA off-target sites (Extended Data Fig. 6b). If we use the approach of recent publications[22] that interrogate only off-target insertions at sites that partially match the gRNA sequence, we find that TATSI has an off-target rate as low as other state-of-the-art target-site integration technologies in plants (Extended Data Fig. 6b). These data demonstrate that the mPing insertions that are at other locations besides PDS3 represent free-transposition sites generated by the active mPing transposition system and not off-target sites of CRISPR–Cas9 cleavage. We observe that the number of free-transposition sites decreases in either fused or unfused lines with the CRISPR–Cas system (Fig. 2f and Extended Data Fig. 6a), suggesting that mPing is channelled to a targeted location by the double-stranded break generated by CRISPR–Cas.

Programmability of targeted insertions

We demonstrated the programmability of TATSI by changing the CRISPR gRNA and targeting mPing insertion to either an exon of the ALCOHOL DEHYDROGENASE1 (ADH1) gene or to the non-coding region upstream of ACTIN8 (ACT8). These gRNAs were verified to be functional on the basis of a Cas9 mutation-detection assay (Extended Data Fig. 3a). Targeted insertions of mPing at these loci were detected by PCR and verified by Sanger sequencing of the insertion junctions (Fig. 3a). We also performed multiplexed targeted insertion by generating two gRNAs (targeting ADH1 and ACT8) from a single transcript (Extended Data Fig. 5a). We detected mPing at both the ADH1 and ACT8 loci (Extended Data Fig. 5c–h), demonstrating that mPing can be distributed to multiple targeted loci from one transgenesis event.

Fig. 3: Programmability of the insertion site and cargo.

a, Sanger sequencing analysis of the junctions of mPing-targeted insertion events in the ADH1 gene and in the non-coding region upstream of ACT8. b, Visualization of the rate of targeted insertion upstream of ACT8. Each dot represents a distinct T1 transgenic plant, and the plants with mPing excision (blue) and targeted insertion (orange) are marked. c, Measurement of the excision frequency of mPing from the donor site (left) and rate of targeted insertion (right). n is the number of T1 transgenic Arabidopsis plants analysed. The colour of each data bar corresponds to the mPing cargo colour code in d. d, The cargo of different mPing versions demonstrated to excise and undergo targeted insertion in the Arabidopsis genome. NOS P–bar–NOS T is an expression cassette that expresses a herbicide-resistance gene, and bar CDS is the protein-coding region without the promoter and terminator. mPing versions are not drawn to scale, and the size of each is indicated.

As TEs are the targets of DNA methylation in plants[34] and, through transposition, they can recruit methylation to new loci[35], we tested whether mPing brings DNA methylation to an unmethylated ADH1 exon after TATSI insertion. After targeted insertion into ADH1, both mPing and the flanking ADH1 exon are not methylated above the background level found at this locus before mPing insertion (Extended Data Fig. 7a). The lack of DNA methylation attracted by mPing is probably due to it being an unexpressed non-autonomous TE and the fact that it is a foreign TE that is not recognized by identity-based silencing in Arabidopsis[36].

Rate of targeted insertion

The above targeted insertions were tested using a two-component transformation strategy in which the donor mPing was previously integrated into the genome on a separate transgene from the ORF1/ORF2/Cas9/gRNA transgene. We generated a one-component system in which the mPing donor site was present on the same transgene (Extended Data Fig. 7b). Similar to the two-component system, this one-component system successfully produced targeted insertions of mPing (Extended Data Fig. 7c,d), simplifying the transgenesis required for targeted insertion. We used this one-component system and the non-deleterious intergenic target site upstream of ACT8 to test the rate of targeted insertion in Arabidopsis plants. In 120 individual first-generation (T1) transgenic plants with the ORF2–Cas9 fusion, we detected 75.0% with mPing excision and 6.7% with targeted insertion (Fig. 3b,c). We validated that these plants have both sides of the mPing element at the targeted insertion site by PCR. In comparison, the unfused +ORF2, +Cas9 configuration generated higher rates for both mPing excision (98.7%) and targeted insertion (35.5%) (Fig. 3b,c). Our observed rate of targeted insertion is an improvement on the reported rates of T1 site-specific integration compared with HR (0.24%)[37], HDR (0.68–2.4%)38,39 or NHEJ knock-in (4.8%)[40] in Arabidopsis. Only plants that displayed excision had the potential for targeted insertion (Fig. 3b) and, of the plants that have excision, 36–45% have targeted insertion, suggesting a limiting step for targeted insertion is mPing excision. Furthermore, we performed a knock-in strategy similar to homology-independent targeted integration (HITI) in which CRISPR–Cas9 both excises the cargo from the donor site and creates the break at the target site[41]. We found rates of targeted insertion for HITI intermediate to the fused and unfused versions of TATSI (Fig. 3c), suggesting that the higher rate of targeted insertion in the unfused configuration of TATSI may be due to transposase binding of the extrachromosomal mPing element.

Insertion of enhancer and gene cargos

To test the cargo capacity that can be delivered by mPing, we engineered four distinct variations that add additional sequences to the rice 430 bp mPing element. The first adds a synthetic array of enhancers composed of six heat shock elements (HSEs) (Fig. 3d and Extended Data Fig. 8a). This synthetic 444 bp mPing_HSE element is capable of excision (Fig. 3c and Extended Data Fig. 8b) and targeted insertion into the region upstream of the ACT8 gene (Fig. 3c and Extended Data Fig. 8c,d). Sanger sequencing verified that all six HSEs were delivered to the targeted insertion site (Extended Data Fig. 8e).

We next tested larger cargos by embedding the protein-coding region of the herbicide bialaphos resistance gene (bar) into mPing, generating the 1,002 bp synthetic element mPing_bar_CDS (Fig. 3d). We also embedded the bar gene including the NOS promoter and terminator into mPing, creating the 1,563 bp mPing_bar element (Fig. 3d). The agricultural use of herbicide-resistant plants has reduced cost and increased yields[42], and targeting the insertion of these resistance genes avoids the creation of new mutations and allows for trait stacking at closely linked positions[43]. Both the mPing_bar_CDS and mPing_bar elements were capable of excision (Fig. 3c and Extended Data Fig. 9a) and targeted insertion into the non-coding region upstream of ACT8 (Fig. 3c and Extended Data Fig. 9b,c) in 26.7% and 27.7% of T1 Arabidopsis plants, respectively (Fig. 3c). We confirmed using Sanger sequencing that the entire cargo (bar expression cassette in mPing_bar and CDS region in mPing_bar_CDS) were delivered intact and mutation-free to the targeted insertion site (Extended Data Fig. 10 and 11). We further tested the size limit of cargo delivery and found the TATSI system can deliver cargo of two Arabidopsis endogenous genes totalling 8.6 kb (creating the 8,994 bp mPing_EPSPS; Fig. 3c,d). The frequency of targeted insertion was reduced to 8.3% with this large cargo (Fig. 3c), suggesting that smaller cargos are more efficient for targeted insertion using TATSI.

Targeted insertions in soybean

To demonstrate the commercial use of TATSI in a crop plant, a series of mPing vectors was transformed into soybean (Glycine max var. Williams 82). Soybean has the estimated fourth largest global harvest and is a critical global source of protein and oil[44]. Soybean has been a target of crop improvement, with 94% of the US soybean crop now transgenic[45]; however, considerable agricultural improvement remains possible for soybeans[46]. Target-site integration of transgenes in the soybean genome remains inefficient, with new approaches at multinational seed companies reaching rates of only 3.4% (ref. [47]). Previous reports demonstrated the free transposition of the rice mPing element by ORF1 and ORF2 when transformed into soybean[48]. To determine whether mPing can be used for improved target-site integration in soybean, we tested seven TATSI transgene configurations (Fig. 4a), and each of these was co-expressed with a gRNA targeting the intergenic region DD20, a safe-harbour location to target transgene integration.

Fig. 4: Targeted insertions in the soybean genome.

a, The configurations of seven different transgenes that were tested for targeted insertion. The label colour corresponds to the data bars in b. b, Rates of mPing excision (top left), Cas9-mediated mutations (SNPs) (top right), plants with both excision and mutation (bottom left) and targeted insertions (bottom right) in transgenic regenerated (R0) soybean plants. Both junctions of mPing must be found at the DD20-targeted insertion site to be counted as a positive targeted insertion event. n is the number of transgenic individuals tested. c, Sanger sequencing of the junctions of mPing, mPing_HSE and mPing_bar insertions into the soybean DD20 non-coding target site. d, Insertion-seq defines the locations of mPing in the genome of a single R0 soybean plant. The soybean nuclear genome is displayed on the x axis. Insertion is detected at the DD20 targeted insertion site (red datapoint) as well as at six other sites. These other sites do not have similarity to the gRNA sequence, but are TAA/TTA sites favoured for insertion from free transposition of mPing. The triangles denote the orientation of mPing insertion. A black dashed line at 10,000 RPM is shown.

Both ORF2 + Cas9 fused and unfused configurations functioned to generate high rates of mPing targeted insertion in soybean plants (Fig. 4b). The same ORF2–Cas9 fusion with a 1×G4S linker that was functional in Arabidopsis (Fig. 1b–d) did not have Cas9 activity in soybean (Fig. 4b (light blue)), whereas a longer 3×G4S linker generated both the mPing excision (76.2%) and Cas9 activity (38.1%) required for TATSI targeted insertion (15.9%) (Fig. 4b (dark blue)). These data are consistent with mPing excision rates in yeast, in which the longer 3×G4S linker displayed higher activity than the 1×G4S linker (Extended Data Fig. 3d). Similar to Arabidopsis, the ORF2 + Cas9 unfused transgene configuration in soybean (1) has higher rates of mPing excision (92.7%) and targeted insertion (18.2%) (Fig. 4b (yellow)); and (2) the insertion junctions also have small flanking deletions. One mPing insertion at the DD20 site has a fully accurate junction on the 3′ side and a three-nucleotide deletion of the flanking TTA sequence on the 5′ side (Fig. 4c). (3) Insertion sequencing (insertion-seq) of an R0 regenerated soybean plant with the ORF2 + Cas9 unfused transgene indicated seven major mPing insertion sites in this plant, one of which is the targeted insertion at the DD20 location (Fig. 4d (red)). The other sites of mPing insertion do not have sequence similarity to the gRNA (Fig. 4d) and, rather, are at sites of the known TTA/TAA preference sequence for mPing insertion[9], thus representing free mPing transpositions.

The delivery of enhancer elements and a gene cassette was also tested in soybean by replacing mPing with mPing_HSE or mPing_bar in the unfused +ORF2, +Cas9 transgene configuration (Fig. 4a (orange and red)). Targeted insertion of mPing_HSE occurs with similar flanking NHEJ deletions as seen in Arabidopsis (Fig. 4c), while the rate of targeted insertion is reduced to 6.3% (Fig. 4b). The 1.5 kb mPing_bar element was delivered at a rate of 6.7% (Fig. 4b). This experiment confirms the targeted delivery of custom DNA into the soybean genome using TATSI, while also suggesting that there is an efficiency penalty for altering the mPing sequence.

To test whether mPing_bar was functional in soybean, we constructed a transgene in which the only herbicide-resistance gene in the vector was present within mPing (Fig. 4a (purple)). In soybean plants, this mPing_bar element confers herbicide resistance, enabling the recovery of transgenic cells after transformation and growth on medium with herbicide. Similar to other mPing cargos, this mPing_bar element undergoes excision (94.1%) and targeted insertion (9.8%) (Fig. 4b). We also identified regenerated soybean plants with mPing_bar at the DD20 insertion site but that lack the mPing_bar element at the parent transgene donor site (Extended Data Fig. 12a,b). One plant has a partial parent transgene integration without the donor mPing_bar element (Extended Data Fig. 12b (plant 2)), while a different plant has no parent transgene (Extended Data Fig. 12b (plant 3)). Both plants with targeted mPing_bar insertion are herbicide resistant, and therefore the mPing_bar element is capable of functionally driving this trait when mobilized away from the parent transgene.

Discussion

Prokaryotic CRISPR-associated transposases and similar synthetic systems have been demonstrated in bacterial cultures, animal cell culture and tissue cultures12,28. Here we produced a functional TATSI system to generate targeted insertions in whole individuals of the model plant Arabidopsis, and translated this technology into soybean plants, which represent a critically important crop for global oil and protein production. Two configurations were successful for targeted insertion, with the ORF2 transposase protein either translationally fused or unfused to the programmable nuclease. The fusion decreases transposase activity but also increases the ratio of on-target insertions. TATSI has the potential to work in any transformable crop genome, with the exception of rice, in which it is likely to be epigenetically silenced. The rice mPing/Pong system avoids identity-based silencing in Arabidopsis and soybean because it is foreign to these genomes, and it avoids expression-based silencing because mPing is not expressed and ORF1/ORF2 are expressed in trans by genic promoters and terminators that drive TE activity[36]. For rice, a TE from a different genome could be engineered for TATSI.

The critical factor for any targeted integration tool is the frequency of on-target site insertion. TATSI represents a fold-change improvement over other methods of targeted insertion in the soybean genome. TATSI offers an improvement in Arabidopsis compared with HR, HDR or NHEJ knock-in. These other technologies are the product of years of refinement, and we expect that TATSI will further improve in the coming years. Data suggest that a limiting factor for targeted insertion is the rate of TE excision. Thus, the use of hyperactive versions of mPing such as mmPing20[49] may quickly increase the rate of targeted insertion. Moreover, all of the necessary components for targeted integration can be packaged into an all-in-one transgene for one-step delivery, and we identified a case in which the targeted integration is present in a R0 regenerated soybean plant without the parent transgene (Extended Data Fig. 12), skipping the requirement to segregate away the machinery required for targeted insertion.

Transpose proteins continue to bind to TE ends after excision from the donor site, protecting the free ends of the TEs while they are extrachromosomal3,4. We find that, after targeted insertion, the delivered TE is most often complete and rarely mutated, probably because the transposase proteins bind to mPing ends and protect these regions from nucleases while the DNA is extrachromosomal. We speculate that this is why TATSI in the unfused configuration results in a higher insertion rate compared with HITI, in which the cargo is unbound and unprotected while extrachromosomal. Our results suggest that mPing insertion is primarily mediated by NHEJ and not by transposase proteins[41]. We found that single-nucleotide insertions and small deletions are common at both the flanking TTA/TAA bases and the flanking insertion-site DNA. These small deletions probably represent degradation of the cut site after nuclease cleavage but before mPing insertion and repair of the junctions. The TTA/TAA bases flanking the TE are necessary for mPing excision[32] and, as one or both of these sites is often mutated after insertion mediated by TATSI, this results in targeted insertions being unable to excise out from their targeted integration sites. By contrast, insertions generated by free transposition will have an intact target-site duplication (TTA sequences at both ends) and will be capable of excision out of their insertion site.

Drawbacks to TATSI that can be improved in the future include controlling the orientation of mPing insertion (which is currently uncontrolled) and reducing free transpositions. Off-target insertions occur with both prokaryotic CRISPR-associated transposases and similar eukaryotic synthetic systems12,28,29. In TATSI, these untargeted insertions are the product of the active TE, and not off-target cleavage by the catalytically active CRISPR–Cas9. Free transposition insertions of mPing are reduced by the presence of Cas9, suggesting that the insertion of mPing is being directed to the on-target site by the presence of the Cas9-induced double-stranded DNA break. Off-target insertions during targeted integration are generally tolerated in crop production to a much higher extent compared with in medical or therapeutic applications. First, our method of detecting off-target effects is much more sensitive than other reports. Second, during transgenic crop production, the transgene is inserted into the genome of a transformation variety, and then introgressed (repeatedly back-crossed) into the ever-changing elite germplasm before deployment for agriculture[50]. This introgression process segregates away the rest of the genome and places the insertion event into a new genetic background, effectively removing any free-transposition events that are not tightly linked.

Methods

Transgene production

Transgenes were generated using the oligos listed in Supplementary Table 1. First, Pong ORF1 and ORF2, including promoters and terminators (sequence information is from pWMD_23)[51], were synthesized by GeneArt (Thermo Fisher Scientific) and cloned into a pHEE401E vector through In-Fusion cloning (Takara). Second, the Cas9, Cas9(D10A) nickase and dCas9 derived from S. pyogenes52,53,54 were amplified from pHEE401E, pDe-Cas9-D10A and pDIRECT_21B, respectively. Third, a G4S flexible linker was added through PCR and each Cas9 version was fused to ORF1 or ORF2 by In-Fusion reactions. Fourth, a gRNA cassette was created and inserted through a GoldenGate reaction. Each transgene used a CRISPR gRNA that was previously demonstrated to cleave within the Arabidopsis PDS3 gene[55], ALCOHOL DEHYDROGENASE1 (ADH1) or upstream of the ACTIN8 gene (ACT8) (Supplementary Table 1). For the one-component system, the mPing donor was amplified from genomic DNA of Arabidopsis line with a previously integrated mPing element[9], and cloned into the ORF2-Cas9 fusion vector described above. All mPing elements and derivatives have flanking TTA sites engineered into the donor site.

To test LbCas12a, DNA fragments containing multiplexed gRNA sequences were synthesized by Genewiz and cloned into the vector that expresses Pong ORF1 and ORF2 by an In-Fusion reaction. The multiplexed targeting strategy includes two gRNAs (targeting ADH1 and ACT8) from a single transcript (as described previously[56]). DNA fragments of Cas12a were synthesized by IDT and added by In-Fusion reactions. The Cas12a sequence from Lachnospiraceae bacterium was optimized and provided by Bayer Crop Science.

To test the delivery of HSEs, mPing_HSE was synthesized by IDT and cloned into the base vector containing the gRNA and protein machinery required to obtain targeted insertion (+ORF1, +ORF2-Cas9) through In-Fusion reactions.

To test the delivery of the herbicide bialaphos resistance gene (bar)[57], multiple inserts were amplified for In-Fusion reactions. DNA fragments of the bar CDS and NOS P–bar–NOS T were amplified from pB2GW7, embedded in mPing and fused into the base vector containing the gRNA and protein machinery required to obtain targeted insertion (+ORF1, +ORF2, +Cas9).

For the HITI experiment, two gRNAs (one targets mPing upstream of ACT8; the other cuts the flanking sequence on either side of mPing, excising the entire mPing element) were added into the Cas9-only vector through a GoldenGate reaction. mPing with flanking gRNA target sequences (targeted by the gRNA mentioned above) was amplified and added to the vector through In-Fusion reactions.

The soybean vectors are based on pTF101.2. The mPing donor, gRNA cassette, and ORF1 and ORF2-Cas9 were amplified from the above vectors constructed for Arabidopsis transformation and cloned into pTF101.2 through In-Fusion reactions. The DD20[58] gRNA sequence was added on the overhangs of In-Fusion primers.

Plant growth and transformation

Wild-type Arabidopsis plants (Columbia ecotype) were grown at 22 °C on Pro-Mix FPX soil in Conviron MTPS-120 growth chambers with 16 h per day of 200 µmol m−2 s−1 light. Binary vectors were introduced into Agrobacterium tumefaciens GV3101. All of the transgenic lines were transformed using the Agrobacterium-mediated floral dip method and subsequent selection for hygromycin-resistant plants.

Soybean plants were transformed by the Plant Transformation Facility at the Donald Danforth Plant Science Center. Binary vectors were introduced into A. tumefaciens AGL1. Mature half-seeds of Soybean (G. max var. Williams 82) were transformed using Agrobacterium to generate transgenic plants using methodology adapted from a previous study[59]. Transgenic plants were selected for Basta resistance and confirmed by PCR. Soybean plants were grown at 25 °C during the day and 23 °C during the night in growth chambers with 14 h per day of 200–600 µmol m−2 s−1 light, and transferred to a greenhouse at 25 °C during the day and 23 °C during the night.

Determination of mPing excision

Images of Arabidopsis seedlings were captured using the Axio Zoom.V16 microscope (ZEISS) with a PlanNeoFluar Z ×1.0 objective. The excitation wavelength was 450–490 nm, and the emission wavelength was 500–550 nm. The mPing excision was also evaluated by PCR analysis (Fig. 1b), with the primers listed in Supplementary Table 1.

Determination of Cas9 mutation rate

The T7 endonuclease I-based mutation detection assay (NEB) was used to access the Cas9 mutation rate. The targeted DNA regions were amplified using Q5 High-Fidelity DNA Polymerase (NEB). A list of the PCR primers used in these reactions is provided in Supplementary Table 1. Heteroduplex formation and T7 endonuclease I (NEB) digestion were performed on the amplified PCR products, and digested products were then visualized by agarose gel electrophoresis.

Determination of targeted insertion

For each gRNA-targeted site, four PCR reactions were conducted as described in Fig. 1c. A list of the PCR primers used in these reactions is provided in Supplementary Table 1. The PCR products of the expected size were then Sanger sequenced to confirm the presence of the targeted insertions. In some cases the PCR products were cloned into the pCR4_TOPO TA vector (Thermo Fisher Scientific) and sequenced using purified plasmids from single colonies. All of the Sanger sequencing data were aligned and visualized using the Benchling Biology Software.

Amplicon-seq of targeted insertion at PDS3

PCR amplicons were generated using the primers shown in Supplementary Table 1. For the amplicon sequencing (amplicon-seq) in Fig. 2, the placement of primers is shown in Fig. 2b. Sequencing libraries were constructed from the amplicons using the Nextera DNA Flex kit (Illumina) and Hackflex protocol[60], as described previously[61]. After quality control, the library with pooled amplicons was sequenced with single-end 300 bp reads on the Illumina MiSeq system at the University of Delaware DNA Sequencing & Genotyping Center. 3′ adapter sequences were removed using cutadapt[62] (parameters: -a CTGTCTCTTATACACATCT -m 10). Bioinformatic analyses are described below.

Bioinformatic analysis of insertion-site precision

Sequencing runs were managed in and initially processed using Illumina BaseSpace software. To determine the precise site of mPing integration within PDS3 from amplicon-seq data, first the mPing sequence was identified and removed using cutadapt[62] (parameters: -a (or -g) “XXX….XXX;min_overlap=8” --discard-untrimmed). The 20 nucleotide mPing sequence used as the adapter was different if the orientation of insertion is forward or reverse and whether the left or right junction is investigated. Similarly, the mPing sequence was searched for on the 5′ (-g) or 3′ (-a) depending on which junction was investigated. After removing the mPing sequence, the rest of the sequence was mapped to the PDS3 reference sequence with the flanking TTA at the targeted location using bwa mem[63] (default parameters). The left-most or the right-most base pair location of the mapped read (depending on right or left junction investigation) was reported as the insertion site. The flanking TTA was included in the reference sequence to ensure that presence or absence of flanking TTA does not impact the determination of the insertion site.

Bioinformatic analysis of mPing intactness

To determine whether the full-length mPing was delivered to the target site, we investigated the reads that contain the mPing/PDS3 junction defined as having both the ≥20 nucleotide PDS3 border sequence and ≥20 nucleotide mPing sequence that is bordering PDS3. Cutadapt[62] parameters to identify these sequences were non stringent to allow for imperfect insertion sites to be included in the analyses. First, cutadapt was used to identify and remove the PDS3 sequence (parameters: --discard-untrimmed --rc --action=trim -g (or -a) “XXX…XXX;min_overlap=8;e = 0.11”). Second, cutadapt was used to identify the 20 nucleotide mPing sequence and convert the full mPing sequence to lowercase using parameters: --discard-untrimmed –rc –action=lowercase -a (or -g) “XXX…XXX;miin_overlpa=8;e = 0.11;anywhere”). Lowercasing helped identify the mPing sequence away from the flanking TTA sequences that could impact the downstream mapping. The full mPing sequence was then extracted and mapped to the corresponding reference sequence using the default parameters of bwa mem[63]. The start (or end) positions of all mapped reads were counted and displayed in Fig. 2b. Forward and reverse insertion orientations of mPing were merged.

Investigation of junction nucleotides at the site of targeted insertion

Reads were mapped to the expected targeted sequence with flanking TTA separating PDS3 border sequence and the inserted mPing sequence using bwa mem[63] with the default parameters. HaplotypeCaller from the Gatk toolkit[64] was then used to identify all of the variants—insertions, deletions and SNPs across the target sequence (parameters: --max-reads-per-alignment-start 0 --disable-tool-default-read-filters -ERC BP_RESOLUTION). For Fig. 2d, only the variants in the junction between mPing and PDS3 are shown. Data for forward and reverse orientation of mPing insertion were merged and R package ggplot2 was used for the data display.

To calculate the statistical difference between the level of polymorphism at the target site between the fused +ORF2-Cas9 and unfused +ORF2, +Cas9 configurations, first the percentage of reads reporting 0 polymorphisms at each position was calculated. Then, the distribution of this percentage across the junction was compared between the fused and unfused configurations and Pearson’s χ2 tests were used to test statistical significance. In Fig. 2d, the difference between the two configurations was found to be statistically significant for both left (P < 0.01) and right (P < 0.001) junctions.

As Tn5 was used to add the Illumina adapters after PCR amplification, it is impossible to detect PCR duplicates and the number of unique insertions therefore cannot be calculated. Thus, we determined the minimum number of insertions by counting the number of reads with unique sequences after clustering the reads, that is, reads that overlap with exact match in the overlapping region would be merged to be called as a single insertion. This method will underestimate the number of unique insertions. First, all of the reads that cover the junction shown in Fig. 2d are collected. These reads are then clumped to remove exact duplicates using bbduk clumpify.sh[65] (parameters: containment=t subs=0 addcount=t dedupe=t). Finally, clusters were called based on overlapping reads also using bbduk dedupe.sh (parameters: storename=f uniquenames=f Sort=length absorbrc=t absorbmatch=t absorbcontainment=t findoverlap=t cluster=t processclusters=t cc=t exact=t minoverlap=20 k = 20). The number of unique clusters was counted for each sample and shown in Fig. 2d.

Determination of mPing free-transposition sites

Libraries for insertion-seq were constructed using an adapted HtStuf protocol[66] with the following modifications to reduce the abundance of the mPing donor site in the library. High molecular mass DNA was isolated from 50 Arabidopsis seedlings for each line using the NucleoBond HMW DNA kit (Takara), and digested by the restriction enzymes XbaI and AluI (sites not present in mPing). The XbaI enzyme was selected because the donor mPing position has XbaI sites just outside of mPing, generating a fragment of 446 bp. Fragmented DNAs above 450 bp were purified from agarose gels, A-tailed by Klenow fragment (3′−5′ exo-; NEB) and ligated to the pGEM-T Easy vector (Promega). Then, 1 µl of this ligation product was used as a template for primary PCR, followed by secondary PCR using nested primers. mPing-specific primers were used with the pGEM-T Easy vector primers for primary and secondary PCRs. Barcoded sequencing adapters were added to the amplicons through their inclusion in the PCR primers. A list of all of the primers used is provided in Supplementary Table 1. PCR products were purified using the DNA Clean & Concentrator kit (ZYMO Research) and pooled. After passing quality controls, libraries were run on the MiSeq (Illumina) system with V2 output (single-end, 300 bp) at the University of Delaware DNA Sequencing & Genotyping Center. The sequencing library for soybean was constructed similarly, except that the genomic DNA was isolated from leaf tissues and digested by XbaI, PmlI and AluI.

Bioinformatic analysis to determine free-transposition sites

Sequencing runs were managed in and initially processed using Illumina BaseSpace software. To identify and characterize reads that have both mPing and other regions of the genome, the 3′ adapter was trimmed from raw reads using cutadapt[62] (parameters: -a “ATCACTAGTGAATTCGCGGCC;min_overlap=10;e = 0.1” -q 10). Next, only reads containing mPing sequence were identified and the matching mPing sequence was trimmed from the 5′ end using cutadapt (parameters: -g “EXPECTEDMPINGSEQUENCE;min_overlap=35;e = 0.1” -q 10). Reads shorter than 30 nucleotides were discarded. To remove reads that show mPing at its donor location, these 5′ and 3′ trimmed sequences were mapped to the reference mPing donor sequence using the default parameters of bowtie2[67] with the additional parameter to store donor-unmapped reads (--un). These donor-unmapped reads were then mapped to the genome (TAIR10 reference genome for Arabidopsis and Williams 82 reference genome Wm82.a4.v1 from Phytozome for soybean) using the default parameters of bowtie2. The start position of each mapping read was collected as the mPing insertion site into the genome. The counts for insertion sites were summed over 10 nucleotide non-overlapping bins across the genome and normalized to the sequenced raw read counts in each sample to calculate the RPM for each ten-nucleotide bin. The R package CMplot[68] was used to generate the Manhattan plots displayed in Figs. 2e and 4d and Extended Data Fig. 6b.

The number of bins with ≥100 RPM in each sample was counted as the number of free-transposition sites in the genome. PDS3 bins were excluded from the free-transposition list for samples with the PDS3 gRNA. The overlap of free transposition between biological replicates is shown in Extended Data Fig. 6a. The free-transposition sites were sorted on the basis of their RPM values and shown on the x axis in decreasing order in Fig. 2f with the RPM plotted. To only interrogate sites with partial matches to gRNA, first a list of Arabidopsis genomic regions was created using Cas-OFFinder[69] with the least stringent criteria (≤9 bp mismatch to the gRNA and ≤2 bp bulge size). Next, only overlapping bins of free-transposition sites were retained and the Manhattan plots were created for Extended Data Fig. 6b.

Western blotting

In liquid nitrogen, inflorescence tissue was ground into fine powder, then thawed in lysis buffer (50 mM Tris-HCl pH 7.5, 150 mM NaCl, 5 mM MgCl_2, 10% glycerol, 1% NP-40 (IGEPAL), 0.5 mM DTT, 1 mM PMSF, 1% plant protease inhibitor cocktail (GoldBio)) for 15 min at 4 °C. The lysate was centrifuged for 15 min at 4 °C to remove debris. Equal amount of 2× LDS sample loading buffer (NuPAGE) was added to the clarified lysate. The samples were incubated at 95 °C for 5 min and then loaded onto a 4–20% gradient Tris-Glycine gel (BioRad). Proteins were separated at 150 V for 75 min and then transferred to a PVDF membrane (Immobilon-FL, MilliporeSigma) using the BioRad semi-dry transblot for 1 h. The membranes were blocked for 1 h at room temperature in Azure Fluorescent Blot Blocking Buffer (Azure). Primary antibodies, anti-Actin 11 (Agrisera, AS10 702) and anti-Cas9 (Diagenode, C15310258-100), were diluted 1:2,000 and 1:5,000, respectively, in the blocking buffer and incubated with the blot for three nights. The membranes were washed five times at room temperature with 1× PBS-T. The secondary antibodies (anti-Actin 11: AzureSpectra, goat anti-mouse 800, AC2135; anti-Cas9: AzureSpectra goat anti-rabbit 800, AC2134) were diluted 1:5,000 and incubated with the membranes for 1 h. The membranes were washed five times at room temperature with 1× PBS-T, and with 1× PBS for the last wash. The blots were dried and visualized using the Azure Sapphire Biomolecular Imager. Raw images of the western blots are shown in Supplementary Fig. 1.

HRM analysis to detect mutations

High resolution melt (HRM) analysis was used to detect mutations at specific cut sites by Cas9. A list of the primers used for the analysis is provided in Supplementary Table 1. PCR reactions were performed using the QuantStudio 5 real-time quantitative PCR (qPCR) system and MeltDoctor HRM reagent (Applied Biosystems) according to the manufacturer’s instructions. The qPCR data were then analysed using the High Resolution Melt Software from Applied Biosystems.

DNA methylation analysis

DNA methylation was analysed using DNA isolated from Arabidopsis seedlings using the NucleoBond HMW DNA kit (Takara). High-molecular-mass DNA was digested with XbaI and HincII or XbaI, DraI and SmaI to enrich for targeted integrations and remove donor locations of mPing. Digested high-molecular-mass DNA was run on an agarose gel and fragments above 1 kb were extracted. Fragmented DNA was converted (unmethylated Cytosines enzymatically converted to uracil) using a modified protocol of NEBNext Enzymatic Methyl-seq Conversion Module (New England Biolabs). In brief, the modifications included: 3 times the suggested volume of enzyme TET2 and oxidation enhancer were used to compensate for an increased input of 500 ng of genomic DNA; and, in the DNA clean-up steps, ethanol precipitation was used rather than bead purification. Amplicons were generated for sequencing by PCR for each locus with primers including degenerate bases that can be found in Supplementary Table 1. Amplification was performed using the My Taq HS Mix (Bioline) and the correct-size band was then extracted from the agarose gel. To calculate the conversion rate of each enzymatically converted DNA sample, amplification was also performed on AT2G20610, a known unmethylated gene. Library preparation and amplicon-seq was performed as described in the ‘Amplicon-seq of targeted insertion at PDS3’ section above. Analysis was performed as previously described[61].

Yeast assays

Yeast transposition assays were performed as described previously using BY4741-derived yeast that contain a genomic copy of the mmPing20 element in the ADE2 gene[49]. ORF1 (ORF1SC1 ONE[70]), ORF2 (Pong TPase L418A, L420A)[71] and Cas9 were supplied separately or as fusions cloned into pAG423 GAL (Addgene, 14149) and pAG425 GAL (Addgene, 14153) plasmids using standard gateway cloning. p426-SNR52p-gRNA.CAN1.Y-SUP4t (Addgene, 43803) was used to supply the CAN1 gRNA[72]. The number of ADE2 revertant colonies were counted after 10 days incubation on Galactose CSM-ADE-HIS-LUE-URA. A total of 48 colonies were grown on a CSM-ADE plate, then replicated onto CSM-ARG + canavanine medium and scored for growth the next day.

Statistics and reproducibility

Statistical analysis for individual experiments is described within their figure legends. Figure 1a and Extended Data Fig. 2b are representative images from 160 independent plants (10 from each of the 16 lines) that were analysed with similar results. The analysis of mPing excision (Fig. 1b and Extended Data Fig. 2c) and targeted integration (Fig. 1d and Extended Data Fig. 4a) was repeated with independent transgenic plants twice with the same results. The Western blots in Extended Data Fig. 4b were repeated twice. Excision assays of mPing with Cas12a fusion proteins (Extended Data Fig. 5b) and targeted insertion assays (Extended Data Fig. 5e,f) were independently performed with two pooled samples (each including 50 independent T_1 transgenic plants) with similar results. The targeted insertion assay with the one-component transgene in Extended Data Fig. 7c was repeated twice with similar results. The excision assay for mPing_HSE (Extended Data Fig. 8b) and the targeted insertion assay for this element (Extended Data Fig. 8c) was repeated with independent transgenic plants twice with similar results. The excision assay for mPing_bar and mPing_bar_CDS (Extended Data Fig. 9a) and the targeted insertion assay for these elements (Extended Data Fig. 9c) was repeated using pooled samples (each including 30 independent T_1 transgenic plants) twice with similar results. The PCR genotyping in Extended Data Fig. 12b was repeated for two technical replicates on the same plants.

Biological material availability

The LbCas12a plasmid and sequences are from Bayer Crop Science and are not controlled or distributed by the corresponding author. Otherwise, there are no restrictions on the availability of the biological materials. Materials are available from the authors and the Cas9 plasmid vectors and are being made available from the Arabidopsis Biological Resource Center (http://abrc.osu.edu).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

There are no restrictions on the presented data. Amplicon-sequencing and Insertion-seq data from Figs. 2 and 4 for Arabidopsis and soybean are provided through the NCBI Sequence Read Archive (GSE227105). Genome sequences and annotations used come from TAIR10 (Columbia ecotype Arabidopsis) (https://www.arabidopsis.org/download/) and Williams 82 Wm82.a4.v1 from Phytozome (soybean; https://phytozome-next.jgi.doe.gov/info/Gmax_Wm82_a4_v1).

Code availability

All tools used for the bioinformatic analyses are publicly available. Unless specified otherwise, the default parameters were used.

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Acknowledgements

We thank K. Czymmek for help with microscopy; T. Finley, P. Vallal, M. O. Luis and D. Arasu for supporting the soybean transformation workflow; S. Wessler for her comments; and the staff at the University of Delaware DNA Sequencing & Genotyping Center. This project is supported by NSF grants IOS-2149964 to R.K.S. and MCB-1651666 to C.N.H., the Danforth Plant Science Center Proof of Concept funding, The Danforth Plant Science Center Big Ideas Competition and a sponsored research project from Bayer Crop Science.

Author information

Authors and Affiliations

1. Donald Danforth Plant Science Center, St Louis, MO, USA

Peng Liu, Kaushik Panda, Seth A. Edwards, Ryan Swanson, Pratheek Pandesha, Yu-Hung Hung, Gerald Klaas & R. Keith Slotkin

2. Division of Biological Sciences, University of Missouri, Columbia, MO, USA

Seth A. Edwards, Ryan Swanson & R. Keith Slotkin

3. Plant Transformation Facility, Donald Danforth Plant Science Center, St Louis, MO, USA

Hochul Yi & Veena Veena

4. Division of Biology and Biomedical Sciences, Washington University, St Louis, MO, USA

Pratheek Pandesha

5. Bayer Crop Science, St Louis, MO, USA

Xudong Ye & Larry A. Gilbertson

6. University of South Carolina-Aiken, Aiken, SC, USA

Megan V. Collins, Kaili N. Renken & C. Nathan Hancock

Contributions

The concept was developed by P.L. and R.K.S. Methodology was developed by P.L., C.N.H. and R.K.S. Bioinformatics analysis was developed by K.P. The investigation was carried out by P.L., K.P., S.A.E., P.P., Y.-H.H., R.S., K.N.R. and G.K. Data curation was the responsibility of P.L., K.P. and R.K.S. Soybean transformation was performed by V.V. and H.Y. Design of Cas12a vectors and analysis was aided by X.Y. and L.A.G. Yeast assays were performed by M.V.C. and C.N.H. Materials were provided by C.N.H.; P.L. and R.K.S. wrote the paper. Funding was acquired by R.K.S.

Corresponding author

Correspondence to R. Keith Slotkin.

Ethics declarations

Competing interests

Part of the funding that supported this research in the Slotkin laboratory was provided by Bayer Crop Science. X.Y. and L.A.G. work to improve crop gene editing for the for-profit company Bayer Crop Science. Patents have been filed with inventors R.K.S. and P.L. at the Donald Danforth Plant Science Center, and the technology is licensed to Bayer Crop Science for research purposes. The following patents have been filed, with R.K.S. and P.L. listed as inventors on both: (1) “Targeted insertion via transposition”; pending application numbers, US18/282,139, EP22772096.8, CA3,212,093 and AU2022237499A1; applicant, Donald Danforth Plant Science Center. (2) “Targeted insertion via transposition”; pending application number PCT/US2023/078837; applicant, Donald Danforth Plant Science Center.

Peer review

Peer review information

Nature thanks Zhixi Tian and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Published data in support of the model of mPing excision.

The ORF1 and ORF2 proteins are expressed from the Pong transposon and bind the mPing element to form a transposition complex31,73. ORF1 is a Myb-like DNA binding protein that binds to at least 15 base pairs of the mPing terminal inverted repeat (TIR) sequence[49]. ORF2 is the canonical transposase (TPase) with the DDE catalytic motif necessary for mPing excision and insertion71,74. The flanking nucleotides (TTA or TAA) that are immediately adjacent to the TIRs at the donor site are necessary for efficient mPing excision[32]. The ORF1 and ORF2 proteins directly interact[75] and are both required for mPing excision from the donor site9,71. After excision, the donor site is repaired by NHEJ using the microhomology of the staggered cut overhangs left by excision[32]. This allows for very precise repair of the excision site, often reestablishing the coding frame of the mPing donor site71,76,77. The transposition complex remains associated with the extra-chromosomal mPing DNA as it is also responsible for catalysing insertion.

Extended Data Fig. 2 Transposable element excision generated by Cas9-fused proteins.

a. Diagram of fusion proteins tested. Twelve different transgenes were created and transformed into Arabidopsis. Cas9 and derivative proteins were fused either to the Pong transposase ORF1 or ORF2 protein coding regions. Both N- and C-terminal translational fusions were created using the G4S flexible linker. Three different versions of Cas9 were used: double-strand cleavage Cas9, the single stranded nickase, and the catalytically dead dCas9. When a functional transposase protein is generated by expression of ORF1 and ORF2, it excises mPing out of the 35S-GFP donor location in the Arabidopsis genome, producing fluorescence. b. Excision of the mPing TE from GFP restores the plant’s ability to generate fluorescence. Images of representative Arabidopsis seedlings showing GFP fluorescence for all 12 fusion proteins. The cotyledons are outlined with a white dashed line. Size bars represent 500 μm. A subset of this experiment is shown as Fig. 1a. c. Excision of mPing assayed by PCR of pooled seedlings of the twelve different translational fusions from part a, and controls. The top band represents mPing within GFP (donor position), and the smaller band is generated upon mPing excision. The arrows indicate the pair of primers used for PCR. d. Sanger sequencing of the PCR product upon mPing excision. Grey bars behind the sequencing peaks represent quality scores for each base call.

Extended Data Fig. 3 Efficiency of ORF2-Cas9 fusion proteins.

a. High Resolution Melt (HRM) analysis to test gRNA efficiency. Mutations created by Cas9 were detected for genomic loci PDS3, ADH1, or the region upstream of ACT8. PCR product melting dynamics differed between the WT plants (pink) and Cas9 positive control lines with the indicated gRNA (purple). The melting temperature difference is caused by the generation of short indels and SNPs upon Cas9 cleavage and repair by NHEJ, verifying that all three gRNAs are functional in Arabidopsis plants. b. Representative pds3 homozygous mutant white seedling from plants with the catalytically-active Cas9 fusion protein. c. The ratio of white pds3 T2 seedlings for all Cas9 fusion proteins tested. d. Efficiency of ORF2-Cas9 fusion proteins in yeast. mPing excision frequency (blue, left Y-axis) and Cas9 mutation frequency (orange, right Y-axis) measured for unfused and fused ORF2 and Cas9 with three different protein-protein linker sequences. The ORF2 protein’s C-terminus is fused to Cas9 via a 1xG4S, 3xG4S or 16AA linker. mPing excision was measured as the number of ADE2 revertant colonies due to mPing excision per million cells. The average and standard deviation for multiple biological replicates (n = 6) are shown (blue). Cas9 mutation frequency was measured by testing the gRNA-targeted canavanine resistance of the ADE2 revertant colonies (n = 48). This experiment was performed two times independently to ensure reproducibility.

Extended Data Fig. 4 Targeted insertions of mPing at the PDS3 gRNA target site.

a. PCR assay as described in Fig. 1c for the 12 fusion proteins generated in Extended Data Fig. 2a and controls. PCR negative controls include a line lacking the Cas9, ORF1 and ORF2 proteins (-ORF1,-ORF2), a line with ORF1 and ORF2 but no Cas9 (+ORF1, + ORF2), and a no-template DNA PCR reaction (water). Among the 12 fusion proteins, only ORF2-Cas9 displays the correct size band for targeted insertions. Insertions were verified by Sanger sequencing of the PCR products. b. Western blots using the Cas9 and Actin11 antibodies, showing that the ORF2-Cas9 and ORF2-dCas9 proteins are expressed in transgenic plants as full-length fusion proteins. Upper panel shows that both ORF2-Cas9 and ORF2-dCas9 have the expected size of ~216 kDa (Cas9 is 150 kDa and ORF2 is 66 kDa). Lower panel compares the size of the unfused Cas9 with the ORF2-Cas9 fusion protein. Raw images of the Westerns are shown in Supplementary Fig. 1. c. Sanger sequencing of the junctions of targeted integration events into the PDS3 gene. PCR products from panel a were cloned into the pCR4_TOPO TA vector and 9 individual colonies were sequenced per PCR reaction. The triangle represents where Cas9 cuts in the gRNA target sequence. The flanking TTA/TAA sequence is present at some insertion junctions and absent in others. The PDS3 sequence is shown in blue, the gRNA target site is highlighted in grey, mPing is shown as red text, and the flanking TTA/TAA sequences are shown in black text.

Extended Data Fig. 5 Cas12a-mediated targeted insertions.

a. Diagram of the multiplexed vector cassette that generates two distinct Cas12a gRNAs, one that targets ADH1 and one that targets upstream of ACT8. b. PCR assay to detect excision of mPing generated by functional ORF1 and ORF2 proteins. Fusing these proteins to Cas12a does not stop excision activity. c. Diagram of the four PCR reactions to detect targeted insertions into ADH1. Arrows indicate primers used to detect targeted insertions: U + L, D + R, U + R, D + L. d. Diagram of the four PCR reactions to detect targeted insertions into the region upstream of ACT8. e. PCR assay to detect targeted insertion of mPing into ADH1. Targeted insertions are detected for both protein fusions to Cas12a as well as in the unfused configuration. f. PCR assay to detect targeted insertion of mPing into the region upstream of ACT8. Targeted insertions are detected for both protein fusions to Cas12a as well as in the unfused configuration. g. Sanger sequencing of a mPing targeted insertion into ADH1 mediated by Cas12a cleavage. h. Sanger sequencing of a mPing targeted insertion into the region upstream of ACT8 mediated by Cas12a cleavage.

Extended Data Fig. 6 mPing insertion at gRNA off-target sites.

a. Venn diagrams of mPing insertion sites (excluding PDS3) in common between biological replicates. Data is from Fig. 2e,f. b. Insertion of mPing at CRISPR/Cas9 predicted off-target sites. Data display is the same as Fig. 2e. Different from Fig. 2e, only PDS3 and the predicted CRISPR/Cas9 off-target regions of the genome are interrogated.

Extended Data Fig. 7 DNA methylation analysis and a one-component transgene system.

a. Amplicon deep sequencing of enzymatic-converted DNA methylation patterns. The average methylation level across the amplicon is shown for each cytosine context (CG, CHG, CHH (H = A,T,C)), with 95% confidence intervals calculated using the Wilson score interval method. On the left is the ADH1 insertion site before mPing insertion, broken where mPing will insert and either side of the insertion site is analysed separately. On the right is the methylation after mPing insertion. A dash line denotes the background non-conversion rate of the enzymatic reaction determined for each sample by sequencing an unmethylated region of the genome. This conversion percentage is also listed below each genotype. Biological replicates are denoted as “Rep 1” vs. “Rep 2”. n= the number of total cytosines assayed for each amplicon. b. Map of a single vector containing the mPing donor element, the gRNA and protein machinery required to obtain mPing targeted insertions (+ORF1, + ORF2-Cas9). c. PCR-based targeted insertion assay (as in Fig. 1c,d) in pooled seedlings using the one-component transgene system. Targeted insertions are detected in each reaction. d. Sanger sequencing of the junctions of a targeted insertion event in the Arabidopsis PDS3 gene generated from the single vector one-component transgene system shown in panel b.

Extended Data Fig. 8 Insertion of heat shock elements (HSEs) as mPing cargo.

a. Experimental design and generation of the synthetic 444 bp ‘mPing_HSEs’ element. b. Excision assay by PCR (as in Fig. 1b) in pooled seedlings shows the mPing_HSEs element is capable of excision. The excised product is easier to detect if the genomic DNA is digested with the SspI restriction enzyme before PCR (SspI site is in mPing_HSEs). c. PCR assay detecting targeted insertions (as in Extended Data Fig. 5d) of mPing_HSEs into the region upstream of the ACT8 gene. The ‘T2 (pooled)’ sample represents a pool of T2 seedlings, while ‘T2 #1’, ‘T2 #2’, etc… are individual T2 plants. Red arrowheads denote PCR products that were verified as targeted insertions by Sanger sequencing. d. Sanger sequencing of the junctions of a mPing_HSEs targeted insertion into the region upstream of ACT8. e. Sanger sequencing across the majority of the mPing_HSEs element and into the region upstream of ACT8 demonstrates that all six HSEs were successfully delivered to this region. The arrows on the top cartoon indicate the pair of primers used for PCR. The Sanger sequencing represents the contig of several sequencing reactions from a single TOPO TA plasmid clone of a PCR product. The sequence is annotated above, including the six HSEs as red pointed boxes.

Extended Data Fig. 9 Targeted insertion of a gene and CDS as mPing cargos.

a. Excision assay by PCR (as in Fig. 1b) in pooled seedlings shows the mPing_bar CDS and mPing_bar versions are capable of excision. Blue arrowheads indicate the expected size of the amplicon with different sized mPing versions before excision. b. PCR strategy and primer placement to detect targeted insertions of mPing_bar CDS and mPing_bar into the region upstream of ACT8. Arrows indicate primers used to detect targeted insertions: U + L, D + L. The “L” primer is the same for mPing_bar CDS and mPing_bar versions. c. PCR detecting targeted insertions of mPing_bar CDS and mPing_bar into the region upstream of the ACT8 gene. Red arrows indicate correct size PCR products that were verified as targeted insertions by Sanger sequencing. There is no PCR product in the ‘mPing’ sample because the “L” PCR primer site is in the bar CDS region (see panel b).

Extended Data Fig. 10 Targeted insertion of the intact bar gene cassette.

a. PCR strategy and primer placement to detect targeted insertions of the mPing_bar element into the region upstream of the ACT8 gene. The arrows indicate the pair of primers used for PCR. b. Sanger sequencing across the majority of the mPing_bar element and into the region upstream of ACT8 demonstrates the successful delivery of the complete bar gene cassette (including promoter and terminator) into this region. The Sanger sequencing represents the contig of several sequencing reactions from a single TOPO TA plasmid clone of a PCR product.

Extended Data Fig. 11 Targeted insertion of the intact bar CDS.

a. PCR strategy and primer placement to detect targeted insertions of the mPing_bar CDS element into the region upstream of the ACT8 gene. The arrows indicate the pair of primers used for PCR. b. Sanger sequencing across the majority of the mPing_bar CDS element and into the region upstream of ACT8 demonstrates the successful delivery of the complete bar CDS into this region. The Sanger sequencing represents the contig of several sequencing reactions from a single TOPO TA plasmid clone of a PCR product.

Extended Data Fig. 12 The mPing_bar element confers the herbicide resistance trait in soybean.

a. Transgene design and PCR primer placement for “PCR1” to “PCR6” used to genotype for the presence of the mPing_bar/gRNA/ORF1/ORF2/Cas9 parent transgene in R0 transformed soybean plants. b. PCR assay to genotype for the presence of the mPing_bar/gRNA/ORF1/ORF2/Cas9 parent transgene in R0 transformed soybean plants. “PCR1” detects both the original mPing_bar donor and its excision product. “PCR2” to “PCR6” detect different locations on the mPing_bar/gRNA/ORF1/ORF2/Cas9 transgene. GmLe1 is a control gene. The combined data demonstrates that R0 plant #1 has the full transgene in the genome, plant #2 has a partial transgene insertion that lacks the mPing_bar donor site, and plant #3 does not have the mPing_bar/gRNA/ORF1/ORF2/Cas9 transgene.

Supplementary information

Supplementary Fig. 1

Full scanned images of western blots from Extended Data Fig. 4b

Reporting Summary

Peer Review file

Supplementary Table 1

PCR primers and guide RNA sequences used in this report.

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Liu, P., Panda, K., Edwards, S.A. et al. Transposase-assisted target-site integration for efficient plant genome engineering. Nature 631, 593–600 (2024). https://doi.org/10.1038/s41586-024-07613-8

- Received: 10 March 2023

- Accepted: 28 May 2024

- Published: 26 June 2024

- Issue Date: 18 July 2024

- DOI: https://doi.org/10.1038/s41586-024-07613-8

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Brainstem Dbh+ neurons control allergen-induced airway hyperreactivity

Yujuan Su,

Jinhao Xu,

Ziai Zhu,

Jisun Chin,

Le Xu,

Haoze Yu,

Victoria Nudell,

Barsha Dash,

Esteban A. Moya,

Li Ye,

Axel Nimmerjahn &

…

Xin Sun

Nature volume 631, pages 601–609 (2024)

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Abstract

Exaggerated airway constriction triggered by repeated exposure to allergen, also called hyperreactivity, is a hallmark of asthma. Whereas vagal sensory neurons are known to function in allergen-induced hyperreactivity1,2,3, the identity of downstream nodes remains poorly understood. Here we mapped a full allergen circuit from the lung to the brainstem and back to the lung. Repeated exposure of mice to inhaled allergen activated the nuclei of solitary tract (nTS) neurons in a mast cell-, interleukin-4 (IL-4)- and vagal nerve-dependent manner. Single-nucleus RNA sequencing, followed by RNAscope assay at baseline and allergen challenges, showed that a Dbh+ nTS population is preferentially activated. Ablation or chemogenetic inactivation of Dbh+ nTS neurons blunted hyperreactivity whereas chemogenetic activation promoted it. Viral tracing indicated that Dbh+ nTS neurons project to the nucleus ambiguus (NA) and that NA neurons are necessary and sufficient to relay allergen signals to postganglionic neurons that directly drive airway constriction. Delivery of noradrenaline antagonists to the NA blunted hyperreactivity, suggesting noradrenaline as the transmitter between Dbh+ nTS and NA. Together, these findings provide molecular, anatomical and functional definitions of key nodes of a canonical allergen response circuit. This knowledge informs how neural modulation could be used to control allergen-induced airway hyperreactivity.

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Main

Interoception defines a fundamental biological process whereby the nervous system senses and responds to the inner state of the body. Many open questions remain regarding complete multisynaptic circuits that sense signals from organs, integrate them in the nervous system and respond through modulation of organ function. The lung is a prominent source of interoceptive signals. In the lung, airway constriction, triggered by exposure to irritants including allergens, is a physiological response. Following chronic exposure, allergens will trigger exacerbated constriction, also termed airway hyperreactivity, a primary asthma morbidity and mortality.

In repeated allergen-challenged mice, but not in naive mice, either vagotomy, ablation or repression of vagal neurons abolished airway hyperreactivity1,2,3. A key knowledge gap is the brainstem neurons that are important for transmitting the chronic allergen signal. Lung-innervating vagal sensory neurons project exclusively to the nucleus of the solitary tract (nTS) region of the brainstem4,5,6. Aside from the lung, nTS integrates sensory inputs from many organs4,7,8. Systematic analyses of nTS neuronal diversity and connectivity are critical for interrogation of functional heterogeneity and specificity. In this study, we delineated a complete interoception neural circuit composed of multiple nodes that are both necessary and sufficient for allergen-induced airway hyperreactivity, mimicking exacerbated airway constriction in asthma.

nTS neurons were activated by allergen

To investigate the neurons involved in allergen response, we used a mouse model of asthma with chronic intranasal instillation of house dust mites (HDM), a known trigger for asthmatic responses in both humans and rodents. We used a commonly used regimen, with multiple doses of allergen administration to the lung (Fig. 1a). This triggered the expected asthmatic responses including goblet cell metaplasia, type 2 immune infiltration and exacerbated airway smooth muscle constriction, also termed airway hyperreactivity (Extended Data Fig. 1a–d and Supplementary Fig. 1a,b). To address the issue of whether allergen administered to the lung could trigger brainstem neuron activation, we performed immunostaining for immediate early-factor FOS, a neuronal activation marker. Staining at 2 h following the fourth challenge showed an increase of FOS+ cells in HDM-treated mice compared with saline-treated control mice (Fig. 1b,c and Extended Data Fig. 1e–y). Increased FOS+ cells were enriched in the nTS region between bregma −7.20 and −8.08 mm (Extended Data Fig. 1k–v) compared with the adjoining area postrema, dorsal motor nucleus of the vagus (DMV) and hypoglossal nucleus (12N) (Extended Data Fig. 2a–d). This enrichment in the nTS is consistent with findings that lung-innervating sensory nerves project to the nTS4,5,6. This increase is found to be statistically significant only after the fourth HDM challenge, but not after the previous three doses (Extended Data Fig. 2e–m), a finding corroborated by Fos transcript signal (Extended Data Fig. 2n–q). These findings suggest that there is conditioning of nTS neurons by repeated administration of allergen.

Fig. 1: Activation of nTS neurons following repeated allergen challenges to lung.

a, Experimental scheme for HDM treatment. b,c, Representative FOS antibody staining (b) and quantification (c). Dashed areas represent nTS. n = 6 saline and n = 13 HDM mice, unpaired t-test, P < 0.0001. d,e, Representative FOS antibody staining (d) and quantification (e), showing decreased FOS+ cells in the nTS of c-Kitw-sh/w-sh mice, compared to c-Kitw-sh/+ mice. n = 4 saline and n = 5 HDM, c-Kitw-sh/+ mice, P < 0.0001; n = 6 saline and n = 10 HDM, c-Kitw-sh/w-sh mice, P = 0.0339; for comparison between c-Kitw-sh/+ and c-Kitw-sh/w-sh mice following HDM, P < 0.0001, two-way analysis of variance (ANOVA) with Bonferroni post hoc test. f, Experimental scheme for treatment with anti-IL-4 antibody. g,h, Representative FOS antibody staining (g) and quantification (h). n = 4 mice for both groups, unpaired t-test, P = 0.0095. i, Experimental scheme for HDM treatment after vagotomy. j,k, Representative FOS antibody staining (j) and quantification (k). n = 5 mice for both sham and vagotomy, P = 0.0001 between vagotomy ipsilateral and vagotomy contralateral; P < 0.0001 between sham ipsilateral and vagotomy ipsilateral; not significant (NS) for remaining pairs, two-way ANOVA (Bonferroni post hoc). l, Experimental scheme for labelling allergen-activated neurons in TRAP2; Ai14 mice. m,n, Coronal view of CLARITY-cleared brainstem hemisphere (m, dashed areas) and quantification (n, n = 5 saline and n = 7 HDM mice, unpaired t-test, P = 0.0010). o, Experimental scheme for ablating allergen-activated neurons in TRAP2; Ai14; DTR mice. p,q, Representative FOS antibody staining (p) and quantification (q). n = 4 vehicle and n = 7 DTX mice, unpaired t-test, P < 0.0001. r,s, FlexiVent-measured maximal resistance (Rrs, cm H_2O s ml−1, airway pressure (cm H_2O) per time derivative of tidal volume, r) and elastance (Ers, cm H_2O ml−1, airway pressure per tidal volume, s) of wild-type airways following increasing doses of methacholine (MCh), demonstrating hyperreactivity following HDM. n = 5 saline and n = 6 HDM mice, unpaired t-test was performed at each MCh concentration separately, for Rrs (r), at 12 mg ml−1 MCh, P = 0.0087; at 24 mg ml−1, P = 0.0007; for Ers (s), at 12 mg ml−1, P = 0.0450; and at 24 mg ml−1, P = 0.0009; NS for all other pairwise comparisons. t,u, Blunted airway hyperreactivity in DTX + HDM group. n = 4 vehicle and n = 7 DTX mice, unpaired t-test, for Rrs (t), at 12 mg ml−1, P = 0.0069; for Ers (u), at 12 mg ml−1, P = 0.0436; P < 0.0001 for both Rrs (t) and Ers (u) at 24 mg ml−1; NS for all other pairs. Data are presented as mean ± s.e.m., two-sided for unpaired t-test. b,d,g,j,p, Scale bars, 200 µm. Ctrl, control; i.p. intraperitoneal.

Source Data

It is reported that mast cells are required for the development of allergen-induced airway constriction[9]. Using the same mast cell-deficient c-Kitw-sh/w-sh mice as in previous studies, we found a statistically significant decrease of FOS+ neurons in the nTS following HDM challenges compared with heterozygous c-Kitw-sh/+ controls (Fig. 1d,e). These data suggest that signals relayed through mast cells are an important contributor to allergen-induced nTS activation.

Mast cells are among those immune cells that produce interleukin-4 (IL-4) and are critical for allergen-induced airway hyperreactivity[10]. It is reported that neutralization of IL-4 using antibodies can abrogate allergen-induced airway hyperreactivity[10]. Using the same IL-4-neutralizing antibody and regimen, we found that administration of anti-IL-4, but not of isotype control antibody, significantly decreased the number of FOS+ neurons in the nTS (Fig. 1f–h).

To determine whether vagal nerves are essential for allergen activation of the nTS, we performed unilateral vagotomy (Fig. 1i). This led to a statistically significant decrease in FOS+ neurons on the ipsilateral operated side compared with the contralateral control side following HDM challenge (Fig. 1j,k), suggesting that vagal nerves are required for transmission of allergen signals to the nTS.

To quantify cumulative allergen-induced activation and test a tool to manipulate these neurons, we crossed Fos2A-iCreER (TRAP2) mice with Rosa-lxl-tdTomato (Ai14) mice to label activated neurons. Following injection of 4-hydroxytamoxifen (4-OHT) after each HDM challenge or saline administration (Fig. 1l), we cleared the whole brainstem using CLARITY[11]. We found a statistically significant increase of activated tdTomato+ (tdTom+) neurons in the nTS of HDM-treated mice compared with saline-treated controls (Fig. 1m,n), further confirmed through sectioning (Extended Data Fig. 3a–c). Consistently, increase in tdTom+ neurons is found to be statistically significant only after the fourth HDM challenge (Extended Data Fig. 3d). After performing FOS antibody staining following the fifth challenge, we found an overlap of roughly 70% with previously activated tdTom+ neurons (Extended Data Fig. 3e–g). This difference of around 30% may be due to either incomplete lineage labelling of activated neurons or activation of fresh new neurons in the most recent challenge.

Using TRAP2 mice coupled with rabies virus injection in the nTS, we further found that there are more traced, mostly Trpv1+, neurons in the vagal ganglia of mice challenged with HDM compared with saline control (Extended Data Fig. 3h–l). This result suggests that there are enhanced synaptic connections between vagal ganglia and allergen-activated nTS neurons following HDM. Together, our data indicate that multiple allergen challenges to the lung act through immune cells and vagal ganglia to activate nTS neurons.

Activated neurons drive hyperreactivity

To determine whether activated nTS neurons have a role in allergen-induced responses, we crossed Fos2A-iCreER; Rosa-lxl-tdTomato (TRAP2; Ai14) mice with Rosa-lxl-DTR mice to express diphtheria toxin receptor (DTR) in allergen-activated neurons (TRAP2; Ai14; DTR; Fig. 1o). Following bilateral nTS injection of diphtheria toxin (DTX), we confirmed a decrease in allergen-activated neurons (Fig. 1p,q).

To determine whether this ablation affects airway hyperreactivity, we utilized a well-established flexiVent assay. Following the last dose of HDM or saline, increasing doses of methacholine were intratracheally administered to mimic how acute triggers elicit chronically heightened airway constriction in humans, commonly referred to as ‘asthma attack’. In wild-type mice, as expected, multiple doses of HDM sensitization and challenge led to further increase in respiratory system resistance (Rrs) and elastance (Ers) compared with the saline control group, demonstrating allergen-induced airway hyperreactivity (Fig. 1r,s). This hyperreactivity is abrogated by vagotomy[1], suggesting that differential airway constriction between the HDM and saline groups is primarily due to methacholine-activated vagal response rather than a direct effect of methacholine on airway smooth muscles. By comparing the extent of airway constriction triggered by the same concentration of methacholine, we used the differences between experimental and controls as a measure of the impact of the chronically adaptive vagal circuit.

In TRAP2; Ai14; DTR mice, compared with vehicle-injected control, DTX ablation of allergen-activated neurons led to blunted HDM-induced airway hyperreactivity (Fig. 1t,u). This blunting effect was not observed when saline-trapped (handling- and injection-activated) nTS neurons were ablated (Extended Data Fig. 3m–o). Ablation of allergen-activated neurons did not lead to any change in HDM-induced goblet cell metaplasia, type 2 immune cell recruitment or expression of type 2 cytokine genes Il4, Il5 and Il13 (Extended Data Fig. 3p–x).

Single-nucleus transcriptome of the nTS

To identify which subset of nTS neurons are activated, we first defined the overall diversity of nTS neurons through single nucleus RNA sequencing (snRNA-seq). Dissected and fresh-frozen nTS regions from either (1) naive mice (n = 4 biological repeats, 2 males in each group), (2) mice at 1.5 h following the fourth saline treatment (2 males in the group) or (3) mice at 1.5 h following the fourth HDM challenge (2 males in the group) were profiled (Fig. 2a). Following data integration from the 3 conditions, 42,157 nuclei passed quality control with more than 2,000 unique feature counts, which is a commonly used cutoff in brainstem neuronal snRNA-seq studies12,13,14. Following removal of glial and other non-neuron clusters, 39,626 neurons remained. We purified nTS neurons in silico by exclusion of known area postrema, DMV and cuneate nucleus clusters based on their snRNA-seq profiles15,16, as well as on the Allen Brain in situ database[17]. The resulting 32,880 nTS neurons segregated into 18 clusters, as shown by uniform manifold approximation and projection (UMAP) (Fig. 2b, Extended Data Fig. 4a,b, Supplementary Note 1, Supplementary Fig. 2a–i and Supplementary Tables 1 and 2). Following extraction of snRNA-seq data from the naive condition (Extended Data Fig. 4c), we confirmed that all 18 clusters express pan-neuronal markers but not glial markers (Fig. 2c and Extended Data Fig. 4d–g). Among the 18 neuronal clusters, 7 are excitatory and 11 are inhibitory (Fig. 2d,e). Individual neuronal populations are distinguished by a set of markers (Fig. 2f and Supplementary Table 3).

Fig. 2: Single-nucleus transcriptomic signatures of the nTS.

a, Diagram illustrating the relative locations of brainstem regions of interest. AP, area postrema. b, UMAP plots of integrated nTS snRNA-seq data from naive adult males (n = 4 biological repeats, n = 2 mice in each group), mice at 1.5 h after the fourth saline treatment (2 males in the group) and mice at 1.5 h after the fourth HDM challenge (2 males in the group). c–e, Feature plots showing pan-neuronal marker Rbfox3 (c), excitatory marker Scl17a6 (d) and inhibitory marker Slc32a1 (e). f, Dotplot showing top markers for each cluster. Genes in red were used for validation by either Feature plots or RNAscope. Using default Seurat[29] dotplot settings (Methods), percentage expressed was plotted from 0 to 60% detected and the colour bar shows the average of scaled normalized expression values across cells in a given cluster. g–t, Feature plots (g,i,k,m,o,q,s) and RNAscope (h,j,l,n,p,r,t) of excitatory markers Gli3 (g), Mecom (h), C1ql4 (i), Nr4a2 (j), Lpar1 (k), Lhx9 (l), Pou3f1 (m,n), Gmnc (o), Tac2 (p), Mafa (q,r) and Dbh (s,t). Bregma levels with maximal signals are shown. Scale bars, 200 µm. u, Diagram summarizing spatial distribution of the 18 nTS clusters with coronal (middle) and transverse (left and right) views, based on RNAscope data on serial nTS sections. Whereas clusters 3 and 13 are found throughout the rostral–caudal axis (left), cluster 13 is ventral to cluster 3 in the rostral portion while intermingled in the caudal portion. Neurons in the other clusters are more regionally restricted to selective bregma regions, as illustrated by curved coloured lines corresponding to coloured boxes (right). Dorsal–ventral and medial–lateral distributions of each cluster are reflected by placement of coloured dots. Brainstem illustration in u was created with BioRender.com.

We validated the top markers using RNAscope on serial sections of the nTS (Fig. 2g–t and Extended Data Fig. 4h–c’) and generated a comprehensive map of the spatial distribution of nTS clusters (Fig. 2u). Along the rostral–caudal axis, most clusters are concentrated between bregma −6.80 and −8.24 mm. Some clusters are found in discrete domains—for example, clusters 5, 11 and 17 are found only in the more caudal regions between bregma −7.76 and −8.24 mm whereas cluster 15 is predominantly in the more rostral regions between bregma −6.24 and −6.80 mm. In comparison, other clusters—for example, clusters 3 and 13—are distributed widely along the rostral–caudal axis.

Dbh + neurons are preferentially activated

Compared with naive nTS, the same 18 neuron subtypes were detected in saline- and HDM-challenged nTS (Fig. 3a,b). To quantitatively determine the identity of allergen-activated nTS neurons, we performed double RNAscope of Fos with the top markers of each of the 18 nTS clusters. Dbh+ cluster 13 neurons showed the highest percentage of overlap with Fos+ neurons among all clusters assayed (Fig. 3c–e and Supplementary Table 3). Using RNAscope, Dbh was detected in an average of 2,342 ± 169 neurons in the nTS (Extended Data Fig. 5a). Dbh+ neurons reside in close proximity to Trpv1+ vagal projections, which are required for allergen-induced hyperreactivity[2] (Extended Data Fig. 5b,c). Furthermore, the Dbh+ domain between bregma −6.96 and −8.08 mm overlaps substantially with that where allergen-induced FOS+ cells were enriched (bregma −7.20 to −8.08 mm; Extended Data Fig. 1k–v). Compared with saline, Fos+Dbh+ nTS neurons were increased following HDM challenge (Extended Data Fig. 5d). Together, these data led us to focus on Dbh+ nTS neurons to determine their role in allergen responses.

Fig. 3: Dbh+ neurons in the nTS were preferentially activated following allergen challenge to lung.

a,b, UMAP plots (a) and stacked bar plot (b) showing the corresponding 18 neuron clusters in naive (n = 4 biological repeats, n = 2 mice in each group), saline- (2 males in the group) and HDM-challenged (2 males in the group) nTS. c, Quantification from double RNAscope showing the overlap between Fos and the top marker gene of each individual nTS cluster 1.5 h following the fourth HDM. Red and blue indicate excitatory and inhibitory clusters, respectively. Data are mean ± s.e.m. Each data point represents an individual animal. n = 3 for cluster 1 (C1), n = 4 for C2, n = 3 for C3, n = 4 for C4, n = 3 for C5, n = 4 for C6, n = 4 for C7, n = 3 for C8, n = 4 for C9, n = 3 for C10, n = 4 for C11, n = 3 for C12, n = 10 for C13, n = 3 for C14, n = 3 for C15, n = 4 for C16, n = 3 for C17, n = 3 for C18. Multiple-comparisons one-way ANOVA (Bonferroni post hoc test), P < 0.0001 for comparison between C13 Fos and Dbh overlap and that between Fos and marker genes of the other 17 nTS clusters. d,e, Dbh and Fos double RNAscope (d) and quantification (n = 12 mice, e) in nTS; boxed areas are enlarged on the right, arrowheads indicate overlapping expression. Scale bars, 200 µm.

Source Data

Dbh + neurons are necessary for hyperreactivity

To determine whether Dbh+ nTS neurons are essential for hyperreactivity, we used three approaches. First, we performed chemical ablation by injecting anti-dopamine beta-hydroxylase (DBH) antibody conjugated to saporin (SAP), shown to be specific for DBH+ neurons[18], into the nTS (Fig. 4a). Compared with scrambled, peptide-conjugated SAP (blank–SAP) control, the anti-DBH–SAP group showed a clear reduction in DBH+ neurons, confirming ablation efficiency (Fig. 4b). Following HDM treatment, the anti-DBH–SAP group showed reduced FOS+ cells compared with blank–SAP control (Extended Data Fig. 6a–c). In naive mice, DBH+ neuron ablation had no effect on methacholine responses (Extended Data Fig. 6d–f). In contrast, following HDM challenge, the anti-DBH–SAP-injected group showed blunted airway hyperreactivity compared with blank–SAP control (Fig. 4c,d).

Fig. 4: Dbh+ neurons in the nTS mediate airway hyperreactivity.

a, Experimental scheme for chemical ablation of Dbh+ nTS neurons. Bi-nTS, bilateral nTS. b, DBH antibody staining in nTS. c,d, FlexiVent data showing blunted airway hyperreactivity following DBH–SAP treatment. n = 4 blank–SAP and n = 8 DBH–SAP mice, unpaired t-test at 0, 6, 12 and 24 mg ml−1 MCh separately, at 24 mg ml−1 MCh of both Rrs (c) and Ers (d), P < 0.0001; NS for all other pairwise comparisons. e, Experimental scheme for genetic ablation of Dbh+ nTS neurons. f, Dbh RNAscope in nTS. g,h, FlexiVent data showing blunted airway hyperreactivity following DTX injection. n = 4 vehicle and n = 10 DTX mice, unpaired t-test, for Rrs (g) at 12 mg ml−1 MCh, P = 0.0022; for Ers (h) at 12 mg ml−1 MCh, P = 0.0002; and for both Rrs (g) and Ers (h) at 24 mg ml−1, P < 0.0001; NS for all other pairwise comparisons. i, Experimental scheme for chemogenetic inhibition of Dbh+ nTS neurons. j,k, FlexiVent data showing blunted airway hyperreactivity following CNO injection. n = 5 vehicle and n = 9 CNO mice, unpaired t-test, for Rrs (j) at 24 mg ml−1 MCh, P < 0.0001; for Ers (k) at 12 mg ml−1 MCh, P = 0.0370, at 24 mg ml−1 MCh, P < 0.0001; NS for all other pairwise comparisons. l, Experimental scheme for chemogenetic activation of Dbh+ nTS neurons. m,n, FlexiVent data showing partially increased airway hyperreactivity following CNO injection, in place of the fourth HDM. All groups received the first, second and third HDM challenges. n = 4 fourth HDM, n = 11 fourth CNO and n = 7 fourth saline mice, multiple-comparisons one-way ANOVA (Bonferroni post hoc test), for Rrs (m) at 12 mg ml−1 MCh, P < 0.0001 between fourth HDM and fourth saline, between fourth CNO and fourth saline, at 24 mg ml−1 MCh, P < 0.0001 for all pairs; for Ers (n) at 12 mg ml−1 MCh, P = 0.0384 between fourth HDM and fourth saline, P = 0.0252 between fourth CNO and fourth saline, at 24 mg ml−1 MCh, P < 0.0001 for all pairs; NS for all other pairwise comparisons. Data are mean ± s.e.m., two-sided for unpaired t-test. b,f, Scale bars, 200 µm.

Source Data

Second, we performed genetic ablation. In Dbh-cre; Ai14 mice, the RNA of Dbh and tdTomato showed over 95% overlap, confirming cre specificity (Extended Data Fig. 6l,m). We injected DTX into the nTS of Dbh-cre; Rosa-lxl-DTR (Dbh-cre; DTR) mice (Fig. 4e), leading to efficient loss of Dbh+ neurons in the nTS (Fig. 4f). Following ablation, we carried out HDM challenge and found that allergen-induced airway hyperreactivity was blunted in the DTX-injected group compared with the vehicle-injected control group (Fig. 4g, h).

Third, we performed chemogenetic inactivation using the designer receptors exclusively activated by designer drugs (DREADD) system. We injected AAV-flex-hM4D(Gi)-mCherry into the nTS of Dbh-cre mice, eliciting efficient and specific expression (Fig. 4i and Extended Data Fig. 6t,u). We administered clozapine-N-oxide (CNO) to activate hM4D(Gi) 1 h before the second to fourth HDM, sparing the first HDM which is required for sensitization of the immune system[2]. We found that the CNO group showed blunted airway hyperreactivity response compared with the vehicle group (Fig. 4j,k). In mice expressing mCherry without hM4D(Gi), administration of CNO had no effect (Extended Data Fig. 6c’–f’).

We also tested other nTS populations for their requirement in the allergen circuit. Our early RNAscope data showed that both Th[19] and Tacr1 (ref. [20]) had some overlap with Fos (Extended Data Fig. 7a–c). Furthermore, there is minimal overlap between Th+Fos+ neurons and Dbh+Fos+ neurons (Extended Data Fig. 7d). Ablation of Th+ nTS neurons by DTX into the nTS of Th-cre; Rosa-lxl-DTR mice did not affect allergen-induced airway hyperreactivity (Extended Data Fig. 7e–g). In contrast, most, if not all, Tacr1+Fos+ neurons are also Dbh+ (Extended Data Fig. 7h). We found that chemical ablation of Tacr1+ nTS neurons using anti-substance P receptor (SSP/TACR1) antibody conjugated to saporin (SSP–SAP) blunted hyperreactivity (Extended Data Fig. 7i–k). Together, the data from chemical ablation, genetic ablation and chemogenetic inactivation demonstrate that Dbh+ nTS neurons are necessary for allergen-induced airway hyperreactivity.

Dbh + neurons can induce hyperreactivity

To address whether Dbh+ neurons are sufficient for driving allergen response, we used DREADD-targeted activation. We injected AAV-DIO-hM3D(Gq)-mCherry into the nTS of Dbh-cre mice (Fig. 4l and Extended Data Fig. 8a), then administrated CNO in place of the fourth HDM challenge. Although not to the full extent as HDM, CNO activation was partially sufficient to induce increased airway hyperreactivity compared with saline control (Fig. 4m,n). Partial hyperreactivity was also observed following repeated CNO-mediated activation of nTS Dbh+ neurons (Extended Data Fig. 8b–d) and following repeated CNO-mediated activation of allergen-induced TRAP2 neurons in the nTS (Extended Data Fig. 8e–g). Injection of AAV-DIO-mCherry control virus did not induce airway hyperreactivity in sensitized airway (Extended Data Fig. 8h–j). In naive mice not exposed to HDM, CNO activation (the fourth dose) of Dbh+ neurons had no effect (Extended Data Fig. 8k–m).

In none of the three loss-of-function experiments, nor in the chemogenetic gain-of-function experiment or TRAP2; Ai14; DTR experiments, did we observe any changes in HDM-induced goblet cell metaplasia, immune cell infiltration or expression of Il4, Il5 and Il13 (Extended Data Figs. 3p–x, 6g–k,n–b’ and 8n–u). We also assayed for potential effects on other aspects of lung function. Following either HDM challenge or CNO activation, compared with saline controls, we found no statistically significant difference in minute ventilation, respiratory frequency, tidal volume or metabolic rate as measured by plethysmography (Extended Data Fig. 8v–b’). This is consistent with a previous report that HDM challenge in mice did not alter respiratory parameters[21]. These results indicate that CNO activation of Dbh+ nTS neurons induced airway hyperreactivity in sensitized airways without affecting respiration. Together, data from functional tests demonstrate that Dbh+ nTS neurons are necessary and partially sufficient for allergen-induced airway hyperreactivity.

Downstream NA and parasympathetic neurons

To map Dbh+ nTS targets and their role in hyperreactivity, we injected AAV-flex-tdTomato into the nTS of Dbh-cre mice (Fig. 5a). No fibre was detected directly in the lung (Extended Data Fig. 9a). Following screening of the whole brainstem, we found tdTom+ fibres projecting to the NA (Fig. 5b). We also found fibres in the lateral parabrachial nucleus, dorsal raphe nucleus in the brainstem, arcuate hypothalamic nucleus and other hypothalamic structures, consistent with published data22,23 (Extended Data Fig. 9b,c).

Fig. 5: Parasympathetic neurons in the NA are necessary and sufficient downstream of the nTS for allergen-induced airway hyperreactivity.

a, Brainstem section showing injection of AAV-flex-tdTomato into Dbh-cre mouse. b, In the same mouse, tdTom+ nerves project to the NA (vesicular acetylcholine transporter; VAChT+). c, Injection of AAV-flex-tdTomato into the NA of a Chat-cre mouse. d,e, In the same mouse, tdTom+ projects to both trachea (d) and extrapulmonary bronchi (e). f, NA-originated tdTom+ fibres innervate postganglionic parasympathetic ganglia (VAChT+) on extrapulmonary bronchi. Arrowheads indicate innervated signals. g, In Chat-cre; Ai14 mice, injection of CTB488 in dorsal trachea labelled CTB488+tdTom+ NA neurons. Arrowheads indicate overlapping expression. h, Scheme for chemogenetic inhibition of Chat+ neurons in bilateral NA (bi-NA). i, AAV-flex-hM4D-mCherry signals. j,k, FlexiVent, n = 4 vehicle and n = 5 CNO mice, unpaired t-test, at 24 mg ml−1 MCh, P < 0.0001 for both Rrs (j) and Ers (k); NS for all other pairs. l, Scheme for inhibiting NA-innervating Dbh+ nTS neurons by injecting AAV2/retro-flex-hM4D-mCherry bilaterally into NA of Dbh-cre mice. m,n, FlexiVent, n = 3 vehicle and n = 4 CNO mice, unpaired t-test, for Ers (n), at 12 mg ml−1 MCh, P = 0.0127; at 24 mg ml−1 MCh, P < 0.0001 for both Rrs (m) and Ers (n); NS for all other pairs. o, Scheme for chemogenetic activation of Chat+ neurons in bilateral NA. p, AAV-DIO-hM3D-mCherry signals. q,r, FlexiVent, n = 4 fourth HDM, n = 11 fourth CNO and n = 4 fourth saline mice, multiple-comparisons one-way ANOVA (Bonferroni post hoc test), for Rrs (q), at 12 mg ml−1 MCh, P < 0.0001 between fourth HDM and fourth saline, P = 0.0092 between fourth CNO and fourth saline, P = 0.0081 between fourth HDM and fourth CNO; at 24 mg ml−1 MCh, P < 0.0001 for all comparisons; for Ers (r), at 12 mg ml−1 MCh, P = 0.0022 between fourth HDM and fourth saline, P = 0.0145 between fourth HDM and fourth CNO; at 24 mg ml−1 MCh, P = 0.0371 between fourth CNO and fourth saline, P < 0.0001 for remaining pairs; NS for all other pairwise comparisons. Data are mean ± s.e.m., two-sided for unpaired t-test. Scale bars, 500 µm (a,b), 100 µm (c,i,p), 200 µm (f, 50 µm in magnified views; g, 100 µm in magnified views).

Source Data

To address whether cholinergic NA neurons project to the lung, we injected AAV-flex-tdTomato into the NA of Chat-cre mice (Fig. 5c). NA-originated tdTom+ fibres project to postganglionic parasympathetic ganglia residing in both the trachea and extrapulmonary bronchi, but not in the lung (Fig. 5d–f and Extended Data Fig. 9d–h). In turn, these postganglionic parasympathetic ganglia neurons project into the lung (Extended Data Fig. 9i,j). In comparison with NA, stereotaxic injection of AAV-flex-Tom into DMV and the adjacent 12N of Chat-cre mice labelled fibres that passed by the space between the trachea and oesophagus without innervating the trachea or bronchi (Extended Data Fig. 9k,l). To validate from the retrograde direction, we introduced CTB488 dorsal to the trachea, in which postganglionic parasympathetic ganglia are enriched. In whole-brainstem sections, we found cell body labelling in the NA, with little labelling in other regions (Fig. 5g).

To address NA function in hyperreactivity, we injected AAV-flex-hM4D(Gi)-mCherry into the NA of Chat-cre mice followed by the HDM regimen (Fig. 5h,i). The CNO group showed blunted airway resistance and elastance compared with vehicle-injected controls (Fig. 5j,k). To further validate the functional hierarchy between Dbh+ nTS neurons and NA neurons, we inhibited NA-innervating Dbh+ nTS neurons by injecting AAV2/retro-flex-hM4D-mCherry into the NA of Dbh-cre mice. Such inhibition was sufficient to abolish airway hyperreactivity (Fig. 5l–n).

To activate Chat+ neurons in the NA, we injected AAV-DIO-hM3D(Gq)-mCherry bilaterally into the NA of Chat-cre mice (Fig. 5o,p). Although not as potent as HDM, activation of Chat+ NA neurons by either single or repeated CNO injections was partially sufficient to induce increased airway hyperreactivity compared with saline (Fig. 5q,r and Extended Data Fig. 9m–o). In naive mice not exposed to HDM, CNO activation of Chat+ NA neurons had no effect (Extended Data Fig. 9p–r).

Calb1+ neurons in the general vicinity of the NA were shown to have a role in acute bronchoconstriction[12]. Using the same Calb1-cre as in ref. [12], modulation of Calb1+ neurons did not affect allergen-induced airway hyperreactivity (Extended Data Fig. 10a–h). On closer review, the results in the published study[12] show that the Calb1-cre labelled neurons were in the AmbEx region, distant from the Chat+ NA region. Consistently, we found that Calb1-cre-labelled neurons are outside of the region in which VAChT+ NA neurons reside (Extended Data Fig. 10i). Furthermore, our DREADD-targeted neurons in Calb1-cre mice were located in regions surrounding VAChT+ cholinergic NA neurons but did not overlap with them (Extended Data Fig. 10b,f). Together, these data suggest that hyperreactivity is dependent on Chat+ NA neurons but not on nearby Calb1+ neurons.

To test whether DMV, a known target of nTS neurons[24], may also act downstream in the allergen circuit, we performed DREADD-based inactivation or activation of Chat+ neurons in the DMV (Extended Data Fig. 10j,m). These DMV perturbations showed little effect on hyperreactivity (Extended Data Fig. 10k,l,n,o). Our data together demonstrate that NA neurons are necessary and partially sufficient for allergen-induced hyperreactivity.

Noradrenaline to NA in hyperreactivity

To determine the molecular nature of signalling from the nTS to NA, we profiled NA using snRNA-seq by dissection of the green fluorescent protein (GFP)+ region that corresponds to NA in Chat-cre; CAG-Sun1/sfGFP mice. Among all the neurons identified in the snRNA-seq dataset, we focused on the 188 Chat+ neurons. We then integrated our data with two recently published NA datasets12,13 following 2,000 unique feature counts cutoff. The integrated data with 534 NA neurons segregated into 5 clusters defined by markers that overlap with those published12,13 (Fig. 6a,b, Extended Data Fig. 10p,q and Supplementary Table 4).

Fig. 6: Blocking noradrenaline receptors in the NA blunted allergen-induced airway hyperreactivity.

a, UMAP plot of our snRNA-seq data integrated with published datasets12,13. b, Dotplot showing top marker genes from the integrated dataset which overlap with those in published datasets12,13. Using default Seurat[29] dotplot settings (Methods), percentage expressed was plotted from 0 to 30% detected, and the colour bar shows the average of the scaled normalized expression values across cells in a given cluster. c,d, Double RNAscope of NA showing overlap (arrowheads) for Adra1a (c) and Adra1b (d). e,f, Feature plots of Adra1a (e) and Adra1b (f). g,h, Injection of CTB488 into dorsal trachea in wild-type mouse labelled NA neurons for Adra1a (g) and Adra1b (h). i, Experimental scheme for noradrenaline receptor antagonist treatment. j,k, FlexiVent, n = 5 vehicle, n = 7 prazosin and n = 6 terazosin mice, multiple-comparisons one-way ANOVA (Bonferroni post hoc test), for Rrs (j), at 12 mg ml−1 MCh, P = 0.0017 prazosin versus vehicle, P = 0.0348 terazosin versus vehicle; at 24 mg ml−1 MCh, P < 0.0001 prazosin or terazosin versus vehicle; for Ers (k), at 12 mg ml−1 MCh, P = 0.0288 prazosin versus vehicle; at 24 mg ml−1 MCh, P < 0.0001 prazosin or terazosin versus vehicle; NS for all other pairwise comparisons. l, Experimental scheme for chemogenetic activation of the Dbh+ nTS neurons and delivering noradrenaline receptor antagonists into the NA of the same mouse. m,n, FlexiVent, n = 3 vehicle, n = 3 prazosin and n = 3 terazosin mice, multiple-comparisons one-way ANOVA (Bonferroni post hoc test), for Rrs (m), at 12 mg ml−1 MCh, P = 0.0047 prazosin versus vehicle, P = 0.0014 terazosin versus vehicle; at 24 mg ml−1 MCh, P = 0.0002 prazosin versus vehicle, P < 0.0001 terazosin versus vehicle; for Ers (n), at 12 mg ml−1 MCh, P = 0.0010 prazosin versus vehicle, P = 0.0002 terazosin versus vehicle; at 24 mg ml−1 MCh, P < 0.0001 prazosin or terazosin versus vehicle; NS for all other pairwise comparisons. o, Diagram illustrating multiple nodes of the complete allergen neural circuit. Data are mean ± s.e.m. Scale bars, 100 µm in c,d,g,h.

Source Data

Dopamine beta-hydroxylase converts dopamine to noradrenaline, raising the possibility that noradrenaline may be the signal between Dbh+ nTS and NA. Among all noradrenaline receptor genes, only Adra1a and Adra1b are expressed in the NA (Fig. 6c–f). Tracheal injection of CTB488 labelled Adra1a+ and Adra1b+ neurons in the NA (Fig. 6g,h). To address whether blocking of noradrenaline signal reception in the NA can blunt airway hyperreactivity, we infused either prazosin or terazosin, both of which are ADRA1 antagonists, into the NA (Fig. 6i and Extended Data Fig. 10r). Compared with vehicle control, targeted administration of prazosin or terazosin significantly blunted airway hyperreactivity (Fig. 6j,k). To address whether Dbh+ nTS neurons act through adrenergic signalling to the NA, we chemogenetically activated Dbh+ nTS neurons and delivered noradrenaline receptor antagonists into the NA of the same mouse (Fig. 6l). This blunted the ability of activated Dbh+ nTS neurons to trigger airway hyperreactivity (Fig. 6m,n). Together, these results suggest that noradrenaline acts as the neurotransmitter between Dbh+ nTS neurons and NA neurons in their function in allergen-induced airway hyperreactivity.

Discussion

In this study, our findings delineate a full airway hyperreactivity neural circuit in which chronic allergen challenges to the lung are transmitted through immune cells and ascending vagal afferents to the Dbh+ nTS integrator, which then descend through efferent Adra1a+/Adra1b+ NA neurons before projecting back to postganglionic neurons and innervating the airway (Fig. 6o). This is a circuit identified in wild-type mice following repeated conditioning by allergen, suggesting the presence of an endogenous molecular and cellular response machinery. Repeated allergen exposure is central to asthma pathogenesis. Our findings defined a disease-relevant circuit with nodes from the immune system, the nervous system and structural cells of the organ.

We found that only repeated, but not single, allergen challenges led to a statistically significant increase in activated nTS neurons, similar to current-clamp recording data in the rhesus monkey[25]. Such chronic conditioning is distinct from mechanisms that drive acute airway constriction[26]. Our data demonstrate that ablation or inactivation of Dbh+ nTS neurons blunted the airway hyperreactivity present only following repeated allergen challenge. We note that this airway hyperreactivity circuit may utilize nodes that also have a role in acute airway constriction. For example, activation of thoracic cholinergic nerves in naive animals induced acute airway constriction in the absence of exogenous methacholine[26]. Our findings suggest that repeated allergen exposures may hijack this existing node to the delivery of elevated airway constriction above the threshold level of acute response, mimicking asthma. Our results further define the identity of the brainstem neurons that connect both afferent and efferent nodes to control airway hyperreactivity. The snRNA-seq datasets of both nTS and NA, together with existing lung and vagal neuron single-cell datasets, and the knowledge that acetylcholine is the probable neurotransmitter from NA to postganglionic neurons and then to airway smooth muscle cells4,27,28, can be used to complete the molecular connectome of the allergen circuit. Such molecular definition of the allergen-induced hyperreactivity circuit enables the use of neuromodulation to bypass the systemic side effects of current asthma treatments.

Methods

Mice

All mice were housed, and all experimental procedures were carried out in American Association for Accreditation of Laboratory, Animal Care-certified laboratory animal facilities at the University of California, San Diego (UCSD). All animal procedures were approved by the Institutional Animal Care and Use Committee at UCSD. Animals were maintained under constant environmental conditions (temperature in rooms is 68–72 °F and humidity 30–70%), with food and water provided ad libitum in a 12/12 h light/dark cycle. Adult mice from strains C57BL/6 J (no. JAX 000664), c-Kitw-sh/w-sh (no. JAX 030764), Fos2A-iCreER (TRAP2, no. JAX 030323), Rosa-lxl-tdTomato (Ai14, no. JAX 007914), Rosa-lxl-DTR (no. JAX 016603), Th-cre (no. JAX 008601), Chat-cre (no. JAX 031661), CAG-Sun1/sfGFP (no. JAX 030952) and Rosa-Zsgreen (no. JAX 007906) were purchased from the Jackson laboratory. Strain Dbh-cre (MMRRC 036778) was purchased from the Mutant Mouse Resource and Research Center. All cre lines were maintained in a B6 background and were viable and fertile with no detectable abnormal phenotypes. Both male and female mice were used for experiments. Mice were at least 6 weeks old when subjected to HDM challenge, stereotaxic injection or surgery.

HDM challenge

Mice were anaesthetized using isoflurane. 50 μg 20 μl−1 HDM extract (Dermatophagoides pteronyssinus, GREER Labs) was introduced intranasally for 4 consecutive weeks on days 0, 7, 14 and 21. For controls, 20 μl of saline was used instead of HDM on the same regime. Mice were euthanized 1.5 h following the last challenge for Fos RNAscope or nTS snRNA-seq; 2 h after the last challenge for FOS antibody immunostaining; 24 h after the last challenge for flexiVent assay, periodic acid–schiff (PAS) staining and quantitative PCR (qPCR); and 3 days after the last challenge for flow cytometry.

Lung tissue PAS staining

Mice were euthanized by CO_2 inhalation. The lungs were inflated with 4% paraformaldehyde (PFA) at 35 cm H_2O airway pressure; they were postfixed in 4% PFA overnight and then prepared for paraffin sections at width 6 μm. Goblet cells were stained using a PAS staining kit (Sigma).

Tissue collection and immunofluorescence staining

Mice were euthanized by CO_2 inhalation followed by transcardial perfusion with PBS and 4% PFA. Subsequent to postfixing overnight in 4% PFA, tissues were washed in PBS followed by overnight sucrose dehydration. Brainstem blocks were sectioned at 25, 40 or 99 μm thickness in a rostral to caudal sequence, and lung blocks were sectioned at 99 or 300 μm thickness for parasympathetic neuron/airway staining. All sections were processed for immunostaining following the standard protocol. Primary antibodies used include rabbit anti-c-FOS (SYSY, no. 226 008, 1:300), rabbit anti-DBH (Sigma, no. AB1585, 1:300), rabbit anti-Dsred (Takara, no. 632496, 1:300), rabbit anti-VAChT (SYSY, no. 139 103, 1:300), mouse anti-alpha Smooth Muscle Actin-FITC (Sigma, no. F3777, 1:300), rabbit anti-TRPV1 (Alomone labs, no. ACC-030, 1:300) and chicken anti-GFP (abcam, no. ab13970, 1:300). Secondary antibodies used include goat anti-rabbit FITC, goat anti-rabbit Cy3 and goat anti-rabbit Cy5 (all from Jackson Immuno Research Labs, all 1:300). Slides were mounted with Vectashield (Vector Labs) and imaged using an Olympus VS200 Slide Scanner or Leica SP8 confocal microscope. For quantification of the total number of FOS+ neurons by antibody staining and the overlap between Fos and the top marker gene of each individual nTS cluster by RNAscope following HDM, we counted total cell numbers in 20 serial sections (at 25 µm thickness each, with 75 µm interval between sections, together representing ¼ of the whole nTS regions (2 mm)) and recorded these as one data point for one animal. For quantification of FOS+ neurons without the total label on the y axis, we counted three sections of nTS bregma regions with the most concentrated signals following serial section of the whole nTS (three sections were chosen between bregma −7.20 and −8.08 mm at the same stereotaxic coordinates between groups), and the average number was used as one data point for one animal. We used Qupath for overlap quantification in the nTS.

RNAscope

Vagal ganglia or brainstems were sectioned at 25 μm thickness. All staining procedures were performed using the RNAscope Fluorescent Multiplex Kit (Advanced Cell Diagnostics, no. 320850) following the manufacturer’s instructions. The following probes from Advanced Cell Diagnostics were used: Mm-Fos (no. 316921), Mm-Dbh (no. 407851), Mm-Phox2b (no. 407861), Mm-Otp (no. 516391), Mm-Mecom (no. 432231), Mm-Kcns3 (no. 467371), Mm-Mafa (no. 556931), Mm-Egr3 (no. 431101), Mm-Crym (no. 466131), Mm-Gbx2 (no. 314351), Mm-Col23a1 (no. 432681), Mm-Npy (no. 313321), Mm-Cdh3 (no. 514591), Mm-Nr4a2 (no. 423351), Mm-Lhx9 (no. 495431), Mm-Pou3f1 (no. 436421), Mm-Tac2 (no. 446391), Mm-St18 (no. 443271), Mm-Igf1 (no. 443901), Mm-Lama2 (no. 424661), Mm-Th (no. 317621), Mm-Tacr1 (no. 428781), Mm-Trpv1 (no. 313331), Mm-Gfp (no. 400281), Mm-tdTomato (no. 317041), Mm-Chat (no. 408731), Mm-Adra1a (no. 408611) and Mm-Adra1b (no. 413561).

Vagotomy

Mice were anaesthetized with a mixture of ketamine (100 mg kg−1) and xylazine (10 mg kg−1) by intraperitoneal injection (the same approach was used for all anaesthesia in our study unless otherwise notated). Fully anaesthetized mice were placed ventral side up on a stereotaxic frame, then 70% ethanol was sprayed on the throat to wet the fur. The skin was lifted to make a vertical cut (1 cm) on the throat, then one side of the vagal nerve was isolated and teased away from the carotid artery using small, curved forceps. Unilateral vagotomy was conducted by lifting the vagal nerve and cutting with straight scissors. Control sham operations were performed by lifting the vagal nerve and releasing it intact. Following vagotomy, the wounds were sutured and the area was disinfected with povidone-iodine. For all surgeries in our study, unless otherwise notated, mice were positioned on a heating pad to maintain body temperature and ophthalmic ointment was applied to maintain eye lubrication during surgery. Postoperative analgesia was provided with Buprenorphine SR (0.1 mg kg−1, subcutaneous injection). Mice were allowed to recover for 1 week before being subjected to allergen challenge.

4-OHT injection and TRAP2 labelling

4-Hydroxytamoxifen (4-OHT, Sigma, no. H6278) was dissolved at 20 mg ml−1 in ethanol by shaking at 37 °C for 15 min, followed by aliquoting and storage at −20 °C for up to several weeks. Before use, 4-OHT was redissolved in a 1:4 mixture of castor oil/sunflower seed oil (Sigma, nos. 259853 and S5007). Ethanol was fully evaporated by vacuum under centrifugation. To determine brainstem neurons activated by allergen challenge but not by food consumption[30], mice were fasted for 12 h before allergen challenge and 4-OHT injection; they were then placed back on a regular diet following cre activation. Mice were maintained in their home cages for 1 week to allow tdTomato expression before treatment.

CLARITY-based brain clearing

A hydrogel based on 1% acrylamide (1% acrylamide, 0.125% Bis, 4% PFA, 0.025% (w/v) VA-044 initiator, in 1× PBS) was used for all CLARITY preparations. Following transcardial perfusion with 4% PFA and postfixation, brainstems were transferred to 1% hydrogel for 48 h to allow monomer diffusion. Samples were degassed and polymerized for 4–5 h at 37 °C. Samples were washed with 200 mM NaOH-boric buffer (pH 8.5) containing 8% SDS for 6–12 h and then transferred to a flow-assisted clearing device using a temperature-control circulator. Next, 100 mM Tris-boric buffer (pH 8.5) containing 8% SDS was used to accelerate clearing, after which samples were washed in PBS + 0.1% Triton X for at least 24 h at 37 °C. Samples were incubated in a refractive index-matching solution (refractive index = 1.45) for 8 h at 37 °C and then 6–8 h at room temperature before confocal imaging.

Brainstem stereotaxic injection

Fully anaesthetized mice were placed in a stereotaxic frame with the head angled at 45°. A midline incision was made though the animal’s skin, posterior neck muscles and dura mater were pulled to expose the medulla between the occipital bone and C1 vertebra. Based on the stereotaxic coordinates of mouse brain[31] and using obex as a reference point, injections were made into either bilateral nTS (0.1 mm rostral to obex, 0.2 mm lateral to midline, 0.25 mm under the medullary surface), bilateral NA (0.65 mm caudal to obex, 1.25 mm lateral to midline, 0.45 mm under the medullary surface) or bilateral DMV (0.05 mm caudal to obex, 0.1 mm lateral to midline, 0.1 mm under the medullary surface) using a calibrated glass micropipette attached to a Nanoject II injector (Drummond) and microprocessor pump (Pneumatic PicoPump, WPI). Each injection lasted no less than 10 min. Following injection, the glass micropipette was left in place for an additional 10 min before slow withdrawal. DTX (2 ng 200 nl−1), anti-DBH–SAP (42 ng 200 nl−1, advanced targeting system, no. IT-03), SSP–SAP (3.25 ng 200 nl−1, advanced targeting system, no. IT-11), blank–SAP (advanced targeting system, no. IT-21) or virus (AAV2/9-flex-tdTomato, 1.3 × 10^13 genome copies (gc) ml−1, AAV2/8-flex-hM4D(Gi)-mCherry, 2.07 × 10^13 gc ml−1, AAV2/8-DIO-hM3D(Gq)-mCherry, 8 × 10^12 gc ml−1, AAV2/8-CMV-flex-TVA-mCherry-2A-oG, 1.39 × 10^13 gc ml−1, EnvA G-Deleted Rabies-EGFP, 5.0 × 10^7 gc ml−1; Boston Children’s Hospital Vector Core and Salk GT3 core) were used. Blank–SAP cannot enter cells and is thus non-toxic to neurons, serving as the appropriate control for DBH–SAP or SSP–SAP administration. DBH–SAP was previously validated as specifically ablating DBH+ neurons with no effect on neighbouring neurons18,32,33. Because a period of 2 weeks is necessary to eliminate DBH+ neurons using DBH–SAP[32], mice were injected with SAP 1 week before the first sensitization and 2 weeks before the second challenge.

Chemogenetic manipulation

Either CNO (Sigma, no. C0832, 1 mg kg−1, dissolved in 0.9% NaCl) or vehicle (0.9% NaCl) was injected intraperitoneally following expression of hM3D(Gq), hM4D(Gi) or mCherry in bilateral nTS or NA. The CNO concentration of 1 mg kg−1 used was effective34,35,36 and without apparent non-specific effects37,38. For hM4D(Gi) inhibition, CNO was injected 1 h before every of the second to fourth challenge; for hM3D(Gq) activation, CNO was injected in place of the fourth HDM challenge. Mice were euthanized 24 h later for flexiVent assay.

Airway hyperreactivity assayed by flexiVent

Anaesthetized mice were paralysed with acepromazine (10 mg kg−1, intraperitoneal injection). Mice were tracheotomized with a 20 G sterile catheter and attached to a flexiVent pulmonary mechanics apparatus (SCIREQ). Mice were ventilated at 9 ml kg−1 tidal volume and a frequency of 150 beats min−1. The weight of each animal was entered into flexiVent at the start of each round of assay. Pre-scans were carried out as part of the flexiVent programme, allowing for the calculation of lung size. Positive end-expiratory pressure was set at 300 mm H_2O. The nebulizer was activated for 10 s to deliver each dose of methacholine (0, 6, 12 or 24 mg ml−1, dissolved in 0.9% NaCl). The Rrs and Ers of the respiratory system were determined in response to aerosolized methacholine challenges, and the mean maximal elastance and resistance of 12 measurements by dose were then calculated. Statistical analysis at each methacholine concentration was performed separately.

Tissue processing and flow cytometry

Anaesthetized mice were injected with AF700-counjugated CD45 (BioLegend, no. 103128, 10 µg per mouse) by intravenous injection to distinguish circulating immune cells and resident immune cells within the lung. Mice were euthanized 5 min later for lung harvest. Whole lungs were mechanically dissociated in GentleMACS C tubes (Miltenyi Biotec) containing 5 ml of RPMI 1640 (Thermo Scientific) with 10% fetal bovine serum, 1 mM HEPES (Life Technology), 1 mM MgCl_2 (Life Technology), 1 mM CaCl_2 (Sigma), 0.525 mg ml−1 collagenase/dispase (Roche) and 0.25 mg DNase I (Roche) by running the mouse lung 1-2 program on GentleMACS (Miltenyi Biotec). Lung pieces were then digested by shaking at around 150 rpm for 30 min at 37 °C. Following incubation, lung pieces were mechanically dissociated further using the mouse lung 2-1 program on GentleMACS, followed by straining through a 70 μm filter. Red blood cells were removed by the addition of 1 ml of RBC lysis buffer (BioLegend) to each tube and incubation at room temperature for 1 min. Single-cell suspensions were pelleted (1,500 rpm, 4 °C, 5 min), counted with a haemocytometer and diluted to around 1 × 10^6 cells ml−1. Diluted cells were stained with Fc blocking antibody (5 mg ml−1, BD) before incubation with a surface marker antibody cocktail. For lung myeloid tissue, the following antibodies were used: 1:100 BV605-conjugated anti-F4/80 (BioLegend, no. 123133), 1:500 BV510-conjugated anti-CD45 (BioLegend, no. 110741), 1:1,000 APC-conjugated anti-CD11c (BioLegend, no. 117310), 1:1,000 PE-Cy7-conjugated anti-Ly6G (BioLegend, no. 560601) and 1:2,000 PE-CF594-conjugated anti-CD11b (BioLegend, no. 101256). For lung lymphoid tissue, the following antibodies were used: 1:200 FITC-conjugated anti-CD45 (BioLegend, no. 103108), 1:100 APC-Cy7-conjugated anti-IL-7Ra (BioLegend, no. 135040), V450-conjugated Lineage mix (1:200 anti-CD19, TONBO, no. 50-201-4944), 1:500 anti-CD11c (TONBO, no. 50-201-4937), 1:500 anti-F4/80 (TONBO, no. 50-201-4978), 1:100 anti-NK1.1 (BD, no. 560524), 1:100 anti-TER119 (BD, no. 560504), 1:100 anti-TCR gamma delta (Invitrogen, no. 48-5711-82), 1:100 BV510-conjugated anti-ST2 (BD, no. 745080), 1:200 PE-Cy7-conjugated anti-TCR-beta (BioLegend, no. 109222), 1:100 BV604-conjugated anti-CD4 (BioLegend, no. 100548) and 1:2,000 PerCP-Cy5.5-conjugated anti-CD90.2 (BioLegend, no. 105338). Cells were then stained using live/dead dye (1:1,000, Ghost Dye Red 780 (TONBO, no. 13-0865-T100 for myeloid tissue, 1:500 and Ghost Dye Violet 450, TONBO, no. 13-0863-T100)) before fixing using BD Stabilizing Fixative and transfer to fluorescent activated cell sorting tubes. Flow cytometry was analysed on a BD FACS Canto RUO - ORANGE analyser with three lasers (405, 488 and 640 nm) at the Flow Cytometry Core at VA San Diego Health Care System and San Diego Veterans Medical Research Foundation. All data were further analysed and plotted with FlowJo software (Tree Star). Eosinophils, group 2 innate lymphoid cells and T-helper 2 cells were gated on live, resident CD45+ singlets.

Isolation of nTS and NA nuclei

Mice were euthanized using CO_2 inhalation. For nTS, brainstems were acutely harvested from either (1) 4 groups of adult wild-type naive mice (n = 4 biological repeats, two males in each group); (2) mice at 1.5 h following the fourth saline treatment (2 males in the group); or (3) mice at 1.5 h following the fourth HDM challenge (2 males in the group). The nTS was visualized by microscopy and harvested based on anatomical landmarks. The reason for using males was based on our observation that males show less Fos background in saline control groups compared with females, providing a more consistent baseline for our study. Similarly, male mice were used in multiple, recently published single-cell RNA-seq datasets14,15,27,39. For NA, brainstems were acutely harvested from adult Chat-cre; CAG-Sun1/sfGFP mice (n = 7, 4 males and 3 females). NA was identified by nucleus-localized GFP fluorescence signals based on anatomical landmarks while avoiding DMV and 12N regions that also express Chat. NA samples from 7 mice were pooled for the snRNA-seq experiment. One pooled sample was assayed similarly in the other two studies with single-cell/-nucleus RNA-seq of the NA12,13. Dissected tissues were placed in liquid N_2 immediately and either stored at −80 °C or sent directly for nuclei isolation.

On the day of nuclei dissociation, dissected tissues were transferred into 1 ml of douncing buffer (0.25 M sucrose, 25 mM KCl, 5 mM MgCl_2, 10 mM Tris-HCl pH 7.5, 1 mM DTT (no. D9779, Sigma) and 1× cOmplete EDTA-free protease inhibitor (no. 05056489001, Roche, DB-DP, 0.1% Triton-X)). Pestled samples were filtered with 30 μm CellTrics and transferred to prechilled low-bind Eppendorf tubes. Samples were spun and sequentially resuspended in douncing buffer, permeabilization buffer (5% IGEPAL-CA630, no. I8896, Sigma, 0.2% DTT, 1 mM cOmplete EDTA-free protease inhibitor and 1× PBS) and tagmentation buffer (66 mM Tris-acetate pH 7.8 (no. BP-152, Thermo Fisher Scientific)), 132 mM K-acetate (no. P5708, Sigma), 20 mM Mg-acetate (no. M2545, Sigma) and 32 mM DMF (no. DX1730, Millipore) and counted using a haemocytometer.

snRNA-seq and data analysis

Single-nucleus RNA sequencing experiments were carried out by the Center for Epigenomics, UCSD. Nuclei were processed into complementary DNA libraries using the Chromium Single Cell 3’ v3 kit (10X Genomics) and sequencing was carried out on the NovaSeq (Illumina) platform. The CellRanger software package from 10X Genomics (v3.0.2) was used to align raw reads onto the mouse reference genome (GRCm38) and generate the feature-barcode matrix. CellBender (v0.3.0)[40] was then used to remove technical artefacts and ambient RNA to produce improved estimates of gene expression. The R package Seurat (v4.0)[29] was then used to perform data quality control, normalization, principal components analysis, UMAP generation and differential gene expression testing. Nuclei with above 5% mitochondrial reads and greater than 2,000 unique genes were considered high-quality cells and were filtered for further analyses, following the filtering criteria commonly used in neuronal snRNA-seq studies including those on brainstem neurons12,13,14. In addition, DoubletFinder (v2.0)[41] was used to remove doublets and SCTransform was used to normalize feature expression. Harmony (v1.2.0)[42] was used to integrate individual datasets across three conditions (naive, saline and HDM). In total 42,157 nuclei were recovered, including 39,626 neurons. We then extracted nTS data from baseline naive condition (n = 4 biological repeats, n = 2 mice in each group) to profile marker gene expression across all nTS clusters. To determine dimensions for optimized clustering, we tested up to 50 principal components and evaluated the optimal cutoff using an elbow plot. In our study, we settled on using the first 20 principal components for clustering and projection with both UMAP and t-distributed stochastic neighbour embedding. We also tested a range of clustering resolutions (from 0.1 to 2.0) that were evaluated with clustree (v0.5.1; https://github.com/lazappi/clustree)[43]. In this dataset the resolution was set to 1.0, resulting in 25 interim clusters. We plotted a density UMAP using geom_density_2d and stat_density_2d (https://ggplot2.tidyverse.org/reference/geom_density_2d.html) from ggplot2 (v3.3.2)[44] for visual identification of high-density regions that represent potential unique cell populations. Using these two methods, coupled with manual inspection of top markers, we combined several clusters with shared markers to ensure that annotated clusters would show unique transcriptional profiles, resulting in 18 distinct clusters (further details in Supplementary Note 1, Supplementary Fig. 2a–i and Supplementary Tables 1 and 2). Following combination, we reordered cluster ID based on the number of cells in each cluster and renumbered the largest cluster as cluster 1. For rigorous definition of marker genes for each cluster, we screened each cluster’s top marker genes (Seurat, FindAllMarkers) using ViolinPlot, FeaturePlot and DotPlot. Using the Seurat default dotplot setting (R package Seurat (v4.0)[29], percentage expressed was plotted based on the actual percentage of cells expressing selected marker genes in a given cluster. Average expression was plotted based on the average normalized single-cell expression value, using the Seurat default dotplot setting (R package Seurat v4.0)[29], with maximum average expression threshold set at 2.5 and everything higher set to this; and with minimum average expression threshold set at −2.5 and everything lower set to this. The colour bar shown on the right of the plot represents the range of scaled normalized expression values for the genes shown in that plot. We provide a list of the top 100 marker genes of nTS clusters in Supplementary Table 3.

For NA, 10,072 nuclei were recovered, including 7,664 neurons. Using Chat as a positive control gene to identify NA neurons, we removed clusters not showing Chat expression from ViolinPlot, FeaturePlot and DotPlot. We then followed the same pipeline given above to process our data and used Harmony (v1.2.0)[42] to integrate our data with two published NA single-cell datasets12,13. We provide a list of the top 100 marker genes in Supplementary Table 4.

qPCR

Total RNA was extracted from lungs using Trizol (Invitrogen) and the RNeasy Mini RNA extraction kit (Qiagen). qPCR with reverse transcription was then performed to obtain corresponding cDNA using THE iScript Select cDNA Synthesis Kit (Bio-Rad). qPCR was performed with the CFX ConnectTM system (Bio-Rad) using SYBR Green (Bio-Rad). At least three technical and three biological replicates were performed for each gene. Primer sequence: 5′-CGGCCAGGTCATCACTATTGGCAAC-3′, 5′-GCCACAGGATTCCATACCCAAGAAG-3′ for Actb (β-actin); 5′-TGACTCAATCTGCGTGCCTT-3′, 5′-AGGCCTTCTTTTGGCAGGTT-3′ for Muc5ac; 5′-GGTCTCAACCCCCAGCTAGT-3′, 5′-GCCGATGATCTCTCTCAAGTGAT-3′ for Il4; 5′-CCTCTTCGTTGCATCAGGGT-3′, 5′-GATCCTCCTGCGTCCATCTG-3′ for Il5; 5′-AAAGCAACTGTTTCGCCACG-3′, 5′-CCTCTCCCCAGCAAAGTCTG-3′ for Il13.

Plethysmography

Ventilatory parameters during normoxia was measured in unrestrained male mice using a whole-body barometric plethysmograph modified for continuous flow45,46,47. Flow was maintained constant through the chamber while a pressure transducer (45 mMP with 2 cm H_2O diaphragm, Validyne) recorded changes attributable to the warming and expansion of inhaled gases. Mice were weighed and sealed into an individual plethysmograph chamber along with a temperature and humidity probe (Thermalert TH5, Physitemp). A constant gas flow (335 ml min−1) was delivered using a rotameter (no. 603, Matheson) and measured with a flow meter (Sables System International, Inc.) upstream of the chamber. Gases exited the chamber through a valve and into a vacuum pump (Model 25, Precision Scientific Co.) to isolate pressure changes from respiration in the chamber during constant flow with high input and output impedances. All ventilatory parameters were recorded on an analogue-digital acquisition system (PowerLab 8SP, AD Instruments) and analysed with LabChart 8-Pro Software, sampling at a rate of 1 kHz. Mice were allowed to acclimatize to the chamber and constant air flow for 45 min (normoxia, 21% of O_2) and were then exposed to a 5 min challenge of hypoxic gas (10% O_2) to test responsiveness; they were then exposed to normoxic gas for 15 min. A minimum of 30 s between 10 and 15 min of normoxia exposure was analysed. Respiratory frequency (breaths min−1) was measured from cyclic peaks in the plethysmograph pressure pulses, and tidal volume (ml) was measured from calibration pulses using equations from ref. [48]. Minute ventilation (\({\dot{{\rm{V}}}}_{{\rm{i}}}\), ml min−1 kg) was calculated from respiratory frequency and tidal volume and normalized to body mass ventilation. Oxygen consumption \(({\dot{{\rm{V}}}}_{{{\rm{O}}}_{2}})\) and carbon dioxide production \(({\dot{{\rm{V}}}}_{{{\rm{CO}}}_{2}})\) were calculated by recording inspired and expired oxygen and carbon dioxide fractions using an O_2/CO_2 analyser. The ratio of \({\dot{{\rm{V}}}}_{{\rm{i}}}\,/{\dot{{\rm{V}}}}_{{{\rm{O}}}_{2}}\) to \({\dot{{\rm{V}}}}_{{\rm{i}}}\,/{\dot{{\rm{V}}}}_{{{\rm{CO}}}_{2}}\) thus provides a more precise estimation of mouse ventilation without the confounding factors produced by changes in metabolic rate.

CUBIC tissue clearing and LSFM

Clear, unobstructed brain/body imaging cocktails and computational analysis protocol (CUBIC) buffers were prepared accordingly[49]. Following sufficient tissue clearing in R1 buffer, tissues were embedded in 2% low-melting agarose and then incubated in R2 solution before imaging. Cleared samples were imaged using a Zeiss Z.1 light sheet fluorescence microscope (LSFM). Vagal ganglia were imaged using a ×5 objective (LSFM ×5/0.1 numerical aperture (NA)) and a 1.45 ×5 CLARITY specific chamber. Lung samples were imaged using a ×2.5 objective (LSFM ×2.5/0.1 NA) and a 1.45 ×2.5 CLARITY specific chamber (Translucence Biosystems).

CTB488 injection

Fully anaesthetized mice were placed ventral side up on a stereotaxic frame. The extrathoracic trachea was exposed via a neck incision. The trachea was carefully lifted without bleeding. Using a 10 μl Hamilton glass micropipette fitted with a 32 G needle, 10 μl of CTB488 (1 mg ml−1, Thermo Scientific, no. C22841) was injected into the dorsal aspect of the trachea from both sides, about three to five cartilage rings caudal to the larynx. Sutured mice were allowed to recover on a thermal pad before being returned to housing. Mice were euthanized 1 week following CTB488 injection for brainstem collection.

Cannula implantation at bilateral NA

Fully anaesthetized mice were mounted on a stereotaxic frame. A midline incision was made to show bregma, the skull was cleaned with hydrogen peroxide and small holes were drilled through the skull at the designated stereotaxic coordinates of NA (6.7 mm caudal to bregma, 1.25 mm lateral to midline, 4.25 mm under the dura)[31]. A bilateral guide cannula affixed with two units of 26 G stainless-steel tubing (P1 Technologies, Inc.) was stereotaxically implanted 0.5 mm above the NA region. Guide cannulae were secured to the skull using superglue and dental cement. A matching dummy cannula were inserted into the guide cannula and secured with a dust cap to ensure guide cannula patency. Mice were allowed to recover in their home cages for 1 week before challenge and drug delivery. On the day of flexiVent assay, starting from baseline measurement, 1 μl of prazosin (0.4 mg ml−1 in ultrapure water, no. P7791, Sigma), terazosin (0.4 mg ml−1 in ultrapure water, no. T4680, Sigma) or ultrapure water control was consecutively microinjected into bilateral NA through the guided cannula using a two-channel syringe pump (no. R462, RWD).

AAV infection of vagal ganglia

The vagal ganglia of anesthetized mice were surgically exposed by making an incision along the neck. A micropipette containing 200 nl of AAV2/9-syn-flex-GFP (2 × 10^13 gc ml−1; Boston Children’s Hospital Vector Core) was inserted into bilateral vagal ganglia. Mice were euthanized 3 weeks later for harvesting of vagal ganglia and brainstem.

Statistics and reproducibility

Statistical analyses were calculated with Microsoft Excel and performed using Prism (GraphPad), with statistical tests and sample sizes reported in figure legends. Data in graphs are presented as mean ± s.e.m. and statistical tests are two-sided, unless otherwise indicated. All replicates were biological, unless otherwise indicated. All representative images are from at least three independent experiments, and details are described in figure legends. Sample sizes were determined based on previous expertise and publications in the field. Exact sample sizes are described in each figure legend. Investigators were blinded to group allocations for FOS antibody staining and flexiVent experiments associated with Figs. 1 and 3–6 and Extended Data Figs. 1–3 and 6–10; group allocation was not blinded in other experiments. Significance is defined as P < 0.05, with significance annotations of *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001. Absence of significant differences (P > 0.05) is indicated by NS (not significant).

Materials availability

All reagents and materials used in this study are commercially available.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The publicly available mouse genome reference mm10 (GENCODE vM23/Ensembl 98) from 10X Genomics was used for snRNA-seq analysis. Raw and fully processed snRNA-seq data reported in this study have been deposited in the Gene Expression Omnibus and are publicly available under accession numbers GSE200003 (for nTS) and GSE211538 (for NA). Additional data related to this paper may be requested from the authors; requests should be made directly to xinsun@health.ucsd.edu. Source data are provided with this paper.

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Acknowledgements

We thank members of the Sun laboratory for inputs and discussions. We thank T. Komiyama, D. Kleinfeld and B. Lim at UCSD, A. Wang and N. Zemke at the Center for Epigenomics in the UCSD School of Medicine and S. Pfaff at the Salk Institute for inputs and discussions. We thank the Center for Epigenomics at UCSD for performing snRNA-seq experiments. Work at the Center for Epigenomics was supported in part by the UCSD School of Medicine. This publication includes data generated at the UCSD Institute for Genomic Medicine utilizing an Illumina NovaSeq 6000 that was purchased with funding from a National Institutes of Health SIG grant (no. S10 OD026929). Flow cytometry was performed with the support of the Flow Cytometry Research Core at the San Diego Center for AIDS Research (no. P30 AI036214), the VA San Diego Health Care System and the San Diego Veterans Medical Research Foundation. We thank J. Santini and M. Erb at the UCSD School of Medicine Microscopy Core (National Institute of Neurological Disorders and Stroke Grant no. P30 NS047101) for assistance with imaging. Fundings were provided by the National Institutes of Health (nos. R01AT011676 and OT2OD023857 to X.S.), the Tobacco-Related Disease Research Program (no. T29IR0475 to X.S.) and the American Heart Association (no. 19POST34450103 to Y.S.).

Author information

Authors and Affiliations

1. Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA

Yujuan Su, Jinhao Xu, Ziai Zhu, Jisun Chin, Le Xu, Haoze Yu & Xin Sun

2. Department of Biological Sciences, University of California San Diego, La Jolla, CA, USA

Jinhao Xu & Xin Sun

3. Department of Neuroscience, Scripps Research Institute, La Jolla, CA, USA

Victoria Nudell & Li Ye

4. La Jolla Institute for Immunology, La Jolla, CA, USA

Barsha Dash

5. Division of Pulmonary, Critical Care & Sleep Medicine, Department of Medicine, University of California, San Diego, CA, USA

Esteban A. Moya

6. Waitt Advanced Biophotonics Center, The Salk Institute for Biological Studies, La Jolla, CA, USA

Axel Nimmerjahn

Contributions

Y.S. and X.S. conceived and designed experiments. Y.S. performed experiments. Y.S., A.N. and X.S. interpreted data. Y.S. prepared figures and drafted the manuscript. J.X. helped with TRAP2 and flexiVent experiments and performed PAS staining. Y.S. and J.C. analysed snRNA-seq data and generated figures with help from Z.Z., L.X. and H.Y. V.N. and L.Y. performed CLARITY on TRAP2 mice and performed confocal imaging. B.D. designed antibody cocktails for flow cytometry. E.A.M. performed plethysmography and analysed data. Y.S. and X.S. edited and revised the manuscript, with input from the other authors. Y.S., J.X., Z.Z., J.C., L.X., H.Y., V.N., B.D., E.A.M., L.Y., A.N. and X.S. approved the final version of the manuscript.

Corresponding author

Correspondence to Xin Sun.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Isaac Chiu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 nTS neurons were activated after allergen challenge to lung.

(a) PAS staining shows that HDM challenge effectively induced mucus-secreting airway goblet cells (blue), as expected. (b-d) Flow cytometry analyses of innate lymphoid cells (ILC2s), eosinophils (Eos) and T-helper type 2 cells (Th2) from whole lungs after consecutive challenges showing significant increase following HDM, when compared to saline controls. Data are presented as mean values ± SEM. n = 6 (first saline, first HDM, second saline, second HDM, third saline, third HDM), 3 (fourth saline) and 4 (fourth HDM) mice. Unpaired t-test (two-sided) was used between first saline and first HDM, second saline and second HDM, third saline and HDM, fourth saline and fourth HDM, separately. For b, P = 0.0012 (1st), P < 0.0001 (2nd), P = 0.0002 (3rd), P = 0.0052 (4th), for c, P = 0.0022 (1st), P = 0.0001 (2nd), P < 0.0001 (3rd), P = 0.0027 (4th), for d, P = 0.0369 (1st), P = 0.0004 (2nd), P < 0.0001 (3rd), P = 0.0005 (4th). (e-x) FOS antibody staining two hours after the fourth challenge in serial sections of the whole brainstem. Increased FOS+ cells were enriched in the nTS region from Bregma −7.20 to −8.08 mm in HDM-treated mice (outlined in yellow) compared to the saline-treated control mice. Outlines delineate nTS regions at indicated Bregma levels. (y) Quantification (unpaired t-test, two-sided, P = 0.0003) of total number of FOS+ cells after HDM (n = 11 mice) or saline (n = 5 mice) treatment in the whole nTS regions (Bregma −6.24 mm to −8.24 mm). Scale bars, 100 µm (a), 200 µm (e-x).

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Extended Data Fig. 2 nTS neurons were activated only after repeated allergen challenges to the lung of B6 mice.

(a,b) Fos RNAscope from brainstem section of naïve mice 1.5 h after HDM challenge to lung showing Fos RNA signals are not found in the AP as defined by Gfral (a) or Glp1r (b). (c) FOS antibody staining from brainstem section of Chat-cre; Ai14 mice 2 h after HDM showing that there is little overlap between FOS+ cells and tdTom+ DMV and 12 N regions. (d) Quantifications of the number of Fos+ neurons in the AP (n = 6 mice), DMV (n = 7 mice) or 12 N (n = 7 mice). (e,h,k) Experiment scheme for the first, second or third HDM challenge in wild-type mice. (f,g,i,j,l,m) FOS antibody staining (f,i,l) and quantification (g,j,m) showing no statistically significant increase of FOS+ cells after the first, second or third HDM challenge. n = 6 mice each, unpaired t-test, NS. (n) Experiment scheme for single saline or HDM challenge in naive mice and tissue collection 30 min after the challenge for Fos RNAscope. (o) No significant difference in the number of Fos+ neurons in the nTS 30 min following a single saline (n = 3 mice) or HDM (n = 3 mice) challenge, unpaired t-test, NS. (p) Experiment scheme for HDM challenge and tissue collection in wild-type mice for Fos RNAscope. (q) The number of Fos+ neurons in the nTS are increased in the HDM-treated (n = 11 mice) group, compared to saline-treated controls (n = 11 mice), unpaired t-test, P = 0.0024. Outlines in a,b,c,f,i and l delineate nTS regions at indicated Bregma levels. Scale bars, 200 µm. Data are mean values ± s.e.m., statistical tests are two-sided.

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Extended Data Fig. 3 Genetic ablation of HDM-activated nTS neurons did not affect goblet cell metaplasia or immune cell infiltration.

(a) Experiment scheme for labeling HDM-activated trapped neurons. (b,c) Representative image (b, nTS outlined) and quantification (c) from sections of TRAP2; Ai14 mice. n = 8 saline and 11 HDM mice, unpaired t-test, P < 0.0001. (d) Quantification of tdTom+ nTS neurons of TRAP2; Ai14 mice after consecutive HDM challenges. n = 5 saline and 10 HDM mice for first 4-OH, 6 saline and 6 HDM mice for both second and third 4-OH, 8 saline and 11 HDM mice for fourth 4-OH, unpaired t-test, P < 0.0001 fourth 4-OH saline versus fourth HDM. NS. for all other pairwise comparisons. (e) Experiment scheme for FOS immunostaining after trapping HDM-activated neurons. (f,g) Representative image (f, nTS outlined) and quantification (g, n = 9 mice) showing the overlap between FOS+ and tdTom+ nTS neurons. Arrowheads indicate tdTom−FOS+ nTS neurons. (h) Experiment scheme for rabies retrograde tracing of HDM-activated neurons from nTS. (i,j) Representative vagal ganglia (i) and quantification (j). n = 5 saline and 5 HDM mice, unpaired t-test, P = 0.0373. (k,l) Representative RNAscope (k) or immunostaining (l) images showing TRPV1+GFP+ neurons (arrowheads) in the vagal ganglia of TRAP2 mice after rabies nTS injection and HDM challenge. (m) Experiment scheme for ablating saline-activated, or HDM-activated nTS neurons. (n,o) FlexiVent, n = 4 saline+DTX and 4 HDM + DTX mice, unpaired t-test, at 24 mg ml−1 MCh, P = 0.0002 for Rrs (n) and P = 0.0003 for Ers (o). NS for all other pairwise comparisons. (p) Experimental scheme for DTX ablation of HDM-activated nTS neurons. (q,r) PAS staining (q) and qPCR (r) showing no change in goblet cell metaplasia. n = 3 vehicle and 3 DTX mice, unpaired t-test, NS. (s-u) Flow cytometry analyses of ILC2s, Eos and Th2 (n = 4 vehicle and 7 DTX mice) and qPCR of Il4, Il5 and Il13 (n = 3 vehicle and 3 DTX mice), unpaired t-test, NS for all pairs. Scale bars, 200 µm (b,f,i, k-l), 100 µm (q). Data are mean ± s.e.m., statistical tests are two-sided.

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Extended Data Fig. 4 snRNA-seq provided a transcriptomic atlas of nTS.

(a) Quality control data including nFeature RNA (the number of genes detected in each cell), nCount RNA (the total number of molecules detected within a cell) and mitochondrial percentage of our integrated nTS snRNA-seq datasets. (b) Dot Plot showing little expression of genes enriched in AP[15] (Gfral, Prlhr, Glp1r, Casr and Ntrk1) or DMV[16] (Chat, Gm4881 and Nppb) compared to pan-neuronal marker genes (Rbfox3 and Snap25) in our integrated nTS snRNA-seq datasets. Using default Seurat[29] dotplot settings (Methods), percent expressed was plotted from 0 to 75% detected, and the colour bar shows the average of the scaled normalized expression values across cells in a given cluster. (c) Stacked bar plots showing relative contribution from four replicates of naïve nTS snRNA-seq datasets (Naïve_rep1, Naïve_rep2, Naïve_rep3 and Naïve_rep4). (d-g) Feature plots showing little to no expression of glia marker genes-Gfap (d), Olig1 (e), Aqp4 (f) or Tmem119 (g) in the neuronal clusters. (h-c’) Feature plots (left, h,j,l,n,p,r,t,v,x,z, b’) and RNAscope images (right, i,k,m,o,q,s,u,w,y,a’, c’) showing expression of marker genes in the nTS (outlined) inhibitory clusters. Bregma levels shown are tailored to layers with optimal signal for each marker. Genes validated here by Feature plots or RNAscope were indicated in red in Fig. 2f. Scale bars, 200 µm.

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Extended Data Fig. 5 Dbh+ neurons in the nTS were activated upon allergen challenge to lung.

(a) RNAscope of Dbh on serial sections (each section was at 20 µm thickness, 80 µm interval between sections) of naïve brainstem shows Dbh cover most of the nTS from Bregma −6.96 mm to Bregma −8.08 mm. Dbh is detected in an average of 2,342 ± 169 neurons in the nTS (n = 7 mice). (b) Vagal ganglia of Trpv1-cre mouse after bilateral microinjection of AAV-flex-GFP to the vagal ganglia. n = 6 mice. (c) GFP+ Trpv1+ vagal fibers project to the vicinity of Dbh+ neurons in the nTS. Boxed areas magnified at the bottom. n = 6 mice. (d) Quantification of the number of Dbh+Fos+ neurons in the nTS after HDM challenge to lung by RNAscope. Data are mean values ± s.e.m. n = 5 saline and 10 HDM mice, unpaired t-test (two-sided), P = 0.0004. Dashed circles in a and c outline nTS at indicated Bregma levels. Scale bars, 200 µm.

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Extended Data Fig. 6 Chemical depletion, genetic depletion or genetic inactivation of Dbh+ neurons did not affect goblet cell metaplasia or cytokine gene expressions.

(a) Experiment schemes for FOS+ cells detection after DBH-SAP injection and HDM. (b,c) Representative staining (b) and quantification (c, n = 4 blank-SAP and 4 DBH-SAP mice, unpaired t-test, P = 0.0155) showing reduced FOS+ cells following DBH-SAP treatment. (d) Experiment scheme for flexiVent after DBH-SAP injection and saline treatment. (e,f) FlexiVent, n = 3 blank-SAP and 4 DBH-SAP mice, unpaired t-test, NS. for all pairwise comparisons. (g-k) PAS staining (g) and qPCR (h-k) showing no change in goblet cell metaplasia or expression of type 2 cytokine genes. n = 3 blank-SAP and 3 DBH-SAP mice, unpaired t-test, NS. (l,m) Representative RNAscope (l) and quantification (m, n = 3 mice) showing extensive overlap of signals (arrowheads in l). (n) Experiment scheme for DTX-mediated ablation of Dbh+ neurons in the nTS. (o-s) PAS staining (o) and qPCR (p-s) showing that Dbh+ neuron ablation has no effect on goblet cell metaplasia or expression of type 2 cytokine genes. n = 3 vehicle and 3 DTX mice, unpaired t-test, NS. (t) Experiment scheme for Dbh+ neuron chemogenetic inhibition. (u) Representative AAV-flex-hM4D-mCherry signals in the nTS. (v,w) PAS staining (v) and qPCR (w, n = 3 vehicle and 3 CNO mice, unpaired t-test, NS) showing that chemogenetic inhibition of Dbh+ nTS neurons has no effect on goblet cell metaplasia. (x-b’) Flow cytometry analyses of ILC2s, Eos and Th2, and qPCR from whole lungs showing that chemogenetic inhibition of Dbh+ nTS neurons has no effect on immune cell infiltration. For x-z, n = 5 vehicle and 5 CNO mice, for a’ and b’, n = 3 vehicle and 3 CNO mice, unpaired t-test, NS. (c’) Experiment scheme for control virus AAV-flex-mCherry injection. (d’) Representative AAV-flex-mCherry signals in the nTS. (e’,f’) FlexiVent, n = 3 vehicle and 4 CNO mice, unpaired t-test, NS for all pairwise comparisons. Scale bars, 100 µm (g,o,v), 200 µm (b,l,u, d’). Data are mean values ± s.e.m., statistical tests are two-sided.

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Extended Data Fig. 7 Ablation of Tacr1+ neurons, but not Th+ neurons in the nTS led to reduced airway hyperreactivity.

(a,b) Double RNAscope showing the overlap between Th and Fos (a), Tacr1 and Fos (b) in the nTS at 1.5 h after the fourth HDM challenge to lung. Arrowheads indicate cells with overlapping signals. (c) Quantification of Th+Fos+ (n = 5 mice) and Tacr1+Fos+ (n = 4 mice) neurons as compared to Dbh+Fos+ (n = 10 mice) neurons in the nTS after the fourth HDM challenge to lung by RNAscope. Multiple comparisons under one-way ANOVA (Bonferroni post hoc test), P = 0.0001 between Dbh+Fos+ and Th+Fos+, P = 0.0020 between Dbh+Fos+ and Tacr1+Fos+, NS between Th+Fos+ and Tacr1+Fos+. (d) Representative image showing Dbh+Fos+ neurons and Th+Fos+ neurons have only a few triple overlaps by RNAscope. Arrows indicate Dbh+Fos+ neurons or Th+Fos+ neurons, arrowheads indicate overlapping Dbh+Th+Fos+ neurons. (e) Experiment scheme for genetic ablation of Th+ nTS neurons. (f,g) FlexiVent data showing no effect on airway hyperreactivity after DTX injection. n = 3 vehicle and 4 DTX mice, unpaired t-test (two-sided), NS for all pairwise comparisons. (h) Representative image showing Dbh+Fos+ neurons and Tacr1+Fos+ neurons have clear triple overlaps by RNAscope. Arrowheads indicate overlapping Dbh+Tacr1+Fos+ neurons. (i) Experiment scheme for chemical ablation of Tacr1+ nTS neurons. (j,k) FlexiVent data showing blunted airway hyperreactivity after SSP-SAP treatment. n = 4 blank-SAP and 6 SSP-SAP mice, unpaired t-test (two-sided), for Rrs (j), at 12 mg ml−1 MCh, P = 0.0224, at 24 mg ml−1, P = 0.0254; for Ers (k), at 6 mg ml−1, P = 0.0033, at 24 mg ml−1, P = 0.0297. NS for all other pairwise comparisons. Outlines in a,b,d and h delineate nTS regions at indicated Bregma levels. Boxed areas in d and h are magnified as labeled. Scale bars, 200 µm. Data are mean values ± s.e.m.

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Extended Data Fig. 8 Chemogenetic activation of Dbh+ nTS neurons did not affect goblet cell metaplasia, immune cell infiltration, or respiratory parameters.

(a) Representative mCherry signals (with Dsred antibody staining) following AAV-DIO-hM3D-mCherry injection in the nTS (outlined, no expression in AP). (b) Experiment scheme for repeated chemogenetic activation of Dbh+ nTS neurons. (c,d) FlexiVent, n = 3 HDM, n = 4 CNO*4 and n = 3 Saline mice, multiple comparisons under one-way ANOVA (Bonferroni post hoc test), for Rrs, at 12 and 24 mg ml−1 MCh, P < 0.0001 HDM versus saline or CNO*4, at 24 mg ml−1, P = 0.0010 CNO*4 versus saline, P < 0.0001 for the other two pairs; for Ers, at 12 mg ml−1, P = 0.0016 CNO*4 versus saline, P = 0.0001 HDM versus saline; at 24 mg ml−1, P = 0.0003 CNO*4 versus saline, P < 0.0001 HDM versus saline, P = 0.0034 HDM versus CNO*4. NS for remaining pairs. (e) Experiment scheme for repeated chemogenetic activation of allergen-activated Fos2A-iCreER (TRAP2) neurons. (f,g) FlexiVent, n = 3 HDM, n = 4 CNO*4 and n = 3 saline mice, multiple comparisons one-way ANOVA (Bonferroni post hoc test), for Rrs, at 12 mg ml−1 MCh, P = 0.0162 HDM versus saline, P = 0.0392 HDM versus CNO*4, at 24 mg ml−1, P = 0.0108 CNO*4 versus saline, P < 0.0001 for the other two pairs; for Ers, at 12 mg ml−1, P = 0.0002 HDM versus saline, P = 0.0074 HDM versus CNO*4, at 24 mg ml−1, P = 0.0002 CNO*4 versus saline, P < 0.0001 for the other two pairs. NS for remaining pairs. (h) Experiment scheme for control virus AAV-flex-mCherry injection to the nTS of Dbh-cre mice. (i,j) FlexiVent, n = 3 HDM, n = 3 saline and n = 3 CNO mice, multiple comparisons one-way ANOVA (Bonferroni post hoc test), for Rrs (i), at 12 and 24 mg ml−1 MCh, P < 0.0001 HDM versus saline or CNO; for Ers (j), at 12 mg ml−1, P = 0.0004 HDM versus saline, P < 0.0001 HDM versus CNO, at 24 mg ml−1, P < 0.0001 HDM versus saline or CNO. NS for remaining pairs. (k) Experiment scheme for activation of nTS Dbh+ neurons in naïve mice. (l,m) FlexiVent, n = 5 CNO versus n = 3 vehicle mice, unpaired t-test (two-sided), NS. (n) Experiment scheme for activation of nTS Dbh+ neurons in sensitized mice. (o,p) PAS staining (o) and qPCR (p). n = 3 mice for each group, unpaired t-test (two-sided), P = 0.0058 HDM versus saline, P = 0.0078 HDM versus CNO; NS CNO versus saline. (q-u) Flow cytometry (q and s, n = 4 saline, n = 4 HDM and n = 6 CNO; r, n = 6 saline, n = 6 HDM and n = 8 CNO) and qPCR (t,u, n = 4 saline, n = 3 HDM and n = 3 CNO). Multiple comparisons under one-way ANOVA (Bonferroni post hoc test), for q, P = 0.0144 HDM versus saline, P = 0.0040 HDM versus CNO; for r, P = 0.0261 HDM versus saline, P = 0.0141 HDM versus CNO; for s, P = 0.0256 HDM versus CNO; for t, P = 0.0344 HDM versus saline; for u, P = 0.0169 HDM versus saline, P = 0.0131 HDM versus CNO. NS for remaining pairs. (v-b’) Measurements of respiratory parameters. n = 3 mice for each group, multiple comparisons under one-way ANOVA (Bonferroni post hoc test), NS. Scale bars, 200 µm (a), 100 µm (o). Data are mean values ± s.e.m.

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Extended Data Fig. 9 Parasympathetic neurons in the NA project to the airway-innervating postganglionic neurons.

(a-c) tdTom+ fibers from Fig. 5a were absent in the lung (a), while present in the lateral parabrachial nucleus (PBN, b) and dorsal raphe nucleus (DR, b) and arcuate hypothalamic nucleus (ARC, c). 3 V, the third ventricle. Yellow boxed areas are magnified on the right. (d-g) tdTom+ neurons/ganglia (arrowheads) reside in the trachea, main bronchi (d) and main airway entrance (hilum) of lung lobes (e-g) of Chat-cre; tdTomato mice. (h) tdTom+ nerves innervate postganglionic parasympathetic ganglia (VAChT+, arrowhead) in the extrapulmonary airway, rather than directly on smooth muscle cells (SMA+). (i,j) Zsgreen+ parasympathetic nerve terminals near both airway smooth muscles and immune cells. (k) Stereotaxic injection of AAV-flex-tdTomato into bilateral DMV (also labeled neurons in ventral adjoining 12 N) of Chat-cre mice. Yellow boxed area was magnified on the right, ovals outline DMV regions. (l) tdTom+ nerves were found passing through the space between trachea and esophagus (arrowheads). n = 3 mice for a-l. (m) Experiment scheme for measuring airway hyperreactivity after repeated chemogenetic activation of Chat+ neurons in the NA. (n,o) FlexiVent, n = 3 fourth HDM, 4 fourth CNO*4 and 3 fourth saline mice, multiple comparisons under one-way ANOVA (Bonferroni post hoc test), for Rrs (n), at 12 mg ml−1 MCh, P < 0.0001 fourth HDM versus fourth saline or fourth CNO, at 24 mg ml−1, P = 0.0002 fourth CNO*4 versus fourth saline, P < 0.0001 for remaining pairs; for Ers (o), at 12 mg ml−1, P = 0.0238 fourth HDM versus fourth saline, at 24 mg ml−1, P = 0.0014 fourth HDM versus fourth CNO*4, P < 0.0001 for remaining pairs. NS for all other pairs. (p) Experiment scheme for CNO activation of NA Chat+ neurons in naïve mice. Both groups received the 1st–3rd saline challenge. (q,r) FlexiVent, n = 3 fourth saline and 6 fourth CNO mice, unpaired t-test (two-sided), NS for all pairs. Scale bars, 500 µm (a,d-g), 200 µm (b-c,i,j,k,l), 100 µm (h). Data are mean values ± s.e.m.

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Extended Data Fig. 10 Modulation of Calb1+ neurons or parasympathetic neurons in the DMV did not affect allergen-induced airway hyperreactivity.

(a) Experiment scheme for chemogenetic inhibition of Calb1+ neurons. Bi-DMV, bilateral DMV. (b) AAV-flex-hM4D-mCherry signals in the NA of Calb1-cre mouse. VAChT stains for cholinergic neurons in the NA. (c,d) FlexiVent, n = 4 HDM and 4 CNO mice, unpaired t-test (two-sided), NS for all pairs. (e) Experiment scheme for chemogenetic activation of Calb1+ neurons. (f) AAV-DIO-hM3D-mCherry signals in the NA of Calb1-cre mouse. VAChT stains for cholinergic neurons in the NA. (g,h) FlexiVent, n = 3 fourth HDM, 4 fourth CNO and 3 fourth saline mice, multiple comparisons under one-way ANOVA (Bonferroni post hoc test), for Rrs (g), at 12 mg ml−1 MCh, P = 0.0002 fourth HDM versus fourth saline, P = 0.0004 fourth HDM versus fourth CNO, at 24 mg ml−1, P < 0.0001 fourth HDM versus fourth saline or fourth CNO; for Ers (h), at 6 mg ml−1, P = 0.0418 fourth HDM versus fourth CNO, at 12 and 24 mg ml−1, P < 0.0001 fourth HDM versus fourth saline or fourth CNO. NS for all other pairs. (i) Calb1-cre; tdTom labeled neurons. (j) Experiment scheme for chemogenetic inhibition of Chat+ neurons in the DMV. (k,l) FlexiVent, n = 3 HDM and 6 CNO mice, unpaired t-test (two-sided), NS for all pairs. (m) Experiment scheme for chemogenetic activation of Chat+ neurons in the DMV. (n,o) FlexiVent, n = 4 fourth HDM, 4 fourth CNO and 4 fourth saline mice, multiple comparisons under one-way ANOVA (Bonferroni post hoc test), for Rrs (n), at 12 mg ml−1 MCh, P = 0.0023 fourth HDM versus fourth saline, P = 0.0248 fourth HDM versus fourth CNO, at 24 mg ml−1, P < 0.0001 fourth HDM versus fourth saline or fourth CNO; for Ers (o), at 24 mg ml−1, P < 0.0001 fourth HDM versus fourth saline or fourth CNO. NS for all other pairs. (p) UMAP plots of the integrated NA dataset. (q) Stacked bar plots. (r) Representative cryosection of the brainstem showing bilateral cannula implantation (tract indicated by arrowheads) target NA region (VAChT+). Arrowheads in b,f,i, labeled neurons that are outside of the region of the VAChT+ cholinergic NA neurons. Scale bars, 200 µm (b, f,i), 500 µm(r). Data are mean values ± s.e.m.

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Supplementary information

Supplementary Information

This file contains Supplementary Note 1 and legends for Supplementary Figs. 1 and 2 and Tables 1–4.

Reporting Summary

Supplementary Fig. 1

Gating strategies used for cell sorting. (a) Gating strategies to determine the percentage of eosinophils (Eos) from whole lungs presented on Extended Data Figs. 1c, 3t, 6y and 8r. Red colored boxes indicate cells used for following analysis. AM, alveolar macrophage. (b) Gating strategies to determine the percentage of innate lymphoid cells (ILC2s) and T-helper type 2 cells (Th2) from whole lungs presented on Extended Data Figs. 1b,1d,3s,3u,6x,6z,8q and8s. Colored boxes indicate cells used for following analysis.

Supplementary Fig. 2

Clustree output and Density plot of the integrated nTS dataset. (a) Output from clustree for the different clustering resolutions using integrated nTS dataset. In this dataset, the resolution was set to 1.0 (indicated by arrowhead). (b-h) UMAP plots of the integrated nTS dataset from Resolution 0.4 to Resolution 1.0 (see Supplementary Note 1 for details). (i) Density UMAP plot of the integrated dataset.

Supplementary Table 1

Table of Top 200 differentially expressed marker genes of nTS Cluster 18 at Resolution 0.9 and Cluster 10 at Resolution 1.0. Marker genes for both clusters from “FindAllMarkers” analysis were sorted by adjusted p-value, following the default Seurat pipeline and based on Bonferroni correction using all features in the dataset. Shared marker genes were highlighted in yellow. See Supplementary Note 1 for details.

Supplementary Table 2

Table of Top 200 differentially expressed marker genes of 25 interim nTS clusters at Resolution 1.0. Marker genes for each cluster from “FindAllMarkers” analysis are sorted by adjusted p-value, following the default Seurat pipeline and based on Bonferroni correction using all features in the dataset. Shared marker genes between Clusters 8 and 3; Clusters 18, 19 and 1; Clusters 20 and 17; Clusters 22 and 7; Clusters 23 and 10; Clusters 24 and 16 were highlighted in yellow. See Supplementary Note 1 for details.

Supplementary Table 3

Table of Top 100 differentially expressed marker genes of nTS clusters. Top 100 marker genes for each cluster from “FindAllMarkers” analysis as sorted by adjusted p-value, following the default Seurat pipeline and based on Bonferroni correction using all features in the dataset. Marker genes that were plotted in Fig. 2f were highlighted in yellow. Marker genes that were validated in Fig. 2g-t and Extended Data Fig. 4h-c’, as well as used for quantification in Fig. 3c were bolded in red.

Supplementary Table 4

Table of Top 100 differentially expressed genes of NA clusters. Top 100 marker genes for each cluster from “FindAllMarkers” analysis as sorted by adjusted p-value, following the default Seurat pipeline and based on Bonferroni correction using all features in the dataset. Marker genes that were plotted in Fig. 6b were highlighted in yellow.

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Su, Y., Xu, J., Zhu, Z. et al. Brainstem Dbh+ neurons control allergen-induced airway hyperreactivity. Nature 631, 601–609 (2024). https://doi.org/10.1038/s41586-024-07608-5

- Received: 18 September 2022

- Accepted: 24 May 2024

- Published: 10 July 2024

- Issue Date: 18 July 2024

- DOI: https://doi.org/10.1038/s41586-024-07608-5

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Semantic encoding during language comprehension at single-cell resolution

Mohsen Jamali,

Benjamin Grannan,

Jing Cai,

Arjun R. Khanna,

William Muñoz,

Irene Caprara,

Angelique C. Paulk,

Sydney S. Cash,

Evelina Fedorenko &

…

Ziv M. Williams

Nature volume 631, pages 610–616 (2024)

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Abstract

From sequences of speech sounds1,2 or letters[3], humans can extract rich and nuanced meaning through language. This capacity is essential for human communication. Yet, despite a growing understanding of the brain areas that support linguistic and semantic processing4,5,6,7,8,9,10,11,12, the derivation of linguistic meaning in neural tissue at the cellular level and over the timescale of action potentials remains largely unknown. Here we recorded from single cells in the left language-dominant prefrontal cortex as participants listened to semantically diverse sentences and naturalistic stories. By tracking their activities during natural speech processing, we discover a fine-scale cortical representation of semantic information by individual neurons. These neurons responded selectively to specific word meanings and reliably distinguished words from nonwords. Moreover, rather than responding to the words as fixed memory representations, their activities were highly dynamic, reflecting the words’ meanings based on their specific sentence contexts and independent of their phonetic form. Collectively, we show how these cell ensembles accurately predicted the broad semantic categories of the words as they were heard in real time during speech and how they tracked the sentences in which they appeared. We also show how they encoded the hierarchical structure of these meaning representations and how these representations mapped onto the cell population. Together, these findings reveal a finely detailed cortical organization of semantic representations at the neuron scale in humans and begin to illuminate the cellular-level processing of meaning during language comprehension.

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Main

Humans are capable of communicating exceptionally detailed meanings through language. How neurons in the human brain represent linguistic meaning and what their functional organization may be, however, remain largely unknown. Initial perceptual processing of linguistic input is carried out by regions in the auditory cortex for speech1,2 or visual regions for reading[3]. From there, information flows to the amodal language-selective[9] left-lateralized network of frontal and temporal regions that map word forms to word meanings and assemble them into phrase- and sentence-level representations4,5,13. Processing meanings extracted from language also engages widespread areas outside this language-selective network, with diverging evidence suggesting that semantic processing may be broadly distributed across the cortex[11] or that it may alternatively be concentrated in a few semantic ‘hubs’ that process meaning from language as well as other modalities7,12. How linguistic and semantic information is represented at the basic computational level of individual neurons during natural language comprehension in humans, however, remains undefined.

Despite a growing understanding of semantic processing from imaging studies, little is known about how neurons in humans process or represent word meanings during language comprehension. Further, although speech processing is strongly context dependent[14], how contextual information influences meaning representations and how these changes may be instantiated within sentences at a cellular scale remain largely unknown. Finally, although our semantic knowledge is highly structured15,16,17, little is understood about how cells or cell ensembles represent the semantic relationships among words or word classes during speech processing and what their functional organization may be.

Single-neuronal recordings have the potential to begin unravelling some of the real-time dynamics of word and sentence comprehension at a combined spatial and temporal resolution that has largely been inaccessible through traditional human neuroscience approaches18,19,20. Here we used a rare opportunity to record from single cells in humans18,19,21 and begin investigating the moment-by-moment dynamics of natural language comprehension at the cellular scale.

Single-neuron recordings during speech processing

Single-neuronal recordings were obtained from the prefrontal cortex of the language-dominant hemisphere in a region centred along the left posterior middle frontal gyrus (Fig. 1a and Methods (‘Acute intraoperative single-neuronal recordings’) and Extended Data Fig. 1a). This region contains portions of the language-selective network together with several other high-level networks22,23,24,25, and has been shown to reliably represent semantic information during language comprehension11,26. Here recordings were performed in participants undergoing planned intraoperative neurophysiology. Moreover, all participants were awake and therefore capable of performing language-based tasks, providing the unique opportunity to study the action potential dynamics of individual neurons during comprehension in humans.

Fig. 1: Semantic selectivity by single neurons during naturalistic speech processing.

a, Left: single-neuron recordings were obtained from the left language-dominant prefrontal cortex. Recording locations for the microarray (red) and Neuropixels (beige) recordings (spm12; Extended Data Table 1) as well as an approximation of language-selective network areas (brown) are indicated. Right: the action potentials of putative neurons. b, Action potentials (black lines) and instantaneous firing rate (red trace) of each neuron were time-aligned to the onset of each word. Freq., frequency. c, Word embedding approach for identifying semantic domains. Here each word is represented as a 300-dimensional (dim) vector. d, Silhouette criterion analysis (upper) and purity measures (lower) characterized the separability and quality of the semantic domains (Extended Data Fig. 2a). permut., permutations. e, Peri-stimulus spike histograms (mean ± standard error of the mean (s.e.m.)) and rasters for two representative neurons. The horizontal green bars mark the window of analysis (100–500 ms from onset). sp, spikes. f, Left: confusion matrix illustrating the distribution of cells that exhibited selective responses to one or more semantic domains (P < 0.05, two-tailed rank-sum test, false discovery rate-adjusted). Spatiotemp., spatiotemporal.; sig. significant. Top right: numbers of cells that exhibited semantic selectivity. g, Left: SI of each neuron (n = 19) when compared across semantic domains. The SIs of two neurons are colour-coded to correspond to those shown in Fig. 1e. Upper right: mean SI across neurons when randomly selecting words from 60% of the sentences (mean SI = 0.33, CI = 0.32–0.33; across 100 iterations). Bottom right: probabilities of neurons exhibiting significant selectivity to their non-preferred semantic domains when randomly selecting words from 60% of the sentences (1.4 ± 0.5% mean ± s.e.m. different (diff.) domain). h, Relationship between increased meaning specificity (by decreasing the number of words on the basis of the words’ distance from each domain’s centroid) and response selectivity. The lines with error bars in d,g,h represent mean with 95% confidence limits.

Altogether, we recorded from 133 well-isolated single units (Fig. 1a, right, and Extended Data Fig. 1a,b) in 10 participants (18 sessions; 8 male and 2 female individuals, age range 33–79; Extended Data Table 1) using custom-adapted tungsten microelectrode arrays27,28,29 (microarray; Methods (‘Single-unit isolation’)). To further confirm the consistency and robustness of neuronal responses, an additional 154 units in 3 participants (3 sessions; 2 male individuals and 1 female individual; age range 66–70; Extended Data Table 1) were also recorded using silicon Neuropixels arrays30,31 (Methods (‘Single-unit isolation’) and Extended Data Fig. 1c,d) that allowed for higher-throughput recordings per participant (287 units across 13 participants in total; 133 units from the microarray recordings and 154 units from the Neuropixels recordings). All participants were right-hand-dominant native English speakers and were confirmed to have normal language function by preoperative testing.

During recordings, the participants listened to semantically diverse naturalistic sentences that were played to them in a random order. This amounted to an average of 459 ± 24 unique words or 1,052 ± 106 word tokens (± s.e.m) across 131 ± 13 sentences per participant (Methods (‘Linguistic materials’) and Extended Data Table 1). Additional controls included the presentations of unstructured word lists, nonwords and naturalistic story narratives (Extended Data Table 1). Action potential activities were aligned to each word or nonword using custom-made software at millisecond resolution and analysed off-line (Fig. 1b). All primary findings describe results for the tungsten microarray recordings unless stated otherwise for the Neuropixels recordings (Extended Data Fig. 1).

Selectivity of neurons to specific word meanings

A long-standing observation[32] that lies at the core of all distributional models of meaning[33] is that words that share similar meanings tend to occur in similar contexts. Data-driven word embedding approaches that capture these relationships through vectoral representations11,34,35,36,37,38,39 have been found to estimate word meanings quite well and to accurately capture human behavioural semantic judgements[40] and neural responses to meaning through brain-imaging studies11,26,37,39,41.

To first examine whether and to what degree the activities of neurons within the population reflected the words’ meanings during speech processing, we used an embedding approach that replaced each unique word heard by the participants with pretrained 300-dimensional embedding vectors extracted from a large English corpus (Methods (‘Word embedding and clustering procedures’))35,37,39,42. Thus, for instance, the words ‘clouds’ and ‘rain’, which are closely related in meaning, would share a smaller vectoral cosine distance in this embedding space when compared to ‘rain’ and ‘dad’ (Fig. 1c, left). Next, to determine how the words optimally group into semantic domains, we used a spherical clustering and silhouette criterion analysis34,37,43,44 to reveal the following nine putative domains: actions (for example, ‘walked’, ‘ran’ and ‘threw’), states (for example, ‘happy’, ‘hurt’ and ‘sad’), objects (for example, ‘hat’, ‘broom’ and ‘lampshade’), food (for example, ‘salad’, ‘carrots’ and ‘cake’), animals (for example, ‘bunny’, ‘lizard’ and ‘horse’), nature (for example, ‘rain’, ‘clouds’ and ‘sun’), people and family (for example, ‘son’, ‘sister’ and ‘dad’), names (for example, ‘george’, ‘kevin’ and ‘hannah’) and spatiotemporal relationships (for example, ‘up’, ‘down’ and ‘behind’; Fig. 1c right and Extended Data Tables 2 and 3). Purity and d′ measures confirmed the quality and separability of these word clusters (Fig. 1d and Extended Data Fig. 2a).

We observed that many of the neurons responded selectively to specific word meanings. The selectivity or ‘tuning’ of neurons reflects the degree to which they respond to words denoting particular meanings (that is, words that belong to specific semantic domains). Thus, a selectivity index (SI) of 1.0 would indicate that a cell responded to words within only one semantic domain and no other, whereas an SI of 0 would indicate no selectivity (that is, similar responses to words across all domains; Methods (‘Evaluating the responses of neurons to semantic domains’)). Altogether, 14% (n = 19 of 133; microarray) of the neurons responded selectively to specific semantic domains indicating that their firing rates distinguished between words on the basis of their meanings (two-tailed rank-sum test comparing activity for each domain to that of all other domains; false discovery rate-corrected for the 9 domains, P < 0.05). Thus, for example, a neuron may respond selectively to ‘food’ items whereas another may respond selectively to ‘objects’ (Fig. 1e). The domain that elicited the largest change in activity for the largest number of cells was that of ‘actions’, and the domain that elicited changes for the fewest cells was ‘spatiotemporal relations’ (Fig. 1f). The mean SI across all selective neurons was 0.32 (n = 19; 95% confidence interval (CI) = 0.26–0.38; Fig. 1g, left) and progressively increased as the semantic domains became more specific in meaning (that is, when removing words that lay farther away from the domain centroid; analysis of variance, F(3,62) = 8.66, P < 0.001; Fig. 1h and Methods (‘Quantifying the specificity of neuronal response’)). Findings from the Neuropixels recordings were similar, with 19% (n = 29 of 154; Neuropixels) of the neurons exhibiting semantic selectivity (mean SI = 0.42, 95% CI = 0.36–0.48; Extended Data Fig. 3a,b), in aggregate, providing a total of 48 of 287 semantically selective neurons across the 13 participants. Many of the neurons across the participants and recording techniques therefore exhibited semantic selectivity during language comprehension.

Most of the neurons that exhibited semantic selectivity responded to only one semantic domain and no other. Of the neurons that demonstrated selectivity, 84% (n = 16; microarray) responded to one of the nine domains, with only 16% (n = 3) showing response selectivity to two domains (two-sided rank-sum test, P < 0.05; Fig. 1f, top right). The response selectivity of these neurons was also robust to analytic choice, demonstrating a similarly high degree of selectivity when randomly sub-selecting words (SI = 0.33, CI = 0.32–0.33, rank-sum test when compared to the original SI values, z value = 0.44, P = 0.66, Fig. 1g, top right, and Methods (‘Evaluating the responses of neurons to semantic domains’)) or when selecting words that intuitively fit within their respective domains (SI = 0.30; rank-sum test compared to the original SI values, z value = 0.60, P = 0.55; Extended Data Fig. 2b and Extended Data Table 2). Moreover, they exhibited a similarly high degree of selectivity when selecting nonadjacent content words (SI = 0.34, CI = 0.26–0.42; Methods), further confirming the consistency of neuronal response.

Finally, given these findings, we tested whether the neurons distinguished real words from nonwords (such as ‘blicket’ or ‘florp’, which sound like words but are meaningless), as might be expected of cells that represent meaning. Here we found that many neurons distinguished words from nonwords (27 of 48 neurons; microarray, in 7 participants for whom this control was carried out; two-tailed t-test, P < 0.05; Methods (‘Linguistic materials; Nonwords’)), meaning that they exhibited a consistent difference in their activities. Moreover, the ability to differentiate words from nonwords was not necessarily restricted to semantically selective neurons (Extended Data Fig. 3f, Neuropixels, and Extended Data Fig. 4, microarray), together revealing a broad mixture of response selectivity to word meanings within the cell population.

Generalizable and robust meaning representations

Meaning representations by the semantically selective neurons were robust. Training multi-class decoders on the combined response patterns of the semantically selective cells, we found that these cell ensembles could reliably predict the semantic domains of randomly selected subsets of words not used for training (31 ± 7% s.d.; chance: 11%, permutation test, P < 0.01; Fig. 2a and Methods (‘Model decoding performance and the robustness of neuronal response’)). Moreover, similar decoding performances were observed when using a different embedding model (GloVe[45]; 25 ± 5%; permutation test, P < 0.05; Fig. 2b) or when selecting different recorded time points within the sentences (that is, the first half versus the second half of the sentences; Extended Data Fig. 5a). Similar decoding performances were also observed when randomly subsampling neurons from across the population (Extended Data Fig. 5c–e), or when examining multi-unit activities for which no spike sorting was carried out (permutation test, P < 0.05; Methods (‘Multi-unit isolation’) and Extended Data Fig. 5b). In tandem, these analyses therefore suggested that the words’ meanings were robustly represented within the population’s response patterns.

Fig. 2: Decoding word meanings during language comprehension.

a, Left: projected probabilities of correctly predicting the semantic domain to which individual words belonged over a representative sentence. Right: the cumulative decoding performance (±s.d.) of all semantically selective neurons during presentation of sentences (blue) versus chance (orange); see also Extended Data Fig. 4b. b, Decoding performances (±s.d.) across two independent embedding models (Word2Vec and GloVe). c, Left: the absolute difference in neuronal responses (n = 115) for homophone pairs that sounded the same but differed in meaning (red) compared to that of non-homophone pairs that sounded different but shared similar meanings (blue; two-sided permutation test). Right: scatter plot displaying each neuron’s absolute difference in activity for homophone versus non-homophone pairs (P < 0.0001, one-sided t-test comparing linear fit to identity line). d, Peri-stimulus spike histogram (mean ± s.e.m.) and raster from a representative neuron when hearing words within sentences (top) compared to words within random word lists (bottom). The horizontal green bars mark the window of analysis (100–500 ms from onset). e, Left: SI distributions for neurons during word-list and sentence presentations together with the number of neurons that responded selectivity to one or more semantic domains (inset). Right: the SI for neurons (mean with 95% confidence limit, n = 9; excluding zero firing rate neurons) during word-list presentation. These neurons did not exhibit changes in mean firing rates when comparing all sentences versus word lists independently of semantic domains (rank-sum test, P = 0.16).

We also examined whether the activities of the neurons could be generalized to an entirely new set of naturalistic narratives. Here, for three of the participants, we additionally introduced short story excerpts that were thematically and stylistically different from the sentences and that contained new words (Extended Data Table 1; 70 unique words of which 28 were shared with the sentences). We then used neuronal activity recorded during the presentation of sentences to decode semantic domains for words heard during these stories (Methods (‘Linguistic materials; Story narratives’)). We find that, even when using this limited subset of semantically selective neurons (n = 9; microarray), models that were originally trained on activity recorded during the presentation of sentences could predict the semantic domains of words heard during the narratives with significant accuracy (28 ± 5%; permutation test, P < 0.05; Extended Data Fig. 6).

Finally, to confirm the consistency of these semantic representations, we evaluated neuronal responses across the different participants and recording techniques. Here we found similar results across individuals (permutation test, P < 0.01) and clinical conditions (χ^2 = 2.33, P = 0.31; Methods (‘Confirming the robustness of neuronal response across participants’) and Extended Data Fig. 2c–f), indicating that the results were not driven by any single participant or a small subset of participants. We also evaluated the consistency of semantic representations in the three participants who underwent Neuropixels recordings and found that the activities of semantically selective neurons in these participants could be used to reliably predict the semantic domains of words not used for model fitting (29 ± 7%; permutation test, P < 0.01; Extended Data Fig. 3c) and that they were comparable across embedding models (GloVe; 30 ± 6%). Collectively, decoding performance across the 13 participants (48 of 287 semantically selective neurons in total) was 36 ± 7% and significantly higher than expected from chance (permutation test, P < 0.01; Methods). These findings therefore together suggested that these meaning representations by semantically selective neurons were both generalizable and robust.

Sentence context dependence of meaning encoding

An additional core property of language is our ability to interpret words on the basis of the sentence contexts in which they appear46,47. For example, hearing the sequences of words “He picked the rose…” versus “He finally rose…” allows us to correctly interpret the meaning of the ambiguous word ‘rose’ as a noun or a verb. It also allows us to differentiate homophones—words that sound the same but differ in meaning (such as ‘sun’ and ‘son’)—on the basis of their contexts.

Therefore, to first evaluate the degree to which the meaning representations by neurons are sentence context dependent, seven of the participants were presented with a word-list control that contains the same words as those heard in the sentences but were presented in random order (for example, “to pirate with in bike took is one”; Extended Data Table 1), thus largely removing the influence of context on lexical (word-level) processing. Here we find that, the SI of the neurons that exhibited semantic selectivity in the sentence condition dropped from a mean of 0.34 (n = 9 cells; microarray, CI = 0.25–0.43) to 0.19 (CI = 0.07–0.31) during the word-list presentation (signed-rank test, z(17) = 40, P = 0.02; Fig. 2d,e), in spite of similar mean population firing rate[48] (two-sided rank-sum test, z value = 0.10, P = 0.16). The results were similar for the Neuropixels recordings, for the SI dropped from 0.39 (CI = 0.33–0.45) during the presentation of sentences to 0.29 (CI = 0.19–0.39) during word-list presentation (Extended Data Fig. 3e; signed-rank test, z(41) = 168, P = 0.035). These findings therefore suggested that the response selectivity of these neurons was strongly influenced by the word’s context and that these changes were independent of potential variations in attentional engagement, as evidenced by similar overall firing rates between the sentences and word lists[48].

Second, to test whether the neurons’ activity reflected the words’ meanings independently of their word-form similarity, we used homophone pairs that are phonetically identical but differ in meaning (for example, ‘sun’ versus ‘son’; Extended Data Table 1). Here we find that neurons across the population exhibited a larger difference in activity for words that sounded the same but had different meanings (that is, homophones) compared to words that sounded different but belonged to the same semantic domain (permutation test, P < 0.0001; n = 115 cells; microarray, for which data were available; Figs. 2c and 3a and Methods (‘Evaluating the context dependency of neuronal response using homophone pairs’)). These neurons therefore encoded the words’ meanings independently of their sound-level similarity.

Fig. 3: Sentence context dependence and word meaning predictions.

a, Differences in neuronal activity comparing homophone (for example, ‘son’ and ‘sun’; blue) to non-homophone (for example, ‘son’ and ‘dad’; red) pairs across participants using a participant-dropping procedure (two-sided paired t-test, P < 0.001 for all participants). b, Left: decoding accuracies for words that showed high versus low surprisal based on the preceding sentence contexts in which they were heard. Words with lower surprisal were more predictable on the basis of their preceding word sequence. Actual and chance decoding performances are shown in blue and orange, respectively (mean ± s.d., one-sided rank-sum test, z value = 26, P < 0.001). Right: a regression analysis on the relation between decoding performance and surprisal.

Last, we quantified the degree to which the words’ meanings could be predicted from the sentences in which they appeared. Here we reasoned that words that were more likely to occur on the basis of their preceding word sequence and context should be easier to decode. Using a long short-term memory model to quantify each word’s surprisal based on its sentence context (Methods (‘Evaluating the context dependency of neuronal response using surprisal analysis’)), we find that decoding accuracies for words that were more predictable were significantly higher than for words that were less predictable (comparing top versus bottom deciles; 26 ± 14% versus 10 ± 9% respectively, rank-sum test, z value = 26, P < 0.0001; Fig. 3b). Similar findings were also obtained from the Neuropixels recordings (rank-sum test, z value = 25, P < 0.001; Extended Data Fig. 3d), indicating that information about the sentences was being tracked and that it influenced neuronal response. These findings therefore together suggested that the activities of these neurons were dynamic, reflecting processing of the words’ meanings based on their specific sentence contexts and independently of their phonetic form.

Organization of semantic representations

The above observations suggested that neurons within the population encoded information about the words’ meanings during comprehension. How they may represent the higher-order semantic relationships among words, however, remained unclear. Therefore, to further probe the organization of neuronal representations of meaning at the level of the cell population, we regressed the responses of the neurons (n = 133) onto the embedding vectors of all words in the study vocabulary (that is, a matrix of n words × 300 embedding dimensions), resulting in a set of model weights for the neurons (Fig. 4a, left, and Methods (‘Determining the relation between the word embedding space and neural response’)). These model weights were then concatenated (dimension = 133 × 300) to define a putative neuronal–semantic space. Each model weight can therefore be interpreted as the contribution of a particular dimension in the embedding space to the activity of a given neuron, such that the resulting transformation matrix reflects the semantic relationships among words as represented by the population11,34,37.

Fig. 4: Hierarchical semantic relationship between word representations.

a, Left: the activity of each neuron was regressed onto 300-dimensional word embedding vectors. A PC analysis was then used to dimensionally reduce this space from the concatenated set model parameters such that the cosine distance between each projection reflected the semantic relationship between words as represented by the neural population. Right: PC space with arrows highlighting two representative word projections. The explained variance and correlation between cosine distances for word projections derived from the word embedding space versus neural data (n = 258,121 possible word pairs) are shown in Extended Data Fig. 7a,b. b, Left: activities of neurons for word pairs based on their vectoral cosine distance within the 300-dimensional embedding space (z-scored activity change over percentile cosine similarity, red regression line; Pearson’s correlation, r = 0.17). Right: correlation between vectoral cosine distances in the word embedding space and difference in neuronal activity across possible word pairs (orange) versus chance distribution (grey, n = 1,000, P = 0.02; Extended Data Fig. 7c). c, Left: scatter plot showing the correlation between population-averaged neuronal activity and the cophenetic distances between words (n = 100 bins) derived from the word embedding space (red regression line; Pearson’s correlation, r = 0.36). Right: distribution of correlations between cophenetic distances and neuronal activity across the different participants (n = 10).

Applying a principal component (PC) analysis to these weights, we find that the first five PCs accounted for 46% of the variance in neural population activity (Fig. 4a right and Extended Data Fig. 7a) and 81% of the variance for the semantically selective neurons (Extended Data Fig. 3g for the Neuropixels recordings). Moreover, when projecting words back into this PC space, we find that the vectoral distances between neuronal projections significantly correlated with the dimensionally reduced word distances in the original word embeddings (258,121 possible word pairings; r = 0.04, permutation test, P < 0.0001; Extended Data Fig. 7b). Significant correlations between word similarity and neuronal activity were also observed when using a non-embedding approach based on the ‘synset’ similarity metric (WordNet; r = −0.76, P = 0.001; Extended Data Fig. 7d) as well as when comparing the vectoral distances in the word embeddings to the raw firing activities of the neurons (r = 0.17; permutation test, one-sided, P = 0.02, Fig. 4b and Extended Data Fig. 7c for microarray recordings and r = 0.21; Pearson’s correlation, P < 0.001; Extended Data Fig. 3h for Neuropixels recordings). Our findings therefore suggested that these cell populations reliably captured the semantic relationships among words.

Finally, to evaluate whether and to what degree neuronal activity reflected the hierarchical semantic relationship between words, we compared differences in firing activity for each word pair to the cophenetic distances between those words49,50,51 in the 300-dimension word embedding space (Methods (‘Estimating the hierarchical structure and relation between word projections’)). Here the cophenetic distance between a pair of words reflects the height of the dendrogram where the two branches that include these two words merge into a single branch. Using an agglomerative hierarchical clustering procedure, we find that the activities of the semantically selective neurons closely correlated with the cophenetic distances between words across the study vocabulary (r = 0.38, P = 0.004; Fig. 4c). Therefore, words that were connected by fewer links in the hierarchy and thus more likely to share semantic features (for example, ‘ducks’ and ‘eggs’)50,51 elicited smaller differences in activity than words that were connected by a larger number of links (for example, ‘eggs’ and ‘doorbell’; Fig. 5 and Methods (‘t-stochastic neighbour embedding procedure’)). These results therefore together suggested that these cell ensembles encoded richly detailed information about the hierarchical semantic relationship between words.

Fig. 5: Organization of semantic representations within the cell population.

a, An agglomerative hierarchical clustering procedure was carried out on all word projections in PC space obtained from the neuronal population data. The dendrogram shows representative word projections, with the branches truncated to allow for visualization. Words that were connected by fewer links in the hierarchy have a smaller cophenetic distance. b, A t-stochastic neighbour embedding procedure was used to visualize all word projections (in grey) by collapsing them onto a common two-dimensional manifold. For comparison, representative words are further colour-coded on the basis of their original semantic domain assignments in Fig. 1c.

Discussion

Neurons are the most basic computational units by which information is encoded in the brain. Yet, despite a growing understanding of the neural substrates of linguistic4,5,6,7,8,9,10,11,12 and semantic processing11,37,41, understanding how individual neurons represent semantic information during comprehension in humans has largely remained out of reach. Here, using single-neuronal recordings during natural speech processing, we discover cells in the prefrontal cortex of the language-dominant hemisphere that responded selectively to particular semantic domains and that exhibited preferential responses to specific word meanings. More notably, the combined activity patterns of these neurons could be used to accurately decode the semantic domain to which the words belonged even when tested across entirely different linguistic materials (that is, story narratives), suggesting a process that could allow semantic information to be reliably extracted during comprehension at the cellular scale. Lastly, to understand language, the meanings of words likely need to be robustly represented within the brain, entailing not only similar representations for words that share semantic features (for example, ‘mouse’ and ‘rat’) but also sufficiently distinct representations for words that differ in meaning (for example, ‘mouse’ and ‘carrot’). Here we find a putative cellular process that could support such robust word meaning representations during language comprehension.

Collectively, these findings imply that focal cortical areas such as the one from which we recorded here may be potentially able to represent complex meanings largely in their entirety. Although we sampled cells from a relatively restricted prefrontal region of the language-dominant hemisphere, these cell populations were capable of decoding meanings—at least at a relatively coarse level of semantic granularity—of a large set of diverse words and across independent sets of linguistic materials. The responses of these cell ensembles also harboured detailed information about the hierarchical relationship between words across thousands of word pairs, suggesting a cellular mechanism that could allow semantic information to be rapidly mapped onto the population’s response patterns, in real time during speech.

Another notable observation from these recordings is that the activities of the neurons were highly context dependent, reflecting the words’ meanings based on the specific sentences in which they were heard even when they were phonetically indistinguishable. Sentence context is essential to our ability to hone in on the precise meaning or aspects of meaning needed to infer complex ideas from linguistic utterances, and is proposed to play a key role in language comprehension46,47,52. Here we find that the neurons’ responses were highly dynamic, reflecting the meaning of the words within their respective contexts, even when the words were identical in form. Loss of sentence context or less predictive contexts, on the other hand, diminished the neurons’ ability to differentiate among semantic representations. Therefore, rather than simply responding to words as fixed stored memory representations, these neurons seemed to adaptively represent word meanings in a context-dependent manner during natural speech processing.

Taken together, these findings reveal a highly detailed representation of semantic information within prefrontal cortical populations, and a cellular process that could allow the meaning of words to be accurately decoded in real time during speech. As the present findings focus on auditory language processing, however, it is also interesting to speculate whether these semantic representations may be modality independent, generalizing to reading comprehension53,54, or even generalize to non-linguistic stimuli, such as pictures or videos or nonspeech sounds. Further, it remains to be discovered whether similar semantic representations would be observed across languages, including in bilingual speakers, and whether accessing word meanings in language comprehension and production would elicit similar responses (for example, whether the representations would be similar when participants understand the word ‘sun’ versus produce the word ‘sun’). It is also unknown whether similar semantic selectivity is present across other parts of the brain such as the temporal cortex, how finer-grained distinctions are represented, and how representations of specific words are composed into phrase- and sentence-level meanings.

Our study provides an initial framework for studying linguistic and semantic processing during comprehension at the level of individual neurons. It also highlights the potential benefit of using different recording techniques, linguistic materials and analytic techniques to evaluate the generalizability and robustness of neuronal responses. In particular, our study demonstrates that findings from the two recording approaches (tungsten microarray recordings and Neuropixels recordings) were highly concordant and suggests a platform from which to begin carrying out similar comparisons (especially in light of the increasing emphasis on robustness and replicability in the field). Collectively, our findings provide evidence of single neurons that encode word meanings during comprehension and a process that could support our ability to derive meaning from speech —opening the door for addressing a multitude of further questions about human-unique communicative abilities.

Methods

Study participants

All procedures and studies were carried out in accordance with the Massachusetts General Hospital Institutional Review Board and in strict adherence to Harvard Medical School guidelines. All participants included in the study were scheduled to undergo planned awake intraoperative neurophysiology and single-neuronal recordings for deep brain stimulation targeting. Consideration for surgery was made by a multidisciplinary team including neurologists, neurosurgeons and neuropsychologists18,19,55,56,57. The decision to carry out surgery was made independently of study candidacy or enrolment. Further, all microelectrode entry points and placements were based purely on planned clinical targeting and were made independently of any study consideration.

Once and only after a patient was consented and scheduled for surgery, their candidacy for participation in the study was reviewed with respect to the following inclusion criteria: 18 years of age or older, right-hand dominant, capacity to provide informed consent for study participation and demonstration of English fluency. To evaluate for language comprehension and the capacity to participate in the study, the participants were given randomly sampled sentences and were then asked questions about them (for example, “Eva placed a secret message in a bottle” followed by “What was placed in the bottle?”). Participants not able to answer all questions on testing were excluded from consideration. All participants gave informed consent to participate in the study and were free to withdraw at any point without consequence to clinical care. A total of 13 participants were enrolled (Extended Data Table 1). No participant blinding or randomization was used.

Neuronal recordings

Acute intraoperative single-neuronal recordings

Microelectrode recording were performed in participants undergoing planned deep brain stimulator placement19,58. During standard intraoperative recordings before deep brain stimulator placement, microelectrode arrays are used to record neuronal activity. Before clinical recordings and deep brain stimulator placement, recordings were transiently made from the cortical ribbon at the planned clinical placement site. These recordings were largely centred along the superior posterior middle frontal gyrus within the dorsal prefrontal cortex of the language-dominant hemisphere. Here each participant’s computed tomography scan was co-registered to their magnetic resonance imaging scan, and a segmentation and normalization procedure was carried out to bring native brains into Montreal Neurological Institute space. Recording locations were then confirmed using SPM12 software and were visualized on a standard three-dimensional rendered brain (spm152). The Montreal Neurological Institute coordinates for recordings are provided in Extended Data Table 1, top.

We used two main approaches to perform single-neuronal recordings from the cortex18,19. Altogether, ten participants underwent recordings using tungsten microarrays (Neuroprobe, Alpha Omega Engineering) and three underwent recordings using linear silicon microelectrode arrays (Neuropixels, IMEC). For the tungsten microarray recordings, we incorporated a Food and Drug Administration-approved, biodegradable, fibrin sealant that was first placed temporarily between the cortical surface and the inner table of the skull (Tisseel, Baxter). Next, we incrementally advanced an array of up to five tungsten microelectrodes (500–1,500 kΩ; Alpha Omega Engineering) into the cortical ribbon at 10–100 µm increments to identify and isolate individual units. Once putative units were identified, the microelectrodes were held in position for a few minutes to confirm signal stability (we did not screen putative neurons for task responsiveness). Here neuronal signals were recorded using a Neuro Omega system (Alpha Omega Engineering) that sampled the neuronal data at 44 kHz. Neuronal signals were amplified, band-pass-filtered (300 Hz and 6 kHz) and stored off-line. Most individuals underwent two recording sessions. After neural recordings from the cortex were completed, subcortical neuronal recordings and deep brain stimulator placement proceeded as planned.

For the silicon microelectrode recordings, sterile Neuropixels probes[31] (version 1.0-S, IMEC, ethylene oxide sterilized by BioSeal) were advanced into the cortical ribbon with a manipulator connected to a ROSA ONE Brain (Zimmer Biomet) robotic arm. The probes (width: 70 µm, length: 10 mm, thickness: 100 µm) consisted of 960 contact sites (384 preselected recording channels) that were laid out in a chequerboard pattern. A 3B2 IMEC headstage was connected via a multiplexed cable to a PXIe acquisition module card (IMEC), installed into a PXIe chassis (PXIe-1071 chassis, National Instruments). Neuropixels recordings were performed using OpenEphys (versions 0.5.3.1 and 0.6.0; https://open-ephys.org/) on a computer connected to the PXIe acquisition module recording the action potential band (band-pass-filtered from 0.3 to 10 kHz, sampled at 30 kHz) as well as the local field potential band (band-pass-filtered from 0.5 to 500 Hz, sampled at 2,500 Hz). Once putative units were identified, the Neuropixels probe was held in position briefly to confirm signal stability (we did not screen putative neurons for speech responsiveness). Additional description of this recording approach can be found in refs. 20,30,31. After completing single-neuronal recordings from the cortical ribbon, the Neuropixels probe was removed, and subcortical neuronal recordings and deep brain stimulator placement proceeded as planned.

Single-unit isolation

For the tungsten microarray recordings, putative units were identified and sorted off-line through a Plexon workstation. To allow for consistency across recording techniques (that is, with the Neuropixels recordings), a semi-automated valley-seeking approach was used to classify the action potential activities of putative neurons and only well-isolated single units were used. Here, the action potentials were sorted to allow for comparable isolation distances across recording techniques59,60,61,62,63 and unit selection with previous approaches27,28,29,64,65, and to limit the inclusion of multi-unit activity (MUA). Candidate clusters of putative neurons needed to clearly separate from channel noise, display a voltage waveform consistent with that of a cortical neuron, and have 99% or more of action potentials separated by an inter-spike interval of at least 1 ms (Extended Data Fig. 1b,d). Units with clear instability were removed and any extended periods (for example, greater than 20 sentences) of little to no spiking activity were excluded from the analysis. In total, 18 recording sessions were carried out, for an average of 5.4 units per session per multielectrode array (Extended Data Fig. 1a,b).

For the Neuropixels recordings, putative units were identified and sorted off-line using Kilosort and only well-isolated single units were used. We used Decentralized Registration of Electrophysiology Data (DREDge; https://github.com/evarol/DREDge) software and an interpolation approach (https://github.com/williamunoz/InterpolationAfterDREDge) to motion correct the signal using an automated protocol that tracked local field potential voltages using a decentralized correlation technique that realigned the recording channels in relation to brain movements31,66. Following this, we interpolated the continuous voltage data from the action potential band using the DREDge motion estimate to allow the activities of the recorded units to be stably tracked over time. Finally, putative neurons were identified from the motion-corrected interpolated signal using a semi-automated Kilosort spike sorting approach (version 1.0; https://github.com/cortex-lab/KiloSort) followed by Phy for cluster curation (version 2.0a1; https://github.com/cortex-lab/phy). Here, an n-trode approach was used to optimize the isolation of single units and limit the inclusion of MUA67,68. Units with clear instability were removed and any extended periods (for example, greater than 20 sentences) of little to no spiking activity were excluded from analysis. In total, 3 recording sessions were carried out, for an average of 51.3 units per session per multielectrode array (Extended Data Fig. 1c,d).

Multi-unit isolation

To provide comparison to the single-neuronal data, we also separately analysed MUA. These MUAs reflect the combined activities of multiple putative neurons recorded from the same electrodes as represented by their distinct waveforms57,69,70. These MUAs were obtained by separating all recorded spikes from their baseline noise. Unlike for the single units, the spikes were not separated on the basis of their waveform morphologies.

Audio presentation and recordings

The linguistic materials were given to the participants in audio format using a Python script utilizing the PyAudio library (version 0.2.11). Audio signals were sampled at 22 kHz using two microphones (Shure, PG48) that were integrated into the Alpha Omega rig for high-fidelity temporal alignment with neuronal data. Audio recordings were annotated in semi-automated fashion (Audacity; version 2.3). For the Neuropixels recordings, audio recordings were carried out at a 44 kHz sampling frequency (TASCAM DR-40× 4-channel 4-track portable audio recorder and USB interface with adjustable microphone). To further ensure granular time alignment for each word token with neuronal activity, the amplitude waveform of each session recording and the pre-recorded linguistic materials were cross-correlated to identify the time offset. Finally, for additional confirmation, the occurrence of each word token and its timing was validated manually. Together, these measures allowed for the millisecond-level alignment of neuronal activity with each word occurrence as they were heard by the participants during the tasks.

Linguistic materials

Sentences

The participants were presented with eight-word-long sentences (for example, “The child bent down to smell the rose”; Extended Data Table 1) that provided a broad sample of semantically diverse words across a wide variety of thematic contents and contexts[4]. To confirm that the participants were paying attention, a brief prompt was used every 10–15 sentences asking them whether we could proceed with the next sentence (the participants generally responded within 1–2 seconds).

Homophone pairs

Homophone pairs were used to evaluate for meaning-specific changes in neural activity independently of phonetic content. All of the homophones came from sentence experiments in which homophones were available and in which the words within the homophone pairs came from different semantic domains. Homophones (for example, ‘sun’ and ‘son’; Extended Data Table 1), rather than homographs, were used as the word embeddings produce a unique vector for each unique token rather than for each token sense.

Word lists

A word-list control was used to evaluate the effect that sentence context had on neuronal response. These word lists (for example, “to pirate with in bike took is one”; Extended Data Table 1) contained the same words as those given during the presentation of sentences and were eight words long, but they were given in a random order, therefore removing any effect that linguistic context had on lexico-semantic processing.

Nonwords

A nonword control was used to evaluate the selectivity of neuronal responses to semantic (linguistically meaningful) versus non-semantic stimuli. Here the participants were given a set of nonwords such as ‘blicket’ or ‘florp’ (sets of eight) that sounded phonetically like words but held no meaning.

Story narratives

Excerpts from a story narrative were introduced at the end of recordings to evaluate for the consistency of neuronal response. Here, instead of the eight-word-long sentences, the participants were given a brief story about the life and history of Elvis Presley (for example, “At ten years old, I could not figure out what it was that this Elvis Presley guy had that the rest of us boys did not have”; Extended Data Table 1). This story was selected because it was naturalistic, contained new words, and was stylistically and thematically different from the preceding sentences.

Word embedding and clustering procedures

Spectral clustering of semantic vectors

To study the selectivity of neurons to words within specific semantic domains, all unique words heard by the participants were clustered into groups using a word embedding approach35,37,39,42. Here we used 300-dimensional vectors extracted from a pretrained dataset generated using a skip-gram Word2Vec[11] algorithm on a corpus of 100 billion words. Each unique word from the sentences was then paired with its corresponding vector in a case-insensitive fashion using the Python Gensim library (version 3.4.0; Fig. 1c, left). High unigram frequency words (log probability of greater than 2.5), such as ‘a’, ‘an’ or ‘and’, that held little linguistic meaning were removed.

Next, to group words heard by the participants into representative semantic domains, we used a spherical clustering algorithm (v.0.1.7, Python 3.6) that used the cosine distance between their representative vectors. We then carried out a k-means clustering procedure in this new space to obtain distinct word clusters. This approach therefore grouped words on the basis of their vectoral distance, reflecting the semantic relatedness between words37,40, which has been shown to work well for obtaining consistent word clusters34,71. Using pseudorandom initiation cluster seeding, the k-means procedure was repeated 100 times to generate a distribution of values for the optimal number of cluster. For each iteration, a silhouette criterion for cluster number between 5 and 20 was calculated. The cluster with the greatest average criterion value (as well as the most frequent value) was 9, which was taken as the optimal number of clusters for the linguistic materials used34,37,43,44.

Confirming the quality and separability of the semantic domains

Purity measures and d′ analysis were used to confirm the quality and separability of the semantic domains. To this end, we randomly sampled from 60% of the sentences across 100 iterations. We then grouped all words from these subsampled sentences into clusters using the same spherical clustering procedure described above. The new clusters were then matched to the original clusters by considering all possible matching arrangements and choosing the arrangement with greatest word overlap. Finally, the clustering quality was evaluated for ‘purity’, which is the percentage of the total number of words that were classified correctly[72]. This procedure is therefore a simple and transparent measure that varies between 0 (bad clustering) to 1 (perfect clustering; Fig. 1d, bottom). The accuracy of this assignment is determined by counting the total number of correctly assigned words and dividing by the total number of words in the new clusters:

$$\text{purity}\left(\Omega ,{\mathbb{C}}\right)=\frac{1}{n}\mathop{\sum }\limits_{i=1}^{k}{\max }_{j}\left|{\omega }_{i}\cap {c}_{j}\right|$$

in which n is the total number of words in the new clusters, k is the number of clusters (that is, 9), \({\omega }_{i}\) is a cluster from the set of new clusters \(\Omega \), and \({c}_{j}\) is the original cluster (from the set of original clusters \({\mathbb{C}}\)) that has the maximum count for cluster \({\omega }_{i}\). Finally, to confirm the separability of the clusters, we used a standard d′ analysis. The d′ metric estimates the difference between vectoral cosine distances for all words assigned to a particular cluster compared to those assigned to all other clusters (Extended Data Fig. 2a).

The resulting clusters were labelled here on the basis of the preponderance of words near the centroid of each cluster. Therefore, although not all words may seem to intuitively fit within each domain, the resulting semantic domains reflected the optimal vectoral clustering of words based on their semantic relatedness. To further allow for comparison, we also introduced refined semantic domains (Extended Data Table 2) in which the words provided within each cluster were additionally manually reassigned or removed by two independent study members on the basis of their subjective semantic relatedness. Thus, for example, under the semantic domain labelled ‘animals’, any word that did not refer to an animal was removed.

Neuronal analysis

Evaluating the responses of neurons to semantic domains

To evaluate the selectivity of neurons to words within the different semantic domains, we calculated their firing rates aligned to each word onset. To determine significance, we compared the activity of each neuron for words that belonged to a particular semantic domain (for example, ‘food’) to that for words from all other semantic domains (for example, all domains except for ‘food’). Using a two-sided rank-sum test, we then evaluated whether activity for words in that semantic domain was significantly different from activity in all semantic domains, with the P value being false discovery rate-adjusted using a Benjamini–Hochberg method to account for repeated comparisons across all of the nine domains. Thus, for example, when stating that a neuron exhibited significant selectivity to the domain of ‘food’, this meant that it exhibited a significant difference in its activity for words within that domain when compared to all other words (that is, it responded selectively to words that described food items).

Next we determined the SI of each neuron, which quantified the degree to which it responded to words within specific semantic domains compared to the others. Here SI was defined by the cell’s ability to differentiate words within a particular semantic domain (for example, ‘food’) compared to all others and reflected the degree of modulation. The SI for each neuron was calculated as

$${\rm{SI}}=\frac{\left|{{\rm{FR}}}_{{\rm{domain}}}-{{\rm{FR}}}_{{\rm{other}}}\right|}{\left|{{\rm{FR}}}_{{\rm{domain}}}+{{\rm{FR}}}_{{\rm{other}}}\right|}$$

in which \({{\rm{FR}}}_{{\rm{domain}}}\) is the neuron’s average firing rate in response to words within the considered domain and \({{\rm{FR}}}_{{\rm{other}}}\) is the average firing rate in response to words outside the considered domain. The SI therefore reflects the magnitude of effect based on the absolute difference in activity for each neuron’s preferred semantic domain compared to others. Therefore, the output of the function is bounded by 0 and 1. An SI of 0 would mean that there is no difference in activity across any of the semantic domains (that is, the neuron exhibits no selectivity) whereas an SI of 1.0 would indicate that a neuron changed its action potential activity only when hearing words within one of the semantic domains.

A bootstrap analysis was used to further confirm reliability of each neuron’s SI across linguistic materials in two parts. For the first approach, the words were randomly split into 60:40% subsets (repeated 100 times) and the SI of semantically selective neurons was compared in both subsets of words. For the second, instead of using the mean SI, we calculated the proportion of times that a neuron exhibited selectivity for another category other than their preferred domain when randomly selecting words from 60% of the sentences.

Confirming the consistency of neuronal response across analysis windows

The consistency of neuronal response across analysis windows was confirmed in two parts. The average time interval between the beginning of one word and the next was 341 ± 5 ms. For all primary analysis, neuronal responses were analysed in 400-ms windows, aligned to each word, with a 100-ms time-lag to further account for the evoked response delay of prefrontal neurons. To further confirm the consistency of semantic selectivity, we first examined neuronal responses using 350-ms and 450-ms time windows. Combining recordings across all 13 participants, a similar proportion of cells exhibiting selectivity was observed when varying the window size by ±50 ms (17% and 15%, χ^2(1, 861) = 0.43, P = 0.81) suggesting that the precise window of analysis did not markedly affect these results. Second, we confirmed that possible overlap between words did not affect neuronal selectivity by repeating our analyses but now evaluated only non-neighbouring content words within each sentence. Thus, for example, for the sentence “The child bent down to smell the rose”, we would evaluate only non-neighbouring words (for example, child, down and so on) per sentence. Using this approach, we find that the SI for non-overlapping windows (that is, every other word) was not significantly different from the original SIs (0.41 ± 0.03 versus 0.38 ± 0.02, t = 0.73, P = 0.47); together confirming that potential overlap between words did not affect the observed selectivity.

Model decoding performance and the robustness of neuronal response

To evaluate the degree to which semantic domains could be predicted from neuronal activity on a per-word level, we randomly sampled words from 60% of the sentences and then used the remaining 40% for validation across 1,000 iterations. Only candidate neurons that exhibited significant semantic selectivity and for which sufficient words and sentences were recorded were used for decoding purposes (43 of 48 total selective neurons). For these, we concatenated all of the candidate neurons from all participants together with their firing rates as independent variables, and predicted the semantic domains of words (dependent variable). Support vector classifiers (SVCs) were then used to predict the semantic domains to which the validation words belonged. These SVCs were constructed to find the optimal hyperplanes that best separated the data by performing

$$\mathop{min}\limits_{w,b,\zeta }\left(\frac{1}{2}{w}^{{\rm{T}}}{\rm{w}}+{\rm{C}}\mathop{\sum }\limits_{{\rm{i}}=1}^{{\rm{n}}}{\zeta }_{{\rm{i}}}\right)$$

subject to

$${y}_{i}({w}^{{\rm{T}}}\varphi ({x}_{i})+b)\,\ge 1-{\zeta }_{i}$$

in which \(y\in {\left\{1,-1\right\}}^{n}\), corresponding to the classification of individual words, \(x\) is the neural activity, and \({{\rm{\zeta }}}_{i}=\max \left(0,\,1-{y}_{i}\left(w{x}_{i}-b\right)\right)\). The regularization parameter C was set to 1. We used a linear kernel and ‘balanced’ class weight to account for the inhomogeneous distribution of words across the different domains. Finally, after the SVCs were modelled on the bootstrapped training data, decoding accuracy for the models was determined by using words randomly sampled and bootstrapped from the validation data. We further generated a null distribution by calculating the accuracy of the classifier after randomly shuffling the cluster labels on 1,000 different permutations of the dataset. These models therefore together determine the most likely semantic domain from the combined activity patterns of all selective neurons. An empirical P value was then calculated as the percentage of permutations for which the decoding accuracy from the shuffled data was greater than the average score obtained using the original data. The statistical significance was determined at P value < 0.05.

Quantifying the specificity of neuronal response

To quantify the specificity of neuronal response, we carried out two procedures. First, we reduce the number of words from each domain from 100% to 25% on the basis of their vectoral cosine distance from each of their respective domains’ centroid. Thus, for each domain, words that were closest to its centroid, and therefore most similar in meaning, were kept whereas words farther away were removed. The SIs of the neurons were then recalculated as before (Fig. 1h). Second, we repeated the decoding procedure but now varied the number of semantic domains from 2 to 20. Thus, a higher number of domains would mean fewer words per domain (that is, increased specificity of meaning relatedness) whereas a smaller number of domains would mean more words per domain. These decoders used 60% of words for model training and 40% for validation (200 iterations). Next, to evaluate the degree to which neuron and domain number led to improvement in decoding performance, models were trained for all combinations of domain numbers (2 to 20) and neuron numbers (1 to 133) using a nested loop. For control comparison, we repeated the decoding analysis but randomly shuffled the relation between neuronal response and each word as above. The percentage improvement in prediction accuracy (PA) for a given domain number (d) and neuronal size (n) was calculated as

$${\rm{improvement}}\left(d,\,n\right)=100\times \frac{\left[{{\rm{PA}}}_{{\rm{actual}}}\left(d,\,n\right)-{{\rm{PA}}}_{{\rm{shuffle}}}\left(d,\,n\right)\right]}{{{\rm{PA}}}_{{\rm{actual}}}\left(d,\,n\right)}$$

Evaluating the context dependency of neuronal response using homophone pairs

We compared the responses of neurons to homophone pairs to evaluate the context dependency of neuronal response and to further confirm the specificity of meaning representations. For example, if the neurons simply responded to differences in phonetic input rather than meaning, then we should expect to see smaller differences in firing rate between homophone pairs that sounded the same but differed in meaning (for example, ‘sun’ and ‘son’) compared to non-homophone pairs that sounded different but shared similar meaning (for example, ‘son’ and ‘sister’). Here, only homophones that belonged to different semantic domains were included for analysis. A permutation test was used to compare the distributions of the absolute difference in firing rates between homophone pairs (sample x) and non-homophone pairs (sample y) across semantically selective cells (P < 0.01). To carry out the permutation test, we first calculated the mean difference between the two distributions (sample x and y) as the test statistic. Then, we pooled all of the measurements from both samples into a single dataset and randomly divided it into two new samples x′ and y′ of the same size as the original samples. We repeated this process 10,000 times, each time computing the difference in the mean of x′ and y′ to create a distribution of possible differences under the null hypothesis. Finally, we computed the two-sided P value as the proportion of permutations for which the absolute difference was greater than or equal to the absolute value of the test statistic. A one-tailed t-test was used to further evaluate for differences in the distribution of firing rates for homophones versus non-homophone pairs (P < 0.001). To allow for comparison, 2 of the 133 neurons did not have homophone trials and were therefore excluded from analysis. An additional 16 neurons were also excluded for lack of response and/or for lying outside (>2.5 times) the interquartile range.

Evaluating the context dependency of neuronal response using surprisal analysis

Information theoretic metrics such as ‘surprisal’ define the degree to which a word can be predicted on the basis of its antecedent sentence context. To examine how the preceding context of each word modulated neuronal response on a per-word level, we quantified the surprisal of each word as follows:

$${\rm{surprisal}}\left({w}_{i}\right)=-\log P({w}_{i}{\rm{| }}{w}_{1}\ldots {w}_{i-1})$$

in which P represents the probability of the current word (w) at position i within a sentence. Here, a pretrained long short-term memory recurrent neural network was used to estimate P(wi | w_1…wi−1)[73]. Words that are more predictable on the basis of their preceding context would therefore have a low surprisal whereas words that are poorly predictable would have a high surprisal.

Next we examined how surprisal affected the ability of the neurons to accurately predict the correct semantic domains on a per-word level. To this end, we used SVC models similar to that described above, but now divided decoding performances between words that exhibited high versus low surprisal. Therefore, if the meaning representations of words were indeed modulated by sentence context, words that are more predictable on the basis of their preceding context should exhibit a higher decoding performance (that is, we should be able to predict their correct meaning more accurately from neuronal response).

Determining the relation between the word embedding space and neural response

To evaluate the organization of semantic representations within the neural population, we regressed the activity of each neuron onto the 300-dimensional embedded vectors. The normalized firing rate of each neuron was modelled as a linear combination of word embedding elements such that

$${F}_{i,w}={v}_{w}{\theta }_{i}+{\varepsilon }_{i}$$

in which \({F}_{i,w}\) is the firing rate of the ith neuron aligned to the onset of each word w, \({\theta }_{i}\) is a column vector of optimized linear regression coefficients, \({v}_{w}\) is the 300-dimensional word embedding row vector associated with word w, and \({\varepsilon }_{i}\) is the residual for the model. On a per-neuron basis, \({\theta }_{i}\) was estimated using regularized linear regression that was trained using least-squares error calculation with a ridge penalization parameter λ = 0.0001. The model values, \({\theta }_{i}\), of each neuron (dimension = 1 × 300) were then concatenated (dimension = 133 × 300) to define a putative neuronal–semantic space θ. Together, these can therefore be interpreted as the contribution of a particular dimension in the embedding space to the activity of a given neuron, such that the resulting transformation matrix reflects the semantic space represented by the neuronal population.

Finally, a PC analysis was used to dimensionally reduce θ along the neuronal dimension. This resulted in an intermediately reduced space (θpca) consisting of five PCs, each with dimension = 300, together accounting for approximately 46% of the explained variance (81% for the semantically selective neurons). As this procedure preserved the dimension with respect to the embedding length, the relative positions of words within this space could therefore be determined by projecting word embeddings along each of the PCs. Last, to quantify the degree to which the relation between word projections derived from this PC space (neuronal data) correlated with those derived from the word embedding space (English word corpus), we calculated their correlation across all word pairs. From a possible 258,121 word pairs (the availability of specific word pairs differed across participants), we compared the cosine distances between neuronal and word embedding projections.

Estimating the hierarchical structure and relation between word projections

As word projections in our PC space were vectoral representations, we could also calculate their hierarchical relations. Here we carried out an agglomerative single-linkage (that is, nearest neighbour) hierarchical clustering procedure to construct a dendrogram that represented the semantic relationships between all word projections in our PC space. We also investigated the correlation between the cophenetic distance in the word embedding space and difference in neuronal activity across all word pairs. The cophenetic distance between a word pair is a measure of inter-cluster dissimilarity and is defined as the distance between the largest two clusters that contain the two words individually when they are merged into a single cluster that contains both49,50,51. Intuitively, the cophenetic distance between a word pair reflects the height of the dendrogram where the two branches that include these two words merge into a single branch. Therefore, to further evaluate whether and to what degree neuronal activity reflected the hierarchical semantic relationship between words, as observed in English, we also examined the cophenetic distances in the 300-dimension word embedding space. For each word pair, we calculated the difference in neuronal activity (that is, the absolute difference between average normalized firing rates for these words across the population) and then assessed how these differences correlated with the cophenetic distances between words derived from the word embedding space. These analyses were performed on the population of semantically selective neurons (n = 19). For further individual participant comparisons, the cophenetic distances were binned more finely and outliers were excluded to allow for comparison across participants.

t-stochastic neighbour embedding procedure

To visualize the organization of word projections obtained from the PC analysis at the level of the population (n = 133), we carried out a t-distributed stochastic neighbour embedding procedure that transformed each word projection into a new two-dimensional embedding space θtsne (ref. [74]). This transformation utilized cosine distances between word projections as derived from the neural data.

Non-embedding approach for quantifying the semantic relationship between words

To further validate our results using a non-embedding approach, we used WordNet similarity metrics[75]. Unlike embedding approaches, which are based on the modelling of vast language corpora, WordNet is a database of semantic relationships whereby words are organized into ‘synsets’ on the basis of similarities in their meaning (for example, ‘canine’ is a hypernym of ‘dog’ but ‘dog’ is also a coordinate term of ‘wolf’ and so on). Therefore, although synsets do not provide vectoral representations that can be used to evaluate neuronal response to specific semantic domains, they do provide a quantifiable measure of word similarity[75] that can be regressed onto neuronal activity.

Confirming the robustness of neuronal response across participants

Finally, to ensure that our results were not driven by any particular participant(s), we carried out a leave-one-out cross-validation participant-dropping procedure. Here we repeated several of the analyses described above but now sequentially removed individual participants (that is, participants 1–10) across 1,000 iterations. Therefore, if any particular participant or group of participants disproportionally contributed to the results, their removal would significantly affect them (one-way analysis of variance, P < 0.05). A χ^2 test (P < 0.05) was used to further evaluate for differences in the distribution of neurons across participants.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All primary data supporting the findings of this study are available online at https://figshare.com/s/94962977e0cc8b405ef3. Details of the participants’ demographics and task conditions are provided in Extended Data Table 1.

Code availability

All primary Python codes supporting the findings of this study are available online at https://figshare.com/s/94962977e0cc8b405ef3. Software packages used in this study are listed in the Nature Portfolio Reporting Summary along with their versions.

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Acknowledgements

M.J. is supported by the Canadian Institutes of Health Research, a Brain & Behavior Research Foundation Young Investigator Grant and the Foundations of Human Behavior Initiative; B.G. is supported by the Neurosurgery Research & Education Foundation and a National Institutes of Health (NIH) National Research Service Award; A.R.K. and W.M. are supported by NIH R25NS065743; A.C.P. is supported by UG3NS123723, Tiny Blue Dot Foundation and P50MH119467; S.S.C. is supported by R44MH125700 and Tiny Blue Dot Foundation; E.F. is supported by U01NS121471 and R01 DC016950; and Z.M.W. is supported by NIH R01DC019653 and U01NS121616. We thank the participants; J. Schweitzer for assistance with the recordings; D. Lee, B. Atwater and Y. Kfir for data processing; and J. Tenenbaum, R. Futrell and Y. Cohen for their valuable feedback and suggestions.

Author information

Author notes

1. These authors contributed equally: Mohsen Jamali, Benjamin Grannan

Authors and Affiliations

1. Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Mohsen Jamali, Benjamin Grannan, Jing Cai, Arjun R. Khanna, William Muñoz, Irene Caprara & Ziv M. Williams

2. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Angelique C. Paulk & Sydney S. Cash

3. Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA

Angelique C. Paulk & Sydney S. Cash

4. Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA

Evelina Fedorenko

5. Harvard-MIT Division of Health Sciences and Technology, Boston, MA, USA

Ziv M. Williams

6. Harvard Medical School, Program in Neuroscience, Boston, MA, USA

Ziv M. Williams

Contributions

M.J., B.G., A.R.K., W.M., I.C. and Z.M.W. carried out the experiments; M.J., B.G. and J.C. carried out neuronal analyses; A.C.P., S.S.C. and Z.M.W. developed the Neuropixels recording approach; M.J., W.M. and I.C. processed the Neuropixels data; E.F. provided linguistic materials and feedback; M.J., B.G., J.C., A.R.K., W.M., I.C., A.C.P., S.S.C. and E.F. edited the manuscript; and Z.M.W. conceived and designed the study, wrote the paper and supervised all aspects of the research.

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Correspondence to Ziv M. Williams.

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The authors declare no competing interests.

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Nature thanks Peter Hagoort, Frederic Theunissen and Kareem Zaghloul for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Language-related activity, recording stability, waveform morphology and isolation quality across recording techniques.

a, Example of waveform morphologies displaying mean waveform ± 3 s.d and associated PC distributions used to isolate putative units from the tungsten microarray recordings. The horizontal bar indicates a 500 µs interval for scale. The gray areas in PC space represent noise. All single units recorded from the same electrode were required to display a high degree of separation in PC space. b, Isolation metrics of the single units obtained from the tungsten microarray recordings. c, Left, waveform morphologies observed across contacts in a Neuropixels array. Right, PC distributions used to isolate and cluster single units. d, Isolation distance and nearest neighbor noise overlap of the recorded units obtained from the Neuropixels arrays.

Extended Data Fig. 2 Cluster separability and consistency of neuronal responses across participants.

a, The d’ (d-prime) indices measuring separability between the distribution of the vectoral cosine distances among all words within a cluster (purple) and those among all words across clusters (gray). The d’ indices were all above 2.5 reflecting strong separability. b, Selectivity index of neurons (mean with 95% CL, n = 19) when semantic domains were refined by moving or removing words whose meanings did not intuitively fit with their respective labels (Extended Data Table 2). c, There was no significant difference (χ^2 = 2.33, p = 0.31) in the proportions of neurons that displayed semantic selectivity based on the participants’ clinical conditions of essential tremor (ET), Parkinson’s disease (PD) or cervical dystonia (CD). d, Left, the proportional contribution per participant based on the total percentage of neurons contributed. Right, the proportional contribution of semantically selective cells per participant based on the fraction contributed. Participants without selective cells are not shown. e, A leave one out cross-validation participant-dropping procedure demonstrated that population results remained similar. Here, we sequentially removed individual participants (i.e., participants #1-10) and then repeated our selectivity analysis. Semantic selectivity across neurons was largely unaffected by removal of any of the participants (one-way ANOVA, F(9, 44) = 0.11, p = 0.99). Here, the mean selectivity indices (± s.e.m.) are separately presented after removing each participant. f, A cross-validation participant-dropping procedure was used to determine whether any of the participants disproportionately contributed to the population decoding. Average decoding results and comparison to the shuffled data are separately presented after removing each participant (permutation test, p < 0.01; #1-10).

Extended Data Fig. 3 Confirming consistency of semantic representations by neurons using Neuropixels recordings.

a, Coincidence matrix illustrating the distribution of cells obtained from Neuropixels recordings that displayed selective responses to one or more semantic domains (two-tailed rank-sum test, p < 0.05, FDR adjusted). Inset, proportions of cells that displayed selective responses to one or more semantic domains. b, The distributions of SIs are shown separately for semantically-selective (n = 29, orange) and non-selective (n = 125, grey) cells. The mean SI of cells that did not display semantic selectivity (n = 125) was 0.16 (one-sided rank-sum test, z-value = 7.2, p < 0.0001). Inset, selectivity index (SI) of each neuron (n = 29) when compared across different semantic domains. c, The cumulative decoding performance (± s.d.) of all semantically selective neurons during sentences (blue) versus chance (orange). Inset, decoding performances (± s.d.) across two independent embedding models (Word2Vec and GloVe). d, Decoding accuracies for words that displayed high vs. low surprisal based on the preceding sentence contexts in which they were heard. Actual and chance decoding performances are shown in blue and orange, respectively (mean ± s.d., one-sided rank-sum test z-value = 25, p < 0.001). The inset shows a regression analysis on the relation between decoding performance and surprisal. e, Left, SI distributions for neurons during word list and sentence presentations together with the number of neurons that responded selectivity to one or more semantic domains (Inset). Right, the SI for neurons (mean with 95% CL, n = 21; excluding zero firing rate neurons) during word-list presentation. The SI dropped from 0.39 (CI = 0.33-0.45) during the sentences to 0.29 (CI = 0.19-0.39) during word list presentation (signed-rank test, z(41) = 168, p = 0.035). f, The selectivity index of neurons for which nonword lists presentation was performed (n = 26 of 153 cells were selective) when comparing their activities during sentences vs. nonwords (mean SI = 0.34, CI = 0.28-0.40). Here, the selectivity of each neuron reflects the degree to which it differentiates any semantic (meaningful) compared to non-semantic (nonmeaningful) information. g, Contribution to the variance explained in PC space for word projections across participants using a participant-dropping procedure. h, Activities of neurons for word pairs based on their vectoral cosine distance within the 300-dimensional embedding space (z-scored activity change over percentile cosine similarity; Pearson’s correlation r = 0.21, p < 0.001).

Extended Data Fig. 4 Selectivity of neurons to linguistically meaningful versus nonmeaningful information.

a, The distributions of SIs are shown separately for cells that displayed significance for semantic information (n = 19, orange) and those that did not (n = 114, grey). The mean SI of cells that did not display semantic selectivity (n = 114) was 0.14 (one-sided rank-sum test, z-value = 5.8, p < 0.0001). b, Decoding performances (mean ± s.d.) for cells that were not significantly selective for any particular semantic domain but which had an SI greater than 0.2 (n = 11) compared to that of shuffled data (21 ± 6%; permutation test, p = 0.046). c, The selectivity index of neurons for which nonword lists presentation was performed (n = 27 of 48 cells for which this control was performed displayed a significant difference in activity using a two-sided t-test) when comparing their responses to nonwords (i.e., that carried no linguistic meaning) versus sentences (i.e., that carried linguistic meaning; mean SI = 0.43, CI = 0.35-0.51). The semantically selective cells (n = 6, red) displayed a similar word vs. nonword SI when compared to the non-semantically selective cells (n = 21, orange; two-sided t-test, df = 26, p = 1.0). d, Peristimulus histograms (mean ± s.e.m.) and rasters of representative neurons when the participants were given words heard within sentences (red) or sets of nonwords (gray). The horizontal green bars display the 400 ms window of analysis.

Extended Data Fig. 5 Generalizability and robustness of word meaning representations.

a, Average decoding performances (± s.d., purple, n = 1000 iterations) were found to be slightly lower for words heard early (first 4 words) vs. late (last 4 words) within their respective sentences (23 ± 7% vs. 29 ± 8% decoding performance, respectively; One-sided rank sum test, z-value = 17, p < 0.001)76,77. The orange bars represent control accuracy with shuffling neuronal activities. b, Cumulative mean decoding performance (±s.d., purple) for multi-units (MUs) compared with chance (orange). The mean decoding accuracy for all MUs was 23 ± 6% s.d. (one-sided permutation test, p = 0.02) and reflect the unsorted activities of units obtained through recordings (Methods). c, Relationship between the number of neurons considered, the number of word clusters modeled, and prediction accuracy. Here, a lower number of clusters leads to more words per grouping and therefore domains that are not as specific in meaning (e.g., “sun”, “rain”, “clouds”, and “sky”,) whereas a higher number of clusters means fewer words and therefore domains that are more specific in meaning (e.g., “rain” and “clouds”). d, The percent improvement in decoding accuracy (mean ± s.e.m) corresponds to decoding performance minus chance probability using 60% of randomly selected sentences for modeling and 40% for decoding (n = 200 iterations). Inset, relation between log of odds probability (mean ± s.e.m) of predicting the correct semantic domains and number of clusters (i.e., not accounting for chance probability). e, The relation between the number of word clusters modeled and the percent improvement in decoding accuracy (mean ± s.e.m) when considering semantically selective (high SI) and non-selective (low SI) cells separately.

Extended Data Fig. 6 Semantic selectivity during naturalistic story narratives.

a, Comparison of average decoding performances (± s.d., blue, n = 200 iterations) for sentences and naturalistic story narratives, matched based on the number of neurons (left: 2 neurons, right: 5 neurons). b, Comparison of average decoding performances (± s.d., blue, n = 200 iterations) for sentences, matched based on the number of single-units or multi-units (left: 2 units, right: 5 units). Chance decoding performances are given in gray.

Extended Data Fig. 7 Population organization of semantic representations.

a, Contribution to percent variance explained in PC space for word projections across participants using a participant-dropping procedure (first 5-15 PCs; two-sided z-test; p > 0.7). b, Correlation between the vectoral cosine distances between PC-reduced word-projections derived from the neural data and PC-reduced vectors derived from the 300-dimensional word embedding space (n = 258,121 possible word-pairs; note that not all pairs were used for all recordings per neuron since certain words were not heard by all participants). c, Difference in neuronal activities (n = 19 neurons, p = 0.048, two-sided paired t-test, t(18) = 2.12) for word pairs whose vectoral cosine distances were far versus near in the word embedding space. d, Relation between neuronal activity and word meaning similarity using a non-embedding based ‘synset’ approach (n = 100 bins, Pearson’s correlation r = −0.76, p = 0.001). Here, the degree of similarity ranges from 0 to 1.0, with a value of 1.0 indicating that the words are highly similar in meaning (e.g., “canine” and “dog”) and 0 indicating that their meanings are largely distinct.

Extended Data Table 1 Demographic data of study participants and task materials

Extended Data Table 2 Semantic domains and word labels

Extended Data Table 3 Grouping words into smaller word clusters

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Jamali, M., Grannan, B., Cai, J. et al. Semantic encoding during language comprehension at single-cell resolution. Nature 631, 610–616 (2024). https://doi.org/10.1038/s41586-024-07643-2

- Received: 26 May 2020

- Accepted: 31 May 2024

- Published: 03 July 2024

- Issue Date: 18 July 2024

- DOI: https://doi.org/10.1038/s41586-024-07643-2

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SARS-CoV-2 variants acquire mutations in the spike protein that promote immune evasion[1] and affect other properties that contribute to viral fitness, such as ACE2 receptor binding and cell entry2,3. Knowledge of how mutations affect these spike phenotypes can provide insight into the current and potential future evolution of the virus. Here we use pseudovirus deep mutational scanning[4] to measure how more than 9,000 mutations across the full XBB.1.5 and BA.2 spikes affect ACE2 binding, cell entry or escape from human sera. We find that mutations outside the receptor-binding domain (RBD) have meaningfully affected ACE2 binding during SARS-CoV-2 evolution. We also measure how mutations to the XBB.1.5 spike affect neutralization by serum from individuals who recently had SARS-CoV-2 infections. The strongest serum escape mutations are in the RBD at sites 357, 420, 440, 456 and 473; however, the antigenic effects of these mutations vary across individuals. We also identify strong escape mutations outside the RBD; however, many of them decrease ACE2 binding, suggesting they act by modulating RBD conformation. Notably, the growth rates of human SARS-CoV-2 clades can be explained in substantial part by the measured effects of mutations on spike phenotypes, suggesting our data could enable better prediction of viral evolution.

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Over the past 4 years of SARS-CoV-2 evolution, the virus has accumulated mutations throughout its genome. The most rapid evolution has occurred in the viral spike, for instance, the XBB-descended variants that dominated in 2023 have 45–48 spike protein mutations relative to the earliest known strains from Wuhan in late 2019. The reason for this rapid evolution is that spike mutations can strongly affect both the virus’s inherent transmissibility and ability to escape pre-existing immunity1,3. A crucial aspect of interpreting and forecasting SARS-CoV-2 evolution is therefore understanding the impact of current and potential future mutations on the spike.

Here we measure how thousands of mutations to the spike glycoprotein of the XBB.1.5 and BA.2 SARS-CoV-2 strains affect three molecular phenotypes critical to viral evolution: cell entry, ACE2 binding and neutralization by human polyclonal serum (Fig. 1a). To do this, we extend a recently described pseudotyped lentivirus deep mutational scanning system[4] that enables safe experimental characterization of mutations throughout the spike[5]. We demonstrate that mutations outside the RBD can substantially affect spike binding to ACE2. We also define the mutations that escape neutralization by sera from humans who have been multiply vaccinated and also recently infected by XBB or one of its descendant lineages (XBB*), and show there is appreciable heterogeneity in the antigenic impact of mutations across individuals. Finally, we show that the spike phenotypes we measure explain much of the changes in viral growth rate among different SARS-CoV-2 clades that have emerged in humans over the past few years.

Fig. 1: Deep mutational scanning to measure phenotypes of the XBB.1.5 and BA.2 spikes.

a, We measure the effects of mutations in spike on cell entry, receptor binding and serum escape using deep mutational scanning (DMS). We then use these measurements to predict the evolutionary success of human SARS-CoV-2 clades. b, Distribution of effects of mutations in XBB.1.5 and BA.2 spikes on entry into 293T-ACE2 cells for all mutations in the deep mutational scanning libraries, stratified by the type of mutation and the domain in spike. Negative values indicate worse cell entry than the unmutated parental spike. Note that the library design favoured introduction of substitutions and deletions that are well tolerated by spike, explaining why many mutations of these types have neutral to only modestly deleterious effects on cell entry. c, Cell entry effects of mutations F456L, P1143L and deletion of V483 relative to the distribution of effects of all substitution and deletion mutations in the libraries. Interactive heat maps with effects of individual mutations across the whole spike on cell entry are at https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/293T_high_ACE2_entry_func_effects.html and https://dms-vep.org/SARS-CoV-2_Omicron_BA.2_spike_ACE2_binding/htmls/293T_high_ACE2_entry_func_effects.html. The boxes in b and c span the interquartile range, with the horizontal white line indicating the median. Whiskers in b indicate 0.75 of the interquartile range plotted from the smallest value of the first and highest value of the third quartile. For c, the effect of deleting V483 was not measured in the BA.2 spike. The effects of mutations are the mean of two biological replicate measurements made with different deep mutational scanning libraries.

Design of spike mutant libraries

We created mutant libraries of the spikes from the XBB.1.5 and BA.2 strains. We chose these strains because nearly all human SARS-CoV-2 circulating at present descends from either BA.2 or XBB.1.5’s parent lineage XBB[6], and because XBB.1.5 is the sole component of the COVID-19 booster vaccine recommended by the WHO in 2023 (ref. [7]). We wanted the libraries to contain all evolutionary accessible amino-acid mutations tolerable for spike function. We therefore included all mutations observed an appreciable number of times among the millions of SARS-CoV-2 sequences in Global Initiative on Sharing All Influenza Data (GISAID). In addition, we included all possible mutations at sites that change often during SARS-CoV-2 evolution or are antigenically important1,8, and deletions at key N-terminal domain (NTD) and RBD sites. These criteria led us to target roughly 7,000 amino-acid mutations in each of the XBB.1.5 and BA.2 libraries (Extended Data Fig. 1a). We created two independent libraries for each spike so we could perform all deep mutational scanning in full biological duplicate. The actual libraries contained between 69,000 and 102,000 barcoded spike variants with an average of two mutations per variant, and covered 99% of the targeted mutations, as well as some extra mutations (Extended Data Fig. 1a). To retrospectively validate that this library design covered most evolutionarily important mutations, we confirmed that our XBB.1.5 libraries provided adequate coverage for high-confidence experimental measurements of nearly all spike mutations now present in XBB, BA.2 and BA.2.86-descended Pango clades—despite the fact that BA.2.86 had not even emerged yet at the time we designed the library (Extended Data Fig. 1b). So although our libraries do not contain all spike mutations, they cover nearly all mutations that are relevant in the near- to mid-term evolution of SARS-CoV-2. Because the RBD is an especially important determinant of ACE2 binding and serum antibody escape[9], we also made duplicate XBB.1.5 libraries that saturated all amino-acid mutations in only the RBD (Extended Data Fig. 1a).

Effects of spike mutations on cell entry

We measured the effects of all library mutations on spike-mediated cell entry in 293T-ACE2 cells (Extended Data Fig. 1c,d and interactive heat maps at https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/293T_high_ACE2_entry_func_effects.html and https://dms-vep.github.io/SARS-CoV-2_Omicron_BA.2_spike_ACE2_binding/htmls/293T_high_ACE2_entry_func_effects.html). These measurements were highly correlated between the replicate libraries for each spike, indicating the experiments have good repeatability (Extended Data Fig. 1e). The effects of mutations were also well correlated between the XBB.1.5 and BA.2 spikes (Extended Data Fig. 1f), consistent with previous reports that most but not all mutations have similar effects on the spikes of different SARS-CoV-2 variants10,11. As expected, stop codons were highly deleterious for cell entry (Fig. 1b). Because our full-spike library design strategy favours functionally tolerated mutations in spike, most amino-acid mutations in our libraries just slightly impaired cell entry and some but not all single-residue deletions were also well tolerated (Fig. 1b). SARS-CoV-2 has acquired numerous deletions in the NTD’s flexible loops during its evolution12,13, and consistent with that evolution we find that the flexible loops but not the core β sheets of the NTD are relatively tolerant of deletions (Extended Data Fig. 1g). Overall, the effects of mutations on cell entry were fairly well correlated with the effects of amino-acid mutations on viral fitness estimated from millions of natural human SARS-CoV-2 sequences[14] (Extended Data Fig. 1h).

No individual mutation in either the XBB.1.5 or BA.2 spikes notably increased pseudovirus cell entry, although some mutations did marginally improve entry (Fig. 1b and interactive heat maps linked in figure legend). One mutation that slightly improves pseudovirus entry in both XBB.1.5 and BA.2 is P1143L (Fig. 1c), which is found in the recently emerged BA.2.86 lineage[15]. We previously reported that mutations to P1143 also improve cell entry for BA.1 and Delta pseudoviruses[4]. The deletion mutations in our libraries are usually more deleterious for cell entry than substitutions (Fig. 1b); however, deletion of V483 in the RBD is well tolerated for cell entry, consistent with emergence of this mutation in the BA.2.86 variant[15]. The F456L mutation, which has emerged repeatedly in XBB clades after being rare in earlier BA.2-derived clades, is well tolerated for cell entry in XBB.1.5 but substantially deleterious in BA.2 (Fig. 1c).

Non-RBD mutations affect ACE2 binding

To measure how mutations in spike affect receptor binding, we leveraged the fact that the soluble ACE2 ectodomain neutralizes spike-mediated infection with a potency proportional to the strength of spike binding to ACE2 (refs. 1,16). To validate this fact, we made pseudoviruses with six different spike variants and quantified their neutralization by monomeric ACE2 (Fig. 2a). Compared to the BA.2 spike, the Wuhan-Hu-1+D614G spike is neutralized less potently by soluble ACE2 consistent with its weaker ACE2 binding17,18, whereas four mutants of BA.2 known to have higher ACE2 binding[2] (N417K, N417F, R493Q and Y453F) were all neutralized more potently by soluble ACE2 (Fig. 2a). The quantitative neutralization by soluble ACE2 was highly correlated with previously measured monomeric RBD-ACE2 affinities2,18,19 (Fig. 2b).

Fig. 2: Effects of mutations on full-spike ACE2 binding measured using pseudovirus deep mutational scanning.

a, Neutralization of pseudoviruses with the indicated spikes by soluble monomeric ACE2. Viruses with spikes that have stronger binding to ACE2 are neutralized more efficiently by soluble ACE2 (lower half-maximal neutralizing titers(NT50)), whereas viruses with spikes with worse binding are neutralized more weakly. Error bars indicate standard error between two replicates. ACE2 affinity values measured by surface plasmon resonance for BA.2 and Wu-1+D614G are shown in brackets[18]. b, Correlation between neutralization NT50 by soluble ACE2 versus the RBD affinity for ACE2 as measured by titrations using yeast-displayed RBD[2]. c, Correlations between the effects of RBD mutations on ACE2 binding measured using the pseudovirus-based approach (this study) and yeast-based RBD display2,20. d, Distribution of effects of individual mutations on full-spike ACE2 binding for all functionally tolerated mutations in our libraries, stratified by RBD versus non-RBD mutations. Note that effects of magnitude greater than two are clamped to the limits of the plots’ x axes. The effects of individual XBB.1.5 spike mutations on ACE2 binding are shown in Extended Data Fig. 3.

Using this approach, we measured how mutations across both the XBB.1.5 and BA.2 spikes affect apparent ACE2 binding (Extended Data Fig. 2 and interactive heat maps of all mutation effects at https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/monomeric_ACE2_mut_effect.html and https://dms-vep.github.io/SARS-CoV-2_Omicron_BA.2_spike_ACE2_binding/htmls/monomeric_ACE2_mut_effect.html). Because our assay measures ACE2 neutralization rather than 1:1 ACE2-RBD affinity there are several distinct mechanisms that could affect what we refer to as ACE2 binding: direct changes in 1:1 RBD-ACE2 binding affinity2,20, changes in spike that modulate the conformation of the RBDs (such as up and down movements)21,22 and ACE2-induced shedding of the S_1 subunit23,24.

The effects of RBD mutations on ACE2 binding to the spike measured using pseudovirus deep mutational scanning correlate well with previously reported measurements from RBD yeast display for both XBB.1.5 and BA.2 (ref. [20]) (Fig. 2c). We also measured ACE2 binding for the XBB.1.5 pseudovirus libraries with saturating RBD mutations using both monomeric and dimeric soluble ACE2. The RBD-only pseudovirus measurements were highly correlated with the full-spike library measurements (Extended Data Fig. 3a), and the measured values were highly similar for monomeric versus dimeric soluble ACE2 (Extended Data Fig. 3b). ACE2 binding and pseudovirus cell entry are distinct properties, with no strong correlation between these properties among tolerated mutations (Extended Data Fig. 3c), probably reflecting the fact that cell entry can be limited by factors unrelated to receptor binding, especially in target cells expressing moderate to high levels of ACE2, such as those used in our experiments.

A striking observation from the deep mutational scanning is that some mutations outside the RBD appreciably affect binding to ACE2 (Fig. 2d and Extended Data Figs. 2 and 3). To validate these findings, we used mass photometry to measure binding of the soluble native ACE2 dimer to the spike ectodomain trimer (Fig. 3a). Mass photometry measures protein-protein interactions in solution by detecting changes in light scattering that are proportional to protein molecular mass[25], which allows us to detect binding of one or more ACE2 molecules to the spike (Fig. 3a). We produced prefusion-stabilized HexaPro[26] BA.2 and XBB.1.5 spikes, along with mutants that our deep mutational scanning experiments showed to modulate ACE2 binding, and performed mass photometry in the presence of a series of ACE2 concentrations (Fig. 3a,b, Extended Data Fig. 4 and Supplementary Figs. 1–3). As expected, we observed better and worse ACE2 binding for RBD mutations that have been previously identified to either increase (R493Q) or abrogate (R498V) ACE2 engagement, respectively[2] (Fig. 3b, left panels). Furthermore, we detected increased ACE2 binding for all but one of the BA.2 and XBB.1.5 spike trimers harbouring S_1 subunit mutations (in NTD, RBD and SD1 domains) that our deep mutational scanning indicated had better binding (Fig. 3b middle panel, Extended Data Fig. 4 and Supplementary Figs. 2 and 3), as well as decreased ACE2 binding for S_1 mutations that our deep mutational scanning indicated had worse binding (Fig. 3b). However, mutations to the BA.2 and XBB1.5 S_2 subunit found to increase binding to ACE2 in our deep mutational scanning did not lead to increased ACE2 binding detectable by mass photometry (Fig. 3b right panel, Extended Data Fig. 4b,c and Supplementary Figs. 2 and 3). Notably, some of these S_2 mutations were previously reported to affect spike fusion27,28,29 suggesting that they may indeed affect S_1 shedding and in turn affect ACE2 binding consistent with our deep mutational scanning. However, unlike the spikes in deep mutational scanning experiments, the spikes used in mass photometry experiments are prefusion stabilized by introduction of the HexaPro mutations in the fusion machinery[26]. These modifications to spike may limit the propagation of long-range allosteric changes induced by S_2 subunit mutations, possibly explaining the discrepancy between deep mutational scanning and mass photometry. Concurring with this hypothesis, we previously showed that ACE2-induced allosteric conformational changes that drive fusion peptide exposure were inhibited by the prefusion-stabilizing 2P mutations[30].

Fig. 3: Non-RBD mutations affect ACE2 binding.

a, ACE2 binding measurements using mass photometry. The histogram on the left shows distribution of spike molecular mass when no (S0xACE2), one (S1xACE2), two (S2xACE2) or three (S3xACE2) ACE2 molecules are bound. We measure how this mass distribution changes as spike is incubated with increasing concentrations of soluble dimeric ACE2. RBD occupancy is the fraction of RBDs bound to ACE2, calculated using Gaussian components for S0xACE, S1xACE2, S2xACE2 and S3xACE2 at each ACE2 concentration. b, RBD occupancy measured using mass photometry for different BA.2 and XBB.1.5 spike variants. Top left panel shows that a BA.2 spike mutation known to increase ACE2 binding (R493Q/blue) has greater RBD occupancy relative to unmutated BA.2 (black) spike, by contrast a mutation known to decrease ACE2 binding (R498V/green) has lower RBD occupancy in both BA.2 (top left panel) and XBB.1.5 (bottom left panel) backgrounds. Panels on the right show RBD occupancy for BA.2 (top right) and XBB.1.5 (bottom right) spike variants with mutations in S_1 or S_2 subunits measured to increase ACE2 binding in the deep mutational scanning. Values shown in parentheses after the mutation in the legend are the effect on ACE2 binding measured by deep mutational scanning. Error bars in plots a and b indicate standard error between two replicates. c, Non-RBD mutations measured to increase ACE2 binding in deep mutational scanning experiments that have arisen independently as defining mutations in at least four XBB-descended clades.

Non-RBD mutations that enhance ACE2 binding have played an important role in SARS-CoV-2 evolution. The following non-RBD mutations that enhance ACE2 binding occurred in the main pre-Omicron variants of concern: A570D (Alpha), A222V (several moderate-frequency Delta sublineages), T1027I (Gamma) and D950N (Delta) (Extended Data Fig. 2d). In addition, the following non-RBD mutations that occurred in Omicron variants, all of which represent reversions to pre-Omicron residue identities, increase ACE2 binding: K969N, K764N and Y655H. Consistent with previous studies showing that the original D614G mutation increased the proportion of RBDs in the up conformation[21], we find that G614D decreases full-spike ACE2 binding (Fig. 3b and Extended Data Fig. 2d).

To systematically examine the recent evolutionary role of non-RBD-ACE2 binding-enhancing mutations, we tabulated non-RBD mutations that enhance binding and are new mutations in at least four XBB-descended Pango clades (Fig. 3c). Some of these mutations may explain why certain clades had a growth advantage. For example, the NTD mutation Q52H provided the EG.5.1 lineage with a clear growth advantage over EG.5 (ref. [6]), despite not measurably affecting serum neutralization[31]. Our deep mutational scanning provides an explanation for the success of EG.5.1 by showing that Q52H enhances ACE2 binding. Similarly, T572I is now appearing convergently in JN.1-descended lineages[6], and our results show that mutation enhances ACE2 binding.

Heterogeneous sera escape

We next mapped how mutations in spike affect neutralization by the polyclonal antibodies in sera from ten vaccinated individuals who either had a confirmed XBB* infection or whose last infection was during a period when XBB lineages were the dominant circulating variants (Supplementary Table 1). We performed these measurements with the full-spike XBB.1.5 libraries using 293T cells expressing moderate levels of ACE2 that better capture the activities of non-RBD antibodies32,33, although the key sites of escape were mostly similar if we used 293T cells expressing high levels of ACE2 or the RBD-only libraries (Extended Data Fig. 5). The sites of greatest serum escape were mainly in the RBD (Fig. 4a–c and interactive plot at https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/summary_overlaid.html). These sites include 357, 371, 420, the 440–447 loop, 455–456 and 473, as well as a few sites in the NTD, such as positions 200 and 234. At some sites, the escape mutations are strongly deleterious to ACE2 binding (Fig. 4c). For instance, mutations at Y473 cause strong neutralization escape but greatly reduce ACE2 binding, probably explaining their low frequency among circulating SARS-CoV-2 variants. In addition, only some of the antibody escape mutations mapped in our experiments are accessible by single-nucleotide mutations to XBB.1.5 (Fig. 4c). Several escape mutations that are single-nucleotide accessible and do not strongly impair ACE2 binding are found in recent variants, including mutations at site 456 in EG.5.1 and many other XBB variants, mutations at 455 in HK.3.1 and JN.1, mutations at 420 in GL.1 and mutations at 200 in XBB.1.22 (ref. [6]).

Fig. 4: Serum antibody escape mutations for individuals with previous XBB* infections.

a, Escape at each site in the XBB.1.5 spike averaged across ten sera collected from individuals with previous XBB* infections. The points indicate the total positive escape caused by all mutations at each site. See https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/summary_overlaid.html for an interactive version of this plot with extra mutation-level data. b, Enlarged view of the escape at each site in RBD with each line representing one of the ten sera. Key sites are labelled with red circles indicating escape for each of the ten sera. Red data points indicate escape for each individual at select RBD positions. c, Logo plots showing the 16 sites of greatest total escape after averaging across the sera. Letter heights indicate escape caused by mutation to that amino acid, and letters are coloured light yellow to dark brown depending on the impact of that mutation on ACE2 binding (see colour key). The top plot shows all amino-acid mutations measured, and the bottom plot shows only amino acids accessible by a single-nucleotide mutation to the XBB.1.5 spike. d, The left shows a correlation between DMS escape scores and pseudovirus neutralization assay IC_90 values for three sera. The right is a logo plot showing escape for all sites with mutations validated in the neutralization assays, with the specific validated mutations in red.

Whereas the same mutations often escape many sera, there is also heterogeneity such that the sera-average is not fully reflective of the effects of mutations on any individual serum (Fig. 4b,d and Extended Data Fig. 6). For example, whereas mutations to site Y473 strongly escape neutralization by most sera, two sera we analysed (493C and 501C) are largely unaffected by mutations at that site. Other key sites of escape, including 420 and 456, show similar heterogeneity across sera. To validate that escape mutations can have very different effects across sera, we performed standard pseudovirus neutralization assays[5] against a panel of point mutants to the XBB.1.5 spike (Fig. 4d). The changes in neutralization in these validation assays were highly correlated with the escape measured by deep mutational scanning, and confirmed the serum-to-serum heterogeneity. For example, Y473S strongly reduces neutralization by sera 287C and 500C, but actually slightly increases neutralization by serum 501C. Similarly, F456L substantially reduces neutralization by only some sera (Fig. 4d).

The deep mutational scanning identifies mutations that increase, as well as escape, neutralization (Extended Data Fig. 7). Sensitizing mutations often occur at sites that are mutated in XBB.1.5 relative to earlier variants, such as sites 373, 405, 417, 460, 486 and 505 (Extended Data Fig. 7). Presumably in many cases, reverting mutations at these sites restores neutralization by antibodies elicited by infection or vaccination with earlier viral strains. To confirm that the sensitizing mutations identified in the deep mutational scanning actually increased neutralization, we validated the sensitizing effects of R403K and N405K in standard pseudovirus neutralization assay (Fig. 4d). In addition, some sensitizing mutations seem to act by placing the RBD in a more up conformation as discussed in the next subsection.

RBD conformation affects serum escape

Most sites of strong escape described in the previous section are proximal to the ACE2-binding motif in the RBD that is the target of many potent neutralizing antibodies34,35. However, the deep mutational scanning also reveals individual mutations at non-RBD or ACE2-distal RBD sites that strongly escape neutralization. Some of these sites, such as 42, 200 and 234 in the NTD, 572 in SD1 and 852 in S2 have mutations that cause as much escape as ACE2-proximal RBD mutations, decreasing serum neutralization by as much as sixfold (Fig. 4a,d). Whereas most mutations at any given site have similar effects on escape (that is, either promoting or sensitizing) at many ACE2-proximal RBD sites, different mutations at the same non-RBD or ACE2-distal RBD site can have opposing effects on neutralization (Fig. 5a–c). Furthermore, there is a strong correlation between mutational effects on neutralization and ACE2 binding at these sites: mutations that reduce neutralization also reduce ACE2 binding, and mutations that increase neutralization also increase ACE2 binding (Fig. 5a,b). No such consistent correlation exists between neutralization and ACE2 binding for RBD escape sites in close proximity of ACE2 binding interface (Fig. 5c).

Fig. 5: Sera escape and ACE2 binding are inversely correlated for non-RBD and ACE2-distal RBD sites.

a, The left shows a correlation between ACE2 binding and sera escape for amino-acid mutations at non-RBD sites with the highest mutation-level sera escape (each point is a distinct amino-acid mutation). Average escape for each mutation across all sera is shown. The right shows a logo plot for the same sites, with letter heights proportional to escape caused by that mutation (negative heights mean more neutralization), and letter colours indicating effect on ACE2 binding (green means better binding). b, A similar plot for RBD sites that are distal (at least 15 Å) from ACE2. c, A similar plot for RBD sites proximal (within 15 Å) to ACE2. Only sites with at least seven different mutations measured are included in the logo plots. d, Top-down view of XBB spike (Protein Data Bank ID 8IOT) with the non-RBD and ACE2-distal sites shown in a and b highlighted as spheres. The RBD is pink, the NTD is blue and sites in SD1 are green.

We propose that non-RBD and ACE2-distal RBD mutations that increase both neutralization and ACE2 binding do so by shifting the RBD to a more upright position, whereas those that decrease neutralization and ACE2 binding do so by shifting the RBD to a more downwards position36,37,38. Previous work has shown that mutations that put the RBD in a down position reduce neutralization by antibodies that target RBD residues only accessible in the up position, whereas antibodies that can bind both the up and down RBD are unaffected by such mutations15,39. Consistent with this previous work, we confirmed that the mutations at ACE2-distal sites identified in our full-spike deep mutational scanning as probably affecting RBD conformation only affect neutralization by monoclonal antibodies that bind only to the up conformation of the RBD (Extended Data Fig. 8).

Our results show that mutations that affect neutralization and ACE2 binding by modulating RBD conformation are common in certain regions of spike: a result that makes structural sense, because most of these mutations are located near the interfaces between the RBD and other spike domains (Fig. 5d and Extended Data Fig. 9). Furthermore, many of these strong escape sites, including N234, F371, P373, F375, A376, S408, A570 and T572, have been previously shown by structural methods to affect RBD conformation22,36,37,38,40,41,42,43 or the conformation of key RBD epitopes19,44.

Spike phenotypes and clade growth

SARS-CoV-2 evolution in humans is characterized by the repeated emergence of new viral clades, which often possess extra amino-acid mutations in spike relative to their predecessors. To test whether our deep mutational scanning measurements could help explain which clades are evolutionarily successful, we estimated the relative growth rates in humans of sufficiently-sampled SARS-CoV-2 clades using multinomial logistic regression[45] (Extended Data Fig. 10a–c). As expected, more recent clades generally had higher growth rates, consistent with evolution selecting for viral clades that are more fit (Extended Data Fig. 10a), presumably in part due to further mutations in spike[46].

We sought to determine whether the growth of clades could be predicted from how their mutations affect the spike phenotypes measured by deep mutational scanning. Note that almost any mutation-based measurement (such as just counting mutations) trivially correlates with clade growth because newer clades typically have both better growth rates and more spike mutations (Extended Data Fig. 10a,d). For instance, clade growth rates strongly correlate with the number of spike mutations relative to the early Wuhan-Hu-1 sequence (Extended Data Fig. 10e). But this correlation is not informative because the question of evolutionary interest is not whether SARS-CoV-2’s spike will acquire more mutations over time (we already know this will happen), but rather which of the various mutant viruses present at any given time will spread. Furthermore, phylogenetic correlations can exaggerate associations between mutations and clade growth[47]. Therefore, we focused on predicting changes in clade growth for each pair of parent–descendant clades separated by at least one spike mutation (Extended Data Fig. 10b). This approach eliminates the confounding effects of phylogenetic relatedness and the accumulation of mutations over time (Extended Data Fig. 10e,f), and better answers the question of how specific mutations affect clade growth.

Changes in growth between parent–descendant clade pairs were positively correlated with all three experimentally measured spike phenotypes both among just XBB-descended clades (Fig. 6a and Extended Data Fig. 11) and among all BA.2, BA.5 and XBB-descended clades (Extended Data Fig. 12). The correlations were statistically significant for sera escape and cell entry as assessed by randomization of the measurements among mutations. However, these univariate correlations do not fully capture the information in the experiments, as the effects of mutations on the spike phenotypes are themselves correlated (for example, mutations that cause sera escape sometimes decrease ACE2 binding). We therefore performed ordinary least-squares multiple linear regression of changes in clade growth versus all three phenotypes. The predictions of this regression correlated with changes in clade growth better than any individual phenotype, and were highly statistically significant as assessed by randomization of the measurements among mutations (Fig. 6b and Extended Data Fig. 12). Sera escape uniquely explained the largest fraction of the variance in changes in clade growth, but ACE2 binding and cell entry effects also explained some variance. By contrast, neither RBD yeast-display deep mutational scanning of antibody escape8,48 and ACE2 affinity[20] nor the EVEscape deep learning model[49] were consistently better than randomized data at predicting changes in clade growth at a significance level of P = 0.05 (Extended Data Figs. 11 and 12).

Fig. 6: Spike phenotypes measured by deep mutational scanning partially predict the evolutionary success of SARS-CoV-2 clades.

a, Correlation between the changes in growth rate for parent–descendant clade pairs versus the change in each spike phenotype measured in the XBB.1.5 full-spike deep mutational scanning (several mutations are assumed to have additive effects). The text above each plot shows the Pearson correlation (r) and a P value computed by comparing the actual correlation to that for 100 randomizations of the experimental data among mutations. b, Ordinary least-squares multiple linear regression of changes in growth rate versus all three measured spike phenotypes. The small text indicates the unique variance explained by each variable, as well as the coefficients (coef.) in the regression. See https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/current_dms_clade_pair_growth.html and https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/current_dms_ols_clade_pair_growth.html for interactive versions of both panels in which points can be hovered over for details on clades and their mutations. P values are for one-sided tests of the hypothesis that the tested predictor outperforms randomizations, and are reported individually for each comparison. See Extended Data Fig. 12 for a similar analysis that also includes BA.2 and BA.5 descended clades.

We also sought to test the ability of full-spike deep mutational scanning to explain the high fitness of BA.2.86 and its descendant clades (for example, JN.1), which were identified after the completion of the experiments described in this study[50]. Because there are not yet sufficient distinct BA.2.86-descended clades to make meaningful comparisons with clade growth, instead we performed a different test inspired by Thadani et al.[49]: we generated sequences with random sets of naturally observed spike amino-acid mutations that had the same number of differences relative to BA.2 as did BA.2.86, or relative to BA.2.86 as all designated BA.2.86-descended clades. Our XBB.1.5-based full-spike deep mutational scanning could distinguish the true BA.2.86 and BA.2.86-descended clades from sequences with the same number of mutations with high statistical significance, and did so better than RBD yeast-display deep mutational scanning or EVEscape (Supplementary Fig. 4).

Discussion

More than 16 million human SARS-CoV-2 genomes have been sequenced to date, enabling rapid identification of variants with new mutations at the sequence level. However, interpreting the consequences of these mutations on viral spread in a partially immune population remains a major challenge. Here we show how pseudovirus-based deep mutational scanning can characterize the effects of mutations throughout spike on three distinct phenotypes critical to viral fitness: cell entry, ACE2 binding and serum antibody escape.

The full-spike pseudovirus data we generate enables several key insights that were not apparent from previous yeast-display RBD deep mutational scanning approaches1,2,48. Most obviously, the data encompass all spike domains, not just the RBD. These data show that non-RBD mutations can affect ACE2 binding, probably by altering the conformation of the RBD in the context of the spike trimer (for example, in up versus down position). Such mutations are highly relevant for SARS-CoV-2 evolution—for instance, enhancement of ACE2 binding by non-RBD mutations appears to explain why EG.5.1 spread so rapidly after it acquired Q52H, why A222V subvariants of Delta spread widely, why A570D was selected in Alpha, and why T572I is now arising so frequently in BA.2.86-descended variants.

Pseudovirus deep mutational scanning also enables us to directly measure how mutations affect neutralization by polyclonal sera. By contrast, previous RBD-display deep mutational scanning could only measure how mutations affect antibody binding[51], and so to estimate mutational effects on serum neutralization escape it was necessary to characterize hundreds of individual antibodies assumed to represent the polyclonal neutralizing repertoire of humans1,8. The ability to directly map how mutations affect serum neutralization leads to two new insights. First, it reveals the heterogeneity in how mutations affect neutralization by sera from different individuals. For instance, we characterize sera from XBB* infected individuals that are both strongly affected and almost completely unaffected by mutations at key sites such as 456 or 473. The sera examined in this study came from individuals with varied immunization and infection histories, which probably contributes to observed escape heterogeneity, although individual-to-individual variation in humoral response may also play a role. This person-to-person heterogeneity in the antigenic effects of spike mutations will increase as individuals accumulate increasingly distinct exposure histories, and could come to play an important role in shaping SARS-CoV-2 evolution and disease susceptibility as it does for influenza virus52,53,54.

The second major insight from direct mapping of serum escape is that mutations outside the RBD can have marked effects on neutralization. For instance, NTD mutations such as Y42F and N234T decrease neutralization by some sera by nearly sixfold. The existence of such strong non-RBD escape mutations may seem surprising given that most neutralizing activity in human sera come from antibodies that bind the RBD9,32,51,55. However, our data indicate that the strongest non-RBD serum escape mutations act primarily by shifting the RBD to the down conformation, thereby indirectly escaping class 1 and 4 antibodies that bind to RBD surfaces only accessible in the up conformation15,39. Of course, such mutations come at a cost to ACE2 binding, because the RBD cannot bind receptor in the down conformation56,57. Nonetheless, the ubiquity of such mutations suggests that this mechanism of escape merits monitoring and is in line with previous observations made with endemic human coronaviruses58,59,60. For instance, the RBD of the CoV-229E spike has never been observed in the up conformation61,62 despite the fact that this spike somehow manages to bind its receptor during infection. Whether SARS-CoV-2’s spike could eventually evolve to also far more strongly favour a down RBD conformation is unknown.

The most important indication of the relevance of our work is that our measurements of spike phenotypes partially explain the evolutionary success of different SARS-CoV-2 clades in humans. A longstanding goal of evolutionary biology is to understand the molecular phenotypes that contribute to fitness[63], and then measure them with sufficient accuracy to predict which mutants will actually spread in the real world. We have taken a real step towards this goal, because the spike phenotypes measured by our deep mutational scanning explain a substantial amount of the changes in growth rates of recent SARS-CoV-2 clades. Of course, pseudovirus spike deep mutational scanning will never perfectly predict SARS-CoV-2 evolution: evolution itself is partially stochastic[64], pseudovirus experiments do not capture all phenotypes of spike relevant to transmission or multicycle replication and our experiments completely ignore mutations to non-spike genes that contribute to fitness14,65. Furthermore, it remains technically challenging for deep mutational scanning to account for epistatic interactions among mutations[66], and we need modelling approaches that better account for how person-to-person heterogeneity in immune-escape mutations shape viral evolution[52]. However, the fact that our deep mutational scanning has substantial power to explain clade growth shows that we have reached the point at which experiments can enable useful predictions about SARS-CoV-2 evolution. An important area of future work will be integrating these highly informative experimental measurements into even more sophisticated models of viral evolution49,67,68.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data described in this paper are available in both interactive and numerical form in various levels of detail. For easy interactive visualization of the data, we suggest the following interactive charts of how mutations affect all measured phenotypes after applying a reasonable set of filters to remove lower confidence measurements: XBB.1.5 spike, https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/summary_overlaid.html; BA.2 spike, https://dms-vep.github.io/SARS-CoV-2_Omicron_BA.2_spike_ACE2_binding/htmls/summary_overlaid.html and XBB.1.5 RBD, https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_RBD_DMS/htmls/summary_overlaid.html. For numerical data on mutational effects on all measured phenotypes after applying the same reasonable set of filters, see XBB.1.5 spike, https://github.com/dms-vep/SARS-CoV-2_XBB.1.5_spike_DMS/blob/main/results/summaries/summary.csv; XBB.1.5, spike, per-serum escape: https://github.com/dms-vep/SARS-CoV-2_XBB.1.5_spike_DMS/blob/main/results/summaries/per_antibody_escape.csv; BA.2 spike, https://github.com/dms-vep/SARS-CoV-2_Omicron_BA.2_spike_ACE2_binding/blob/main/results/summaries/summary.csv and XBB.1.5 RBD, https://github.com/dms-vep/SARS-CoV-2_XBB.1.5_RBD_DMS/blob/main/results/summaries/summary.csv. Raw sequencing data files have been uploaded to BioProjects under the following accession codes: PRJNA1034580 for the XBB.1.5 full-spike library, PRJNA1035795 for the XBB.1.5 RBD-only library and PRJNA1035933 for the BA.2 full-spike library.

Code availability

In addition to the above interactive charts and numerical data, the entire computational pipelines are available along with rich interactive HTML displays of results. These numerical data and HTML displays include extra options to filter the data for higher and lower confidence values, such as by examining the measurements in each of the two replicate libraries or filtering measurements by how many variants a mutation is seen in. Specifically, full interactive HTML documentation for each deep mutational scanning experiment are rendered on GitHub Pages as follows: XBB.1.5 full spike, https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/; BA.2 full spike, https://dms-vep.github.io/SARS-CoV-2_Omicron_BA.2_spike_ACE2_binding/ and XBB.1.5 RBD, https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_RBD_DMS/. GitHub repositories with the actual computer code, as well as numerical data are at: XBB.1.5 spike, https://github.com/dms-vep/SARS-CoV-2_XBB.1.5_spike_DMS; BA.2 spike, https://github.com/dms-vep/SARS-CoV-2_Omicron_BA.2_spike_ACE2_binding and XBB.1.5 RBD, https://github.com/dms-vep/SARS-CoV-2_XBB.1.5_RBD_DMS. XBB.1.5 full spike, XBB.1.5 RBD spike and BA.2 full spike repositories are published via Zenodo at https://doi.org/10.5281/zenodo.10981249 (ref. [69]), https://doi.org/10.5281/zenodo.10981257 (ref. [70]) and https://doi.org/10.5281/zenodo.10981262 (ref. [71]), respectively. Note that most of the analysis in these GitHub repositories is performed using dms-vep-pipeline-3 (https://github.com/dms-vep/dms-vep-pipeline-3), v.3.5.3. Python notebooks and raw event data used for mass photometry analysis are available at https://github.com/JackTaylorBrown/massphotometry.

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Acknowledgements

We thank R. Hisner for helpful comments during library design. We thank C. Roemer and R. Neher for helpful comments on interpreting the effects of mutations in the context of SARS-CoV-2 evolution. We thank T. Peacock for useful discussions on ACE2 binding. We gratefully acknowledge all data contributors, including the authors and their originating laboratories responsible for obtaining the specimens, and their submitting laboratories that generated the genetic sequence and metadata and shared via the GISAID Initiative, the data on which part of this research is based. J.D.B. is an investigator at the Howard Hughes Medical Institute. D.V. is an Investigator of the Howard Hughes Medical Institute and the Hans Neurath Endowed Chair in Biochemistry at the University of Washington. This work was supported by the grant no. NIH/NIAID R01AI141707 and contract no. 75N93021C00015 to J.D.B., as well as grant nos. NIH/NIAID P01AI167966 and DP1AI158186 and contract no. 75N93022C00036 to D.V.), a Pew Biomedical Scholars Award (D.V.), an Investigators in the Pathogenesis of Infectious Disease Awards from the Burroughs Wellcome Fund (D.V.), the University of Washington Arnold and Mabel Beckman cryoEM centre and the National Institute of Health grant no. S10OD032290 (to D.V.). This research was also supported by the Genomics & Bioinformatics Shared Resource, RRID:SCR_022606, of the Fred Hutchinson Cancer Center/University of Washington Cancer Consortium (grant no. P30 CA015704), by the Flow Cytometry Shared Resource, RRID:SCR_022613, of the Fred Hutchinson Cancer Center/University of Washington/Seattle Children’s Cancer Consortium (grant no. P30 CA015704), and by Fred Hutchinson Cancer Center Scientific Computing, NIH grant nos. S10-OD-020069 and S10-OD-028685. B.M. was funded by SciLifeLab’s Pandemic Laboratory Preparedness programme (grant no. VC-2022-0028) and the Erling Persson Foundation (grant no. 2021 0125). T.B. is an investigator at the Howard Hughes Medical Institute.

Author information

Authors and Affiliations

1. Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA

Bernadeta Dadonaite, Teagan E. McMahon, Ariana G. Farrell & Jesse D. Bloom

2. Department of Biochemistry, University of Washington, Seattle, WA, USA

Jack Brown, Daniel Asarnow, Cameron Stewart, Jimin Lee & David Veesler

3. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA

Marlin D. Figgins & Trevor Bedford

4. Department of Applied Mathematics, University of Washington, Seattle, WA, USA

Marlin D. Figgins

5. University of Washington, Department of Medicine, Division of Allergy and Infectious Diseases, Seattle, WA, USA

Jenni Logue & Helen Y. Chu

6. Department of Epidemiology, University of Washington, Seattle, WA, USA

Trevor Bedford

7. Howard Hughes Medical Institute, Seattle, WA, USA

Trevor Bedford, David Veesler & Jesse D. Bloom

8. Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden

Ben Murrell

Contributions

Conceptualization was the responsibility of B.D. and J.D.B. Methodology was developed by B.D., J.B., B.M., D.V. and J.D.B. Experiments were carried out by B.D., J.B., T.E.M., J.L., A.G.F., D.A. and C.S. Computational analysis was done by B.D., J.B., B.M., D.A., D.V., T.B., M.D.F. and J.B.D. Writing of the original draft was done by B.D. and J.D.B. Writing, review and editing were carried out by B.D., D.V. and J.D.B. Resources came from J.L. and H.Y.C. Supervision of the project was by J.D.B. Funding was acquired by J.D.B.

Corresponding author

Correspondence to Jesse D. Bloom.

Ethics declarations

Competing interests

J.D.B. and B.D. are inventors on Fred Hutchinson Cancer Center licensed patents related to the pseudovirus deep mutational scanning system used in this paper. J.D.B. consults for Apriori Bio, Invivyd, Aerium Therapeutics, GlaxoSmithKline and the Vaccine Company on topics related to viral evolution. H.Y.C. reports consulting with Ellume, Pfizer and the Bill and Melinda Gates Foundation. She has served on advisory boards for Vir, Merck and Abbvie. She has conducted continuing medical education teaching with Medscape, Vindico and Clinical Care Options. She has received research funding from Gates Ventures, and support and reagents from Ellume and Cepheid, all outside the submitted work. D.V. is named as inventor on patents for coronavirus vaccines filed by the University of Washington. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Yunlong Cao, Theo Sanderson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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Extended data figures and tables

Extended Data Fig. 1 XBB.1.5 and BA.2 spike deep mutational scanning libraries.

a, Number of targeted and final number of mutations and barcoded variants in the XBB.1.5 and BA.2 full spike and XBB.1.5 RBD pseudovirus-based deep mutational scanning libraries. b, Total number of unique spike amino-acid mutations present in BA.2, BA.5, BA.2.86, and XBB descended Pango clades and the number of those mutations that are present in at least three barcoded variants in each replicate of the XBB.1.5 full spike libraries, which was the minimum number of occurrences we needed to make high-confidence estimates of the mutational effects on cell entry. The first number is the total number of mutations meeting the criteria and the number in parentheses is the number of these mutations covered in the libraries: for example, there are 108 spike amino-acid mutations that occur in more than one XBB-descended clade, and 107 of those mutations are well covered in our XBB.1.5 full spike libraries. c, Method for creating genotype-phenotype linked spike deep mutational scanning libraries, as previously described in Dadonaite et al.[4]. Lentivirus backbone plasmids encoding barcoded mutagenised spike genes together with helper and VSV-G expression plasmids are transfected into 293 T cells to make VSV-G pseudotyped virus. These viruses are used to infect 293T-rtTA cells at MOI < 0.01 so that no more than one spike variant is integrated into each cell. Transduced cells are selected for lentiviral integration, and spike pseudotyped virus libraries are produced from these cells by transfecting helper plasmids in the presence of doxycycline to induce spike expression. In the absence of doxycycline and with added VSV-G expression plasmid, VSV-G pseudotyped virus libraries are also produced from the same cell lines; these VSV-G pseudotyped viruses are used to help estimate effects of spike mutations on cell entry as described in the next panel. d, Method used to measure effects of mutations in spike on cell entry. The ability of each spike variant to mediate cell entry is assessed by quantifying its relative frequency in 293T-ACE2 cells infected with spike-pseudotyped versus VSV-G pseudotyped libraries. e, Correlations between the effects of mutations on cell entry measured using each of the two independent full spike libraries of XBB.1.5 or BA.2. Throughout the rest of this paper, we report the mean value between the two libraries. f, Correlation between mutational effects on cell entry measured for the XBB.1.5 versus BA.2 full spike libraries. g, Cell-entry effects as measured in the deep mutational scanning of mutations in either the flexible loops or core β-sheets of the NTD. The left plot shows the effects of amino-acid mutations; the right plot shows the effects of single-residue deletions. The black line indicates the median entry effect, and the boxes indicate the interquartile range. Mutational effects are the median of two biological replicates. Whiskers indicate 0.75 of the interquartile range plotted from the smallest value of the 1st and highest value of the 3rd quartile. h, Correlation between mutational effects measured with the XBB.1.5 or BA.2 full spike libraries and fitness effects of those mutations estimated from actual human SARS-CoV-2 sequences[14].

Extended Data Fig. 2 Correlations among measured mutational effects on ACE2 binding.

a, Correlation between effects of mutations on ACE2 binding measured with XBB.1.5 full spike and XBB.1.5 RBD pseudovirus libraries. b, Correlation between effects of mutations on ACE2 binding measured using XBB.1.5 RBD pseudovirus library with monomeric and dimeric ACE2. Heatmaps with the XBB.1.5 RBD pseudovirus measurements made using monomeric and dimeric ACE2 are at https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_RBD_DMS/htmls/monomeric_ACE2_mut_effect.html and https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_RBD_DMS/htmls/dimeric_ACE2_mut_effect.html, respectively c, Correlation between effects of mutations on ACE2 binding and spike-mediated cell entry for different libraries. d, ACE2 binding heat map showing key non-RBD sites that have mutated in the past major SARS-CoV-2 variants. Specific variant mutations are highlighted in red outline. Table on the right indicates variants in which these mutations occurred. See https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/monomeric_ACE2_mut_effect.html for an interactive plot showing ACE2 binding for all mutations measured in spike is at.

Extended Data Fig. 3 Effects of NTD and RBD mutations on full-spike ACE2 binding.

Mutations that enhance ACE2 binding are shaded blue, mutations that decrease affinity are shaded orange, mutations that are too deleterious for cell entry to be measured in the binding assay are dark gray, and light gray indicates mutations not present in our libraries. Interactive heatmaps showing mutational effects on ACE2 binding for the full XBB.1.5 and BA.2 spikes are at https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/monomeric_ACE2_mut_effect.html and https://dms-vep.org/SARS-CoV-2_Omicron_BA.2_spike_ACE2_binding/htmls/monomeric_ACE2_mut_effect.html. Note that a few sites are missing in the static heatmap in this figure due to lack of coverage or deletions in the XBB.1.5 spike; see the interactive heatmaps for per-site numbering.

Extended Data Fig. 4 Mass photometry measurements for S[1] and S[2] occupancy.

a, Illustration of Gaussian components for no (S0xACE2), one (S1xACE2), two (S2xACE2), or three (S3xACE2) ACE2-bound spikes. S1xRBD occupancy is the fraction of spikes bound by one ACE2 molecule and S2xRBD occupancy is the fraction of spikes bound by two ACE2 molecules. b, Top row - S1xRBD occupancy measured using mass photometry for different BA.2 spike mutants. Bottom row - S2xRBD occupancy for different BA.2 spike mutants. c, Top row - S1xRBD occupancy for different XBB.1.5 spike mutants. Bottom row - S2xRBD occupancy for different XBB.1.5 spike mutants. Error bars in plots b-c indicate standard error between two biological replicates.

Extended Data Fig. 5 Correlation among serum escape mapping experiments.

a, Correlation between mutation escape scores for experiments using the full-spike XBB.1.5 libraries performed on 293 T cells expressing high or medium amounts of ACE2 for four sera. Note that the medium cells were used for all other figures shown in this paper. b, Correlation between mutation escape scores for mutations in the XBB.1.5 full spike and RBD-only libraries. See https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/compare_high_medium_ace2_escape.html and https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/compare_spike_rbd_escape.html for interactive versions of these scatter plots that also show line plots of per-site escape values for each serum.

Extended Data Fig. 6 Escape at key sites for each serum.

Logoplots showing XBB.1.5 spike escape at 16 highest escape sites for each of the 10 sera measured. Letter heights indicate the escape caused by mutation to that amino acid. Letters are colored light yellow to dark brown depending on mutation effect on ACE2 binding. Left: all mutations measured. Right: mutations accessible with a single-nucleotide substitution.

Extended Data Fig. 7 Mutations in XBB.1.5 spike that increase serum neutralization.

Escape at each site in the XBB.1.5 spike averaged across the 10 sera from individuals with prior XBB* infections, showing negative as well as positive values (Fig. 4 only shows positive values). Sites with negative escape in this plot are ones where many mutations make spike more sensitive to neutralization. Interactive plots with site and mutation-level escape are at https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/htmls/summary_overlaid.html (set ‘floor escape at zero’ at the bottom of the chart to false to show negative escape).

Extended Data Fig. 8 Only antibodies that bind RBD in the up conformation are escaped by mutations outside the structural epitope.

This figure shows previously generated and published deep mutational scanning escape maps for three monoclonal antibodies, two of which bind to RBD only in the up conformation (REGN10933 and SC27) and one of which binds to the RBD in both the up and down conformation (LY-CoV1404). All antibodies are escaped by mutations in their direct structural epitope, but only the antibodies that bind only the up conformation are escaped by ACE2-distal mutations outside their epitope that affect RBD up/down conformation. a, REGN10933 escape profile mapped using a Delta full spike deep mutational scanning library[4]. REGN10933 only binds RBD in the up position72,73. Line plot shows mean escape at each position in Delta spike with sites that modulate RBD movement highlighted in red. Heatmap shows mutation escape scores for sites highlighted in red on the line plot. Surface representation of RBD is coloured by site mean escape score with sites showing escape in the RBD outside the main antibody labeled (PDB ID: 6XDG). b, SC27 antibody escape profile mapped using the XBB.1.5 saturated RBD deep mutational scanning library[74]. SC27 only binds RBD in the up conformation. (PDB ID: 7MMO). c, LY-CoV1404 escape profile mapped using the BA.1 full spike deep mutational scanning library[4]. LY-CoV1404 binds RBD in both up and down conformations[75]. (PDB ID: 7MMO).

Extended Data Fig. 9 Sites with highest inverse correlation between ACE2 binding and serum escape.

a, Correlation between ACE2 binding and serum escape for sites in XBB.1.5 spike. Only sites with at least 7 mutations measured and Pearson r < 0.82 are shown. b, Most sites with strongly negative correlations between mutational effects on ACE2 binding and escape are at positions that could plausibly impact the RBD conformation in the context of the full spike, since they tend to be at the interface of the RBD and other spike domains. Left: all sites from a shown on spike structure as spheres. RBD is colored in light pink, NTD light blue, SD1 green and the S_2 subunit in yellow. Spheres are shown on only one chain for each domain for clarity (PDB ID: 8IOU). Right: RBD sites from a shown on RBD in up position engaged with ACE2. RBD is colored in light pink and ACE2 is gray.

Extended Data Fig. 10 Correlations in absolute clade growth with absolute clade phenotypes.

a, Phylogenetic tree of XBB-descended Pango clades, colored by their relative growth rates. The tree shows only clades with at least 400 sequences and at least one new spike mutation, and their ancestors. Ancestor clades with insufficient sequences for growth rate estimates are in white. b, The same phylogeny but with branches colored by the change in growth rate between parent-descendant clade pairs. c, Correlation between clade growth estimates made using the Murrell lab multinomial logistic regression model (see methods) or a hierarchical multinomial logistic regression implemented by the Bedford lab[68] (see https://github.com/nextstrain/forecasts-ncov/). Both sets of estimates are for clades designated after Jan-1-2023 and use the data available as of Oct-2-2023. The estimates are highly correlated, and everywhere else in this paper we report analyses using the Murrell lab estimates. d, Number of spike amino-acid mutations relative to the early Wuhan-Hu-1 virus in all SARS-CoV-2 Pango clades versus the clade designation dates. XBB-descended clades are in orange. As can be seen from this plot, newer clades tend to have more spike mutations. e, Because newer clades tend to have both more mutations and better growth, clade growth rate is trivially correlated with a clade’s relative distance (number of spike mutations) from Wuhan-Hu-1. However, this correlation is not informative as it is already known that new clades tend to have more mutations. f, If we instead correlate the change in growth rate between parent-descendant clade pairs separated by at least one spike mutation (Fig. 6b) with the change in spike mutational distance to Wuhan-Hu-1 there is no correlation, since this approach removes the co-variation with total mutation count. Therefore, simple mutation counting is not informative for predicting changes in clade growth. g, Correlations for the phenotypes measured by the full spike deep mutational scanning in the current paper; h, the phenotypes measured in yeast display RBD deep mutational scanning; i, predicted by the EVEscape method. These plots differ from Fig. 6a and Extended Data Fig. 11 in that they show the correlations in absolute clade growth with the absolute clade phenotypes, rather than comparing the changes in both for each parent-descendant clade pair. Absolute clade phenotypes are computed as the sum of mutation effects. The P-values above the plots is a one sided test that computes the fraction of times the correlation is greater than that for the actual data after randomizing the phenotypic effects among mutations. Note that the correlations are not reflective of the P-values (there can be high correlations but non-significant P-values) for the reasons noted in the main text and in e—phylogenetic correlations, and the fact that new clades have both more mutations and higher growth so that any “phenotype” that amounts to counting mutations gives a correlation in these plots. For this reason, comparing changes in clade growth to changes in spike phenotypes as done in Fig. 6a and Extended Data Fig. 11 is the correct approach to test whether a method can actually predict which new clades will be successful.

Extended Data Fig. 11 Correlations of changes in growth with various other properties of spike for XBB descended clades.

This figure shows the change in growth rate between parent-descendant clade pairs versus the change in various spike phenotypes, rather than showing the absolute clade growth and absolute spike phenotypes as in Extended Data Fig. 10. Comparing the changes removes phylogenetic correlations as discussed in the main text. a, Correlation between the changes in growth rate for parent-descendant clade pairs versus the change in each spike phenotype measured in the XBB.1.5 full-spike deep mutational scanning described in the current paper (multiple mutations are assumed to have additive effects). These panels are the same as those shown in Fig. 6a, and are re-printed here to enable easier comparison to other panels in this figure. b, Correlations of changes in clade growth with changes in site-level antibody escape, ACE2 affinity, and RBD expression measured for RBD mutations in yeast-display deep mutational scanning. c, Correlation of changes in the EVEscape score with changes in clade growth. d, Ordinary least-squares regression of changes in the yeast-display RBD deep mutational scanning phenotypes versus changes in XBB-descendant clade growth. The small text indicates the unique variance explained by each variable as well as the coefficients in the regression. e, Ordinary least squares multiple linear regression of changes in XBB-descendant clade growth rate versus all three measured spike phenotypes using the XBB.1.5 full spike deep mutational scanning. This panel is the same as Fig. 6b, and is re-printed here to enable easier comparison to other panels in this figure. All panels are labeled with the Pearson correlation (r) and a P-value which is a one-sided test determined by computing how many randomizations of the mutational data yield correlations as large as the actual one.

Extended Data Fig. 12 Correlations of changes in growth with various other properties of spike for BA.2, BA.5, and XBB descended clades.

This figure is the same as Extended Data Fig. 11 except that it includes clades descended from any of BA.2, BA.5, and XBB whereas Extended Data Fig. 11 includes just clades descended from XBB.

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Dadonaite, B., Brown, J., McMahon, T.E. et al. Spike deep mutational scanning helps predict success of SARS-CoV-2 clades. Nature 631, 617–626 (2024). https://doi.org/10.1038/s41586-024-07636-1

- Received: 13 November 2023

- Accepted: 31 May 2024

- Published: 03 July 2024

- Issue Date: 18 July 2024

- DOI: https://doi.org/10.1038/s41586-024-07636-1

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Plasmacytoid dendritic cells control homeostasis of megakaryopoiesis

Florian Gaertner,

Hellen Ishikawa-Ankerhold,

Susanne Stutte,

Wenwen Fu,

Jutta Weitz,

Anne Dueck,

Bhavishya Nelakuditi,

Valeria Fumagalli,

Dominic van den Heuvel,

Larissa Belz,

Gulnoza Sobirova,

Zhe Zhang,

Anna Titova,

Alejandro Martinez Navarro,

Kami Pekayvaz,

Michael Lorenz,

Louisa von Baumgarten,

Jan Kranich,

Tobias Straub,

Bastian Popper,

Vanessa Zheden,

Walter Anton Kaufmann,

Chenglong Guo,

Guido Piontek,

Saskia von Stillfried,

Peter Boor,

Marco Colonna,

Sebastian Clauß,

Christian Schulz,

Thomas Brocker,

Barbara Walzog,

Christoph Scheiermann,

William C. Aird,

Claus Nerlov,

Konstantin Stark,

Tobias Petzold,

Stefan Engelhardt,

Michael Sixt,

Robert Hauschild,

Martina Rudelius,

Robert A. J. Oostendorp,

Matteo Iannacone,

Matthias Heinig &

…

Steffen Massberg

Nature volume 631, pages 645–653 (2024)

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Abstract

Platelet homeostasis is essential for vascular integrity and immune defence1,2. Although the process of platelet formation by fragmenting megakaryocytes (MKs; thrombopoiesis) has been extensively studied, the cellular and molecular mechanisms required to constantly replenish the pool of MKs by their progenitor cells (megakaryopoiesis) remains unclear3,4. Here we use intravital imaging to track the cellular dynamics of megakaryopoiesis over days. We identify plasmacytoid dendritic cells (pDCs) as homeostatic sensors that monitor the bone marrow for apoptotic MKs and deliver IFNα to the MK niche triggering local on-demand proliferation and maturation of MK progenitors. This pDC-dependent feedback loop is crucial for MK and platelet homeostasis at steady state and under stress. pDCs are best known for their ability to function as vigilant detectors of viral infection[5]. We show that virus-induced activation of pDCs interferes with their function as homeostatic sensors of megakaryopoiesis. Consequently, activation of pDCs by SARS-CoV-2 leads to excessive megakaryopoiesis. Together, we identify a pDC-dependent homeostatic circuit that involves innate immune sensing and demand-adapted release of inflammatory mediators to maintain homeostasis of the megakaryocytic lineage.

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Main

Platelets are anucleate cells circulating in the blood to maintain vascular barrier function in health and disease1,2. They are produced in the bone marrow (BM) by their precursors, MKs, in a process called thrombopoiesis[3]. During thrombopoiesis, MKs show signs of apoptosis[6] and release platelets in a process in which the MK cell body is entirely consumed[7]. Consequently, replenishment of fragmented MKs from progenitors (megakaryopoiesis) is continuously required to ensure MK homeostasis and sustained platelet production.

Here we identify a homeostatic circuit[8] that balances thrombopoiesis and megakaryopoiesis in BM tissue. Patrolling pDCs—a unique subset of innate immune sentinel cells[5]—sense MK turnover by detecting cell-free DNA released from apoptotic MKs. Innate immune signalling through the MYD88–IRF7 pathway activates the release of IFNα by pDCs, which in turn triggers megakaryopoiesis to replenish MKs and to maintain platelet homeostasis during steady state and stress. Thus, our data establish innate immune sensing by pDCs as a key mechanism controlling cellular homeostasis in the BM and blood. Perturbed pDC function, such as strong activation during viral infection with SARS-CoV-2, increases megakaryopoiesis, leading to marked hyperplasia of the megakaryocytic lineage. Our data may therefore provide a mechanistic explanation for alterations in platelet counts frequently observed during inflammation and infection and opens routes for therapeutic intervention.

Cellular dynamics of megakaryopoiesis

The primary site of megakaryopoiesis in mammals is the BM[3]. To analyse the spatial distribution of MKs (CD41+CD42+) and their progenitors (MKPs) (CD41+CD42−) we performed three-dimensional immunofluorescence imaging of mouse calvarial BM[9] (Fig. 1a, Extended Data Fig. 1a and Supplementary Video 1). The vast majority of mature MKs (around 82%) resides within a distance of ≤5 µm to sinusoids (Fig. 1b) without preferential association to the endosteum or other sites (Extended Data Fig. 1b). MKPs were significantly smaller than MKs (Extended Data Fig. 1c), were largely spherical (Extended Data Fig. 1d) and showed a similar distribution to mature MKs (around 70% of MKPs) (Fig. 1a,b).

Fig. 1: Spatiotemporal coordination of thrombopoiesis and megakaryopoiesis.

a, 3D-rendered z stack of mouse BM (sternum). n = 3. MKPs (green): CD41+CD42−; MKs (yellow): CD41+CD42+; sinusoids (grey): CD144+; bone (blue): second harmonic generation. b, The distribution of MKs and MKPs relative to their distance to sinusoids. n = 3. Data are mean ± s.d. Statistical analysis was performed using multiple unpaired t-tests; NS, not significant. c, Chronic 2P-IVM analysis of the calvaria. n = 7 mice. Top, images of VwfeGFP/+ cells (green); TRITC–dextran (sinusoids; magenta). The arrow indicates an MKP migrating at the perivascular niche before growth. The arrowhead indicates MKP growth in the proximity of thrombopoiesis (which is indicated by an asterisk). Bottom, 3D-rendering. d, The speed of MKs (n = 52), arrested MKPs (n = 33) and motile MKPs (n = 31). Cells were pooled from 7 mice. Statistical analysis was performed using one-way ANOVA with Tukey’s test; NS, P = 0.2029; ****P = 0.0000000005. Data are mean ± s.d. e, The diameters of arrested and motile MKPs tracked over time (2P-IVM). n = 28 cells from 5 mice (arrested MKPs) and n = 6 cells from 3 mice (motile MKPs). f, The change in cell volume per hour during MKP maturation (growth; yellow) (n = 14 cells pooled from 4 mice) and platelet release (reduction; cyan) (n = 11 cells pooled from 4 mice). Data are mean ± s.d. g, VwfeGFP/+ cells per field of view (FOV) tracked over time. n = 12 FOVs from 5 mice. h, The homeostatic circuit of MKs in BM. i, Chronic 2P-IVM after PD. The histogram shows an increased frequency of MKPs at the perivascular niche. n = 4 mice per group. Statistical analysis was performed using multiple Mann–Whitney U-tests; *P = 0.0286. Data are mean ± s.d. Arrowheads, new MK progenitors. j, The fold change in platelet counts (haemocytometer). n = 9 (baseline), n = 6 (0.5 days), n = 8 (1 day), n = 9 (2 days), n = 9 (4 days), n = 4 (8 days) mice. MK/MKP density (counts per mm^3) (BM whole-mount immunostainings) (n = 3 mice) were measured at the indicated timepoints after PD. k, The percentage of MKPs attached to MKs (BM whole-mount immunostainings). n = 4 mice. Statistical analysis was performed using an unpaired t-test; **P = 0.009. Data are mean ± s.d. l, The frequency of apoptotic MKs (live/dead-stain-405−CD41-PE+CD42-APC+CD11b−CD8a−Apotracker green+) increases after PD (light pink, 6 h; pink, 24 h), as determined using FACS. Left, the fluorescence intensity (Apotracker). The frequency of apotracker+ MKs. n = 4 (control and PD (6 h)) and n = 6 (PD (12 h)) mice. Statistical analysis was performed using one-way ANOVA with Tukey’s test; ***P = 0.00041; ****P = 0.0000059. Data are mean ± s.d. For a, c and i, scale bars, 50 μm.

Source data

To study the spatiotemporal patterns of megakaryopoiesis in vivo, we performed two-photon intravital microscopy (2P-IVM) analysis of Vwf-eGFP reporter mice, specifically labelling the entire megakaryocytic lineage including MKPs and mature MKs[10] (Extended Data Fig. 1e–g). We visualized the same field of view for up to 3 days with an imaging window implanted onto the calvaria to track individual MKPs and MKs (Extended Data Fig. 2a). We identified small, motile VwfeGFP/+ cells within the BM parenchyma that arrest along BM sinusoids (Fig. 1c and Extended Data Fig. 2b,c) and increase their volume by around tenfold (Fig. 1c–f and Supplementary Video 2), representing MKPs undergoing cytoplasmic maturation into large, sessile MKs (Fig. 1d). This provides real-time evidence that perivascular positioning of immotile MKs is determined by their motile progenitors, as previously proposed by others[11].

Mature MKs lodged within the perivascular niche release proplatelets to produce platelets[3] (Fig. 1c and Extended Data Fig. 2b). Once entering thrombopoiesis MKs rapidly reduce their volume and disappear completely within hours (Fig. 1c,f and Extended Data Fig. 2b,c). Consumption of platelet-producing MKs is irreversible as we did not observe recovery once MKs completed thrombopoiesis. Instead, new VwfeGFP/+ MKPs appear in proximity to vanished MKs giving rise to mature MKs (Fig. 1c, Extended Data Fig. 2b,c and Supplementary Video 2). Consequently, the total number of VwfeGFP/+ cells within one field of view remains highly stable over several hours to days (Fig. 1g). At the BM-tissue level megakaryopoiesis and thrombopoiesis are therefore well synchronized processes that ensure immediate replenishment of platelet-producing MKs from their progenitors to maintain MK homeostasis (Fig. 1h).

We tested whether megakaryopoiesis and thrombopoiesis also remain synchronized in situations of high platelet demand. We removed the entire circulating platelet pool by antibody-mediated platelet depletion (PD) (Extended Data Fig. 2d). During PD, MKs lose sphericity, indicating activation (Extended Data Fig. 2e), and engage in emergency platelet production through intrasinusoidal proplatelet extensions or MK fragmentation resulting in a rapid reduction in MK size (Extended Data Fig. 2f). Multi-day four-dimensional imaging revealed that the fast release of platelets from MKs is accompanied by an increased MKP proliferation that peaks at 12–24 h after treatment and was most prominent at the perivascular niche, while MK growth dynamics was not affected (Fig. 1i and Extended Data Fig. 2g,h). Accelerated proliferation of MKPs fully compensated for the high MK demand during emergency thrombopoiesis and replenished the circulating platelet pool within 4 days while maintaining MK homeostasis (Fig. 1j). Together, our data show that thrombopoiesis and megakaryopoiesis are tightly coordinated to maintain MK homeostasis in BM tissue both in steady state and pathological platelet consumption, raising the question of the underlying mechanism[8] (Fig. 1h).

pDCs regulate megakaryopoiesis

Liver-derived thrombopoietin (TPO) is the most potent cytokine promoting megakaryopoiesis. Its plasma levels are tightly regulated through TPO sequestration by TPO receptors (cMPL) on circulating platelets[12] (Extended Data Fig. 3a). Elevated plasma TPO levels drove global proliferation of BM MKPs, but did not trigger characteristic local perivascular megakaryopoiesis (Extended Data Fig. 3b–e) as observed after PD (Fig. 1i). Consistent with these data, both TPO- and cMPL-deficient mice have been shown to produce small numbers of morphologically and functionally normal MKs and platelets at steady state[13], and were able to produce normal platelet counts in response to stress[14]. This suggests that additional TPO-independent signals are involved, potentially arising locally from the BM niche[15].

Whole-mount analysis of mouse BM showed that a considerable fraction of MKPs was located in close proximity to mature MKs[16] (Fig. 1a,k). A large proportion of mature MKs showed signs of apoptosis, and the apoptotic MK fraction further increased when we induced emergency thrombopoiesis by depleting platelets (Fig. 1l). On the basis of these two observations, we hypothesized that vanishing MKs that release platelets and show signs of apoptosis[6] may trigger their own replacement from local MKPs within the perivascular niche. Different phagocyte subsets are equipped to sense and clear apoptotic bodies and cell-free DNA. In particular, macrophages have an important role in homeostasis of various tissues, including erythropoietic islands of the BM[17] and aged BM-resident macrophages were shown to expand platelet-biased haematopoietic stem cells (HSCs)[18]. We found that approximately 12% of mature MKs colocalized with CD68+ macrophages in the steady state (Extended Data Fig. 4a). These macrophage–MK contacts did not change during immune-mediated thrombocytopenia despite the increase in apoptotic MKs (Extended Data Fig. 4a). Furthermore, depletion of macrophages (through CSF1R inhibition (PLX5622)[19] or by using Cd11b-DTR mice[20]) did not significantly alter MK, MKP and platelet counts (Extended Data Fig. 4b,c), indicating a minimal contribution to megakaryopoiesis. We obtained similar results after depletion of phagocytic neutrophils (Extended Data Fig. 4d).

pDCs are another subset of innate immune cells that are specialized in detecting apoptotic cells and nuclei acids21,22. Although pDCs are rare in peripheral tissues, they are abundant in the BM, where they originate[23]. pDCs migrate in the BM with mean speeds of around 4 µm min−1 and without any clear directionality (Fig. 2a, Extended Data Fig. 4e–g and Supplementary Video 3). Compared with simulations of random localizations, pDCs showed an increased probability of residing in close proximity (<10 µm) to MKs (Fig. 2b). This distance to MKs was maintained in situations with increased MK turnover (Extended Data Fig. 4h). Approximately 15% of mature MKs colocalized with BST2+ pDCs during steady state (Extended Data Fig. 4i) and these co-localizations increased by twofold in response to PD (Extended Data Fig. 4i). The total number of pDCs in the BM remained unaffected by PD, suggesting specific rather than stochastic recruitment to the megakaryocytic niche (Extended Data Fig. 4i).

Fig. 2: pDCs are BM niche cells that regulate megakaryopoiesis.

a, 2P-IVM analysis of pDC migration in close proximity to the megakaryocytic lineage. MK/MKPs: VWF–eGFP+ (green); pDCs: anti-SIGLECH–PE (2 µg per 25 g intravenously (i.v.) 15 min before imaging) (magenta). b, The distribution of pDCs relative to their distance from MKs compared with calculated random spots. n = 3 mice. Data are mean ± s.d. Statistical analysis was performed using multiple unpaired t-tests with Holm–Šidák test; **P = 0.0068 (0 μm), **P = 0.0093 (10 μm). c,d, Impaired megakaryopoiesis at steady state and under stress after pDC depletion in BDCA2-DTR mice. c, Cell numbers were quantified using histology or FACS (see also Extended Data Fig. 5b). n = 6 mice. DT, diphtheria toxin. d, Platelet counts (haemocytometry) (n = 8 mice) and the fraction of reticulated (ret.) platelets (FACS; thiazole orange). n = 6 mice. Data are mean ± s.d. Statistical analysis was performed using one-way ANOVA with Tukey’s test; pDCs: ****P = 0.00000000002 (control and BDCA-DTR), ****P = 0.000000000003 (PD and BDCA-DTR + PD), NS, P = 0.89; MKPs: ****P = 0.0000000002 (control and BDCA-DTR), ****P = 0.00000000000002 (control and PD), ****P = 0.00000000000002 (PD and BDCA-DTR + PD), **P = 0.0068; MKs: ***P = 0.00012, ****P = 0.000014, NS, P = 0.98; platelets: ****P = 0.000000000000001 (control and BDCA-DTR), NS, P = 0.997; reticulated platelets: *P = 0.0102 (control and BDCA-DTR), *P = 0.0105 (PD and BDCA-DTR + PD) and ****P = 0.000005. e, Mice with constitutively reduced pDC numbers show altered megakaryopoiesis. n = 6 (control A (RS26WT/WT;Tcf4fl/fl BM chimera)), n = 5 (control B (RS26creERT2/WT;Tcf4WT/WT BM chimera)) and n = 8 (Tcf4−/−(RS26creERT2/WT;Tcf4fl/fl BM chimera)) mice. Data are mean ± s.d. Statistical analysis was performed using one-way ANOVA with Holm–Šidák test; pDCs: ***P = 0.00024; MKPs: *P = 0.033 (RS26creERT2/WT;Tcf4fl/fl and control A), *P = 0.025 (RS26creERT2/WT;Tcf4fl/fl and control B); MKs: *P = 0.0124; platelets: *P = 0.0228; reticulated platelets: ****P = 0.00000068 (RS26creERT2/WT;Tcf4fl/fl and control A), ****P = 0.00000045 (RS26creERT2/WT;Tcf4fl/fl and control B). TAM, tamoxifen. f, Delayed recovery after PD in Tcf4−/− BM chimeras. n = 6 mice. Data are mean ± s.d. Statistical analysis was performed using two-way ANOVA with Tukey’s test, showing a significant delay in recovery: day 0 versus day 8: P = 0.084 (control A), P = 0.22 (control B), P = 0.0000398 (RS26creERT2/WT;Tcf4fl/fl). Scale bar, 50 µm (a).

Source data

To investigate whether pDCs are essential to control megakaryopoiesis and MK homeostasis in vivo, we depleted pDCs by treating mice expressing the diphtheria toxin receptor under the Clec4c promoter (hereafter BDCA2-DTR mice) with diphtheria toxin[24]. After 3 days of treatment, about 80% of pDCs (BST2+SIGLECH+B220+) were cleared from the BM (Fig. 2c and Extended Data Fig. 5a–d). Analysis of the megakaryocytic lineage using fluorescence-activated cell sorting (FACS) and whole-mount immunostaining analysis revealed severely impaired megakaryopoiesis in response to pDC depletion with a substantial decrease in MKP (50%) and MK (25%) numbers (Fig. 2c and Extended Data Fig. 5b,c). The positioning of MKs and MKPs within the BM compartment was altered compared with control mice, indicating a crucial role of pDCs in maintaining the megakaryocytic niche (Extended Data Fig. 5e). Defective MK homeostasis in pDC-ablated mice was associated with reduced platelet production, as indicated by a twofold decrease in the reticulated (young) platelet fraction, and a 40% drop in total circulating platelet counts at steady state (Fig. 2d). Notably, the sharp increase in megakaryopoiesis induced by thrombocytopenia was completely blocked by pDC depletion, indicating their critical role in both steady-state and stress situations (Fig. 2c,d). Moreover, we obtained similar results after depletion of pDCs by antibodies (anti-BST2; clone 927)[25] (Extended Data Fig. 5f,g) and in mice with constitutively reduced pDC numbers (RS26creERT2/WT;Tcf4fl/fl)[26] (Fig. 2e and Extended Data Fig. 5h).

To characterize the precise kinetics of pDC-regulated megakaryopoiesis and platelet production, we transiently depleted pDCs and monitored the peripheral platelet count. Three days after the first injection of the pDC-depleting antibody, when the numbers of BM pDCs have efficiently dropped, platelet counts decreased by approximately 40% (Extended Data Fig. 5g). After the last injection of pDC-depleting antibodies (day 3), platelet counts remained diminished for another 3 days before returning to the baseline (Extended Data Fig. 5g), which was paralleled by normalization of pDC numbers, as well as MK and MKP numbers in the BM (Extended Data Fig. 5f). Thus, these data show that pharmacological alteration of pDC-driven megakaryopoiesis has immediate and reversible consequences on the circulating platelet pool. We assessed how mice with constitutively reduced pDC numbers cope with acute platelet demand. We depleted the entire circulating platelet pool in control and RS26creERT2/WT;Tcf4fl/fl mice and continuously monitored its recovery (Fig. 2f). While control mice recovered to the baseline within 8 days, platelet recovery in RS26creERT2/WT;Tcf4fl/fl mice was substantially delayed by more than a week (Fig. 2f), highlighting that pDCs are indispensable for on-demand platelet production.

Taken together, our data show that pDCs stimulate megakaryopoiesis to ensure MK and platelet homeostasis at steady state and during stress.

pDCs sense MK-derived extracellular DNA

pDCs encountering apoptotic cells become activated and release IFNα into their microenvironment in an interferon regulatory factor 7 (IRF7)-dependent manner[23]. Depletion of pDCs significantly reduced IFNα levels in BM lavages, indicating that pDCs are a major source of IFNα in the BM at steady state (Fig. 3a,b). The numbers of apoptotic MKs increases in response to high platelet demand (Fig. 1l). This was accompanied by increased activation of pDCs and phosphorylation of IRF7 (p-IRF7) (Fig. 3c and Extended Data Fig. 6a) as well as a considerable increase in IFNα levels in the BM extracellular fluid, which was absent in pDC-depleted mice (Fig. 3b). Accordingly, pDCs that were co-cultured with apoptotic MKs in vitro released high amounts of IFNα, while IFNα was barely detectable in co-cultures with vital MKs (Fig. 3d). Incubation of pDCs with cell-free supernatant from apoptotic MKs was sufficient for robust activation, suggesting that physical cell–cell contact is not required for pDC activation (Fig. 3e and Extended Data Fig. 6b). Apoptotic MKs are a rich source of cell-free DNA[27] (Extended Data Fig. 6c), which is a potent activator of pDCs through the TLR9–MYD88 pathway leading to IRF7 activation and type I interferon production[23]. The presence of DNase efficiently blocked activation of pDCs (Fig. 3e and Extended Data Fig. 6b), and MYD88-deficient pDCs showed impaired release of IFNα in response to supernatants of apoptotic MKs (Fig. 3d). Similar to pDC-depleted mice, MYD88-deficient animals exhibited reduced MKP, MK and platelet counts, while both pDC numbers and TPO levels were not significantly altered in these mice (Fig. 3f and Extended Data Fig. 6d). Collectively, these data indicate that pDCs sense and respond to MK-derived cell-free DNA through MYD88–IRF7 signalling and IFNα release, which is critical for homeostasis of the megakaryocytic lineage.

Fig. 3: Innate immune sensing drives pDC activation in response to MK-derived extracellular DNA.

a,b, IFNα levels in the BM are pDC dependent at steady state and under stress. a, The experimental schematic. b, IFNα levels in the BM are pDC dependent, as determined using ELISA. BDCA2-DTR-neg (C57BL/6J): n = 12 (control) and n = 10 (PD (6 h) and PD (24 h)) mice; BDCA2-DTR-pos: n = 6 (control, PD (6 h) and PD (24 h)) mice. Data are mean ± s.d. Statistical analysis was performed using unpaired t-tests with Welch’s correction; **P = 0.0029 (control (BDCA-DTR-neg versus BDCA-DTR-pos)), ****P = 0.00002 (PD 6 h (BDCA-DTR-neg versus BDCA-DTR-pos)), ****P = 0.00001 (PD 24 h (BDCA2-DTR-neg versus BDCA2-DTR-pos)), ****P = 0.00004 (BDCA2-DTR-neg (control versus PD 6 h)), NS, P = 0.0812 (BDCA2-DTR-neg (PD 6 h versus PD 24 h)). c, Elevated p-IRF7 in pDCs after PD as determined using FACS. n = 4 (control and PD (6 h)) and n = 6 (PD (24 h)) mice. Data are mean ± s.d. Statistical analysis was performed using Brown–Forsythe ANOVA with Dunnett’s test; *P = 0.036, **P = 0.004. d, Co-culture of BM-derived pDCs (WT and Myd88−/−) and MKs. MK cell death was induced by DT injection in PF4-cre;RS26-iDTR mice. n = 6; PBS-injected mice were used as controls (n = 4). After 18 h of co-culture, IFNα was measured in the supernatants using ELISA. Data are mean ± s.d. Statistical analysis was performed using two-way ANOVA with Tukey’s test; ****P = 0.00000001 (WT pDCs (vital MKs versus dead MKs)), NS, P = 0.406 (Myd88−/− pDCs (vital MKs versus dead MKs)), P = 0.559 (vital MKs (WT pDCs versus Myd88−/− pDCs)), ****P = 0.0000002 (dead MKs (WT pDCs versus Myd88−/− pDCs)). e, MK-derived cell-free DNA activates pDCs. IFNα was measured using ELISA 30 min after incubation with MK supernatants. n = 3. Data are mean ± s.d. Statistical analysis was performed using one-way ANOVA with Tukey’s test; NS, P = 0.708 (no MKs versus vital MKs), P = 0.995 (vital MKs versus vital MKs + DNase), ****P = 0.00003 (vital MKs versus dead MKs), ****P = 0.00001 (dead MKs versus dead MKs + DNase). f, Myd88−/− mice show impaired megakaryopoiesis. n = 6 (MKPs/MKs), n = 11 (platelets) and n = 8 (reticulated platelets) mice. Data are mean ± s.d. Statistical analysis was performed using unpaired t-tests with Welch’s correction; **P = 0.0038 (MKPs), *P = 0.033 (MKs), *P = 0.016 (platelets), **P = 0.0068 (reticulated platelets).

Source data

IFNα drives pDC-dependent megakaryopoiesis

pDCs were required to maintain basal IFNα levels of the BM extracellular fluid at steady state and to increase levels during stress (Fig. 3a–c). Cells of the MK lineage expressed the IFNα receptor (IFNAR) (Extended Data Fig. 7a,b) and IFNα and TPO synergistically boosted megakaryopoiesis in an IFNAR-dependent manner in vitro (Fig. 4a). Moreover, systemic treatment of mice with IFNα induced rapid and immediate megakaryopoiesis in vivo—doubling MK numbers within 2 h and MKP numbers within 4 h—and an increase in circulating platelets by 1.5-fold (Fig. 4b). IFNα was previously reported to fuel megakaryopoiesis by upregulating cell cycle and translational activity of MK-primed stem and progenitor cells in the BM28,29. Accordingly, bulk RNA-sequencing (RNA-seq) analyses of MK-primed progenitors (CD41+CD42−CD9+KIT+) in thrombocytopenic mice revealed pDC-dependent enrichment of genes associated with cell division and translation, as well as moderate induction of interferon-response genes, consistent with previous reports[28] (Fig. 4c,d and Extended Data Fig. 7c–f).

Fig. 4: pDC-dependent IFNα drives megakaryopoiesis.

a, MK colony-forming unit (CFU) assay after TPO (50 ng ml−1) and IFNα (as indicated) treatment. Conditional deletion in MKPs (Vwf-cre;Ifnarfl/fl; n = 6 mice) and global deletion (Ifnar−/−; n = 5 mice) confirmed a direct and IFNAR-dependent role of IFNα. Data are mean ± s.d. Statistical analysis was performed using two-way ANOVA with Šidák’s test; P values are shown. b, Increased megakaryopoiesis after IFNα treatment. MKs: n = 6 (control), n = 4 (2 h and 4 h), n = 3 (24 h); MKPs: n = 4 (control, 2 h and 4 h), n = 3 (24 h); and platelets: n = 4 (control, 2 h, 4 h and 24 h) mice. Data are mean ± s.d. Statistical analysis was performed using Brown–Forsythe ANOVA with Dunnett’s test; *P = 0.0198 (MKPs); *P = 0.029 (MKs), ****P = 0.00003 (MKs); *P = 0.031 (platelets), **P = 0.0088 (platelets). c, The experimental design of the RNA-seq experiments. d, Metabolic activation of MKPs in bulk RNA-seq (CD41+CD42−CD9+KIT+). The scatter plot shows deregulated genes (log_2[FC]) in PD versus control and PD + pDC depletion (pDC-D) versus PD, plotted against each other. GO analysis revealed upregulated genes (false-discovery rate (FDR) < 0.05) associated with terms for transcription and translation (top five terms). e, UMAP plot of scRNA-seq data (sorted CD41+CD42−CD9+KIT+ progenitors). GMP, granulocyte–monocyte progenitor. f, The frequency of each cell type and condition. g, Annotation by canonical gene expression markers. h, Trajectory analysis of MKP clusters. Top left, pseudotemporal ordering (Monocle3) of MKPs superimposed onto UMAP clusters (colour coded on the basis of progression in pseudotime). Top right, the proportion of MKP subsets for each condition along pseudotime. Bottom, heat map of genes associated with pseudotime (q < 0.01) clustered by pseudotemporal expression pattern. Selected genes are shown for each cluster (1–6) (the full list is provided in Supplementary Table 1). i, Genes upregulated after PD and downregulated after PD + pDC depletion defined from bulk RNA-seq analysis were summarized into a gene score (average expression across the gene set) and visualized by MKP clusters (scRNA-seq). j,k, Differentially expressed genes (Wilcoxon test). The horizontal dashed line indicates P = 0.05. The vertical dashed line indicates log_2[FC] = 0.25; red, P < 0.05 and log_2[FC] > 0.25; blue, P ≧ 0.05 and log_2[FC] > 0.25. l, Decreased megakaryopoiesis in Ifnar−/− mice. pDC depletion in BDCA2-DTR;Ifnar−/− mice had no additive effect. MKs, MKPs and pDCs: n = 10 (control and Ifnar−/−) and n = 6 (BDCA2-DTR;Ifnar−/−) mice; platelets: n = 16 (control), n = 11 (Ifnar−/−) and n = 6 (BDCA2-DTR;Ifnar−/−) mice. Data are mean ± s.d. Statistical analysis was performed using Brown–Forsythe ANOVA with Dunnett’s test; P values are shown. m, Graphical summary.

Source data

To further examine the molecular signature of MK-primed progenitors responding to increased platelet demand and to characterize their genomic states, we sorted CD41+CD42−CD9+KIT+ BM cells from wild-type and thrombocytopenic mice and analysed their transcriptome at the single-cell level (Fig. 4c–f and Extended Data Fig. 8a). We identified nine transcriptionally distinct clusters of progenitors, five of which were MK primed and expressed MK marker genes (Pf4, Itga2b, Vwf) and transcription factors (Fli1, Pbx1, Mef2c, Runx1), albeit to varying degrees30,31,32 (Fig. 4g and Extended Data Fig. 8b and Supplementary table 1). We annotated MK-primed progenitors on the basis of their differentially expressed marker genes and enriched Gene Ontology (GO) into cycling and non-cycling MK primed megakaryocyte–erythrocyte progenitors (MK-MEPs)[33] (Eng, Car2), early MKPs (Pbx1, Mef2c, Fli1) and late MKPs (Gp9, Ppbp) as well as metabolically active MKPs expressing high levels of genes involved in ribosome biogenesis (Ncl, Npm1), translation initiation (Eif4a1, Eif2s2) and protein chaperones (Hsp90) (Fig. 4g and Extended Data Fig. 8b,c). Pseudotime analysis revealed that MKP subsets aligned along a distinct developmental trajectory consistent with the expression of maturation stage-dependent marker genes of megakaryopoiesis (Fig. 4h). We next investigated whether increased platelet demand affected MKP developmental stages. Indeed, the number and proportion of metabolically active and late-stage MKPs increased significantly at the expense of MK-MEPs and early MKPs, indicating PD-induced differentiation of MK-primed progenitors[34] (Fig. 4f,h). The shift toward more mature MKPs was attenuated in pDC-depleted mice (Fig. 4f,h), consistent with pDCs supporting efficient cell cycle induction and protein synthesis of MK-primed progenitor cells (Fig. 4d). Integration of bulk RNA-seq results and scRNA-seq clusters revealed that, among MK-primed progenitors, cycling MK-MEPs showed the highest expression of genes responsive to PD and regulated by pDCs (Fig. 4i and Extended Data Fig. 8c–e). Accordingly, differentially upregulated genes at the single-cell level were enriched in ribosome biogenesis (for example, Npm1, Ncl) and initiation of translation (such as Eif2s2, Eif4a) (Fig. 4j), suggesting increased metabolic activity of cycling MK-MEPs in response to PD, which was attenuated after pDC-depletion (Fig. 4k). Among the highest differentially downregulated genes in response to PD was Pdcd4, a translation inhibitor that was previously reported to have a role in cell growth[35] and emergency megakaryopoiesis[29] and to be regulated by IFNα signalling[36]. Indeed, depletion pDCs in thrombocytopenic mice resulted in significantly increased expression of Pdcd4 in cycling MK-MEPs (Fig. 4j,k). Thus, our data suggest that pDCs drive megakaryopoiesis by initiating protein translation in early MK-primed progenitors[29], consistent with their role as major carriers of IFNα.

pDCs are a major source of IFNα in unperturbed BM (Fig. 3b), suggesting a role for pDC-derived IFNα also in steady-state MK homeostasis. Similar to pDC-depletion, disruption of IFNα signalling in IFNAR-deficient mice reduced MKP numbers at steady state and altered MK and platelet homeostasis (Fig. 4l). Ifnar−/− BM chimeras phenocopied global Ifnar deletion (Extended Data Fig. 8f). Ablation of pDCs in Ifnar−/− mice (BDCA2-DTR;Ifnar−/− mice) had no additive effect on either MKP, MK or platelet numbers (Fig. 4l), indicating that IFNα is the major mediator in pDC-regulated megakaryopoiesis.

Taken together, our data suggest that pDCs encountering apoptotic MKs release IFNα into the BM niche, which in turn fuels expansion and maturation of MK-primed progenitors through IFNAR signalling to maintain MK homeostasis at steady state and under stress (Fig. 4m).

Increased pDC–MK contacts in patients with ITP

To define whether pDC–MK contacts are present in humans, we analysed BM sections from healthy individuals (Fig. 5a,b). Consistent with our findings in mice, approximately 12% of MKs co-localized with pDCs under steady state (Fig. 5c). We then examined the BM of a cohort of patients with severe immune thrombocytopenic purpura (ITP) (secondary ITP with non-Hodgkin lymphoma without BM involvement; patient characteristics are provided in Supplementary Table 2). Similar to PD in mice, the number of circulating platelets was severely reduced in patients with ITP (Fig. 5b). pDC–MK contacts were threefold higher in ITP compared with in control patients (Fig. 5c) while the number of MKs doubled (Fig. 5b). This suggests that pDCs also act as sentinels of MK turnover in human BM.

Fig. 5: Infection alters pDC-regulated MK homeostasis in humans and mice.

a, Immunohistology of human BM biopsies from healthy controls and patients with secondary ITP with non-Hodgkin lymphoma (without BM involvement). MKs (CD41+, >15 μm; green), pDCs (CD123+; magenta), nuclei (DAPI; blue). Scale bar, 50 µm. b,c, Quantification of the number of pDCs, MKs per high power field (HPF) size of 0.9 mm × 0.7 mm and platelets (b) and the fraction of MKs with pDC contact (c) from the experiment in a. n = 5 patients. Data are mean ± s.d. Statistical analysis was performed using unpaired t-tests with Welch’s correction; NS, P = 0.158 (pDCs), **P = 0.0011 (MKs), **P = 0.0014 (platelets), ****P = 0.000002 (MKs/pDCs). d, Infection may alter the role of pDCs as homeostatic sensors. e,f, Immunohistology of human BM biopsies from healthy control patients (the same patients as shown in a and b) and from autopsies of patients with COVID-19 (see also Extended Data Fig. 9d). Quantification of the number of pDCs and the fraction of MKs in contact with pDCs (e) and the number of MKs (f) is shown. n = 5 (control) and n = 12 (COVID-19) individuals. Data are mean ± s.d. Statistical analysis was performed using unpaired t-tests with Welch’s correction; ****P = 0.00007 (pDCs), ****P = 0.0000000007 (MKs/pDCs), **P = 0.0018 (MKs). g, Increased activation of pDCs in the BM of patients with COVID-19. Quantification of activation marker CD69 (left) and IFNα expression (right) (Immunohistology; see also Extended Data Fig. 9e). n = 3 patients. Data are mean ± s.d. Statistical analysis was performed using unpaired t-tests with Welch’s correction; *P = 0.0304, **P = 0.0069. h, BM from FVB;K18-hACE2 mice infected with SARS CoV-2 (10^5 median tissue culture infectious dose (TCID_50) SARS-CoV-2 per mouse in 25 μl intranasally (i.n.)) were analysed in the presence (n = 3) or absence (n = 3) of IFNAR1 blocking antibody and compared to untreated control mice (PBS, n = 2) (immunohistology). Data are mean ± s.d. Statistical analysis was performed using unpaired t-tests with Welch’s correction; **P = 0.0015 (pDCs), **P = 0.0041 (percentage of MK–pDC-contacts), **P = 0.0011 (MKPs), **P = 0.0014 (MKs).

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Infection alters MK homeostasis

pDCs are specialized to sense viral infections and are the major source of IFNα in antiviral immunity37,38. We hypothesized that, in the emergency of acute infection, viral-triggered activation of pDC and the associated release of IFNα may exceed the homeostatic range required for megakaryopoiesis. This could explain hyperplasia of MKs associated with viral infections such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)[39] (Fig. 5d). To address whether SARS-CoV-2 infection is associated with dysregulation of pDC-driven megakaryopoiesis, we analysed BM of humanized mice susceptible to SARS-CoV-2 (FVB-K18-hACE2). Six days after infection, mice showed elevated pDC numbers within the BM (Extended Data Fig. 9a,b). pDCs engaged in close contact with MKs, and both MKP and MK numbers increased compared with in the uninfected controls, suggesting pDC-driven hyperplasia of the MK lineage (Extended Data Fig. 9b). We next analysed human BM from a cohort of patients who died from COVID-19 (Fig. 5e–g; patient characteristics are provided in Supplementary Table 2). Similar to humanized mice, we found a greater than twofold increase in pDC numbers, and a threefold increase in MK–pDC contacts in patients with COVID-19 compared with the control individuals (Fig. 5e), which was associated with MK hyperplasia (Fig. 5f). pDCs respond to SARS-CoV-2 by producing type I interferons[40]. Accordingly, the fraction of activated, CD69+ and IFNα-expressing pDCs in the COVID-19 BM was significantly increased compared with in the BM of healthy controls, both in humans and mice (Fig. 5g and Extended Data Fig. 9c,e). To test whether IFNα signalling drives MK hyperplasia, we treated FVB-K18-hACE2 mice with an IFNAR-blocking antibody before SARS-CoV-2 infection. In contrast to the infected control animals, antibody-treated mice showed reduced pDC–MK contacts in the BM and did not develop hyperplasia of the megakaryocytic lineage (Fig. 5h). These data suggest that the homeostatic circuit of megakaryopoiesis controlled by pDCs can be perturbed by severe systemic infections and that the resulting imbalanced release of IFNα from pDCs contributes to MK alterations in COVID-19.

Discussion

Type 1 interferons are mainly recognized for their protective role in viral infections[41]. However, numerous physiological processes beyond antiviral defence have been identified to rely on IFNs, including immunomodulation, immunometabolism[42], cell cycle regulation, cell survival and cell differentiation42,43. Within the BM compartment, constitutive IFNα levels are required for maintenance of the HSC niche, but long-term systemic elevation of IFNα levels may cause exhaustion of HSCs28,44,45. Moreover, long-term treatment with high-dose IFNα, as well as infections and diseases associated with persistently high type I IFN levels are associated with impaired platelet production and platelet function29,46,47,48,49,50. This indicates that IFNα levels must be precisely controlled to preserve homeostasis of the haematopoietic system.

Here we characterize a homeostatic circuit[8] of BM tissue that maintains stable cellularity of the megakaryocytic lineage through pDC-dependent release of IFNα (Extended Data Fig. 9f). Our data establish patrolling pDCs as homeostatic sensors that monitor MK turnover by detecting MK-derived cell-free DNA. Innate sensing of self-DNA by pDCs occurs through the TLR9–MYD88–IRF7 pathway[23] and results in the precisely controlled delivery of IFNα to MK progenitors to prevent MK loss by eliciting on-demand megakaryopoiesis. Thus, these data demonstrate a critical role of inflammatory signalling in the control of MK homeostasis at the tissue level, which complements the well-characterized systemic regulation of MK lineage homeostasis through TPO (Extended Data Fig. 9f). Although our data indicate that pDCs have a central role in both constitutive and stress-induced IFNα release within the BM, it is worth noting that current technical constraints hinder definitive confirmation of pDCs as the exclusive source of IFNα in vivo, primarily due to the absence of IFNα-deficient mouse models.

The control of MK numbers by pDCs may have functions beyond platelet homeostasis: the BM microenvironment is functionally compartmentalized by a heterogeneous population of niche cells that provide physical and soluble signals to spatiotemporally organize haematopoiesis[51]. Besides giving birth to platelets, MKs constitute niche cells of haematopoietic origin that regulate HSCs during homeostasis and stress34,52,53,54. Consequently, maintenance of the megakaryocytic HSC niche requires seamless replenishment of consumed, platelet-producing MKs. Here we establish that pDCs are crucial players in the niche orchestrating thrombopoiesis and megakaryopoiesis to maintain MK homeostasis and may therefore also contribute to the maintenance of the megakaryocytic HSC niche34,52,53,54.

Platelets not only prevent blood loss but also counteract infections through interaction with immune cells[2]. To compensate peripheral platelet consumption during acute infections and to maintain homeostasis of the circulating platelet pool, the systemic inflammatory response must initiate emergency megakaryopoiesis[29]. Our results argue for a role of pDCs as homeostatic sensors responsive to inflammatory stimuli, such as viruses. By monitoring the perivascular MK niche, pDCs are strategically positioned to instantaneously detect systemic inflammatory signatures, which may allow them to anticipate the risk of platelet exhaustion and promptly initiate emergency megakaryopoiesis. Thus, pDC-driven megakaryopoiesis has a role beyond steady-state homeostasis and is probably beneficial in any type of acute tissue injury associated with loss of vascular integrity and platelet consumption.

However, it may also be detrimental, when pDC-driven megakaryopoiesis is mismanaged, for example, during severe viral diseases. A case in point is infection with coronavirus SARS-CoV-2, which dysregulates the fine-tuned IFNα production of pDCs[55]. While analysis of peripheral blood of patients with SARS-CoV-2 infection revealed reduced pDC counts with muted IFNα production[55], we identify an accumulation of activated, IFNα-releasing pDCs in the BM of patients with severe disease progression. pDCs engage in close contact with MKs, which is accompanied by marked megakaryocytic hyperplasia. Although the mechanisms linking MK hyperplasia to disease progression are still unclear, previous studies have shown a correlation with severe courses of COVID-19 in particular[39]. It is therefore tempting to speculate that pharmacological modulation of the pDC-mediated homeostatic circuit may be of benefit to these patients. In conclusion, we identified a role of pDCs in orchestrating MK and blood platelet homeostasis. Targeting pDC-driven megakaryopoiesis offers options to boost or suppress platelet production in different clinical scenarios.

Methods

Materials

A list of all reagents and resources with the source and identifier is provided in Supplementary Table 3.

Mouse strains

C57BL/6J, C57BL/6J (CD45.1), PF4-cre (C57BL/6-Tg(Pf4-icre)Q3Rsko/J)[56], Rosa26-iDTRflox (C57BL/6Gt(ROSA)26Sortm1(HBEGF)Awai/J)[57], Ifnar−/− (B6.129S2-Ifnar1tm1Agt/Mmjax)[58], Ifnar1flox (B6(Cg)-Ifnar1tm1.1Ees/J)[59], RS26-creERT2 (B6.129-Gt(ROSA)26Sortm1(cre/ERT2)Tyj/J)[60], Myd88−/−(B6.129P2(SJL)-Myd88tm1.1Defr/J)[61], CD11b-DTR (B6.FVB-Tg(ITGAM-HBEGF/EGFP)34Lan/J)[62], LysM-cre (B6.129P2-Lyz2tm1(cre)Ifo/J)[63], Mcl-1fl/fl (B6;129-Mcl1tm3Sjk/J)[64] and BDCA2-DTR (C57BL/6-Tg(CLEC4C-HBEGF)956Cln/J)[24] mice were purchased from The Jackson Laboratory. Vwf-cre mice were generated by W. Aird and were described previously[65]. Vwf-eGFP mice were generated by C. Nerlov and described previously[10]. Tcf4fl/fl (C57BL/6N-Tcf4tm1c(EUCOMM)Wtsi/WtsiH)[66] mice were obtained from Wellcome Sanger Institute and INFRAFRONTTIER/EMMA partner (Vienna) from which the mouse was received. PF4-cre mice were crossed with Rosa26-iDTR mice to induce MK cell death in vivo (PF4-cre; RS26-iDTR)[52]. PF4-cre;RS26-iDTR mice were crossed with Vwf-eGFP mice to visualize the megakaryocytic lineage after induction of MK cell death. Vwf-cre mice were crossed with IFNαR1fl/fl mice to conditionally delete Ifnar in the megakaryocytic lineage. BDCA2-DTR and Ifnar−/− were cross bred to achieve pDC depletion in Ifnar−/− animals (BDCA2-DTR;Ifnar−/−). RS26-creERT2 mice were cross bred with Tcf4fl/fl (C57BL/6N-Tcf4tm1c(EUCOMM)Wtsi/WtsiH) mice to constitutively reduce pDC numbers. FVB-K18-hACE2 expressing humanized ACE2 were bred in the Iannacone laboratory[67].

Both male and female mice were used in this study. Unless otherwise stated, mice of the control and experimental group were sex- matched and age-matched (6–12 weeks). Animals were bred and maintained in the animal facilities of the Walter-Brendel Zentrum (Wbex), the Zentrum für Neuropathologie und Prionforschung (ZNP) or the Biomedical center of the LMU Munich, Germany or IRCCS San Raffaele Scientific Institute, Italy or Institute of Science and Technology Austria, Austria. All mice live in standardized conditions in which temperature, humidity and hours of light and darkness are maintained at a constant level all year round. The housing of laboratory mice was in accordance with European and German animal welfare legislations (5.1-231 5682/LMU/BMC/CAM/), Wbex and ZNP. Room temperature and relative humidity ranged from 20 to 22 °C to 45 to 55%. The light cycle was adjusted to a 12 h–12 h light–dark period. Room air was exchanged 11 times per hour and filtered with HEPA-systems. All of the mice were housed in individually ventilated cages (Typ II long, Tecniplast) under specified-pathogen-free conditions. Hygiene monitoring was performed every 3 months based on the recommendations of the FELASA-14 working group. All of the animals had free access to water and food (irradiated, 10 mm pellet; 1314P, Altromin). The cages were equipped with nesting material (5 × 5 cm, Nestlet, Datesand), a red corner house (Tecniplast) and a rodent play tunnel (7.5 × 3.0 cm, Datesand). Soiled bedding (LASbedding, 3–6 mm, PG3, LASvendi) was removed every 7 days. All of the animal experiments were performed in compliance with all relevant ethical regulations for studies involving mice and were approved by the local legislation on protection of animals (Regierung von Oberbayern, Munich; ROB 55.2-1-54-2532-190-2015; ROB 55.2-2532; Vet 02-17-194).

Mouse anaesthesia

If not stated otherwise, anaesthesia was performed by isoflurane induction, followed by intraperitoneal injection of medetomidine (0.5 mg per kg body weight), midazolam (5 mg per kg body weight) and fentanyl (0.05 mg per kg body weight). Toe pinching reflexes and breathing pattern were used to determine the adequate depth of anaesthesia. Core body temperature was maintained by heating pads, and narcosis was maintained by repetitive injections of 50% of the induction dose, if necessary.

Human samples

LMU Munich: BM samples of five patients with clinically proven ITP, of five patients with non-Hodgkin lymphoma without BM involvement and 12 patients who died of COVID-19 were analysed. The samples of patients with ITP and lymphoma were archived material, and the COVID-19 specimens were taken during autopsy. Clinical details are provided in Supplementary Table 2. The study was approved by and conducted according to requirements of the ethics committees at the Ludwig Maximilians University of Munich (20-1039). There was no commercial support for this study. University Clinic Aachen: we included 6 consecutive clinical autopsies of patients who were positive for COVID-19 between 9 March 2020 and 5 May 2020 performed at the Institute of Pathology of the University Clinic Aachen. Each patient had a positive clinical SARS-CoV-2 PCR test from upper or lower respiratory tract before autopsy, confirmed by post-mortem PCR with reverse transcription (RT–PCR). Consent to autopsy was obtained by the legal representatives of the deceased patients. The study was approved by the local ethics committee (EK 304/20, EK 119/20 and EK 092/20). BM samples were obtained using an electric autopsy saw (Medezine 5000, Medezine) from the vertebral bodies. The autopsies were performed in two steps according to a modified standard protocol to further increase employee safety and sample acquisition (developed in the frame of the German Registry of COVID-19 autopsies, www.DeRegCOVID.ukaachen.de). The samples were decalcified in formic acid or EDTA before dehydration and embedding in paraffin. Formalin-fixed, paraffin-embedded BM blocks were cut on a microtome at 1–3 µm thickness and decalcified again in EDTA if necessary.

Drug treatments

DT was purchased from Sigma-Aldrich (322326) and was intraperitoneally injected into CD11b-DTR mice as a single dose of 25 ng per g for 2 days and BDCA2-DTR and BDCA2-DTR;Ifnar−/− mice with a dose of 8 ng per g per day for consecutive 3 days. A single dose was injected into Pf4-cre;iDTRfl/fl mice 24 h before the experiment. Platelet-depleting antibodies (R300, anti-GPIbα) and isotype control (C301) were purchased from Emfret and used according to the manufacturer’s protocol. pDC-depleting antibodies (ultra-LEAF purified anti-PDCA-1, 927, BioLegend) were injected intraperitoneally for up to 3 consecutive days at a concentration of 150 µg per mouse at day 1 and 100 µg per mouse on the following days. The isotype control (ultra-LEAF purified rat IgG2bk isotype control, RTK4530, BioLegend) was injected accordingly. Type I IFNα was applied by injecting universal IFNα (PBL, assay science) with 5000 U per mouse intraperitoneally in 200 µl PBS. For macrophage ablation, wild-type mice were feed with PLX 5622 chow (D19101002i, AIN-76A), or control chow (D10001i, AIN-76A) from Research Diets, for 7 consecutive days.

Tamoxifen injection

Cre-recombinase in RS26creERT2/WT;Tcf4fl/fl mice was induced by intraperitoneal injection of tamoxifen (Sigma-Aldrich, 10540-29-1) dissolved in corn oil (Sigma-Aldrich C8267) three times every other day (1 mg per day), and the mice were analysed 10 days after the first administration[26].

BM transplantation

Lin−Sca-1+KIT+ (LSK) cells were isolated and sorted from BM of Ifnar−/−, RS26creERT2/WT;Tcf4fl/fl and control mice (Lin-Pacific Blue (Ter-119, CD3, CD8a, CD45R, CD11b, Ly-6G), Sca-1–PE–Cy7, KIT–APC, all purchased from BioLegend, 1:100). A total of 8 × 10^3 LSK cells was intravenously injected into lethally irradiated C57BL/6J female mice (CD45.1) (two doses of 6.5 Gy with a time interval of 8 h). The BM of chimeras was analysed 8 weeks after the transplantation.

Mouse model of SARS-CoV-2 infection

B6.Cg-Tg(K18-ACE2)2Prlmn/ J mice (on the C57BL/6 background) were purchased from The Jackson Laboratory and bred against FVB mice to obtain C57BL/6 × FVB F_1 hybrids. Mice were housed under specific-pathogen-free conditions and heterozygous mice were used at 6–10 weeks of age. All of the experimental animal procedures were approved by the Institutional Animal Committee of the San Raffaele Scientific Institute and all infectious work was performed in designed BSL-3 workspaces. Mice were infected intranasal with 10^5 TCID_50 of SARS-CoV-2/human/ITA/Milan-UNIMI-1/2020 (GenBank: MT748758.1) in 25 μl. Then, 5 days after infection, the mice were perfused fixed with 4% PFA and the femurs were embedded in Tissue Tek (also see below). The frozen femurs were cut until the marrow was exposed. The femurs were rinsed with PBS and post-fixed with 4% PFA for 15 min at room temperature. The femurs were washed with PBS and incubated with 10% goat serum for 1–2 h at room temperature. BM was stained with anti-mouse CD41 (for MK/MKP), anti-mouse BST2 (for pDC) and DAPI for nucleus staining. In selected experiments, K18-hACE2 mice were injected intraperitoneally with 2 mg per mouse of anti-IFNAR1 blocking antibody (BioXcell, BE0241, MAR1-5A3) 1 day before infection. All the COVID-19 mouse infection experiments were approved by the Authorization no 270/2022-PR (6EEAF.228).

Immunohistology of human BM samples

BM biopsies of five patients with confirmed immune thrombocytopenia and platelet counts <30 × 10^9 per l were compared with age-matched controls (normal BM biopsies performed for lymphoma staging). Tissue was fixed for 12 h in 4% formalin and embedded in paraffin. For immunohistochemistry, 1.5 µm sections were used. Multiplex immunofluorescence or confocal laser-scanning microscopy imaging were performed after antigen retrieval with epitope retrieval buffer (PerkinElmer). Slides were incubated sequentially for 1 h using the following antibodies: pDCs (anti-human CD123, ab257307, Abcam, 1:100); and MKs (anti-human CD41, ab134131, Abcam, 1:100, or MCA467G Bio-Rad, 1:100) and detection was performed using by TSA-Opal620 (PerkinElmer) and TSA-Opal650 (PerkinElmer). Multispectral imaging was performed using the PerkinElmer Vectra Polaris platform. Images were analysed using HALO (Indica labs) software. Furthermore, the samples were imaged on the LSM 880 confocal microscope using the Airyscan module (Carl Zeiss), Plan-Apo ×20/0.8 or ×63/1.46 objectives and analysed using Zen Blue (v.2.3; Carl Zeiss). The study was approved by and conducted according to requirements of the ethics committees at the Ludwig Maximilians University of Munich (20-1039) and the local ethics committee (EK 304/20, EK 119/20 and EK 092/20).

BM autopsies from patients with COVID-19 (embedded in paraffin) were deparaffinized with xilol twice for 5 min, ethanol (100%) twice for 2 min, ethanol (96%) once for 3 min, ethanol (70%) once for 2 min, and submitted for antigen retrieval with Tris-EDTA pH 9 for 20 min, washed once in 0.5% BSA-PBS-Tween-20 (0.1%) for 5 min. The samples were blocked in 10% donkey serum with 0.5% saponin for 1 h at room temperature. To monitor pDC activation and IFNα production, the following primary antibodies were used: mouse CD69 anti-human (MA5-15612, Thermo Fisher Scientific, 1:200), IFNα rabbit polyclonal (PA5-115430 Thermo Fisher Scientific, 1:50). Anti-CD123 goat polyclonal (ab257307, Abcam, 1:100) was used to label pDCs. Primary antibodies were incubated at 4 °C overnight and samples were subsequently washed three times with 0.5% BSA-PBS-Tween-20 (0.1%) for 5 min before adding secondary antibodies. Secondary antibodies (1:200) were as follows: donkey anti-rabbit-AF488 (A-21206), donkey anti-mouse-AF555 (A-31570) and donkey anti-goat-AF647 (A-21447), all from Thermo Fisher Scientific. DAPI (1:1,000) was used for nucleus staining (15 min at room temperature). The samples were washed three times with PBS 5 min before mounting with DAKO (S3023, DAKO) mounting medium.

Immunohistology of mouse BM whole mounts

Mice were euthanized and bones (sternum, femur and tibiae) were collected and post-fixed in 4% PFA for 1 h at room temperature, and incubated in 15% sucrose for 2 h at 4 °C and in 30% sucrose at 4 °C overnight. Next, the bones were embedded in Tissue-Tek O.C.T. Compound and frozen and stored at −80 °C. Frozen bones were cut on the Histo Serve NX70 cryostat until the exposure of the BM. The sternum was cut as sagittal section. The femurs and tibiae were cut as coronal section or cross-section, according to purpose. Bones were carefully removed from O.C.T. and gently washed in 1× PBS. For whole-mount staining, the cut bones were fixed again in 4% PFA for 15 min at room temperature, washed in PBS and incubated in 10% normal goat serum (Thermo Fisher Scientific) for at least 45 min at room temperature (blocking/permeabilization). The bones were then incubated with primary antibodies at room temperature overnight and washed with PBS before adding secondary antibodies for 2 h at room temperature. Labelling of MKs/MKPs was as follows: primary antibodies: CD41–FITC+ (BioLegend, 133903, 1:100) and CD42-purified hamster anti-mouse (BioLegend, 148501, (1:100)); secondary antibodies: goat anti-hamster Alexa Fluor 647 (Abcam, ab173004, 1:100). Labelling of vessels was as follows: primary antibodies: anti-VE-cadherin (CD144) biotin purified (eBioacience, 13-1441-82, 1:100); secondary antibodies: streptavidin-PE (eBioscience, 12-4317-87, 1:200). Labelling of pDCs was as follows: primary antibodies: anti-SIGLECH-PE or FITC (BioLegend, 129606 or 129603, 1:100) or BST2 (CD317/PDCA-1, Thermo Fisher Scientific, PA5-120152, or eBioscience, 16-3172-81, 1:100) anti-mouse purified, anti-mouse rabbit polyclonal; secondary antibodies: goat anti-rat Alexa Fluor 647 (Abcam, ab150159) or goat anti-rabbit Alexa Fluor 594 (Thermo Fisher Scientific, A-11012) all at a dilution of 1:200. To label the nucleus, Hoechst 33342 or DAPI (Thermo Fisher Scientific, 1:1,000) was used. Lineage-biotin antibodies (Ter-119, CD3e, CD45R, CD11b, Ly-6G) and streptavidin-PE were used at a dilution of 1:200; all antibodies were purchased from eBioscience (San Diego). After staining, bone samples were imaged using the multiphoton LaVision Biotech TrimScope II system connected to an upright Olympus microscope, equipped with a Ti;Sa Chameleon Ultra II laser (Coherent) tunable in the range of 680 to 1,080 nm and a ×16 water-immersion objective (numerical aperture 0.8, Nikon). Single images were acquired at a depth of 50–80 μm, with a z interval of 2 μm. The signal was detected by photomultipliers (G6780-20, Hamamatsu Photonics, Hamamatsu). ImSpector Pro 275 (LaVision) was used as acquisition software. Alternatively, a LSM 880 laser-scanning confocal microscope equipped with an Aryscan module (Carl Zeiss), and the Zen Black acquisition software v.2.3 was used. The images were acquired using the Plan-Apo ×20/0.8 or ×63/1.46 objectives, z-step size of 2 µm, range in z-stack of 40 µm.

IFNα staining was as follows: primary antibodies: IFNα polyclonal antibody (PA5115430, Thermo Fisher Scientific, 1:100); secondary antibodies: goat-anti-rabbit 594 (Thermo Fisher Scientific, 1:200). Macrophage staining was performed as follows: primary antibodies: anti-CD68 monoclonal (Bio-Rad, MCA1957GA, 1:50); secondary goat-anti-rat Alexa 647 (Abcam, 1:200). IFNAR staining was performed as follows: primary antibody: IFNAR1 anti-mouse (BioLegend, 127302, 1:100); secondary antibodies: goat-anti mouse Alexa 555 (Thermo Fisher Scientific, 1:200). Bones were imaged using the LSM 880 confocal microscopy using the Airyscan module, objective Plan-Apo ×20 objective NA, 0.8 or with ×63/1.46 oil Plan-Apo. Images were taken with a z step size of 2 µm, range in z stack of 40 µm and analysed using Zen Blue v.2.3. 3D projections and rendering were performed using Imaris v.9.2 (Oxford Instruments/Imaris).

Multi-photon intravital imaging of the calvarian BM

Anaesthetized mice were placed onto a metal stage with a warming pad to maintain the body temperature. The hair over the skull was carefully removed using an electric hair clipper. The skin on the skull was then cut in the midline to expose the frontal bone. For short-term imaging (<4 h), a custom-built metal ring was glued directly onto the centre of the skull, and the mouse’s head was immobilized by fixing the ring on a stereotactic metal stage. After imaging, the mice were euthanized by cervical dislocation. For long-term (chronic) imaging, a chronic window was implanted on the skull. In brief, a round cover glass (diameter: 6 mm) was centred on top of the frontal bone with sterile saline in between glass and the bone surface. The surrounding area of the glass was then filled with dental glue (Cyano veneer) and a custom plastic ring with inner diameter 8 mm was carefully centred on the frontal bone, with the glass exactly in the middle of the ring. The ring was further immobilized by applying the glue in the gap between the outer edge of the glass and the inner edge of the ring, as well as the gap between the outer edge of the ring and the tissue. Surgery was performed under sterile conditions. The mouse calvarium was imaged using a multiphoton LaVision Biotech TrimScope II system connected to an upright Olympus microscope, equipped with a Ti;Sa Chameleon Ultra II laser (Coherent) tunable in the range of 680 to 1,080 nm and additionally an optical parametric oscillator (OPO) compact to support the range of 1,000 to 1,600 nm and a ×16 water-immersion objective (NA 0.8, Nikon). Time-lapse videos of 3D stacks were recorded within 30 μm to 40 μm depth, with a z interval of 2 or 3 μm and a frame rate of 1 min. Chronic imaging was performed at frame rates of <6 h. Blood vessels and bone structure were taken as landmarks to retrieve the same imaging area of the BM. 3D z stacks were acquired with a z interval of 2 μm; 870 nm or 900 nm was used as an excitation wavelength. The signal was detected by Photomultipliers (G6780-20, Hamamatsu Photonics, Hamamatsu). ImSpector Pro 275 (LaVision) was used as acquisition software. Imaging was performed at 37 °C using a customized incubator. Blood vessels were visualized by intravenous injection of dextran tetramethylrhodamine 500,000 Da (TRITC-dextran, 100 μg in 100 μl solution, D7136, Thermo Fisher Scientific) or Dextran Cascade Blue 10,000 Da molecular mass (D1976, Thermo Fisher Scientific) before imaging. Vwf-eGFP mice were used to visualize the megakaryocytic lineage; pDCs were labelled with SIGLECH-PE antibody (BioLegend, 129606) injected intravenously 20 min before imaging (20 µl diluted with 100 µl NaCl).

Image processing

Videos and images were analysed using Imaris v.9.2 (Oxford Instruments/Imaris) or ZEN Blue software v.2.3 (Carl Zeiss) or FIJI[68]. Image denoising using Noise2Void[69] was performed in representative micrographs shown in Fig. 1c and Extended Data Fig. 2c. Mosaic images were stitched in Imaris. The numbers of MKs, MKPs and pDCs were quantified in the whole mosaic images and normalized to the total volume of the BM in the image. The cell distance to vessels and/or endosteal surface was measured manually in Imaris Slice mode or by using ZEN Blue (v.2.3). The mean diameter of an MKP or MK was calculated by the average of the longest and shortest axis of the cell. Cell volumes of 3D-rendered BM stacks were measured automatically in Imaris. Cell migration was analysed in 3D time-lapse videos by tracking the cell at every timepoint using Imaris. The cell speed was calculated by dividing the track length with the track duration. The distance of migrating pDCs to MK surfaces was measured and compared to computed random spots using Imaris v.10.9 (Oxford Instruments/Imaris).

Isolation of mouse BM cells

Mice were anaesthetized and euthanized by cervical dislocation. Long bones (femurs, tibiae, humerus) were collected into ice-cold sterile PBS. Bones were flushed with PBS + 2% FCS using a 26-Gauge needle and the BM suspension was further filtered through a 70 μm or 100 μm cell strainer (Miltenyi Biotec) and pelleted at 4 °C and 300g for 5 min. The supernatant was discarded and cells were resuspended and incubated in red blood cell lysis buffer for 5 min. Lysis was terminated by adding 30 ml PBS + 2 mM Ethylenediaminetetraacetic acid (EDTA, Sigma-Aldrich), followed by centrifugation at 4 °C and 300g for 5 min. Cells were resuspended with PBS + 0.5% BSA (Carl Roth).

Flow cytometry

BM isolated cells (as described above) were enriched by removing CD19+ and CD11b+ cells by negative selection using the EasySep selection kit II (StemCell Technologies) for the cell sorting experiments. Cells were incubated with mouse CD16/CD32 (BD Pharmingen (Fc block) before staining (1:100). The following antibodies were used to identify MKs: 1:100 anti-mouse CD41-FITC+ and anti-mouse CD42d-APC+ (BioLegend, 1:100); and MKPs: anti-mouse CD41-FITC+, Pacific Blue Lin− (Ter-119−CD3e−CD45R−CD11b−Ly-6G−), anti-mouse CD105-PE/PercCy7−, CD150-Brillant violet 510+ and anti-CD9-PercCy5.5+ (BioLegend) (all 1:100). We identified pDCs using the following antibodies: anti-mouse SIGLECH-FITC+, CD11b-PE-Cy7− and B220-APC+ from BioLegend (1:100). pDC activation: anti-mouse CD69-FITC (1:200), CD86-PE (1:400), CD11b-APC-Cy7 (1:200), CD317-APC (1:100), SiglecH-PercCy5.5 (1:100) antibodies all from BioLegend and Life/Dead fixable Aqua dead marker (405 nm excitation; Thermo Fisher Scientific, 10 μg ml−1); macrophages: anti-CD45.2+ (BioLegend, PE/Cyanine 7, 1:200), anti-CD45.1 (BioLegend, FITC 1:100), anti-F4/80+ (BioLegend, PerCP/Cyanine 5.5, 1:100), anti-CD64+ (BioLegend, APC, 1:100); anti-CD115− (BioLegend, Brilliant Violet 421, 1:100); neutrophils: CD11b+ (BioLegend, APC/Cyanine 7, 1:200), Ly6G/G1+ (BioLegend, PE/Cyanine 7, 1:200), CD115− (BioLegend, Brilliant Violet 421, 1:100); p-IRF7 expression by pDCs: after staining for pDC surface markers (see above), cells were fixed with PFA and methanol and stained with anti-mouse rabbit monoclonal phospho-IRF7 antibody (Ser437/438, Cell Signaling, 1:100) in Perm buffer III (BD) as previously described[42] followed by secondary goat anti-rabbit-APC antibodies (Thermo Fisher Scientific, 1:200). Before loading the samples, 10 μg ml−1 Sytox Orange for the live/dead cell gating and counting beads (1,2,3count beads, Thermo Fisher Scientific), were added to the cell suspension, with exception of the p-IRF7 stain. Apoptosis was measured using Apotracker Green (BioLegend) according to the manufacturer’s instructions. For reticulated platelet staining, 2 µl of blood was fixed with PFA 1%. The blood samples were stained with anti-CD42d-APC (1:100) and thiazole orange (TO) (1 μg ml−1) (Sigma-Aldrich) for 25 min at room temperature in the dark and submitted to flow cytometry analyses[70]. MK ploidy was quantified after propidium iodide staining in MKs. Measurements were performed on the FACS Canto II cell analyzer equipped with FACSDiva software v.6.0 (BD Biosciences) or on the Cytoflex-S system with CytExpert acquisition software v.2.3 (Beckman Coulter). FACS data were analysed using FlowJo v.10.6.2 or v.10.9. The gating strategies for all FACS data are shown in Supplementary Data 1.

Bulk RNA-seq analysis

For RNA-seq analysis, BM cells were isolated by flushing the long bones with FACS buffer (2 mM EDTA, 1% FCS, PBS) and treated with Pharm Lyse buffer (BD). Cells were enriched by magnetic removal of CD11b+ and CD19+ cells (EasySep, Stem Cell Technologies). The negative fraction was stained for B220-BV421, SIGLECH-PE, CD9-PerCP-Cy5.5, CD41-FITC, CD42-APC, KIT-APC-Cy7 (all from BioLegend, (1:100). A total of 2,000 cells was sorted using the BD FACS ARIA III Cell sorter (FACSDiva acquisition software v.7.0), into NEB-lysis buffer and processed for sequencing using the NEBNext Single Cell/Low Input RNA Library Kit according to the manufacturer’s protocol (at IMGM). Libraries were pooled in equimolar amounts and sequenced on the NovaSeq 6000 (Illumina) system in a single-end 75-nucleotide run, yielding between 15 and 25 million reads per sample. Reads were mapped against GRCm38.p4 using CLC Genomics Workbench (Qiagen) with the following parameters: mismatch cost 2; insertion/deletion cost, 3; length fraction, 0.8; similarity fraction 0.8; global alignment “no”; strand specific “both”; maximum number of hits per read 5. CLC Genomics Workbench was also used to generate gene expression matrices.

GSEA

To prepare the data for gene set enrichment analysis (GSEA), DESeq2 (v.1.30.0) analysis was performed using Galaxy with the default parameters71,72. Genes were filtered for an expression of transcripts per million (TPM) > 1 in any condition (42,868 genes) to remove non-expressed or very-low-abundance genes, and then sorted according to the log_2-transformed fold change of the respective analysis. For further analysis, the tool GSEA (v.4.0.3) of UC San Diego and Broad Institute was used73,74, referring to their RNA-seq manual pages for analysis. The normalized counts of each replicate as the ranked list generated above were submitted to the GSEA tool with the following parameters: gene sets of their Molecular Signatures Database (MSigDB) in the categories ‘canonical pathways’ (C2) and ‘gene ontology’ (C5) were chosen to contain Ifna1 gene (94 gene sets). Mouse gene symbols were mapped to the human gene symbol (Chip platform: Mouse_Gene_Symbol_Remapping_Human_Orthologs_MSigDB.v7.4.chip), permutation type was set to gene set and gene set size was set to contain between 15 and 2,000 genes.

GO analysis

Genes were filtered for log_2-transformed fold change greater or lower than 1 and submitted to the Database for Annotation, Visualization and Integrated Discovery (DAVID) v.6.8 (ref. [75]). Resulting GO terms were filtered for q < 0.05. The data visualization tool ClustVis (http://biit.cs.ut.ee/clustvis) was used to generate the heat map of genes expressed in MKPs in Extended Data Fig. 7 (ref. [76]). Bulk RNA-seq data are accessible at the Gene Expression Omnibus (GEO; GSE185488).

Sample preparation for scRNA-seq

BM cells were isolated as described above, by flushing the long bones with PBS + 2% FCS, without EDTA using a 26 gauge needle. The BM suspension was further filtered through a 70 μm and 40 μm cell strainer (Miltenyi Biotec) and pelleted at 4 °C and 300g for 5 min. The pellet was resuspended with 1 ml 1× red blood cell lysis buffer and incubated at room temperature for 5 min. After incubation, 15 ml of PBS + 2% FCS without EDTA was added. The cell suspension was centrifuged at 300g for 5 min, the supernatant was discarded and the pellet was resuspended in 1 ml PBS + 2% FCS, and a negative selection kit for CD11b+ and CD19+ (StemCell Technologies) was used according ot the manufacture’s instructions to remove the CD11b+ and CD19+ cells. The final pellet was incubated with the respective TotalSeqB anti-mouse Hashtag antibody (that is, BioLegend, TotalSeq-B0301 anti-mouse Hashtag 3; of this family, Hashtags 3, 4, 5 and 10 were used). After incubation for 30 min on ice and three subsequent washing steps, cells were resuspended in FACS buffer with 2% FBS, followed by centrifugation at 300g for 5 min at 4 °C. The supernatant was discarded and the cell pellet was stained for MKPs as described above. MKPs were sorted using BD FACSMelody Cell Sorter (BD FACS Chorus acquisition software v.1.1.20.0), for 10x scRNA-seq analysis.

scRNA-seq

The Chromium Next GEM Single Cell 3′ reagent kit v3.1 (CG000206 Rev D) from 10x Genomics protocol was used for sequencing of FACS-sorted BM MKPs. To decrease batch-effect related artefacts, sample multiplexing using TotalSeqB anti-mouse Hashtag antibodies, which were included into the FACS antibody mix, was performed. Four samples were multiplexed into one library. In total, 1 × 10^5 cells across runs were loaded for generating gel beads in emulsion (GEMs). According to the kit protocol, first, GEMs were generated, then reverse transcription was performed, and cDNA was cleaned up, amplified and size selected. After a quality control and quantification step, gene expression libraries and cell surface libraries were subsequently constructed. The libraries were sequenced using the Illumina NovaSeq system by IMGM laboratories, as described previously[77].

Analysis of scRNA-seq data

Sequencing reads were processed using the Cell Ranger software with the mm10 mouse reference genome index provided by 10x Genomics (https://cf.10xgenomics.com/supp/cell-exp/refdata-gex-mm10-2020-A.tar.gz). This resulted in a count matrix for 16,045 cells and 32,285 genes. The count data were analysed using Seurat[78]. Background contamination was removed using the soupX method, setting the contamination fraction parameter of 0.1 (ref. [79]). Quality control included removal of cells with less than 250 or more than 6,000 features (expressed genes), removal of cells with total UMI counts below 400 and above 20,000, removal of cells with more than 5% of UMIs mapping to mitochondrial genes and removal of genes expressed in less than 3 cells. Furthermore, ribosomal genes were removed. Count data were size normalized to a total UMI count of 10,000 per cell and subsequently log transformed (plus one pseudocount). The top 2,000 highly variable genes were selected on the basis of VST (variance stabilizing transformation)-transformed expression values. Cell cycle scoring was performed and expression values were adjusted for the percentage of mitochondrial UMIs, the S and G2M cell cycle scores. Cells were assigned to samples by demultiplexing the Hashtag oligos, resulting in 1,918 cells for control, 3,243 cells for platelet depletion plus pDC depletion and 1,900 cells for platelet depletion. For differential gene expression analysis, expression levels per gene were centred and scaled across cells. Nearest neighbour graphs (k = 30) were built based on the first 30 principal components. On the basis of the graph, ten clusters were identified using the Leiden algorithm with a resolution of 0.25. Cluster-specific marker genes were identified using Wilcoxon tests, testing only for overexpression, requiring at least 25% of the cluster to express the marker and a log-transformed fold change of at least 0.25. Clusters were assigned to cell types based on the gene annotations of these marker genes. For each cluster, differential gene expression analysis between conditions was performed using the Wilcoxon rank-sum test (wilcox). The DE genes were then selected based on an average log_2-transformed fold change cut-off of greater than 0.25 and an adjusted P-value cut-off of less than 0.05. Cell-type-specific gene expression of the gene sets defined from bulk RNA-seq analysis were summarized into gene scores (average expression across the gene set) and visualized by cell type cluster. Trajectory analysis was performed on the following cell types: metabolic MKPs, late MKP, MK-MEPs, cycling MK-MEPs and early MKPs using Monocle380,81. This assigned each cell to an estimated pseudotime along a trajectory. The graph_test function was used to determine genes with pseudotime-associated gene expression patterns (FDR < 0.05 and Moran’s I > 0.25). Gene expression values of genes with pseudotime-associated gene expression were fitted using a spline function with 3 degrees of freedom and corresponding z scores were visualized as a heat map. scRNA-seq data are accessible at the GEO (GSE261996). Code is available at GitHub (https://github.com/heiniglab/gaertner_megakaryocytes).

MK culture from mouse BM

BM cells (see above) were cultured in DMEM medium containing 10% fetal bovine serum, 1% penicillin–streptomycin and 70 ng μl−1 TPO (ImmunoTools) for 5 days at 37 °C and 5% CO_2. On day five, a BSA step gradient was prepared by placing PBS containing 1.5% BSA on top of PBS with 3% BSA (PAA). Cells were loaded on top of the gradient, and MKs were settled to the bottom within 30 min at 1× gravity at room temperature. Mature MKs formed a pellet at the bottom of the tube.

In vitro co-culture of pDCs with MKs

For pDC generation, BM cells were isolated (see above) from control and Myd88−/− mice and cultured for 7 days in RPMI-1640 GlutaMAX-I (GIBCO) supplemented with 10% FCS (GIBCO), 1 mM sodium pyruvate (GIBCO), 1% penicillin–streptomycin (Thermo Fisher Scientific), 1% MEM non-essential amino acids (GIBCO), 0.05 mM β-mercaptoethanol MeEtOH (GIBCO) and recombinant 100 ng ml−1 FLT3L (BioLegend). Cells were collected by flushing Petri dishes with cold PBS. The purity of pDCs was 70–75% as determined by FACS. MK-iDTR mice were injected with DT to induce death of MKs. Control mice received PBS. After 6 h of DT injection, mice were euthanized and femurs were flushed with DMEM medium containing 10% fetal bovine serum, 1% penicillin–streptomycin and 70 ng ml−1 thrombopoietin (TPO, ImmunoTools). MKs were isolated using a BSA gradient as described above. pDCs and MK (1:1) were incubated together for 8 h at 37 °C and 5% CO_2. After incubation, the supernatant was collected and analysed for IFNα level (ELISA, see below).

ELISA

For serum TPO measurement, 1 ml anti-coagulated blood was collected intracardially and kept overnight at −20 °C. The next day, the blood was centrifuged at 2,000g for 20 min and the supernatant (serum) was collected for TPO measurement using the Quantikine Mouse Thrombopoietin ELISA Kit (R&D Systems) to measure the serum TPO levels. IFNα was measured by ELISA (Mouse IFN Alpha All Subtype ELISA Kit, High Sensitivity, PBL Assay Science). Blood was left at room temperature for 20 min and, after centrifugation, the serum was frozen at −20 °C until further analysis. To measure the IFN levels in the BM, one femur was flushed with 200 µl of PBS and cells were centrifuged at 300g. The supernatants were stored at −20 °C until analysis.

pDC culture with MK supernatants and DNase treatment

PF4-cre;iDTRfl/fl mice were treated with DT for 6 h. The long bones were collected and the BM was isolated by flushing the femurs, tibias and humerus with 200 µl of PBS + 2% FCS using a 26 gauge needle. The BM suspension was further filtered through a 100 μm cell strainer (Miltenyi Biotec) and pelleted at 4 °C and 300g for 5 min. The supernatant was discarded and cells were resuspended and incubated in red blood cell lysis buffer for 5 min. The MKs were isolated as described above and cell suspension was centrifuged for 5 min at 5,000g and 4 °C, followed by 1 min at 11,000g to obtain a tight pellet. The supernatant was collected and transferred to new tubes and centrifuged for 15 min at 2,500g at room temperature (Eppendorf 5415D with the F45-24-11 rotor; Eppendorf). To obtain the MP pellet, the supernatant was transferred into new tubes (homo-polymer, Axygen) and centrifuged for 40 min at 20,000g at room temperature (Mikro200R with the 2424-B rotor; Hettich)[82]. The resultant MK pellet was collected and the supernatant containing exosomes/extracellular vesicles was transferred into a new tube and treated or not with DNase (1 µl ml−1; Sigma-Aldrich) at 37 °C for 20 min. The treated or non-treated supernatant was added to the pDC cell culture and incubated of 60 min at 37 °C. The pDC supernatant was collected and the IFNα levels were measured using the ELISA kit according to the manufacturer’s instructions (Mouse IFN Alpha All Subtype ELISA Kit, High Sensitivity, PBL Assay Science). The pDCs were collected and stained for FACS analysis for pDC-activation markers (anti-CD69 (1:200) and anti-CD86 (1:400)).

NanoDrop experiment

A NanoDrop spectrophotometer (Thermo Fisher Scientific, NANODROP 2000, Peqlab), was used to measure the concentration of DNA in a 2 µl drop of the MK apoptotic supernatant treated or non-treated with DNase I.

MK CFU assay

CFU assays were performed using the MegaCult kit (StemCell Technologies) according to the manufacturer’s protocol. In brief, femurs and tibias of Vwf-cre;Ifnar−/−, Vwf-cre;Ifnar−/−, Ifnar−/− and Ifnar+/+ mice were flushed with Iscove’s MDM with 2% FBS to isolate BM cells. Cells were washed in Iscove’s MDM (without FBS) before culture. Then, 2.2 × 10^6 cells were resuspended in cold MegaCult-C medium containing collagen, TPO 50 ng ml−1 and IFNα type 1 universal (5 U, 10 U, 100 U, 500 U or 1,000 U; PBL-Biomedical Laboratories). The final cell suspension (1.5 ml) was loaded into six-well plates and cultivated for 7 days at 37 °C under 5% CO_2. After incubation, well plates were imaged using a stereo microscope (Axio Zoom v16 with Objective Plan-NEOFLUAR Z ×1.0/0.25 FWD 56 mm) and Zen Blue software (v.2.6) was used for imaging acquisition (Carl Zeiss). MK-CFUs colonies were classified according to the manufacturer’s protocol (a minimum of 3 cells in close contact).

EdU proliferation assay

The Click-it EdU Cell Proliferation Assay Kit (Thermo Fisher Scientific) was used to analyse the MKP proliferation. In vivo labelling of BM cells with 5-ethynyl-2′-deoxyuridine (EdU) was described previously[83]. In brief, Vwf-eGFP mice were intraperitoneally injected with 0.5 mg EdU in DMSO. After 4 h, mice were anaesthetized and euthanized by cervical dislocation and long bones (femurs and tibiae) were collected. BM cells were prepared as described above. The detection of EdU was performed according to the manufacturer’s protocol. In brief, cells were stained with surface marker antibodies (CD41, CD42) for 30 min at room temperature in the dark, followed by fixation for 15 min (4% PFA, provided in the kit) and permeabilization for 15 min (saponin-based permeabilization and wash reagent, provided in the kit). The samples were washed with 1% BSA between each step. The samples were then incubated for 30 min at room temperature in the dark in EdU reaction cocktail containing PBS, copper protectant, Pacific Blue picolyl azide and reaction buffer additive according to the manufacturer’s protocol. The samples were next washed and analysed by flow cytometry (LSRFortessa cell analyzer equipped with BD FACSDiva v.8.0.1, from BD Biosciences). VWF+CD41+CD42− cells were gated and EdU+ cells were measured within this population using FlowJo (v.10.6.2).

RT–PCR analysis of Ifnar1

MKs and MKPs from unfractionated mouse BM cell suspensions were directly sorted into RLT buffer (Qiagen) containing 143 mM β-mercaptoethanol (Sigma Aldrich) and total RNA was isolated using the RNeasy Micro Kit (Qiagen) including an on-spin column DNase I digest to remove remaining traces of genomic DNA. First-strand cDNA was synthesized from total RNA with the High Capacity cDNA Reverse Transcription kit (Applied Biosystems) using random primers in 20 µl reaction volumes. RT–PCR was performed using the SsoAdvanced Universal SYBR Green Supermix (Bio-Rad) and the primers for murine Ifnar1 and Actb in the MyiQ Single-Colour Real-Time PCR System (Bio-Rad). Products of RT–PCR were separated by electrophoresis on a 2.5% agarose gel in 1× TBE buffer. Images were taken using a Gel iX Imager (Intas). Primers were as follows: Mm_Ifnar1 Fw, TCTCTGTCATGGTCCTTTATGC (Eurofins); Mm_Ifnar1 Rev, CTCAGCCGTCAGAAGTACAAG (Eurofins); and the Mm_Actb_1_SG primer assay (400 × 25 µl reactions; QT00095242, Qiagen).

Statistics

GraphPad Prism (v.9.1.2) was used for all statistical analysis. All data were assumed to have Gaussian distribution, unless otherwise specified. Before performed the statistical analysis, the data were confirmed to have equal variance using F-tests, and Student’s unpaired t-tests were used for the comparison of two groups; otherwise, unpaired t-tests with Welch’s correction were used when variances were significantly different. For comparison of multiple groups, one-way or two-way ANOVA was used. Error bars indicate the s.d. All reported probabilities were two-sided. P < 0.05 was considered to be significant.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Imaging and flow cytometry raw data are available on request. scRNA-seq data are accessible at the GEO (GSE261996). Bulk RNA-seq data are accessible at the GEO (GSE185488). Source data are provided with this paper.

Code availability

Code for scRNA-seq analysis is available at GitHub (https://github.com/heiniglab/gaertner_megakaryocytes).

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Acknowledgements

We thank S. Helmer, N. Blount, E. Raatz and Z. Sisic for technical assistance. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) SFB 1123 (S.M. project B06); SFB 914 (S.M. projects B02 and Z01, H.I.-A. project Z01, S.S. project A06, K.S. project B02, C. Schulz project A10, B.W. project A02, C. Scheiermann project B09); SFB 1054 (T.B. project B03); FOR2033 (F.G., R.A.J.O., S.M.); Individual research grant project ID: 514478744 (F.G.); Heisenberg Programme project ID: 514477451 (F.G.); the DZHK (German Center for Cardiovascular Research) (MHA 1.4VD (S.M.), Postdoc Start-up Grant, 81×3600213 (F.G.)); and LMUexcellence NFF (F.G.). W.F. received funding from China Scholarship Council (CSC, no. 201306270012). P.B. is supported by the German Research Foundation (DFG, project IDs 322900939, 432698239 and 445703531), European Research Council (ERC Consolidator grant no. 101001791) and the Federal Ministry of Education and Research (BMBF, STOP-FSGS-01GM2202C and NATON within the framework of the Network of University Medicine, no. 01KX2121). S.v.S. is supported by the START-Program of the Faculty of Medicine of the RWTH Aachen University (AZ 125/17). A.D. and S.E. are supported by the German Research Foundation (SFB TRR 267); S.E. by the BMBF in the framework of the Cluster4future program (CNATM—Cluster for Nucleic Acid Therapeutics Munich). This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 833440 to S.M.). F.G. received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 747687. The project is funded by the European Union (ERC, MEKanics, 101078110). Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

Author information

Author notes

1. These authors contributed equally: Florian Gaertner, Hellen Ishikawa-Ankerhold, Susanne Stutte, Wenwen Fu

Authors and Affiliations

1. Department of Medicine I, University Hospital, LMU Munich, Munich, Germany

Florian Gaertner, Hellen Ishikawa-Ankerhold, Wenwen Fu, Jutta Weitz, Bhavishya Nelakuditi, Dominic van den Heuvel, Larissa Belz, Gulnoza Sobirova, Zhe Zhang, Anna Titova, Alejandro Martinez Navarro, Kami Pekayvaz, Michael Lorenz, Chenglong Guo, Sebastian Clauß, Christian Schulz, Konstantin Stark, Tobias Petzold & Steffen Massberg

2. Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria

Florian Gaertner, Vanessa Zheden, Walter Anton Kaufmann, Michael Sixt & Robert Hauschild

3. DZHK (German Centre for Cardiovascular Research), Partner site Munich Heart Alliance, Munich, Germany

Florian Gaertner, Anne Dueck, Kami Pekayvaz, Christian Schulz, Konstantin Stark, Tobias Petzold, Stefan Engelhardt, Matthias Heinig & Steffen Massberg

4. Institute of Cardiovascular Physiology and Pathophysiology, Biomedical Center, LMU Munich, Planegg-Martinsried, Germany

Susanne Stutte, Barbara Walzog & Christoph Scheiermann

5. Walter Brendel Center of Experimental Medicine, University Hospital, LMU Munich, Munich, Germany

Susanne Stutte, Barbara Walzog & Christoph Scheiermann

6. Institute for Immunology, Faculty of Medicine, LMU Munich, Munich, Germany

Susanne Stutte, Jan Kranich & Thomas Brocker

7. Institute of Pharmacology and Toxicology, Technical University of Munich (TUM), Munich, Germany

Anne Dueck & Stefan Engelhardt

8. Institute of Computational Biology, Deutsches Forschungszentrum für Gesundheit und Umwelt, Helmholtz Zentrum München, Neuherberg, Germany

Bhavishya Nelakuditi & Matthias Heinig

9. Department of Computer Science, TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany

Bhavishya Nelakuditi & Matthias Heinig

10. Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy

Valeria Fumagalli & Matteo Iannacone

11. Department of Dynamics of Immune Responses, Vita-Salute San Raffaele University, Milan, Italy

Valeria Fumagalli & Matteo Iannacone

12. Department of Neurology, Ludwig-Maximilians-University School of Medicine, Munich, Germany

Louisa von Baumgarten

13. Biomedical Center, Bioinformatic Core facility, LMU Munich, Planegg-Martinsried, Germany

Tobias Straub

14. Biomedical Center, Core Facility Animal Models, LMU Munich, Planegg-Martinsried, Germany

Bastian Popper

15. Institute of Pathology, Ludwig-Maximilians-University Munich, Munich, Germany

Guido Piontek & Martina Rudelius

16. Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany

Saskia von Stillfried & Peter Boor

17. Washington University, School of Medicine, St Louis, MO, USA

Marco Colonna

18. Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, Geneva, Switzerland

Christoph Scheiermann

19. Department of Medicine, Center for Vascular Biology Research, Beth Israel Deaconess Medical Center, Boston, MA, USA

William C. Aird

20. MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK

Claus Nerlov

21. Department of Cardiology, Angiology and Intensive Care Medicine, Campus Benjamin Franklin, Deutsches Herzzentrum der Charité (DHZC) University Hospital Berlin, Berlin, Germany

Tobias Petzold

22. DZHK (German Centre for Cardiovascular Research), Partner site Berlin, Berlin, Germany

Tobias Petzold

23. Friede Springer - Centre of Cardiovascular Prevention @ Charité, Charité - University Medicine Berlin, Berlin, Germany

Tobias Petzold

24. Laboratory of Stem Cell Physiology, Department of Internal Medicine III—Hematology and Oncology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany

Robert A. J. Oostendorp

Contributions

Initiation: F.G., H.I.-A. and S.S. Conceptualization: F.G. with input from S.M., H.I.-A. and S.S. Methodology: H.I.-A., S.S., W.F., F.G., C. Scheiermann, C. Schulz, L.v.B., A.D., V.F., M.I., K.P., A.M.N., B.N., M.H. and Z.Z. Investigation: H.I.-A., S.S., W.F., C.G., J.W., Z.Z., D.v.d.H., A.D., T.S., V.F., M.L., M.R., G.P., A.M.N., B.N., A.T., L.B., G.S., V.Z. and W.A.K. Resources: K.S., K.P., M.C., S.C., M.R., J.K, T.B., B.P., B.W., S.E., W.C.A., T.P., M.S., C.N., M.I., R.A.J.O., S.v.S., P.B. and S.M. Formal analysis: H.I.-A., S.S., W.F., F.G., M.L., D.v.d.H., Z.Z., C.G., J.W., C. Schulz, M.R., A.D., R.H., B.N. and M.H. Writing—original draft: F.G. Writing—editing: F.G., S.M., H.I.-A. and S.S. with input from all of the authors. Visualization: H.I.-A., W.F., F.G., J.W., C.G., A.D., D.v.d.H., B.N. and M.H. Supervision: F.G., H.I.-A. and S.M. Project administration: F.G. and H.I.-A. Funding acquisition: F.G. and S.M.

Corresponding author

Correspondence to Florian Gaertner.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Cristina Lo Celso and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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Extended data figures and tables

Extended Data Fig. 1 MK and MKP distribution in the bone marrow niche.

a, Representative whole-mount immunostaining of megakaryocyte progenitors and mature megakaryocytes in murine sternum bone (n = 3 mice). MKPs (green): CD41+/CD42−; MKs (yellow): CD41+/CD42+; blood vessels (grey): CD144+; bone (blue): second harmonic generation. Scale bars = 200 μm (upper). Also see Fig. 1a. b, Histogram showing BM distribution of MKs and MKPs relative to their distance to endosteum. n = 3 mice; Mean ± SD. c, d, Cell diameter and sphericitity; for cell diameter MK n = 68 cells and MKP n = 55 cells; for sphericity MK n = 53 cells and MKP n = 17 cells; pooled from 7 mice; ****(Sphericity): p = 0.0000002, ****(Cell diameter): p = 0.000000000000001; unpaired t-test/Welch’s correction; Mean ± SD. e, Representative whole-mount immunostaining of MKs/MKPs in VwfeGFP/+ mice. MKs/MKPs of VwfeGFP/+ mice show no significant difference in cell number (p = 0.69/p = 0.84) and size (p = 0.38/p = 0.10) compared to C57Bl/6 J mice (stained with anti-CD41). n = 3 mice; unpaired t-test/Welch’s correction; Mean ± SD. Scale bar = 30 μm. f, VWF-eGFP does not co-localize with erythrocytes (Ter-119), granulocytes (CD11b, Ly-6G) and lymphocytes (CD3e, CD45R) in BM (n = 1). Scale bar = 100 µm. g, Platelet counts of VwfeGFP/+ compared to C57Bl/6 J; WT group n = 14 mice, VwfeGFP/+ group n = 7 mice. unpaired t-test/Welch’s correction; Mean ± SD; ns: p = 0.14.

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Extended Data Fig. 2 Megakaryocytic lineage tracing by chronic time-lapse 2P-IVM.

a, Schematic showing experimental setup of chronic 2-photon intravital microscopy (2P-IVM) of mouse calvarian bone marrow. Lower: Time series of BM in VwfeGFP/+ mice. MK lineage (VWF-eGFP; green); Bone (second harmonic generation). Arrow: Fragmentation of MK results in the release of platelet-like particles. Note that after fragmentation, platelet-like particles exit the BM and new MKs grow in size before undergoing fragmentation. b, VwfeGFP/+ mice were i.v. injected with TRITC-dextran to track the megakaryocytic lineage (green) and blood vessels (red) respectively (left time series). Max. intensity projections of raw data and 3D rendering of z-stacks from the same 2P-IVM time series are shown. Scale bars = 50 μm. c, Representative raw data and 3D-rendering corresponding to 3D-rendered images shown in Fig. 1c (n = 7). Scale bars = 50 µm. d-h: Spatiotemporal dynamics of the megakaryocytic lineage in response to immune-mediated thrombocytopenia. d, Schematic showing experimental setup of PD. Peripheral blood platelet counts monitored at indicated time points after a single injection (i.p.) of R300 or isotype control; R300: before (n = 9), 30 min (n = 3), 0.5d (n = 6), 1d (n = 8), 2d (n = 9), 4d (n = 9), 8d (n = 4) mice; isotype: before (n = 7), 30 min (n = 3), 0.5d (n = 4), 1d (n = 5), 2d (n = 7), 4d (n = 4), 8d (n = 3) Mean ± SD. e, Morphometric analysis of MKs shows decreased sphericity in response to PD corresponding to an increase of cellular protrusions; Control n = 63 cells and PD 12 h n = 51 cells, pooled from 4 mice; unpaired t-test/Welch’ correction, ***: p = 0.0002; Mean ± SD. Scale bars = 50 μm. f, Representative 2P-IVM time series of proplatelet formation and MK fragmentation. Single cell tracking of MK volumes over time reveals a significant faster decrease of volume following PD. Control n = 14 and PD n = 11 cells, pooled from 3 mice; unpaired t-test; ns: p = 0.0206; Mean ± SD. g, Upper: Small ( < 15 μm) VWF-eGFP+ cells (arrows) appear 12 h after PD (2P-IVM). Also see Fig. 1i. Scale bar = 50 µm. Middle: PD triggers an instantaneous proliferation of MKPs peaking 1d following platelet depletion; MKPs (CD41+/CD42–) were counted in whole-mount BMs. n = 3 mice; one-way ANOVA/Dunnett, **: p = 0.0023, ****: p = 0.000009, ***: p = 0.0008, ns: p = 0.063 and p = 0.072; Mean ± SD. Lower: Proliferation of MKPs (VWF-eGFP+/CD41+/CD42–) was measured after in vivo labelling of BM cells with 5-ethynyl-2′-deoxyuridine (EdU) using FACS. Mean fluorescent intensity of EdU and Frequency of EdU-pve cells significantly increases after PD (12 h); n = 3 mice; paired t-test; **: p = 0.0046; *: p = 0.0115; error bar=SD; Mean ± SD. h, Left: MKPs lodged within the perivascular niche of the BM grow in volume. Volume increase of single cells was tracked over time. Arrested MKP n = 27 cells and mobile MKP n = 6 cells. Notably, the speed of cell growth after PD did not significantly differ from steady state control; Control n = 54 cells and PD = 18 cells, pooled from 3 mice, unpaired t-test Welch’s correction ns: p = 0.7503; Mean ± SD.

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Extended Data Fig. 3 TPO triggers global megakaryopoiesis without preferential localization to the perivascular niche.

a, Left: Scheme of platelet homeostasis regulated by TPO. TPO released from liver is scavenged by circulating platelets that express the TPO-receptor (Mpl). Thrombocytopenia leads to an increase of unbound plasma TPO which drives megakaryopoiesis in the BM. Right: Plasma TPO levels increase in response to PD, reaching the highest levels 12 h after platelet depletion (ELISA); n = 4 mice per group, one-way ANOVA/Dunnett; *: p = 0.018, **: p = 0.008, ****: p = 0.00000005; Mean ± SD. b, Left: Plasma TPO levels increase after i.p. injection (8 ng/g body weight on 3 consecutive days, i.p.) (ELISA after 30 min) (n = 6 mice); Mean ± SD; unpaired t-test/Welch’s correction; *: p = 0.014. Right: Thrombopoiesis is not affected by TPO treatment as indicated by unaffected platelet counts (hemocytometer); n = 5 mice, unpaired t-test Welch’s correction ns: p = 0.267; Mean ± SD. c, 3D-rendered micrographs of BM wholemount staining show increased numbers of MKPs (green: CD41 + / < 20 μm) and MKs (yellow: CD41+/ > 20 μm) after TPO treatment, vessels (grey: CD144); scale bar = 50 µm. d, TPO-treatment increased megakaryopoiesis (MKP and MK numbers) to an extent similar to platelet depletion (PD). Of note, TPO-treatment leads to an accumulation of mature MKs in the BM while MK numbers in the BM remained unaffected after PD due to increased MK consumption (BM whole-mount immunostainings); control n = 3 mice, PD n = 4 mice and TPO n = 3 mice, one-way ANOVA/Dunnett; MKPs, **(PD): p = 0.0064, **(TPO): p = 0.0032; MK, ns(PD) = 0.44, **(TPO): p = 0.0023; Mean ± SD. e, Increased megakaryopoiesis in response to TPO followed a different spatial pattern compared to PD. During PD, the local increase in megakaryopoiesis is restricted to the perisinusoidal compartment (see Fig. 1g). In contrast, TPO treatment increased megakaryopoiesis throughout the BM compartment. Consequently, the distribution of MKPs relative to the perivascular niche was unaffected by TPO treatment as analysed in BM whole-mount immunostaining. The distances were binned into 5 μm intervals; n = 4 mice per group; Unpaired t-test; ns: p = 0.99; Mean ± SD.

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Extended Data Fig. 4 MK-immune cell interaction in the bone marrow.

a-d: Macrophages, monocytes and neutrophils are dispensable for megakaryopoiesis a, Left: MK-Macrophage contacts were quantified by whole-mount BM immunohistology; MK: CD41+; Macrophages: CD68+. Scale bar = 10 µm. Right: PD did not increase MK-Macrophage contacts; n = 4 mice; unpaired t-test Welch’s correction ns: p = 0.9299 and ns: p = 0.6882; Mean ± SD. b, Macrophage depletion with Csf1R-Inhibitor (PLX5622) did not impair megakaryopoiesis (n = 4 mice). Mean ± SD; Welch’s t test; ****: p = 0.000018. c, Macrophage / monocyte depletion in CD11b-DTR mice did not impair megakaryopoiesis (n = 3 mice). Mean ± SD; Welch’s t test; *: p = 0.02. d, Neutrophil-depletion in LysM-Cre; Mcl-1fl/fl mice has no impact on megakaryopoiesis (n = 3 mice). Mean ± SD; Welch’s t test; **: p = 0.009. e-i: pDCs reside in close proximity to bone marrow MKs. e, Whole mount histology of BM. MKs: CD41 (grey); pDCs: BST2 (red) and SiglecH (green). The vast majority of bright SiglecH-positive cells also show bright BST2 signal, indicative of pDCs. f, Experimental setup of 2P-IVM and platelet depletion (PD) and MK cell death (MKD). g, 2P-IVM of calvaria bone. MKs: VWF-eGFP (green); pDCs: SiglecH-PE (magenta). Also see Fig. 2a. pDCs migrate with mean speeds of 3-6 μm/min, without significant alteration by platelet depletion of MK cell death. Left: Mean of 3 experiments is plotted (each data-point represents one mouse). Right: Mean of all pooled frames (each data point represents speed at a single frame pooled from 3 experiments). Mean ± SD; one-way ANOVA/Tukey; ns: p = 0.07; ****: p = 0.0000000000000001. Scale bar: 100 μm. h, Histogram showing the distribution of pDCs relative to their distance from MKs (n = 3 mice per group); Mean ± SD; multiple unpaired t-tests; ns: no statistical significance. i, Whole mount histology of control and PD. MKs (green, CD41); pDCs (magenta, BST2+); nuclei (blue, Hoechst). Bar plot: frequencies of pDC-MK- and pDC-MKP contacts; n≥5 mice; unpaired Welch’s t-test; ****(%MK with pDC-contacts): p = 0.000002, ****(%MKP with pDC-contacts): p = 00003; Mean ± SD. PD did not increase the number of bone marrow pDCs (n = 6 mice). Mean ± SD; unpaired t-test Welch’s correction; ns: no statistical significance. Scale bars = 50 µm.

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Extended Data Fig. 5 pDCs control megakaryopoiesis.

a-d, Impaired megakaryopoiesis following pDC-depletion in BDCA2-DTR mice. a, DT treatment does not affect megakaryopoiesis. C57BL/6 J mice treated with DT (Control A) were compared to untreated BDCA2-DTR mice (Control B). Cell numbers were quantified by FACS; n = 6 mice; Mean ± SD; unpaired t-test Welch’s correction; ns: no statistical significance. b, Cell numbers were quantified by FACS; n = 12 mice; Mean ± SD; unpaired t-test Welch’s correction, ****(MKs and MKPs): p = 0.000000001, ****(pDCs): p = 0.000000000001. c, Cell numbers were quantified by FACS; n = 6 mice; Mean ± SD; unpaired t-test Welch’s correction, ***: p = 0.0002, **: p = 0.0083, *: p = 0.048, ns: no statistical significance. d, Representative confocal micrograph of BM; MKs (green, CD41); pDCs (magenta, BST2+); nuclei (blue, Hoechst). Scale bars = 100 µm. e, pDC-depletion disrupts the megakaryocytic niche and alters the distribution of MKPs and MKs within the BM. BM whole-mounts were stained for MKs (CD41+CD42+) and MKP (CD41+CD42−) and positioning was quantified in relation to blood vessels (CD144+). Frequency of MKs and MKPs in close contact to blood vessels significantly decreased following pDC-depletion; n = 12 mice; multiple unpaired t-test/Holm-Sidak, ****(MK(0-5): p = 0.00000000001, **(MK(10-15): p = 0.003, ****(MK( > 20): p = 0.000000001, ****(MKP(0-5): p = 0.000005, *(MKP(5-10): p = 0.01, ****(MK( > 0): p = 0.00000000005; Mean ± SD. Notably, the percentage of high ploidy and therefore large MKs increased after pDC depletion, suggesting that misguided positioning rather reduced MK size underlie the increased distance to the vasculature; n = 3 mice; multiple unpaired t-tests/Holm-Sidak; **(2):p = 0.003, ***(4): p = 0.0003, **(8): p = 0.002, *(16): p = 0.017, ***(32): p = 0.0003, ***(64): p = 0.0007; Mean ± SD. f, Transient pDC depletion and recovery after anti-BST2 treatment (n = 6 mice); Mean ± SD; one-way ANOVA/Tukey; pDCs: ***: p = 0.0001, ****: p = 0.00003, MKPs: ****: p = 0.00000000008 and p = 0.000000003, MKs: ****: p = 0.0000003 and p = 0.0000008. g, Thrombocytopenia and recovery after transient pDC-depletion (n = 6); Mean ± SD; multiple unpaired t-tests/Holm-Sidak; ****: p = 0.000000004 and p = 0.000000001. h, Neutrophil, macrophage and monocyte counts in mice with constitutively reduced pDC-numbers (RS26Cre-ERT2/wt;Tcf4fl/fl BM chimera) (n = 5-7). Mean ± SD; BM: one-way ANOVA/Holm-Sidak; **: p = 0.016; Blood: 2way ANOVA/Holm-Sidak, ****: p = 0.00001 and p = 0.000002.

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Extended Data Fig. 6 Innate immune sensing drives pDC activation in response to MK-derived extracellular DNA.

a, FACS analysis of activation markers of BM pDCs following PD (pink: 6 h; red: 24 h). Ctrl/PD6h: n = 4 mice, PD24h: n = 7; one-way ANOVA/Dunnnett; CD69-postive: **: p = 0.0072 and *: p = 0.015, CD86-positive ***: p = 0.0001, ns=0.124; Mean ± SD. b, FACS analysis of activation markers of BM pDCs co-cultured with vital or apoptotic MKs or supernatants and in the presence or absence of DNAseI (n = 3 experiments). Mean ± SD; 2way ANOVA/Tukey; CD86: ****(vital MK vs. dead MK): p = 0.0000003, ****(dead MK vs. dead MK + DNAse): p = 0.000008, ****(vital supernatant vs. dead supernatant): p = 0.000001, ****(dead supernatant vs. dead supernatant + DNAse): p = 0.0000006; CD69: ****(vital MK vs. dead MK): p = 0.00009, ***(dead MK vs. dead MK + DNAse): p = 0.0003, ****(vital supernatant vs. dead supernatant): p = 0.00002, ****(dead supernatant vs. dead supernatant + DNAse): p = 0.0000001. c, MK supernatants contain cell-free DNA (n = 4 experiments). Mean ± SD; one-way ANOVA/Tukey; ****(vital vs. dead): p = 0.00000005, ****(dead vs. dead+DNAse): p = 0.0000003. d, MyD88−/− mice have normal pDC counts in BM (n = 6 mice; FACS) and normal plasma TPO levels (n = 5 mice; ELISA). Mean ± SD; unpaired t test; ns: no statistical significance.

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Extended Data Fig. 7 Bulk RNA-seq of MK-primed progenitors.

a, Expression (mRNA) of IFNaR was analysed in sorted MKs and MKPs by RT-PCR. MKPs and MKs from IFNaR−/− mice served as negative control; ß-actin (housekeeping gene); n = 2. b, Representative immunofluorescence staining confirmed expression of IFNaR in isolated MKPs and MKs. IFNaR−/− (negative control) (n = 3). Scale bar=10 µm. c, Left: Schematic of experimental design. MKPs were sorted from BM of mice treated with isotype antibody (grey; n = 2), R300 (PD, purple; n = 3) or R300 plus DT (PD plus pDC depletion, green; n = 2). Right: Expression heatmap of MKP RNA-Seq data. Heatmap shows MKP genes de-regulated (log2FC < 1 or >1 with FDR < 0.05) in either PD versus control or PD vs. platelet depletion with additional pDC depletion. Heatmap was generated using non-hierarchical clustering on rows and columns using ClustVis R package. d, Principal component analysis (PCA) of MKP RNA-Seq data. MKPs exhibit a strong variance shift following PD (grey to magenta). This variance is abrogated when animals were additionally depleted for pDCs (magenta to blue). e, Gene set enrichment analysis was performed on RNA-seq data (Reactom). “Response to type I interferon” is shown. PD compared to control (left) and PD with additional pDC depletion (right). f, Left: intersection analysis (Venn diagram) confirms a high overlap of type I interferon response genes to be inversely regulated between conditions (21 genes of Top 30). Heatmap show unsupervised clustering of differentially regulated genes (unique genes of both Top30 lists).

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Extended Data Fig. 8 scRNAseq of MK-primed progenitors.

a, UMAP plot of Control, platelet depletion (PD) and PD+pDC-depletion. Colours indicate the cluster assignment. b, Dot plot of cluster defining genes; Top 10 differentially expressed genes are shown. Identity and colours indicate the clusters shown in panel a. c, Heat map shows cell cycle genes[84]. The colour bar on the top x-axis indicates the clusters shown in panel a. d, Integration of bulk RNAseq data and scRNAseq data. Genes upregulated in response to PD both in bulk and cycling MK-MEPs are shown. Right: Enrichment analysis of identified process associated with increased metabolic activity. e, Integration of bulk RNAseq data and scRNAseq data. Response to type I interferon genes upregulated in response to PD either in bulk and cycling MK-MEPs are shown. f, Decreased MKP and MK numbers in BM chimeric IFNaR−/− mice. Irradiated WT mice received BM from IFNaR−/− or IFNaR +/+ (Control) donors, respectively. Mice were subjected to analysis 8 weeks after transplantation; Mean ± SD; unpaired t-test/Welch’s correction; ****: p = 0.00000000361, ***: p = 0.0010, **: p = 0.0035, *: p = 0.0170.

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Extended Data Fig. 9 pDC-driven megakaryopoiesis is altered in infection.

a, Augmented megakaryopoiesis in a humanized mouse model of SARS-CoV-2 infection. BM from FVB;K18hACE2 mice infected with SARS CoV-2 (10^5 TCID50 SARS-CoV-2/mouse in 25μl intranasally or untreated control mice were analysed by immunohistology (n = 3). MKs (green; CD41; >15 μm); MKPs (green; CD41; <15 μm); pDCs (magenta; BST2). Scale bar = 100 µm. b, n = 3 mice; Mean ± SD; unpaired t-test with Welch’s correction; MK/pDC contact**: p = 0.0081, pDC number: **: p = 0.0037, MK number: ****: p = 0.000049, MKP **: p = 0.0039. c, Frequency of CD69+ and IFN-α expressing pDCs in BM increases after SARS-CoV-2 infection (immunohistology). Mean ± SD; Unpaired t-test with Welch’s correction; *: p = 0.0481, ***: p = 0.0004. d, e, Representative confocal micrographs of human BM (see quantification in Fig. 5e–g). d, white arrows indicate pDCs in contact with MKs (control: n = 5, covid-19: n = 12). e, yellow arrows indicate pDCs expressing large amounts of IFN-α (n = 3). Scale bar = 50 µm. f, pDCs act as BM niche cells that control tissue homeostasis of MKs, complementing systemic regulation by TPO (Graphical summary). Left: TPO is the most studied regulator of platelet homeostasis. TPO is constitutively released from the liver and is scavenged by c-Mpl, the TPO receptor expressed on platelets (see 1). Consequently, a decrease in the number of circulating platelets (see 2) is inherently associated with an increase in plasma TPO levels (see 3), which activates HSCs and MK-primed progenitors in the BM via c-Mpl to drive megakaryopoiesis to meet platelet demand (see 4). Thus, the TPO-dependent homeostatic circuit regulating megakaryopoiesis involves circulating platelets as sensors operating at the systemic level. Right: Mature MKs release extracellular DNA (see 5), which is sensed by pDCs via TLRs (see 6) and leads to the release of IFN-α (see 7). IFN-α drives the proliferation and maturation of HSCs and MK-primed progenitors to replenish megakaryocytes in their BM niche (see 8), preventing MK exhaustion and ensuring continuous platelet production. The pDC-dependent homeostatic circuit thus involves innate immune sensing of apoptotic MKs and operates at the tissue level. Loss of pDC-dependent MK homeostasis at the tissue level cannot be compensated for by TPO-dependent MK homeostasis at the systemic level, indicating a non-redundant role of both pathways in regulating megakaryopoiesis.

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Supplementary information

Supplementary Data 1

Gating strategies for FACS analysis.

Reporting Summary

Supplementary Table 1

List of differentially expressed genes of MKP clusters (related to Fig. 4g) and a full list of differentially expressed genes shown in pseudotime heat map (related to Fig. 4h).

Supplementary Table 2

Patient characteristics (related to Fig. 4a–g).

Supplementary Table 3

List of all reagents and resources with the source and identifier.

Supplementary Video 1

MKs and MKPs reside along BM sinusoids. Two-photon microscopy of sternal whole-mount (2D slices and 3D rendered stack are shown and animated). MKs (orange; CD42+CD41+); MKPs (green; CD42−CD41+); sinusoids (grey; CD144+); bone (blue; SHG). Related to Fig. 1a.

Supplementary Video 2

Thrombopoiesis and megakaryopoiesis are synchronized processes. Chronic 2P-IVM of cavarial bone marrow. MKs/MKPs: VWF–eGFP (green); blood vessels: TRITC-dextran (magenta); bone: second harmonic generation (blue). Animation of 3D rendered data shows mature MKs (colour coded in cyan) that disappear from the niche and small MKPs (colour-coded in yellow) that appear and grow in size. 3D stacks were recorded at the four indicated timepoints. Related to Fig. 1c and Extended Data Fig. 2c.

Supplementary Video 3

Migrating pDCs monitor megakaryocytes in the bone marrow. 2P-IVM videos of pDCs (magenta; anti-SiglecH-PE) migrating in close proximity to MKs (green; VWF–eGFP+) in the bone marrow. Maximum intensity projections of raw data and 3D rendered animations are shown. Related to Fig. 2a and Extended Data Fig. 4g.

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Gaertner, F., Ishikawa-Ankerhold, H., Stutte, S. et al. Plasmacytoid dendritic cells control homeostasis of megakaryopoiesis. Nature 631, 645–653 (2024). https://doi.org/10.1038/s41586-024-07671-y

- Received: 13 August 2021

- Accepted: 04 June 2024

- Published: 10 July 2024

- Issue Date: 18 July 2024

- DOI: https://doi.org/10.1038/s41586-024-07671-y

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Emergence of large-scale cell death through ferroptotic trigger waves

Hannah K. C. Co,

Chia-Chou Wu,

Yi-Chen Lee &

…

Sheng-hong Chen

Nature volume 631, pages 654–662 (2024)

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Abstract

Large-scale cell death is commonly observed during organismal development and in human pathologies1,2,3,4,5. These cell death events extend over great distances to eliminate large populations of cells, raising the question of how cell death can be coordinated in space and time. One mechanism that enables long-range signal transmission is trigger waves[6], but how this mechanism might be used for death events in cell populations remains unclear. Here we demonstrate that ferroptosis, an iron- and lipid-peroxidation-dependent form of cell death, can propagate across human cells over long distances (≥5 mm) at constant speeds (around 5.5 μm min−1) through trigger waves of reactive oxygen species (ROS). Chemical and genetic perturbations indicate a primary role of ROS feedback loops (Fenton reaction, NADPH oxidase signalling and glutathione synthesis) in controlling the progression of ferroptotic trigger waves. We show that introducing ferroptotic stress through suppression of cystine uptake activates these ROS feedback loops, converting cellular redox systems from being monostable to being bistable and thereby priming cell populations to become bistable media over which ROS propagate. Furthermore, we demonstrate that ferroptosis and its propagation accompany the massive, yet spatially restricted, cell death events during muscle remodelling of the embryonic avian limb, substantiating its use as a tissue-sculpting strategy during embryogenesis. Our findings highlight the role of ferroptosis in coordinating global cell death events, providing a paradigm for investigating large-scale cell death in embryonic development and human pathologies.

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Large-scale cell death is a common occurrence in embryonic development and diseases. During embryogenesis, massive cell death acts as a tissue-sculpting process to shape cavities and tubules for organ formation1,7,8. Under pathological conditions, excessive tissue damage frequently occurs in ischaemia–reperfusion and degenerative diseases3,4,5,9. These cell death events usually occur contiguously across the tissue over considerable distances, eliminating large cell populations. Although it is known that a cell death event can affect neighbouring cells through diffusive signals such as cytotoxic molecules (for example, calcium and ROS)10,11 or signalling factors (including TNF)[12], this bystander effect is quickly abrogated in space and time, curtailing cell death extent (to around 20 µm or 2–3 cells). Thus, it is unclear how a localized and short-lived death signal overcomes the spatial limitations of simple diffusion to cause a global cell death event.

Collective cell death requires robust signal transmission, which is made possible by the mechanism of trigger waves. In contrast to simple diffusion, whereby signals dissipate quickly in space, trigger waves are self-regenerating chemical fronts that propagate over long distances without compromising speed or amplitude6,13. Trigger waves are ubiquitous in nature, being responsible for neuron firing[14], mitotic and apoptotic waves in Xenopus eggs15,16, and infectious disease epidemics[17]. Although they differ in molecular details, all of these examples rely on excitable or bistable media over which signals are regenerated and transmitted6,18. Considering the prevalence of large-scale cell death, we postulated that human cells could be primed to become bistable cell populations that permit the propagation of death signals via trigger waves.

Propagative nature of ferroptosis

Ferroptosis, a form of cell death through iron-catalysed lipid peroxidation, has recently been shown to exhibit profound bystander effects that allow cell death to spread over relatively long distances (up to 600 µm)[19]. In support of that observation, we found that ferroptosis, caused by either cystine deprivation or treatment with ferroptosis inducers (such as erastin (an inhibitor of the cystine–glutamate antiporter xCT) and RSL3 (an inhibitor of glutathione peroxidase 4; GPX4)), spreads across human retinal pigment epithelial (RPE) cells as a series of spatially coupled death events (Extended Data Fig. 1a,b,d,f and Supplementary Video 1). Consistently, we found that ferroptotic death kinetics presented high spatiotemporal order, as reflected by the lower entropies of cell death vectors compared with those of apoptotic and calcium-induced death (Extended Data Fig. 1c–j). These well-ordered ferroptotic death kinetics were also observed after xCT suppression in 16 additional human cell lines derived from 12 tissue types (Extended Data Fig. 1k,m and Supplementary Video 1). By contrast, these ordered death kinetics were absent after inhibition of GPX4 in some of the cell lines that we tested (Extended Data Fig. 1l,m). These results support the generality of the propagative nature of ferroptosis when induced by xCT suppression, but not by GPX4 inhibition. This difference may be attributable to the involvement of distinct regulatory pathways, depending on how ferroptosis is induced[20]. Nevertheless, our results, along with the large-scale ferroptosis observed under pathological conditions3,21,22, prompted us to examine the possibility that large-scale cell death may be coordinated through ferroptosis.

Ferroptosis propagates as trigger waves

Despite previous observations of the wave-like spread of ferroptosis, the challenge of obtaining time-resolved, long-distance ferroptosis quantifications has precluded investigations of its regulatory principles. For example, an assay that monitors cell death propagation over distances of millimetres would enable definitive distinction between simple diffusion-driven ferroptotic spread, which would slow down in space, and ferroptotic trigger waves, which would maintain a constant speed. To develop such an assay, we used RPE cells, which are sensitive to blue-light irradiation[23] and exhibit excessive ferroptosis during age-related retinal degeneration[24]. After erastin treatment, ferroptosis initiated and propagated across RPE cells (Extended Data Fig. 1d). However, annihilation of the ferroptotic waves from multiple cell-death-initiating sites impeded their long-distance measurement (Extended Data Fig. 2a). We found initiation of ferroptosis to be a random process, with the frequency and time interval between consecutive initiation events following Poisson and geometric distributions, respectively (Methods and Extended Data Fig. 2b–d). Consistently, cellular ROS and iron levels adopted a continuous probability distribution (Extended Data Fig. 2e,f), probably owing to the intrinsic stochasticity of the redox system. Thus, to reduce the number of ferroptotic initiations, we modulated the cell growth medium (Methods and Extended Data Fig. 2g,h) and then used RPE cell photosensitivity to control the timing and location of ferroptosis initiations through blue-light irradiation (432 nm) to introduce exogenous ROS (Extended Data Fig. 3a). Blue-light exposure elevated cellular ROS and prompted subsequent cell death of erastin-treated RPE cells (Extended Data Fig. 3b), similar to the effect of adding exogeneous H_2O_2 (Extended Data Fig. 3c). Importantly, this light-induced cell death proved ferroptosis specific, as ferroptosis inhibitors (Fer-1, DFO and Lip-1) suppressed it, whereas inhibitors of apoptosis and necroptosis did not (Extended Data Fig. 3d).

In response to blue-light irradiation of erastin-treated cells, ferroptotic cell death initiated from the light-exposed area and propagated at a constant speed over a distance of several millimetres (in this experiment, at 5.48 µm min−1 across 5 mm; Fig. 1a–d and Supplementary Video 2). Three independent experiments revealed that ferroptosis propagated at a constant speed of 5.52 ± 0.09 µm min−1 (mean ± s.d.) over distances of ≥5 mm. Compared with the diffusive spread of either calcium[25] or a small protein[26], ferroptosis propagated linearly over time (Fig. 1e), supporting the idea that ferroptosis propagates through trigger waves and not through simple diffusion. Notably, the speed of ferroptotic propagation did not change according to photoinduction light intensity (Extended Data Fig. 3e–j), indicating that the cellular state of the population, but not the intensity of the initiating signal, is the determinant of wave speed. Again, this propagating cell death was ferroptosis specific as only ferrostatin-1 halted its propagation, whereas inhibitors of apoptosis and necroptosis did not (Extended Data Fig. 3k,l).

Fig. 1: Ferroptosis propagates across RPE-1 cells at a constant speed through lipid peroxidation wavefronts.

a,b, Ferroptosis initiates from the photoinduction area (red circle) and propagates across erastin-treated cells over 5 mm for 18 h. The contours (white outline) represent the border of cell death at specific timepoints. a, Nuclear dye fluorescence image (11 h after photoinduction) overlaid with contours 2–18 h after photoinduction. b, Time-lapse images of cell death after photoinduction, showing magnified views of the orange box in a. Cell death is indicated by cell rupture (bright field) and increased nuclear dye fluorescence (cyan to white). c,d, Data derived from a. c, Time-lapse image array of ferroptosis propagation. d, Kymograph for ferroptosis propagation. The white line is a linear least-squares fit to the nuclear dye fluorescent fronts, where its slope represents the speed of propagation. e, Comparison of travelling distances over time among ferroptosis propagation and diffusive molecules (Methods). f,g, Merged images of lipid peroxidation (yellow) and nuclear dye fluorescence (cyan). Lipid peroxidation was monitored using C11-BODIPY581/591. The yellow contours represent the border of lipid peroxidation at specific timepoints. f, Image (7 h after photoinduction) overlaid with contours 1–10 h after photoinduction (top). Bottom, fluorescence intensities of cell death and lipid peroxidation quantified across the bottom region of the image. g, Magnified view of the box in f. h, Time-lapse image array of lipid peroxidation and cell death in f. i, Kymograph for lipid peroxidation propagation in f. The slopes of the yellow and white lines represent the speeds of lipid peroxidation and cell death propagation, respectively. Data shown are representative of three biological repeats. Scale bars, 400 μm (a, b and f) and 250 μm (g).

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One hallmark of ferroptosis is the elevation of lipid peroxidation[22]. Using a lipid peroxidation probe, we observed wavefronts of lipid peroxidation that preceded cell death (Fig. 1f and 1g (magnified image) and Supplementary Video 3). These lipid peroxidation wavefronts propagated at a speed similar to ferroptotic cell death (Fig. 1h,i; in this experiment, at 6.05 µm min−1 (5.83 ± 0.40 µm min−1, mean ± s.d.) for three biological repeats). Similarly, cellular ROS—detected using a general ROS probe for hydroxyl radicals (·OH), superoxides (O_2−) and hydrogen peroxides (H_2O_2)—were also observed as wavefronts (Extended Data Fig. 4a–d and Supplementary Video 4), again propagating at a speed similar to that of ferroptotic waves (in this experiment, at 5.29 µm min−1 (5.46 ± 0.25 µm min−1, mean ± s.d.) for three biological repeats). Addition of various ROS scavengers halted the propagating cell death (Extended Data Fig. 4e), suggesting that multiple ROS can collectively contribute to the signalling wavefronts that drive ferroptosis propagation.

Diffusion for spatial coupling

The formation of self-regenerating ROS wavefronts requires two critical components: (1) a spatial coupling mechanism for intercellular ROS transmission; and (2) an intracellular ROS amplification mechanism (for example, ROS bistability). One possible spatial coupling mechanism is the diffusion of ROS or an ROS-inducing molecule that is independent of direct cell–cell contact. To test that possibility, we created physical gaps (35–380 µm) between a wave-initiated region and a non-initiated region. Ferroptosis propagated across small gap sizes of 2–8 cells (35–118 µm in width) with no sign of stalling (Fig. 2), supporting the idea that direct cell–cell contact is not essential for ferroptosis propagation. When the gap width was large (>200 µm), ferroptosis propagation consistently ceased (Fig. 2). For medium-width gaps (120–200 µm), ferroptosis spread across these gaps in 50% of cases (that is, 5 out of 10 waves halted; Fig. 2b). In the cases in which waves halted, the cells at the edge of the non-initiated region exhibited a gradual increase in ROS (magnified views of the traces for the 156 µm and 224 µm gaps are shown in Fig. 2a). This ROS accumulation is probably caused by a diffusive molecule from ferroptotic cells of the wave-initiating region, as creating gaps alone does not increase ROS levels (Extended Data Fig. 5a). To characterize this diffusive molecule, we collected the erastin-free conditioned medium containing the secreted molecules from ferroptotic cells (Methods). Cells exposed to this conditioned medium underwent ferroptosis, indicative of the presence of the diffusive molecule secreted by ferroptotic cells (Extended Data Fig. 5b). However, when the conditioned medium was pretreated with antioxidants to scavenge lipid peroxides (Trolox, Fer-1), hydrogen peroxide, superoxide and hydroxyl radicals (TEMPO, Tiron), the cell death caused by the conditioned medium was greatly suppressed, indicating that this diffusive molecule may be a type of ROS whose activity is suppressed by ROS scavengers. One possible ROS candidate is H_2O_2, which is known to be long lived and freely diffusive. To test the involvement of H_2O_2, we subjected the conditioned medium to centrifugal filtration. In contrast to the eluate of culture medium containing exogenously added H_2O_2, which caused cell death, the eluate of the centrifuged conditioned medium did not induce cell death, implying that the diffusive molecule is unlikely to be H_2O_2 (Extended Data Fig. 5c). It is therefore possible that the spatial coupling mechanism underlying ferroptosis propagation is the diffusion of ROS molecules, such as peroxidized lipids[27] and/or their byproducts. These lipid peroxidation byproducts, such as lipid-derived electrophiles with a peroxide group, can be released from ferroptotic cells[28], and they are sufficiently stable chemically to freely diffuse[29]. Moreover, other signalling molecules (such as proteins, extracellular vesicles and/or their cargoes)30,31 with oxidative capabilities may also mediate the spread of ferroptosis. Further studies are required to determine the identity of these diffusive ROS molecules.

Fig. 2: Diffusion of ROS signal as the coupling mechanism for ferroptotic trigger waves.

a, Gaps were created between a wave-initiated region (left) and a non-initiated region (right). Time-lapse image sequence of cell death (cyan) and ROS (yellow) propagation across a gap. ROS was monitored in erastin-treated cells using CellROX dye. The mean fluorescence intensity of ROS was quantified along the 2 mm distance at specific timepoints. Scale bar, 200 μm. b, The probability of a wave passing through different gap widths (35–380 μm). The propagating probability was obtained by fitting the data (from 38 wells) to a logistic model, \(p\left(x\right)=\frac{1}{1\,+\,{e}^{-\left({\beta }_{0}+{\beta }_{1}x\right)}}\), where p is the propagating probability and x is the gap width. Gaps were created by scratching the bottom of the plate with needles of different tip sizes (20–400 μm) after wave initiation. The time-lapse video for this experiment is shown in Supplementary Video 5. Data are representative of three biological repeats.

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ROS amplification through feedback loops

For ferroptosis to propagate as trigger waves, intercellular diffusive ROS need to activate a ROS-amplification mechanism in neighbouring cells, in turn leading to their further production of diffusive ROS and ferroptotic cell death. At least three ROS feedback loops could potentially operate to amplify ROS in the ferroptosis network: a glutathione (GSH)-mediated double-negative-feedback loop and the two positive-feedback loops of the Fenton reaction[32] and NADPH oxidase (NOX) signalling33,34,35 (Fig. 3a). To examine the roles of these feedback loops in regulating ferroptosis propagation, we modulated their strengths by chemical perturbation (Fig. 3b).

Fig. 3: ROS feedback loops modulate the speed of ferroptotic trigger waves.

a, A ferroptosis network includes three ROS feedback loops. b, Chemical perturbations applied to modulate the strength of Fenton and NOX feedback loops in erastin-treated cells. c,d, The speed of ferroptotic trigger waves declines after iron chelation. c, Nuclear dye fluorescence image (11 h after photoinduction) overlaid with cell death contours. The yellow contour indicates the timepoint at which DFO (80 µM) was added. d, Kymograph for the experiment in c. e,f, The speed of ferroptotic trigger waves increases after iron addition. e, Nuclear dye fluorescence image (15 h after photoinduction) overlaid with cell death contours. The yellow contour indicates the timepoint at which FC (250 µM) was added. f, Kymograph for the experiment in e. g–i, Addition of GKT137831 (1.25 µM) (g), LY294002 (100 µM) (h) or dasatinib (0.6 µM) (i) slows down ferroptotic trigger waves. j–n, Wave speed as a function of DFO (j), FC (k), GKT137831 (l), dasatinib (m) and LY294002 (n) concentrations. The dose–response curves (dashed line) were obtained by fitting the data to a Michaelian inhibition function for DFO, LY294002 and GKT137831; to a Michaelian activation function for FC; and to a biphasic inhibition function[49] for dasatinib (Methods). The shaded area bounded by grey lines represents the 95% confidence interval of model prediction. Data are derived from five (DFO, 0–0.6 µM and 2.5–160 µM), four (DFO, 1.25 µM), four (FC) and six (GKT137831, LY294002, dasatinib) technical replicates. Fitted parameters are shown in Extended Data Table 1. For c–n, data are representative of three biological repeats. Scale bars, 500 μm (c and e).

Source Data

The Fenton reaction is driven by cellular labile iron. It converts H_2O_2 to ·OH—a highly reactive ROS that elicits autocatalytic lipid peroxidation[32]. To examine the role of iron in ferroptosis propagation, we perturbed its level using the iron chelator deferoxamine (DFO) and the iron supplement ferric citrate (FC). After DFO treatment (80 µM), we observed that the speed of ferroptosis propagation diminished from 5.28 µm min−1 to 2.33 µm min−1 (Fig. 3c,d and Supplementary Video 6). This deceleration of ferroptosis propagation proved to be dose dependent, with propagation being completely halted at a DFO concentration of 160 µM (Fig. 3j). By contrast, supplying free iron through FC accelerated ferroptosis propagation from 5.04 µm min−1 to 9.40 µm min−1 (Fig. 3e,f and Supplementary Video 7). Similarly, we observed dose dependency for ferroptotic wave speed (Fig. 3k). Using a labile-iron probe, we observed elevated iron levels at the ferroptosis wavefronts, supporting a critical role for iron in regulating ferroptosis propagation (Extended Data Fig. 6a,b).

NOXs are a major class of enzymes that generate cellular ROS (O_2− and H_2O_2). NOX-generated ROS inhibit tyrosine kinase phosphatases, resulting in activation of tyrosine kinases[36]. In turn, active tyrosine kinases and their downstream effectors (such as PI3K) can activate NOXs to further promote ROS production33,34,35, thereby forming a positive-feedback loop (Fig. 3a). As NOX4 is the predominant isoform expressed in RPE cells (Extended Data Fig. 7a), it is likely that this positive-feedback loop depends primarily on NOX4. Accordingly, we applied inhibitors of NOX1/NOX4 (GKT137831), PI3K (LY294002) and tyrosine kinases (dasatinib) to RPE cells (Fig. 3b). These small-molecule inhibitors are target specific and do not exhibit antioxidant activity (Extended Data Fig. 7b). All three inhibitors slowed down ferroptosis propagation (Fig. 3g–i) and presented dose dependency (Fig. 3l–n).

In addition to chemical perturbations, we genetically modulated the strength of NOX signalling by overexpressing ERK2, encoding an intermediate signalling protein in the RTK–ERK–NOX cascade[37] (Extended Data Fig. 8a). RPE cells overexpressing ERK2 presented greater ERK2 activity and NOX activity (Extended Data Fig. 8b,c). Accordingly, ferroptotic waves propagated at a higher speed in ERK2-overexpressing cells (Extended Data Fig. 8d,e and Supplementary Video 8), supporting a primary role for the NOX-mediated feedback loop in wave propagation.

Activation of these ROS feedback loops not only enhanced ferroptosis propagation, but also induced it in cell populations that were otherwise resistant to ferroptosis. For example, one factor that causes ferroptosis resistance is high confluency, which can downregulate iron uptake[38], thereby probably suppressing the Fenton-mediated ROS feedback loop. Using lung carcinoma epithelial A549 cells and bone osteosarcoma U-2 OS cells as examples, we found that high cell densities resulted in ferroptosis resistance. By supplementing iron in combination with erastin treatment, we found that ferroptosis initiated and propagated across both these cell populations (Extended Data Fig. 6c,d), further highlighting the critical role of activating ROS feedback loops in ferroptosis propagation. Moreover, in addition to the regulators involved in amplifying ROS, other well-known ferroptosis regulators (such as ferroptosis suppressor protein 1; FSP1)[39] also contributed to ferroptosis propagation, with ferroptotic wave speed increasing after inhibition of FSP1 (Extended Data Fig. 9). Together, these results indicate that molecular regulators (amplifiers or suppressors) of ROS can govern the occurrence and progressivity of ferroptotic waves.

ROS bistability on ferroptotic stress

At the core of the ferroptosis network is the GSH–ROS double-negative-feedback loop (Fig. 3a). To maintain ROS at physiological levels, cells depend heavily on de novo GSH synthesis through nutrient catabolism (for example, of glucose and cystine)22,40. Our previous study revealed that a glucose-deprivation-induced decline in GSH results in ROS bistable switches[41]. Similarly, suppression of cystine uptake may cause ROS bistability, allowing ferroptosis to propagate as trigger waves. To gain a quantitative understanding of this process, we built a mathematical model for ROS trigger waves incorporating (1) cell-intrinsic ROS feedback loops in the ferroptosis network; and (2) simple diffusion as the coupling mechanism between cells (Extended Data Fig. 10a).

Our simulations show that an increased erastin concentration can lead to a bifurcation in the ROS steady state, that is, switching from a monostable (<0.39 µM erastin) to a bistable regime (0.39–11.7 µM erastin) in which two ROS stable steady states (lower and upper) coexist (Fig. 4a). As expected from a bistable system[6], an activation threshold (that is, unstable steady state (USS)) separates the two steady states such that ROS levels above this threshold are amplified to the upper steady state. ROS usually propagate as trigger waves within this bistable regime (Extended Data Fig. 10b–d). When ROS levels are elevated sufficiently to surpass the activation threshold (Fig. 4a (blue to yellow)), either by exposure to blue light or by diffusive ROS from neighbouring ferroptotic cells, ROS feedback loops are activated for its switch to the upper steady state (Fig. 4a (yellow to red)). This process iterates as a series of ROS activation–diffusion events, emerging as self-regenerating trigger waves of ROS. Notably, our model predicts that increasing the erastin concentration (from 0 to 11.7 µM) exerts a relatively mild effect on the low-ROS steady state (Fig. 4a), but it apparently accelerates ferroptotic propagation and enhances the intensity of ROS wavefronts (Extended Data Fig. 10d,e). Thus, our simulations provide two testable predictions when the erastin concentration is increased: (1) the transition from monostable to bistable ROS; and (2) the dose-dependent response of ferroptosis propagation speed.

Fig. 4: Ferroptosis stress primes cells for ROS bistability and promotes the propagation of ferroptotic trigger waves.

a, In silico simulations showing ROS steady state as a function of erastin concentration. ROS steady state bifurcates from a monostable (low) to a bistable regime (yellow area) with increased erastin concentration. Stable low and high ROS steady states and the USS are denoted by blue, red and white circles, respectively. ROS elevation by photoinduction (blue arrow, elevation from blue to yellow circle) allows cells to surpass the USS, above which ROS is amplified (red arrow) to the high steady state. b, Images of ROS fluorescence (yellow) 20 min after photoinduction at different erastin concentrations. c, Single-cell ROS steady states were measured before and after photoinduction at different erastin concentrations in b. For each erastin level, 80 cells are shown. d, Increasing erastin concentrations promote ROS wavefront propagation. Simulations (top) and experiments (bottom) of ROS propagation in cell populations treated with different erastin concentrations 6 h after photoinduction. e–g, Wave speed (e), wavefront width (f) and amplitude (g) at different erastin concentrations. Data are mean ± s.d. For wave speed, four technical replicates; wavefront width and amplitude were measured from twelve directions. Statistical analysis was performed using two-sided Wilcoxon rank-sum tests with false-discovery rate adjustment; P values are shown at the top. For b–g, data are representative of three biological repeats. Scale bars, 100 μm (b) and 800 μm (d).

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To experimentally test erastin-induced ROS bistability, we used photoinduction to modulate cellular ROS for the activation of its bistable switch. Within the predicted bistable regime, we anticipated that photoinduction (Fig. 4a (blue arrow)) would activate the ROS bistable switch by elevating ROS above the activation threshold. Consistently, when we increased the erastin concentration above 1.25 μM, the cellular ROS steady state switched to the upper level after photoinduction (Fig. 4b,c), similar to an ROS bistable switch. By contrast, at an erastin concentration of lower than 0.63 μM, cells maintained low ROS levels, resembling the monostable ROS regime. In particular, cells without erastin treatment did not exhibit ROS elevation regardless of the photoinduction intensity (Extended Data Fig. 11a), supporting their redox monostability. At the threshold level of 0.63 μM erastin, cells displayed a bimodal distribution of ROS levels, indicative of its proximity to the monostable/bistable bifurcation point. This monostable-to-bistable transition represents a cellular priming process during which individual cells acquire ROS bistability and the cell population becomes a bistable medium over which ROS propagate as trigger waves. Thus, erastin-treated cells exhibit properties of the ROS bistable medium predicted by our model, with features of an activation threshold for the ROS bistable switch, and a monostable-to-bistable bifurcation point.

We next examined how erastin-mediated cellular priming may alter the states of ROS feedback loops. Increasing the concentration of erastin elicited a hyperbolic decrease in cellular GSH (Extended Data Fig. 11b). In contrast to the bistable nonlinear switch of ROS (Fig. 4c), a linear decrease in GSH was observed when the erastin concentration was increased from 0.31 µM to 1.25 µM. Thus, the bistable ROS switch is probably not a direct result of suppressing de novo GSH synthesis alone but an emergent property of ROS feedback-loop activation. Consistently, we found that labile iron and NOX activity, respective markers for the strength of the Fenton and NOX-signalling loops, increased with the erastin concentration (Extended Data Fig. 11c,d and 11f (bottom)), indicating interactions among these ROS feedback loops and their concerted activation during the cellular priming process. Concurrently, we detected a mild but consistent increase in ROS with increasing erastin concentration (Extended Data Fig. 11e and 11f (top)).

We also examined how the cellular priming status might quantitatively influence the behaviour of ferroptotic trigger waves. Consistent with our model prediction, the majority of the bistable regime enabled ferroptosis propagation, with wave speed increasing (from 3.08 μm min−1 to 5.38 μm min−1) in accordance with erastin concentration (Fig. 4d,e and Supplementary Video 9). Moreover, a faster propagating wave exhibited larger ROS wavefronts of higher amplitudes (Fig. 4f,g). Thus, ferroptosis stress primes cell populations, thereby endowing them with different degrees of progressivity for ferroptotic waves.

Ferroptosis in the developing limb

Our cell-based assays show how ferroptosis propagates across a large population of cells as trigger waves. During embryonic development of complex organisms such as vertebrates, large-scale cell death is a recurrent mechanism that is responsible for shaping tissue structures and removing temporary embryonic-stage-specific tissues1,2. If ferroptotic waves act as a tissue-sculpting mechanism, it is conceivable that developmental signals (for example, morphogens) may encode spatial information that primes cells in different regions to exhibit distinct redox states. Regions in which cells are redox bistable would allow propagation of ferroptosis to eliminate unwanted structures. By contrast, cellular regions of redox monostability could act as boundaries to restrict the spread of ferroptosis, thereby facilitating region-specific tissue sculpting.

We investigated this possibility in the developing avian limb, in which large-scale cell death has been found to be a critical mechanism for muscle remodelling[42]. During this remodelling process, muscle masses are sculpted by cell death to form the anatomically distinct muscle bellies of the foot, shank and thigh42,43. At day 6.5 of development (stage HH30), foot muscles are continuous with the shank muscles, presenting longitudinal alignment (HH30; Extended Data Fig. 12a). From day 7 onwards, the foot muscles are progressively separated from the shank (7.5 day limb, stage HH32; Extended Data Fig. 12a), and this process is facilitated by massive cell death occurring along the central proximodistal axis of the zeugopod, as detected by TUNEL staining (muscular ventral death zone; Extended Data Fig. 12b). Concurrently, the foot and shank muscle masses segregate into individual muscle bellies through cell death, as indicated by myosin-positive cells with a rounded appearance (Extended Data Fig. 12d,e (top)). Notably, we detected colocalization of 4-hydroxynonenal (4-HNE), a lipid peroxidation marker and ferroptosis indicator, with dead cells in the muscular ventral death zone (Extended Data Fig. 12c) and in degenerating muscles (Extended Data Fig. 12d,e), indicating that muscle cells undergo ferroptosis during the process of muscle remodelling.

We next examined the occurrence of ferroptosis in the developing limb more broadly. We found that, in addition to the muscle layer, the dorsal and ventral ectodermal layers of the limb also exhibited abundant 4-HNE signals (Fig. 5a). Moreover, large-scale lipid peroxidation was detected at the ventral ectodermal layer, extending across the proximodistal axis of the limb zeugopod over a distance of greater than 2 mm (Fig. 5b). Consistently, whole-mount staining of 4-HNE indicated the occurrence of lipid peroxidation at the central region of the limb zeugopod (Fig. 5c), which coincided with the cell death observed in the same region detected by TUNEL staining (Fig. 5d). Quantification of 4-HNE and TUNEL signals in multiple limbs revealed stronger signals at the central region relative to the lateral regions of the zeugopod (Extended Data Fig. 12f–h), indicative of region-specific large-scale ferroptosis at the central ectodermal layer of the developing limb.

Fig. 5: Ferroptosis and its propagation facilitate muscle remodelling in the avian limb.

a,b, Co-immunostaining of lipid peroxidation marker (4-HNE, yellow) and myosin heavy chain (myosin, magenta) in transverse (a) and longitudinal (b) sections (HH33 limbs). The transverse section (a) is located at the zeugopod (box in b). The longitudinal section (b) features the ectodermal layer. c,d, Whole-mount immunostaining of 4-HNE (c) and TUNEL staining (d) in HH32 limbs outlined in red (magnified views are shown at the bottom). e, Nuclear dye fluorescence image (HH31 limb) (left). Right, time-lapse images of cell death (magnified views of the box are shown on the left). f, Cell death area with or without DFO (10 mM, n = 18), UAMC-3203 (1 μM, n = 20), Fer-1 (10 μM, n = 22) and Z-VAD-FMK (200 μM, n = 24). Data are mean ± s.d. Statistical analysis was performed using two-sided Wilcoxon rank-sum tests; P values are shown at the top. g,h, PALP assay of C11-BODIPY581/591 (C11-B)-stained limbs (merged images of oxidized and reduced C11-BODIPY581/591). g, C11-BODIPY581/591-stained limb overlaid with the laser target sites. h, Magnified views of the box in g (top). Bottom, lipid peroxidation levels at target sites in the top images. i, Lipid peroxidation levels in the central (within 100 μm of the centre axis) and lateral (≥350 μm away from the centre axis) regions. Data are mean ± s.d. of 28 (central) and 61 (lateral) target sites. j, Immunostaining of myosin from UAMC-3203-treated and vehicle-control-treated embryos (HH33) (top). Bottom, muscle fibres colour coded according to their orientations. For a–e and g–j, data are representative of three biological repeats. Scale bars, 200 μm (a, c (bottom), d (bottom), h and e (right seven images)), 400 μm (b, c (top) and d (top)), 500 μm (g), 600 μm (e (left)) and 300 μm (j). Ventral views are shown unless otherwise stated.

Source Data

Ferroptotic waves in the limb zeugopod

As ferroptosis has the potential to propagate over large areas, we examined whether the above-described large-scale cell death could be caused by ferroptosis waves. To this end, we used an ex vivo limb culture system (Methods) to monitor cell death occurrence in real time. As shown in Fig. 5e and Supplementary Video 10, cell death spread across the limb from distal to proximal regions at a speed of 1.15 μm min−1, that is, the same order of magnitude relative to the wave speeds measured in our cell-based assay. Mean cell death propagation speed across three embryonic limbs was 1.30 ± 0.13 μm min−1 (mean ± s.d.). Furthermore, the cell death area across the limb was significantly reduced by treatment with various ferroptosis inhibitors (UAMC-3203, DFO and Fer-1), but not by the apoptosis inhibitor Z-VAD-FMK (Fig. 5f), indicating natural occurrence of ferroptosis in the ectodermal layer of the limb zeugopod.

Although it can extend over a large area, ferroptosis propagation is evidently confined to the central region of the proximodistal axis of the developing limb, with cells located in lateral regions remaining viable. As ferroptosis is involved in executing this cell death event, it is possible that spatial patterning of polyunsaturated fatty acids (PUFAs) could act as a natural boundary to confine its propagation. To measure relative PUFA levels from the lateral to central regions along the proximodistal limb axis, we used photochemical activation of membrane lipid peroxidation (PALP)—an assay designed to measure relative PUFA levels using targeted laser pulses that photochemically induce lipid peroxidation[44] (Fig. 5g). As shown in Fig. 5h, PALP-induced lipid peroxidation was much greater in the central regions in which cell death propagates than in the lateral regions in which cells remain viable. The increase in lipid peroxidation in the lateral regions was transient, with lipid peroxidation reverting to basal levels soon after the application of laser pulses, indicative of their redox monostability (Fig. 5i). By contrast, the levels of lipid peroxidation at the central regions remained elevated, indicative of redox bistability. This result supports the hypothesis that different regions of the embryonic limb may exhibit distinct redox states to confine ferroptosis propagation.

Limb muscle remodelling by ferroptosis

To examine a possible functional role of ferroptosis during muscle remodelling, we examined the consequence of suppressing ferroptosis on muscle development through in ovo injection of the ferroptosis inhibitor UAMC-3203. As shown in Fig. 5j, untreated embryos exhibited individualized muscle bellies at the anterior–posterior axis, each with a compact unit of muscle fibres well aligned with one another along a distinct orientation plane, represented by a coherent cell orientation angle. By contrast, suppressing ferroptosis resulted in excessive muscle fibres (quantification is shown in Extended Data Fig. 12i) and compromised the proper anterior–posterior segregation of the muscle masses. This phenotype was also accompanied by fibre disorganization, as revealed by the higher entropy of fibre orientations (quantification is shown in Extended Data Fig. 12j). These observations support the involvement of ferroptosis in regulating the number of muscle fibres and the subsequent individualization of the muscle mass during the development of avian limbs.

Ferroptotic cell death propagation in cell culture and in embryonic limbs exhibits several common properties. For example, their dependency on lipid peroxidation and cellular iron implies activation of shared ROS feedback loops that give rise to redox bistability. Supporting this notion, cell death propagates with speeds in the same order of magnitude in both systems. Moreover, similar to the multiple spontaneous death initiation events detected in cell culture, we also uncovered variation in the number of death initiation events in embryonic limbs (Supplementary Video 11), which may also be attributable to the stochastic nature of the cellular redox system (Extended Data Fig. 2). Nevertheless, the ferroptosis priming signals of both systems are distinct. Whereas ferroptosis propagation in cell culture results from erastin-mediated cellular priming, the wave-like propagation of ferroptosis in the embryonic limb arises from as-yet-uncharacterized developmental signals. Another distinct aspect of the two systems is the spatial area over which cell death waves extend. In contrast to the complete elimination of an entire cell population in culture, ferroptosis waves are confined to a specific region of the embryonic limb. One type of spatially encoded information from the priming signal is the level of PUFAs (Fig. 5g–i), the distribution of which constrains ferroptosis waves in the embryonic limb. Whether other ferroptosis regulators (for example, GPX4)[45] are modulated by developmental signals to exhibit region-specific activities remains to be examined.

Discussion

Large-scale cell death during embryogenesis was initially observed in the nineteenth century[1]. These observations inspired decades of study to dissect the molecular mechanisms through which cell death is regulated, but few have pursued the fundamental question of how cell death is coordinated in space to permit the elimination of large populations of cells. These developmental cell death events were previously attributed to apoptosis. However, this conventional view has been challenged by several studies46,47, suggesting the involvement of an as-yet-unclear mechanism that could act collaboratively and/or synergistically with apoptosis to coordinate developmental cell death events (such as ROS-mediated cell death45,48). Apart from during development, excessive cell death is also widely observed as occurring contiguously across tissues in disease pathologies, for example, during ischaemia–reperfusion injuries and degenerative diseases3,4,5,9. Our study provides one possible explanation for the occurrence of such massive cell death events. We show that ROS, as a ferroptotic death signal, can spread as trigger waves across cells over long distances without diminishing in speed. We have demonstrated that ferroptotic stress primes cell populations to become redox bistable by stimulating ROS feedback loops. Consequently, these redox bistable cell populations permit ferroptosis to propagate through a series of ROS amplification–diffusion events by means of short-range ROS diffusion as a spatial coupling mechanism. This mechanism of ferroptotic trigger waves overcomes the spatial limitations of simple diffusion to coordinate ferroptosis across cells, allowing the emergence of tissue-scale cell death. Finally, our study reveals ferroptosis propagation as a mechanism for eliminating temporary cell populations during muscle remodelling in developing limbs, substantiating its use as a tissue-sculpting strategy for shaping tissues and organs into appropriate forms during embryonic development. Our findings support the emerging notion that other modalities of cell death (for example, ferroptosis) alongside apoptosis are involved in organismal development, opening up future avenues for investigating the interplay between developmental signals and ferroptosis.

Methods

Reagents

Erastin (Cayman, 17754), RLS-3 (MedChemExpress, HY-100218A), ferrostatin-1 (Cayman, 17729), necrostatin-1 (Cayman, 11658), Z-VAD-FMK (Cayman, 14463), ionomycin (Sigma-Aldrich, I9657), staurosporine (MedChemExpress, HY-15141), GKT137831 (MedChemExpress, HY-12298), LY294002 (LC laboratories, L-7962), dasatinib (Cayman, 11498), FSEN1 (Cayman, 38025) and Trolox (MedChemExpress HY-101445) were all dissolved in DMSO and stored at −80 °C before usage. l-Buthionine sulfoximine (BSO) (Sigma-Aldrich, B2515), DFO mesylate salt powder (Sigma-Aldrich, D9533), Tiron (Sigma-Aldrich, 172553), TEMPO (Sigma-Aldrich, 176141), N-acetyl-l-cysteine (Sigma-Aldrich, A9165) and catalase (Sigma-Aldrich, C1345) were freshly prepared before each experiment by dissolving them in distilled H_2O. FC (Sigma-Aldrich, 3522-50-7) was prepared in distilled H_2O and stored at −20 °C. Sytox Green nucleic acid stain (Invitrogen, S7020), siR-DNA probe (Spirochrome, SC007), siR700-DNA probe (Spirochrome, SC015), CellROX deep red (Invitrogen, C10422), C11 BODIPY581/591 (Invitrogen, D3861), FeRhoNox-1 iron dye (Sigma-Aldrich, SCT030), DAPI (AAT Bioquest, 17513) and lucigenin (Cayman, 14872) were stored at −20 °C. NADPH (Sigma-Aldrich, N7505) was reconstituted in 10 mM Tris-HCl (pH 8) and stored at −80 °C. Phosphatase inhibitor cocktail (524629) and protease inhibitor cocktail (539134) were sourced from Calbiochem. Human holo-transferrin (R&D Systems, 2914-HT) was reconstituted in distilled H_2O and stored at −80 °C. Phenol-red-free RPMI medium was made according to RPMI 1640 (Gibco, 11875) medium formulation using cell-culture-grade inorganic salts, amino acids and vitamins purchased from Sigma-Aldrich.

Cell culture

hTERT RPE-1 (ATCC, CRL-4000), 786-O (ATCC, CRL-1932), G-402 (ATCC, CRL-1440), HOS (ATCC, CRL-1543), LN-18 (ATCC, CRL-2610), U-118 MG (ATCC, HTB-15), PANC1 (ATCC, CRL-1469), MDA-MB-231 (ATCC, HTB-26), HT-1080 (ATCC, CCL-121), NCI-H1650 (ATCC, CRL-5883), A549 (ATCC, CRM-CCL-185) and U-2 OS (ATCC, HTB-96) cells were cultured in RPMI 1640 medium (Gibco, 11875) containing 5% fetal bovine serum (FBS) (Sigma-Aldrich, TMS-013-BKR). HuH-7 (JCRB Cell Bank, JCRB0403), A-172 (ATCC, CRL-1620), Hs 895.T (ATCC, CRL-7637), HeLa (ATCC, CCL-2) and SH-SY5Y (ATCC, CRL-2266) cells were cultured in DMEM (Gibco, 11965) with 5% FBS. All cell lines were grown at 37 °C under 5% CO_2. Cells were passaged routinely to maintain around 80% confluency. All cell lines were tested and found to be free of mycoplasma.

RPE-1 cells with ERK2 overexpression

RPE-1 cell lines stably expressing ERK2 and mCherry (control) were generated by lentivirus infection according to a standard protocol. In brief, lentivirus production was performed by HEK-293T transfection with the target construct (ERK2-P2A-eGFP or mCherry), packaging vector (pVSV-G) and envelope plasmid (psPAX2) using FuGENE HD transfection reagent (Promega, E2311), followed by lentivirus infection in RPE-1 cells. To generate the ERK2-P2A-eGFP lentiviral construct, the ERK2 sequence (Addgene, 116760) was subcloned into a lentiviral backbone fused to a P2A-eGFP sequence using a Gateway recombination system (Invitrogen).

Live-cell fluorescence microscopy

Time-lapse imaging experiments were performed using a Zeiss Axio Observer 7 inverted microscope equipped with a X-Cite Xylis (Excelitas Technologies, XT720S) LED illumination system, Definite Focus 2 system and a Prime BSI Scientific CMOS camera (Photometrics), under controlled temperature (37 °C), atmosphere (5% CO_2) and humidity (90–100%) in an environmental chamber. Images were taken with 2 × 2 binning using either a Zeiss ×10/0.45 M27 or ×20/0.8 M27 Plan-Apochromat objective. The fluorescence filter sets used for imaging experiments were as follows: Cy5 (Semrock, LED-Cy5-A-000), iRFP (Semrock, Cy5.5-C-000), FITC (Semrock, LED-FITC-A-000) and mCherry (Semrock, LED-mCherry-A-000).

Enhanced nuclear dye fluorescence as a cell death reporter

To simplify imaging experiments, cell death was monitored according to the increased nuclear dye (siR-DNA or siR700-DNA dye) fluorescence that co-occurs with cell rupture (Extended Data Fig. 1a). To further validate this approach, we compared the increase in nuclear dye fluorescence with that of a cell death indicator, Sytox Green (3 nM), during ferroptosis. The increase in nuclear fluorescence consistently occurred 1–2 h earlier than the increase in Sytox Green fluorescence in all dead cells (Extended Data Fig. 1a,b). Sytox Green, siR-DNA and siR700-DNA were imaged using the FITC, Cy5 and iRFP filter sets, respectively.

Comparison of cell death kinetics of different death types in RPE-1 cells

Cells (5.4 × 10^4 cells per cm^2) were seeded on a 96-well μ-plate (Ibidi, 82406) in phenol-red-free RPMI medium with 5% FBS and siR-DNA (1 μM) 1 day before treatment with different death inducers (Extended Data Fig. 1d–j). For the cystine-starvation experiments, cells were washed three times and treated with cystine-free RPMI medium with 5% dialysed FBS (Gibco, 26400-044).

Ferroptosis death kinetics in different cell lines

To obtain the death kinetics of erastin-induced (10 μM) ferroptosis in 16 different cell lines, and RSL3-induced (0.15 μM) ferroptosis in U-2 OS and A549 cells, the cells were seeded at densities of 25% confluency, followed by the indicated treatments after 2 days.

Cell-based assay for ferroptotic trigger waves in RPE-1 cells

Two days before time-lapse imaging, 5.4 × 10^4 cells per cm^2 were seeded on a 24-well μ-plate (IBIDI, 82406) or a 96-well μ-plate coated with Matrigel matrix (Corning, 356231). Cells were grown in DMEM/F12 (Gibco, 21041025) supplemented with physiological levels of holo-transferrin (0.8–1.3 mg ml−1). One day after seeding, the growth medium was replaced with homemade phenol-red-free RPMI with 0.15% FBS and the nuclear dye siR-DNA (1 μM). Cells were treated with erastin (10 μM) the next day, and imaged by fluorescence microscopy. To initiate ferroptotic death propagation, blue-light photoinduction (see the next section for details) was performed 8 h after erastin treatment. Generally, tiled (3 × 3 or 5 × 5) images were collected with a ×10 objective at a 1 h time interval.

Photoinduction

For our standard photoinduction experiments, cells were exposed to blue light (Semrock, FF01-432/36-25) using a ×20 objective (60 mW for 10 s), unless otherwise stated. The size of the exposed area (~0.2 mm^2) was adjusted with an aperture diaphragm slider. To experimentally test ROS responses after light irradiation at different wavelengths (Extended Data Fig. 3e), we applied different filters (Semrock, FF01-378/52-25, FF01-432/36-25, FF01-474/27-25, FF01-509/22-25, FF01-554/23-25, FF01-578/21-25, FF01-635/18-25) to irradiate cells. A Gigahertz Optik Radiometer (PT-9610) was used to measure light power.

Chemical perturbation of ferroptotic trigger waves

To quantify changes in trigger wave speed after chemical perturbations, we applied chemical inhibitors 11–15 h after photoinduction in 24-well μ-plates. To obtain the dose–response curves of wave speed in response to chemical inhibitors, experiments were performed in 96-well μ-plates into which chemical inhibitors had been added 4 h after photoinduction. To prevent potential toxicity, the final DMSO concentration was kept below 0.1%.

Imaging cellular ROS and lipid peroxidation

To image cellular ROS and lipid peroxidation during ferroptosis propagation, cells were stained with CellROX (0.6 μM) or C11-BODIPY581/591 (1.25 μM), respectively, in phenol-red-free RPMI medium with 0.15% FBS for 30 min. After staining, cells were washed twice with the growth medium before imaging. Filter sets were Cy5 for CellROX, and FITC and mCherry for C11-BODIPY581/591.

Intercellular gap creation

Intercellular gaps were created by scratching the bottom of the plate with needles of different tip sizes (20 μm to 400 μm) after wave initiation.

Characterization of conditioned medium from ferroptotic cells

The cell death induced by conditioned medium from ferroptotic cells was assessed in a similar manner to a protocol described previously[27] with some modifications. Specifically, the recipient and donor RPE-1 cells were seeded at 4.5 × 10^3 cells per cm^2 and 5.4 × 10^4 cells per cm^2, respectively, according to the assay described in the ‘Cell-based assay for ferroptotic trigger waves in RPE-1 cells’ section above. Two days after seeding, the donor RPE-1 cells were treated with erastin (10 µM). After 12 h of treatment, when around 50% of cells were dead, erastin was washed out and replaced with erastin-free medium. After 4 h, these conditioned media were collected and pretreated with different ROS scavengers (Trolox, Fer-1, TEMPO and Tiron) (Extended Data Fig. 5b). In a separate experiment (Extended Data Fig. 5c), the conditioned media and H_2O_2-containing (100 µM) media underwent centrifugal filtration with a molecular mass cut-off of 30 kDa (Amicon ultracentrifugal filter). These ROS-scavenger-treated or filtered conditioned media were transferred to the recipient cells, before undergoing time-lapse imaging. As an erastin-wash control, we used a culture dish without cells to prepare the conditioned medium.

GSH measurement

Total GSH level was measured using the GSH/GSSG-Glo Assay kit (Promega) according to the manufacturer’s protocol. In brief, cells were seeded for 2 days in white-walled 96-well plates (Thermo Fisher Scientific, 136101). Before GSH measurement, cells were treated with erastin for 8 h. Luminescence signals were measured in relative light units using a SpectraMax Paradigm (Molecular Devices) microplate reader at 1 s integration time per well. GSH concentrations were interpolated from the linear range of the standard curve (R^2 = 0.99).

Measurement of cellular labile iron

Intracellular labile iron (Fe2+) levels were measured in RPE-1 cells 8 h after erastin treatment (Extended Data Fig. 11c and 11f (bottom)). Cells were stained with FeRhoNox-1 (5 μM) in phenol-red- and FBS-free RPMI medium. After incubation for 1 h, the cells were washed twice with the growth medium, then fixed (4% paraformaldehyde) and imaged. For image analysis, DAPI staining (30 nM) was applied to stain nuclei. FeRhoNox-1 and nuclear DAPI fluorescence signals were acquired using mCherry and DAPI filter sets, respectively. Similarly, to measure iron levels during cell death propagation, cells were stained as described above, and then photoinduced and processed for time-lapse imaging (Extended Data Fig. 6a,b).

Measurement of NOX activity

NOX activities were measured using a lucigenin-derived chemiluminescence assay, as described previously[50] with some modifications. After 8 h of erastin treatment, whole-cell homogenates were collected with modified HEPES buffer (140 mM NaCl, 5 mM KCl, 0.8 mM MgCl_2, 1.8 mM CaCl_2, 1 mM Na_2HPO_4, 25 mM HEPES, 1% glucose, pH 7) supplemented with phosphatase inhibitor cocktail and protease inhibitor cocktail. After three freeze (−80 °C)–thaw cycles, lucigenin was added to the homogenates to a final concentration of 5 μM and incubated at 37 °C for 30 min in the dark. The homogenates were then added into 96-well plates (equivalent to 2 × 10^5 cells per well). Immediately before luminescence measurement, 400 μM NADPH was added to each well. Luminescence was measured every 3 min using the EnSpire Multilabel Plate Reader with an integration time of 1 s per well and the temperature was maintained at 37 °C. Data are presented as relative luminescence units after 30 min of recording.

DPPH assay

The antioxidant potential of small molecule inhibitors (GKT137831, LY294002, dasatinib) was determined using a 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay kit (Dojindo) according to the manufacturer’s protocol. The absorbance at 517 nm was measured using the EnSpire Multilabel Plate Reader.

Western blot analysis

Cells were lysed in RIPA buffer supplemented with phosphatase and protease inhibitor cocktails, and then allowed to homogenize on ice for 30 min. Protein lysates were collected from the supernatants after centrifugation at 14,000g for 10 min. After protein quantification using the BCA assay kit (Thermo Fisher Scientific, 23227), protein lysates (15 μg per lane) were mixed with sample buffer and then incubated at 70 °C for 10 min before loading. Proteins were separated by 10% SDS–PAGE and blotted onto PVDF membranes (Millipore, IPFL85R). After blocking with 5% bovine serum albumin at room temperature for 30 min, the membranes were incubated with primary antibodies against ERK (1:2,000, Cell Signaling Technology, 9102) and phosphorylated-ERK (1:1,000, Cell Signaling Technology, 9106S) at 4 °C overnight, followed by incubation with secondary antibodies (IRDye 680RD goat anti-mouse IgG, LiCOR, 926-68070; IRDye 800CW donkey anti-rabbit IgG, LiCOR, 925-32213) at room temperature for 1 h. The bands were visualized using the Typhoon laser scanner. Finally, the membranes were stained with Ponceau S to validate the transfer efficiency and to quantify protein loading.

RT–qPCR analysis

Total RNA was extracted with TRIzol reagent (Invitrogen, 15596018) and reverse transcribed using the iScript cDNA synthesis kit (Bio-Rad, 1708891). Quantitative PCR with reverse transcription (RT–qPCR) was performed with iTaq Universal SYBR Green (Bio-Rad, 1708882) and monitored with a BioRadCFX96 system equipped with CFX Maestro software 2.3 (v.5.3.022.1030). The primers for qPCR were as follows: NOX1, fw: 5′-GTCTGCTCTCTGCTTGAAT-3′, rv: 5′-ATGAGATAGGCTGGAGAG-3′; NOX2, fw: 5′-CCCTTTGGTACAGCCAGTGAAGAT-3′, rv: 5′-CAATCCCGGCTCCCACTAACATCA-3′; NOX3, fw: 5′-ATGAACACCTCTGGGGTCAGCTGA-3′, rv: 5′-GGATCGGAGTCACTCCCTTCGCTG-3′; NOX4, fw: 5′-CAGAAGGTTCCAAGCAGGAG-3′, rv: 5′-GTTGAGGGCATTCACCAGAT-3′.

TUNEL staining of chicken limbs

No ethical approval was required for experiments on chicken embryos at the desired Hamburger–Hamilton (HH) stage[51] 30–33. Fertilized Leghorn chicken eggs were incubated at 37.5 °C in a humidified incubator. Chicken hindlimbs were dissected and fixed for 12 h with 4% paraformaldehyde. The terminal deoxynucleotidyl-transferase-mediated dUTP-TRIC nick-end labelling (TUNEL) assay was performed using the In Situ Cell Death Detection Kit (Roche) according to the manufacturer’s protocol. In brief, limbs and tissue sections were permeabilized at 37 °C with 2% Triton X-100 in PBS for 12 h. Antigen retrieval was performed by incubating in 0.1 M sodium citrate with 0.1% Triton X-100 in PBS at 70 °C for 30 min. TUNEL staining was conducted at 37 °C for 1 h, followed by imaging under the Zeiss LSM980 confocal microscope equipped with Zen Blue software (v.3.8). For tissue sections (Extended Data Fig. 12b,c), limbs were sliced to a thickness of 200 µm using a vibratome. Immunostaining was applied to the tissue sections (see the ‘Immunostaining of chicken limbs’ section below), followed by double-labelling with TUNEL.

Immunostaining of chicken limbs

Chicken limbs were fixed for 12 h with 4% paraformaldehyde at the indicated embryonic stages. For immunostaining of tissue sections, limbs were sliced to a thickness of 200 µm using a vibratome. Limbs and tissue sections were permeabilized with 2% Triton X-100 in PBS for 12 h. After permeabilization, blocking was conducted for 8 h with 10% FBS in PBS, followed by incubation with primary antibodies for 2 days, and with secondary antibody for 12 h in 2% Triton X-100 in PBS with 20% DMSO, unless otherwise specified. Whole-mount immunostaining of 4-HNE (Fig. 5c) was done in 0.5% Triton X-100 in PBS. All of the incubation steps were performed at room temperature. Imaging was conducted under the Zeiss LSM980 confocal microscope.

The following primary antibodies were used: anti-4-HNE (1:250, Abcam, ab46545; and 1:250, Abcam, ab48506) and anti-myosin heavy chain (2 µg ml−1, Developmental Studies Hybridoma Bank, MF20). The following secondary antibodies were used: goat anti-rabbit IgG Alexa Fluor Plus 488, goat anti-mouse IgG Alexa Fluor Plus 647 and goat anti-rabbit IgG Alexa Fluor 568 (1:500, Invitrogen, A32731, A32728, A11036). The results of 4-HNE staining using anti-4-HNE (Abcam, ab46545) antibody was further validated with an additional anti-4-HNE antibody (Abcam, ab48506) in 2% Triton X-100 in PBS with 1% FBS.

Time-lapse imaging of cell death in chicken limb ex vivo

Chicken limbs were dissected at stage HH31 and cultured in DMEM/F12 with 5% FBS and siR-DNA (5 μM). For imaging purposes, limbs were anchored to the bottom of the glass-bottomed plate (Mattek, P24G-1.0-13-F) by applying silicon glue (Picodent Twinsil, 13001000) to the farthest proximal region of the limb. To further stabilize the limb, we added a cover glass above the distal region of the limb. Time-lapse imaging experiments were performed using a Zeiss LSM980 confocal microscope or a Zeiss Axio Observer 7 inverted microscope under controlled temperature (35 °C), atmosphere (5% CO_2) and humidity (90–100%). Cell death was detected as increased fluorescence signal of siR-DNA dye, as described above. Images were taken every 1.5 h with a 639 nm laser (LSM980) and Cy5 filter set (Axio Observer 7) using the Zeiss ×10/0.45 M27 Plan-Apochromat objective.

In ovo injection of ferroptosis inhibitor UAMC-3203

In ovo injection into the amniotic sac was conducted on stage HH30 embryos. Egg candling was performed to locate the target amniotic sac, and a hole was created using a 26 G 1/2 needle. Solutions of UAMC-3203 (4 mM) and DMSO (vehicle control) were prepared in saline (0.9% sodium chloride), and 120 µl of either solution was injected into the amniotic sac using a 26 G 1/2 needle. The success rate (>90%) of in ovo injection into the amniotic sac was determined by trial injections of a food dye solution. Chicken limbs were dissected at stage HH33 and fixed with 4% paraformaldehyde, before undergoing immunostaining.

PALP assay

PALP assays[44] were performed using the Zeiss LSM980 confocal microscope equipped with Chameleon Ultra (Coherent) for two-photon laser excitation. Specific circular areas (98 μm^2) were stimulated by an 800 nm two-photon laser at 5% power output with eight iterations (scan speed = 7 fps; pixel time = 0.51 μs). To acquire images for C11-BODIPY fluorescence, 488 nm and 561 nm lasers were used.

Computation of the travelling distances of diffusive molecules

The travelling distances for diffusive molecules in Fig. 1e were calculated using the diffusion equation \(d=\sqrt{2Dt}\), where d is the distance, D is the diffusion coefficient and t is time (D of calcium = 200 µm^2 s−1 and D of a small globular protein = 10 μm^2 s−1).

Summary of image processing and data analysis

The image preprocessing procedures—including flatfield correction, ratiometric image calculation, image alignment and stitching of tiled images—were implemented using ImageJ (v.1.54 f). For image presentation, raw images were median-filtered with a circular area of 20 μm^2.

Wave outlines, representing the boundaries of wave propagation, were generated by identifying the top 1% of the image’s fluorescence intensity by image binarization, followed by pixel dilation and mean filtering to visualize wavefronts of cell death (Fig. 1b,c), lipid peroxidation (Fig. 1g,h) and ROS (Fig. 2a and Extended Data Fig. 4a,b). The wavefronts of lipid peroxidation and ROS represent the increase in their signals, obtained by the fluorescence intensity difference between two consecutive images along time. Size filters were applied to exclude debris from analysis.

Fluorescence intensities of cell death, lipid peroxidation (Fig. 1f (bottom)) and cellular ROS (Extended Data Fig. 4a (bottom)) across space were obtained by calculating the mean signal intensities along the indicated distance with width (250 μm), and normalizing to their minimum and maximum intensities. The same procedure was done for quantifying ROS across space in Fig. 2a, but without signal normalization. The mean fluorescence intensities were smoothed with a window size of 50 μm.

To analyse the spatial and temporal patterns of cell death events, vector fields (Extended Data Fig. 1c) were constructed from the cell death outlines described above. The vectors, which indicate the directionality between death events, were generated based on two consecutive cell death outlines using the gradient function in MATLAB (v.R2023b). As an index to quantify spatial and temporal associations between death events, we computed the entropy of the vectors’ angle distribution along different directions from an initial cell death event. Specifically, a distribution of vectors along a specific direction was obtained from the initial death event to the next successive death events across 3 h within its neighbourhood (typically 100 μm × 350 μm). The entropy of the vectors was calculated as \(H=-{\sum }_{i=1}^{30}{p}_{i}{\log }_{2}{p}_{i}\), where pi is the frequency that the angle of vector falls in the ith bin (30 bins in 360°).

Kymographs were generated using an array of cropped images of the fluorescent signals (cell death, lipid peroxidation, ROS or iron) along the direction of wave propagation (y axis). For each cropped image, the value for each pixel on the y axis represents the maximum signal along its width (250 μm). This operation was repeated on each cropped image for all timepoints, and the intensity lines were stacked along the x axis. For speed measurement, we first marked the earliest time for each position on the y axis that the threshold of fluorescence signals was reached (defined as the top 10% of the signal intensity for the kymograph). This yielded a distribution of spatial locations representing the wavefront for each timepoint. The top 11 locations closest to the mode of the distribution at each timepoint were used to estimate the wave speed using the polyfit function in MATLAB.

To characterize the ferroptosis initiation sites (Extended Data Fig. 2), a threshold-based segmentation was initially applied to binarized bright-field images to identify dead cells. We define a ferroptosis initiation site as the area where: (1) >5 dead cells are initially found within a circle of 40 μm radius, and (2) the cell death area increases (less than 20-fold) over time. All automatically identified initiation sites were manually inspected to eliminate false-positive sites (such as debris). In total, 761 ferroptosis initiation sites across 756 positions (area of 1.26 × 1.26 mm^2) were identified over a 5 h period. The distribution of the number of ferroptosis initiation sites was then compared with a Poisson distribution with the same mean (1.01 initiation events per 5 h) with P = 1 using a two-sample Kolmogorov–Smirnov test. To examine the distribution of time interval between two consecutive initiation events, we randomly combined the 756-time series of ferroptosis initiation events to calculate the time interval distribution. When compared with a geometric distribution with the same mean (5.16 h), we determined a P = 0.23 using a two-sample Kolmogorov–Smirnov test.

The dose–response curves in Fig. 3j–n were obtained by fitting the data to a Michaelian inhibition function, \(y={y}_{0}+(\,{y}_{M}{-y}_{0})\frac{K}{K+x}\), for DFO, LY294002 and GKT137831; to a Michaelian activation function, \(y={y}_{0}+(\,{y}_{M}{-y}_{0})\frac{x}{K+x}\), for FC; and to a biphasic inhibition function, \(y={y}_{0}+{y}_{M}\left(1-{f}_{1}\frac{x}{{K}_{1}+x}-(1-{f}_{1}\,)\frac{x}{{K}_{2}+x}\right)\), for dasatinib[49], where y is the trigger wave speed, x is the drug concentration, and all of the other parameters are determined by model fitting.

In Fig. 4c, the ROS levels in individual cells were quantified 1 h before and after photoinduction using a semi-automatic cell tracking program, as described previously[41]. The slope of increased ROS was computed as an estimate for the ROS steady state.

The mean intensities of ROS and iron dye fluorescence (Extended Data Fig. 11c,e) were quantified in whole cells by nuclear segmentation using the ImageJ StarDist plugin, followed by nuclear dilation 7 pixels from the nuclear border. Size filtering was applied to exclude incorrect segmentation and dividing cells.

The widths and amplitudes of a ROS wavefront (Fig. 4f,g) were measured as the distances and the maximal intensities of ROS signal between two consecutive ROS outlines in multiple directions.

Maximum intensity projections of confocal (Fig. 5a–d,g,h,j and Extended Data Fig. 12) and epifluorescence (Fig. 5e) image stacks were obtained using the ImageJ Stack Focuser plugin. The margins of the limbs were outlined by threshold-based segmentation.

To quantify the co-localization of degenerating muscles (myosin heavy chain-positive and rounded cells) with 4-HNE, the images were initially binarized on the basis of the thresholds of their respective backgrounds, which enabled identification of signal-positive regions. The binary images of degenerating muscles were then used to construct 3D objects using the regionprops3 function in MATLAB, followed by dilation (3 μm from the object). The percentage of 4-HNE colocalization with the degenerating muscles was calculated as the ratio of the number of 4-HNE-positive degenerating muscles to the total number of degenerating muscles.

For the images in Fig. 5j, 3D median filter and background subtraction was applied using ImageJ. To further remove the background signals, threshold-based 3D surface segmentation was performed in Imaris (v.10.0.1). To quantify the numbers of muscle fibres and their orientations (Extended Data Fig. 12i,j), we better visualized the fibre structures by applying a tubeness filter (ImageJ), followed by ridge detection (ImageJ) to identify individual muscle fibres. The muscle fibre count (Extended Data Fig. 12i) represents the total number of detected fibres across all z stacks encompassing the ventral foot muscles. The muscle fibre orientations were determined using the ImageJ OrientationJ plugin and are colour coded in Fig. 5j. To quantify the entropy of the fibre orientations, fibres within a 900 μm^2 area were considered, and the entropy of the fibre orientations was calculated as \(H=-{\sum }_{i=1}^{90}{p}_{i}{\log }_{2}{p}_{i}\), where pi is the frequency with which the fibre orientation falls in the ith bin (90 bins from 0° to 180°).

In Fig. 5h,i, lipid peroxidation was quantified using the following formula: \(\frac{{\rm{O}}{\rm{x}}{\rm{i}}{\rm{d}}{\rm{i}}{\rm{z}}{\rm{e}}{\rm{d}}\,{\rm{C}}11-{\rm{B}}}{{\rm{O}}{\rm{x}}{\rm{i}}{\rm{d}}{\rm{i}}{\rm{z}}{\rm{e}}{\rm{d}}\,{\rm{C}}11-{\rm{B}}+{\rm{r}}{\rm{e}}{\rm{d}}{\rm{u}}{\rm{c}}{\rm{e}}{\rm{d}}\,{\rm{C}}11-{\rm{B}}}\). The mean intensities of lipid peroxidation were quantified at the laser target sites before and after photoinduction.

The wave speed in Fig. 5e was measured by generating kymographs as described above. To quantify the area of cell death in Fig. 5f, a threshold was applied to identify the area of cell death, which was normalized to the total limb area. For image presentation in Fig. 5e, the debris outside the limb were threshold-filtered followed by manual removal.

Computational modelling of ROS trigger waves

We developed a reaction–diffusion model to model propagation of ROS trigger waves. Two types of ROS feedback loops were considered, that is, positive-feedback loops (through NOX and Fenton loops) and a double-negative feedback loop (through GSH). For simplicity, we modelled the two positive-feedback loops as a single term. The resulting partial differential equation is:

$$\begin{array}{c}\frac{{\rm{\partial }}{\rm{R}}{\rm{O}}{\rm{S}}}{{\rm{\partial }}t}=D\frac{{{\rm{\partial }}}^{2}{\rm{R}}{\rm{O}}{\rm{S}}}{{\rm{\partial }}{x}^{2}}+\mathop{\bar{{k}_{{\rm{p}}{\rm{o}}{\rm{s}}{\rm{i}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}\frac{{{\rm{R}}{\rm{O}}{\rm{S}}}^{{n}_{{\rm{p}}{\rm{o}}{\rm{s}}{\rm{i}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}}}{{\rm{E}}{\rm{C}}{50}_{{{\rm{p}}{\rm{o}}{\rm{s}}{\rm{i}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}^{{n}_{{{\rm{p}}{\rm{o}}{\rm{s}}{\rm{i}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}}+{{\rm{R}}{\rm{O}}{\rm{S}}}^{{n}_{{{\rm{p}}{\rm{o}}{\rm{s}}{\rm{i}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}}}}}\limits^{\text{positive-feedback loop}}\,-\mathop{\overline{{k}_{{\rm{d}}-{{\rm{n}}{\rm{e}}{\rm{g}}{\rm{a}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}\left({c}_{{\rm{G}}{\rm{S}}{\rm{H}}}+\frac{{{\rm{E}}{\rm{C}}50}_{{\rm{e}}{\rm{r}}{\rm{a}}{\rm{s}}{\rm{t}}{\rm{i}}{\rm{n}}}}{{{\rm{E}}{\rm{C}}50}_{{\rm{e}}{\rm{r}}{\rm{a}}{\rm{s}}{\rm{t}}{\rm{i}}{\rm{n}}}+E}\right)\frac{EC{50}_{{\rm{d}}-{{\rm{n}}{\rm{e}}{\rm{g}}{\rm{a}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}^{{n}_{{\rm{d}}-{{\rm{n}}{\rm{e}}{\rm{g}}{\rm{a}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}}}{{{\rm{E}}{\rm{C}}50}_{{\rm{d}}-{{\rm{n}}{\rm{e}}{\rm{g}}{\rm{a}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}^{{n}_{{\rm{d}}-{{\rm{n}}{\rm{e}}{\rm{g}}{\rm{a}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}}+{{\rm{R}}{\rm{O}}{\rm{S}}}^{{n}_{{\rm{d}}-{{\rm{n}}{\rm{e}}{\rm{g}}{\rm{a}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}}}{\rm{R}}{\rm{O}}{\rm{S}}}}\limits^{\text{double-negative-feedback loop}}\\ \,\,-{k}_{{\rm{d}}{\rm{e}}{\rm{g}}}{\rm{R}}{\rm{O}}{\rm{S}}+{k}_{{\rm{s}}{\rm{y}}{\rm{n}}{\rm{t}}{\rm{h}}}\end{array}$$

where ROS denotes the cellular ROS level and E denotes the erastin concentration. The parameters were as follows: D = 178 μm^2 min−1, \({k}_{{{\rm{p}}{\rm{o}}{\rm{s}}{\rm{i}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}\) = 1.2 μM min−1, \({{\rm{E}}{\rm{C}}50}_{{{\rm{p}}{\rm{o}}{\rm{s}}{\rm{i}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}\) = 1 μM, \({n}_{{{\rm{p}}{\rm{o}}{\rm{s}}{\rm{i}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}\) = 3, \({k}_{{\rm{d}}-{{\rm{n}}{\rm{e}}{\rm{g}}{\rm{a}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}\)= 1.5 min−1, cGSH = 0.1, EC50erastin = 0.27μM, \({{\rm{E}}{\rm{C}}50}_{{\rm{d}}-{{\rm{n}}{\rm{e}}{\rm{g}}{\rm{a}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}\) = 2 μM, \({n}_{{\rm{d}}-{{\rm{n}}{\rm{e}}{\rm{g}}{\rm{a}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}\) = 3, kdeg = 0.26 min−1 and ksynth = 0.1 μM min−1.

The ROS positive and double-negative feedback loops were modelled as hyperbolic functions with Hill coefficients (\({n}_{{{\rm{p}}{\rm{o}}{\rm{s}}{\rm{i}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}\) and \({n}_{{\rm{d}}-{{\rm{n}}{\rm{e}}{\rm{g}}{\rm{a}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}\)) equal to 3. The impact of erastin on the ROS double-negative feedback loop follows a hyperbolic response, with EC50erastin = 0.27 μM. cGSH represents the basal production of GSH independently of cystine import.

In our simulations, the diameter of a cell was assumed to be 16 µm with an intercellular distance of 5 µm. To simulate the initiation of ROS trigger waves, we allowed all cells to first reach their lower ROS steady states as a function of erastin concentration. A local ROS elevation (photoinduction area = 0.2 mm^2) was simulated to surpass the USS threshold, mimicking blue light irradiation in the initiating cells (Fig. 4a (blue arrow)). When the USS threshold was surpassed, ROS underwent a bistable switch to its higher steady state (Fig. 4a (red arrow)). We defined the ROS threshold for cell death to be 90% of the higher steady state. After reaching this ROS threshold for 30 min, ROS production was stopped by setting \({k}_{{{\rm{p}}{\rm{o}}{\rm{s}}{\rm{i}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}\), \({k}_{{\rm{d}}-{{\rm{n}}{\rm{e}}{\rm{g}}{\rm{a}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}-{\rm{f}}{\rm{b}}}_{}}\) and ksynth to be zero, representing cell death.

Reproducibility and statistical analysis

All experiments were independently performed at least three times with similar results. Technical repeats were performed in independent wells, and all the data from technical repeats are consistent across different biological replicates. The number of biological replicates is indicated in the figure legend as n. Details of statistical testing can be found in the corresponding figure legends. Wilcoxon rank-sum tests and the two-sample Kolmogorov–Smirnov tests were conducted in MATLAB. The wave propagation probability (Fig. 2b) was obtained by fitting the data to a logistic model using the fitglm function in MATLAB. The dose–response curves (Fig. 3j–n) were obtained by fitting the data to hyperbolic functions (indicated in the Methods above) using the nlinfit function in MATLAB.

Data reporting

No statistical method was used to predetermine sample size. For the animal experiment in Fig. 5f, the left and right limbs of an individual animal were randomly allocated in control and experimental groups. For Fig. 5j and Extended Data Fig. 12h,i, animals were randomly allocated in control and experimental groups. Investigators were not blinded during data collection. However, data quantification was performed automatically using computational algorithms as described.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All data are available in the Article and its Supplementary Information. Microscopy data have been deposited at Figshare[52] (https://doi.org/10.6084/m9.figshare.25762806). Additional supporting microscopy data are available from the corresponding author on request, without any restrictions. Source data are provided with this paper.

Code availability

Custom codes used for mathematical simulations and imaging analyses are available at GitHub (https://github.com/imb-lcd/ftw2024).

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Acknowledgements

We thank the members of our laboratory, Y.-C. Hsu, M.-R. Wu and J.-Y. Leu for discussions; J.-A. Chen, E. S. Liau, J.-H. Wei and Y.-C. Liu for their assistance with embryo experiments; and S.-P. Lee for her technical support with imaging. We acknowledge the funding support from Academia Sinica (AS-GCS-111-L04 and AS-GCP-113-L02) to S.-h.C., and the National Center for Theoretical Sciences, Physics Division, for the Postdoctoral Fellowship to C.-C.W.

Author information

Author notes

1. These authors contributed equally: Hannah K. C. Co, Chia-Chou Wu

Authors and Affiliations

1. Molecular and Cell Biology, Taiwan International Graduate Program, Academia Sinica and Graduate Institute of Life Science, National Defense Medical Center, Taipei, Taiwan

Hannah K. C. Co & Sheng-hong Chen

2. Laboratory for Cell Dynamics, Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan

Hannah K. C. Co, Chia-Chou Wu, Yi-Chen Lee & Sheng-hong Chen

3. National Center for Theoretical Sciences, Physics Division, Taipei, Taiwan

Chia-Chou Wu & Sheng-hong Chen

4. Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei, Taiwan

Sheng-hong Chen

Contributions

S.-h.C. and H.K.C.C. conceptualized the project. S.-h.C. and H.K.C.C. designed the experiments. S.-h.C., H.K.C.C. and Y.-C.L. conducted the experiments and analysed the data. C.-C.W. developed computational tools, and performed data analysis and simulations with intellectual inputs from S.-h.C. and H.K.C.C.; S.-h.C. and H.K.C.C. wrote the paper with input from all of the authors. S.-h.C. supervised and acquired funding for the research project.

Corresponding author

Correspondence to Sheng-hong Chen.

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Competing interests

The authors declare no competing interests.

Peer review

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Nature thanks Scott Dixon and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Ferroptosis propagates across different cell types.

a, Time-lapse images showing cell rupture (bright-field) co-occurring with an increase in nuclear dye fluorescence (cyan to white), followed by an increase in sytox green (orange) after erastin (10 μM) treatment. b, Single-cell quantification of nuclear fluorescence and sytox green signal upon ferroptosis. Data represent median and interquartile range for 20 cells. c, Vector field of cell death representing directionality of death events was generated from cell death contours (1). Each vector and each area bounded by contours are colour-coded for a specific time-point. The angles of the vectors along a specific direction (i.e., 0°) from the initial death event are computed (2). The distribution of vector angles is shown in the polar histogram (3). Entropy (H) is calculated as an index for the randomness of the vector angles (Methods). d-i, Spatial and temporal analysis of cell death events in RPE-1 cells after treatment with erastin (d, e), RSL3 (f, g), or staurosporine (h, i). d, f, h, Time-lapse images of cell death in RPE-1 cells treated with erastin (10 μM) (d), RSL3 (0.15 μM) (f) or staurosporine (0.15 μM) (h). Shown are bright-field and nuclear dye fluorescence images overlaid with cell death contours (orange outlines). e, g, i, Upper panel: vector field of cell death. Lower panel: polar histogram with its entropy (H). j, Spatial and temporal analysis of cell death in RPE-1 cells induced by different chemicals: from left to right, ferroptosis inducers BSO (8 mM) + FC (1 mM), cystine starvation (- Cys2), sodium iodate (12.5 mM); and the intracellular calcium inducer ionomycin (2.5 μM). Upper panels: vector fields of cell death. Lower panels: example polar histogram of cell death vectors. k, l, Spatial and temporal analysis of erastin (10 μM)-induced ferroptosis in 16 different cell lines (k), and RSL3 (0.15 μM)-induced ferroptosis in U-2 OS and A549 cells (l). Upper panels: vector fields of cell death. Lower panels: example polar histogram of cell death vectors. m, Entropy calculation of cell death vector fields in (e-l). Data represent mean ± s.d. (18 angles from three cell death areas for each condition). The entropies of staurosporine- and ionomycin-induced cell death in RPE-1 cells, and RSL3-induced cell death in U-2 OS and A549 cells are significantly different from other conditions. Significance was tested by two-sided Wilcoxon rank-sum tests (* FDR-adjusted P < 1 × 10−5). Representative time-lapse movies for this experiment are shown in Supplementary Video 1. Scale bars, 100 (a, A549 and U-2 OS in k), and 200 (d-l) μm.

Source Data

Extended Data Fig. 2 Ferroptosis initiation is a random process.

a, A representative image of RPE-1 cells cultured in RPMI media + 5% FBS, followed by erastin (10 μM) treatment for 7 h. Orange crosses indicate sites of ferroptosis initiation events. Scale bar, 500 μm. b, Time series of ferroptosis initiation events from 756 positions. Time interval between frames is 50 min. c, Distribution of the number of initiation events identified over 5 h with its fit to a Poisson distribution (mean = 1.01 initiation events in 5 h, two-sample Kolmogorov-Smirnov test P = 1, indicating no significant difference from the Poisson distribution). d, Distribution of the time interval between two consecutive initiation events with its fit to a geometric distribution (mean = 5.16 h, two-sample Kolmogorov-Smirnov test P = 0.23, indicating no significant difference from the geometric distribution). e, f, Distributions of single-cell iron (e) and ROS (f) levels in RPE cells after 8 h of erastin treatment with their fit to the logistic distribution. The two-sample Kolmogorov-Smirnov test P are 0.35 (e) and 0.51 (f). g, h, Adjusting the concentrations of transferrin and FBS reduces the number of ferroptotic initiation sites. Number of initiation sites (left panel) and timing of initiation (right panel) after erastin (10 μM) treatment as a function of FBS (g) and transferrin (h) concentrations. Cells were seeded in RPMI media for experiments in (g). For experiments in (h), cells were seeded in DMEM/F12 supplemented with different transferrin concentrations without FBS, followed by media-change to RPMI with 0.15% FBS one day after seeding. Data represent mean ± s.d. from at least four technical repeats. All experiments were independently repeated three times with similar results.

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Extended Data Fig. 3 Blue light irradiation elevates cellular ROS levels, and causes ferroptosis cell death and its propagation in erastin-treated cells.

a, A cartoon illustrating a cell-based assay for long-distance measurement of ferroptosis propagation (Methods). RPE-1 cells were treated with erastin prior to blue light (432 nm) irradiation. A local area (the photoinduction area marked with a red circle, ~0.2 mm^2) was irradiated with blue light to initiate ferroptosis at a desired time and location. b-d, The area of the red circles in (b) and (d) was irradiated with blue light (432 nm, 60 mW) 8 h after erastin (10 μM) treatment. b, c, Time-lapse images of ROS (yellow) and cell death (nuclear dye, cyan) after photoinduction in (b), and after local addition of H_2O_2 (80 µM, 2 µL) (approximately within the area indicated by the red circles) in (c). d, Chemical inhibitors of ferroptosis (DFO, 200 μM; Lip-1, 30 nM; Fer-1, 60 nM), necroptosis (Nec-1, 10 μM), and apoptosis (Z-VAD-FMK, 10 μM) were added before photoinduction. Cell death was monitored for 3 h after photoinduction using nuclear dye. e, Time-lapse image sequences of ROS (yellow) in erastin-treated cells before and after photoinduction with different wavelengths of light (358 nm, 432 nm, 495 nm, 509 nm, 550 nm, 587 nm, 646 nm). f, Quantification of ROS at the photoinduction area in (e). Data represent mean ± s.d. of four wells. g, Speeds of ferroptosis propagation initiated by different wavelengths of light. Data represent mean ± s.d. from four wells with more than two directions calculated for each. h, Time-lapse image sequences of ROS (yellow) in erastin-treated cells before and after photoinduction with 432 nm light of different durations (0.6, 1.25, 2.5, 5, 10 sec). i, Quantification of ROS at the photoinduction area in (h). Data represent mean ± s.d. of four wells. j, Speeds of ferroptosis propagation initiated by different durations of light exposure (432 nm). Data represent mean ± s.d. from three wells with more than two directions calculated for each. k, Kymographs of cell death propagation in erastin-treated cells with or without addition of cell death inhibitors. Chemical inhibitors of ferroptosis (Fer-1, 60 nM), necroptosis (Nec-1, 10 μM), and apoptosis (Z-VAD-FMK, 10 μM) were added 2.5 h after photoinduction (white arrow). l, Speed measurements for experiments in (k). Data represent mean ± s.d. from four wells with three directions calculated for each. All experiments were independently repeated three times with similar results. Scale bars, 100 (b, h, e), 500 (c), and 200 (d) μm.

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Extended Data Fig. 4 Cellular ROS (·OH, O[2]− and H[2]O[2]) wave fronts precede ferroptosis propagation.

a, b, Cellular ROS was monitored in erastin-treated cells using a general ROS dye (CellROX) that detects ·OH, O_2− and H_2O_2. Images are derived from merging ROS (yellow) and nuclear dye fluorescence (cyan). Each yellow contour represents the border of the ROS wave front at a specific time-point. a, Upper panel: Image (8 h after photoinduction) overlaid with ROS contours 1-11 h after photoinduction. Lower panel: Fluorescence intensities of cell death and ROS signals were quantified across the bottom region of the image. b, Zoomed-in view of the box in (a). c, Time-lapse image array of ROS (yellow) and cell death (cyan) over 14 h. The image sequence was cropped from the same experiment in (a). d, Kymograph for ROS propagation in (a). The slopes of the yellow and white lines represent the speeds of ROS wave fronts and cell death propagation, respectively. The time-lapse movie for this experiment is shown in Supplementary Video 4. e, Kymographs of cell death propagation in erastin-treated cells after addition of ROS scavengers (Trolox, 6 µM; Tiron, 2 mM; catalase, 2000 U/mL; TEMPO, 125 µM; NAC, 15 µM) 4 h after photoinduction (white arrow). Data shown are representative of three biological repeats. Scale bars, 400 (a), and 250 (b) μm.

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Extended Data Fig. 5 The spatial coupling mechanism involves the diffusion of a type of ROS.

a, Time-lapse image sequence of ROS (yellow) in erastin-treated cells across a gap (width = 172 μm). Lower panel: the mean intensity of ROS was calculated along the 2-mm distance at specific time-points. b, Left panel: Nuclear fluorescence and bright field images 20 h after incubation with erastin-free conditioned media (C.M.), ROS scavenger-treated C.M. (Fer-1, 60 nM; Trolox, 20 µM; TEMPO, 125 µM; Tiron, 2 mM), and media after washing out erastin-containing media in a culture dish without cells (Wash Ctrl). c, Nuclear fluorescence and bright field images 12 h after incubation with C.M., the eluate and retention fractions of the centrifugally filtered C.M., and eluate of the centrifugally filtered H_2O_2-containing media. Right panels in (b, c): Cell death (%) quantified from left panel. Data represent mean ± s.d. of four wells. All experiments were independently repeated three times with similar results. Scale bars, 200 (a), and 100 (b, c) μm.

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Extended Data Fig. 6 Fenton-mediated ROS feedback loop is critical for ferroptosis occurrence and propagation.

a, Time-lapse image array of cellular labile iron in pseudocolor (magenta) and cell death (cyan) propagation in erastin-treated cells. Cellular labile iron was monitored using an iron dye (FeRhoNox-1). b, Kymograph for cellular labile iron propagation from the image sequence shown in (a). The slopes of the yellow and white lines represent the speed of labile iron at the wave fronts and cell death propagation, respectively. c, d, Enhancing Fenton-mediated ROS feedback loop induces wave propagation in cells gaining ferroptosis resistance at high confluency. Time-lapse images of A549 (c) and U-2 OS (d) cells at the indicated time points after erastin treatment (10 μM) with or without ferric citrate (FC, 125 μM). Cell death is indicated by increased nuclear dye fluorescence signal. The white outlines represent the boundaries of cell death areas. Experiments were independently repeated three times with similar results. Scale bar, 300 μm (c, d).

Extended Data Fig. 7 Targeting the NOX feedback loop by chemical inhibitors.

a, NOX4 is the dominant NOX isoform in RPE-1 cells. RT-qPCR was performed to quantify the relative mRNA levels of NOX1-4 in RPE-1 cells. Data represent mean ± s.d. of three technical replicates. Data shown is a representative of three biological repeats. b, Small-molecule inhibitors targeting the NOX loop do not exhibit antioxidant potential. Cell-free antioxidant potentials of Trolox (32 µM), GKT137831 (5 µM), LY294002 (100 µM), and dasatinib (10 µM) were measured as the DPPH absorbance at 517 nm relative to DMSO (vehicle control). Data represent mean ± s.d. from three wells.

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Extended Data Fig. 8 ERK2 overexpression in RPE-1 cells increases phosphorylated ERK2, NOX activity, and the speed of ferroptotic trigger waves.

a, Genetic modulation of NOX signalling by ERK2 overexpression. b, Western blot analysis of overexpressed ERK2 (ERK2-P2A) and its phosphorylated form (phospho-ERK2-P2A) with or without erastin treatment (10 μM) in ERK2-overexpressing and control RPE-1 cells. The size of ERK2-P2A is bigger than wild type ERK2 and co-migrates with ERK1 due to its fusion to P2A peptide. For blot source data, see Supplementary Fig. 1. c, Relative NOX activity measured in ERK2-overexpressing and control RPE-1 cells. Data represent mean ± s.d. with four technical repeats. NOX activity of ERK2-overexpressing cells is significantly different from that of control cells (* two-sided Wilcoxon rank-sum test P = 0.0286). d, e, ERK2 overexpression increases the speed of ferroptotic trigger waves. Kymographs representing ferroptosis propagation in control cells (d) and ERK-overexpressing cells (e). Data shown (b-e) are representative of three biological repeats.

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Extended Data Fig. 9 FSP1 inhibition increases ferroptosis wave speed.

a, Kymographs of cell death propagation in RPE-1 cells treated with erastin (left panel) or erastin + FSEN1 (0.25 μM) (right panel). b, Wave speed as a function of FSEN1 concentration. Data represent mean ± s.d. with three technical repeats. Experiments were repeated three times with similar results.

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Extended Data Fig. 10 In silico simulations of ROS trigger waves.

a, A diagram of the ROS feedback loops and parameters used to build the mathematical model. b, c, Time-course simulations of ROS levels in a 200 × 200 population of cells treated with 10 µM erastin. Photoinduction was simulated by elevating ROS levels above the unstable steady state (USS) within the area of the red circle. Scale bar, 400 μm (b). c, Cross-section of ROS kinetics along the midline in (b). d, Speed of ROS propagation as a function of erastin concentration. e, Width (left panel) and amplitude (right panel) of the ROS wave front as a function of erastin concentration.

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Extended Data Fig. 11 Erastin quantitatively modulates ROS feedback loops.

a, ROS steady state remains low regardless of photoinduction intensity in the absence of erastin treatment. Single-cell ROS steady states were measured in cells (40 cells) treated with or without erastin (10 µM) after photoinduction with different light intensities (1: 60 mW for 10 s; 2: 240 mW for 10 s; 3: 240 mW for 40 s). b-e, Cellular levels of GSH (b), labile iron (c), NOX activity (d), and ROS (e) after treatment with different erastin concentrations. Data represent mean ± s.d. (GSH: four technical replicates; labile iron: ≥ 257 cells; NOX activity: three biological repeats, with three technical repeats each; and ROS: ≥ 249 cells). Measurements are significantly different from those of untreated (0 µM) cells (two-sided Wilcoxon rank-sum tests, * FDR-adjusted P < 0.05 (b), <2 × 10−30 (c), <4 × 10−3 (d), <2 × 10−5 (e)). (f) Representative images of ROS (upper panels) and labile iron (lower panels) 8 h after different erastin treatments. Scale bar, 50 μm. All experiments were independently repeated three times with similar trends.

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Extended Data Fig. 12 Ferroptosis is involved in muscle remodelling during avian limb development.

a, Whole-mount immunostaining of myosin heavy chain (myosin) in avian hindlimb at stages HH30 and HH32 of embryonic development. Ventral views of the limbs are shown, with limb margins outlined in yellow. Lower panels: Zoomed-in views of the boxes in the upper panels. The foot and shank muscles are labelled. b-e, Longitudinal sections of stage HH33 limbs. b, Co-staining of myosin and TUNEL, showing the muscular ventral death zone (MVDZ) at the zeugopod area. c, Co-staining of TUNEL and 4-HNE at the MVDZ in (b). d, e, Co-immunostaining of myosin and 4-HNE in the foot (d) and shank (e) regions. Degenerating muscles display a rounded and beaded appearance. Data shown (a-e) are representative of three biological repeats. f, g, 4-HNE and TUNEL signals are abundant at the central region relative to the lateral regions of the limb zeugopod. Upper panels: Immunostaining of 4-HNE (f) and TUNEL staining (g) in stage HH32 limbs. Lower panels: The relative mean values of 4-HNE (f) and TUNEL (g) signal intensities were calculated along the indicated region (lateral and central, grey box 1600 × 1000 µm^2) for three biological repeats (yellow, blue, orange curves). h, The 4-HNE and TUNEL signal intensities at the central and lateral regions normalized to the total signal intensities. i, j, In ovo ferroptosis suppression impairs muscle remodelling in avian embryonic limb. Muscle fibre count (i) and distribution of entropy of the muscle fibre orientation (j) in embryonic limbs dissected from UAMC-3203-treated and vehicle control (DMSO)-treated embryos. Data represent mean ± s.d. of three biological repeats. The muscle fibre count and entropy of the muscle fibre orientations for limbs dissected from UAMC-3203-treated embryos are higher than those from the control embryos (* two-sided Wilcoxon rank-sum tests, P = 0.0495 and 0, respectively). Scale bars, 500 (upper panels in a), 300 (lower panels in a), 200 (b), 50 (c), 100 (d, e), and 400 (f, g) μm.

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Extended Data Table 1 The parameters derived from model fitting in Fig. 3j–n

Supplementary information

Supplementary Figure 1

Uncropped blot for Extended Data Fig. 8b.

Reporting Summary

Peer Review file

Supplementary Video 1

Ferroptosis spreads across cells in different cell types. Ferroptosis propagates across cell populations of 15 different cell lines (1a), and across individual colonies of A549 and U-2 OS cells (1b). Ferroptotic cell death is shown as an increase in nuclear dye fluorescence (cyan to white). Scale bars, 200 μm (1a), 300 μm (1b) and 100 μm (magnified views in 1b).

Supplementary Video 2

Ferroptosis propagates across RPE-1 cells over a 5 mm distance. Ferroptotic cell death is represented by an increase in nuclear dye fluorescence (cyan to white). The photoinduction area (red circle) was irradiated with blue light to initiate ferroptosis propagation. Scale bar, 400 μm.

Supplementary Video 3

Ferroptosis propagates through lipid peroxidation wavefronts. Lipid peroxidation and cell death were monitored using C11-BODIPY581/591 dye (yellow) and an increase in nuclear dye fluorescence (cyan to white), respectively. Photoinduction (red circle) initiated ferroptosis propagation. Scale bar, 400 μm.

Supplementary Video 4

Ferroptosis propagates through cellular ROS wavefronts. Cellular ROS and cell death were monitored using CellROX dye (yellow) and an increase in nuclear dye fluorescence (cyan to white), respectively. Photoinduction (red circle) initiated ferroptosis propagation. Scale bar, 400 μm.

Supplementary Video 5

Propagation of ferroptosis across gaps of different widths. Cellular ROS and cell death were monitored using CellROX dye (yellow) and an increase in nuclear dye fluorescence (cyan to white), respectively. Intercellular gaps are indicated by the dashed lines. Scale bar, 150 μm.

Supplementary Video 6

Ferroptosis propagation decelerates after DFO addition. Ferroptotic cell death is represented by an increase in nuclear dye fluorescence (cyan to white). Photoinduction (red circle) initiated ferroptosis propagation. DFO was added 11 h after photoinduction. Scale bar, 400 μm.

Supplementary Video 7

Ferroptosis propagation accelerates after FC addition. Ferroptotic cell death is represented by an increase in nuclear dye fluorescence (cyan to white). Photoinduction (red circle) initiated ferroptosis propagation. FC was added 15 h after photoinduction. Scale bar, 400 μm.

Supplementary Video 8

Ferroptosis propagation in ERK2-overexpressing cells compared with control cells. Ferroptotic cell death is represented by an increase in nuclear dye fluorescence (cyan to white). Photoinduction (red circle) initiated ferroptosis propagation. Scale bar, 400 μm.

Supplementary Video 9

Increasing concentrations of erastin promote the propagation of ferroptotic trigger waves. Cellular ROS and cell death were monitored using CellROX dye (yellow) and an increase in nuclear dye fluorescence (cyan to white), respectively. Scale bar, 400 μm.

Supplementary Video 10

Cell death spreads across the central proximodistal axis of the embryonic avian limb. Cell death was monitored as an increase in nuclear dye fluorescence (cyan to white) in a stage HH31 limb. Scale bars, 500 μm and 200 μm (magnified view).

Supplementary Video 11

Cell death initiates from multiple sites in the embryonic avian limb. Cell death was monitored as an increase in nuclear dye fluorescence (cyan to white) in a stage HH31 limb. Scale bars, 200 μm.

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Co, H.K.C., Wu, CC., Lee, YC. et al. Emergence of large-scale cell death through ferroptotic trigger waves. Nature 631, 654–662 (2024). https://doi.org/10.1038/s41586-024-07623-6

- Received: 26 March 2023

- Accepted: 29 May 2024

- Published: 10 July 2024

- Issue Date: 18 July 2024

- DOI: https://doi.org/10.1038/s41586-024-07623-6

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NBS1 lactylation is required for efficient DNA repair and chemotherapy resistance

Hengxing Chen,

Yun Li,

Huafu Li,

Xiancong Chen,

Huafeng Fu,

Deli Mao,

Wei Chen,

Linxiang Lan,

Chunming Wang,

Kaishun Hu,

Jia Li,

Chengming Zhu,

Ian Evans,

Eddie Cheung,

Daning Lu,

Yulong He,

Axel Behrens,

Dong Yin &

…

Changhua Zhang

Nature volume 631, pages 663–669 (2024)

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Abstract

The Warburg effect is a hallmark of cancer that refers to the preference of cancer cells to metabolize glucose anaerobically rather than aerobically1,2. This results in substantial accumulation of lacate, the end product of anaerobic glycolysis, in cancer cells[3]. However, how cancer metabolism affects chemotherapy response and DNA repair in general remains incompletely understood. Here we report that lactate-driven lactylation of NBS1 promotes homologous recombination (HR)-mediated DNA repair. Lactylation of NBS1 at lysine 388 (K388) is essential for MRE11–RAD50–NBS1 (MRN) complex formation and the accumulation of HR repair proteins at the sites of DNA double-strand breaks. Furthermore, we identify TIP60 as the NBS1 lysine lactyltransferase and the ‘writer’ of NBS1 K388 lactylation, and HDAC3 as the NBS1 de-lactylase. High levels of NBS1 K388 lactylation predict poor patient outcome of neoadjuvant chemotherapy, and lactate reduction using either genetic depletion of lactate dehydrogenase A (LDHA) or stiripentol, a lactate dehydrogenase A inhibitor used clinically for anti-epileptic treatment, inhibited NBS1 K388 lactylation, decreased DNA repair efficacy and overcame resistance to chemotherapy. In summary, our work identifies NBS1 lactylation as a critical mechanism for genome stability that contributes to chemotherapy resistance and identifies inhibition of lactate production as a promising therapeutic cancer strategy.

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Main

Altered metabolism and genome instability are two hallmarks of cancer cells[1]. Cancer cells alter their nutrient uptake and utilization to fulfil their need for sustained cell survival[4]. The principle aim of radiotherapy and most chemotherapeutic drugs is to directly or indirectly cause DNA damage, leading to cell death[5]. Cancer cells must rapidly repair damaged DNA to ensure cell survival[6]. Accumulated metabolites such as 2-hydroxyglutarate, fumarate and succinate inhibit DNA repair and promote genome instability[7]. However, little is known about metabolites that promote DNA repair. It is therefore essential to uncover mechanisms of metabolism-driven DNA repair that can promote tumour survival, as these may highlight novel cancer vulnerabilities.

Lactate promotes HR-mediated DNA repair

To investigate the metabolic profiles of DNA damage responses, we combined proteomics and non-targeted metabolomics analyses on postoperative gastric cancer specimens from patients who had received platinum-based neoadjuvant chemotherapy (NAC). Nine of the enroled patients were sensitive to platinum-based NAC, and 15 were resistant (Fig. 1a and Extended Data Fig. 1a). A total of 2,347 proteins were quantified by proteomics (Supplementary Table 1). Lactate dehydrogenase (LDHA), the glycolytic enzyme that catalyses the conversion of pyruvate to lactate, was one of the top upregulated proteins in resistant tumours (Fig. 1b). Non-targeted metabolomics identified 327 metabolites (Supplementary Table 2), and lactate was one of the most abundant metabolites in resistant tumours (Fig. 1c). The combined proteomics and metabolomics analysis showed that the anaerobic glycolysis pathway was activated in resistant tumours (Fig. 1d). Compared with cisplatin-sensitive parental cells (A549-P, AGS-P, HCT116-P, HGC27-P and MGC803-P), the cisplatin-resistant cells (A549-R, AGS-R, HCT116-R, HGC27-R and MGC803-R, respectively) exhibited significantly increased extracellular acidification rate (ECAR) (Extended Data Fig. 1b–d) and lactate production (Extended Data Fig. 1e).

Fig. 1: Lactate enhances DNA repair and resistance to DNA-damaging therapy.

a, Schematic describing quantitative proteomics and non-targeted metabolomics analyses of 24 postoperative tumour specimens. b,c, Volcanic map of proteomic (b) or metabolic (c) differences between NAC-resistant and NAC-sensitive gastric cancer tissues. Red dots indicate enzymes or metabolites from the glycolytic pathway. Vertical and horizontal dashed lines indicate cutoffs of log_2 fold change (0.58 or −0.58) and P value (0.05), respectively. d, Changes in the proteins and metabolites of glycolytic pathway between NAC-resistant and NAC-sensitive gastric cancer tissues. Black dots represent metabolites that were not detected by mass spectrometry. P, phosphate; TCA, tricarboxylic acid. e,f, The growth of MGC803-P (e) and PDX-1 (f) tumours was assessed in NSG mice. g, Survival curve analysis of NSG mice transplanted with PDX-1 tumours. h, The growth of PDX-1 tumours was assessed in NSG mice. i, The growth of with wild-type or MCT1-knockdown (MCT1 is encoded by SLC16A1) MGC803-P tumours was assessed in NSG mice treated with or without lactate. All mice were treated with cisplatin. shMCT1, knockdown with short hairpin RNA targeting SLC16A1; shNC, non-targeting short hairpin RNA. e–i, n = 6. j, Dose–response curves for cisplatin in PDO1 treated with or without lactate (20 mM). k,l, AGS-P cells were treated with lactate (20 mM) for 24 h and then treated with IR (2 Gy). Cells were collected at the indicated time after IR treatment. k, Cells were lysed for immunoblotting analyses. Gel source data are presented in Supplementary Fig. 1. l, Comet assays (left) and analysis of tail moment. n = 60. Scale bars, 25 μm. m, Schematic representation of the HR reporter (left). HR repair efficiency was measured in lactate-treated and control HeLa reporter cells (right). BRCA2 overexpression was used as a positive control. j,m,n, n = 3. Data are mean ± s.d. P values by two-sided t-test (b–d,l,m) or two-way ANOVA (e,f,h,i). Log-rank test (g). In all box plots, the centre line indicates the median, box edges delineate third and first quartiles and whiskers extend to 1.5 times the interquartile range above and below the box.

Source Data

In NSG mice bearing MGC803-P xenografts or in mice with patient-derived xenografts (PDXs) derived from two cases of gastric cancer, lactate alone had no effect on tumour growth. However, lactate administration decreased the efficacy of cisplatin treatment to reduce tumour growth (Fig. 1e,f and Extended Data Fig. 2a) and shortened the survival time of the mice (Fig. 1g and Extended Data Fig. 2b). Lactate also promoted resistance to ionizing radiation (IR), another type of DNA-damaging therapy (Fig. 1h). Notably, lactate did not promote cisplatin resistance when the lactate uptake transporter MCT1 was depleted in the tumour cells (Fig. 1i and Extended Data Fig. 2c). We also validated that tumours from the lactate-treated mice contained much higher lactate levels than those from control mice (Extended Data Fig. 2d). Lactate facilitated resistance of patient-derived organoids (PDOs) from individuals with gastric cancer and of cell lines to a variety of DNA-damaging therapies, including cisplatin, etoposide, adriamycin and IR (Fig. 1j and Extended Data Figs. 2e–i and 3a–e).

DNA-damaging agents induce double-strand breaks (DSBs), which cause rapid increases in histone H2AX phosphorylation at serine 139 (referred to as γH2AX) at the DSB sites. Compared with control groups, lactate caused a significant increase in γH2AX levels (Fig. 1k and Extended Data Fig. 3f). Similar results were obtained when examining γH2AX foci through immunofluorescence assays (Extended Data Fig. 3g). Comet assays showed that tail moments of lactate-treated cells were markedly shorter than those of control cells 6 h after IR treatment (Fig. 1l). The two main DSB repair pathways are HR and non-homologous end-joining8,9 (NHEJ). To determine which of these two pathways might be regulated by lactate, we used HeLa DR-GFP and HeLa EJ5-GFP reporter assays and found that lactate significantly increased HR repair efficiency but only slightly elevated NHEJ repair efficiency (Fig. 1m and Extended Data Fig. 3h). These results suggested that lactate is mainly involved in HR-mediated DNA repair.

Lactate induces NBS1 K388 lactylation

Lactate enables lysine lactylation of proteins[10]. We next explored whether lactate promotes resistance to DNA-damaging agents through lactylation. We observed that the global levels of lactylated lysine (Kla) were significantly higher in chemo-resistant gastric cancer tissues and resistant cancer cell lines using pan-Kla antibody (Fig. 2a,b). We used UV-laser microirradiation to induce DSBs in sub-nuclear volumes. Lysine-lactylated proteins were recruited to DSB sites (Fig. 2c). To gain a global view of DNA repair-related lactylation, we used a 4D label-free proteomics analysis to explore lysine-lactylated substrates in AGS-P and AGS-R cells (Extended Data Fig. 4a and Supplementary Table 3). We identified 4,028 lysine lactylation sites across 1,603 proteins, and quantified 2,485 lysine lactylation sites in 1,140 proteins (Extended Data Fig. 4b). We found that 909 lysine lactylation sites on 543 proteins were significantly upregulated (fold change ≥ 1.5, P < 0.05), but only 8 lysine lactylation sites on 8 proteins were downregulated (Fig. 2d). We subsequently analysed the DNA repair interaction network in upregulated lysine lactylation substrates based on the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database. Among these DNA repair-related proteins, NBS1 was highlighted owing to its crucial role in sensing and repairing of DNA damage (Extended Data Fig. 4c). Crucially, lactate did not promote cisplatin resistance when NBS1 was depleted in AGS-P cells (Extended Data Fig. 4d). Thus, we focused on NBS1 lactylation.

Fig. 2: Lactate induces NBS1 K388 lactylation and TIP60 mediates NBS1 K388 lactylation.

a, IHC staining with pan-Kla antibody in postoperative tumour specimens from patients with NAC-sensitive or NAC-resistant gastric cancer. Scale bars, 50 μm. b, AGS-P and AGS-R cells were lysed for immunoblotting. c, AGS-P cells were subjected to laser microirradiation and stained with anti-pan-Kla and anti-γH2AX. Representative of n = 20 cells. Scale bars, 5 μm. d, Volcano plot showing global lysine lactylation changes in AGS-R cells compared with AGS-P cells. e, Cell lysates of AGS-P and AGS-R were immunoprecipitated with anti-NBS1 or control IgG, followed by immunoblotting. f, Identification and quantification of NBS1 K388 lactylation. LC–MS/MS analysis of modified VS(Kla)MEQK is shown. g, AGS-P and AGS-NBS1(K388R) cells were treated with lactate (20 mM) for 24 h, and whole-cell extracts were collected for immunoprecipitation with anti-NBS1 antibody, followed by immunoblotting. h, The cofactor pocket of TIP60 (PDB: 2OU2) bound to acetyl-CoA (left) and lactyl-CoA (right). TIP60 is shown in cartoon representation. The transfer group in acetyl-CoA or lactyl-CoA is indicated with red circles. i, Endogenous co-immunoprecipitation assays in AGS-P cells. j, Purified NBS1 was incubated with GST-TIP60, followed by GST pull-down assay and immunoblotting with anti-NBS1. k, AGS-P cells were treated with cisplatin (2.5 μM) for 6 h, and whole-cell extracts were collected for immunoprecipitation with the indicated antibodies, followed by immunoblot analysis. l,m, AGS-P cells were transfected with Flag–TIP60 (l) or small interfering RNA (siRNA) targeting TIP60 (siTIP60) (TIP60 is encoded by KAT5) or non-targeting siRNA (NC) and analysed by immunoblotting. 1 and 2 represent two independent small interfering RNAs. n, Left, in vitro lactylation assay showing lysine lactyltransferase activity of TIP60. Right, TIP60-mediated histone H4 acetylation served as positive control. Data are mean ± s.d. P values by two-sided Mann–Whiney test (a) or two-sided t-test (d). Gel source data are presented in Supplementary Fig. 1.

Source Data

To further confirm lactylation of NBS1, we used three approaches. First, total cellular extracts from cancer cells were immunoprecipitated with anti-NBS1 followed by immunoblotting using pan-Kla antibody. NBS1 was lactylated in parental cells and NBS1 lactylation increased in cisplatin-resistant cells (Fig. 2e and Extended Data Fig. 4e). Both lactate and cisplatin administration induced NBS1 lactylation (Extended Data Figs. 4f,g and 5a). Second, liquid chromatography–mass spectrometry (LC–MS/MS) revealed that K338 of NBS1 was lactylated (Fig. 2f). We used prime editing of the genome of AGS-P cells to replace NBS1 lysine 338 with arginine (Extended Data Fig. 5b). NBS1 protein could not be lactylated in AGS-NBS1(K388R) cells even under lactate or cisplatin stimulation (Fig. 2g and Extended Data Fig. 5a). Finally, we generated K388-specific antibodies (anti-NBS1-K388la) that specifically recognize NBS1 K388 lactylation. The specificity of anti-NBS1-K388la was verified by dot blotting and immunohistochemistry (IHC) assays (Extended Data Fig. 5c,d). NBS1 K388 lactylation was increased by addition of lactate to the medium, and was also increased in cisplatin-resistant cancer cells compared with parental cells (Extended Data Fig. 5e,f).

To investigate the possible lysine lactyltransferase that lactylates NBS1, we performed immunoprecipitation assays to capture NBS1-interacting proteins from AGS-P cell lysates using anti-NBS1 and then analysed the eluted samples by LC–MS/MS (Supplementary Table 4). In addition to classical NBS1-binding proteins including MRE11, RAD50 and TCOF111,12,13, we identified TIP60 as a novel binding partner of NBS1 (Extended Data Fig. 5g). TIP60, a member of the MYST sub-family of histone acetyltransferases, is a vital enzyme that is directly involved in early DNA repair and cell survival14,15,16. Thus, we identified TIP60 as a potential lysine lactyltransferase responsible for NBS1 lactylation. To explore this possibility, we first docked lactyl coenzyme A (lactyl-CoA) into the structure of TIP60 (Protein Data Bank (PDB): 2OU2). Lactyl-CoA was well accommodated in the cofactor pocket of TIP60, similar to the structure of the acetyl coenzyme A (acetyl-CoA)–TIP60 complex (Fig. 2h). Second, co-immunoprecipitation assays showed that NBS1 interacts with TIP60 (Fig. 2i and Extended Data Fig. 5h). Glutathione S-transferase (GST) pull-down assay demonstrated that NBS1 interacted directly with TIP60 (Fig. 2j). The interaction between TIP60 and NBS1 was increased following cisplatin treatment (Fig. 2k). Finally, NBS1 K388 lactylation was increased when TIP60 was overexpressed, whereas it was suppressed by TIP60 knockdown (Fig. 2l,m). In vitro lysine lactylation confirmed that TIP60 mediated NBS1 K388 lactylation (Fig. 2n). Together, these data indicated that TIP60 directly lactylates NBS1.

Class I histone deacetylases (HDAC1–3) have been reported to function as histone lysine delactylases[17]. Overexpression of HDAC3, but not HDAC1 or HDAC2, reduced NBS1 K388 lactylation (Extended Data Fig. 5i), whereas knockdown of HDAC3 increased NBS1 K388 lactylation (Extended Data Fig. 5j). Co-immunoprecipitation assays showed that HDAC3 interacts with NBS1 (Extended Data Fig. 5k), suggesting that HDAC3 is the de-lactylase for NBS1.

NBS1 lactylation promotes MRN complex formation

Next, we investigated the role of NBS1 K388 lactylation in the response to DNA-damaging treatment. Compared with AGS-P cells, AGS-NBS1(K388R) cells showed decreased cellular viability and increased apoptosis after cisplatin or IR treatment (Extended Data Fig. 6a–f). Further, lactate could not promote cisplatin or IR resistance in the AGS-NBS1(K388R) cells, indicating that the lactate-induced cisplatin or IR resistance is dependent on NBS1 K388 lactylation (Fig. 3a and Extended Data Fig. 6g–j). Moreover, LDHA overexpression increased resistance to cisplatin in AGS-P cells, but did not exert an additional effect on cisplatin survival in AGS-NBS1(K388R) cells (Extended Data Fig. 6k,l).

Fig. 3: NBS1 K388 lactylation facilitates DNA repair by promoting MRN complex formation.

a, Dose–response curves for IR in AGS-P and AGS-NBS1(K388R) cells treated with or without lactate (20 mM). n = 3 biologically independent samples. b, AGS-P cells and AGS-NBS1(K388R) cells were collected for comet assay at indicated times after IR treatment (10 Gy). Scale bars, 25 μm. n = 60 biologically independent cells. c, HeLa DR-GFP reporter cells were transfected with I-SceI-DsRed and control vector, Flag-tagged wild-type NBS1 (NBS1-WT) or NBS1(K388R). HR efficiency was assessed by counting GFP-positive cells. n = 3 biologically independent samples. d,e, AGS-P and AGS-NBS1(K388R) cells were treated with IR (2 Gy), and stained with anti-BRCA1 (d) or anti-RAD51 (e) 60 min after IR treatment. A cell containing ten or more foci was considered as a foci-positive cell. n = 60 cells were examined over three independent experiments. f, AGS-P and AGS-NBS1(K388R) cells were treated with lactate (20 mM) for 24 h, and whole-cell extracts were immunoprecipitated with anti-NBS1 antibody, followed by immunoblotting. g, Fractions of AGS-P or AGS-NBS1(K388R) cells were separated by sucrose gradient centrifugation. Indicated proteins were quantified using ImageJ. h, AGS-P cells were treated with cisplatin (2.5 μM) for 0.5, 1, 2 or 4 h. Chromatin and soluble fractions were isolated for immunoblotting. i, eGFP–NBS1-WT or eGFP–NBS1(K388R) were transfected into AGS-P cells. At 18 h post-transfection, cells were treated with or without lactate for 12 h. Cells were then laser micro-irradiated and monitored by live-cell microscopy. Accumulation of eGFP–NBS1-WT or eGFP–NBS1(K388R) on the DNA damage tracks was quantified. n = 20 biologically independent cells. Data are mean ± s.d. P values by two-sided t-test (b–e). Gel source data are presented in Supplementary Fig. 1.

Source Data

We further investigated the potential role of NBS1 lactylation in DNA damage response. Levels of γH2AX were lower in AGS-NBS1(K388R) cells compared with AGS-P cells after IR or cisplatin treatments (Extended Data Fig. 6m,n). Comet assay showed that tail moments of AGS-P cells were markedly shorter than those of AGS-NBS1(K388R) cells 7 h after IR treatment, although there was little difference 0.5 h after IR treatment (Fig. 3b). NBS1 is responsible for activating HR repair[18]. Whereas overexpression of wild-type NBS1 significantly increased HR repair efficiency, overexpression of NBS1(K388R) had no apparent effect on HR repair efficiency (Fig. 3c). Formation of BRCA1 and RAD51 foci—key steps in HR repair5,19—were significantly decreased after IR treatment in AGS-NBS1(K388R) cells compared with AGS-P cells (Fig. 3d,e and Extended Data Fig. 6o,p).

NBS1 forms the trimeric MRN complex with MRE11 and RAD50. The MRN complex has a key role in sensing DSBs and activating the DNA repair pathway18,20,21. We investigated whether NBS1 lactylation affects the formation of MRN complex. Co-immunoprecipitation assays revealed that after cisplatin treatment, the interaction between MRE11–RAD50 and NBS1 was strongly decreased in AGS-NBS1(K388R) cells (Extended Data Fig. 7a,b). To investigate the molecular mechanisms underlying an essential role of NBS1 K388 lactylation in HR repair, we used crosslinking mass spectrometry (CLMS) (Extended Data Fig. 7c). NBS1(K388R) purified from AGS-NBS1(K388R) cells exhibited a different crosslinking pattern compared with the wild-type NBS1 protein, suggesting that the K388R mutation alters the conformation of NBS1 (Extended Data Fig. 7d). We identified several crosslinking sites between NBS1 and MRE11 in AGS-P cells, including the amino acid residues located at the MRE11-binding domain region of NBS1 and at the NBS1-binding domain region of MRE11. However, these crosslinking sites between NBS1 and MRE11 were not observed in AGS-NBS1(K388R) cells (Extended Data Fig. 7d). Specifically, NBS1 K388 is one of these crosslinking sites, indicating it is located on the interaction interface (Extended Data Fig. 7d).

Lactate promoted the interaction between MRE11–RAD50 and NBS1 in AGS-P cells, but not in AGS-NBS1(K388R) cells (Fig. 3f and Extended Data Fig. 7e). We performed in vitro TIP60-mediated lactylation assay of NBS1 in the presence or absence of lactyl-CoA, and subsequently, bio-layer interferometry assays to measure the interaction between lactylated NBS1 and MRE11. When lactyl-CoA was present and NBS1 was lactylated, the lactylated NBS1 formed a direct interaction with MRE11. However, in the absence of lactyl-CoA, NBS1 did not interact with MRE11 (Extended Data Fig. 7f).

The interaction between MRE11–RAD50 and NBS1 were facilitated by TIP60 overexpression (Extended Data Fig. 7g). TIP60 is known to stimulate ATM kinase activity by acetylating ATM. Knockdown of ATM or administration of an ATM inhibitor (KU-55933) decreased TIP60-induced NBS1 K388 lactylation and interaction between MRE11–RAD50 and NBS1 (Extended Data Fig. 7h,i). Furthermore, sucrose gradient analysis of extracts of AGS-P cells and AGS-NBS1(K388R) cells showed that most MRE11–RAD50 cosedimented with NBS1 in AGS-P cells, whereas fractions containing MRE11–RAD50 and NBS1 almost completely mutually exclusive in AGS-NBS1(K388R) cells (Fig. 3g).

The MRN complex is one of the earliest factors to be recruited to DSB sites21,22. We investigated whether NBS1 lactylation regulates the recruitment of MRN complex to DSB sites. Enrichment of MRE11, RAD50 and NBS1 in the chromatin fraction after cisplatin treatment was markedly decreased in AGS-NBS1(K388R) cells compared with wild-type cells (Fig. 3h). The proportion of MRE11, RAD50 and NBS1 foci-positive cells were all significantly reduced in AGS-NBS1(K388R) cells after IR treatment (Extended Data Fig. 8a–c). We also monitored the localization kinetics of MRE11 and RAD50 in response to laser-induced DNA damage in both AGS-P and AGS-NBS1(K388R) cells. Recruitment of eGFP–MRE11 and eGFP–RAD50 to DSB sites were delayed in AGS-NBS1(K388R) cells (Extended Data Fig. 8d). In addition, NBS1(K388R) was recruited to DSB sites much more slowly than wild-type NBS1 (Fig. 3i and Extended Data Fig. 8e). Moreover, lactate accelerated the recruitment of wild-type NBS1 to DSB sites, but had no such effect on NBS1(K388R) (Fig. 3i and Extended Data Fig. 8e). Overall, these data indicate that NBS1 lactylation promotes MRN complex formation and recruitment of HR proteins to DSB sites.

Lactate deprivation disrupts DNA repair

On the basis of the above findings, we speculated that inhibition of lactate metabolism would disrupt DNA repair. LDHA predominantly catalyses pyruvate reduction to lactate. MCT1 is responsible for the transportation of lactate from the tumour microenvironment into tumour cells3,23. We observed that the amount of LDHA and NBS1 K388 lactylation, but not the amount of MCT1, were increased in cisplatin-resistant cells (Extended Data Fig. 8f). CRISPR-mediated LDHA knockout reduced lactate production and NBS1 K388 lactylation (Extended Data Fig. 9a). Sodium oxamate and stiripentol are distinct LDHA inhibitors. Both sodium oxamate and stiripentol significantly reduced lactate production and NBS1 K388 lactylation (Extended Data Fig. 9b,c). Comet assay showed that LDHA knockdown resulted in defective DNA repair in AGS-P cells, but not in AGS-NBS1(K388R) cells (Extended Data Fig. 9d). These findings suggested that LDHA inhibition suppresses NBS1 K388 lactylation and disrupts DNA repair.

Stiripentol has been used clinically as an anti-epileptic treatment24,25. We examined whether stiripentol could render resistant cancer sensitive to DNA-damaging treatment. We established five PDOs from patients with chemotherapy-naive gastric cancer (Extended Data Fig. 9e) and tested them for cisplatin sensitivity. PDO4 and PDO5 were intrinsically resistant to cisplatin compared with PDO1–3 (Extended Data Fig. 9f). We therefore further analysed PDO4 and PDO5. The combination of stiripentol and cisplatin was highly synergistic in both PDO4 and PDO5 (Fig. 4a). In NSG mice bearing MGC803-R xenografts or chemo-resistant gastric cancer PDX (PDX-R), combined stiripentol and cisplatin or IR also elicited marked tumour regression (Fig. 4b,c and Extended Data Fig. 9g). The combined stiripentol and cisplatin or IR treatments were well tolerated (Extended Data Fig. 9h). Moreover, combined stiripentol and cisplatin or IR prolonged the survival of NSG mice bearing PDX-R (Fig. 4d,e).

Fig. 4: Stiripentol overcomes resistance to DNA-damaging treatment and LDHA expression and NBS1 K388 lactylation were increased in NAC-resistant tumours.

a, Indicated PDOs were treated as indicated for 72 h and analysed for cell viability by ATPlite assay. Synergy graphs were generated with Combenefit (Loewe model). b,c, The growth of PDX-R tumours was assessed in mice treated with: control (saline), cisplatin (2 mg kg−1, once a week) (b) or IR (2 Gy per fraction, once daily for consecutive 4 days per week) (c) alone or combined with stirtpentol (150 mg kg−1, once daily for consecutive 5 days per week). d,e, Survival curve analysis of NSG mice transplanted with chemo-resistant PDX tumours. b–e, n = 6. f, Top, representative IHC staining of LDHA and NBS1 K388 lactylation in NAC-sensitive or NAC-resistant gastric cancer tissues. Bottom, quantification of IHC staining of LDHA and NBS1 K388la in NAC-sensitive and NAC-resistant gastric cancer tissues. g, Correlation between LDHA expression and NBS1 K388 lactylation in gastric cancer tissues from 94 patients who received NAC. Note that some dots represent more than one specimen. h, High lactylation of K388 NBS1 and high expression of LDHA are correlated with the lowest overall survival rate. Data are mean ± s.d. f–h, n = 94. P value by two-way ANOVA (b,c), log-rank test (d,e,h), two-sided Mann–Whiney test (f) or two-sided Pearson correlation test (g).

Source Data

NBS1 K388 lactylation predicts poor survival

We further investigated the clinical relevance of LDHA and NBS1 K388 lactylation. According to the Gene Expression Profiling Interactive Analysis (GEPIA) database, LDHA expression was significantly upregulated in pancreatic, stomach, lung and ovarian cancers (Extended Data Fig. 10a). Next, we collected 94 tumour specimens obtained by biopsy from patients with gastric cancer prior to NAC (Supplementary Table 5). All of the enroled patients received platinum-based NAC, and were divided into NAC-resistant and NAC-sensitive categories on the basis of their responses to subsequent NAC. IHC showed that the amounts of LDHA and NBS1 K388 lactylation were increased in the NAC-resistant tumours (Fig. 4f). LDHA was positively correlated with NBS1 K388 lactylation (Fig. 4g). The overall survival rate of patients with high levels of NBS1 K388 lactylation and LDHA was much lower than that of the patients with low levels of NBS1 K388 lactylation and LDHA (Fig. 4h and and Extended Data Fig. 10b). Of the 94 enroled patients, paired pre- and post-treatment tumour tissues were available from 55 individuals. The levels of LDHA and NBS1 K388 lactylation were increased following NAC in patients with NAC-sensitive or NAC-resistant tumours (Extended Data Fig. 10c). Together, these results revealed that LDHA expression and NBS1 K388 lactylation are correlated with clinical resistance to NAC.

Discussion

Owing to the Warburg effect, lactate accumulation is a prominent feature in many solid tumours. Here we identify a direct link between lactate accumulation, efficient DNA repair and chemoresistance. In the context of lactate accumulation, TIP60 acts as a lysine lactylation transferase and mediates NBS1 lactylation at K388, a lysine residue located on the interaction interface between NBS1 and MRE11. NBS1 K388 lactylation decreased when ATM was depleted (Extended Data Fig. 7h,i), suggesting that ATM is involved in lactylation of NBS1 at K388. TIP60-mediated NBS1 K388 lactylation was essential for MRN complex formation and efficient DNA repair (Extended Data Fig. 10d). Thus, lactate serves as a protective metabolite for genome integrity, conferring cancer cell survival under the action of genotoxic agents. Our data clarify that lactate promotes DNA repair machinery and chemoresistance. Chemotherapy remains a major component of mainstay therapies for treating human malignancies, and the resistance of cancer cells to chemotherapy is responsible for deaths of many cancer patients. Our data demonstrate strong synergy between the clinically available LDHA inhibitor stiripentol and genotoxic therapies, including cisplatin and IR. Thus, interventions of lactate may represent a promising approach to improve chemotherapy outcomes and survival of cancer patients.

Our analysis of the lactylation-enriched proteome reveals that many DNA repair-related proteins are lactylated in chemo-resistant cells, implying an extensive role of lactylation in regulating DNA repair. MRE11 lactylation has recently been shown to enhance its DNA binding and DNA end resection[26]. Further work will be required to explore the unknown effects of lactylation on DNA repair machinery.

Methods

Animals

All animal studies were performed in accordance with the Animal Care and Use Committee of Sun Yat-sen University. For all mouse experiments (including PDXs), the maximum permitted tumour volume below 1,600 mm^3 was not exceeded. Mice were kept under specific pathogen-free or germ-free conditions, with an ambient temperature of 20 ± 2 °C, humidity of 55 ± 10% and a dark:light cycle of 12 h. Six-week-old male NSG mice were allowed to acclimatize to housing conditions in animal facility for 1 week before being used in the experiments. Both male and female mice were used for experiments, but within each experiment, they were sex-matched. Cells were resuspended in 1:1 PBS:Matrigel and subcutaneously transplanted into the bilateral dorsal flanks of NSG mice. The subcutaneous PDX model was established by transplanting minced fresh tumour tissue into NSG mice.

In animal experiments involving lactate treatment, the mice were assigned randomly to the following groups: (1) control (saline); (2) lactate (100 μl of 1 mM, 3 times a week); (3) cisplatin (2 mg kg−1, once a week) or IR (2 Gy per fraction, once daily for 4 consecutive days per week); (4) a combination of both agents at the aforementioned doses (n =  6 mice per group). Sample size in each group was determined by our preliminary experiments. For cisplatin and lactate administration, mice in the treated groups received intraperitoneal injections.

In animal experiments involving stiripentol treatment, mice were treated as follows: (1) control (saline); (2) IR (2 Gy per fraction, once daily for consecutive 4 days per week) or cisplatin (2 mg kg−1, once a week); (3) stiripentol (150 mg kg−1, once daily for consecutive 5 days per week); (4) the combination of both agents at the aforementioned doses. For cisplatin and stiripentol administration, mice in the treated groups received intraperitoneal injections. Tumour volume and body weight were measured every three days weekly. Tumour volume was calculated using the following formula: volume (mm^3) = [width (mm)]2 × length (mm)/2.

Organoid cultures

Gastric cancer organoids were established as described[27]. In brief, gastric cancer tissue used for organoid culture was obtained following surgery from patients with gastric cancer. Tumour tissues were isolated and transported to the laboratory on ice within 1 h of removal from the patients in ice-cold DMEM/F-12 with 50 U ml−1 penicillin-streptomycin. Tissues were washed three times with cold DMEM/F-12 with antibiotics and cut into small pieces with sterile blades. The minced tissue was incubated in DMEM containing 1 mg ml−1 collagenase V (Sigma) for 1 h at 37 °C. The tissues were washed in ice-cold PBS, followed by centrifugation (300g, 5 min, and 4 °C). TrypLE (Thermo Fisher Scientific) was used to digest the sample for 5 min at 37 °C, followed by stopping with ice-cold PBS and centrifugation. The sample was resuspended in 50 μl culture medium and then filtered through a 40-μm nylon mesh. One-hundred microlitres Matrigel (Corning) was added to the suspension, which was allowed to solidify on pre-warmed 24-well culture plates (Corning) for 15 min at 37 °C. One millilitre culture medium was added to the well after gelation. The medium was changed every 3–4 days, and the organoids were passaged with TrypLE every 2 weeks. The medium for culturing gastric cancer organoids was as described previously[28].

Cell lines

Cell lines were cultured in a humidified incubator at 5% CO_2 and 37 °C. All cell lines were validated by STR DNA profiling and tested negative for mycoplasma by PCR. Culture media were supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin.

293 T, AGS, A549, HCT116 and HeLa cell lines were obtained from American Type Culture Collection (ATCC). MGC803 cells were obtained from Cell Bank, Shanghai Institute of Biochemistry and Cell Biology (SIBCB). U2OS-265 cells were provided by R. Greenberg. 293 T and HeLa cells were cultured in DMEM medium (Gibco). AGS and A549 cells were cultured in F12K medium (Gibco). HCT116, U2OS-265 and MGC803 cells were cultured in RPMI 1640 medium (Gibco).

Patients and tumour samples

Tumour samples from patients with gastric cancer, pathologically and clinically diagnosed at the Seventh Affiliated Hospital of Sun Yat-sen University, were collected. Informed consent was obtained from all patients, and approvals were obtained from the ethics board of the Seventh Affiliated Hospital of Sun Yat-sen University for the use of these specimens in research. The Institutional Review Board or IRB (Number KY-2022-011-01 and KY-2022-039-02) at the Seventh Affiliated Hospital of Sun Yat-sen University. Clinical information on these patients, including age, chemotherapy regimens, and survival situation, was obtained from medical and follow-up records (Supplementary Tables 5 and 6). Pathological tumour regression grade (TRG) was used to evaluate the efficacy of NAC. TGR was classified into four tiers according to Ryan’s score[29]. Scores of 0 to 2 were categorized as responders or sensitive, whereas score 3 was categorized as non-responders or resistant.

Antibodies

The following antibodies were generated by Cell Signaling: anti-NBS1 (14956); anti-caspase-3 (14220); anti-H2AX (7631); anti-H2AX (Ser139) (9718); anti-histone H3 (4620); anti-p300 (86377); anti-HDAC3 (3949); anti-histone H3 (4499). The following antibodies were generated by Novus: anti-NBS1 (NB100-143SS). The following antibodies were generated by ABclonal: anti-Flag (AE005); anti-β-actin (AC004). The following antibodies were generated by Proteintech: anti-β-tubulin (10068-1-AP); anti-MCT1 (20139-1-AP); anti-LDHA (19987-1-AP); anti-TIP60 (10827-1-AP); anti-GFP (50430-2-AP); anti-c-MYC (10828-1-AP). The following antibodies were generated by BD: anti-H2AX (pS139) (560446); anti-RAD50 (611010). The following antibodies were generated by Abcam: anti-RAD51 (ab88572); anti-TIP60 (ab300522); anti-histone H4 (ab31830). The following antibodies were generated by PTM BIO: anti-pan-Kla (PTM-1401); anti-pan-Kac (PTM-101); anti-histone H4K8ac (PTM-120); anti-NBS1-K388la (N/A). The following antibodies were generated by Santa Cruz: anti-BRCA1 (sc-6954).

For western blots, antibodies were diluted 1:1,000. For immunofluorescence, antibodies were diluted 1:200. For IHC, antibodies were diluted 1:100.

Prime editing-mediated genome editing

The prime editing system was used to construct genomic NBS1(K388R) mutations in AGS parental cells. Prime editing was performed as described previously[30]. In brief, the pegRNA-NBS1 spacer sequence and 3′ extension sequence were designed using the prime-editing guide RNAs design tool (http://pegfinder.sidichenlab.org/). Prime editing-NBS1 spacer sequence: GAAATCAAAGTCTCCAAAA. Prime editing-NBS1 3′ extension sequence: TTTTTGTTCCATTCTGGAGACTTTGAT. The digested pU6-pegRNA-GG-vector plasmid was assembled with the spacer sequence, 3′ extension sequence, and scaffold sequence by Golden Gate assembly. The ligation product was transformed into Escherichia coli. The resulting clonal transformants were isolated and sequenced. PCMV-PE2 and assembled pU6-pegRNA-GG-Vector plasmid were transfected into AGS parental cells. After 24 h post-transfection, cells were diluted and seeded into 96-well plates with only one cell per well. After cultivation, Genomic DNA was then extracted from the monoclonal cells. The PCR products spanning the mutation sites were sequenced.

PDX model

The collection of gastric cancer tumour surgical specimens was approved by the Seventh Affiliated Hospital of Sun Yat-sen University. The informed consent of patient was obtained according to institutional regulatory standards before surgery. Tumour tissues were collected, and transported to the laboratory within 1 h in ice-cold DMEM with 50 U ml−1 penicillin-streptomycin. Tumour tissues were washed three times with cold DMEM with 50 U ml−1 penicillin-streptomycin and cut into small pieces with sterile blades. A small incision was made on the bilateral dorsal flanks of anaesthetized NSG mice and minced fresh tumour surgical specimens were subcutaneously transplanted. The incision was closed up with sutures and tumour formation was monitored for the next 3 months.

Neutral comet assays

Neutral comet assays were performed using the Comet Assay Kit (Trevigen) according to the manufacturer’s protocol. In brief, the lysis solution was prepared and chilled at 4 °C for at least 20 min before use. Agarose was melted in a water bath of boiling water for 5 min and then cooled in a 37 °C water bath for at least 20 min. Cells (1 × 10^5 ml−1) were combined with molten agarose at a ratio of 1:10 (v/v), and 50 µl was placed onto the comet slide. The slides were placed in a 4 °C refrigerator for 10 min, and then were immersed in a 4 °C lysis solution for 1 h, followed by neutral electrophoresis buffer for 30 min. The slides were subjected to electrophoresis at 21 V for 45 min and immersed in DNA precipitation solution and 70% ethanol for 30 min at room temperature. The samples were dried at 37 °C for 10 min and stained with SYBR green for 10 min before the images were captured under an epifluorescence microscope (Olympus). The tail moment was analysed using the Comet Assay Software Project (CASP).

HR and NHEJ reporter assays

HeLa cells were stably integrated with DR-GFP (Addgene, #26475) and EJ5-GFP (Addgene, #44026) reporters respectively. I-SceI-T2A-dsRed was a gift from L. Li. In brief, 1 × 10^6 HeLa DR-GFP or EJ5-GFP reporter cells were transfected with 3 μg of I-SceI using Lipofectamine 3000 Transfection Kit (Invitrogen). After 48 h, cells were collected and subjected to flow cytometry analysis (CytoFLEX). The efficiency of repair was determined by the ratio of cells expressing both GFP and dsRed signals to all dsRed-positive cells. Three independent experiments were performed.

Laser microirradiation, imaging and immunofluorescence

Cells were transfected with the indicated GFP-tagged expression plasmids and seeded onto 35-mm glass-bottom dishes (NEST). After 24 h transfection, cells were placed into a cell culture chamber (37 °C, 5% CO_2) on an inverted microscope (Olympus). Laser microirradiation was carried out by scanning the regions of interest using fixed wavelength of ultraviolet laser (405 nm). Time-lapse images were captured and the fluorescence intensity of the micro-irradiated regions within the nucleus relative to the non-irradiated regions was calculated using Olympus software.

For immunofluorescence assay, cells were seeded into glass-bottom dishes (NEST), and then treated with cytoskeleton buffer (10 mM PIPES, pH 7.0, 100 mM NaCl, 300 mM sucrose, and 3 mM MgCl_2, 0.7% Triton X-100, 0.3 mg ml−1 RNase A) for 5 min. Next, cells were fixed with 4% (w/v) paraformaldehyde (Sigma) for 15 min at room temperature, washed with 1× PBS 3 times. Cells were permeabilized with 0.2% Triton X-100 for 5 min and blocked in immunostaining blocking solution (Beyotime) for 30 min. Subsequently, the cells were washed three times with PBS and then incubated with the indicated primary antibody at 4 °C overnight. Finally, images were captured with a fluorescence microscope (Olympus or Leica).

Establishment of cisplatin-resistant cell lines

For cancer cell lines (AGS, MGC803, HCT116, HGC27 and A549), cells reaching approximately 70% density in 100 mm dishes were treated with one-tenth of the half-maximal inhibitory concentration (IC_50) of cisplatin. Fresh drug-containing complete culture medium was then changed every two days. The cells were treated with sequentially increasing concentrations of cisplatin for nearly six months. The IC_50 values of the cisplatin-resistant cells were at least 5-fold higher than those of the corresponding parent cells.

Measurement of lactate

To measure lactate levels in tumour tissue, the tissue was homogenized with lysis buffer on ice, and the supernatant was obtained by centrifugation at 12,000g for 10 min at 4 °C. The supernatants were collected, and lactate levels were measured using an l-Lactate Assay Kit (Abcam, ab65330) following the manufacturer’s instructions.

Crosslinking mass spectrometry

AGS-P cells and AGS-NBS1(K388R) cells were grown to 80% confluence, lysed with NETN buffer, and clarified via centrifugation. To enrich NBS1-interacting proteins, 5 μg anti-NBS1 antibody was incubated with 40 μl protein A/G beads at room temperature for 2 h. After washing twice, the beads were added to 1 mg of cell lysate and incubated at room temperature for 2 h. Proteins bound to beads were resuspended with HEPES buffer, added with DSSO at 2.5 mM final concentration, and crosslinked at room temperature for 1 h with shaking. Subsequently, 1 M Tris-HCl (pH 8.0) was added to a final concentration of 62 mM to quench the crosslinked reaction. The crosslinked NBS1-interacting proteins were reduced with 50 mM dithiothreitol at 37 °C for 1.5 h, alkylated with 50 mM iodoacetamide for 15 min at room temperature in darkness, and digested with 1 μg trypsin at 37 °C overnight. After desalination, the crosslinked peptides were analysed by LC–MS/MS and identified through database searching, as previously described.

4D label-free quantitative lactylproteomics analysis

Cells were collected, and the 4D label-free quantitative lactylproteomics analysis was performed by Jingjie PTM BioLabs. For protein extraction, cell sample was grinded by liquid nitrogen, and then the powder was sonicated three times in lysis buffer (50 µM PR-619, 1% Triton X-100, 50 mM NAM, 10 mM dithiothreitol, 1% protease inhibitor cocktail, 3 µM trichostatin A (TSA) and 2 mM EDTA) on ice using a high-intensity ultrasonic processor (Scientz). An equivalent volume of Tris-saturated phenol was added to the sample, which was then vortexed for 5 min. The upper phenol phase was transferred to a new tube tube after centrifugation (4 °C, 10 min, 5,000g). Proteins were precipitated by adding at least four volumes of ammonium sulfate-saturated methanol. The mixture was further incubated for at least 6 h at −20 °C. The supernatant was discarded after centrifugation (4 °C, 10 min). The remaining precipitate was washed once with ice-cold methanol and then three times with ice-cold acetone. Proteins were re-dissolved with 8 M urea and the protein concentration was measured with a BCA kit.

For digestion, the protein solution was reduced with 5 mM dithiothreitol for 30 min at 56 °C, and was alkylated with 11 mM iodoacetamide for 15 min at room temperature in darkness. Subsequently, 100 mM triethylammonium bicarbonate (TEAB) was added to urea in the protein sample that was then digested overnight by trypsin at 1:50 trypsin-to-protein mass ratio.

To enrich lactyl-modified peptides, tryptic peptides were dissolved in NETN buffer (1 mM EDTA, 100 mM NaCl, 50 mM Tris-HCl, 0.5% NP-40, pH 8.0) and incubated with pre-washed antibody beads at 4 °C overnight with gentle shaking. The breads were washed four times with NETN buffer and twice with water. The peptides were eluted from the beads with 0.1% TFA and then vacuum-dried.

For LC–MS/MS analysis, tryptic peptides were dissolved in solvent A (2% acetonitrile in water and 0.1% formic acid) and then loaded onto a home-made reversed-phase analytical column. Peptides were separated with a gradient from 6% to 24% solvent B (0.1% formic acid in acetonitrile) in 70 min, 24% to 35% over 14 min, 35% to 80% over 3 min, and held at 80% for the last 3 min, all at a constant flow rate of 450 nl min−1 using a nanoElute UHPLC system (Bruker Daltonics). The peptides were subjected to capillary source and analysed using the timsTOF Pro (Bruker Daltonics) mass spectrometry. The timsTOF Pro was operated in parallel accumulation serial fragmentation (PASEF) mode. Precursors and fragments were analysed on TOF detector (a MS/MS scan range from 100 to 1,700 m/z). The dynamic exclusion was set to 30 s. Precursors with charge states 0 to 5 were selected for fragmentation, and 10 PASEF-MS/MS scans were acquired per cycle. The electrospray voltage applied was 1.75 kV.

For database search, the MS/MS data were processed with Maxquant search engine (v.1.6.6.0). Tandem mass spectra were searched against the Homo_sapiens_9606_SP_20200509. Fasta concatenated with reverse decoy database. Trypsin/P was specified as cleavage enzyme allowing up to 2 missing cleavages. The mass tolerance for precursor ions was set as 20 ppm in main search and 20 ppm in first search and the mass tolerance for fragment ions was set as 0.04 Da. Carbamidomethyl on Cys was specified as fixed modification, and lactylation on Lys and oxidation on Met were specified as variable modifications. False discovery rate was adjusted to <1%.

Label-free proteomics analysis

For tumour tissue, the tissue was washed wash away the remaining blood and other body fluids on the tissue surface by using PBS. The tissue was cut with scissors, and sonicated 3 times in lysis buffer (50 µM PR-619, 1% Triton X-100, 50 mM NAM, 10 mM dithiothreitol, 1% protease inhibitor cocktail, 3 µM TSA and 2 mM EDTA) on ice using a high-intensity ultrasonic processor (Scientz). An equivalent volume of Tris-saturated phenol was added to the sample, which was then vortexed for 5 min. The upper phenol phase was transferred to a new tube and centrifuged (4 °C, 10 min, 16,000g). Proteins were precipitated by adding at least four volumes of ammonium sulfate-saturated methanol. The mixture was further incubated for at least 6 h at −20 °C. The supernatant was discarded after centrifugation (4 °C, 10 min). The remaining precipitate was washed once with ice-cold methanol and then three times with ice-cold acetone. Proteins were re-dissolved with 8 M urea and the protein concentration was measured with a BCA kit. Proteins were reduced with 5 mM dithiothreitol at 37 °C for 1 h. Proteins were alkylated with 10 mM iodoacetamide at 25 °C for 45 min in the dark. Samples were digested with trypsin (Promega) at 1:50 enzyme-to-protein ratio. After 18 h of digestion, peptides were eluted with 0.1% TFA and vacuum-dried. Peptides were analysed by LC–MS/MS (Thermo Fisher Easy1200-Faims Fusion Orbitrap).

For cell samples, cells were lysed with lysis buffer (50 mM Tris-HCl [pH 8.0], 1% Triton X-100, 0.5% Nonidet P-40, 10 mM dithiothreitol, 1% protease inhibitor cocktail, 150 mM NaCl and 5 mM EDTA) on ice for 30 min, followed by centrifugation (12,000g, 20 min, and 4 °C). The protein solution was precipitated with acetone, and was reduced with 50 mM dithiothreitol for 1.5 h at 30 °C. The protein solution was alkylated with 50 mM iodoacetamide for 15 min at room temperature in darkness. Subsequently, 100 mM TEAB was added to urea in the protein sample that was then digested overnight by trypsin at 1:50 trypsin-to-protein mass ratio. Finally, the peptides were analysed by LC–MS/MS (Thermo Fisher Easy1200-Faims Fusion Orbitrap).

Metabolomics analysis

Tumour tissue were pulverized after being frozen in liquid nitrogen with the addition of 250 µl of mixed solvent (chloroform:methanol:water, 1:2:1). The lysate was sonicated and centrifuged for 10 min at 12,000 rpm. Aqueous supernatant was transferred to a gas chromatography vial containing internal standards. The deposit was rehomogenized with a T10 basic homogenizer at 4 °C for 30 s after adding 250 µl of methanol. An aliquot of supernatant was added to the mixture in the vial and vacuum-dried after a second centrifugation. Samples were run on an LC–MS/MS (Thermo Fisher Ult3000-Exploris 480 Orbitrap).

Quantification and statistical analysis

All statistical analyses were performed with GraphPad Prism 7.0 (GraphPad). Values were obtained from at least three independent experiments, using three technical replicates per condition, unless otherwise indicated in the figure legend. No animals or tumour samples were excluded from data analyses. Student’s t-test, two-sided, unpaired, two-tailed, two-way or one-way analysis of variance (ANOVA) were used to analyse data as indicated. The Kaplan–Meier method was used to calculate the cumulative overall survival data, and the log-rank test was used for analysis.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Mass spectrometry data (PXD050906) have been deposited at the ProteomeXchange Consortium through the PRIDE partner repository. Accession numbers are listed in the key resources table. All data reported in this paper available in a publicly accessible repository. LDHA RNA levels were obtained from GEPIA database (http://gepia.cancer-pku.cn/). The data deposited and made public is compliant with the regulations of Ministry of Science and Technology of the People’s Republic of China. Source data are provided with this paper.

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Acknowledgements

The authors thank R. Greenberg for providing the U2OS-265 reporter cell lines and L. Li for the gift of I-SceI-T2A-dsRed plasmid. We greatly appreciate the gift of I-SceI-T2A-dsRed plasmid from Lin Li (University of Chinese Academy of Sciences). We would like to thank W.-G. Zhu (Shenzhen University) for their kind suggestion on in vitro nuclease activity assay. Mass spectrometry analysis was performed by the Bioinformatics and Omics Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University. This study was supported by the Breast Cancer Now Toby Robins Research Centre at the Institute of Cancer Research (CTR-Q5-Y2) and grants from the National Key Research and Development Program of China (2021YFA0909300), National Natural Science Foundation of China (82073148 to C. Zhang, 81772579 to Y.H., U20A20379 to Y.H., 82220108013 to Y.H., 82102716 to H.C., 82073067, 82373023 to D.Y.), China Postdoctoral Science Foundation (2022M713588), China National Postdoctoral Program for Innovative Talents (BX20230440 to Y.L.), Guangdong Science and Technology Department (2019B020226003 to D.Y., 2023B1212060013, 2020B1212030004), Guangdong Provincial Key Laboratory of Digestive Cancer Research (2021B1212040006), Science and Technology Program of Guangdong (2016A020213002), Guangdong–Hong Kong–Macau University Joint Laboratory of Digestive Cancer Research (2021LSYS003), Sanming Project of Medicine in Shenzhen (SZSM201911010 to C. Zhang), Shenzhen Key Medical Discipline Construction Fund (SZXK016 to Y.H.), Shenzhen Sustainable Project (KCXFZ20200201101059392 to C. Zhang), Shenzhen Fundamental Research Program (JCYJ20200109142605909 to C. Zhang) and Shenzhen-Hong Kong-Macau Technology Research Programme (Type C) (SGDX2020110309260100 to C. Zhang).

Author information

Author notes

1. Yun Li

Present address: Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University, Guangzhou, China

2. These authors contributed equally: Hengxing Chen, Yun Li, Huafu Li

Authors and Affiliations

1. Guangdong Provincial Key Laboratory of Digestive Cancer Research, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China

Hengxing Chen, Xiancong Chen, Huafeng Fu, Deli Mao, Wei Chen, Linxiang Lan, Chunming Wang, Jia Li, Chengming Zhu, Eddie Cheung, Yulong He & Changhua Zhang

2. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China

Yun Li, Kaishun Hu, Daning Lu & Dong Yin

3. Cancer Stem Cell Laboratory, The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK

Huafu Li, Linxiang Lan, Ian Evans & Axel Behrens

4. Digestive Diseases Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China

Xiancong Chen, Huafeng Fu, Deli Mao, Wei Chen, Chunming Wang, Jia Li, Yulong He & Changhua Zhang

Contributions

H.C., Y.L. and H.L. participated in manuscript preparation and formal analysis and performed most of the experiments. W.C. generated organoids and performed cell viability assays. X.C. and D.M. contributed to cell culture and western blot. H.F. and C.W. performed IHC assays and animal experiments. J.L. performed immunofluorescence assays. L.L., K.H., D.L. and C. Zhu analysed the data. E.C. and I.E. supervised statistical analysis. Y.H. provided intellectual feedback. C. Zhang, D.Y. and A.B. conceived and directed the project.

Corresponding authors

Correspondence to Yulong He, Axel Behrens, Dong Yin or Changhua Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Lactate was upregulated in resistant cancer cells.

a, Characteristics of 24 patients with gastric cancer undergoing neoadjuvant chemotherapy. Green, patient classed as sensitive; dark red, patient classed as resistant. TP53, MYC and LDHA genomic aberration status: no detectable alteration (light green), pathogenic mutation (purple), amplification (red) and deletion (black) in baseline biopsies. Differentiation status: poor (dark brown), moderate (light brown), moderate & poor (brown). Clinical TNM stage before NAC: T4 (blue), T3 (yellow). Lauren type: 1 = intestinal type; 2 = diffuse type; 3 = mix type. b, Dose-response curves for cisplatin in resistant cells and the corresponding parental cells. c, ECAR was measured between resistant cells and the corresponding parental cells. Analysis was performed using the Seahorse XF96e Extracellular Flux Analyzer. d, Statistical analysis of glycolytic activity between resistant cells and the corresponding parental cells. e, The level of lactate in AGS-P, AGS-R, MGC803-P, MGC803-R, A549-P, and A549-R cells was measured. Data are presented as mean ± SD. n = 3 biologically independent samples. P value was determined by t-test (two-sided) for c, d, e.

Source Data

Extended Data Fig. 2 Lactate facilitates DNA-damaging treatment resistance in vivo and in vitro.

a, The growth of PDX2 tumors was assessed in NSG mice treated with: (1) Control (saline); (2) lactate (100 mL of 1 mM, three times a week); (3) cisplatin (2 mg/kg, once a week); (4) the combination of both agents at the aforementioned doses; (n =  6 mice per group). b, Survival curve analysis of NSG mice transplanted with MGC803-P tumors and treated with: (1) Control; (2) lactate; (3) cisplatin; (4) the combination of both agents; (n =  6 mice per group). c, Immunoblot analysis of MCT1 knockdown in MGC803-P cells. d, Relative lactate levels in MGC803 and PDX tumors. e, Dose-response curves for cisplatin in PDO2-5 treated with or without lactate (20 mM). f, Dose-response curves for cisplatin in AGS-P, MGC83-P, and A549-P cells treated with or without lactate (20 mM). g, Cell viability was measured by colony formation assays. h, Cell lysates were immunoblotted for caspase-3. i, Cell apoptosis was determined by flow cytometry. Data are presented as mean ± SD. n = 6 biologically independent samples for d, n = 3 biologically independent samples for e-f, h-i. P value was determined by two-way ANOVA for a, log rank test for b, t-test (two-sided) for d, h, i. For gel source data, see Supplementary Fig. 1.

Source Data

Extended Data Fig. 3 Lactate affects DNA repair.

a, Dose-response curves for etoposide and adriamycin in AGS-P cells treated with or without lactate (20 mM). b, Cell lysates were immunoblotted for caspase-3. c, Dose-response curves for IR in AGS-P cells treated with or without lactate (20 mM). d, Cell apoptosis was determined by flow cytometry. e, Cell lysates were immunoblotted for caspases and caspase substrates. f, MGC803-P cells were treated with lactate (20 mM) for 24 h and then treated with IR (2 Gy). Cells were harvested at the indicated time after IR treatment. Cells were lysed for immunoblotting analyses. g, AGS-P cells were treated with or without lactate (20 mM) for 24 h. Cells were harvested at the indicated time points after IR (2 Gy) treatment for IF assay. Scale bars, 10 μm. n = 60 cells examined three independent experiments. h, Schematic representation of NHEJ reporter (above). NHEJ repair efficiency was measured (below). Ku70 overexpression as a positive control for promoting NHEJ repair. Data are presented as mean ± SD. n = 3 biologically independent samples for a, c, d, h. P value is determined by t-test (two-sided) for d, g, h. For gel source data, see Supplementary Fig. 1.

Source Data

Extended Data Fig. 4 Lactate or cisplatin inducs NBS1 lactylation.

a, Schematic view of the experimental workflow for quantification of Kla in AGS-P and AGS-R cells. b, Summary of identified and quantified peptides and proteins in lactylation proteome. c, Protein-protein interaction network analysis of the DNA damage repair-related Kla proteins based on the STRING database. d, Dose-response curves for cisplatin in AGS-P and AGS-sh-NBS1 cells treated with or without lactate (20 mM) (Left). NBS1 knockdown in AGS cells was confirmed by western bolt assay (Right). e, A549-P, A549-R, MGC803-P, and MGC803-R were lysed with RIPA buffer. Immunoprecipitations of cell lysates with anti-NBS1 or anti-immunoglobulin G (IgG), followed by immunoblotting with antibodies against the indicated proteins. f, AGS-P cells were treated with lactate for the indicated concentrations, and WCEs were collected for IP with anti-NBS1 antibody, followed by immunoblot analysis. g, HEK293T and Hela cells were treated with cisplatin for 6 h, and WCEs were collected for IP with anti-NBS1 antibody, followed by immunoblotting. Data are presented as mean ± SD. n = 3 biologically independent samples for d. For gel source data, see Supplementary Fig. 1.

Source Data

Extended Data Fig. 5 TIP60 and HDAC3 regulates NBS1 K388 lactylation.

a, AGS-P, AGS-NBS1-K388 cells were treated with cisplatin (2.5 μM) for 6 h, and WCEs were collected for IP with anti-NBS1 antibody, followed by immunoblotting. b, Sanger DNA sequencing traces of the NBS1 target site from the genome isolated from AGS-P cells (unedited cells) and AGS-NBS1-K388R cells (edited cells). c, ddH_2O containing different peptides was added onto the nitrocellulose membrane, followed by immunoblotting using anti-NBS1-K388la antibody. d, NBS1 K388 lactylation-specific antibody was validated for IHC staining. Representative images showing gastric cancer tissues stained with the antibody of NBS1-K388la incubated in the presence or absence of lactylated NBS1 peptides. Scale bar = 50 μm. e, AGS-P, A549-P, and MGC803-P cells were treated with lactate for the indicated concentrations, followed by immunoblot analysis. f, AGS-P, AGS-R, A549-P, A549-R, MGC803-P and MGC803-R cells were immunoblotted for analysis. g, Table summarizes the vital proteins identified by mass spectrometry analysis. We marked the protein of interest in red font. h, Exogenous IP assays showed that TIP60 interacts with NBS1. i, Flag-HDAC1, Flag-HDAC2 or Flag-HDAC3 was transfected into AGS-P cells. Cells were lysed for immunoblotting analyses. j, HDAC3 knockdown enhanced NBS1 K388 lactylation in MGC803-P cells. k, Co-IP assays showed that HDAC3 interacted with NBS1. Data are presented as mean ± SD. For gel source data, see Supplementary Fig. 1.

Extended Data Fig. 6 NBS1 K388 lactylation promotes DNA-damaging treatment resistance via HR repair.

a-d, Cytotoxic effects of cisplatin on AGS-P and AGS-NBS1-K388R cells were examined by cell viability (a), western blot (b), flow cytometry (c), and colony formation assay (d). e-f, Cytotoxic effects of IR on AGS-P and AGS-NBS1-K388R cells were examined by cell viability (e), western bolt (f). g-h, The protective effects of lactate on cisplatin resistance were examined in AGS-P and AGS-NBS1-K388R cells by western blot (g) and flow cytometry (h). i-j, The protective effects of lactate on IR resistance were examined in AGS-P and AGS-NBS1-K388R cells by cell viability (i) and flow cytometry (j). k-l, The protective effects of LDHA on cisplatin resistance were examined in AGS-P and AGS-NBS1-K388R cells by flow cytometry (k) and cell viability (l). m, n, Immunoblotting of γ-H2AX in both AGS-P and AGS-NBS1-K388R cells after cisplatin (2.5 μM) (m) or IR (10 Gy) (n) treatment for indicated time. o-p, Immunostaining with anti-BRCA1 (o) and anti-RAD51 (p) was performed at 60 min after IR (2 Gy). Scale bars, 15 μm. Data are presented as mean ± SD. n = 3 biologically independent samples for a, c, d, e, h, i, j, k, l. P value was determined by t-test (two-sided) for c, d, h, j, k. For gel source data, see Supplementary Fig. 1.

Source Data

Extended Data Fig. 7 NBS1 K388 lactylation promotes MRN complex formation.

a, b, AGS-P and AGS-NBS1-K388R cells were treated with cisplatin for indicated time, and WCEs were collected for IP with anti-MRE11 (a) or anti-NBS1 antibody (b), followed by immunoblotting. c, AGS-P and AGS-NBS1-K388R cells were treated with IR (10 Gy), and WCEs were collected for IP with anti-NBS1 antibody. Then, purified NBS1 was supplemented with DSSO for crosslinking assay. Subsequently, the samples were analyzed by mass spectrometry. d, Graphical overview of the chemical cross-linking mass spectrometry results (CLMS). The green lines represent the interlinks within NBS1. The black lines represent the exterlinks between NBS1 and MRE11. Colored portions indicate structurally characterized parts of each protein. e, AGS-P and AGS-NBS1-K388R cells were treated with lactate, and WCEs were collected for IP with anti-MRE11, followed by immunoblotting. f, BLI assays of the lactylated NBS1 and the naïve NBS1 binding to MRE11. The analysis was performed by Pall Fortebio Octet Red96. g, Increasing expression of TIP60 enhanced the MRE11/RAD50 and NBS1 interaction. h, AGS-P cells were transfected with indicated si-RNA and TIP60-expressing plasmid for 48 h. WCEs were collected for IP with anti-NBS1 antibody, followed by immunoblotting. i, AGS-P cells were transfected with TIP60 for 12 h, then cells were treated with KU-55933 (ATM inhibitor) for 12 h. WCEs were collected for IP with anti-NBS1 antibody, followed by immunoblotting. For gel source data, see Supplementary Fig. 1.

Extended Data Fig. 8 NBS1 lactylation regulates the recruitment of MRN complex to DSB sites.

a, b, Immunostaining with anti-MRE11 and anti-RAD50 was performed after IR. A cell containing 10 or more foci was considered as a foci-positive cell. The percentage of anti-MRE11 and anti-RAD50 foci-positive cells was plotted. Scale bars, 10 μm. c, IF staining was performed with anti-NBS1 and anti-γ-H2AX at 30 min following IR as indicated. Scale bars, 10 μm. d, EGFP-MRE11 or EGFP-RAD50 was transfected into AGS-P or AGS-NBS1-K388R cells. At 24 h post-transfection, cells were laser micro-irradiated and monitored using a live-cell imaging microscope. Scale bars, 5 μm. e, EGFP-NBS1-WT or EGFP-NBS1-K388R mutant was transfected into AGS-P cells. At 18 h post-transfection, cells were treated with or without lactate for 12 h. Cells were then laser micro-irradiated and monitored using a live-cell imaging microscope. Scale bars, 5 μm. f, AGS-P, AGS-R, A549-P, A549-R, HCT116-P, HCT116-R, HGC27-P, HGC27-R, MGC803-P and MGC803-R cells were immunoblotted for analysis. For gel source data, see Supplementary Fig. 1. Data are presented as mean ± SD. n = 60 cells examined three independent experiments for a-c, n = 20 biologically independent cells for d, e. P value is determined by t-test (two-sided) for a-c.

Source Data

Extended Data Fig. 9 Inhibition of LDHA reverses DNA-damaging treatment resistance.

a, LDHA-Knockout MGC803-R cell line was generated using the CRISPR/ Cas9 system. b, MGC803-R cells were treated with sodium oxamate (10 mM). c, MGC803-R cells were treated with stiripentol (0.5 mM). a-c, Cells were divided into two parts. One part was lysed for immunoblotting and the other was used to detect lactate production. d, AGS-P cells and AGS-NBS1-K388R cells were transfected with the indicated siRNA for 2 days and then exposed to IR (8 Gy). Cells were harvested for comet assay at the indicated time after IR treatment. Scale bars, 25 μm. n = 60. e, Characteristics of PDOs from five gastric cancer patients. Green, patient classed as sensitive; dark red, patient classed as resistant. TP53, MYC and LDHA genomic aberration status: no detectable alteration (light green), pathogenic mutation (purple), amplification (red) and deletion (black) in baseline biopsies. Gender status: Male (blue), female (yellow). Differentiation status: poor (dark brown), moderate (light brown). Age: Years. Histology: Adenocarcinoma. f, Dose-response curves for cisplatin in PDOs. PDOs were cultured with various concentrations of cisplatin for 72 h. g, The growth of MGC803-R tumors was assessed in mice treated with: (1) Control (saline); (2) cisplatin (2 mg/kg, once a week); (3) stirtpentol (150 mg/kg, once daily for consecutive 5 days per week); (4) the combination of both agents at the aforementioned doses. h, Mouse body weights from each group in MGC803-R and PDX-R were measured. Data are presented as mean ± SD. n = 3 biologically independent samples for a-c, f. n = 6 individual mice per group for g, h. P value is determined by t-test (two-sided) for a-d, two-way ANOVA for g. For gel source data, see Supplementary Fig. 1.

Source Data

Extended Data Fig. 10 NBS1 K388 lactylation and LDHA predicts poor survival.

a, Box plot showing LDHA RNA levels across biologically independent tissues from various cancer types in GEPIA database. The box plot indicates the median (central line), the third and first quartiles (box limits) and 1.5 times the interquartile range above and below the box (whiskers). b, High levels of LDHA and NBS1 K388 lactylation were associated with poor overall survival of gastric cancer patients. c, Representative images of IHC staining of LDHA and NBS1 K388 lactylation in tumor specimens obtained before and after NAC. IHC scores of LDHA and NBS1 K388 lactylation are shown. n = 19 biologically independent individuals (Sensitive groups). n = 36 biologically independent individuals (Resistant groups). Scale bar = 50 μm. We shot it with a Nikon microscope. d, Cartoon illustrating that LDHA induces lactate, which in turn signals via TIP60 to increase NBS1 K388 lactylation. Lactylated NBS1 promotes MRN complex formation and the recruitment of MRN complex to DSB sites and thus favors DNA repair and cell survival. P value was determined by One-way ANOVA (two-sided) for a. * indicates P < 0.05, log rank test for b, or paired Wilcoxon test (two-sided) for c.

Source Data

Supplementary information

Supplementary Information

This file contains Supplementary Figs. 1 and 2 and Supplementary Table 7.

Reporting Summary

Supplementary Table 1

Proteomic analysis of NAC-sensitive and NAC-resistant tumours.

Supplementary Table 2

Non-targeted metabolomic analysis of NAC-sensitive and NAC-resistant tumours.

Supplementary Table 3

All of the identified lysine-lactylated proteins.

Supplementary Table 4

NBS1-interacting proteins detected in AGS cells using LC–MS/MS.

Supplementary Table 5

Clinicopathological characteristics of patients with gastric cancer who received NAC.

Supplementary Table 6

Clinicopathological characteristics of patients with gastric cancer who received NAC.

Source data

Source Data Fig. 1

Source Data Fig. 2

Source Data Fig. 3

Source Data Fig. 4

Source Data Extended Data Fig. 1

Source Data Extended Data Fig. 2

Source Data Extended Data Fig. 3

Source Data Extended Data Fig. 4

Source Data Extended Data Fig. 6

Source Data Extended Data Fig. 8

Source Data Extended Data Fig. 9

Source Data Extended Data Fig. 10

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Chen, H., Li, Y., Li, H. et al. NBS1 lactylation is required for efficient DNA repair and chemotherapy resistance. Nature 631, 663–669 (2024). https://doi.org/10.1038/s41586-024-07620-9

- Received: 18 April 2023

- Accepted: 29 May 2024

- Published: 03 July 2024

- Issue Date: 18 July 2024

- DOI: https://doi.org/10.1038/s41586-024-07620-9

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Amendments & Corrections

- Author Correction: Microglia regulate central nervous system myelin growth and integrity

- Author Correction: Chemoproteomic discovery of a covalent allosteric inhibitor of WRN helicase

- Author Correction: A high-performance neuroprosthesis for speech decoding and avatar control

- Publisher Correction: Mental navigation in the primate entorhinal cortex

- Retraction Note: Microbiome analyses of blood and tissues suggest cancer diagnostic approach

Author Correction Open access Published: 03 July 2024

Author Correction: Microglia regulate central nervous system myelin growth and integrity

Niamh B. McNamara,

David A. D. Munro,

Nadine Bestard-Cuche,

Akiko Uyeda,

Jeroen F. J. Bogie,

Alana Hoffmann,

Rebecca K. Holloway,

Irene Molina-Gonzalez,

Katharine E. Askew,

Stephen Mitchell,

William Mungall,

Michael Dodds,

Carsten Dittmayer,

Jonathan Moss,

Jamie Rose,

Stefan Szymkowiak,

Lukas Amann,

Barry W. McColl,

Marco Prinz,

Tara L. Spires-Jones,

Werner Stenzel,

Karen Horsburgh,

Jerome J. A. Hendriks,

Clare Pridans,

Rieko Muramatsu,

Anna Williams,

Josef Priller &

…

Veronique E. Miron

Nature volume 631, page E11 (2024)

- 746 Accesses

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The Original Article was published on 14 December 2022

Correction to: Nature https://doi.org/10.1038/s41586-022-05534-y Published online 14 December 2022

In the version of this article originally published, Figs. 1b, 2f, and 2n contained errors. Figure 1b had accidental merging of the same Hoechst image with the Tmem119 stain for both genotypes. Figure 2f had incorrect statistical results which had not been updated following quantification of a larger number of axons. Figure 2n, a magnified inset of Fig. 2l in Fire∆/∆, had been rotated 180° from the lower magnification image in 2l and the green asterisks were too small. These figures have now been corrected, and the original and revised versions can be seen below in Figs. 1 and 2.

Fig. 1: Original and revised Fig. 1b

.

Fig. 2: Original and revised Fig. 2n

.

In addition, Extended Data Fig. 7f had a typographical error in the legend, which should have read n = 3 mice on normal diet and n = 4 mice on PLX diet. The source data for Fig. 2j provided were for ‘total myelinated axons’ but should have been for ‘normally myelinated axons’, and the correct data were provided in the tab for Fig. 2k.

We have now included the background strain of the Fire∆/∆ mice in the methods section.

Please note that the same representative images for IBA1 staining are included in both Fig. 4a and Extended Data Fig. 8a, yet this is not an error as these address different questions: Fig. 4a indicates total IBA1+ cells and Extended Data Fig. 8a indicates the subset of these which are LYVE1+ (perivascular macrophages).

Author information

Author notes

1. These authors contributed equally: Josef Priller, Veronique E. Miron

Authors and Affiliations

1. UK Dementia Research Institute at The University of Edinburgh, Edinburgh, UK

Niamh B. McNamara, David A. D. Munro, Alana Hoffmann, Rebecca K. Holloway, Irene Molina-Gonzalez, Jamie Rose, Stefan Szymkowiak, Barry W. McColl, Tara L. Spires-Jones, Anna Williams, Josef Priller & Veronique E. Miron

2. Centre for Discovery Brain Sciences, Chancellor’s Building, The University of Edinburgh, Edinburgh, UK

Niamh B. McNamara, Alana Hoffmann, Rebecca K. Holloway, Irene Molina-Gonzalez, Katharine E. Askew, Jamie Rose, Stefan Szymkowiak, Barry W. McColl, Tara L. Spires-Jones, Karen Horsburgh & Veronique E. Miron

3. Medical Research Council Centre for Reproductive Health, The Queen’s Medical Research Institute, The University of Edinburgh, Edinburgh, UK

Niamh B. McNamara, Alana Hoffmann, Rebecca K. Holloway, Irene Molina-Gonzalez & Veronique E. Miron

4. Centre for Clinical Brain Sciences, Chancellor’s Building, The University of Edinburgh, Edinburgh, UK

David A. D. Munro & Josef Priller

5. Centre for Regenerative Medicine, Institute for Regeneration and Repair, The University of Edinburgh, Edinburgh, UK

Nadine Bestard-Cuche, Jonathan Moss & Anna Williams

6. Departments of Molecular Pharmacology, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan

Akiko Uyeda & Rieko Muramatsu

7. Department of Immunology and Infection, Biomedical Research Institute, Hasselt University, Hasselt, Belgium

Jeroen F. J. Bogie & Jerome J. A. Hendriks

8. University MS Centre, Hasselt University, Hasselt, Belgium

Jeroen F. J. Bogie & Jerome J. A. Hendriks

9. Barlo Multiple Sclerosis Centre, St Michael’s Hospital, Toronto, Ontario, Canada

Rebecca K. Holloway & Veronique E. Miron

10. Keenan Research Centre for Biomedical Science, St Michael’s Hospital, Toronto, Ontario, Canada

Rebecca K. Holloway & Veronique E. Miron

11. Department of Immunology, The University of Toronto, Toronto, Ontario, Canada

Rebecca K. Holloway & Veronique E. Miron

12. Wellcome Trust Centre for Cell Biology, King’s Buildings, The University of Edinburgh, Edinburgh, UK

Stephen Mitchell

13. Biological and Veterinary Services, Chancellor’s Building, The University of Edinburgh, Edinburgh, UK

William Mungall & Michael Dodds

14. Department of Neuropathology and Neurocure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany

Carsten Dittmayer & Werner Stenzel

15. Institute of Neuropathology, Centre for Basics in NeuroModulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany

Lukas Amann & Marco Prinz

16. Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany

Lukas Amann & Marco Prinz

17. Centre for Inflammation Research, The Queen’s Medical Research Institute, The University of Edinburgh, Edinburgh, UK

Clare Pridans

18. Simons Initiative for the Developing Brain, Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK

Clare Pridans

19. Muir Maxwell Epilepsy Centre, University of Edinburgh, Edinburgh, UK

Clare Pridans

20. Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany

Josef Priller

21. Neuropsychiatry and Laboratory of Molecular Psychiatry, Charité-Universitätsmedizin Berlin and DZNE, Berlin, Germany

Josef Priller

Corresponding author

Correspondence to Veronique E. Miron.

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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McNamara, N.B., Munro, D.A.D., Bestard-Cuche, N. et al. Author Correction: Microglia regulate central nervous system myelin growth and integrity. Nature 631, E11 (2024). https://doi.org/10.1038/s41586-024-07696-3

- Published: 03 July 2024

- Issue Date: 18 July 2024

- DOI: https://doi.org/10.1038/s41586-024-07696-3

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Author Correction Published: 03 July 2024

Author Correction: Chemoproteomic discovery of a covalent allosteric inhibitor of WRN helicase

Kristen A. Baltgalvis,

Kelsey N. Lamb,

Kent T. Symons,

Chu-Chiao Wu,

Melissa A. Hoffman,

Aaron N. Snead,

Xiaodan Song,

Thomas Glaza,

Shota Kikuchi,

Jason C. Green,

Donald C. Rogness,

Betty Lam,

Maria E. Rodriguez-Aguirre,

David R. Woody,

Christie L. Eissler,

Socorro Rodiles,

Seth M. Negron,

Steffen M. Bernard,

Eileen Tran,

Jonathan Pollock,

Ali Tabatabaei,

Victor Contreras,

Heather N. Williams,

Martha K. Pastuszka,

John J. Sigler,

Piergiorgio Pettazzoni,

Markus G. Rudolph,

Moritz Classen,

Doris Brugger,

Christopher Claiborne,

Jean-Marc Plancher,

Isabel Cuartas,

Joan Seoane,

Laurence E. Burgess,

Robert T. Abraham,

David S. Weinstein,

Gabriel M. Simon,

Matthew P. Patricelli &

…

Todd M. Kinsella

Nature volume 631, page E12 (2024)

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The Original Article was published on 24 April 2024

Correction to: Nature https://doi.org/10.1038/s41586-024-07318-y Published online 24 April 2024

In the version of the article initially published, the cell line “SW620” originally appeared as “SW680”. This has now been corrected in Fig. 5h and the legend, and the first paragraph of the “VVD-133214 causes MSI-H tumour regression” section in the HTML and PDF versions of the article.

Author information

Author notes

1. Robert T. Abraham

Present address: Odyssey Therapeutics, San Diego, CA, USA

Authors and Affiliations

1. Vividion Therapeutics, San Diego, CA, USA

Kristen A. Baltgalvis, Kelsey N. Lamb, Kent T. Symons, Chu-Chiao Wu, Melissa A. Hoffman, Aaron N. Snead, Xiaodan Song, Thomas Glaza, Shota Kikuchi, Jason C. Green, Donald C. Rogness, Betty Lam, Maria E. Rodriguez-Aguirre, David R. Woody, Christie L. Eissler, Socorro Rodiles, Seth M. Negron, Steffen M. Bernard, Eileen Tran, Jonathan Pollock, Ali Tabatabaei, Victor Contreras, Heather N. Williams, Martha K. Pastuszka, John J. Sigler, Laurence E. Burgess, Robert T. Abraham, David S. Weinstein, Gabriel M. Simon, Matthew P. Patricelli & Todd M. Kinsella

2. Pharma Research and Early Development pRED F. Hoffmann-La Roche, Ltd, Basel, Switzerland

Piergiorgio Pettazzoni, Markus G. Rudolph, Moritz Classen, Doris Brugger, Christopher Claiborne & Jean-Marc Plancher

3. Vall d’Hebron Institute of Oncology, Vall d’Hebron University Hospital, Universitat Autònoma de Barcelona, CIBERONC, Barcelona, Spain

Isabel Cuartas & Joan Seoane

Corresponding authors

Correspondence to Matthew P. Patricelli or Todd M. Kinsella.

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Baltgalvis, K.A., Lamb, K.N., Symons, K.T. et al. Author Correction: Chemoproteomic discovery of a covalent allosteric inhibitor of WRN helicase. Nature 631, E12 (2024). https://doi.org/10.1038/s41586-024-07595-7

- Published: 03 July 2024

- Issue Date: 18 July 2024

- DOI: https://doi.org/10.1038/s41586-024-07595-7

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Author Correction Published: 04 July 2024

Author Correction: A high-performance neuroprosthesis for speech decoding and avatar control

Sean L. Metzger,

Kaylo T. Littlejohn,

Alexander B. Silva,

David A. Moses,

Margaret P. Seaton,

Ran Wang,

Maximilian E. Dougherty,

Jessie R. Liu,

Peter Wu,

Michael A. Berger,

Inga Zhuravleva,

Adelyn Tu-Chan,

Karunesh Ganguly,

Gopala K. Anumanchipalli &

…

Edward F. Chang

Nature volume 631, page E13 (2024)

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The Original Article was published on 23 August 2023

Correction to: Nature https://doi.org/10.1038/s41586-023-06443-4 Published online 23 August 2023

Due to an error in the settings within the proprietary data-acquisition software used to collect neural data, approximately half of neural channels were misordered in our data files. This did not impact the main claims or outcomes of the paper; real-time decoding performance was unaffected. However, this did affect the mapping of electrodes to respective anatomical locations, electrode-based visualizations across the cortex and analyses that depended on the anatomical assignments of the electrodes. After correction, results that support our finding of persistent somatotopy in speech-motor cortex despite anarthria have been strengthened. One sentence in the main text, Figs. 1 and 5, Extended Data Figs. 7 and 8 and the Supplementary information have all been corrected in the HTML and PDF versions of the article. The Supplementary information that accompanies this notice provides detailed explanations of these amendments.

Supplementary information is available in the online version of this amendment.

Author information

Author notes

1. These authors contributed equally: Sean L. Metzger, Kaylo T. Littlejohn, Alexander B. Silva, David A. Moses, Margaret P. Seaton

Authors and Affiliations

1. Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA

Sean L. Metzger, Kaylo T. Littlejohn, Alexander B. Silva, David A. Moses, Margaret P. Seaton, Ran Wang, Maximilian E. Dougherty, Jessie R. Liu, Gopala K. Anumanchipalli & Edward F. Chang

2. Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, USA

Sean L. Metzger, Kaylo T. Littlejohn, Alexander B. Silva, David A. Moses, Ran Wang, Jessie R. Liu, Karunesh Ganguly, Gopala K. Anumanchipalli & Edward F. Chang

3. University of California, Berkeley–University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA

Sean L. Metzger, Alexander B. Silva, Jessie R. Liu & Edward F. Chang

4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA

Kaylo T. Littlejohn, Peter Wu, Inga Zhuravleva & Gopala K. Anumanchipalli

5. Speech Graphics Ltd, Edinburgh, UK

Michael A. Berger

6. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA

Adelyn Tu-Chan & Karunesh Ganguly

Corresponding author

Correspondence to Edward F. Chang.

Supplementary information

Explanation of updates

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Metzger, S.L., Littlejohn, K.T., Silva, A.B. et al. Author Correction: A high-performance neuroprosthesis for speech decoding and avatar control. Nature 631, E13 (2024). https://doi.org/10.1038/s41586-024-07735-z

- Published: 04 July 2024

- Issue Date: 18 July 2024

- DOI: https://doi.org/10.1038/s41586-024-07735-z

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Publisher Correction Open access Published: 04 July 2024

Publisher Correction: Mental navigation in the primate entorhinal cortex

Sujaya Neupane,

Ila Fiete &

Mehrdad Jazayeri

Nature volume 631, page E14 (2024)

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The Original Article was published on 12 June 2024

Correction to: Nature https://doi.org/10.1038/s41586-024-07557-z Published online 12 June 2024

This article was originally published under standard Springer Nature license (© The Author(s), under exclusive licence to Springer Nature Limited). It is now available as an open-access paper under a Creative Commons Attribution 4.0 International license, © The Author(s). Also, there were errors in the Fig. 3b y-axis labels where, in the text now reading “Cross-validated R^2 at offset”, “offset” originally appeared as “onset”. The errors have been corrected in the HTML and PDF versions of the article.

Author information

Authors and Affiliations

1. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA

Sujaya Neupane, Ila Fiete & Mehrdad Jazayeri

2. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

Ila Fiete & Mehrdad Jazayeri

Corresponding author

Correspondence to Mehrdad Jazayeri.

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Neupane, S., Fiete, I. & Jazayeri, M. Publisher Correction: Mental navigation in the primate entorhinal cortex. Nature 631, E14 (2024). https://doi.org/10.1038/s41586-024-07737-x

- Published: 04 July 2024

- Issue Date: 18 July 2024

- DOI: https://doi.org/10.1038/s41586-024-07737-x

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Retraction Note Published: 26 June 2024

Retraction Note: Microbiome analyses of blood and tissues suggest cancer diagnostic approach

Gregory D. Poore,

Evguenia Kopylova,

Qiyun Zhu,

Carolina Carpenter,

Serena Fraraccio,

Stephen Wandro,

Tomasz Kosciolek,

Stefan Janssen,

Jessica Metcalf,

Se Jin Song,

Jad Kanbar,

Sandrine Miller-Montgomery,

Robert Heaton,

Rana Mckay,

Sandip Pravin Patel,

Austin D. Swafford &

…

Rob Knight

Nature volume 631, page 694 (2024)

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The Original Article was published on 11 March 2020

Retraction to: Nature https://doi.org/10.1038/s41586-020-2095-1 Published online 11 March 2020

The Editors have retracted this article. After publication, concerns about the robustness of specific microbial signatures reported as associated with cancer were brought to the attention of the Editors[1]. The authors have provided responses to the issues in a separate publication[2].

Expert post-publication peer review of the issues raised and the authors’ responses has confirmed that some of the findings of the article are affected and the corresponding conclusions are no longer supported. All authors agree with this retraction.

References

1. Gihawi, A. et al. Major data analysis errors invalidate cancer microbiome findings. mBio 14, e01607–23 (2023).

2. Sepich-Poore, G. D. et al. Robustness of cancer microbiome signals over a broad range of methodological variation. Oncogene 43, 1127–1148 (2024).

Author information

Author notes

1. Evguenia Kopylova

Present address: Clarity Genomics, Beerse, Belgium

2. Tomasz Kosciolek

Present address: Malopolska Centre of Biotechnology, Jagiellonian University in Krakow, Krakow, Poland

3. Stefan Janssen

Present address: Algorithmic Bioinformatics, Department of Biology and Chemistry, Justus Liebig University Gießen, Gießen, Germany

4. These authors contributed equally: Gregory D. Poore, Evguenia Kopylova

Authors and Affiliations

1. Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

Gregory D. Poore, Sandrine Miller-Montgomery & Rob Knight

2. Department of Pediatrics, University of California San Diego, La Jolla, CA, USA

Evguenia Kopylova, Qiyun Zhu, Tomasz Kosciolek, Stefan Janssen, Se Jin Song & Rob Knight

3. Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA

Carolina Carpenter, Serena Fraraccio, Stephen Wandro, Se Jin Song, Sandrine Miller-Montgomery, Sandip Pravin Patel, Austin D. Swafford & Rob Knight

4. Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA

Jessica Metcalf

5. Department of Medicine, University of California San Diego, La Jolla, CA, USA

Jad Kanbar

6. Department of Psychiatry, University of California San Diego, La Jolla, CA, USA

Robert Heaton

7. Moores Cancer Center, University of California San Diego Health, La Jolla, CA, USA

Rana Mckay & Sandip Pravin Patel

8. Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA

Rob Knight

Corresponding author

Correspondence to Rob Knight.

About this article

Cite this article

Poore, G.D., Kopylova, E., Zhu, Q. et al. Retraction Note: Microbiome analyses of blood and tissues suggest cancer diagnostic approach. Nature 631, 694 (2024). https://doi.org/10.1038/s41586-024-07656-x

- Published: 26 June 2024

- Issue Date: 18 July 2024

- DOI: https://doi.org/10.1038/s41586-024-07656-x

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Table of Contents

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If bird flu sparks a human pandemic, your past immunity could help

Stars hint at an unusual black hole lurking in our Galaxy

How anti-obesity drugs cause nausea: finding offers hope for better drugs

This lab-grown meat probably tastes like real beef

How PhD students and other academics are fighting the mental-health crisis in science

Books & Arts

Kyoto review: ‘thrilling’ play shows fight for landmark climate treaty

The Napoleonic tweets, Books in brief

Opinion

Edward C. Stone obituary: physicist who guided Voyager probes to interstellar space

Make gene therapies more available by manufacturing them in lower-income nations

Work

Science on the edge: how extreme outdoor skills enhanced our fieldwork

Your reagent is past its use-by date. Should you bin it?

Serpents of the people: how a religious festival helps me to monitor snake behaviour

Research

Mechanical release of homogenous proteins from supramolecular gels

Chemical reservoir computation in a self-organizing reaction network

Human degradation of tropical moist forests is greater than previously estimated

A deep catalogue of protein-coding variation in 983,578 individuals

Transposase-assisted target-site integration for efficient plant genome engineering

Brainstem Dbh+ neurons control allergen-induced airway hyperreactivity

Semantic encoding during language comprehension at single-cell resolution

Spike deep mutational scanning helps predict success of SARS-CoV-2 clades

Plasmacytoid dendritic cells control homeostasis of megakaryopoiesis

Emergence of large-scale cell death through ferroptotic trigger waves

NBS1 lactylation is required for efficient DNA repair and chemotherapy resistance

Amendments & Corrections

Author Correction: Microglia regulate central nervous system myelin growth and integrity

Author Correction: Chemoproteomic discovery of a covalent allosteric inhibitor of WRN helicase

Author Correction: A high-performance neuroprosthesis for speech decoding and avatar control

Publisher Correction: Mental navigation in the primate entorhinal cortex

Retraction Note: Microbiome analyses of blood and tissues suggest cancer diagnostic approach