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This Week

- Failure is not an option for Africa’s newly launched medicines agency

- Official statistics are vastly undercounting deaths from extreme weather

- For real climate action, empower women

- ‘Almost utopian’: how protecting the environment is boosting the economy in Brazil

EDITORIAL 11 November 2025

Failure is not an option for Africa’s newly launched medicines agency

The inequitable distribution of vaccines during the COVID-19 pandemic was the final proof of the need for more home-grown manufacturing and regulatory capacity across Africa.

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People in African countries waited longer to receive COVID-19 vaccines than did people in high-income countries.Credit: Robert Bonet/NurPhoto/Getty

Who would have thought that the proverbial red carpet would be rolled out for a regulatory agency? That is what happened — and rightly so — with today’s launch of the African Medicines Agency (AMA), which is headquartered in Kigali.

As Nature Africa’s news team reports, the AMA is the most important development for health and science in Africa in years. Medicines regulators are the hidden stars of public health. It is their job to ensure that the pills we consume and the vaccines we receive are quality products, safe to use and do what they say on the label. The AMA has been a decade in the making (B. M. Ncube et al. J. Pharm. Policy Pract. 14, 29; 2021). Now that it has become reality, it must succeed.

The idea of a single medicines regulator for the African continent comes some 30 years after the creation of the European Medicines Agency (EMA), the European Union’s one-stop regulator, now based in Amsterdam. The EMA, which has been advising and funding the AMA during this initial phase, has a number of roles. High on the list is providing scientific advice on proposals for new medicines, vaccines and other medical products. This is often guidance on improving the design of studies so that they are as robust as possible.

However, the EMA is arguably best known for evaluating the safety and efficacy of products after laboratory tests and clinical trials. It can recommend authorization of a drug or vaccine so that it can be sold across the EU, but the actual decision to make it available rests with the European Commission and national regulatory bodies.

The AMA will draw on this experience, but its operating environment couldn’t be more different from that of European countries. The EU is home to some of the world’s largest pharmaceutical companies and some of the best-funded research groups in the areas that it regulates. African countries, by contrast, import more than 70% of medicines and 99% of vaccines. Each EU country has its own national regulatory body for medicines that takes care of authorizing clinical trials, another important part of the regulatory chain. Only 9 of 55 African Union member states have national regulators that have achieved the necessary quality threshold to be allowed to do this, as determined by the World Health Organization (N. Wairagkar et al. PLOS Glob. Public Health 5, e0004276; 2025). They are not authorized to approve vaccines and drugs — another reason why so many countries rely on imported products.

This means that, compared with its European counterpart, the AMA will need to do a lot more training and capacity building so that national authorities have the expertise needed to authorize medicines. It will need to work with universities, funding agencies and industry to build and establish research partnerships.

A core priority will be setting standards for the design and evaluation of clinical trials. Of some 1.3 million clinical trials conducted around the world between 1999 and 2024, less than 3% took place in Africa, write Mwila Mulubwa, Leon Mutesa and Kelly Chilabe in a Comment article in this week’s issue. This must be improved because, as the authors say, African populations are being given treatments that have not been properly tested among the people there, and could therefore be ineffective, or even detrimental. They recommend that the AMA should require candidate medicines and vaccines to be appropriately tested in Africa — both in clinical trials and (at the preclinical stage) using models derived from cells or tissues from individuals of African ancestry.

Funding and the future

A key question is who is going to pay for all of this. Regulatory bodies don’t come cheap — and they are often not a priority for governments when money is tight, as is the case now. The EMA costs around €600 million (US$694 million) to run each year. Estimates for the AMA are a lot lower. For the first five years, roughly €100 million has come from international donors — the European Commission, the EMA and two charitable foundations, the Gates Foundation in Seattle, Washington, and Wellcome in London. But in the medium-to-long run, African Union member states must find a way to shoulder more of the costs. The sudden decision this year by the United States to cease funding its overseas development agency, USAID — and the disaster it has wrought on whole communities of its beneficiaries — is a reminder of the risks of health systems relying wholly on external funders.

If the AMA is to have a stable local funding base, it needs to be fully supported by everyone on the continent. Twenty-five countries have fully ratified the treaty that brought the AMA into force, giving it the formal seal of approval within their national laws. The rest need to complete ratification without delay.

The COVID-19 pandemic laid bare the inequity at the heart of global health care. Countries that had thriving pharma industries and lots of cash procured vaccines more quickly than those that didn’t. African Union member states were right to declare a goal to bring manufacturing home to the continent. That requires safe, efficient and effective regulatory frameworks. The launch of the AMA is a key part of that process, and it must succeed.

Nature 647, 289 (2025)

doi: https://doi.org/10.1038/d41586-025-03668-3

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EDITORIAL 12 November 2025

Official statistics are vastly undercounting deaths from extreme weather

Research reveals that many more people lose their lives because of the effects of rainfall and flooding than are routinely accounted for.

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People wade through Mumbai’s flooded streets during monsoon rains in August.Credit: Indranil Aditya/NurPhoto/Getty

The monsoon season lasts from June to September in southern Asia. During this period, nearly 60% of Bangladesh’s population is at risk from floods. Last year, some five million people were affected by flash floods caused by heavy rains. Increasingly heavy rains are submerging parts of India and Pakistan, too. This year alone, 45% of India has experienced extreme rainfall, leading to some 1,500 deaths. And, in Pakistan, two million people have had to evacuate their homes and more than 800 have died.

More extreme weather events are among the expected impacts of climate change, but a study in Nature now shows that the reported number of excess deaths — how many more deaths occurred than would otherwise be expected — because of rainfall and flooding is probably a considerable undercount[1]. This work is part of an expanding body of knowledge suggesting that many more people are vulnerable to extreme weather events than are being recorded in official data.

Such studies need to re-energize discussions taking place this week at the COP30 climate conference in Belém, Brazil — including those about the size and make-up of a fund being set up to compensate people for the losses and damages caused by climate change. Some initial reports from Brazil suggest that expectations for COP30 are low even by the standards of COP meetings. And that isn’t good news for anyone.

In many countries, calculations of deaths from heavy rains are based on whether relevant words such as flooding or drowning are mentioned on a death certificate. However, fatalities caused indirectly by rainfall or flooding — as when someone loses their life because of electrocution, a waterborne infectious disease or falling debris — will not mention water as the cause of death. Researchers say that this is a key reason why the actual numbers are likely to be higher than those recorded officially.

In their study, economists Tom Bearpark at Princeton University in New Jersey, Ashwin Rode at the University of Chicago in Illinois and Archana Patankar at Green Globe Consulting in Mumbai, India, aimed to improve the accuracy of rainfall-related mortality assessments. Their modelling study focuses on Mumbai. As well as being India’s financial hub, the city also houses at least one million people who live in informal settlements. These consist of unsafe, often overcrowded, homes that lack basic necessities such as running water, access to a toilet and electricity.

The researchers found that, between 2006 and 2015, some 2,500 lives were lost yearly during the monsoon. They also show that many who died from rainfall-related causes were young children and women, and most lived in informal settlements. Their figure for mortality from rainfall in Mumbai is an order of magnitude greater than that recorded officially for the state of Maharashtra, which includes Mumbai, for 2006–14.

Bearpark, Rode and Patankar are not alone in their conclusion that deaths caused by extreme weather events are being undercounted in official statistics. In 2024, scientists Rachel Young and Solomon Hsiang at the University of California, Berkeley, estimated[2] that, between 1930 and 2015, 7,000–11,000 excess deaths annually in the United States were caused by 501 tropical cyclones (or hurricanes). This contrasts with an average of 24 deaths reported in government statistics as directly attributable to such events. Unrecorded causes included limited or no access to health care, and conditions such as sudden infant death syndrome and cardiovascular diseases. Very young children (under one year old), individuals aged 65 years or older and Black people were the most vulnerable. Other studies using similar approaches have found that deaths from extreme heat are also being undercounted.

Recording the number and causes of deaths accurately matters. It matters to families so they know why loved ones have died. Knowing that many more people are at risk of death from extreme weather events also matters to all those in positions of responsibility in their countries, because it means that they can do something about the underlying issues before it is too late.

We know, for example, that around 25% of the world’s urban population lives in informal settlements, a number that has been increasing since 2020. Efforts must be redoubled to improve conditions given that people living in such housing are the most vulnerable to extreme weather.

At the same time, a fund for compensating poorer countries for the climate impacts caused mainly by the emissions of richer ones will need to be resourced sufficiently. The new loss-and-damage fund will issue its first call for proposals at COP30. Last year, an independent expert group on climate finance estimated that poorer countries would need at least US$250 billion a year by 2030 to pay for losses and damages from extreme-weather effects[3]. Taking excess mortality as a proxy, those costs are probably an underestimate, too. These are all reasons for COP delegates to bite the bullet and agree to a credible, accelerated plan to reduce the root cause of extreme weather: planet-warming emissions.

Nature 647, 290 (2025)

doi: https://doi.org/10.1038/d41586-025-03669-2

References

1. Bearpark, T., Rode, A. & Patankar, A. Nature https://doi.org/10.1038/s41586-025-09730-4 (2025).

2. Young, R. & Hsiang, S. Nature 635, 121–128 (2024).

3. Bhattacharya, A., Songwe, V., Soubeyran, E. & Stern, N. Raising Ambition and Accelerating Delivery of Climate Finance (Grantham Research Institute on Climate Change and the Environment, London School of Economics and Political Science, 2024).

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WORLD VIEW 11 November 2025

For real climate action, empower women

In Belize, women are tackling climate change and biodiversity loss on the ground, but gender equity is needed in government, too — COP30 should address this.

By

Elma Kay

This week, as world leaders gather in Belém, Brazil, for the 30th United Nations Climate Change Conference (COP30), once again it seems that there will be more rhetoric than real solutions for the countries that are most vulnerable to the effects of a changing climate.

My native Belize — with its tropical rainforests, coastal wetlands and coral reefs — is one such country. Warming seas are killing our reefs. Hurricanes and wildfires are increasing in frequency and intensity.

As a biologist with two decades of experience in academia and conservation, I have learnt that, to protect natural resources, local communities must be empowered to steward these efforts — and that women are often best placed to drive them, at least in Latin America and the Caribbean, but probably everywhere.

In many cultures, women are the de facto main carers and custodians of community knowledge. They are often in positions, paid or otherwise, to make their communities healthier, more prosperous and more peaceful — making them well-suited to lead climate action and conservation work.

I am the managing director of the Belize Maya Forest Trust, a non-profit organization dedicated to conserving nearly 1,000 square kilometres of the country’s tropical forest. I also chair the board of directors of the Belize Network of NGOs, an umbrella organization for many of the more than 100 non-governmental organizations (NGOs) in the country. In Belize, many prominent conservation organizations are led by women. Take the Community Baboon Sanctuary, a globally recognized conservation effort that protects the habitat of the Yucatán black-howler monkey (Alouatta pigra). Today, the sanctuary is managed by the Community Baboon Sanctuary Women’s Conservation Group, which is directed by women from seven Belizean villages.

And over the past five years, working with the government of Belize and The Nature Conservancy, a global environmental organization headquartered in Arlington, Virginia, women in Belize have had key roles in the execution of game-changing sustainable-financing initiatives. One is Belize’s first large-scale project to reduce emissions by avoiding deforestation, which is helping to protect the Belize Maya Forest, an area that includes some of the world’s most biodiversity-rich lands. Another is Belize Blue Bonds — which, when the deal closed in November 2021, was the world’s largest debt-refinancing scheme for ocean conservation.

The more I participate in advocacy and interact with civil-society organizations — both in Belize and internationally — the more I find myself in rooms full of women striving to protect the environment and defend human rights.

But I have also noticed that women are not usually negotiating for funds, deciding whether to launch large initiatives or setting national priorities.

Belize is again a case in point. In any of the ten elections since Belize gained independence in 1981, at most four women have been elected to the 28–31-member House of Representatives, the most powerful arm of the country’s legislature. Other female parliamentarians have been appointed by the prime minister, but these positions tend to have much less authority than do those elected through popular vote. Also, female ministers are usually put in charge of human or rural-development issues, rather than the country’s economy, land, infrastructure or defence.

For countries to build resilience and protect their biodiversity in the time needed, more women must be made equal partners in decision making.

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Nature 647, 291 (2025)

doi: https://doi.org/10.1038/d41586-025-03630-3

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Competing Interests

The author declares no competing interests.

WORLD VIEW 11 November 2025

‘Almost utopian’: how protecting the environment is boosting the economy in Brazil

This bold approach could galvanize countries attending the COP30 summit.

By

Carolina Grottera

As Brazil hosts the United Nations climate summit COP30 in Belém, its government is taking steps to boost the economy without damaging the environment. The goal sounds almost utopian: by 2050, Brazil aims to double income per capita, slash emissions of every greenhouse gas to attain ‘net zero’, and distribute wealth more fairly to achieve a 20% reduction in the Gini index — a metric that reflects income inequality.

There has already been substantial progress. For example, deforestation has substantially decreased since the start of President Luiz Inácio Lula da Silva’s latest term in 2023, and is now at its lowest rate for 11 years (see go.nature.com/442bqtp). As a member of the team in charge of the Ecological Transformation Plan at Brazil’s Ministry of Finance, I hope that our early successes will encourage other nations to proceed in a similar direction.

Two initiatives, in particular, could fundamentally reshape global climate finance — the funding systems that support climate mitigation and adaptation efforts. The first is the Tropical Forest Forever Facility (TFFF), a fund ran by participating nations, with support from the World Bank, to help low- and middle-income countries (LMICs) to preserve their tropical forests. The fund aims to reach US$125 billion in the coming years. An initial investment of $25 billion should come from nations and philanthropic organizations. Brazil has pledged to invest the first $1 billion, and other countries, including Norway, Indonesia, France and Germany, have announced substantial contributions. Another $100 billion is expected to come from capital markets, through the issuing of bonds that pay interest at the market rate.

The fund is made up of investments rather than donations — the distinction matters. Experience shows that aid flows are volatile and are subject to political winds and fiscal constraints in donor countries. The TFFF breaks this cycle. Here’s how it works: sponsors invest in the fund and receive annual returns that are comparable with those from low-risk sovereign bonds, at, say, 4%. The fund deploys this capital in a diversified portfolio of fixed-income assets, mainly bonds from emerging markets, with an expected yield of 7–8% (historically, a representative portfolio of diversified emerging-market bonds has paid out at around that rate). This portfolio excludes investments that finance fossil fuels or activities linked to deforestation.

When mature, this mechanism could generate nearly $4 billion annually for forest conservation — rewarding ecosystem services and paying for anti-deforestation enforcement and capacity-building to foster the bioeconomy, such as biotechnology for pharmaceutical production.

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Nature 647, 292 (2025)

doi: https://doi.org/10.1038/d41586-025-03631-2

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News in Focus

- ‘Biotech Barbie’ says the time has come to consider CRISPR babies. Do scientists agree?

- ‘Mind-captioning’ AI decodes brain activity to turn thoughts into text

- Personalized gene editing helped one baby: can it be rolled out widely?

- COVID-19 is spreading again — how serious is it and what are the symptoms?

- Biggest black-hole outburst ever seen records death throes of a star

- Chinese scientists increasingly lead joint projects with the UK, US and Europe

- Why India’s controversial ‘cloud seeding’ trial failed to make it rain

- How much protein do you really need? What the science says

- The computers that run on human brain cells

NEWS 03 November 2025 Correction 06 November 2025

‘Biotech Barbie’ says the time has come to consider CRISPR babies. Do scientists agree?

A company’s plan to edit the genomes of human embryos worries some researchers — but it might reflect the changing attitudes towards the controversial approach.

By

Heidi Ledford

Entrepreneur Cathy Tie’s company Manhattan Genomics will work on methods to edit the genomes of human embryos. Credit: Caitlyn Gaurano

Cathy Tie left university to found her first biotechnology company at the age of 18. In the 11 years since, she has launched several more. Her first company helped genetic-testing firms to interpret their results; her second provides digital health-care services.

Her latest venture, which announced some of its first key hires on 30 October, veers out of the mainstream. Tie, who has called herself Biotech Barbie, sometimes refers to her latest company as the Manhattan Project — the name used for the US effort to develop an atomic bomb in the 1940s — and now focuses her entrepreneurial ambitions on a controversial goal: altering the genomes of human embryos to prevent genetic disorders.

“We have a duty to patients with incurable, debilitating diseases,” says Tie. “A majority of Americans are in support of this technology.”

Plenty of scientists, however, are worried. Manhattan Genomics, the official name of her latest company, based in New York City, was launched this summer. Tie co-founded the firm with Eriona Hysolli, former head of biological sciences at Colossal Biosciences, a firm based in Dallas, Texas, that focuses on de-extincting species. Another company, Preventive in South San Francisco, California, announced on 30 October that it also intends to explore gene editing in human embryos.

So far, neither company has revealed the details of its scientific plans, such as which diseases it will target, and which techniques it will use. Tie says Manhattan Genomics will conduct extensive research and safety testing before attempting to create gene-edited babies. Among the new consultants announced last week are a bioethicist and two scientists with expertise in non-human-primate reproductive biology — skills that would be needed for testing the safety of embryo editing.

Even so, some researchers say it is much too early to consider commercializing gene-editing technologies for human embryos — a process that carries added safety risks and ethical quandaries compared with the gene-editing therapies that are currently on the market to treat blood conditions in children and adults.

“The bar for safety is so, so, so, so high,” says Alexis Komor, a biochemist at the University of California San Diego, who studies gene-editing technologies. “We’re definitely not there yet.”

CRISPR therapies

For years, the gene-editing field has laboured in the shadow of He Jiankui, a Chinese biophysicist who, in 2018, announced that he had edited human embryos to bolster the resulting children’s resistance to HIV. Those embryos were implanted into the mother, and two gene-edited girls were born. He was then sentenced to three years in prison for “illegal medical practice”. (Tie and He had a personal relationship earlier this year, but Tie says the two have since parted and that He is not involved in Manhattan Genomics.)

Scientists largely condemned his work, with many calling for a moratorium on all clinical uses of heritable gene editing in humans. Some countries have restrictions on such research. In the United States, for example, federal funds cannot be used for gene-editing studies in human embryos, and the US Food and Drug Administration cannot approve clinical use of genetically manipulated embryos.

Amid this turmoil, gene editing in non-reproductive cells has raced ahead. The world’s first approved gene-editing therapy uses the DNA-snipping system called CRISPR–Cas9 to edit DNA in blood stem cells. The therapy, which was approved in 2023, treats two genetic blood disorders. Earlier this year, a related method called base editing was used to edit DNA in liver cells in a bespoke treatment for an infant with a metabolic disorder.

Side effects

But editing non-reproductive cells is different, ethically and scientifically, from editing embryos, says Junjiu Huang, a biologist who studies reproductive development at Sun Yat-sen University in Guangzhou, China. Gene-editing tools might behave differently in an embryo than in non-reproductive cells. Editing an embryo also means that genetic changes will be passed down to the next generation, with consequences that are difficult to predict.

Unwanted DNA changes — a possibility with any gene-editing technology — could have more catastrophic effects in the embryo than in a single organ, because they can affect every cell in the body during crucial stages in development, says Komor. And a vanishingly small number of people would need to edit genes in their embryos, she says, given that many can already use genetic tests to screen embryos for disease-causing mutations.

Tie and Hysolli note that methods used to edit genes have evolved considerably since He’s experiments. Newer techniques, such as base editing and another approach called prime editing, offer improved precision compared with conventional CRISPR–Cas9 editing. Neither of these techniques requires breaking both strands of DNA, a necessity for the original CRISPR gene-editing method and a step that can cause drastic chromosomal changes in embryos. “A lot of new advances have happened in the gene-editing space to make it safer and more accurate,” says Tie.

Eriona Hysolli, who co-founded Manhattan Genomics, is a veteran of de-extinction company Colossal Biosciences.Credit: Caitlyn Gaurano

But researchers are still learning the full gamut of unwanted genetic changes that base editing can cause, says Komor. Even less is known about newer methods such as prime editing, she adds.

In 2015, Huang and his colleagues were the first to gene edit human embryos[1]. (The embryos were not implanted in a mother.) Since then, he has continued to work with embryos using base editing, and says the technique holds promise, but is not ready for the clinic. Hysolli agrees that the methods need to be studied further, particularly in embryos. The approach that Manhattan Genomics eventually chooses will be dictated at least in part by the diseases it decides to focus on first, she says.

Huang describes the timing of Manhattan Genomics’ launch as “inappropriate”. The technology is not yet mature, he says; nor are the ethics, social consensus and legal framework for the technology’s use.

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Nature 647, 295-297 (2025)

doi: https://doi.org/10.1038/d41586-025-03554-y

Updates & Corrections

- Correction 06 November 2025: An earlier draft of this story referred to the new consultants to Manhattan Genomics as ‘employees’ rather than ‘consultants’ and misstated when their names were announced. The earlier draft also included a picture caption that misstated the name of the firm where Eriona Hysolli used to work.

References

1. Liang, P. et al. Protein Cell 6, 363–372 (2015).

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NEWS 05 November 2025

‘Mind-captioning’ AI decodes brain activity to turn thoughts into text

A non-invasive imaging technique can translate scenes in your head into sentences. It could help to reveal how the brain interprets the world.

By

Max Kozlov

Functional magnetic resonance imaging is a non-invasive way to explore brain activity. Credit: National Institute of Mental Health/National Institutes of Health/SPL

Reading a person’s mind using a recording of their brain activity sounds futuristic, but it’s now one step closer to reality. A technique called ‘mind captioning’ generates descriptive sentences of what a person is seeing or picturing in their mind using a read-out of their brain activity, with impressive accuracy.

The technique, described in a paper published today in Science Advances[1], also offers clues for how the brain represents the world before thoughts are put into words. And it might be able to help people with language difficulties, such as those caused by strokes, to better communicate.

The model predicts what a person is looking at “with a lot of detail”, says Alex Huth, a computational neuroscientist at the University of California, Berkeley. “This is hard to do. It’s surprising you can get that much detail.”

Scan and predict

Researchers have been able to accurately predict what a person is seeing or hearing using their brain activity for more than a decade. But decoding the brain’s interpretation of complex content, such as short videos or abstract shapes, has proved more difficult.

Previous attempts have identified only key words that describe what a person saw rather than the complete context, which might include the subject of a video and actions that occur in it, says Tomoyasu Horikawa, a computational neuroscientist at NTT Communication Science Laboratories in Kanagawa, Japan. Other attempts have used artificial intelligence (AI) models that can create sentence structure themselves, making it difficult to know whether the description was actually represented in the brain, he adds.

Horikawa’s method first used a deep-language AI model to analyse the text captions of more than 2,000 videos, turning each one into a unique numerical ‘meaning signature’. A separate AI tool was then trained on six participants’ brain scans and learnt to find the brain-activity patterns that matched each meaning signature while the participants watched the videos.

Once trained, this brain decoder could read a new brain scan from a person watching a video and predict the meaning signature. Then, a different AI text generator would search for a sentence that comes closest to the meaning signature decoded from the individual’s brain.

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Nature 647, 297 (2025)

doi: https://doi.org/10.1038/d41586-025-03624-1

References

1. Horikawa, T. Sci. Adv. 11, eadw1464 (2025).

2. Tang, J., LeBel, A., Jain, S. & Huth, A. G. Nature Neurosci. 26, 858–866 (2023).

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NEWS 31 October 2025

Personalized gene editing helped one baby: can it be rolled out widely?

In a world first, a bespoke gene-editing therapy benefited one child. Now researchers plan to launch a clinical trial of the approach.

By

Heidi Ledford

Base editors can modify a DNA sequence (shown here in a coloured autoradiogram) at specific target sites, allowing disease-causing mutations to be corrected.Credit: Hank Morgan/Science Photo Library

Late last year, dozens of researchers spanning thousands of miles banded together in a race to save one baby boy’s life. The result was a world first: a cutting-edge gene-editing therapy fashioned for a single person, and produced in a record-breaking six months[1].

Now, baby KJ Muldoon’s doctors are gearing up to do it all over again, at least five times over. And faster.

The ground-breaking clinical trial, described on 31 October in the American Journal of Human Genetics[2], will deploy an offshoot of the CRISPR–Cas9 gene-editing technique called base editing, which allows scientists to make precise, single-letter changes to DNA sequences. The study is expected to begin next year, after its organizers spent months negotiating with US regulators over ways to simplify the convoluted path a gene-editing therapy normally has to take before it can enter trials.

Developing KJ’s treatment was “a pretty hectic and intense six months”, says Kiran Musunuru, a cardiologist at the Perelman School of Medicine at the University of Pennsylvania in Philadelphia, and one of KJ’s doctors. “But I think we can get it shorter.”

The trial is also the next step towards answering a question that has hung over many families of children with rare diseases since the news broke of KJ’s successful treatment: when will it be our turn? “There is no ‘one size fits all’ in this space,” says Ryan Maple, executive director of the Global Foundation for Peroxisomal Disorders in Tulsa, Oklahoma.

Momentum seems to be building. In addition to the planned clinical trial in Philadelphia, the Center for Pediatric CRISPR Cures, which launched in July at the University of California, Berkeley and the University of California, San Francisco, also aims to develop personalized gene-editing therapies. And in September, the US government’s Advanced Research Projects Agency for Health announced two programmes to fund research into the development and manufacturing of “precision genetic medicine”.

“I’m more optimistic now than I have been in the past,” says Joseph Hacia, a medical geneticist at the Keck School of Medicine at the University of Southern California in Los Angeles.

Therapy for one

In August last year, soon after KJ Muldoon was born, doctors realized that he had a genetic mutation that meant he was unable to produce the normal form of a crucial liver enzyme called carbamoyl phosphate synthetase 1 (CPS1). CPS1 detoxifies ammonia, a natural waste product formed when the body breaks down protein. Ammonia can damage the brain if it is not removed from the bloodstream, and many children with CPS1 deficiency do not live long enough to receive the only known cure: a liver transplant.

But one of KJ’s doctors, Rebecca Ahrens-Nicklas at the Children’s Hospital of Philadelphia in Pennsylvania, had been working with Musunuru to develop a base-editing therapy that could be deployed rapidly to treat children with metabolic disorders. KJ would be the first recipient.

In late February, KJ received a base-editing therapy designed for him, and him alone. CPS1 deficiency occurs in around one in one million births. The therapy KJ received was designed to find one of the incorrect letters in the DNA sequence of his CPS1 gene and replace it with a different letter that would allow the full CPS1 protein to be produced.

After the therapy, KJ’s ammonia levels dropped, and he was able to reduce his medications. Since then, he’s been hard at work, learning to stand on his own, eating solid foods and working towards taking his first steps. “We celebrate each milestone that KJ accomplishes,” says his mother, Nicole Aaron. “He has a radiance about him that really brightens up every room he enters.”

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Nature 647, 298-299 (2025)

doi: https://doi.org/10.1038/d41586-025-03566-8

References

1. Musunuru, K. et al. N. Engl. J. Med. 392, 2235–2243 (2025).

2. Ahrens-Nicklas, R. C. & Musunuru, K. Am. J. Hum. Genet. https://doi.org/10.1016/j.ajhg.2025.10.006 (2025).

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NEWS 04 November 2025

COVID-19 is spreading again — how serious is it and what are the symptoms?

Limited COVID-19 surveillance data are hampering vaccination and health strategies, researchers say.

By

Katie Kavanagh

Surveillance data used to track the number of SARS-CoV-2 infections are less robust than during the pandemic.Credit: Orlando Sierra/AFP via Getty

SARS-CoV-2 infections have been rising in the past month — global cases increased by more than 19,000 last month compared with the previous month, according to data posted on the World Health Organization (WHO) COVID-19 dashboard.

But the real number of infections is much higher than that, researchers say, because countries are less focused on collecting data on the infection now than they were during the COVID-19 pandemic.

“Surveillance is happening but it’s at a much lower level than it used to be. We don’t have a complete picture of virus circulation of the variants that are out there,” says Maria Van Kerkhove, interim director of the department of epidemic and pandemic management at the WHO in Geneva, Switzerland. “I think there’s a collective amnesia right now about COVID-19,” she adds.

Even if people do test positive after using a home antibody test, there is no way to report a positive result in the community, says Antonia Ho, a clinical epidemiologist at the University of Glasgow, UK.

Without high-quality surveillance data, Ho warns that health organizations are not well-prepared to recommend corresponding vaccine formulations and time their roll-out. “Surveillance is critical to really understand what’s circulating,” she says.

How is COVID-19 being tracked?

Although surveillance data that are used to track the absolute number of SARS-CoV-2 infections are less robust than during the pandemic, researchers can still track information about the number of people with severe COVID-19 who require hospitalization. “Hospital-based surveillance is what we’re mostly doing. But we also have wastewater surveillance, which is quite a useful indicator of what’s happening in the community,” Ho says.

Genomic analysis of samples containing the SARS‑CoV‑2 virus shows that the most common variants currently circulating globally are XFG, also known as Stratus or the ‘Frankenstein variant’, and NB.1.8.1, known as Nimbus. Stratus accounts for 76% of reported cases and is dominant in Europe and the Americas, whereas Nimbus is dominant in the Western Pacific region, accounting for 15% of cases, says Van Kerkhove.

Stratus and Nimbus have similar symptoms to previous variants, including a fever, cough and runny nose, but the Nimbus variant has one distinct symptom: a ‘razor blade’ sore throat. These two variants are currently on the WHO’s list of ‘currently circulating COVID-19 variants under monitoring’ as of 4 September.

Van Kerkhove says the latest information about SARS‑CoV‑2 strains is far from the complete picture of viral circulation. Even hospitalization data aren’t as complete as they were during the pandemic — fewer than 35 countries still report COVID-19 data. “That’s why we’re trying to make sure that countries are still doing sequencing,” says Van Kerkhove.

However, she adds that the surveillance data currently available are sufficient to “provide information to governments related to the effectiveness of vaccines”.

Is COVID-19 seasonal?

Vaccination campaigns are focused on people over the age of 65 in the United States, or 75 in the United Kingdom and parts of Europe, and those with a weakened immune system who are older than 6 months. Michael Head, an epidemiologist at the University of Southampton, UK, says he would prefer a wider vaccine roll-out for younger populations as well. “It’s still not a pleasant infection, and vaccines do still have a significant public-health benefit, so they are a vital tool in addressing the threat posed by COVID-19.”

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Nature 647, 299-300 (2025)

doi: https://doi.org/10.1038/d41586-025-03412-x

References

1. Byun, W. S. et al. Environ. Res. 196, 110972 (2021).

2. Montcho, Y. et al. Model. Earth Syst. Environ. 11, 114 (2025).

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NEWS 04 November 2025

Biggest black-hole outburst ever seen records death throes of a star

A ‘superflare’ 10 trillion times brighter than the Sun is confirmed as the record-holder for luminosity.

By

Jenna Ahart

The slow consumption of a star by a black hole (artist’s illustration) unleashed a blaze of light that remained bright even after five years. Credit: Caltech/R. Hurt (IPAC)

Black holes can get energy boosts by ‘snacking’, although their dish of choice is rather different from our own. Analysis suggests that the most luminous burst of light ever detected from a black hole — a fireworks show that was, at its peak, more than 10 trillion times brighter than the Sun — flared up as the black hole gobbled up a star that was at least 30 times as massive as the Sun.

The findings were published on 4 November in Nature Astronomy[1].

When astronomers first laid eyes on the object in 2018, they didn’t realize it was a superflare. After noticing the object brighten, researchers zeroed in on it with the Palomar Observatory’s 5.1-metre Hale Telescope. But a graph of the light emitted by the object proved disappointing. “It didn’t seem nearly as interesting as we thought it was,” says Matthew Graham, an astronomer at the California Institute of Technology in Pasadena and a co-author of the paper.

However, in 2023, the team noticed that, even after five years, the black hole remained peculiarly bright. So they took a closer look using the W. M. Keck Observatory in Hawaii, which revealed that the object was roughly 3 million kiloparsecs, or 10 billion light years, away. To appear so bright at such a great distance, the jets of light must have been particularly luminous. Astronomers now say that the flare is 30 times more luminous than any previously detected blaze of light from a black hole.

A trick of the light?

The authors investigated several possible causes of the flare. Perhaps there was a supernova near the black hole, or the flare was merely a trick of the light, appearing much brighter than it was in reality because of gravity’s warping effects. But the team found that neither explanation matched well with observations.

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Nature 647, 300 (2025)

doi: https://doi.org/10.1038/d41586-025-03597-1

References

1. Graham, M. J. et al. Nature Astron. https://doi.org/10.1038/s41550-025-02699-0 (2025).

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NEWS 05 November 2025

Chinese scientists increasingly lead joint projects with the UK, US and Europe

Although China needs to strengthen its leadership in artificial intelligence, semiconductors and energy, say researchers.

By

Mohana Basu

Chinese researchers are more likely to lead projects with the United Kingdom and countries in the European Union than with the United States. Credit: Dai Bin/Xinhua/Alamy

The number of Chinese scientists taking on leadership roles in international science projects is growing rapidly. They now lead more than half of all research projects with the United Kingdom, and are expected to lead an equal number of projects with countries in the European Union and with the United States in the next couple of years, according to a study[1] published in the Proceedings of the National Academy of Sciences last week.

Hongjun Xiang, a physicist at Fudan University in Shanghai, China, says the projections are consistent with what he has observed in the country, particularly in fields such as physics and engineering. But China needs to strengthen its leadership capabilities in disruptive basic research, “as Nobel-level original breakthroughs remain rare”, he adds.

To understand how scientific leadership is changing, researchers analysed authorship data from nearly six million scientific publications. The team analysed ‘author contribution’ statements on journal manuscripts, in which each author’s role is described. When such statements weren’t available, the team developed a model that could predict leadership roles on the basis of author experience, citation histories and the ideas researchers brought from their previous work, says James Evans, a co-author and computational sociologist at the University of Chicago in Illinois.

Researchers who conceived, designed and guided the project or offered mentorship were classified as leaders; first-year students, people providing tech support and those performing experiments under direction were considered followers.

From there, Evans and his colleagues devised two parameters to assess the changes in scientific leadership in bilateral partnerships. Lead share describes the number of people in leadership roles from a given country. Lead premium is the ratio of leaders to followers on the paper.

Rising leaders

The team found that China’s lead share in US–China collaborations rose from 30% in 2010 to 45% in 2023. China’s lead premium, however, is progressing more slowly. Chinese scientists still have supporting roles in many of these projects, says Evans. They are more likely to lead projects when they work with groups in the United Kingdom and EU, he adds.

According to the research, in 2019, China’s lead share reached parity with the United Kingdom’s; it is likely to be on a par with the EU lead share by 2025–27 and the US lead share by 2027–28. In some crucial technology areas, such as artificial intelligence, semiconductors and energy, Chinese leadership is further behind and is expected to catch up to the United States by 2030.

Xiang says that, in the development of core technologies, such as semiconductors, the country faces significant ‘chokehold’ challenges, including the US government’s ban on selling NVIDIA AI chips to China since 2022. China’s dependency on US tech cannot be easily overcome, because it is rooted in decades of deep scientific research, says Xiang. “Addressing these structural imbalances is the key to our journey from being a big science country to a truly strong one, and continued international collaboration remains vital for this endeavour,” Xiang adds.

But Evans says the findings upend the assumption that the United States can shut China out of global scientific opportunities by not collaborating with its researchers. Some US lawmakers want restrictions on research collaborations with Chinese institutions that work with the country’s military. Simulations show that “if the US were to stop collaborating with China on projects related to critical technologies — such as space, artificial intelligence or quantum computing — it would prove to be very costly for the US”, he says.

Evans says that US–China collaborations, particularly in fields such as artificial intelligence, are more likely to be successful than work done separately.

Cultural differences

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Nature 647, 301-302 (2025)

doi: https://doi.org/10.1038/d41586-025-03617-0

References

1. Wu, R., Esposito, C. & Evans, J. Proc. Natl Acad. Sci. USA 122, e2414893122 (2025).

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NEWS 31 October 2025

Why India’s controversial ‘cloud seeding’ trial failed to make it rain

According to the Indian government, the experiment helped to reduce air pollution — but researchers attribute that to the weather.

By

Mohana Basu

Pollution in Delhi has hit hazardous levels, prompting the Indian government to try cloud seeding as a radical approach to tackling the problem.Credit: Prakash Singh/Bloomberg via Getty

An ambitious attempt by the Indian government to combat Delhi’s hazardous air pollution by inducing artificial rain has failed to trigger any precipitation, prompting researchers to question why the experiment went ahead.

The experiment was conducted in collaboration with the Indian Institute of Technology (IIT) Kanpur on 28 October. Two aircraft flew over several areas of Delhi and released flares containing particles of silver iodide and sodium chloride into the atmosphere, with the goal of catalysing condensation and producing rain.

The Delhi government and IIT Kanpur say that the seeding experiment led to a slight decrease in pollution, but scientists think the improvement had little to do with the intervention.

“It seems like the skies have cleared, but it is because the weather conditions have changed. That’s no credit to the seeding efforts,” says Shahzad Gani, an aerosol scientist at IIT Delhi.

Toxic smog

Every winter for almost a decade, Delhi and surrounding areas have battled hazardous levels of fine particulate matter in the air, produced by industry, vehicle emissions and the burning of crop stubble in neighbouring states. On some days, pollution levels are up to 20 times higher than the World Health Organization’s recommended limits.

In October and November, the air in and around Delhi becomes cooler, drier and more stable, which slows the dispersal of pollutants. The Delhi government has tried taking various measures to improve air quality, such as shutting down polluting industries and banning firecrackers during Diwali festivities. But poor compliance has meant that most of these initiatives have had little effect. This year, when pollution levels became hazardous the night after Diwali celebrations, the government decided to take a different approach.

The technique of cloud seeding — firing small particles such as silver iodide and sodium chloride into existing clouds to help water vapour to condense — has been around since the mid-twentieth century. But reliable proof that the process can induce rainfall on demand is lacking, and its ability to disperse pollution has not been tested, says Gani.

Roxy Mathew Koll, a climate scientist at the Indian Institute of Tropical Meteorology, Pune, says the atmospheric conditions were not suitable for the experiment to succeed. “Cloud seeding can only enhance rainfall if there are already moist, convective clouds with sufficient liquid water. In dry or stagnant air, there’s simply nothing to seed,” Koll says. At the time of the experiment, “two storms — a depression in the Arabian Sea and a cyclone in the Bay of Bengal — were pulling the moisture into those areas and making Delhi drier”.

Another trial, scheduled for Wednesday, was cancelled. Manindra Agrawal, director of IIT Kanpur, told Indian news agency ANI that the experiment will be postponed until a day when the moisture content of clouds is at least 40–50%.

Earlier this year, the Royal Rainmaking Project in Thailand tried to combat pollution by spraying cold water from aeroplanes, hoping that this would allow some of the particulate matter trapped in a layer of warm air in the atmosphere to dissipate.

Long-term solution

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Nature 647, 302 (2025)

doi: https://doi.org/10.1038/d41586-025-03555-x

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NEWS FEATURE 12 November 2025

How much protein do you really need? What the science says

Fitness influencers promote super-high-protein diets, but studies show there’s only so much the body can use.

By

Mariana Lenharo

Social-media trends are encouraging people to consume more protein than most official guidelines recommend. Credit: Akos Stiller/Bloomberg/Getty

Snack bars, yogurts, ice cream, even bottled water: it seems like food makers have worked out ways to slip extra protein into just about anything as they seek to capitalize on a growing consumer trend.

Today, protein-fortified foods and protein supplements form a market worth tens of billions of US dollars, with fitness influencers, as well as some researchers and physicians, promoting high-protein diets as the secret to strength and longevity. Protein is undeniably essential, but how much people really need is still a topic of debate.

On the one hand, most official guidelines recommend a minimum of close to one gram of protein per kilogram of body weight per day, or the equivalent of about 250 grams of cooked chicken (which contains around 68 g of protein) for an adult weighing 70 kilograms. On the other hand, a growing narrative in wellness circles encourages people to eat more than double that amount. Many scientists fall somewhere in the middle and take issue with some of the advice circulating online.

“It’s really frustrating because there isn’t evidence to support the claims that they’re making,” says Katherine Black, an exercise nutritionist at the University of Otago in Dunedin, New Zealand, referring to the super-high protein recommendations often shared on social media. What research does show is that protein needs can vary from person to person and can change throughout a lifetime. And people should think carefully about what they eat to meet those needs. “On social media, it’s like everyone’s worried about protein, putting protein powder into everything,” she says.

Health authorities can help to guide people’s dietary choices on the basis of the latest research. The next Dietary Guidelines for Americans, a document that advises on what to eat for maintaining a healthy lifestyle, is due to come out by the end of this year. But its recommendations, which have tended to be broadly influential, might be changing.

Calculating protein needs

Researchers have been trying to estimate how much protein people need for more than a century. In 1840, chemist Justus von Liebig estimated that the average adult required 120 grams of protein a day, on the basis of a group of German workers’ diets. Later, scientists started to use nitrogen to calculate protein requirements. Protein is the only major dietary component that contains nitrogen. So, by measuring how much of it people consume and the amount they excrete, researchers could estimate how much the body uses.

Since the 1940s, this nitrogen-balance method has been used to determine the Recommended Dietary Allowances (RDA), a set of nutrient recommendations developed by the US National Academies of Sciences, Engineering, and Medicine.

The latest such recommendation for protein, from 2005, establishes the RDA for both men and women at 0.8 grams per kilogram of body weight per day, which it states should be enough to meet the needs of 97–98% of healthy people. European and global-health authorities recommend similar or slightly higher levels.

Although scientists recognize that RDAs are a useful reference point, many say that people could benefit from a higher amount. “The RDA is not a target; it’s simply the minimum that appears to prevent any detectable deficiency,” says Donald Layman, a researcher focusing on protein requirements at the University of Illinois Urbana–Champaign. Evidence suggests[1] that the optimal range is between 1.2 and 1.6 grams of protein per kilogram of body weight per day, he says.

That is especially true for older adults, who often experience muscle loss as they age, as well as for certain athletes and people trying to gain muscle.

For example, in an observational study of 2,066 adults aged 70–79, those who reported eating the most protein — about 1.1 gram per kilogram of bodyweight — lost 40% less lean mass during the three years of follow-up than did those who ate the least — around 0.7 grams per kilogram[2].

“For older adults, 1.2 grams per kilogram is just giving them a little extra protection,” says Nicholas Burd, a nutrition and exercise researcher also at the University of Illinois Urbana–Champaign. Furthermore, older people might experience a decline in appetite, which makes it particularly important for them to pay attention to their protein intake. It doesn’t mean that they need to take protein supplements, he says. “It’s all things we can do with just normal incorporation of high-protein foods in our lives.”

For healthy adults, increasing protein can boost the effects of resistance exercise, such as weightlifting. A 2017 systematic review found that, among people engaged in this type of training, taking protein supplements enhanced muscle gain and strength[3]. But increasing protein beyond 1.6 grams per kilogram per day provided no further benefit.

Meanwhile, some fitness influencers swear by eating 2.2 grams per kilogram of body weight. For most people, that’s simply overkill, says Burd. There’s little harm, other than for people with kidney disease, but Burd adds: “You just create an inefficient system where your body gets very good at wasting food protein.”

Some practitioners might recommend higher protein targets to ensure that people get enough, Burd says. But the protein craze has been driven mostly by aggressive marketing of high-protein foods and supplements, he says.

“The myth of increased protein needs has seeped into popular imagination, including among health professionals, and has been conveniently reinforced by the food industry,” says Fernanda Marrocos, a researcher specializing in nutrition and food policy at the University of São Paulo in Brazil.

Amino-acid goals

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Nature 647, 304-305 (2025)

doi: https://doi.org/10.1038/d41586-025-03632-1

References

1. Phillips, S. M., Chevalier, S. & Leidy, H. J. Appl. Physiol. Nutr. Metab. 41, 565–572 (2016).

2. Houston, D. K. et al. Am. J. Clin. Nutr. 87, 150–155 (2008).

3. Morton, R. W. et al. Br. J. Sports Med. 52, 376–384 (2017).

4. Forester, S. M., Reyes, E. M. & Layman, D. K. J. Acad. Nutr. Diet. 125, 888–899 (2025).

5. Park, S. et al. J. Nutr. 151, 1190–1196 (2021).

6. Abramovay, R., Nunes-Galbes, N. M., Marrocos-Leite, F. H., Nilson, E. A. F. & Louzada, M. L. D. C. Rev. Saúde Pública 59, e21 (2025).

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NEWS FEATURE 11 November 2025

The computers that run on human brain cells

Move over silicon: scientists want to use neurons to make powerful computers with minuscule energy needs.

By

David Adam

Illustration by Paweł Mildner

In a town on the shores of Lake Geneva sit clumps of living human brain cells for hire. These blobs, about the size of a grain of sand, can receive electrical signals and respond to them — much as computers do. Research teams from around the world can send the blobs tasks, in the hope that they will process the information and send a signal back.

Welcome to the world of wetware, or biocomputers. In a handful of academic laboratories and companies, researchers are growing human neurons and trying to turn them into functional systems equivalent to biological transistors. These networks of neurons, they argue, could one day offer the power of a supercomputer without the outsized power consumption.

The results so far are limited. But keen scientists are already buying or borrowing online access to these brain-cell processors — or even investing tens of thousands of dollars to secure their own models.

Some want to use these biocomputers as straightforward replacements for ordinary computers, whereas others want to use them to study how brains work. “Trying to understand biological intelligence is a very interesting scientific problem,” says Benjamin Ward-Cherrier, a robotics researcher at the University of Bristol, UK, who rents time on the Swiss brain blobs. “And looking at it from the bottom up — with simple small versions of our brain and building those up — I think is a better way of doing it than top down.”

Biocomputing advocates claim that these systems could one day rival the capability of artificial intelligence and the potential of quantum computers.

Other researchers who work with human neurons are more sceptical of what’s possible. And they warn that hype — and the science-fictional allure of what are sometimes labelled brain-in-a-jar systems — could even be counterproductive. If the idea that these systems possess sentience and consciousness takes hold, there could be repercussions for the research community.

“I’m nervous that, if this kind of work gets a lot of attention and is overstated, that the reaction won’t just be, ‘We need to think about this work a little more carefully’. It will be, ‘We need to stop this work entirely,’” says Madeline Lancaster, a developmental biologist who uses neural tissue to study development and disease at the University of Cambridge, UK, but is not involved in biocomputing projects. “That could bring in regulations that prevent all work, including on the side of the field that’s really doing research to try to help people.”

Power down

Computer scientists have long coveted the astonishing power efficiency of the human brain. Running on less than 20 watts — about enough to work a small desktop fan — its billions of neurons can spin through the equivalent of one billion billion mathematical operations each second. The best supercomputers can match that speed, but consume a million times more power in doing so.

Some researchers are trying to replicate the super-efficient structure of the brain using silicon chips. This approach, broadly called neuromorphic computing, takes inspiration from how neurons connect and fire to communicate. Specifically, some systems seek to mimic how neurons must charge to a threshold before firing an electrical impulse.

Biocomputing, on the other hand, goes back to the biological source material. Starting with induced pluripotent stem (iPS) cells, which can be reprogrammed to become almost any type of cell, researchers culture communities of brain cells and nurture them with nutrients and growth factors. To communicate with them, researchers sit the cells on electrode arrays, then pass signals and commands to them as sequences of electrical pulses. These signals change the way that ions flow into and out of neurons, and might prompt some cells to fire an electrical impulse known as an action potential. The biocomputer electrodes can detect these signals and employ algorithms to convert them to usable information.

The most common biocomputing approach cultures the neurons as 3D clusters called organoids. The composition of these brain-cell communities varies, depending on how the iPS cells differentiate, but typically includes neurons and cells that support them, such as astrocytes and oligodendrocytes.

In August, Ward-Cherrier and his colleagues reported[1] using human brain organoids of around 10,000 neurons to ‘recognize’ Braille letters. They first used a robot fitted with a tactile sensor to read the letters, and then converted the data collected for each letter into a distinct pattern of electrical pulses — varying the timing and intensity, for example — which they passed through a series of eight electrodes placed next to the surface of the organoid. These electrodes record the collective activity of many nearby neurons.

The researchers wanted to learn whether the patterns of firing in the organoid were different depending on the stimulation pattern it received, and whether those responses were consistent.

For each letter, they collected the response from each electrode, averaged them to yield an overall organoid output, and used machine learning to identify any patterns.

The results showed that, when fed electrical pulses corresponding to specific letters, a single organoid would produce the same characteristic response 61% of the time, on average. When responses from three organoids were combined, that went up to 83%. In other words, the organoids could perform a simple processing task: to distinguish between and identify inputs.

For Ward-Cherrier, it’s a solid proof of principle. “It is an initial kind of stab at showing that we can do these types of tasks. The next step is doing something a little bit more complex.” That could include interpreting messages from the cultured cells as instructions for the robot — to read the letter again, for example. Such abilities define what researchers call closed-loop-systems, which have not yet been demonstrated with human brain organoids — although a 2024 study[2] reported that such a system, made of mouse neuronal organoids, could play Cartpole, a computer game in which the aim is to keep a wobbly pole upright on a moving cart.

Because the inputs and outputs in cultured systems are simple electrical signals, it’s easy to offer remote access to organoids through the web. So, even though the Braille-reading robot is based in Ward-Cherrier’s lab in Bristol, the organoids are grown and housed at the company FinalSpark, in Vevey, Switzerland.

Devices containing tiny clumps of human brain cells sit inside a fridge at FinalSpark in Vevey, Switzerland.Credit: Fabrice Coffrini/AFP via Getty

A self-confessed fan of science fiction, FinalSpark co-founder Fred Jordan says he wants to develop systems of biological neurons that can “perform some similar things as are done with AI today”.

There is a long way to go. As computers, the organoid systems are currently “totally useless from a practical perspective”, he admits. “There is a very big difference between dreaming or thinking about something and doing it for real. And I would love to be one of those people who make this step.”

Selected academic groups, such as Ward Cherrier’s, get free access to FinalSpark organoids, and many teams have signed up for this. A team at the University of Michigan in Ann Arbor, for example, is testing different types of stimulation to see how the organoids behave, and researchers at the Free University of Berlin are focusing on how machine-learning tools can best extract information from neural firing patterns.

For clients with deeper pockets, including private companies, monthly fees of US$5,000 can secure exclusive online access to an organoid system. And plenty do. “We have very, very big companies active in individual fields that you would think are disconnected to this,” Jordan says. Unlike with the free work done by academic groups, FinalSpark does not know what paying customers use the organoids for.

Other independent groups with organoid expertise are jumping into biocomputing. The neural organoids in Alysson Muotri’s lab at the University of California, San Diego, each contain about 2.5 million neurons of various types. Muotri aims to apply organoids to a real-world problem: predicting the path of possible oil spills in the Amazon jungle. The project, financed by an oil company, is due to conclude by 2028. “We’re taking the challenge,” he says. “We’ll see where we are in three years.”

Training ground

For many organoid users trying to run more-complex tasks, one immediate aim is to find ways to train the neurons and thereby encourage goal-directed behaviour. At present, the responses from FinalSpark’s lab-grown organoids have more in common with the reflex actions of the peripheral nervous system — when someone’s leg kicks in response to a tap below the knee, for example — than with the malleable processes that guide decision-making in the brain.

To handle more complexity, these neural systems must be able to learn. One way to encourage that, Jordan says, is to deliver neurotransmitters such as dopamine to try to tune the organoids’ responses to particular stimuli. Dopamine makes neurons more likely to fire and strengthens the synapses that connect them — two changes that make the same neural response to a stimulus more likely to be repeated in future.

Another is a technique called pattern training stimulation, which was used in 2022 by researchers at Cortical Labs, a company based in Melbourne, Australia, to encourage lab-grown brain cells to play the 1970s computer game Pong[3].

Rather than working with organoids, they chose to create networks of cells in dishes (see ‘Computing with cells’). Then the researchers wired them up to a computer, which was programmed so that the response of the neurons to stimulation moved a virtual paddle while a virtual ball bounced around. To direct the paddle towards the ball, the researchers fed the neurons with an organized burst of electrical activity if the cells (at first, randomly) got it right. If the neurons moved the paddle in the wrong direction, they were blasted with chaotic white noise. Over time, the neurons learnt to hit the ball to receive the patterned response rather than the random one.

The strategy is drawn from the observation that brain cells tend to repeat activity that yields a predictable outcome, so will learn the kinds of behaviour that trigger familiar stimulation.

Cortical Labs has now developed a modular system that can link increasing numbers of individual wells, each containing at most 1,000 neurons, to form what the company calls a bioengineered intelligence. Each cell culture has a useful life of about six months before it must be replaced.

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Nature 647, 306-308 (2025)

doi: https://doi.org/10.1038/d41586-025-03633-0

References

1. Liu, T., Philamore, H. & Ward-Cherrier, B. Preprint at arXiv https://doi.org/10.48550/arXiv.2508.20850 (2025).

2. Robbins, A. et al. Preprint at bioRxiv https://doi.org/10.1101/2024.12.07.627350 (2024).

3. Kagan, B. J. et al. Neuron 110, 3952–3969 (2022).

4. Watmuff, B. et al. Commun. Biol. 8, 916 (2025).

5. Khajehnejad, M. et al. Cyborg Bionic Syst. 6, 0336 (2025).

6. Balci, F. et al. Neuron 111, 604–605 (2023).

7. Strong, V., Holderbaum, W. & Hayashi, Y. Cell Rep. Phys. Sci. 5, 102151 (2024).

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Books & Arts

- Don’t despair, collective action can address climate change

BOOK REVIEW 10 November 2025

Don’t despair, collective action can address climate change

Even small acts can make a big difference in driving positive environmental impacts.

By

Michael Jakob

Indigenous people in Brazil and elsewhere are pressing for a stronger voice in climate negotiations. Credit: Mauro Pimentel/AFP/Getty

Positive Tipping Points: How to Fix the Climate Crisis Tim Lenton Oxford Univ. Press (2025)

Earth-systems scientist Tim Lenton has long been emphasizing the importance of ‘tipping points’ for climate change. In a seminal paper published in 2008, he and his co-authors identified several elements of Earth’s climate system that might shift to a different state irreversibly once physical thresholds are crossed (T. M. Lenton et al. Proc. Natl Acad. Sci. USA 105, 1786–1793; 2008). For example, the Greenland ice sheet, the Amazon rainforest and circulation patterns in the Atlantic Ocean could change suddenly as the world warms.

Breaches of climate tipping points are a reason for great concern and warrant rapid reductions in greenhouse-gas emissions. Yet some types of tipping point could instead help to slow down climate change. Social ones, for example, could accelerate the transition to climate neutrality. Policies that have helped to reduce the cost of solar panels, wind turbines and batteries, for example, have encouraged consumers and companies to use electric and renewable-energy sources. These trends are unlikely to be reversed even if such policies are revoked.

In Positive Tipping Points, Lenton takes a closer look at these social forces and discusses how they can be harnessed to avoid further drastic climate change. His engaging and thought-provoking discussion of how individuals can bring about positive tipping points and contribute to climate action is based on state-of-the-art research and packed with useful facts and figures. It is a breath of fresh air at a time when people are increasingly prone to feelings of ‘climate doom’, the idea that a climate catastrophe is unavoidable.

Big changes

The first part of the book highlights that changes in the climate system and society are often non-linear and can occur rapidly. For systems with self-reinforcing feedback loops, a small perturbation might be enough to tip the climate system from one equilibrium state to another. For example, higher temperatures accelerate the melting of sea ice, reducing the amount of solar radiation that is reflected back into space, thus triggering further warming.

Past tipping events include the transition, roughly 12,000 years ago, from the last ice age to the current Holocene interglacial period. Such drastic changes in environmental conditions had implications for human societies, too. The end of that big freeze enabled people to switch from foraging to farming as their main means of food production.

Other wholesale cultural shifts might be expected in a warmer world. If humans are forced to live outside the climatic niche in which our societies evolved, social stability might be threatened. But other positive tipping points could emerge that work in favour of climate-change mitigation.

People in Venice, Italy, plant trees to protect crucial salt-marsh ecosystems.Credit: Simone Padovani/Getty

The second part of Lenton’s book discusses how positive tipping points can help to avert a climate catastrophe. He includes numerous examples of how actions taken by small groups of people have resulted in large-scale social change. For example, Lenton describes how his aunt Lilian’s fight for women’s suffrage helped to change social norms, and how a small number of climate activists, such as Greta Thunberg, have sparked a global movement.

The author argues that the key to pushing social change towards a climate-friendly economy lies in feedback loops — progress in one area increases the probability of improvements in others. Social activism, alternative forms of democracy, technological progress and government policies could reinforce each other, allowing societies to overcome the current state of ‘carbon lock-in’ by reducing their reliance on fossil fuels.

Small steps

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Nature 647, 309-310 (2025)

doi: https://doi.org/10.1038/d41586-025-03634-z

Competing Interests

The author declares no competing interests.

Opinion

- Hamilton Smith obituary: molecular biologist who co-discovered precise molecular scissors for cutting DNA

- Africa finally has its own drug-regulation agency — and it could transform the continent’s health

- To reform universities, first tackle global rankings

- Rebuilding Ukraine — one university’s bold vision

OBITUARY 10 November 2025

Hamilton Smith obituary: molecular biologist who co-discovered precise molecular scissors for cutting DNA

Nobel laureate who helped to sequence the first bacterial and human genomes.

By

J. Craig Venter

Credit: Keystone Press/Alamy

Hamilton (Ham) Smith co-discovered type II restriction enzymes, molecular scissors that cut DNA at precise sequence sites, which proved to be an essential tool for the emerging field of molecular biology. For this work he shared the 1978 Nobel Prize in Physiology or Medicine with Werner Arber and Daniel Nathans. Smith began a second career in 1993, when he agreed to collaborate with me on sequencing the genome of the bacterium Haemophilus influenzae. On its publication in 1995, it was the first complete genome of any organism.

That fortunate collaboration led to the first draft human genome sequence in 2001, and then to the first cell made from a synthetic genome in 2010. Smith was a scientist’s scientist, working in the laboratory almost every day and quietly being an inspiration, friend and a collaborator to all his colleagues.

Born in New York City, Smith grew up in Champaign-Urbana, Illinois, where his father was a college professor. Smith discovered biology while studying mathematics at the University of California, Berkeley, and went on to receive his medical degree from Johns Hopkins University School of Medicine in Baltimore, Maryland, in 1956. Following his military service in the US Navy, and a medical residency at Henry Ford hospital in Detroit, Michigan, in 1962, Smith received a fellowship from the US National Institutes of Health to work with Myron Levine — a pioneer in infectious-disease research — at the University of Michigan in Ann Arbor. Studying the bacteriophage P22, a virus that infects the bacterium Salmonella Typhimurium, he mastered DNA isolation techniques that became an essential component of all his main contributions to science.

In 1967, Smith joined the microbiology department at Johns Hopkins. Beginning work on H. influenzae, he discovered a restriction enzyme by studying changes in DNA viscosity. He used several clever techniques to prove that the enzyme recognized a unique DNA sequence, long before the advent of DNA sequencing, and published two key papers in the Journal of Molecular Biology in 1970 (H. O. Smith and K. W. Wilcox J. Mol. Biol. 51, 379–391 (1970); T. J. Kelly Jr and H. O. Smith J. Mol. Biol. 51, 393–409; 1970). His parents were driving to the grocery shop on 12 October 1978 when the news came over their car radio that Hamilton Smith at Johns Hopkins had won the Nobel prize. His mother told him that she looked at his father and said, “Do you suppose there is another Hamilton Smith at Hopkins?”

In 1993, Smith and I met at a conference in Bilbao, Spain. I had founded the Institute for Genomic Research (TIGR) in Rockville, Maryland, the year before and Smith agreed to join the science advisory board. At his suggestion, we decided to collaborate on sequencing the genome of H. influenzae using my idea of whole-genome shotgun sequencing. The method is dependent on achieving a perfect statistical Poisson distribution of the DNA fragments that cover the genome in a genomic sequencing library, and Smith developed techniques to achieve this. We sequenced 25,000 fragments and mathematically assembled them into the complete circular genome. After this, Smith joined TIGR and its successor firm, the J. Craig Venter Institute, where he stayed for the remaining 25 years of his career. We went on to collaborate on dozens of other genomes, including the Neisseria meningitidis genome that led to the development of the first meningococcal B vaccine that has saved thousands of lives.

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Nature 647, 311 (2025)

doi: https://doi.org/10.1038/d41586-025-03635-y

Competing Interests

The author declares no competing interests.

COMMENT 10 November 2025

Africa finally has its own drug-regulation agency — and it could transform the continent’s health

If it gets things right, the first major regulator of medicines to launch for 30 years could empower Africa to tackle African challenges around health and disease.

By

Mwila Mulubwa,

Leon Mutesa &

Kelly Chibale

Biomedical researchers at a laboratory in Yaoundé, Cameroon. Credit: Daniel Beloumou Olomo/AFP via Getty

After more than a decade of planning, the launch of the African Medicines Agency (AMA) is being celebrated in Mombasa, Kenya, this week at the Seventh Biennial Scientific Conference on Medical Products Regulation in Africa. The agency’s establishment marks a pivotal moment in Africa’s public health, at a time when the need for biomedical research conducted in Africa, focused on African health problems, has never been greater.

Africa holds higher levels of human genetic diversity than anywhere else on Earth, but this diversity has not been adequately studied. And many globally approved treatments and vaccines for diseases such as HIV/AIDS, malaria and tuberculosis are less effective, and can even be harmful in some people of African ancestry[1],[2].

This year, cuts of billions of US dollars in international funding for biomedical research and health services in Africa have left millions of people without access to life-saving treatments or, in the case of researchers and health-care workers, unemployed. This demonstrates the immense vulnerability that comes with relying on funding from external donors.

What’s more, Africa’s phenomenal population growth and pace of urbanization is bringing fresh challenges — as well as opportunities — around health and disease. In Africa’s cities today, the inhabitants of increasingly affluent neighbourhoods are demanding high-quality medicines and health care. But in low-income areas, high population density, inadequate housing and poor sanitation are facilitating the spread of respiratory and diarrhoeal infections[3]. And everywhere, inadequate diets, air pollution, smoking and physical inactivity are driving increased rates of cardiovascular disease, diabetes and cancer[4]. By 2100, Africa is expected to host 13 of the world’s 20 largest cities[5], and such inequalities are likely to worsen.

In the context of all these challenges, the AMA is more than just another regulatory body. It represents a test of whether Africa can claim its rightful place in shaping the science that will help to determine the health of its growing population.

If the AMA ensures that preclinical models for drug testing reflect African biology, that African genomes are woven into drug discovery from the start, and that clinical trials conducted on the continent are rigorous, it will improve the health of billions of people of African ancestry. By helping to generate a more complete picture of human genetic diversity and the links between genetic variation, disease and treatments, it will also redefine standards around drug development and clinical trials globally.

Drug discovery for Africa

Multiple studies spanning three decades have shown that individuals from different regions and ethnic backgrounds respond to medicines differently[6]. But although Africa holds around 18% of the world’s population and accounts for 25% of the global disease burden[7], the continent’s genetic diversity is rarely considered in preclinical or clinical research[8].

African biomedical data sets are hugely under-represented. Only 4% of the data in the Pharmacogenomics Knowledgebase (PharmGKB), a public database that provides information on the links between genes, drugs and diseases, come from individuals of African ancestry, for instance.

Likewise, to test drugs, researchers globally are increasingly relying on in vitro models developed from human cells or tissues. These include 3D cell cultures called organoids that mimic the structure and function of specific organs or tissues, and liver microsomes, which are collections of subcellular fragments derived from liver cells. But most of these tools have been derived from samples obtained from individuals of European ancestry[9].

And when it comes to clinical trials, under 3% of them are conducted in Africa (see ‘Poorly represented’). A study published last year found that, between 2015 and 2023, out of tens of thousands of phase I trials (which assess the safety, side effects and optimal dosing regimen of treatments) conducted around the world, only 17 were conducted in Kenya, only 12 in Nigeria and only 3 in Ethiopia[10], despite these countries being among the leaders in clinical research in sub-Saharan Africa.

Source: WHO International Clinical Trials Registry Platform

The result of all of this is the development of treatments that are ineffective or even detrimental for certain African populations. A compound called efavirenz, for example, was the key ingredient of first-line antiretroviral treatment in Africa and around the world until 2018[11]. But Zimbabwean people with HIV who harbour certain genetic polymorphisms in CYP2B6, the enzyme responsible for metabolizing efavirenz, experience numbness in their feet and other symptoms of neurotoxicity when taking the drug[12].

What’s more, diseases that disproportionately affect African populations, such as sickle-cell disease — which affects roughly 1 in 100 people living in West Africa, compared with 1 in 15,000 people living in Western Europe[13] — have tended to be neglected.

Both sickle-cell disease and Parkinson’s disease contribute to about the same number of deaths globally each year, but whereas nearly 50,000 studies on Parkinson’s disease have been published in the past five years (according to a PubMed search), only around 7,800 studies on sickle-cell disease were published in the same period. (Deaths from Parkinson’s disease occur mainly in Asia, Europe and North America.)

To ensure that African genomic diversity — and African needs — are better embedded into all stages of drug discovery and development, the AMA must ensure that African pharmacogenomic data (genetic information that signals how individuals will respond to medications) are integrated into drug discovery. It must mandate or incentivize the use of in vitro models derived from cells or tissues obtained from individuals of African ancestry[9],[14]. And it must incentivize drug developers to focus on interventions for diseases that disproportionately affect African populations.

To do this, the AMA should require that applicants for regulatory approval demonstrate how they have considered genetic variability relevant to African populations in preclinical and clinical trials. Variants in the cytochrome P450 (CYP) gene family can affect the metabolism of drugs and toxins, for instance; variants in transporter proteins can influence how drugs are absorbed by, distributed in and eliminated from the body; and some variants of some genes, such as APOL1, increase people’s risk of developing a disease. (The APOL1 variants G1 and G2 are more common in individuals of African ancestry and a major risk factor for chronic kidney disease.)

The AMA should require that investigators use models derived from cells or tissues from individuals of African ancestry in all toxicity, efficacy, drug-metabolism and pharmacokinetic studies (which examine what the body does to drugs over time) submitted to it for approval. Or, to incentivize those applying for regulatory approval to take their treatment to the next phase of testing, the agency should offer expedited review pathways or reduced application fees when preclinical studies use ‘Africa-relevant’ tools.

An educator gives a talk on reproductive health in Harare, Zimbabwe.Credit: Jekesai Njikizana/AFP via Getty

In collaboration with African research institutions and global consortia, such as the Innovative Medicines Initiative, a public–private partnership involving the European Union and European life-science industries, the AMA should promote the creation of continent-wide pharmacogenomic databases.

Through partnering with African academic institutions, such as the Holistic Drug Discovery and Development Centre at the University of Cape Town in South Africa, and global funders, such as the Gates Foundation, the agency can help to drive the development of biobanks across Africa — facilities that collect, store and manage human biological samples. This would give more researchers across the continent access to Africa-relevant preclinical tools.

Ultimately, pharmaceutical companies, both in Africa and worldwide, could use these data and resources, in conjunction with artificial intelligence and other computational tools, to make predictions about how certain compounds will affect a given population. In fact, to encourage multinational drug developers to use such data sets and biorepositories, the AMA could help to ensure that open-access African data sets are interoperable with other platforms used in drug development, such as PharmGKB.

Harmonizing regulation

Alongside these steps to make biomedical research and drug discovery more relevant to African populations, the AMA needs to make it much easier for drug developers to conduct clinical trials on the continent.

Currently, drug developers wanting to work in Africa are hampered by insufficient or inadequate laboratories and infrastructure, a shortage of trained scientists who can run clinical trials, a lack of experts in research ethics and regulation, and variability in countries’ laws and standards around the handling of biological samples.

The AMA can help to increase the number of phase I trials being conducted in Africa by fostering partnerships between Africa-based research institutions, such as the Kenya Medical Research Institute in Nairobi, and international funders. It could also work with some of Africa’s continent-wide organizations, such as the International Vaccine Institute’s Africa Regional Office, which was established last year in Kigali, Rwanda, to boost research and development (R&D) of vaccines on the continent.

To ensure that efforts in different countries are coordinated, the agency must help to develop a central database for clinical trials conducted in Africa, perhaps building on the work of the Clinical Trials Community Africa Network. This group of African research-ethics bodies, research institutions and pharmaceutical companies is coordinating a network of clinical trial sites and laboratories to harmonize clinical trials conducted in Africa.

Africa’s rapid population growth is posing fresh health challenges.Credit: Olasunkanmi Ariyo/Getty

As a central, umbrella organization, the AMA should coordinate the reviews of applications for clinical trials and thereby facilitate the design of large-scale, multicountry phase II studies, which assess the efficacy and safety of treatments. Such studies, when conducted independently in any one country, often lack the sample sizes needed to reveal clinically meaningful differences in how genetic subpopulations differ in their response to a treatment.

By harmonizing the regulatory environment across the continent, the AMA can help to facilitate phase III clinical trials (which aim to provide evidence of therapeutic efficacy and safety in thousands of patients in multiple countries), phase IV studies (evaluations of treatments in real-world settings), and the monitoring of fake medicines, too.

Five traps to avoid

To promote a healthy ecosystem for biomedical research and drug and vaccine development in Africa, the AMA must beware some of the pitfalls that have hampered efforts of other continent-wide health initiatives, including the European Medicines Agency (EMA) in its early years.

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Nature 647, 313-316 (2025)

doi: https://doi.org/10.1038/d41586-025-03637-w

References

1. Radouani, F. et al. Pers. Med. 17, 155–170 (2020).

2. Tukwasibwe, S. et al. Immunogenetics 75, 207–214 (2023).

3. Fu, H. & Zhu, C. Front. Public Health 12, 1344306 (2024).

4. Barry, A. et al. Sci. Rep. 15, 12288 (2025).

5. Hoornweg, D. & Pope, K. Environ. Urban. 29, 195–216 (2017).

6. Sibomana, O. Pharm. Pers. Med. 17, 487–496 (2024).

7. Kasprowicz, V. O. et al. BMC Public Health 20, 1104 (2020).

8. Zaaijer, S. & Capes-Davis, A. Cell 184, 2525–2531 (2021).

9. Masimirembwa, C. et al. Drug Metab. Dispos. 51, 1551–1560 (2023).

10. Amboka, P., Kurui, D., Wamukoya, M., Sindi, J. K. & Vicente-Crespo, M. Front. Epidemiol. 4, 1417419 (2024).

11. NAMSAL ANRS 12313 Study Group. N. Engl. J. Med. 381, 816–826 (2019).

12. Dhoro, M. et al. BMC Pharmacol. Toxicol. 16, 4 (2015).

13. Thomson, A. M. et al. Lancet Haematol. 10, e585–e599 (2023).

14. Hurrell, T., Naidoo, J., Ntlhafu, T. & Scholefield, J. Nature Commun. 15, 8581 (2024).

15. Ahonkhai, V., Martins, S. F., Portet, A., Lumpkin, M. & Hartman, D. PLoS ONE 11, e0166515 (2016).

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Supplementary Information

1. Supplementary information AMA

2. WHO trials data sorted by continent

Competing Interests

The authors declare no competing interests.

COMMENT 12 November 2025

To reform universities, first tackle global rankings

Universities are in thrall to a rankings system that prioritizes narrow aspects of academic life. Three changes would give institutions the freedom to explore fresh ways of working.

By

Elizabeth Gadd

Illustration: David Parkins

University reputations and finances often hinge on their position in global ranking tables. Students use rankings to quickly identify the best place to study — or, at least, what might be perceived to be the best by any future employer. Even a small shift in rank can affect how many students apply to a university, altering the income from tuition fees (R.-D. Baltaru et al. Stud. High. Educ. 47, 2323–2335; 2022).

And governments love the simplicity of rankings. Many will fund their citizens’ overseas study only at institutions that are high up the listings. National investment initiatives — such as Russia’s 5-100 Academic Excellence project and Japan’s Top Global University project — often focus on universities that have a chance of making it into the upper echelons of the rankings. The UK government offers its High Potential Individual visas only to candidates who have studied at highly ranked universities.

Such reliance on rankings means that universities are shaped not by the needs of society or by innovations driven from inside the international higher-education community, but by unappointed third-party ranking agencies.

The indicators used by some of the dominant flagship rankings don’t capture the full range of qualities and functions of higher-education institutions. Each agency uses a slightly different ranking method, but all typically focus on a narrow range of criteria. These are centred heavily on publication-based measures, such as citations, and on reputation surveys.

The consequence is that most of the world’s universities tend to pursue one flavour of ‘excellence’, which looks rather like the old, wealthy, conservative, research-intensive institutions of high-income nations.

Meanwhile, universities are facing a series of problems — from diminishing public funding and trust, to decreasing curriculum relevance in a rapidly changing job market and the need to demonstrate real-world impact from research. There is no shortage of ideas about how to reshape universities in response to these challenges, but the dominance of rankings as a measure of institutional success means that universities lack incentives to try. Many fear that stepping away from the status quo might result in a drop down the tables, making it harder for them to attract funding and talent.

Scholarly communities and universities must push for change. Here, I outline how.

A flawed system

In my view, flagship global rankings over-rely on the data sources that they have easy access to: publication data or survey data that they collect themselves. (The Nature Index, produced by Springer Nature, ranks universities solely using contributions to research articles published in natural-science and health-science journals.) In many rankings, evaluations of teaching are based on flimsy proxies, such as staff-to-student ratios or the number of alumni with Nobel prizes. Most rankings place little to no weight on open-science practices, societal impacts, outreach or efforts to improve diversity, equity and inclusion.

Ranking indicators are also weighted in a variety of ways without clear justification. For example, a ranking might allocate a 20% weighting to citations involving faculty members and only 5% to employment outcomes. Rankings are also presented without error bars, even though the data used are imperfect.

Efforts to push back on narrow, publication-dominant forms of assessment have mostly put the onus on universities to change how they evaluate their staff and departments. Many universities have risen to the challenge. Narrative CVs and biosketches — accounts written by researchers that highlight the full range of their contributions — are becoming more common. In European universities, the development of templates to elicit evidence of a broader range of contributions from faculty members, known as Career Assessment Matrices, is expanding.

But there is a limit to how far institutions can move away from citation- and publication-based assessments, if they continue to be judged on these measures by global university rankings.

Students use rankings to choose universities.Credit: Getty

In the past three years, several groups, including the Union of Students in Ireland and the United Nations University think tank, the International Institute for Global Health, have appealed for universities to escape this stranglehold. They have called on institutions to stop supplying data to the rankings, which some, such as the University of Utrecht in the Netherlands and the University of Zurich, Switzerland, have done. And the groups have asked universities to stop promoting their rank and to reduce how much they consider rankings of someone’s previous institution when making decisions, such as which staff members to recruit. The groups also endorse the More Than Our Rank initiative, which encourages institutions to describe their many achievements, activities and aspirations not captured by the rankings, through a narrative statement on their web pages. (I chair the INORMS Research Evaluation Group, which developed the More Than Our Rank initiative.)

These are valid recommendations, but asking individual universities to take responsibility will not lead to global reforms in how university performance is defined and assessed. To achieve this, a three-pronged solution is needed.

Call out current rankings

The higher-education sector should collectively — and vocally — agree that the current rankings are not fit for purpose. It might seem unlikely that institutes currently at the top of the rankings, mostly located in Europe and the United States, would call out a system that benefits them. But geopolitical changes should give pause for thought. Chinese and Indian universities are taking more of the top spots in the rankings than before, with UK, US and Australian institutions in decline. If those currently at the top wait too long to speak out, they might soon find themselves lower down the ranks, with less clout to drive the reforms that would serve all institutions.

The call for change should involve an education campaign aimed at students and policymakers, who rely on rankings for decision-making. This should be led by an independent body that is governed by experts from the international higher-education sector, many of whom have already expressed concern about the harms of global university rankings (see, for example, go.nature.com/4hy1kq9). The goal should be to help consumers of rankings to understand that ‘Which is the best university in the world?’ is not a useful question. ‘Which university might be best for me, given that I care about X and Y?’ is a better question — but one for which current measures are unlikely to provide a good answer.

The campaign should note that good assessments need to be nuanced and contextualized, and will take time to digest. Just as ‘the best’ researcher cannot be identified by the single number that makes up their h-index, ‘the best’ university cannot be determined by the single number that makes up their rank. This message might not be popular, but it is a crucial one.

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Nature 647, 317-319 (2025)

doi: https://doi.org/10.1038/d41586-025-03636-x

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The author chairs the International Network of Research Management Societies (INORMS) Research Evaluation Group, which developed the More Than Our Rank initiative.

COMMENT 10 November 2025

Rebuilding Ukraine — one university’s bold vision

Rector and AI researcher Nataliya Shakhovska tells Nature how her institution is supporting students and staff members amid the war and about her hopes for the future.

By

Aisling Irwin

Nataliya Shakhovska hopes to develop strong connections with European institutions.Credit: Nataliya Shakhovska

One of Ukraine’s largest universities inaugurated its first female rector in May. Now, Nataliya Shakhovska is steering Lviv Polytechnic National University’s 35,000 students and 2,500 academic staff members through the fourth year of the Russian invasion, on top of her research on artificial intelligence.

Shakhovska told Nature how she keeps going and how she’s trying to build up her university to be ‘the best in Europe’.

How do you feel about being the university’s first female rector?

This is incredible. It signals that leadership can and should reflect academic diversity and that the most important criterion is qualification, not gender. And it is a trend across Ukraine. In the past year, women have also won leadership elections at the Kyiv Aviation Institute and the Vasyl Stefanyk Carpathian National University in Ivano-Frankivsk.

In our society, mothers and women have strong roles. The war is a crisis and maybe there is a feeling that a woman can protect us.

You want your university to lead in Europe. How will you achieve that?

Yes, I believe in the transformative power of ambition. Moreover, Ukrainian universities have long been underestimated. Our intellectual resilience, especially in times of crisis, is extraordinary. In my mind, the best university is the one that is the best place for people. We’re not just talking about rankings but about the feeling that ‘I like to spend my time here, not only for work or education, but also because this is a good place for me’.

Brain drain is a problem in Ukraine. That’s why it is important to show our young people that they can and must stay in the country, not only for education but also for their future careers and to rebuild it.

We need sustained investment in research infrastructure. We need new laboratories and — importantly — deeper international partnerships. We need a culture that rewards innovation and inter-disciplinary thinking. But above all, we need to trust it’s possible, and act accordingly.

Your university was bombed in July. What were the consequences?

The event was not just about the damaging of labs and classrooms: those can be rebuilt. It was about the emotional trauma to students and staff members. And it was about the mobilization of students and staff. Our community understands that we must rebuild, we must continue our work, we must continue our studying. Universities are not only the buildings: they are first of all the people.

How have you kept the community together and motivated?

We are replacing all of the damaged windows. We have raised funds from our partner companies to repair our labs. And for our community, we organized a psychological-support group for students and academics.

We have had to think about a trauma- informed educational model. We are a huge university and it is difficult to know whether any particular student is affected by trauma. We have a psychological-support centre for our students. It is busy all the time.

Researchers at Lviv Polytechnic National University in Ukraine are developing hydrogel bandages for treating people with burns and other wounds.Credit: Ukrinform/NurPhoto via Getty

We are also holding a lot of motivational lectures — about new possibilities and time management — and meetings with successful people, not only Ukrainian professors, but also international ones. We organize workshops and courses on non-academic topics for students.

I think these measures are working. Our academic staff members are highly motivated: they want to create something new.

The war has forced your university to innovate — how so?

Conventional education is about lectures, about labs, about practical issues. But having to deal with trauma as well as problems such as access to electricity and materials, it is important that we have a flexible model for our students and staff members.

That’s why we first reviewed our educational facilities. We are currently focusing on opening co-working zones where students and staff members can spend their free time and do their work, and on 24-hour lab access for everyone. In the AI department, students can now access the lab at any time.

And we try to organize ways for our staff members to connect with our international partners. This is one of the most important parts of our strategy. And it is not about emergency support. It is about long-term integration into European research frameworks. This is, I think, the most important innovation.

To what extent has your university’s research pivoted during the war?

Because our economy is working on post-war recovery scenarios, we are having a radical reorganization of research agendas to support this. It is not about publishing papers: we are helping to rebuild the country. We have a renewed sense of purpose.

The most important thing for Ukraine now is the development of technologies, for example, drone systems and energy resilience. For instance, one project is upcycling bomb-damaged concrete into high-quality concrete for reconstruction. It’s funded by the UK government’s InnovateUkraine programme; our engineers have trained using equipment from the University of Leeds, and a mobile rubble-processing line has begun trials near Kyiv. The benefits include carbon dioxide savings, lower construction costs and simpler logistics for reconstruction.

Your output of scientific papers is prolific. How do you manage this?

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Nature 647, 319-320 (2025)

doi: https://doi.org/10.1038/d41586-025-03638-9

This interview has been edited for length and clarity.

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Work

- Parenting, illnesses and medical commitments: the private details grant reviewers shouldn’t need to know

- How genetically encoded sensors have lit up neuroscience

- Witnessing chamois populations recover

CAREER COLUMN 16 October 2025

Parenting, illnesses and medical commitments: the private details grant reviewers shouldn’t need to know

Is it fair for grant reviewers — who are often our colleagues — to judge the legitimacy of career breaks due to personal circumstances?

By

Nathalie Bock

Nathalie Bock and her partner juggled childcare, hospital visits and work commitments after their twin sons were born. Credit: Nathalie Bock

In January 2021, my partner and I were on fixed-term postdoctoral fellowships in Australia when life threw us a rapid sequence of curveballs. We hadn’t planned on four children, but our third pregnancy became twins. Elio, one of our twin boys, was born with intestinal atresia: his small intestine wasn’t connected to his large intestine. A day after birth, surgeons made a life-saving stoma.

Six weeks later, while he was still in the neonatal intensive care unit (NICU), a biopsy revealed that he had total colonic Hirschsprung disease (TCHD). Elio’s colon would never function, meaning that the stoma bag was going to stay for a few years. Living with TCHD entails chronic diarrhoea, vitamin supplementation and a constant risk of dehydration.

Being in a neonatal care unit is hard at any time, but Australia’s strict pandemic border closures meant that our relatives from abroad — we’re from France and Italy — could not fly in to help. After eight weeks, Elio moved from the NICU to the children’s hospital, where a parent had to remain with him at all times. My partner packed a suitcase; he lived in hospital for another three months while we waited for Elio’s stomal output to stabilize. At home I juggled looking after our two older children, aged five and eight, breastfeeding Elio’s brother Enzo, and a daily 45-kilometre commute between our home and the hospital.

Luckily, our institutions granted us extra personal leave, which helped considerably. Still, we were two materials scientists on fixed-term contracts, now a family of six, including a disabled child. We made a pact: we would be there fully for Elio and would not sacrifice one of our careers to save the other, as many others (typically women) are forced to do. We’d spent years of our professional lives pursuing these career paths and, as stubborn optimists, we believed that with the right mindset we could both keep our careers going. We thought this would be the best way to provide our family with stability and fulfilment. Our plan was the same as those we’d put in place during frantic grant-writing seasons — to take turns advancing our work, to keep both of our research careers alive.

From March to June 2021, my partner continued with his fellowship — developing sustainable, biodegradable materials for Australian commercial packaging products — while juggling nasogastric feeds, stoma care and baby cuddles. I advanced my fellowship work on the development of new biomaterials that can be used to culture and test cancer tissue in the laboratory, while running the rest of the family, buoyed by the support of my colleagues and friends.

We made it work from June to December. Elio’s disability was not eligible for government support, however, and despite donations from relatives and friends, we couldn’t afford a specialized carer. So, by late 2021, we started trialling the only nursery that would take Elio. His medical team helped us to adapt his daytime feeding so that he no longer needed nasogastric feeds. Nurses trained the nursery staff to empty his stoma bag and give him multiple medications. Soon after, Elio was fully enrolled at nursery: no more isolation and plenty of fun alongside Enzo and the other toddlers. One of our biggest successes as a family.

Then came a new hurdle: how to respond to questions around career disruption on our grant applications. Many Australian funders offer applicants the opportunity to describe their personal circumstances, so that an individual’s track record of research can be judged fairly, which is a well-meaning attempt to balance the playing field for scientists whose careers have been thrown off course by life events.

Nathalie Bock develops biomaterials to study the role of cellular microenvironments in normal and pathological tissue states.Credit: Nathalie Bock

In practice, however, this meant disclosing intimate details about my family’s and my life to people who were unqualified to assess them. This information gets sent to peer reviewers, who are often colleagues, whose expertise is in science, not socio-medical assessment.

When my partner encountered this request in an important grant submission, he chose not to disclose this information. He felt that it was too personal and with hospital life in full swing, he also knew that he didn’t have the capacity to bring this funding application to a more competitive level. He asked a senior colleague, originally a co-investigator, to step up as the lead applicant. The grant succeeded — a collective win — but my partner relinquished leadership.

I felt similarly reluctant to disclose my recent personal trials. As a grant reviewer myself, I had read deeply personal accounts from others that I had no training to evaluate. In our small research community, I would sometimes find myself working with those same researchers who had shared their experiences in their applications. I’ve made the difficult decision to share personal details here to raise awareness of these issues. But, in the context of grant assessment, I doubt any reseacher wants their colleagues to know such details about them.

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Nature 647, 545-546 (2025)

doi: https://doi.org/10.1038/d41586-025-02913-z

This is an article from the Nature Careers Community, a place for Nature readers to share their professional experiences and advice. Guest posts are encouraged.

Subscribe to Nature Briefing: Careers, an unmissable free weekly round-up of help and advice for working scientists.

TECHNOLOGY FEATURE 11 November 2025

How genetically encoded sensors have lit up neuroscience

Tools that track specific molecules in neurons have enabled researchers to probe previously unexplored aspects of neurobiology — although important caveats remain.

By

Diana Kwon

Neurons expressing the dopamine sensor dLight3.8. Credit: Erin Scott

In the nervous system, synapses are where the action is. There, across a narrow gap between adjacent cells, neurons talk to one another through the dynamic exchange of chemical and electrical signals. Molecules known as neurotransmitters and neuromodulators induce or inhibit action potentials — spikes in voltage across neuronal cell membranes that trigger the release of other molecules. This cross-talk ultimately enables the production of emotions, thoughts, behaviours — everything that makes the brain what it is.

To decode these conversations, researchers have relied on various tools. These include electrophysiology, in which electrodes are inserted into the brain or individual cells (in the case of patch-clamp recording) to measure the voltage changes linked to action potentials; microdialysis, in which some of the fluid surrounding neurons is extracted and analysed; and fast-scan cyclic voltammetry, which uses implanted electrodes to detect certain signalling molecules.

But these methods have limitations. For instance, electrophysiology can precisely measure action potentials, but scientists can’t pinpoint the exact neurotransmitters or neuromodulators (collectively called neurochemicals) that drive them. Microdialysis can identify specific molecules, but it lacks the spatial and temporal resolution to pinpoint exactly when and where these neurochemicals are released, and voltammetry often struggles to distinguish molecules that are similar to each other.

The development of genetically encoded sensors over the past two decades has offered a way for neuroscientists to circumvent these issues. Such sensors were initially developed to identify action potentials in cells by tracking changes in calcium ions, but in recent years, researchers have expanded the toolbox to detect key neurochemicals.

The next generation of these sensors now enables scientists to ask questions such as: how much of a specific neurochemical is released in response to the firing of an action potential? How many action potentials are required to release a given neurochemical? And how long does that molecule stick around? “All these kinds of questions, for the vast majority of molecules — we’re talking about dopamine, serotonin, acetylcholine and many others — we know virtually nothing about,” says Nicolas Tritsch, a neuroscientist at McGill University in Montreal, Canada. “This new class of genetically encoded sensors has really opened up this world.”

Lighting up cells

The revolution in genetically encoded neurochemical sensing began with calcium. Action potentials activate specialized channel proteins on the neuron’s cell membrane and allow calcium to enter, changing the calcium concentration. By fusing the calcium-binding protein calmodulin with a fluorescent protein and genetically targeting the hybrid molecule to specific populations of cells, researchers have developed sensors that light up in response to calcium fluctuations — a proxy for neuronal activity.

One popular variety of genetically encoded calcium indicators, known as GCaMPs, has been around since 2001. Researchers have since optimized the sensors’ speed and sensitivity — and they are now mainstays of neuroscience research. “They’re so ubiquitous that most papers that use GCaMPs stopped citing the relevant papers,” says Loren Looger, a neuroscientist at the University of California, San Diego (UCSD), whose team has been developing these sensors.

Genetically encoded sensors offer several advantages (see ‘Tracking neurochemicals’). Researchers can express them at specific times in particular cells, then pair them with techniques such as optogenetics to cause them to fire in response to light. But they also require genetic manipulation and might change the biology of cells in unexpected ways.

Sources: GCaMP: Y. Yang et al. Ann. Rev. Anal. Chem. 17, 367–392 (2024); PBP & GPCR: Z. Wu et al. Nature Rev. Neurosci. 23, 257–274 (2022)

Still, the success of these sensors motivated many tool developers, including Looger, to dream up similar strategies for detecting other neurochemicals. Among them are neurotransmitters such as glutamate, γ-aminobutyric acid (GABA) and acetylcholine — which activate or inhibit nerve cells — and neuromodulators such as dopamine, serotonin and endocannabinoids, which trigger molecular pathways in the brain[1].

In 2018, two papers[2],[3] published just weeks apart by teams in the United States and China independently described GCaMP-like sensors for dopamine, a neuromodulator involved in reward-based decision-making, learning and many other processes.

Dubbed dLight1 and GRAB-DA, respectively, both teams’ sensors were built on the dopamine receptor, one of a family of proteins known as G-protein-coupled receptors (GPCRs). The teams, led by neuroscientist Lin Tian, then at the University of California, Davis, and by Yulong Li, a neuroscientist at Peking University in Beijing, modified the receptor to fluoresce in the presence of dopamine and inserted that gene into a virus. Researchers could then inject the sensors into a living animal and observe the activity of specific molecules in infected cells in the brain using fibre photometry, a technique that measures changes in fluorescence using an optical fibre implanted in the brain.

These sensors quickly became popular additions to the neuroscience toolkit, and the papers have been cited more than 800 times apiece. In part, says Mark Walton, a neuroscientist at the University of Oxford, UK, their popularity is due to their ease of use. Conventional methods can be difficult to master — voltammetry, in particular, “is a bit of a black art”, he says. But with genetically encoded sensors, “we’ve never looked back”. Among other things, Walton and his team have used them to investigate dopamine fluctuations in almost 200,000 tests in mice, revealing that, contrary to previous results from conventional approaches, reward-based decision-making can happen independently of dopamine[4].

Another benefit of GPCR-based sensors is their generalizability, with a design that has now been extended to receptors for acetylcholine, serotonin and endocannabinoids. “Those initial dopamine sensors and their subsequently optimized variants have been transformative,” says Matthew Banghart, a neuroscientist at UCSD. “It’s so easy to implement, and the sky’s the limit in terms of being able to apply this approach to any GPCR of interest.”

Enabling discoveries

These sensors have begun to fundamentally shift our understanding of how neurotransmitters and neuromodulators work. Take dopamine, for instance. Although scientists have long known what it does, how exactly it transmits information in the brain has remained a mystery.

As a postdoctoral researcher at Brown University in Providence, Rhode Island, in the late 2010s, Arif Hamid wanted to understand how dopamine fluctuates in the brain. One leading hypothesis at the time was that, when dopamine neurons were activated, the neuromodulator would be released across the brain uniformly. But when Hamid and his colleagues used the dLight sensor to track the molecule’s movements in the striatum of mouse brains — a region that contains many dopamine-responsive neurons — they discovered instead that the neuromodulator was released in waves[5]. “We were completely floored,” says Hamid, who is now a neuroscientist at the University of Minnesota in Minneapolis. “It really defied our expectations.”

Tritsch and his team have found that acetylcholine, too, propagates in waves across the striatum. Using two-colour imaging in live mice with GRAB-DA-based sensors for both acetylcholine and dopamine, the researchers reported in 2023 that, rather than there being a stable, baseline concentration of the two molecules, their levels fluctuate with sub-second kinetics[6]. “We found signatures of dopamine and acetylcholine on timescales that are much, much faster than we thought was possible,” Tritsch says. “Our next step is to see how that’s influencing the brain on these timescales.”

The sensors are also revealing the secrets of other important neurochemicals. In 2021, a team led by researchers in California and Canada used a GRAB-based sensor to study endocannabinoid signalling in the mouse brain[7]. And earlier this year, another team used a GPCR sensor, called ntsLight1.1, that responds to the neuropeptide neurotensin, which is involved in feeding behaviour. The aim was to study what drives mice that are fed purely a high-fat diet to start avoiding calorie-rich foods[8]. Among other things, that analysis implicated a decline in neurotensin levels in a brain region linked with dopamine signalling as a possible cause.

And it’s not only in basic neurobiology that these sensors show promise. For instance, Tian, who is now at the Max Planck Florida Institute for Neuroscience in Jupiter, Florida, and her team have found a use that could help to develop treatments for anxiety. They showed that a sensor targeting neurons affected by the psychedelic drug 2,5-dimethoxy-4-iodoamphetamine can be used to identify other cells that contribute to the drug’s anxiety-reducing effects in mice without producing hallucinogenic effects[9]. That’s a finding that could support the development of future psychedelics that could have therapeutic benefits without the mind-bending side-effects, the study authors write.

Mixed signals

Genetically encoded sensors do have some important caveats, however.

One key consideration is how these proteins affect the biology of the cells in which they are expressed. Like most proteins, excessive expression can be toxic to neurons, so identifying the best concentration at which to use a sensor is paramount, says Tritsch. “With every batch, we need to know how much to dilute so we can see it, but so it’s not too much that it kills the neurons.”

Why this happens is an open question, but Tritsch and others suggest two possibilities: The GPCRs could be siphoning off molecules that are required for healthy neuronal functioning, or their overexpression could be overextending the cellular machinery required to build these molecules and deliver them to the membrane.

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Nature 647, 547-549 (2025)

doi: https://doi.org/10.1038/d41586-025-03639-8

References

1. Wu, Z., Lin, D. & Li, Y. Nature Rev. Neurosci. 23, 257–274 (2022).

2. Patriarchi, T. et al. Science 360, eaat4422 (2018).

3. Sun, F. et al. Cell 174, 481–496 (2018).

4. Blanco-Pozo, M., Akam, T. & Walton, M. E. Nature Neurosci. 27, 286–297 (2024).

5. Hamid, A. A., Frank, M. J. & Moore, C. I. Cell 184, 2733–2749 (2021).

6. Krok, A. C. et al. Nature 621, 543–549 (2023).

7. Farrell, J. S. et al. Neuron 109, 2398–2403 (2021).

8. Gazit Shimoni, N. et al. Nature 641, 1238–1247 (2025).

9. Muir, J. et al. Science 386, 802–810 (2024).

10. Aggarwal, A. et al. Nature Methods 20, 925–934 (2023).

11. Marvin, J. S. et al. Nature Methods 16, 763–770 (2019).

12. Unger, E. K. et al. Cell 183, 1986–2002 (2020).

13. Taniguchi, J. et al. eLife 13, e95694 (2024).

14. Mohebi, A., Collins, V. L. & Berke, J. D. eLife 12, e85011 (2023).

15. Lodder, B. et al. Neuron 113, 3554–3566 (2025).

16. Zheng, Y. et al. Science 388, eadt7705 (2025).

WHERE I WORK 10 November 2025

Witnessing chamois populations recover

Haritakis Papaioannou combines a mountaineer’s know-how with a scientific eye to track the slow recovery of chamois populations in western Greece.

By

Ugo Mellone

Haritakis Papaioannou is a conservation biologist and coordinator of Vikos-Aoos UNESCO Global Geopark, Ioannina, Greece. Credit: Ugo Mellone for Nature

“The first time I saw a chamois, a mammal closely related to both goats and antelopes, was more than 40 years ago while I was hiking in Epirus, a mountainous region in western Greece. Chamois were rare then; the desire to learn about and protect them inspired me to become a biologist. Over the past 25 years, their population in my study area — the Northern Pindos National Park — has grown drastically, mainly thanks to a ‘human shield’ effect created by hikers visiting the upper parts of the Northern Pindos mountain range: their presence has made poaching more difficult.

Now, I can easily see dozens of Balkan chamois (Rupicapra rupicapra balcanica) just a few hours from my village. In this picture, I’m counting them on the ledges of a cliff, one of their favourite habitats. We think that there are now around 1,000 individuals in the national park and nearby mountain ranges.

Studying chamois requires the skills of a mountaineer and a scientist’s precision. My colleagues and I have built a habitat suitability model, based on elevation, vegetation, slope and human activity, and are testing it by checking for chamois presence in areas across northern and central Greece. We have discovered small populations in those areas and are in the process of defining wildlife corridors and identifying threats to those populations.

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Nature 647, 552 (2025)

doi: https://doi.org/10.1038/d41586-025-03640-1

This interview has been edited for length and clarity.

Research

- Aligning machine and human visual representations across abstraction levels

- Vector-stimuli-responsive magnetorheological fibrous materials

- A molecularly impermeable polymer from two-dimensional polyaramids

- Global satellite survey reveals uncertainty in landfill methane emissions

- Somatic mutation and selection at population scale

- Sperm sequencing reveals extensive positive selection in the male germline

- Sex and smoking bias in the selection of somatic mutations in human bladder

- Arousal as a universal embedding for spatiotemporal brain dynamics

- ABCA7 variants impact phosphatidylcholine and mitochondria in neurons

- Assessing phylogenetic confidence at pandemic scales

- A new paradigm for outer membrane protein biogenesis in the Bacteroidota

- SARS-CoV-2 mRNA vaccines sensitize tumours to immune checkpoint blockade

- Loss-of-function mutations in PLD4 lead to systemic lupus erythematosus

- Mechanical confinement governs phenotypic plasticity in melanoma

- Design of facilitated dissociation enables timing of cytokine signalling

- Structural basis for mTORC1 activation on the lysosomal membrane

Article Open access Published: 12 November 2025

Aligning machine and human visual representations across abstraction levels

Lukas Muttenthaler,

Klaus Greff,

Frieda Born,

Bernhard Spitzer,

Simon Kornblith,

Michael C. Mozer,

Klaus-Robert Müller,

Thomas Unterthiner &

…

Andrew K. Lampinen

Nature volume 647, pages 349–355 (2025)

Abstract

Deep neural networks have achieved success across a wide range of applications, including as models of human behaviour and neural representations in vision tasks1,2. However, neural network training and human learning differ in fundamental ways, and neural networks often fail to generalize as robustly as humans do3,4, raising questions regarding the similarity of their underlying representations. We need to determine what is missing for modern learning systems to exhibit more human-aligned behaviour. Here we highlight a key misalignment between vision models and humans: whereas human conceptual knowledge is hierarchically organized from fine- to coarse-scale distinctions (for example, ref. [5]), model representations do not accurately capture all these levels of abstraction. To address this misalignment, we first train a teacher model to imitate human judgements, then transfer human-aligned structure from its representations to refine the representations of pretrained state-of-the-art vision foundation models via fine-tuning. These human-aligned models more accurately approximate human behaviour and uncertainty across a wide range of similarity tasks, including a dataset of human judgements spanning multiple levels of semantic abstractions. They also perform better on a diverse set of machine learning tasks, increasing generalization and out-of-distribution robustness. Thus, infusing neural networks with additional human knowledge yields a best-of-both-worlds representation that is both more consistent with human cognitive judgements and more practically useful, paving the way towards more robust, interpretable and human-aligned artificial intelligence systems.

Similar content being viewed by others

Main

Although deep learning has recently driven rapid progress in areas of artificial intelligence such as natural language processing[6] and computer vision7,8,9, even the best of these systems often fail in ways that humans would not4,10,11,12,13. These failures have led to renewals3,12 of older arguments14,15 that neural networks lack the essential ingredients of human intelligence. Therefore, we need to determine how can we build systems that produce more human-aligned behaviour.

Human perception is robust and generalizes across different visual settings4,16,17. However, model performance declines—often markedly—if the data distribution shifts between the training and test sets (for example, refs. 11,18). This lack of robustness in vision model representations poses a challenge for downstream applications that require generalization (for example, refs. 10,11,19). In addition, humans tend to be well calibrated—for example, when they are asked to judge visual similarity[17]—that is, humans’ (un)certainty tends to correlate with their (in)accuracy. Artificial intelligence systems, however, are often overconfident and show high certainty even when their predictions are incorrect[20]. Thus, many differences remain to be reconciled before we can ultimately achieve human-like artificial intelligence.

Here we highlight a key misalignment between humans and deep learning models that may underlie some of these differences: model representations tend to fail to capture the full multi-level conceptual structure of human knowledge. Although model representations successfully encode the local human-perceived similarity structure among closely related entities (for example, different dog breeds), the global relationships between concepts with more abstract semantic relations (for example, dogs and fish, which are both animate but visually dissimilar) are modelled much less systematically. Human neural representations, however, are organized by global features such as animacy5,21, and at multiple finer scales that capture nuanced semantic relationships21,22,23,24. This lack of global organization in the representations of deep learning models across levels of the conceptual hierarchy likely contributes to the aforementioned weaknesses of these models.

A challenge for addressing this misalignment is that collecting representative datasets of human judgements is challenging and expensive. We therefore propose a method for synthesizing simulated (approximately) human-aligned similarity judgements via a surrogate teacher model—a large foundation model that we align using an affine transformation[25] and uncertainty distillation on a small existing dataset[26]. We use this surrogate to produce the AligNet dataset—human-aligned pseudolabels (compare ref. [27]) from the surrogate model for triplets sampled from ImageNet[28] using a clustering-based data-grouping method. We fine-tune various vision foundation models on AligNet using a similarity-space distillation objective. These models show substantially more human-aligned predictions on various cognitive science tasks—including Levels, a dataset of human semantic judgements reflecting multiple levels of abstraction. Furthermore, these aligned models show improved accuracy and out-of-distribution robustness across many downstream machine learning tasks, thus showing the improved generalizability of the aligned model representations. We release our aligned models and training and evaluation datasets.

In summary, our work contributes to better understanding a key difference between artificial and natural intelligence. Moreover, our results illustrate a principle for aligning models to humans—focusing on the multi-scale relational structure of human knowledge—that may contribute to the more general problem of achieving human-aligned artificial intelligence.

To build foundation models with more human-aligned behaviour, we inject additional supervision about human behaviour into the model representations, using a surrogate teacher model: a vision foundation model whose representations are linearly transformed to approximate human judgements and uncertainty on the THINGS dataset[26]. We use clusters from this teacher model’s representations to sample triplets from ImageNet[28] and soft-label them using distances in the teachers representation space, then distil these soft labels into the student representations via a Kullback–Leibler divergence objective (Fig. 1). For details, see Methods.

Towards more human-aligned models

THINGS triplet odd one out

We first validate that our teacher model performs well on the test data for the THINGS dataset[26] used in training; as expected, the teacher achieves high performance (61.7% accuracy, close to the human noise ceiling of 66.67%). We then align a variety of student models—trained with objectives ranging from image captioning to classification or self-supervised learning—using this teacher’s representations; all models show substantially improved human alignment on the THINGS tests (relative performance increases from 21.28–74.47%).

Fig. 1: Overview.

a, An example of the triplet odd-one-out task where a human and a neural network model choose the same (top) and a different (bottom) odd-one-out image, respectively. b, The different parts of the AligNet framework depicted from end to end. First, we develop a teacher model of human judgements using the THINGS dataset. Second, we apply this model to ImageNet and cluster its latent representations into semantically meaningful categories. This allows us to generate arbitrarily many similarity judgements. Third, the obtained human-aligned similarity structure information is distilled into a student vision foundation model using a loss function. KL divergence, Kullback–Leibler divergence. c, Representative human alignment (top) and machine learning downstream (bottom) results show significant performance improvements of the aligned over the non-aligned version of the ViT-B classifier (up to 123% of relative improvement for ML downstream transfer). Error bars are 95% confidence intervals (CIs). For the human alignment results, we ran 100 bootstraps (repetitions) on the item level where we computed model performance for a single bootstrap using a subset of 1,000 randomly (with replacement) selected items from the respective dataset. Triplet odd-one-out datasets are THINGS and coarse-grained Levels respectively. The multi-arrangement panel represents the results for the ViT-B classifier from Fig. 2c. For machine learning downstream results, we computed the error bars using the binomial proportion CI. We used a normal approximation (‘Wald interval’) to compute the binomial proportion 95% CI which can be calculated using the number of data points in a dataset and the observed prediction performance (here, accuracy) of a model for that dataset. Few-shot learning datasets are Flowers (n = 6,149) and UC Merced (n = 1,050). Distribution shift datasets are Entity-13 (n = 167,592) and Entity-30 (n = 153,565) from the BREEDS benchmarks. d, Two-dimensional latent space projection for visualizing the change in the representations after alignment. Although the representations of a standard ViT-B classifier model are unstructured and categories overlap, after alignment the representations are grouped into meaningful categories. All photos are taken from Pixabay and are under a Creative Commons licence CC BY 0.

Other cognitive tasks

Our findings generalize across various object similarity tasks that are commonly used in the cognitive sciences—triplet odd-one-out task (relative performance increases up to 73.35%; Fig 2a), Likert scale similarity ratings (up to 6.3-fold increase in Spearman rank correlation coefficient; Fig 2b) and multiple-arrangement tasks (up to 14.47-fold increase in Spearman rank correlation coefficient; Fig. 2c). All performance increases are statistically significant at α = 0.05; for details, see Supplementary Information.

Alignment at multiple levels of abstraction

Levels dataset

Because previous cognitive datasets were not specifically targeted for assessing knowledge of vision foundation models across levels of abstraction, we collected a dataset of human judgements—which we call Levels—that is based on the triplet odd-one-out task, but stratified across different levels of the semantic hierarchy. Specifically, we collect global coarse-grained semantic, which requires deciding on the odd one out among broadly different categories; local fine-grained semantic, involving discerning subtle distinctions within the same category; and class boundary, testing the capacity to identify category boundaries. For details, see Methods.

Alignment at multiple levels

The Levels dataset allows us to systematically study discrepancies between human and model decisions across these different levels. We find that our soft-alignment method reduces these discrepancies at all levels, but especially for the global coarse-grained judgements, as we predicted.

Global coarse-grained

This level shows the largest improvements. The base models achieved low accuracies of 36.09% (classifier ViT-B (ref. [8])) to 57.38% (self-supervised DINOv2 (ref. [29])). AligNet models significantly improved; all models performed well, with accuracies of 65.70% (ViT-B) to 68.56% (DINOv2)—above the human-to-human reliability score of 61.92% (Fig. 2d, leftmost column). That is, the AligNet models’ responses were more similar to average human responses (as each triplet response is the majority response of the participants) than the level of agreement among the human participants. AligNet models’ relative improvements ranged from 19.48% (DINOv2) to 93.51% (ViT-L).

Fig. 2: Human alignment results.

a, Odd-one-out accuracies on the THINGS dataset and performance averaged across all three levels of abstraction for Levels. b, Spearman rank correlations for the human-response datasets from ref. [24] for the coarse-grained various category and averaged across all fine-grained single-domain categories. c, Spearman rank correlations for the multi-arrangement datasets from refs. [21],[23]. d, Odd-one-out accuracies on our datasets shown individually for the three levels of abstraction. e, Spearman rank correlation of model uncertainties and human response times. Model uncertainties are modelled as discrete Shannon entropy of the pairwise similarities in a triplet. All error bars are 95% confidence intervals obtained by bootstrapping. For each dataset and model, we ran 100 bootstraps (repetitions) on the item level where we computed model performance for a single bootstrap using a subset of 1,000 randomly (with replacement) selected items from the respective dataset. All photos are taken from Pixabay and are under a Creative Commons licence CC BY 0.

Local fine-grained

Most base models did not strongly align to human responses for fine-grained semantics either; all models achieved poor alignment scores of 46.04% (ViT-B) to 57.72% (DINOv2), except for DINOv1, which performed significantly better (62.92%; near the human noise ceiling of 65.92%; Supplementary Table 3). AligNet models achieved increased accuracies of 58.93 (ViT-S) to 62.92% (DINOv1), with relative improvements ranging from 7.84% (DINOv2) to 46.03% (ViT-L) (Fig. 2d, middle column).

Class boundary

Supervised classifiers and image/text contrastive base models performed close to the noise ceiling; accuracies ranged from 81.96% (SigLIP) to 93.67% (ViT-L). Others performed worse; the CapPa (ref. [30]) captioning model achieved 70.37%. AligNet fine-tuning brought all models to a similar level, achieving accuracies up to 93.24% (ViT-L), higher than the human noise ceiling of 89.21% (Fig. 2d, rightmost column). Relative improvements ranged from 0.62% (ViT-L) to 32.29% (CapPa). For more performance details, see Supplementary Table 3.

AligNet model uncertainties correspond to human latencies

We also collected (continuous) human response times, which we use as a proxy of the participants’ uncertainty31,32. We measured model uncertainty as the entropy of the three pairwise similarities within each triplet. Base model uncertainties were not correlated with human response times for the coarse (ρ = −0.014–0.184) and fine-grained (ρ = 0.047–0.160) settings and moderately correlated for the class-boundary setting (ρ = 0.208–0.432). All AligNet models showed substantially increased uncertainty alignment across all levels (Fig. 2e), especially the coarse-grained abstraction level (ρ = 0.479–0.506).

Evaluating other model classes

To confirm that the aforementioned representational weaknesses are present in other classes of deep learning models, we evaluated two state-of-the-art natively multimodal large language models, Gemini 2.0 Flash and Gemini 2.5 pro[33], on Levels. These visual-language models perform similarly to—or slightly better than—the better pretrained vision models across all levels; however, they still substantially underperform our AligNet fine-tuned models (Supplementary Information section 1.7). In addition, we evaluated models trained on Ecoset[34], an ecologically motivated natural image dataset, and find that Ecoset models have severe difficulties matching the human similarity judgements (Supplementary Information section 1.6). These results confirm that the AligNet fine-tuning offers greater improvements in human alignment than merely incorporating language modelling or ecological training.

Aligned models reflect the conceptual hierarchy

Next we consider how model representations change after soft alignment. In Fig. 3, we show that although the model representations are dissimilar before alignment, they become more aligned with each other after soft alignment. This convergence is driven by models aligning better with the human conceptual hierarchy (Fig. 3b; compare ref. [35]). Our soft-alignment procedure embeds this global structure at two levels: first in the cluster-based sampling (Fig. 3c) and then in the soft labels (see above). Because of these factors, the relationships between image representations change during alignment according to their semantic relationship; representations of images from the same basic category tend to move closer together, those of images from the same superordinate category tend to move somewhat closer, and those from different superordinate categories tend to move apart (all effects are highly significant; t > 3.93, P < 0.001).

Fig. 3: Aligned models reflect the semantic hierarchy.

a, Before alignment, models trained with different losses have dissimilar representation structures—particularly those trained for supervised classification. After alignment, however, model representation structures are much more similar to each other. b, To understand the alignment, we study how the models’ representations change across the semantic hierarchy, from relations between images within the same subordinate category to relations across superordinate categories. c, The cluster-driven triplet sampling tends to produce triplets where two images have a closer relation than the third. d, The result is that the relations between image representations change following the semantic hierarchy—images from the same subordinate, basic or superordinate category tend to move closer together, whereas those from different superordinate categories move farther apart. Effects are highly statistically significant: t > 3.93, P < 0.001. e, Visualizing the distance changes in more detail, with some superordinate categories boxed on the diagonal and labelled with icons. Panels d and e are for the representations of ViT-B. Icons in e: Copyright 2021 Google Inc. All Rights Reserved.

As an illustrative example, in a base ViT-B, the representations of lizards are close to those of some plants and fruits owing their similarity in texture, colour or background; after alignment, they become similar to representations of other animals and more distant from those of other, unrelated superordinate categories. This reorganization yields better generalization, for example, when a lizard image is used as an example depicting an abstract category such as animals.

Supplementary Information section 3.2 and Extended Data Fig. 2 present more detailed analyses corroborating these results, including reorganization at higher levels such as living versus non-living, across model layers, and in other models and ablations. Furthermore, Supplementary Information section 3.3 and Extended Data Fig. 3 show that where the AligNet model and a baseline unaligned model disagree, human judgements are strongly correlated with those of AligNet, but not the ablation model (which relies more on superficial stylistic features)—in fact, every human participant in the study agreed more with AligNet.

Alignment improves generalization and robustness

Next we considered how human-aligned representations affect the performance on machine learning tasks. We investigated how alignment improves generalization and out-of-distribution robustness across a variety of downstream tasks.

One-shot classification

We first test an extremely challenging generalization setting: classifying images given only a single labelled example per class. In Fig. 4, we show one-shot performance before and after soft alignment on ten image-classification datasets from varied domains, such as fine-grained bird (Birds[36]) and flower (Flowers[37]) types classification, multi-domain natural image classification (ImageNet[38]) and scene recognition (Places365[39]). The majority of cases (32 of 40) show an improvement, sometimes by a substantial margin (for example, DINOv2 shows a 2.7-fold increase on the Pets dataset); overall, alignment significantly increases the generalization performance on these tasks (P < 0.05). These results show how human-aligned representations support strong generalization from little data. See Supplementary Information section 2.1 for additional results, including the complementary benefits of combining our method with other approaches to few-shot generalization.

Fig. 4: Machine learning downstream results.

We evaluated how AligNet fine-tuning affected the downstream task performances of various pretrained vision foundation models using linear probing. a, One-shot accuracy before (x-axis) versus after (y-axis) AligNet fine-tuning on various image datasets, including the Describable Textures Dataset (DTD) and the UC Merced land use dataset. b, Accuracy improvements on the four BREEDS distribution shift benchmark datasets. c, Accuracy improvements on the ImageNet-A dataset that is used to evaluate model robustness.

Distribution shift

A long-standing problem for applying machine learning algorithms is distribution shift (compare refs. 40,41): in deployment, data often differ in subtle ways from the training data, leading to unexpected model failures. To evaluate whether the global structure induced by alignment helps ameliorate this issue, we evaluated our models on the BREEDS benchmarks[42]—which specifically tests generalization under input distribution shifts, using datasets where training and test data points are sampled from different subpopulations. Figure 4b shows that AligNet fine-tuning consistently improves performance significantly across all benchmarks and models types (especially the image classifier, ViT-B). See Supplementary Information section 2.3 for further results.

Model robustness

Alignment also improves robustness. We evaluate on ImageNet-A[43], a challenging dataset of natural images that are adversarial (that is, models tend to misclassify them, but humans perform better). Again, alignment improves accuracy for all models (compare Fig. 4c), with improvements of up to 9.5 percentage points (1.6-fold improvement for CapPa). Although our method is not designed for improving out-of-distribution robustness, its improvements are comparable to state-of-the-art methods designed for precisely this problem (Supplementary Table 6).

Together, these machine learning results corroborate that AligNet fine-tuning improves the generalization, transfer and robustness of model representations.

Discussion

The differences between natural intelligence and the capabilities of neural networks are the subject of long-standing debates3,14. Despite the recent progress in artificial intelligence, these discussions persist, because deep learning systems still seem to fail in non-human-like ways10,11.

Here, we have highlighted—and addressed—a key deficiency in a broad class of vision foundation models: their representations do not adequately represent the multi-level conceptual structure of human semantic knowledge (‘Towards more human-aligned models’ section). We demonstrate this deficiency through Levels, a dataset of human similarity judgements across multiple levels of abstraction. To address this deficiency, we established a methodological framework for aligning deep learning models’ representations with human similarity judgements to create more human-aligned systems. This framework involves bootstrapping from a small quantity of human data to train a surrogate teacher model, and using this teacher to create a large synthetic dataset (AligNet), which we use to fine-tune various vision foundation models to inject human-aligned structure.

This approach yields significantly increased alignment to human judgements on cognitive science tasks (‘Towards more human-aligned models’ section), and better generalization and robustness on representative machine learning tasks (‘Alignment improves generalization robustness’ section). Thus, soft alignment helps to reduce the brittleness of machine learning models under changing environments. Moreover, our results illustrate how the broader paradigm of studying representational alignment44,45 can not only yield insights about how different systems relate but also be leveraged to actively align model representations with human knowledge to improve the models’ generalization abilities.

These results contribute to long-standing debates over which features of human intelligence neural networks may lack3,14,15. In particular, one line of critique argues that neural networks lack the capability to appropriately represent abstract relations such as same and different46,47, or to organize knowledge into hierarchies of concepts[15]. Although aspects of these critiques have been refuted in simple synthetic settings (for example, ref. [48]), similar criticisms persist for modern foundation models[12]. Our results show that, although standard training objectives do not adequately capture hierarchical category relations, these relations can be distilled into the models—which improves the models’ resilience under the distribution shifts highlighted in previous critiques. These results show that hierarchical representations may emerge from a system that is neither explicitly hierarchically structured nor trained explicitly on the hierarchy.

Although we focused on vision, similar global misalignments probably arise in other areas of research. For instance, in natural language processing, models are similarly trained with objectives that focus on distinguishing between close matches (for example, prediction objectives that primarily distinguish words that are likely to occur, rather than considering their relations to less probable concepts). Applying alignment techniques may therefore analogously help to better capture the global structure of semantic and syntactic relationships among language inputs that these objectives might miss.

More broadly, artificial intelligence systems have been successfully adopted in many areas. However, these deployments lead to practical[49] and conceptual[50] concerns about trustworthiness and safety. It is therefore increasingly important to identify the reasons why these systems occasionally fail and how to alleviate these failures. Our work advances the understanding of the deficiencies of vision model representations, and simultaneously shows a viable path for ameliorating these deficiencies by alignment with human judgements.

Our work has a number of limitations that could be addressed in future efforts. First, the models we used account for neither context in similarity judgements nor higher-order relations. Second, human representations may vary systematically across individuals, cultures and so on. Finally, human judgement is full of flaws, intrinsic contradictions and discrepancies. Given these issues, perfect alignment to human performance may not always be desirable for a technical system. Thus, future work could explore how to best learn from human knowledge without imitating human imperfections.

In summary, we have provided an initial approach to distil global, human-aligned similarity structures into the representations of modern deep neural networks. We have demonstrated an efficient path towards a best-of-both-worlds representation that is both more consistent with human judgements and more practically useful, paving the way towards more robust, interpretable and human-aligned artificial intelligence systems. We hope that our work will inspire more general approaches for aligning foundation models by distiling human priors into their representations.

Methods

Soft alignment

This section is organized as follows. We start by describing how we transform model representations into a space that matches human similarity judgements about coarse-grained semantic object relations. We introduce an affine transformation that matches human similarity judgements and injects the uncertainties that humans assign to their triplet odd-one-out choices into a model’s representation space creating a surrogate teacher model. Using the teacher model’s human-aligned representations, we sample triplets of ImageNet[38] images differently than uniform random sampling by clustering the representations into superordinate categories and using those clusters for data partitioning. We pseudo-label these triplets with human-aligned judgement distributions from the surrogate teacher model. Finally, after having created AligNet triplets, we fine-tune student models with a triplet loss object function.

Representational alignment

Data

To increase the degree of alignment between human and neural network similarity spaces, we begin from the publicly available THINGS dataset, which is a large behavioural dataset of 4.7 million unique triplet responses from 12,340 human participants for m = 1,854 natural object images[51] from the public THINGS object concept and image database[26]. The THINGS dataset can formally be defined as \(D{(\{{a}_{s},{b}_{s}\}|\{{i}_{s},{j}_{s}\,,{k}_{s}\})}_{s=1}^{n}\), which denotes a dataset of n object triplets and corresponding human odd-one-out responses, where \(\{{a}_{s},{b}_{s}\}\subset \{{i}_{s},{j}_{s}\,,\,{k}_{s}\}\) and \(\{{a}_{s},{b}_{s}\}\) is the object pair that was chosen by a human participant among the s-th triplet to have the highest similarity. Let \({\bf{X}}\in \,{{\mathbb{R}}}^{m\times p}\) be the teacher model representations for the m = 1,854 objects in the THINGS dataset, where p is the dimension of the image-representation vector. It is noted that each category in the THINGS dataset is represented by one object image. From X we can construct a similarity matrix for all object pairs \({\bf{S}}:= {\bf{X}}\,{{\bf{X}}}^{{\rm{\top }}}\in {{\mathbb{R}}}^{m\times m}\), where \({S}_{i,j}={{\bf{x}}}_{i}^{{\rm{\top }}}{{\bf{x}}}_{j}\) is the representational similarity for objects i and j, \({\rm{\top }}\) denotes the matrix transpose, and xi refers to the i-th column of X.

Odd-one-out accuracy

The triplet odd-one-out task is frequently used in the cognitive sciences to measure human notions of object similarity52,53,54,55. To measure the degree of alignment between human and neural network similarity judgements in the THINGS triplet task, we embed the m = 1,854 THINGS images into the representation space of a neural network with \({\bf{X}}\in {{\mathbb{R}}}^{m\times p}\). Given vector representations x_1, x_2 and x_3 of the 3 images in a triplet, we first construct a similarity matrix \({\bf{S}}\in {{\mathbb{R}}}^{3\times 3}\) where \({S}_{i,j}\,:= \,{{\bf{x}}}_{i}^{{\rm{\top }}}{{\bf{x}}}_{j}\) is the dot product between a pair of image representations. We identify the closest pair of images in the triplet as \({{\rm{a}}{\rm{r}}{\rm{g}}\,{\rm{m}}{\rm{a}}{\rm{x}}}_{i,j > i}{S}_{i,j}\) with the remaining image being the odd one out. We define odd-one-out accuracy as the fraction of triplets where the odd one out ‘chosen by a model’ is identical to the human odd-one-out choice. Thus, our goal is to learn an affine transformation into the THINGS human object similarity space of the form: \({{\bf{x}}}^{^{\prime} }={\bf{W}}{\bf{x}}+{\bf{b}}\). Here, \({\bf{W}}\in {{\mathbb{R}}}^{p\times p}\) is a learned transformation matrix, \({\bf{b}}\in {{\mathbb{R}}}^{p}\) is a bias and \({\bf{x}}\in {{\mathbb{R}}}^{p}\) is the neural network representation for a single object image in the THINGS dataset. We learn the affine transformation for the representation of the image encoder space of the teacher model (see the ‘Surrogate teacher model’ section for details about the teacher model). Using this affine transformation, an entry in the pairwise similarity matrix S′—which represents the similarity between two object images i and j—can now be written as \({S}_{i,j}^{^{\prime} }\,:= \,{({\bf{W}}{{\bf{x}}}_{i}+{\bf{b}})}^{{\rm{\top }}}({\bf{W}}{{\bf{x}}}_{j}+{\bf{b}})\).

Hard-alignment loss

Given a similarity matrix of neural network representations S and a triplet {i, j, k}, the likelihood of a particular pair, \(\{a,b\}\subset \{i,j,k\}\), being most similar to the remaining object being the odd one out, is modelled by the softmax of the object similarities,

$$\sigma ({\bf{S}},\tau ):= \exp ({S}_{a,b}/\tau )/\exp ({S}_{i,j}/\tau )+\exp ({S}_{i,k}/\tau )+\exp ({S}_{j,k}/\tau )$$

(1)

We can then define the probability of the neural network model to choose the most similar pair (according to the human participants) to be \(q(\{a,b\}|\{i,j,k\},{\bf{S}}):= \sigma ({\bf{S}},\tau )\) with a temperature parameter τ = 1. For n triplet responses, the discrete negative log-likelihood is defined as follows

$${L}_{{\rm{h}}{\rm{a}}{\rm{r}}{\rm{d}}\mbox{-}{\rm{a}}{\rm{l}}{\rm{i}}{\rm{g}}{\rm{n}}}({{\bf{S}}}^{^{\prime} })\,:= \,-\frac{1}{n}\mathop{\sum }\limits_{s=1}^{n}\mathrm{log}\,q(\{{a}_{{\rm{s}}},{b}_{{\rm{s}}}\}|\{{i}_{{\rm{s}}},\,{j}_{{\rm{s}}},\,{k}_{{\rm{s}}}\},\,{{\bf{S}}}^{^{\prime} })$$

Modelling human uncertainties

As each triplet response is a discrete choice, we do not have direct access to the uncertainties of a human participant over the objects in a triplet. Thus, the above loss function optimizes a transform to match the human choice but does not take into account the uncertainties over the three odd-one-out alternatives. However, it is possible to model these uncertainties using variational interpretable concept embeddings (VICE[55]), a recently proposed, approximate Bayesian inference method for learning an interpretable object concept space from human similarity judgements. VICE has shown remarkable performance in predicting the (dis-)agreement in human similarity judgements for multiple similarity judgement datasets, including THINGS[55].

We train a VICE model on the official THINGS train triplet dataset using the (default) hyperparameters recommended by the authors. To capture the uncertainties in human triplet responses, VICE learns a mean, \(\mu \in {{\mathbb{R}}}^{m\times d}\), and a variance, \(\sigma \in {{\mathbb{R}}}^{m\times d}\), for each object image m and each object dimension d, respectively. Therefore, the set of VICE parameters is defined as \(\theta =\{\mu ,\sigma \}\). VICE uses the reparameterization trick56,57 to generate an embedding matrix \({\bf{Y}}\in {{\mathbb{R}}}^{m\times d}\), \({{\bf{Y}}}_{\theta ,{\varepsilon }}=\mu +\sigma \odot {\varepsilon }\), where \(\varepsilon \in {{\mathbb{R}}}^{m\times d}\) is entrywise N(0, 1), and ⊙ denotes the Hadamard (element-wise) product.

After convergence, we can use a VICE model to obtain a posterior probability distribution for each triplet in the data. We approximate the probability distribution using a Monte Carlo estimate58,59,60 from R samples \({{\bf{Y}}}^{({\bf{r}})}={{\bf{Y}}}_{\hat{\theta },\varepsilon (r)}\) for r = 1, …, R, yielding

$$\hat{p}(\{\,{y}_{s},\,{z}_{s}\}|\{{i}_{s},\,{j}_{s},\,{k}_{s}\}):= -\frac{1}{R}\mathop{\sum }\limits_{r=1}^{R}\mathop{\underbrace{p(\{\,{{\rm{y}}}_{s},\,{z}_{s}\}|\{{i}_{{\rm{s}}},{j}_{s},{k}_{s}\},{{\bf{Y}}}^{(r)})}}\limits_{{\rm{M}}{\rm{o}}{\rm{n}}{\rm{t}}{\rm{e}}-{\rm{C}}{\rm{a}}{\rm{r}}{\rm{l}}{\rm{o}}\,{\rm{e}}{\rm{s}}{\rm{t}}{\rm{i}}{\rm{m}}{\rm{a}}{\rm{t}}{\rm{e}}}$$

where we set R = 50 because we found it to yield the best predictive performance on the official THINGS validation set. This gives a representative probability estimate for each of the three pairs in a triplet to be selected as the most similar pair.

Soft-alignment loss

Using the posterior probability estimates obtained from VICE, we transform the original THINGS triplet dataset of discrete triplet choices into a triplet dataset of probability distributions that reflect the human uncertainties of the triplet alternatives. Let \({D}^{\dagger }\,:= \,{({p}_{s}^{\ast }(\{{i}_{s},{j}_{s}\,,\,{k}_{s}\}))}_{s=1}^{n}\) be the transformed triplet dataset, where

$${p}_{s}^{\ast }(\{{i}_{s},{j}_{s}\,,\,{k}_{s}\}):= \hat{p}(\{\,{y}_{s},{z}_{s}\}|\{{i}_{s},{j}_{s}\,,\,{k}_{s}\})\,{\rm{\forall }}\,\{\,y,z\}\subset \{i,j\,,\,k\}.$$

Now, for n triplet responses we can define the negative log-likelihood for the soft alignment loss as

$$\begin{array}{l}{L}_{{\rm{s}}{\rm{o}}{\rm{f}}{\rm{t}}\mbox{-}{\rm{a}}{\rm{l}}{\rm{i}}{\rm{g}}{\rm{n}}}({{\bf{S}}}^{^{\prime} })\,:= \,\frac{1}{n}\mathop{\sum }\limits_{s=1}^{n}{p}_{s}^{\ast }(\{{i}_{s},{j}_{s}\,,\,{k}_{s}\})\mathrm{log}{p}_{s}^{\ast }(\{{i}_{s},{j}_{s}\,,\,{k}_{s}\})\\ \,\,\,\,\,\,-{p}_{s}^{\ast }(\{{i}_{s},{j}_{s}\,,\,{k}_{s}\})\mathrm{log}{q}_{s}^{\ast }(\{{i}_{s},{j}_{s}\,,\,{k}_{s}\})\end{array}$$

(3)

where \({q}_{s}(\{{i}_{s},{j}_{s}\,,\,{k}_{s}\},{\bf{S}})\,:= \,q(\{\,{y}_{s},{z}_{s}\}|\{{i}_{s},{j}_{s}\,,\,{k}_{s}\},{\bf{S}})\,{\rm{\forall }}\,\{\,y,z\}\subset \{i,j,k\}.\)

Uncertainty distillation

We mainly follow the optimization process introduced in ref. [61]. However, we modify their approach by injecting uncertainty measures about human odd-one-out responses into the representation space of the teacher, using a recent approximate Bayesian inference method for learning object concepts from human behaviour[55]. Thus, we replace the negative log-likelihood of the discrete human odd-one-out choices—which we refer to as hard alignment—with the negative log-likelihood of the probabilities for the pairwise triplet similarities obtained from the Bayesian inference model—referred to as soft alignment. The final objective for learning the uncertainty distillation transformation is thus defined as

$$\mbox{arg}\,\mathop{\mbox{min}}\limits_{W,b}\,{L}_{\mbox{soft-align}}({\bf{X}},{\bf{W}},{\bf{b}})+\lambda \Vert {\bf{W}}-(\mathop{\sum }\limits_{j=1}^{p}{W}_{jj}/p){\bf{I}}{\Vert }_{F}^{2},$$

(4)

where \(I\in {{\mathbb{R}}}^{p\times p}\) is the identity matrix and ||.||F^2 denotes the squared Frobenius norm. The right-hand side of the above objective is an ℓ_2-regularization whose aim is to preserve the nearest-neighbour information (or equivalently, the local similarity structure) of the pretrained representations while learning an affine transformation into the THINGS human object similarity space. The above equation is minimized using standard stochastic gradient descent.

Although this expression is similar to the global transform defined in ref. [61], we find it to yield equally strong downstream task performance as the gLocal transform proposed in ref. [61] while predicting human uncertainties better than the global transform. It appears as though there is barely any trade-off between representational alignment and downstream task performance for using the uncertainty distillation, whereas ref. [61] found that the global transform yields slightly better human alignment but worse downstream task performance compared wit the gLocal transform. We use the uncertainty distillation transformation for generating human-like similarity judgements by transforming a model’s representation space with uncertainty distillation.

Data generation

In the following section, we describe the AligNet data-generation process. We start by introducing the data that we use for constructing the triplets. We continue with a detailed description of the different sampling strategies that we consider in our analyses. Finally, we explain how we collect model responses using transformed representations and define the objective function for fine-tuning models on AligNet.

Image data

For creating AligNet, we use the publicly available ImageNet database[38]. ImageNet is a natural image dataset with approximately 10^6 training data points and 1,000 image categories[28]. The categories are almost equally distributed in the data with small variations in the number of images between the different classes. Hence, ImageNet can be considered a highly balanced dataset. ImageNet has been the dominant image dataset for training large computer vision models until the advent of image/text multimodal training a few years ago. Although, so far, larger image datasets exist, ImageNet is still is one of the largest open-source and most widely used image datasets in the field of computer vision.

Triplet sampling

For generating triplets of images, we use three different sampling strategies: random, class-border and cluster-boundary sampling. Let m′ be the number of images in the data where m′ = 1, 281, 167 and C be the number of classes with C = 1,000. Let \({D}_{{\rm{i}}{\rm{m}}{\rm{a}}{\rm{g}}{\rm{e}}}\,:= \,{({x}_{i},\,{y}_{i})}_{i=1}^{{m}^{^{\prime} }}\) be the ImageNet dataset of m′ image–label pairs.

Random

Uniform random sampling is the vanilla sampling approach used to create the THINGS datasets (see above). In random sampling, three images are chosen uniformly at random without replacement from all of the m images in the data to create a triplet. As there are C = 1,000 classes and each class has approximately 1,000 images, most of the triplets generated with this approach contain 3 images from 3 different classes. The number of triplets different from triplets with images from three distinct classes is negligible. It is noted that this is the same sampling approach that was used to generate the THINGS triplets[54]. A triplet generated via random sampling can be defined as the following triplet set \({\bf{S}}:= \{{x}_{i},{x}_{j},{x}_{k}\}\) with the constraint \(({x}_{i}\ne {x}_{j}\ne {x}_{k})\).

Class boundary

Another way to sample image triplets is to exploit the label information associated with each data point. Instead of three random images from three distinct classes, we determine class-boundary triplets to contain two images from the same class and one image from a different class. This is similar to the approach introduced in ref. [62] where each odd-k-out set of images contains a majority class and k odd class singletons. This sampling approach allows models to learn class boundaries similar to the standard supervised learning setting. A triplet generated via class-boundary sampling can be defined as the following triplet set \({\bf{S}}:= \{{x}_{i},\,{x}_{j},\,{x}_{k}\}\) with the constraint \((\,{y}_{i}={y}_{j}\ne {y}_{k})\vee (\,{y}_{i}\ne {y}_{j}={y}_{k})\vee (\,{y}_{i}={y}_{k}\ne {y}_{j})\) where the labels are used for data partitioning.

Cluster boundary

As we want to introduce a general approach that does not rely on label information, we use a third sampling strategy that is, in principle, similar to the class-boundary approach but does not require labels. Let \({\bf{Z}}\in {{\mathbb{R}}}^{{m}^{^{\prime} }\times p}\) be the stacked representations of a neural network model for every image in Dimage. The representations can essentially be computed for any layer of a model. Here we use the image encoder for image/text models and the CLS token representation of the penultimate layer for any other model (as we only use ViT-based models). We then apply k-means clustering to the encoded image representations Z and \({{\bf{Z}}}^{^{\prime} }\,:= \,{({\bf{W}}{{\bf{Z}}}^{{\rm{\top }}}+({{\bf{b}}}_{1},\ldots ,{{\bf{b}}}_{{m}^{^{\prime} }}))}^{{\rm{\top }}}\) respectively (where the transformation variables W and b are computed via uncertainty distillation optimization using equation (4)) into c representation clusters where c can be regarded as similar to C, the number of labels in the original dataset. We use the Elbow criterion to select c. For all of our main experiments, we set c = 500. Hence, the ImageNet dataset is transformed into a ImageNet dataset of image and cluster pairs. After the clustering, we apply the same sampling method as for class-boundary triplets: for each triplet, we choose uniformly at random two images without replacement from one cluster and one image from a different cluster. Thus, a triplet generated via cluster-boundary sampling can be defined as the following set \({\bf{S}}:= \{{x}_{i},{x}_{j},{x}_{k}\}\) with the constraint \(({y}_{i}={y}_{j}\ne {y}_{k})\vee ({y}_{i}\ne {y}_{j}={y}_{k})\vee ({y}_{i}={y}_{k}\ne {y}_{j})\) where instead of the original labels we use the cluster labels for partitioning the data.

Triplet-response generation

We use the responses of a surrogate teacher model (see below) to simulate a dataset of human-aligned triplet odd-one-out responses. More formally, let \({D}_{\mbox{triplets}}\,:= \,{(\{{x}_{i},\,{x}_{j},\,{x}_{k}\})}_{s=1}^{{n}^{^{\prime} }}\) be the dataset of sampled ImageNet triplets for which we want to collect responses using transformed model representations. It is noted that we can sample an arbitrary number of triplets—upper-bounded by the binomial coefficient m′/k with k = 3—and can thus set n′ to essentially any natural number. For the experiments that we report in the main text, we set n′ = 10^7 because we found a larger n′ to not yield any downstream task improvements. For now, we regard our surrogate model as a blackbox model with transformed ImageNet representations \({{\bf{Z}}}^{^{\prime} }\,:= \,{({\bf{W}}{{\boldsymbol{Z}}}^{{\rm{\top }}}+({{\bf{b}}}_{1},\ldots ,{{\bf{b}}}_{{m}^{^{\prime} }}))}^{{\rm{\top }}}\in {{\mathbb{R}}}^{{m}^{^{\prime} }\times p}\) where the affine transformation was found via uncertainty distillation optimization (equation (4)). Given transformed representations z_1′, z_2′ and z_3′ of the three images in a triplet, we can construct a similarity matrix \({{\bf{S}}}^{^{\prime} }\in {{\mathbb{R}}}^{3\times 3}\) where \({{S}^{^{\prime} }}_{i,j}:={{\rm{z}}}_{i}^{{\rm{\top }}}{{\rm{z}}}_{j}\) is the dot product between a pair of of representations. Similarly to how we do this for learning the uncertainty distillation transformation (see above), we identify the closest pair of images in a triplet as \(\arg \mathop{max}\limits_{i,j > i}{S}_{i,j}^{^{\prime} }\) with the remaining image being the odd one out. Let \({D}_{{\rm{a}}{\rm{l}}{\rm{i}}{\rm{g}}{\rm{n}}}\,:= \,{({\{{x}_{a},{x}_{b}\}}_{s}|\{{x}_{i},{x}_{j},{x}_{k}\})}_{s=1}^{{n}^{^{\prime} }}\) then constitute the final AligNet dataset of ImageNet triplets and corresponding model responses, where \(\{{x}_{a},{x}_{b}\}\subset \{{x}_{i},{x}_{j},{x}_{k}\}\) and \(\{{x}_{a},{x}_{b}\}\) is the image pair that was chosen by the transformed model representations to have the highest pairwise similarity. The model choices are the closest approximation to the human choices due to the uncertainty distillation transformation.

It is noted that the dataset includes not only the discrete model choices but also the exact relationships among all pairwise similarities in a triplet obtained from the probability space of the teacher model. Thus, we have access to soft distributions over the labels for use in distillation.

Objective function

Let fθ be a neural network function parameterized by θ, the set of its weights and biases. For every input image x that the function fθ(x) processes it yields a representation fθ(x) = z. Here, z refers to the image encoder representation of image/text models or the CLS token representation before the final linear layer for other model types. From the representations of the three images in a triplet, we can again construct a similarity matrix \({{\boldsymbol{S}}}^{\dagger }\in {{\mathbb{R}}}^{3\times 3}\) where \({S}^{\dagger }:= {{\rm{z}}}_{i}^{{\rm{\top }}}{{\rm{z}}}_{j}\) is the dot product between a pair of image representations. The AligNet loss function is defined as the following KL divergence between teacher and student triplet probabilities,

$$\begin{array}{c}{L}_{\mbox{alignet}}({{\bf{S}}}^{^{\prime} },{{\bf{S}}}^{\dagger })\,:= \,\\ \,-\frac{1}{B}\mathop{\sum }\limits_{s=1}^{B}{\sigma }{([{S}_{i,j}^{^{\prime} },{S}_{i,k}^{^{\prime} },{S}_{j,k}^{^{\prime} }],{\tau }^{^{\prime} })}_{s}\mathrm{log}\,{\sigma }{([{S}_{i,j}^{^{\prime} },{S}_{i,k}^{^{\prime} },{S}_{j,k}^{^{\prime} }],{\tau }^{^{\prime} })}_{s}\\ \,-{\sigma }{([{S}_{i,j}^{^{\prime} },{S}_{i,k}^{^{\prime} },{S}_{j,k}^{^{\prime} }],{\tau }^{^{\prime} })}_{s}\mathrm{log}\,{\sigma }{([{S}_{i,j}^{\dagger },{S}_{i,k}^{\dagger },{S}_{j,k}^{\dagger }],{\tau }^{\dagger })}_{s},\end{array}$$

(5)

where τ′ = 1 and τ† > 1 and B is the batch size. We find τ† via grid search and set it to τ† = 100 for all of our experiments. Recall that σ is a softmax function that models the probabilities over the three image similarity pairs (equation (1)). The final AligNet objective is defined as the following minimization problem

$$\mbox{arg}\,\mathop{\mbox{min}}\limits_{\theta }\,{L}_{\mbox{alignet}}({f}_{\theta })+\lambda {\Vert {\theta }^{\ast }-{\theta }^{\dagger }\Vert }_{2}^{2}$$

(6)

where θ* are the parameters of the pretrained base student model and θ† are the parameters of the fine-tuned student model. This ℓ_2-regularization, which we refer to as weight decay to initialization, encourages the fine-tuned set of parameters to stay close to its base during training. It is similar to the regularization used for learning the uncertainty distillation transformation (equation (4)) but adapted to the set of all model parameters rather than a linear transform.

Surrogate teacher model

Reference [25] showed that image and text models and models trained on large, diverse datasets are better aligned with human similarity judgements than vision models trained with a self-supervised learning objective or supervised models trained on ImageNet. Thus, we use the best-performing image and text model according to various computer vision benchmarks at the time of writing this paper as our teacher model. This model is called SigLIP[63]. SigLIP, similar to contrastive language–image pretraining (CLIP)[64] and ALIGN[65], is trained via contrastive language–image pretraining using millions of image and text pairs. The difference between CLIP and SigLIP is that the latter uses a paired sigmoid loss instead of the standard softmax function usually used for pretraining image and text models via cross-entropy. Image and text pretraining allows the model to learn an aligned representation space for images and text; thus, adding more semantic information about the objects in an image to the model representations.

We use the SigLIP-So400m variant of SigLIP as our teacher model. This variant uses an optimized ViT backbone whose performance is similar to one of the largest ViTs, ViT-g/14[66] while having fewer parameters and thus being smaller. The number of parameters of SoViT-400m/14 lies somewhere between that of ViT-L/16 and ViT-g/14. The output dimensionality of the image and text encoder representations of SoViT-400m/14 is p = 1,152 each. We align the image encoder representations with human odd-one-out choices using the uncertainty distillation optimization outlined in equation (6). This allows us to increase the triplet odd-one-out accuracy of SigLIP-So400m from 44.24% to 61.7% (rightmost column in Supplementary Table 1), which is close to the human noise ceiling of 66.67% for THINGS (compare ref. [54]) and thus among the best human-aligned models without AligNet fine-tuning (compare ref. [25]). It is noted that this is a relative increase in performance of 39.47%. Throughout this paper, we use the human-aligned version of SigLIP-So400m as the surrogate teacher model for generating human-aligned similarity judgements and distiling human-like similarity structure into student vision foundation models (VFMs). We select a diverse and representative set of student VFMs.

Student models

As previous research has demonstrated that a model’s architecture has no significant impact on the degree of alignment with human similarity judgements25,61, we use the same architecture for all student models that we fine-tune on AligNet. Specifically, we use the ViT[8] for the backbone of each student model. We use the ViT rather than a convolutional-neural-network-based model because ViTs have recently emerged as the dominant neural network architecture for computer vision application and VFMs. Every large VFM used in practice is based on the ViT30,63,67,68. Unless otherwise mentioned, we use the base model size, that is, ViT-B. ViT-B has 12 attention layers and an internal (hidden) representation size of p = 768. It has been shown that both the training data and the objective function have a substantial impact on the degree of alignment with human behaviour. Thus, we use student models that were trained on different pretraining task with different training data and objective functions.

Supervised pretraining is still the prevailing mode of training computer vision models. Therefore, we trained ViT-B on the popular ImageNet dataset consisting of 1.4 million natural images[38]. To examine how model performance changes as a function of the model size, we train ViT instances of three different sizes on ImageNet: ViT-S/16, ViT-B/16 and ViT-L/16. The image patch size is the same for each of those models. To evaluate the effect of AligNet on self-supervised pretraining, we use pretrained DINOv1[69] and DINOv2[29] models of which DINOv1 was pretrained on ImageNet and DINOv2 was pretrained on a different, larger image dataset as denoted below. In addition, we investigate multimodal training of vision models that add textual information in the form of both image captioning via the CapPa model[30] and CLIP via SigLIP[63]. The latter model is considered state of the art on many downstream computer vision applications and is used as the image embedding model in modern large visual-language models67,68. The full list of student models that we consider in our analyses is as follows:

- ViT-{S,B,L}

- Training data: ImageNet[38]

- Objective: supervised learning

- CLIP (ViT-B) (see Appendix A.2.4)

- Training data: WebImageText[64]

- Objective: CLIP[64]

- SigLIP (ViT-{B,SO400M})

- Training data: WebLI[63]

- Objective: CLIP[64]

- SigLIP2 (ViT-B) (Supplementary Information section 2.4)

- Training data: WebLI[63]

- Objective: combination of various objectives (see ref. [70] for details)

- DINOv1 (ViT-B)

- Training data: ImageNet[38]

- Objective: self-supervised image pretraining[69]

- DINOv2 (ViT-B)

- Training data: DINOv2 data (see ref. [29] for details)

- Objective: self-supervised teacher-student distillation[29]

- CapPa (ViT-B)

- Training data: JFT-3B (Google proprietary dataset)

- Objective: multimodal image captioning[30]

- Randomly initialized ViT-B (Supplementary Information section 2.5)

- Training data: AligNet

- Objective: AligNet objective (equation (5))

Representational similarity analysis

Representational similarity analysis is a well-established method for comparing neural network representations—extracted from an arbitrary layer of the model—to representations obtained from human behaviour[44]. In representational similarity analysis, one first obtains representational similarity matrices (RSMs) for the human behavioural judgements and for the neural network representations (more specific details can be found in Supplementary Information). These RSMs measure the similarity between pairs of examples according to each source. As in previous work23,25,54,61,71, we flatten the upper triangular of human and model RSMs respectively and quantify their similarities using use the Spearman rank correlation coefficient. In contrast to Pearson correlation, the Spearman rank correlation is scale invariant and thus better suited to measure similarities of judgements obtained from different sources.

Multi-arrangement task

Human similarity judgements for refs. 23,71 were obtained by using a multi-arrangement task. In a multi-arrangement task, participants are presented with a computer screen showing images of several different objects. The participants are asked to arrange the images into semantically meaningful clusters, given the instruction that images of objects that lie close together are considered more similar. From this arrangement, one can infer pairwise (dis-)similarities of the objects and average those across all participants to obtain a representative (dis-)similarity matrix.

Likert scale

In refs. 24,72, pairwise similarity judgements were obtained by asking human participants to rate the similarity of pairs of objects on an ordinal scale that ranges from 0 (‘not similar at all’) to 10 (‘very similar’). The pairwise similarity ratings can be averaged across the different participants, which in turn yields a matrix of similarities between pairs of objects.

Neural network representations

RSMs for neural network representations are obtained by first embedding the same set of images that were presented to the human participants in the p-dimensional latent space of a model. The latent space could be any layer of a neural network. For the base models, we use the representations of the image encoder for SigLIO and the CLS token of the penultimate layer for CapPa, DINOv2 and ViT-B. We do this because previous work has shown that the penultimate layer space and the image encoder space of image and text models, respectively, yield the highest similarity to human behaviour24,25,73. After embedding the images into the neural net’s latent space, we get a representation matrix \({\bf{X}}\in {{\mathbb{R}}}^{n\times p}\) for the n images in the data. Instead of simply computing the dot-product similarity matrix \({\bf{S}}:= {\bf{X}}{{\bf{X}}}^{{\rm{\top }}}\), in representational similarity analysis one typically uses either a cosine similarity or a Pearson correlation kernel to compute the affinity matrix

$$\cos ({{\bf{x}}}_{i},{{\bf{x}}}_{j})\,:= \,\frac{{{{\bf{x}}}_{i}}^{{\rm{\top }}}{{\bf{x}}}_{j}}{{\Vert {{\bf{x}}}_{i}\Vert }_{2}{\Vert {{\bf{x}}}_{j}\Vert }_{2}};\,\,\,\,\,\phi ({{\bf{x}}}_{i},{{\bf{x}}}_{j})\,:= \,\frac{{({{\bf{x}}}_{i}-{\bar{{\bf{x}}}}_{i})}^{{\rm{\top }}}({{\bf{x}}}_{j}-{\bar{{\bf{x}}}}_{j})}{{\Vert {{\bf{x}}}_{i}-{\bar{{\bf{x}}}}_{i}\Vert }_{2}{\Vert {{\bf{x}}}_{j}-{\bar{{\bf{x}}}}_{j}\Vert }_{2}},$$

where the cosine similarity kernel function cos(xi, xj) or the Pearson correlation kernel function ϕ(xi, xj) is applied to every (xi, xj) vector pair of the matrix X for obtaining the final RSM \({{\bf{S}}}^{^{\prime} }\in {{\mathbb{R}}}^{n\times n}\). Here we use the Pearson correlation kernel function ϕ(xi, xj) to obtain a neural net’s RSM. Pearson correlation is the centred version of cosine similarity and the ranking of the obtained similarities does not differ between the two kernel functions but Pearson correlation first centres the vectors to have zero mean and is therefore a more robust measure. For obtaining RSMs with transformed representations, the transforms are first applied to X before computing S′.

Alignment with conceptual hierarchy

When analysing alignment with the conceptual hierarchy, we use the original ImageNet category labels for the images[38]. ImageNet is structured by the WordNet hierarchy, from which we extract basic and superordinate categories aligned with the previous cognitive work. Within and across categories, we measure change in representation distance relative to other changes (by z-scoring across all representation distances for the given model checkpoint), because relative distances are more meaningful than absolute ones (for example, scaling all representations by two would change absolute distances, but not relative ones), and absolute scales of all representations tend to increase during training. We quantify changes with mixed-effects linear regressions that account for the non-independence of representational changes across the different clusters (see Supplementary Information section 3.2 for details).

Levels data

We collected a new multi-level similarity judgement dataset from N = 473 human participants, which we named Levels. The dataset contains odd-one-out judgements on three different types of triplet: coarse-grained semantic, which requires deciding on the odd one out in broadly different categories; fine-grained semantic, which involved discerning subtle within category distinctions; and class boundary, which tested for category-boundary detection. Consistent selection of the same odd-one-out image (for example, i) in multiple participants indicated that the remaining two images (for example, j and k) were closer to each other in the participants’ concept space than either was to the odd one out (see Supplementary Information for details about the data collection). Levels allowed us to evaluate model–human alignment for the same set of stimuli on various levels of abstraction, and to assess how well the models capture the inherent uncertainty in human judgements, inferred from response latencies.

Participants

We recruited N = 508 participants (209 female, 289 male, 3 diverse, N = 7 missing demographic information owing to revocation of study consent; mean age 31.75 ± s.d. = 8.04 years) online via Prolific Academic (https://www.prolific.ac). The eligibility criteria were that participants had to be between 18 and 50 years old, fluent in English, have normal or corrected-to-normal vision, no colourblindness, and have a minimum approval rating of 95% on Prolific. Participants provided informed consent before starting the experiment. The experiment lasted approximately 45 minutes. Participants were reimbursed with £7.70 for completing the experiment and received an additional bonus payment of £0.77. Partial payments were made if the experiment was not completed owing to technical issues (N = 6) or early termination by the participant (N = 1). Participants performing below 90% correct on catch trials (N = 19, 3 female, 16 male), or failing to respond in the allotted time window (15 s) in more than 10 trials (N = 9, 4 female, 4 male, 1 diverse) were excluded. Thus, N = 473 participants remained in the dataset (202 female, 269 male, 2 diverse; mean age 31.82 ± s.d. = 8.03 years). Of these participants, N = 448 were each tested with a different selection of triplets, while ensuring that each triplet was presented N = 5 times across the entire sample of participants (see information on stimuli sampling below). Owing to a server glitch during trial assignment, the remaining N = 25 participants shared their exact triplet selection with one other participant in the sample. These N = 25 participants were excluded from the response times and uncertainty estimation (see ‘Alignment at multiple levels of abstraction’ section) to restrict analysis to participants with different sets of triplets. The experiment was approved by the internal review board of the Max Planck Institute for Human Development.

Stimuli

The experimental stimuli were images taken from the ImageNet dataset[38]. Another nine images were used for instructions only and depicted natural objects selected from the Bank of Standardized Stimuli (BOSS)[74], available at https://drive.google.com/drive/folders/1FpnEFkbqe_huRwfsCf7gs5R1zuc1ZOkn. We grouped the visual stimuli presented in the triplets according to different levels of abstraction: coarse-grained semantic, which comprised three images from three different categories; fine-grained semantic, showing three images from the same category; and class boundary, where two images were from the same and one from a different category.

Instead of randomly sampling triplets—which would reproduce dataset biases—we stratified sampling by superclasses. ImageNet classes follow the WordNet hierarchy28,38, which includes higher-level classes. For instance, all dog breeds can be summarized as the dog superclass. To avoid presenting dogs, birds and other fine-grained classes that are overrepresented in ImageNet more frequently to the participants than other categories, we grouped the ImageNet classes into 717 coarse-grained WordNet superclasses. We uniformly at random sampled images from those 717 superclasses to construct the different kinds of triplets. It is noted that for all superclasses with more than one class, we uniformly at random chose one subclass and uniformly at random sampled one image, two images (without replacement) or three images (without replacement) from that subclass, depending on the triplet type. For most superclasses that comprised a single subclass only, that is, a one-to-one-mapping, we could skip the subclass sampling part. Triplet sampling resulted in N = 450 predefined experiment trial sets, of which N = 448 were used for testing. Across these, each triplet was presented within N = 5 different experiment files. This sampling process ensured a balanced distribution of triplets across the sample, and the repetition of each triplet in five different participants allowed for the calculation of an uncertainty distribution for each triplet.

The triplet odd-one-out task

On each trial, participants were presented with a triplet of images (i, j, k). Participants were asked to select the image that was the most different from the other two, that is, the odd one out. During the instructions, participants saw different triplets with increasing ambiguity regarding which image would likely be picked as the odd one out. Participants were given explanations for potential odd-one-out choices, clarifying that decisions could be based on different criteria, such as semantic or perceptual features of the shown images.

Procedure

The experiment was run online using jsPsych v7.3.3 (www.jspsych.org/7.3/) and custom plugins. Participants were asked to provide demographic information, including their age and gender. Thereafter, they viewed written instructions about the task and performed six practice trials (two trials per triplet level of abstraction). Participants were free to repeat the instructions until they felt confident to perform the experiment. The experiment proper comprised N = 330 experiment trials. Each trial started with a fixation cross (1 s), followed by the presentation of a triplet (maximum 15 s). Participants were asked to select the ‘odd one out’ using the right-, left- or downwards-facing arrow keys on their keyboard. Responses could be entered between 1 s and 15 s after triplet onset, after which the next trial started. Trials in which participants failed to submit a response were rare (M = 0.27% of trials; minimum 0.00%, maximum 6.06%). The serial order of triplet types (for example, fine-grained or coarse-grained semantic) and ImageNet classes (for example, dogs or birds) was counterbalanced across the experiment. We additionally counterbalanced the serial position of trial types across participants using a Latin-square design[75]. Participants could take short breaks (self-paced for up to 2 min) after N = 50, 150 and 200 experiment trials. Experimental trials were interleaved with N = 16 catch trials (class-border triplets), which were predefined based on low model uncertainty and 100% agreement among participants on these specific triplets during piloting. Catch trial performance was used as an indicator of adequate task engagement (see participant inclusion criteria above).

Preprocessing of human response times and uncertainty estimation

Descriptive statistics on response times and uncertainty estimation (see ‘Alignment at multiple levels of abstraction’ section) were calculated based on participants with unique experimental trial sets (N = 448). The response-time data were log transformed (log(RT)), in accordance with current best practices for response-time analysis. Trials with response times longer than 10 s were excluded from analysis (on average M = 2.64% of trials per participant). As responses could be given no earlier than 1 s after triplet onset (see ‘Procedure’ above), no lower bound was set for response-time exclusion. To estimate uncertainty (in terms of the level of (dis-)agreement among observers) for each triplet, we used the discrete (Shannon) entropy of the response distribution across participants.

Human-to-human alignment

We computed the human noise ceiling for each abstraction setting in Levels using a leave-one-out cross-validation approach. In leave one out, the agreement level for a triplet is computed as the average match rate between a held-out participant’s response and the majority response of the remaining population. Thus, for a triplet that was used for five participants, on each leave-one-out iteration, one participant response is held out and the remaining four comprise the population. The human-to-human reliability score is then calculated as the average agreement level across all triplets in the dataset.

Reporting summary

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

Data availability

The synthetically created AligNet data are publicly available at https://console.cloud.google.com/storage/browser/alignet. The Levels data are available on GIN at https://doi.org/10.12751/g-node.hg4tdz.

Code availability

The training code is available at https://github.com/google-deepmind/alignet. The aligned model checkpoints are publicly available at https://console.cloud.google.com/storage/browser/alignet. Both the experiment and analysis code for Levels and for the human validation of the representational differences between AligNet and UnaligNet are hosted on GitHub and archived on Zenodo at https://zenodo.org/records/15554034 (ref. [76]) and https://zenodo.org/records/15554174 (ref. [77]).

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Acknowledgements

L.M., F.B. and K.-R.M. were in part supported by the German Ministry for Education and Research (BMBF) under grants 01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A, 031L0207D and 01IS18037A. K.-R.M. was partly supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP) grants funded by the Korean government (MSIT) (number 2019-0-00079, Artificial Intelligence Graduate School Program, Korea University and number 2022-0-00984, Development of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification of Explanation and number 2024-00457882, AI Research Hub Project). B.S. and F.B. were supported by European Research Council Consolidator Grant ERC-2020-COG-101000972 and Deutsche Forschungsgemeinschaft (DFG) grant 462752742. We thank A. Mahendran, M. S. M. Sajjadi, R. Geirhos and X. Zhai for discussions; and I. Dasgupta, K. Hermann and S. Mohamed for comments on earlier versions of the paper.

Author information

Authors and Affiliations

1. Google DeepMind, Berlin, Germany

Lukas Muttenthaler, Klaus Greff, Klaus-Robert Müller & Thomas Unterthiner

2. Machine Learning Group, Technische Universität Berlin, Berlin, Germany

Lukas Muttenthaler, Frieda Born & Klaus-Robert Müller

3. BIFOLD, Berlin Institute for the Foundations of Learning and Data, Berlin, Germany

Lukas Muttenthaler, Frieda Born & Klaus-Robert Müller

4. Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

Lukas Muttenthaler

5. Max Planck Institute for Human Development, Berlin, Germany

Frieda Born & Bernhard Spitzer

6. TUD Dresden University of Technology, Dresden, Germany

Bernhard Spitzer

7. Anthropic, San Francisco, CA, USA

Simon Kornblith

8. GoogleDeepMind, San Francisco, CA, USA

Simon Kornblith, Michael C. Mozer & Andrew K. Lampinen

9. Department of Artificial Intelligence, Korea University, Seoul, South Korea

Klaus-Robert Müller

10. Max Planck Institute for Informatics, Saarbrücken, Germany

Klaus-Robert Müller

Contributions

L.M. initiated and led the project and had a major role in every part of the project: writing—original draft, conceptualization, investigation, writing—review and editing, methodology, data curation, validation, formal analysis, software, project administration and visualization. K.G. helped lead the project and had a critical role in major parts (for example, running experiments and creating figures) of the project: writing—original draft, conceptualization, investigation, validation, formal analysis, software, project administration and visualization. F.B. led the Levels data collection and was in full charge of everything concerned with it: writing—original draft, investigation, validation, writing—review and editing, data curation, software and visualization. B.S.: writing—review and editing, validation and supervision. S.K.: writing—review and editing, methodology and supervision. M.C.M.: writing—review and editing, validation, resources, methodology and supervision. K.-R.M. helped lead the project and shape some of its ideas: writing—original draft, conceptualization, writing—review and editing, methodology, funding acquisition, resources, supervision and project administration. T.U. hosted L.M. during his first internship at GDM and helped lead the project: conceptualization, investigation, writing—review and editing, methodology, validation, supervision, software, project administration and visualization. A.K.L. had a major role in administering and supervising the project and shaping some of its ideas: writing—original draft, conceptualization, investigation, writing—review and editing, methodology, validation, supervision, project administration and visualization.

Corresponding authors

Correspondence to Lukas Muttenthaler, Klaus-Robert Müller or Andrew K. Lampinen.

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All authors, aside from B.S. and F.B., are or were employed by Google DeepMind.

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Extended data figures and tables

Extended Data Fig. 1 Visualizing the representational reorganization in two dimensions using PCA and TriMap.

Each panel shows a low-dimensional visualization (a: PCA; b: TriMap) the representations of 85k ImageNet images from four different models (columns) onto their first two principal components both before (first row) and after fine-tuning on AligNet (second row). Colors correspond to high-level categories of the images. As shown in both visualizations, AligNet training results in more systematic differentiation of the high-level categories, and more consistent organization across the models. (The third row of the top panel visualizes the proportion of explained variance for the first 32 principal components).

Extended Data Fig. 2 Changes in relative distances between stimuli reflect the superordinate category structure in AligNet models—including at earlier layers and at higher levels of the hierarchy— but not in UnAligNet models.

(a) Representation reorganization is similar across four AligNet models; representations move closer or farther depending on their degree of relatedness in the semantic hierarchy. (a) This reorganization also occurs for the animacy distinction in all four models. (c) By contrast, an UnAlignet ablation model shows reorganization at the basic and subordinate levels that is the opposite of what would be desired; viz. these closely-related categories are moving farther apart, with changes that are larger in magnitude than the changes in the superordinate categories. (d) Changes across the layers of ViT-B/16. Although we only optimize representations at the final representation layer, we see directionally consistent reorganization across all layers, with the magnitude of the changes increasing at higher layers. (Errorbars are bootstrap 95%-CIs. Curves are quadratic regression fits to illustrate trends).

Extended Data Fig. 3 Where AligNet and UnAlignet disagree, AligNet focuses on semantics while UnAligNet relies on more superficial appearance—resulting in humans overwhelmingly favoring AligNet.

Human ratings of semantic similarity on image pairs on which AligNet and UnaligNet maximally disagree. The human participants consistently rate the pairs that AligNet represents more similarly as in fact more semantically similar.

Extended Data Fig. 4 1-shot and 10-shot linear probing performance on 11 common datasets.

This plots compares the few-shot performances of the pre-trained (base) models vs. their performance after being aligned-finetuned. AligNet finetuning is clearly beneficial on most datasets in both 1-shot and 10-shot evaluations. Even models that have previously been trained on ImageNet data (e.g. DINOv1 ViT-B/16 and supervised ViT-B/16) show improvements on 1-shot ImageNet performance, which indicates that the benefits do not merely come from being exposed to ImageNet data, but also from the label information we distilled into AligNet. This plot also includes results for the widely known CLIP[18] model. It generally performs worse compared to the SigLIP model, which is why we picked the latter as the representative text/image model. For the more recent SigLIP2[27] we instead find that it has even stronger baseline performance than SigLIP, but it still does benefit in most cases from AligNet finetuning.

Extended Data Fig. 5 AligNet improves out-of-distribution generalization, and robustness wrt. distributional shifts.

(a) Accuracy of a linear readout head trained on top of the (frozen) base models is plotted on the x-axis, against the corresponding performance a linear readout head trained on top of the (frozen) AligNet-finetuned models on the y-axis. Every symbol above the diagonal indicates improved performance (through AligNet), while symbols below the diagonal indicate a performance degradation. Note that for better readability the range of the plot is zoomed in on the pre-trained models. The model trained from scratch (×) is thus often out of bounds due to its substantially lower performance, and its accuracy before and after finetuning is instead written out in a small inset. (b) Additional results for all student models on the four Breeds datasets: “entity13”, “entity30”, “living17”, and “nonliving26”. Student models are sorted according to their objective function (supervised, captioning, self-supervised, image/text). The randomly initialized ViT-B has the poorest base performance but benefits the most (leftmost column in each panel). Conversely, SigLIP-So400m—the teacher model—has the strongest base performance and benefits the least but consistently (rightmost column in each panel). Vertical arrows indicate improvements in performance compared to the base performance.

Supplementary information

Supplementary Information

Supplementary Notes, including the detailed results of statistical models, and other analysis details (9 tables and 15 figures).

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Muttenthaler, L., Greff, K., Born, F. et al. Aligning machine and human visual representations across abstraction levels. Nature 647, 349–355 (2025). https://doi.org/10.1038/s41586-025-09631-6

- Received: 14 November 2024

- Accepted: 15 September 2025

- Published: 12 November 2025

- Version of record: 12 November 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09631-6

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Vector-stimuli-responsive magnetorheological fibrous materials

Junhong Pu,

Haiqiong Li,

Jin Liu,

Ke Li &

…

Xiaoming Tao

Nature volume 647, pages 375–382 (2025)

Abstract

Fibrous materials that provide reversible actuation1,2 or adapt mechanical properties3,4 in response to external stimuli hold great promise for smart textiles[5], soft robotics[6] and wearable technologies[7]. Although considerable progress has been made in creating fibrous materials responsive to scalar stimuli such as voltage[8], temperature[6], humidity[2] and ion concentration[9], these technologies often lack directional controllability and functional diversity10,11,12,13,14. Here we report a class of vector-stimuli-responsive magnetorheological fibrous materials, guided by our engineering model integrating the structural mechanics of textiles with the magnetics of soft magnetic materials. We mass-produced soft magnetic polymer composite fibres with optimized mechanical and magnetic properties, which we then assembled into concentric helical yarns. These yarns exhibited pronounced bending and stiffening properties controlled by the direction and magnitude of magnetic fields, allowing for customized fabrics with various actuation and stiffening functionalities. We demonstrated innovative smart textiles derived from those fabrics, including an active ventilation fabric for personal moisture management, an integrated conformable gripper for handling objects of varying shapes and stiffness, and a compact remote-controllable haptic finger glove that replicates the sensation of fabric hardness and smoothness. Our work provides insights into stimuli-responsive fibrous materials, elevating them from scalar to sophisticated vector control, heralding an era of smart textile innovation.

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Main

Magnetorheological (MR) materials, a class of smart materials that can reversibly change rheological and mechanical properties under magnetic fields15,16,17, are composed of soft magnetic particles within a fluid or elastomeric carrier18,19. Under external magnetic fields, the magnetized particles attract each other through dipole–dipole interaction to form fibre-like structures—known as the MR effect—that increase the viscosity and stiffness of the MR materials20,21. Among these materials, anisotropic MR elastomers with predefined fibre-like soft magnetic structures exhibit directional responses, including sheer stiffening[22] and rotational actuation[23], to magnetic fields. However, their performance levels are limited because of the inherent rigidity of elastomer matrices22,24, necessitating high magnetic field strengths that are unsafe for human proximity25,26. Recently, magnetic fibres containing hard-magnetic particles have shown marked potential in soft robotics, medical devices and textile-based bioelectronics27,28,29,30,31. However, their use as textile actuators presents several challenges. Integrating magnetized fibres into fabrics with predefined magnetization patterns is limited by inter-fibre magnetic interaction32,33, relatively low precision in textile manufacturing, and the inherently non-bonded hierarchical fabric structures, collectively causing dislocated or disordered magnetization patterns. Magnetizing the fabrics directly is limited because of magnetizer spatial constraints, typically restricting the fabric size to a few centimetres (ref. [34]). Moreover, magnetic interactions between magnetized parts during actuation hinder stable, controllable and reversible movements in fabrics, which allow for hierarchical internal relative motion[23].

We proposed that MR fibrous materials could synergize the vector-stimuli-responsive behaviour of anisotropic MR materials with the flexibility and versatility of textiles, yielding a transformative class of vector-stimuli-responsive fibrous materials. Moreover, soft magnetic MR fibres do not have the limitations of hard-magnetic fibres in textile actuators, as they require no pre-magnetization. These magnetically anisotropic and matrix-free MR fibrous materials, enabled by non-bonding fibre assembly, can achieve high-performance actuation and stiffening functionalities previously unattainable (Fig. 1). Furthermore, the extensibility and programmability of textiles facilitate seamless transitions from two-dimensional fabrics to complex three-dimensional (3D) textile devices for practical applications.

Fig. 1: Schematic of MR fibrous materials with vector-stimuli-responsive functionalities.

Central to the design of anisotropic MR elastomers is their fibre-like architecture, densely packed with magnetic particles embedded in an elastomeric matrix. Although this inactive matrix is essential for structural integrity, it diminishes the performance of the magnetic-active structure. Inspired by this magnetically anisotropic architecture, MR fibrous materials, exemplified by woven and cut-pile MR fabrics, have been innovated. These MR fabrics, characterized by their intrinsic magnetic anisotropy, eliminate the inactive matrix by the textile assembly of MR yarns, thereby enabling fibrous materials with vector-stimuli-responsive behaviour. This advancement results in MR fabrics that offer a range of sophisticated functionalities not achievable with conventional scalar-stimuli-responsive fibrous materials. For example, a woven MR fabric, when fixed at one end, bends in response to non-parallel magnetic fields. By contrast, it stiffens under a vertical magnetic field, exhibiting reduced bending deformation under the same bending moment. Similarly, the cut-pile MR fabric demonstrates surface shearing under non-parallel magnetic fields, whereas under a vertical magnetic field, it significantly stiffens to resist compression. The range of magnetic field directions is indicated in the related Cartesian coordinate systems.

Two fundamental challenges hinder the development of high-performance magnetic fibrous materials, including fibres, yarns and fabrics. First, fibre spinning techniques face a contradiction between high filler content and thin fibre diameter35,36, compromising functional and mechanical properties. Second, the understanding of the multilevel structure–property relationship of stimuli-responsive fibrous materials is still lacking5,7, leading to notable performance decay and inadequate functionalities on the hierarchical assembly of textile structures. Solution spinning techniques such as wet spinning and electrospinning yield fibres with diameters less than 100 μm, but typically containing filler content under 20 wt% (refs. 37,38,39). Extrusion-based fibre processes, such as melt spinning and printing, produce fibres with filler content above 40 wt%. However, high loading can cause filler aggregation and undesirable rheological properties, leading to instability during extrusion and drawing, which results in fibre diameters often exceeding 200 μm (refs. 40,41). This restricts the concurrent achievement of high magnetic susceptibility and flexibility of MR fibres. For fibre assemblies, non-woven fabrics stacked by electrospun fibres exhibit limited durability and processability[39], whereas woven and knitted fabrics integrated by monofilaments often show suboptimal flexibility and deficient resilience34,41.

Design of MR fibres and yarns

Yarn serves as an important intermediary linking the fibres to the final fabrics, fundamentally influencing the design, process and performance of the end textile products. To guide the strategic development of MR yarns, we developed a simple model that relates the bending and stiffening behaviour of MR yarns with the direction and strength of magnetic field vectors, considering both material properties and geometric structures of the yarn. It provided two indicators linked to those vector-stimuli-responsive behaviour: the ratio of material susceptibility (χm) to Young’s modulus (E), χm/E, for composite material development, and the ratio of cross-sectional area (Ay) to the second moment of inertia (Iy), Ay/Iy, for yarn geometric structure design (Supplementary Note 1 and Supplementary Figs. 1 and 2). This model facilitates a methodical approach, starting with the rational design of composite materials, processing through large-scale fabrication of MR fibres and culminating in the refinement of MR yarn geometric structures (Fig. 2a).

Fig. 2: Design and fabrication of MR fibres and yarns.

a, Schematic of the melt spinning process with in situ high-speed drawing, showcasing the uniform dispersion of CIPs and the random alignment of polymer chains in the melt, which become lengthwise oriented in the MR fibres. These fibres are subsequently twisted into MR yarns followed by heat-setting. b, Logarithmic plots of loss factor as a function of angular frequency of molten LDPE and composites. c, χm/E, serving as a criterion for identifying soft magnetic materials with high magnetic activation ability, of LDPE and composites across the magnetic field spectrum. d, Diameter and axial thermal shrinkage of MR fibres with different drawing ratios. Error bars correspond to standard deviations (s.d.) (n = 5). e, Young’s modulus and breaking strength of MR fibres with different diameters. Error bars correspond to s.d. (n = 5). f, Photograph of a 4-km-long MR fibre wrapped on a spool. g, Side and cross-sectional views of the MR fibre. h, Ay/EIy, serving as a criterion for identifying the yarn flexibility, of MR yarns with different fibre diameters and surface helical angles. Error bars correspond to s.d. (n = 3). i, Magnetization curves of the optimal MR yarn in the direction parallel and perpendicular to the yarn axis. j, Photograph of a knot formed by the MR yarn, demonstrating its flexibility and strength. Scale bars, 1 cm (f); 20 μm (g); 500 μm (j).

Source Data

We rationally selected low-density polyethene (LDPE) as the polymer matrix and carbonyl iron particles (CIPs) as the magnetic filler to fabricate a series of polymer composites with uniform filler distribution by twin-screw extruder. We then systematically analysed their rheological characteristics to identify the melt-spinnable composite with the highest χm/E. Rheological characteristics of the molten composites indicate good spinnability, demonstrating good extrusion flowability and high drawing stability for composites with CIPs up to 70 wt% (Fig. 2b, Extended Data Fig. 1 and Supplementary Note 2). The molten composite with 80 wt% CIPs exhibits solid-like behaviour (tan δ < 1) and plastic instability, posing difficulties to realize stable extrusion and drawing. In another aspect, increasing CIP content results in a more significant increase in χm than E (Supplementary Figs. 3 and 4), leading to higher χm/E (Fig. 2c). Therefore, the composite containing 70 wt% CIPs with high χm/E (1.23 emu cm−3 kOe−1 MPa−1 at 3 kOe) and good spinnability was finalized for MR fibre spinning.

The optimal composite was processed into thin MR fibres with aligned polymer chains and CIP distribution by melt spinning and in situ high-speed drawing (Fig. 2a). After experiencing a high drawing ratio of 173, they were drawn down to a diameter of 57 μm (Fig. 2d and Supplementary Note 3). This extensive drawing not only stretched the polymer chains (Fig. 2d), increasing Young’s modulus and breaking strength of MR fibres (Fig. 2e and Supplementary Fig. 5), but also oriented the CIP distribution along the MR fibres (Extended Data Fig. 2), enhancing magnetic anisotropy of MR fibres. Furthermore, the drawing process moved CIPs out of the boundaryless surface (Extended Data Fig. 3), which in turn increased the surface roughness and the static coefficient of friction from 0.13 to 0.36 (Supplementary Fig. 6). The integration of melt spinning with high-speed drawing facilitated the continuous production of thin and high-loading MR fibres on a large scale, achieving a production speed of about 2 km h−1 (Fig. 2f,g).

Seven MR fibres were twisted and subjected to heat-setting to produce MR yarns (Fig. 2a). Similar to Ay/Iy, Ay/EIy was found to inversely correlate the diameter of fibres and directly correlate the surface helical angle of yarn (Fig. 2h). Increasing the surface helical angle slightly influences the tensile characteristics of yarns (Extended Data Fig. 4). We limited it to 26° to reduce the risk of breakage during twisting. Consequently, we meticulously engineered the MR yarn to comprise seven fibres, each 57 μm in diameter, twisted to achieve a surface helical angle of 26°. This specific configuration resulted in a yarn with a high Ay/EIy (0.027 mN−1) and significant magnetic anisotropy (ratio of axial to radial magnetization 1.4 at 3 kOe; Fig. 2i). These characteristics are indicative of the bending and stiffening abilities of MR yarns. Moreover, the MR yarn demonstrated remarkable robustness and flexibility and can withstand 10,000 repeated bending and forming a tight knot without compromising its integrity (Fig. 2j and Supplementary Figs. 7 and 8), thereby making it suitable for standard textile processing.

Characterization of MR yarns

The magnetized MR yarn consistently aligns its magnetic moment with the easy axis to endeavour the energetically favourable direction of magnetization. Figure 3a,d show that any angular misalignment between the easy axis and magnetic field induces magnetic torque. The resulting bending moment (Mbending) either aligns the MR yarn with the field to reduce misalignment or opposes the external force to hinder further misalignment. Moreover, the attraction (Fattraction) between wrapping MR fibres, induced through their demagnetization fields, contributes to the extra stiffening of MR yarn. These mechanisms induce two primary operation modes for MR yarns: bending mode and stiffening mode. As showcased in the comprehensive demonstration (Supplementary Video 1), when a vertical magnetic field is applied, a free-standing MR yarn promptly bends upwards, stiffens and stands upright. On removing the magnetic field, it swiftly returns to its original flexibility, becomes slack and lies down.

Fig. 3: Bending and stiffening properties of MR yarns.

a, Schematic of MR yarn undergoing bending actuation under a specific magnetic field. The yarn, cantilevered at one end, bends towards the direction of the magnetic field because of the angular misalignment between the length of the yarn and the magnetic field, generating a magnetic torque and a resultant bending moment (Mbending). The yarn returns to its original vertical position on removal of the magnetic field. The range of the magnetic field direction is indicated in the related Cartesian coordinate system. b, Bending actuation angle plotted against the applied magnetic field strength and predicted from the bending actuation model for MR yarns with different lengths. Error bars correspond to s.d. (n = 3). c, Moment density of MR yarns with different lengths. Error bars correspond to s.d. (n = 3). d, Schematic of MR yarn stiffening under a specific magnetic field. With the yarn simply supported and under a central vertical load, applying a horizontal external magnetic field induces attraction (Fattraction) between the wrapping MR fibres. This, along with any bending deformation, generates magnetic torques that straighten the yarn, resulting in reduced bending deformation compared with the conditions without a magnetic field. The range of the magnetic field direction is indicated in the related Cartesian coordinate system. e, Three-point bending force–displacement curves of MR yarns at different magnetic field strengths. Shaded areas correspond to s.d. (n = 3). f, Bending rigidity plotted against the applied magnetic field strength and predicted from the stiffening model for MR yarns. Error bars correspond to s.d. (n = 3).

Source Data

All the trends of MR yarns of various lengths aligned with the analytical predictions based on our MR yarn bending model (Fig. 3b), validating our assumption in modelling the yarn as a cylindrical rod (Supplementary Note 1 and Supplementary Fig. 9). Under the same magnetic field strength, the longer MR yarns generated larger bending degrees. The bending output of MR yarns was evaluated using the modified lifting test[42]. The moment densities of MR yarns with different lengths, characterized by moment per unit mass, were proportional to the magnetic field strength and reached about 7 N m kg−1 at 300 mT (Fig. 3c). Among these MR yarns with different lengths, the 10-mm-length MR yarn lifts 185 times of its body weight.

The stiffening property of MR yarns under parallel magnetic fields was assessed by three-point bending with a 15-mm gap. The bending force increased with displacement and magnetic field strength (Fig. 3e). As the magnetic field rose from 0 mT to 280 mT, the bending rigidity of MR yarn increased by about 30 times, from 0.68 mN mm^2 to 20 mN mm^2, aligning well with our analytical prediction (Fig. 3f). This remarkable tunability in stiffness is primarily attributed to two mechanisms. First, the magnetic torque, a known effect in similar anisotropic soft magnetic structures, plays a significant part. Furthermore, we identified an unusual mechanism: an increase in internal friction caused by magnetic attraction among helical fibres within the magnetized MR yarn, which enhances its bending stiffness (Extended Data Fig. 5, Supplementary Note 4, Supplementary Fig. 10 and Supplementary Video 2).

We compared the performance, including safety, bending angle, moment density, stiffening window and maximum flexibility of the MR yarn, with other yarn and fibre materials that demonstrate bending or stiffening properties reported recently (Supplementary Table 1). The MR yarn uniquely combines both bending and stiffening in a single material, unlike others that require multiple activatable components within a single fibre to achieve both functions. Its high flexibility far surpasses that of conventional yarns and fibres, which often use thicker fibres with diameters of several hundred to thousands of micrometres. Together with high magnetic anisotropy, the MR yarn demonstrates superior bending actuation properties. Moreover, it offers a nearly 30-fold tunable stiffness range within safe magnetic field intensities, making it ideal for wearable smart textiles. Although similar stiffening ranges can be achieved with thermal-responsive fibres, their high operating temperatures raise safety concerns about skin contact (Supplementary Note 5).

Characterization of MR fabrics

To scale up the moment output and bending rigidity as well as enable practical functionalities, MR yarns were efficiently assembled into woven and cut-pile MR fabrics in parallel and vertical orientations, respectively. Thin and flexible woven MR fabrics were created by interlacing MR yarns and sewing thread at the right angle (Fig. 4a). The moment density reaches a maximum when a straight yarn was aligned at 45° to the magnetic field (Supplementary Fig. 11 and Supplementary Note 1, Eq. (7)). However, after interlacing the yarns into woven fabrics, the moment density slightly decreases (Fig. 4b). This reduction results from the crimped structure of interlaced yarns that causes small deviations in the orientation of yarn segments from the optimal 45° alignment with the magnetic field. For the plain, twill and satin weaves we fabricated, the weave angle varies inversely with float length, resulting in a statistically significant correlation between float length and moment density at magnetic field strengths between 140 mT and 300 mT (Supplementary Note 6 and Supplementary Fig. 12). The MR yarn and woven fabrics retain at least 30% of their bending degree as the actuation frequency increases from 0.1 Hz to 10 Hz, and exhibit fast response times ranging from 0.07 s to 0.22 s and settling times between 0.52 s and 0.62 s (Extended Data Fig. 6a,c and Supplementary Note 7).

Fig. 4: Structure and property of woven and cut-pile MR fabrics.

a, Photograph of a flexible plain-weave MR fabric with structure diagram at bottom left. b, Moment density of single MR yarn and plain-, twill- and satin-weave MR fabrics. Error bars correspond to s.d. (n = 3). c, Bending rigidity of single MR yarn and plain-, twill- and satin-weave MR fabrics normalized per MR yarn. Error bars correspond to s.d. (n = 3). d, Photographs of a plain-weave MR fabric with the stiff–soft duality. e, Photograph of a soft and conformable cut-pile MR fabric with structure diagram at bottom left. f, Side-view photographs of the cut-pile MR fabric showing in-plane shearing and out-of-plane stiffening under specific magnetic field directions. g,h, Shear force (g) and compression modulus (h) of cut-pile MR fabrics with specific yarn densities. Error bars correspond to s.d. (n = 3). i, Compression fatigue test on MR fabrics at a frequency of 1 Hz with an extension of 1.5 mm for more than 10,000 consecutive cycles, under specific magnetic fields with a strength of 140 mT. Scale bars, 10 mm (a,d,e,f).

Source Data

The bending rigidity normalized per MR yarn of woven MR fabrics is slightly higher than that of the single MR yarn and increases with float lengths (Fig. 4c). This increase is due to the higher MR yarn packing density, lifting constraint and friction experienced by the MR yarns during bending deformation, which ultimately increases the energy dissipation (Supplementary Fig. 13). Among the MR fabrics, the plain-weave MR fabric mostly resembles the MR yarn because of the lowest level of interaction between adjacent MR yarns. A flexible plain-weave MR fabric measuring 35 × 40 mm and weighing 0.5 g supports a 10 g weight at a magnetic field strength of 210 mT (Fig. 4d and Supplementary Video 3).

In comparison with woven MR fabrics consisting of in-plane MR yarns, a cut-pile MR fabric consists of MR yarns vertically inserted into the base fabric. It is soft and conformable (Fig. 4e) and can output shear displacement and force and tune compression rigidity in response to specific external magnetic fields (Fig. 4f). The cut-pile MR fabric generates in-plane shear force when subjected to a magnetic field inclined to the plane. All the bending actuation of MR yarns in the same direction is converted into the directional movement of the surface plane made up of MR yarn tips. The actuation displacement responds quickly within 0.23 s and settles at 1.87 s on the application of magnetic fields, maintaining a displacement of above 2 mm under dynamic actuation at frequencies of up to 2 Hz (Extended Data Fig. 6b,c and Supplementary Note 7). The in-plane displacement results in a global shear force that is proportional to the magnetic field strength and the MR yarn density (Fig. 4g). A 1 cm^2 area of cut-pile MR fabric with a density of 500 yarns cm−2 can generate 110 mN of shear force when exposed to a static magnetic field of 280 mT with an angle of 45° to the plane.

Moreover, the cut-pile MR fabric can tune the out-of-plane compression modulus by varying the strength of the magnetic field applied perpendicular to the plane. Each magnetized MR yarn resists axial compression because the buckling deformation misaligns the MR yarn with the magnetic field. The sum of the force of MR yarns in the axial direction is shown as the apparent compression force of the cut-pile MR fabric in its thickness direction (Supplementary Fig. 14). The absolute range of the compression modulus is customizable by changing the MR yarn density. Doubling the MR yarn density from 250 yarns cm−2 to 500 yarns cm−2 adjusts the modulus range from 0.45–8.3 kPa to 1–22.5 kPa (Fig. 4h). Both the woven and cut-pile MR fabrics are durable to preserve stiffening actuation after a continuous fatigue test of about 10,000 cyclic compression (Fig. 4i) and exhibit minimal creep deformation under consistent loads for 1,000 s, with and without magnetic stiffening (Supplementary Fig. 15).

Smart textile demonstrations

The functionalities and performance of MR fabrics enable a wide range of applications. We designed a linear fabric actuator that operates by converting bending moments into linear motion (Fig. 5a). We fabricated an instantiation of fabric linear actuator that generates a stroke of 5 mm and a force of 150 mN at a magnetic field strength of 280 mT (Fig. 5b). Furthermore, we sandwiched this linear actuator between two fabrics to create an active ventilation fabric (Fig. 5c). Cyclic activation of the linear actuator by a stationary electromagnet induces a fluttering motion of the elastic fabric along with periodic opening of slits, promoting air exchange of the micro-environment (Supplementary Video 4). The breathability of the active ventilation fabrics, characterized by the water vapour transmission rate, is efficiently controlled between 34.5 g m −2  h−1 and 58.5 g m−2 h−1 by operating at frequencies varying from 0 Hz to 2 Hz (Fig. 5d and Supplementary Fig. 16), offering potential benefits in personal moisture and thermal management.

Fig. 5: Demonstration of smart textiles based on MR fabrics.

a, Fabric linear actuator with 5 mm stroke enabled by woven MR fabrics in a four-sided Sarrus linkage. b, Force–stroke relationship of the actuator under specific magnetic field strengths. c, Structure and working principle of the active ventilation fabric driven by the linear actuator. d, Water vapour permeability of the active ventilation fabric under square-wave actuation at selected frequencies with a peak field of 250 mT. Error bars correspond to s.d. (n = 3). e, Conformal gripper using cut-pile MR fabrics on coaxial electromagnets mounted on a robotic arm with six degrees of freedom. f, Close-up of the gripper holding a live worm (1 A input current). g, Untethered haptic finger glove remotely controlled by the mobile magnetic actuation system for haptic feedback. h, Schematic showing the positional and orientational relationship between the finger wearing the MR finger glove and the coaxial electromagnet pair for kinaesthetic feedback. Misalignment between the magnetic field and the finger axis, defined by angle λ, generates a moment (M) on the MR woven fabric. i, At λ = 60°, triangular currents (3 A peak) at 0.2 Hz, 0.5 Hz and 1 Hz were applied to the electromagnet pair, and the resulting output moment of the MR finger glove was measured. j, Schematic showing the MR finger glove with the fingertip pressing on a flat surface to mimic touch. A magnetic field applied perpendicular to the finger axis activates the cut-pile MR fabric under the fingertip, producing a normal force (F). k, At λ = 90°, triangular currents (3 A peak) at 0.2 Hz, 0.5 Hz and 1 Hz were applied to the electromagnet pair, and the resulting output force of the MR finger glove was measured. Scale bars, 5 mm (a); 2 cm (e,f,g).

Source Data

We designed an integrated conformable gripping device by symmetrically integrating cut-pile MR fabrics onto the poles of a mobile magnetic actuation system (Fig. 5e, Supplementary Figs. 17 and 18 and Supplementary Note 9). An array of independently axial compressible MR yarns with tunable modulus enables the cut-pile MR fabrics to have more effective adaptability to irregular shapes with maximized contact area to evenly distribute the gripping force applied on the objects than a conventional cushion. Thus, the conformable gripping device with high workspace flexibility effectively grasps and transfers different items, such as worms, tofu, blueberries, mung bean cake, potato chips and fusilli, with a wide range of modulus at minimal risk of damage or deformation (Fig. 5f and Supplementary Video 5).

Finally, we demonstrated a remote-controllable haptic device by leveraging the mobile magnetic actuation system to activate the finger glove that consists of cut-pile and plain-weave MR fabrics, ensuring a lightweight, comfortable wearing experience (Fig. 5g). This haptic finger glove provides a variety of haptic sensations, including kinaesthetic and tactile effects by controlling the mobile magnetic actuation system to modulate the spatial relationship between the electromagnets and the finger glove and adjust the electric current parameters (Fig. 5h,j, Supplementary Figs. 19–22, Supplementary Note 10 and Supplementary Videos 6 and 7). For instance, for a current of 3 A, the finger glove generates a moment of approximately 3.5 N mm, translating to a moment density of 2.6 N mm g−1 on the finger by the plain-weave MR fabric, whereas it produces a normal force of 150 mN to the fingertip pad (0.7 cm^2) by the cut-pile MR fabric (Fig. 5i,k). These performances are comparable to those of the commercial Dexmo glove (9.5 N mm g−1) (refs. 43,44) and meet the typical threshold of human tactile perception in the order of 10 mN (ref. [45]). It can be controlled to change the softness and smoothness sensations felt by the fingertip, showing great protentional as a fabric handle emulator for rendering the hand feel of different fabrics. Unlike conventional kinaesthetic gloves that rely on motors or pumps, our finger glove based on MR yarns eliminates the complex transmission mechanisms and the need for grounding, resulting in a more lightweight, less-restrictive and more natural design for the wearing finger.

Conclusions

In summary, we have developed an engineering guide for multi-hierarchy fibrous actuating structures by integrating the structural mechanics of textiles with the magnetics of soft magnetic materials. Using a scalable fabrication process, we have created vector-stimuli-responsive MR fibrous materials with various levels of hierarchy architectures. The resultant km-long continuous MR fibres, with a diameter of 57 μm and a particle load of 70 wt%, exhibit excellent alignability subject to an external weak and human-safe magnetic field of up to 300 mT. The MR yarns made from these fibres demonstrate an outstanding bending moment density of 6.5 N m kg−1 and an extraordinarily wide stiffness regulation range of 30 times as compared with other stimuli-responsive materials. Furthermore, we have constructed woven and cut-pile MR fabrics from the yarns. The resultant MR fabrics illustrate a diverse array of actuation abilities, including bending, shearing and linear motion, as well as stiffening properties under bending and compression. Finally, we showcase the versatility of these smart textiles through several demonstrations: an active ventilation fabric, an integrated adaptable gripping device and an untethered all-fabric haptic finger glove. This work not only advances the field of stimuli-responsive materials but also opens up possibilities for the practical application of smart textiles in everyday life and various industries.

Methods

Preparation of MR yarns

We selected LDPE as the flexible matrix because of its high filler capacity enabled by highly branched chains with low molecular packing[46] and good flowability indicated by a broad molecular weight distribution (Supplementary Fig. 23) and significant shear thinning (Extended Data Fig. 1a). CIP was chosen over other soft magnetic materials for its high susceptibility and low remnant magnetization, availability in micro-spherical particles, cost-effectiveness and resistance to oxidation with SiO_2 coating (Supplementary Table 2). CIPs coated with SiO_2 (SQ, BASF SE; Supplementary Fig. 24) were dispersed within LDPE (1700 MN 18C, total energies SE; melt flow rate of 70 g/10 min at 190 °C/2.16 kg) by melt compounding by a twin-screw extruder (Thermo Fisher Hot Melt Extruder Pharma 11). The temperature profile, ranging from the hopper to the die, was set at 10, 80, 130, 150, 150, 150, 150, 150, and 150 °C, respectively, while the screw speed was kept at 50 rpm. Four LDPE/CIP composites were prepared, varying in CIP content at 30, 50, 70, and 80 wt%. Interfacial interactions between LDPE molecules and CIPs (Supplementary Note 8 and Supplementary Fig. 25) promote the wetting of polymer on CIP surfaces, ensuring the uniform CIP dispersion of composites with filler content up to 80 wt% (Extended Data Fig. 7). Among these, the composite containing 70 wt% CIPs was chosen for fibre spinning.

This selected composite was introduced into the barrel of a laboratory melt spinning machine (AT225, Anytester Hefei) to produce MR fibres. The three heating zones of the barrel were set at 140 °C, 150 °C and 160 °C, respectively, allowing the composites to melt for 10 min until reaching a stable temperature of 160 °C. Nitrogen was then filled into the barrel to provide a pressure of 1.2 MPa. The molten polymer strands were extruded through a spinneret and guided by a ceramic wheel onto a collection roller with a diameter of 8.2 cm. A cooling fan positioned between the spinneret and the guide wheel facilitated solidification of the molten composite fibres. The diameter of the resulting MR fibre could be controlled by adjusting the winding speed, and in this case, the extruded molten filaments with a diameter of 750 μm underwent rapid thinning over a short range of about 20 cm (Extended Data Fig. 3). MR fibres with a diameter of 57 μm were consistently produced at a winding speed of 130 rpm. Then, seven of these fully drawn fibres were twisted clockwise to form a yarn, which was subsequently heat-set in an oven at 60 °C for 1 h to alleviate residual stress and stabilize the twist configuration. The primary goal in optimizing the structure of MR yarn was to maximize Ay/Iy, which is inversely related to the diameter of the fibres and directly related to the helical angle of the yarn (Supplementary Note 1). Given that the fibre diameter directly correlates with its modulus, our evaluation focused on optimizing Ay/EIy to refine the MR yarn design.

Fabrication of MR fabrics

Woven MR fabrics were fabricated by interlacing MR yarns as weft and sewing thread as warp at right angles, using a hand weaving machine. Float lengths of 1, 2 and 4 were specifically chosen to fabricate plain-, twill-, and satin-weave MR fabrics that exhibit packing densities of 65 yarns cm−1, 83 yarns cm−1, and 111 yarns cm−1, respectively (Extended Data Fig. 8). These selections enable the examination of the spatial freedom of interlacing MR yarns concerning their float length and packing density. The plain weave has the highest number of interlacing points, followed by the twill weave, and the satin weave.

Cut-pile MR fabrics were fabricated by inserting MR yarns into holes of a base plain-weave fabric using a punch needle kit. On pulling the needle out, a loop of MR yarn formed on the opposite side, its length controlled by the depth of needle insertion. This process repeated until the predefined area was uniformly filled at yarn densities of 250 yarns cm−2 or 500 yarns cm−2. Subsequently, a thin layer of silicone glue was applied to secure the inserted yarns. The loops were then cut at their centre points, followed by the application of silicone precursor at the cut tips. After curing overnight at room temperature, the silicone effectively prevented the free ends from untwisting.

Coefficient of friction

The as-spun filaments, each with a length of 3 cm, were arranged in parallel without any gaps to form a 1-cm wide region and adhered onto a square glass measuring 3 cm × 3 cm and weighing 1.8 g using a double-sided tape. Two identical samples were prepared for the measurement: one with the filaments facing upwards, and the glass side fixed onto a horizontal linear platform, and the other sample with the filaments facing downwards, aligned face-to-face and parallel to the filament region of the first sample. At the left edge of the top glass packed with filament tips, the centre point of the glass edge was horizontally connected to a force gauge using a nylon filament. A series of weights (1 g, 2 g, 5 g, 10 g, 20 g, 50 g and 100 g) were separately placed on top of the sample to provide normal force Fnf = mtmg, where mtm is the total mass of the weight and top glass substrate, and g is the acceleration due to gravity. The linear platform moved away from the force gauge at a velocity of 5 µm s−1. The maximum reading of the force gauge was recorded as the maximum static friction Ffriction, and the static friction coefficient a was calculated using the formula: Ffriction = aFnf. The static coefficient of friction between MR fibres was measured in the same way, by replacing the filaments with fibres.

Differential scanning calorimetry

Differential scanning calorimetry experiments were conducted using a Mettler Toledo DSC3 in a nitrogen atmosphere. Each sample, weighing approximately 1.5 mg, was analysed within a temperature range of 55–135 °C at a heating rate of 10 °C min−1 to record the endothermic curves. The heat of fusion (ΔHf) was determined by integrating the heat flow between 60 °C and 115 °C. This parameter was then used to calculate the crystallinity degree (χ) of both pure LDPE and the composite matrix. The crystallinity degree χ was defined as the ratio of ΔHf/(1 − x), where x represents the content of CIPs in the composite, to the heat of fusion (289.9 J g−1) of the purely crystalline form of polyethylene[47].

MR fibre thermal shrinkage

MR fibre shrinkage was assessed by subjecting the MR fibres with a length of 30–50 mm to a temperature of 100 °C for 10 min without external constraints. Shrinkage was quantified as the percentage change in the original MR fibre length using the formula, Shrinkage (%) = 100 × (Lif − Lsf)/Lif, where Lif represents the initial fibre length and Lsf represents the fibre length after complete shrinkage. The thermal shrinkage results provide insights into both the elongation and arrangement of polymer chains within the MR fibres. This is attributed to the relaxation of polymer chains during the thermal shrinkage process.

Tensile measurement

LDPE and composites containing 30 wt%, 50 wt% and 70 wt% CIPs samples with a dimension of 30 × 10 × 0.105 mm^3 were stretched to break at a velocity of 0.3 mm s−1 by a universal testing machine (Model 5566, Instron).

Structure characterization

Scanning electron microscopy images were obtained using a Tescan VEGA3 microscope equipped with an energy dispersive X-ray spectroscopy detector. Polymer composite samples were subjected to freeze fracture in liquid nitrogen to expose the cross-sectional surfaces, and then sputter coating was done with Au. The imaging was conducted at an accelerating voltage of 20 kV. The energy dispersive X-ray spectroscopy mapping was performed to analyse the distribution of Si, O and Fe on the surface of CIP. Optical photographs were captured using a Leica M165 C High-Performance Stereo Microscope to visualize the structure and extract the geometrical parameters of yarns and fabrics. Nano-computed tomography analysis was conducted using a Zeiss Xradia 520 Versa 3D X-ray microscope to examine the distribution of CIPs within the MR fibre. An MR fibre, approximately 1 mm in length, was mounted on the sample stage. Cross-sectional images, each with a resolution of 480 nm, were sequentially captured along the length at specified intervals. These images were then processed to generate a 3D reconstruction of the MR fibre. The polymer matrix was then extracted, in which the white zones represent CIPs. The directional distribution of CIPs within the MR fibre was assessed using ImageJ software with the following processing steps[48]. Initially, the raw nano-computed tomography images of both transverse and longitudinal sections were converted into binary images using an appropriate threshold to delineate the CIP distribution. Subsequently, the adjustable watershed algorithm was applied to segment the binary image effectively. The areas reflecting concentrated CIPs appeared as white zones within the images and were then fitted with ellipses to account for any orientation in the CIP distribution. Finally, the orientation of each CIP-concentrated area was determined by measuring the tilt angles of the ellipses relative to the horizontal plane. The degree of orientation was analysed based on the percentage distribution of different angles.

Magnetization characterization

Magnetic measurements were conducted at room temperature using the Physical Properties Measurement System (Quantum Design) with a sweep of external magnetic field ranging from −13 kOe to 13 kOe. Samples, including CIPs, LDPE, and composites with CIP contents of 30 wt%, 50 wt%, and 70 wt%, were tested without any preferred direction. The MR yarn was tested twice with the axis oriented parallel and perpendicular to the direction of the applied magnetic field.

Oscillatory shear rheology

Dynamic rheological characterization of both neat LDPE and composites was conducted using a rheometer (AR 2000EX, TA Instruments) equipped with parallel plate geometry. The samples were compressed into disc-shaped specimens with a thickness of 2 mm and a diameter of 25 mm for testing purposes. Frequency sweeps were performed at 160 °C, with the frequency ranging from 100 Hz to 0.01 Hz, while maintaining a strain of 0.5% within the linear viscoelastic region.

Dynamic mechanical analysis

Dynamic mechanical analysis experiments were carried out using a Mettler Toledo DMA1. Samples of LDPE and composites containing 70 wt% CIPs were analysed from −150 °C to −50 °C at a heating rate of 5 °C min−1 to record tan δ curves.

Bending actuation characterization

The bending property of MR yarns was characterized by subjecting vertically cantilevered MR yarns, anchored at their top end, to uniform magnetic fields. MR yarns were cantilevered at the top and positioned vertically within the central space between the two poles of an electromagnet (PEM-20, Litian Magnetoelectrican Science & Technology). The desktop electromagnet measures 260 mm (length) × 180 mm (width) × 200 mm (height) with two 40 × 40 mm poles spaced at an adjustable distance of 0–60 mm (Supplementary Fig. 26a). Within the space between poles, highly uniform magnetic fields can be generated with controllable strength by varying the current and the pole distance (Supplementary Fig. 26b–e). This stationary device allows for precise and uniform magnetic field adjustments across a wide range, providing a versatile workspace for material property characterization and testing in various applications. Static magnetic fields were oriented at 87° to the yarn axis to control the direction of bending; as the magnetic field strength increased, the cantilevered MR yarns gradually bent upwards (Supplementary Fig. 27a). The bending deformation was captured using a camera, and the images were processed using ImageJ to quantify the bending degree. MR yarns with lengths of 10 mm, 15 mm and 20 mm were tested.

To measure the moment output of a MR yarn with length Lyarn and weight myarn, a thin paperboard strip, of weight mps, was attached along the entire free portion of the cantilevered MR yarn (Supplementary Fig. 27b). The magnetic field strength was set to specific values (70 mT, 140 mT, 210 mT, 280 mT and 300 mT), and the cantilevered MR yarn lifted until the midpoint was blocked perpendicular by the PMMA rod connected to the force gauge. At this point, the yarn formed a 45° angle with the vertical direction. The force Fblock required to block the midpoint was recorded. The moment density of the MR yarn was calculated as Mpm = Mbending/myarn = [(mps/myarn + 1)g sin 45° + Fblock]Lyarn/2. The output moment per unit mass of fabrics was measured in a similar way by using woven MR fabrics with float lengths of 1, 2 and 4 measuring 20 × 5 mm with MR yarns aligned along the length direction. For each float length, three independently fabricated samples were evaluated separately.

Stiffening characterization

The MR yarns with a length of 25 mm were measured by a three-point bending test (Supplementary Fig. 28). The lower anvil with a gap of 15 mm was fixed onto an acrylic tube, which was placed on the pan of an analytical balance (ME204, Mettler Toledo) to record the force. To prevent magnetic interference with the balance, a PMMA support with a height of 25 cm was placed beneath the three-point bending support anvil. The upper anvil was connected to an Instron, which provided a downward displacement of 3 mm and returned to the starting position at the rate of 0.1 mm s−1. Three samples of each type of yarns were tested separately. Magnetic fields were generated by a pair of N52 cylindrical magnets (diameter 70 mm and thickness 30 mm) placed parallel to the opposite poles face to face. The magnetic field strength was controlled by the distance between the two magnets. The woven MR fabrics were tested in a similar way by using a load cell (2530-10N) for recording the force.

Bending durability test

The bending durability of the MR yarn was evaluated using a continuous bending setup (Supplementary Fig. 29). A 30-cm sample was fixed at one end to a vertical glass surface. The yarn was threaded horizontally through two pairs of guide pulleys (diameter 13.76 mm) spaced 8 cm apart, followed by a single pulley (diameter 17.2 mm) mounted on a PMMA plate. A pre-tension of 32.7% of the breaking strength of the yarn was applied to the free end using a hook-weight assembly. A pair of guide pulleys (diameter 13.76 mm) was attached to a moving PMMA fixture on an Instron testing machine, programmed to cyclically bend the yarn 90° upwards and 90° downwards for around 5,000 cycles at 0.25 Hz.

Creep test

A plain-weave MR fabric (35 mm × 40 mm) with MR yarns aligned lengthwise was fixed at both ends with a 30-mm gap. A 100-g load on a PMMA block (3.25 g) applied a uniform pressure of 2.95 MPa over an area of 35 mm × 10 mm across the fabric centre. Initial deflection on loading was 0.84 mm, with no observable creep within 1,000 s. Under a magnetic field of 178 mT along the MR yarn axis, the initial deflection of the fabric decreased because of magnetic stiffening, with minimal creep observed over the same period.

A cut-pile MR fabric (40 mm × 40 mm) with a 4-mm-thick MR yarn layer was uniformly pressed with a pressure of 0.71 MPa by a loading of 113.6 g on the MR yarn layer, its thickness decreased to 3.65 mm. No additional thickness reduction occurred across 1,000 s, and under a vertical magnetic field of 187 mT, the initial compression decreased with no detectable creep on this timescale.

Finite-element analysis

A 3D model was constructed using the Magnetomechanical node of COMSOL Multiphysics. Two rectangular magnets measuring 300 μm × 300 μm × 50 μm were built face to face to provide uniform magnetic fields. The surrounding air domain is modelled as a sphere with a radius of 2,300 μm. The two permanent magnets and air domain were from the COMSOL material library, and the mechanical and magnetic properties of the MR yarn were entered based on the experimental results.

Linear actuator fabrication and test

The basic concept uses radially symmetric woven MR fabrics, each resembling a hinge, with a shared wrap yarn acting as a pivot to couple axial force to the bending moments of the woven MR fabrics. The linear fabric actuator was fabricated by integrating four plain-weave MR fabric hinges in the form of a four-sided Sarrus linkage. Each plain-weave MR fabric hinge was assembled by two plain-weave MR fabrics, measuring 5 mm × 5 mm, which shared the same wrap yarn as a pivot. To measure its stroke and force output, the linear actuator was placed on a PMMA plate within a vertical magnetic field (Supplementary Fig. 30). Initially, the actuator was fully extended, with the horizontal part of the PMMA fixture touching its top surface without force. An Instron machine then applied a downward displacement of 5 mm at 0.25 mm s−1, pressing the actuator down and recording its upward force on the PMMA fixture. After each test, the PMMA fixture returned to the starting position by the Instron machine. At the same time, the linear actuator also returned to the starting configuration due to the magnetic actuation.

Ventilation fabric demonstration

The linear actuator was sandwiched in the centre between an elastic fabric with slits and a breathable woven fabric, with an annulus of spacer fabric stitched at the edge to form a dis-shaped active ventilation fabric with a diameter of 90 mm and a thickness of 5 mm.

The water vapour permeability (WVP) of active ventilation fabric was measured according to BS 7209. The active ventilation fabric with elastic fabric facing upwards was firmly fixed on top of a glass vessel (diameter 70 mm) filled with about 30 g distilled water and operated under square-wave actuation at frequencies of 0 Hz, 0.2 Hz, 0.5 Hz, 1 Hz and 2 Hz with a peak magnetic field strength of 250 mT for 30 min. The WVP is calculated as WVP = M/(At), where M is the water loss in mass, t is the time duration and A is the area of the exposed fabric.

Data availability

All data needed to evaluate the conclusions in the paper are present in the paper or the Supplementary Information. Additional data related to this paper may be requested from the corresponding authors. Source data are provided with this paper.

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Acknowledgements

We thank X. Zha and X. Sun for helping with the dynamic mechanical analysis and dynamic rheological characterization. We thank Y. Meng for the discussion on demonstrations. This work was supported by the Research Grants Council, Hong Kong SAR government (no. 15203421 and no. T42-513/24-R); the Endowed Professorship Fund of the Hong Kong Polytechnic University (no. 847 A); the start-up fund of the Hong Kong Polytechnic University (P0046273); and the seed fund of Research Institute for Intelligent Wearable Systems (P0046046). K.L. acknowledges a postgraduate scholarship received from the Hong Kong Polytechnic University.

Author information

Authors and Affiliations

1. Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China

Junhong Pu, Haiqiong Li, Jin Liu, Ke Li & Xiaoming Tao

2. School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong, China

Junhong Pu, Haiqiong Li, Jin Liu, Ke Li & Xiaoming Tao

3. PolyU-Jinjiang Technology and Innovation Research Institute, Jinjiang, China

Xiaoming Tao

Contributions

J.P. and X.T. conceived the research, discussed the plan and obtained funding. J.P. and H.L. fabricated the composite materials, fibrous materials and devices. J.P., H.L., J.L. and K.L. carried out the experiments and characterizations. J.P., H.L., J.L. and X.T. analysed and interpreted the experimental results. J.P., H.L., J.L. and K.L. created the figures and videos. J.P. wrote the paper. All authors reviewed and edited the paper. J.P. and X.T. supervised the research.

Corresponding authors

Correspondence to Junhong Pu or Xiaoming Tao.

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Nature thanks Weihua Li, Xun Zhao 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 Rheology of molten LDPE and composites.

a, Logarithmic plots of complex viscosity. b, Logarithmic plots of storage modulus as a function of angular frequency.

Extended Data Fig. 2 The distribution of CIPs in MR fiber.

a, Nano-CT images of the MR fiber. Half-rose plots of the orientation of CIPs in b, Cross-sectional plane, and c, Longitudinal section plane. Scale bars, 50 μm.

Extended Data Fig. 3 Evolution of MR fiber structure during melt spinning and in-situ high-speed drawing.

a, Sketch of the single filament during the process of melt spinning and in-site high-speed drawing. b, Fiber diameter at different distances away from the spinneret. c, Surface morphology of fibers with diameters corresponding to those presented in b. Scale bars, 5 μm.

Extended Data Fig. 4 Mechanical properties of MR yarns with different twist levels.

a, Photographs of MR yarns with different twist level, scale bar 500 μm. b, Stress-strain curves and c, The extracted Young’s modulus and elongation at break of MR yarns with different helical angles. Error bars correspond to s.d. (n = 3).

Extended Data Fig. 5 Magnetic interaction between MR fibers.

a, Relationship between the minimal magnetic field strength required to bring parallel MR fibers together and the angle between the magnetic field direction and the fiber axis. b, Photos of the attraction between two parallel MR fibers after applying magnetic fields as related to a. Scale bars, 500 μm. c, The contraction of an MR yarn with 1 core and 5 wrapping MR fibers under a lengthwise magnetic field. Scale bar, 200 μm. d, Finite element analysis results identifying the compacting of an MR yarn under a lengthwise magnetic field, where R and Rmax represented the displacement and maximum displacement of wrapping fibers in the radial direction, respectively.

Extended Data Fig. 6 Dynamic actuation properties of MR fabrics and yarn.

a, Bending actuation angles of the MR yarn and woven fabrics under square wave actuation at specific frequencies with a peak magnetic field strength of 135 mT. Error bars correspond to s.d. (n = 3). b, In-plane displacement of the MR yarn tip of cut-pile MR fabric under square wave actuation at specific frequencies with a peak magnetic field strength of 247 mT. Error bars correspond to s.d. (n = 3). c, Bending angle with time of MR yarn and woven fabrics under square wave actuation with a peak magnetic field strength of 135 mT. In-plane displacement with time of MR yarn tip of cut-pile MR fabric under square wave actuation with a peak magnetic field strength of 247 mT.

Extended Data Fig. 7 The dispersion of CIPs in bulk composites.

SEM images of fracture surfaces of the composites with CIP contents of 30, 50, 70, and 80 wt%. Scale bars, 10 μm.

Extended Data Fig. 8 Structure of woven MR fabrics.

Weave diagrams of plain, 2/1 twill, and 4/1 satin MR fabrics. Black and white fill represents weft and wrap, respectively. Photographs with zoom-in views of corresponding fabrics are arranged at the right side. The MR yarns are interlaced as weft with different packing densities along the wrap direction. Scale bars, 1 mm.

Supplementary information

Supplementary Information

This file contains Supplementary Notes 1–10, Supplementary Figs. 1–30, Supplementary Tables 1 and 2, and references.

Supplementary Video 1

MR yarn stands up and lays down.

Supplementary Video 2

Attraction between magnetized wrapping MR fibres. An MR yarn comprising five slightly separated helical MR fibres wrapped around a core MR fibre was laying horizontally. When a magnetic field was applied along the yarn axis, the five helical MR fibres attracted each other. Once the magnetic field was removed, the fibres separated and returned to their original relative positions.

Supplementary Video 3

Fast and sufficient stiffness change of MR woven fabric. The flexible fabric was positioned horizontally on a PMMA supporter, and upon applying a magnetic field, it aligned with the field direction and remained flat. A 10 g load applied at the fabric centre caused a minor deflection, leading to misalignment of the MR yarns with the magnetic field. This deformation created magnetic moments in the fabric to balance the weight. Once the magnetic field was removed, the fabric lost the magnetic moments and could not support the weight, causing it to return to its flexible state and release the load.

Supplementary Video 4

Active ventilation fabric in operation. The left section shows the full view of the ventilation fabric device in action. A stationary electromagnet applies a dynamic magnetic field at different frequencies to the fabric. This causes the linear actuator within the fabric to push the elastic fabric upward when the magnetic field is active. When the field is off, the elastic fabric returns to its flat state due to its own deformation force. The right section offers a close-up view of how the elastic fabric with slits deforms during this dynamic actuation.

Supplementary Video 5

Demonstration of the conformable gripper. Integrated conformable gripping device for handling delicate items with varying shapes, surface conditions and stiffness.

Supplementary Video 6

Kinesthetic feedback generated by remote-controllable haptic finger glove. The haptic MR finger glove was worn on a silicone finger. Dynamic magnetic fields were applied to the glove through a mobile magnetic actuation system, producing various kinesthetic effects. This untethered device demonstrated high feasibility by allowing precise control over the rotation speed and angle of the magnetic field direction relative to the finger axis, as well as adjustments to the current waveform, amplitude and frequency.

Supplementary Video 7

Tactile feedback generated by remote-controllable haptic finger glove. The haptic MR finger glove was worn on a silicone finger. Dynamic magnetic fields were applied to the glove through a mobile magnetic actuation system, producing various haptic effects. This untethered device demonstrated high feasibility by allowing precise control over the current waveform, amplitude and frequency.

Source data

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Pu, J., Li, H., Liu, J. et al. Vector-stimuli-responsive magnetorheological fibrous materials. Nature 647, 375–382 (2025). https://doi.org/10.1038/s41586-025-09706-4

- Received: 30 June 2024

- Accepted: 06 October 2025

- Published: 05 November 2025

- Version of record: 05 November 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09706-4

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A molecularly impermeable polymer from two-dimensional polyaramids

Cody L. Ritt,

Michelle Quien,

Zitang Wei,

Hagen Gress,

Mohan T. Dronadula,

Kaan Altmisdort,

Huong Giang T. Nguyen,

Christopher D. Zangmeister,

Yu-Ming Tu,

Sanjay S. Garimella,

Shahab Amirabadi,

Michael Gadaloff,

Weiguo Hu,

Narayana R. Aluru,

Kamil L. Ekinci,

J. Scott Bunch &

…

Michael S. Strano

Nature volume 647, pages 383–389 (2025)

Abstract

All polymers exhibit gas permeability through the free volume of entangled polymer chains1,2,3. By contrast, two-dimensional (2D) materials including graphene stack densely and can exhibit molecular impermeability4,5,6. Solution-synthesized 2D polymers that exhibit the latter by poly-condensation have been a longstanding goal. Herein, we demonstrate self-supporting, spin-coated 2D polyaramid nanofilms that exhibit nitrogen permeability below 3.1 × 10−9 Barrer, nearly four orders of magnitude lower than every class of existing polymer, and similar for other gases tested (helium, argon, oxygen, methane and sulfur hexafluoride). Optical interference during the pressurization of nanofilm-coated microwells allows measurement of mechanosensitive rim opening and sealing, creating gas-filled bulges that are stable exceeding three years. This discovery enables 2D polymer resonators with high resonance frequencies (about 8 MHz) and quality factors up to 537, similar to graphene. A 60-nm coating of air-sensitive perovskites reduces the lattice degradation rate 14-fold with an oxygen permeability of 3.3 × 10−8 Barrer. Molecularly impermeable polymers promise the next generation of barriers that are synthetically processable, chemically amenable and maximize molecular rejection with minimal material, ultimately advancing sustainability goals.

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Main

Polymeric materials are conventionally thought of as intermediate in order between crystalline and amorphous[7]. This ordering is described as a two-phase mixture of densely packed crystallite domains dispersed throughout an amorphous phase. The former are considered to be gas-impermeable because of the absence of free volume[2]. The free volume contribution from the amorphous phase, present even at high crystallinity, gives rise to gas permeability in polymers1,2. Liquid crystalline polymers, which necessarily assemble as a fluid with minimal free volume, have the lowest reported nitrogen (N_2) permeability at about 10−5 Barrer1,3 (1 Barrer = 3.348 ×  10−16 mol m−1 s−1 Pa−1). By contrast, defect-free graphene, monolayer or multilayer, consists of ideal crystallites that possess no free volume and, consequently, are impermeable to all gases4,5,6. Although 2D lattice materials like graphene can exhibit near-perfect gas rejection as nanofilms, they must be grown at high temperatures and manually transferred as macroscopic layers, limiting their use as barrier materials. The processability and formability of conventional polymers allow them to be easily applied onto surfaces, but their residual free volume limits their use as barrier materials. Eliminating free volume with traditional polymers is extremely challenging, if not impossible, as it requires perfect packing of dynamic segmental chains. Polymers that extend covalently in 2D, as we[8] and others9,10,11 have synthesized in the solution phase, can necessarily pack with negligible free volume. Hence, 2D polymers can potentially exhibit barrier properties thus far only observed for 2D inorganic crystalline nanomaterials.

Here we use a 2D polyaramid polymer (2DPA-1) synthesized from the polycondensation of melamine and trimesoyl chloride (TMC)8,12 to form molecularly impermeable nanofilms between 4 nm and 65 nm thick. When dispersed in trifluoracetic acid (TFA) or dimethylsulfoxide (DMSO), 2DPA-1 forms nanoplatelets with diameters on the order of 10 nm (Fig. 1a,b). Unit cells formed by repeating inert amide linkages with triazine cores afford 2DPA-1 a high degree of chemical stability and processability (Supplementary Figs. 1 and 2)—a consolidation of properties not commonly found in covalent organic framework (COF) analogues synthesized by means of conventional solvothermal methods[13]. Several strategies exist to process COFs for thin film applications14,15,16, but experimental realization of impermeable COFs[17] remains wanting because of the formation of defects[18]. Crystallinity estimates of 20.7% ± 6.2% from powder X-ray diffraction indicates 2DPA-1 lacks long-range order (Supplementary Fig. 3); however, a combination of polarized photoluminescence (Supplementary Fig. 4) and scanning transmission electron microscopy (STEM) of spin-coated 2DPA-1 thin films shows anisotropy in the form of layered, orientational ordering (Fig. 1c–e). The latter indicates a 3.3 ± 0.2 Å centre-to-centre interlayer spacing between stacked 2DPA-1 monolayers. The commensurate thickness of monolayer 2DPA-1 platelets, roughly 3.7 Å [8], is consistent with an absence of free volume between 2DPA-1 layers, mirroring inorganic 2D crystals[19].

Fig. 1: 2DPA-1 nanoplatelets as free-standing thin films for bulge formation.

a, Theoretical structure of 2DPA-1 monolayer platelet. b, TEM image of dispersed 2DPA-1 nanoplatelets adsorbed on the surface of a graphene-supported TEM grid. Image taken at ×110,000 magnification. c, Theoretical structure of a 2DPA-1 lamella with sparse voids formed because of imperfect packing between neighbouring platelets. d,e, Ordering of 2DPA-1 lamella observed at the edge of a spin-coated nanofilm by STEM (d) and corresponding intensity profile (e). Yellow dashed lines indicate the scan region. STEM taken at ×4,000,000 magnification. f–k, Free-standing 2DPA-1 films, after being transferred onto the Si/SiO_2 microwell substrate, forming either positively deflected bulges (f–h) or negatively deflected dimples (i–k). f and i depict cross-sectional illustrations of the film deflections based on the pressure of the internal environment, pint, with respect to the external environment, pext. The green layer represents the 2DPA-1 thin film. g and j are optical micrographs of a bulge and dimple, respectively. h and k are AFM images of the bulge and dimple from g and j, respectively. Height intensities on the nanometre scale. The solid white lines represent cross-sectional deflection profiles taken from AFM, where δ indicates the maximum film deflection. Scale bars, 50 nm (b), 5 nm (d,e), 2 μm (g,h,j,k).

Source Data

To investigate molecular impermeability, nanofilm bulges6,20,21 have been used to measure as low as 10−11 Barrer (10−27 mol m−1 s−1 Pa−1), or 5 molecules s−1 across a 5-µm-diameter microwell at 1 atm (101.3 kPa). We measured the gas permeation properties of 2DPA-1 films (root mean square roughness less than 1 nm) by suspending them over 1-µm-deep microwells (Supplementary Fig. 5) etched into a silicon/silicon oxide (Si/SiO_2) wafer with a 285-nm oxide layer. We find that 2DPA-1 nanofilms, even as thin as about 4 nm, are mechanically robust enough to transfer onto the microwell array without rupture (Extended Data Fig. 1), similar to graphene. Also similar, the transfer at room temperature can result in trapped ambient air, producing positively deflected bulges between 50 nm and 500 nm in height as measured by atomic force microscopy (AFM; Fig. 1f–h and Extended Data Fig. 1). Alternatively, thermal annealing immediately following transfer results in dimpled downwards bulges depressed −500 nm to −190 nm below the well surface (Fig. 1i–k and Extended Data Fig. 2). The deflation rates of the air-trapped bulges lack a thickness dependence, implying that gas leakage is occurring variably through the seal between the 2DPA-1 film and microwell interface.

It is not possible to dynamically observe a well under pressurization using a standard AFM; however, under an optical microscope with an environmental stage, we observe an optical interference effect that allows correlation with physical AFM position by Fourier analysis. Distinct optical profiles arise from changes in the interference of collected light that correspond with the distance between the film and the bottom of the reflective Si/SiO_2 microwell, or rather, the deflection of the film (Supplementary Fig. 6 and Extended Data Figs. 3–6). This phenomenon enabled the development of graphene interferometric modulators[22] and can be exploited to gain access to real-time deflection monitoring.

By using the optical interference described above—and beginning with negatively deflected 35-nm-thick dimples—we can observe dynamic gas filling using a pressurized microscopy stage (Fig. 2a, Supplementary Video 1 and Supplementary Figs. 7 and 8). The as-formed dimple stipulates that ambient air is unable to enter the microwell and balance the pressure, indicating that (1), the 2DPA-1 film itself is impermeable to air; and (2), the seal at the well’s rim, formed from van der Waals adhesion between the 2DPA-1 and SiO_2 interface, remains intact to prevent leakage. Pressurizing the stage to 150 kPa (gauge pressure) initially increases the deflection of the film by 6% (from −354 nm to −332 nm) before reversing and depressing to a minimum of −355 nm after almost 1 h. The dimpled film then reverses again, increasing in height before plateauing at roughly −250 nm after 3 h of pressurization when the gas has presumably filled the microwell to equilibrate pressure on both sides of the film. The film does not return to a neutral suspended state (that is, level with the SiO_2 surface) but instead remains depressed in the microwell because of adhesion of the film to the interior of the microwell (Extended Data Fig. 2). The inflection during bulge pressurization corresponds to two competing, intermittent forces on the bulge: the hydrostatic pressure pushing downwards into the well when the rim is sealed, and inflation from gas filling through the film-substrate interface when the rim is opened. We find that the rim seal can be manipulated in this way using the rate and magnitude of gas pressurization. Further evidence that the pressurized gas, N_2 in this case, is sealed in the 2DPA-1-covered well is provided by the formation of stable, positively deflected bulges with deflections that correspond to the applied N_2 pressure after releasing to 1 atm (101.3 kPa; Extended Data Fig. 7).

Fig. 2: Observation of dynamic filling and N_2 impermeability of 2DPA-1.

a, Deflection of 2DPA-1 dimples before, during and after N_2-pressurization in an environmental stage. White- and red-filled data points represent deflections determined directly by means of AFM measurement and indirectly by means of optical interference analysis, respectively. The dark grey shaded region indicates the pressure ramp-up period from 0 kPa to 150 kPa (gauge pressure), and the light grey shaded region indicates a 150 kPa gauge pressure of N_2. Non-shaded (that is, white) regions represent atmospheric pressure. The insets show cross-sectional profiles of bulge deflections at respective time steps taken from the optical analyses of the imaged bulges (pictured above insets). b, N_2 permeability with respect to degree of crystallinity (Xc). The dashed black lines mark the maximum permeability allowed for a 35-nm bulge to remain inflated over various durations for the microwells used in this study. The red stars represent the threshold permeability for 2DPA-1 based on the observed persistence of 59 2DPA-1-covered microwells over 110 d after filling with 150 kPa N_2. The N_2 permeability of liquid crystalline and glassy polymers, such as poly[bis(trifluoroethoxy phosphazene)] (PTFEP), polyacrylate (PA), poly(p-phenylene terephthalamide) (PPTA), poly(p-hydroxybenzoic acid-co-6-hydroxy-2-naphthioic acid) (HBA/HNA), poly(4-methyl-1-pentene) (PMP), poly(lactic acid) (PLA) and polyethylene (PE) are included from ref. [1] for comparison, along with the H_2 permeability of graphene[5]. aPermeability values are at the method detection limit and may be considered effectively zero. bPowder X-ray diffraction of four bulk samples was used to estimate the upper bound of Xc for 2DPA-1, with error bar denoting the standard deviation.

Source Data

This direct observation of unsealing and resealing at the microwell rim resolves a longstanding puzzle as to how such 2D material bulges form from pressurization with leak-proof seals. Recent work confirms that gas diffusion probably occurs through the interface, where the seal can be improved by means of thermal annealing, applied pressure, metal deposition and engineering atomically smooth interfaces5,23,24,25. The observations herein confirm that the substrate–nanofilm adhesion, forming a manipulatable rim seal, provides a greater degree of control over the contents of the resulting bulge. This provides direct evidence of a mechano-sensitive opening of the rim seal, further supporting its importance to gating molecular transport from sealed microwells.

We tracked the deflections for a set of 8 bulges from the total 59 that were inflated with 150 kPa of N_2 for 110 d (Fig. 2b). No deflation occurred over that time that would indicate gas escaped the microwell at rates above our measurement detection limit. We confirmed the successful trapping of N_2 over that duration by gently puncturing a bulge with an AFM tip, observing it collapse immediately after the pressure was released (Supplementary Fig. 9). The inability of both pressurized N_2 to escape bulges and atmospheric N_2 to equilibrate negatively presured dimples, where \({\rm{\Delta }}{p}_{\mathrm{int}} < {\rm{\Delta }}{p}_{{\rm{atm}}}\), points to a film that is N_2-impermeable. This finding corroborates the low surface area measured for 2DPA-1 platelets by means of Brunauer–Emmett–Teller (BET), where N_2 was used as the probe gas (Supplementary Fig. 10).

Using the half-life (t1/2) of the N_2-pressurized bulges that did not deflate over 110 d of observation as a conservative estimate of the bulge lifetime (Methods), we can threshold the N_2 permeability (PN_2) of 2DPA-1 below 3.1 × 10−9 Barrer (1.0 × 10−24 mol m−1 s−1 Pa−1). This permeability, which is nearly four orders of magnitude lower than that of the least permeable liquid crystalline polymers (PN_2 ≈ 2 ×  10−5 Barrer or 6.7 × 10−21 mol m−1 s−1 Pa−1), is closer in comparison to the H_2 permeability of pristine graphene (Fig. 2b). Furthermore, achieving this level of impermeability necessitates the absence of transmembrane defects, as even the presence of a single 1-nm Knudsen pore would result in PN_2 = 7 × 10−2 Barrer (2.3 × 10−17 mol m−1 s−1 Pa−1) and, consequently, a near-immediate collapse of the bulge (Supplementary Fig. 11).

The impermeability of 2DPA-1 implies an absence of free volume required for molecular transport. The pore in the 2DPA-1 unit cell is estimated to be about 1 nm in diameter; hence, it is unlikely that a monolayer platelet would impose substantial resistance to most gas molecules[26]. We ruled out the possibility that water or other relevant solvent could be occluding the pores by means of simultaneous thermogravimetric analysis with mass spectrometry (TGA-MS) and the observation of several bulges that remain inflated on heating to 120 °C (Supplementary Figs. 12 and 13). The impermeability necessitates a staggered configuration of stacked 2DPA-1 bilayers, similar to AB stacking in COFs27,28, to reduce the effective pore size, enhance chemical interactions and eliminate free volume. Density functional theory (DFT) calculations indicate that staggered pores are the most energetically favourable stacking configurations, with eclipsed stacking (that is, AA stacking) being the least favoured (Supplementary Fig. 14).

Assuming complete steric exclusion, a pore-to-pore offset of 61% imposes the greatest steric restriction to molecular transport, corroborated by hard-sphere, hit-and-miss Monte Carlo simulations29,30,31 (Supplementary Figs. 15 and 16). Using high-throughput DFT and molecular dynamics (MD) simulations (Extended Data Fig. 8 and Supplementary Figs. 17–20), we calculated the N_2 translocation energy barrier across a bilayer pore to be 65.6 kJ mol−1 in the most occluded scenario. The total energy barrier increases to 77.0 kJ mol−1 when accounting for sequential hopping throughout a 100-layer (about 35-nm) film and lies between values measured for extremely thick \((o \sim \mathrm{\mu m})\) crystalline polymers32,33 and pristine monolayer graphene[4].

The ability of 2DPA-1 films to trap gas in the microwells is not exclusive to N_2. We observed similar multiday persistence for bulges filled with sulfur hexafluoride (SF_6), argon (Ar) and methane (CH_4; Fig. 3a–d), indicating that 2DPA-1 is likewise impermeable to gases other than N_2. We note that the formation of persistent bulges from this method was not observed for smaller gases such as oxygen (O_2), carbon dioxide (CO_2), helium (He) or hydrogen (H_2). We measured unary adsorption isotherms for CH_4, CO_2, He and H_2 on 2DPA-1 platelets at 298 K up to 1 MPa (Supplementary Fig. 10). At 150 kPa, negligible He and H_2 adsorption were observed (less than 7 μmol g−1), with only marginal adsorption of CO_2 (380 μmol g−1) and CH_4 (60 μmol g−1). Despite the inability of 2DPA-1 films to form persistent bulges with some of these smaller gases, the adsorption isotherms corroborate the notion that 2DPA-1 pores are inaccessible. The important coupling between gas escape and their interactions with the rim seal remains unknown. For the gases that generated persistent bulges, it is evident that gas transport occurs only during the pressurization process when the rim seal opens.

Fig. 3: Random fluctuations in bulge deflection over time for various gases.

a–d, Normalized deflection of bulges filled with N_2 (a), SF_6 (b), Ar (c) and CH_4 (d), measured over time. The solid, coloured lines and shaded regions represent the average and standard deviation of the normalized bulge measurements at each time point, respectively. The dashed black line marks the normalized initial deflection, δ_0, for all bulges. e–h, MSD analysis for four 2DPA-1 bulge deflections measured over 1,000 d, with measurements for each independent bulge depicted in panels e, f, g, and h. Bulge deflections are represented as grey diamonds (top). The MSDs of these deflection data, represented as light blue circles (bottom), are fit to a squared confinement length, \({R}_{{\rm{C}}}^{2}\), by means of equation 21 for confined diffusion (red line). i, Logarithm of δ_0 (base 10) with respect to the logarithm of \({R}_{{\rm{C}}}^{2}\) (base 10). Data points are taken from e–h and Supplementary Fig. 22. The solid curve represents a linear regression between the two log_10-transformed variables with R^2 = 0.92. The inset illustrates the increase in \({R}_{{\rm{C}}}^{2}\), depicted as variation in the bulge deflection, for systems with greater initial deflections.

Source Data

The fluctuations in the bulge deflections vary randomly from day to day by about 5% on average (Supplementary Fig. 21), similar to leak-free graphene bulges5,34 and resembling a random walk. Mean square displacement (MSD) analyses of the bulge deflections with respect to time (Fig. 3e–h and Supplementary Figs. 22 and 23) follow confined diffusion behaviour, where the MSD plateaus at a limiting value. The MSD of 24 air-trapped 2DPA-1 bulges measured over 1,000 d plateaued at a limiting value around 200 d, mirroring the behaviour of leak-free graphene bulges[5]. Notably, the squared confinement length taken from this analysis correlates strongly (R^2 = 0.92) with the initial deflection of the bulges (Fig. 3i). Given the inherent relationship between the bulge deflection and internal pressure, MSD analysis may present itself as a new, practical tool for estimating the pressure of sealed microwell systems where the internal pressure is unknown. Further, the longitudinal robustness of the inflated bulges indicates 2DPA-1 does not become permeable over time because of hygroscopic degradation common to one-dimensional polyaramids (such as Kevlar) or physical aging phenomena ubiquitous to polymer systems[35], often manifesting in the form of altered free volume due to the relaxation of polymer chains[36]. 2D polyaramids thus represent an entirely new and exciting class of barrier materials—possessing the low permeability properties of 2D crystals in tandem with the synthetic processability of traditional polymers.

Platelet size can have a prodigious effect on the gas permeability of a film, as higher-aspect-ratio platelets are more likely to lead to environments with reduced free volume[37]. The aromatic protons and end group protons on 2DPA-1 are detectable using proton nuclear magnetic resonance (1H NMR), and the ratio of peak integrations between the two (r) can be used to estimate the platelet size38,39 (Supplementary Figs. 24–27). We use the skewness (s), defined as the normalized difference between the mean and mode of the aromatic peak distribution, as an orthogonal measure of polycyclic aramid growth[38]. By combining both r and s parameters in a 2D trajectory, we can differentiate polycyclic aramids from dendrimers (Fig. 4a). Linear scaling of nanoplatelet size with respect to the r parameter is confirmed by TEM and solution-phase fluorescence measurements, and it is found to be critical to forming mechanically robust 2DPA-1 films that can suspend over the microwells (Extended Data Fig. 9 and Supplementary Fig. 28).

Fig. 4: Scaling 2DPA-1 for practical applications.

a, Theoretical and experimental aromatic-to-end group ratio, r, versus aromatic skewness, s. Shown are the dendritic limit (blue circle with cross), the polycyclic curve for r > 4 (solid red line) and experimental (r, s) points for reaction aliquots extracted at various times during synthesis that trace a trajectory towards and then along the polycyclic curve from the dendritic limit. Solid red data points indicate perfect polycyclic 2DPA-1 platelets that can form mechanically stable, suspended thin films. Solid blue data points are 2DPA-1 platelets intermediate between polycyclic and dendritic that cannot form suspended films. b, Illustration of a suspended 2DPA-1 film as a nanoelectromechanical resonator, where the thermal vibrations of the film are detected by a focused helium–neon laser. c, Power spectral density of thermal displacement fluctuations versus frequency, f, for the fundamental mode of a 35-nm-thick 2DPA-1 resonator under 10−5 Pa vacuum. Dark blue data are taken after initial introduction to vacuum, whereas light blue data are the final measurement after complete gas evacuation from the microwell. d, Illustration of 2DPA-1 spin-coating process on MAPbI_3 three-dimensional perovskite to assess its inhibition of O_2 and H_2O (red circles) permeation. e,f, XRD spectra of MAPbI_3 films that were uncoated (e) and coated (f) with a 60-nm 2DPA-1 film. Stacked panels show the change in spectra with respect to time exposed to ambient air. The (110) and (220) peaks represent signature peaks of MAPbI_3. The (001) peak represents the signature peak of the degradation byproduct, PbI_2. Insets are optical images of the perovskite films over time.

Source Data

Nanoelectromechanical devices ranging from nanoscale resonators to molecular valves have implications for information processing, molecular manipulation and sensing applications[40]. Force and mass sensitivity tend to increase with higher resonant frequency and lower mass[41], rendering ultrathin films ideal for applications such as nanoscale force sensing and mass detection. We measured the thermal fluctuation of a 35-nm 2DPA-1 bulge optically under vacuum (about 10−5 Pa) and detected a resonant frequency in the megahertz range (Fig. 4b,c and Supplementary Fig. 29). The initial deflection was measured by AFM. Prolonged vacuum exposure led to total bulge deflation, determined by interferometry, and coincided with an increase in the quality factor (Q) from 87 to 537. Q describes the efficiency of a resonator in terms of energy loss and is defined as the ratio of its resonant frequency to the full width at half maximum of its resonance peak. Although energy losses associated with changes in adhesion at the inner wall of the microwell can affect the measured Q, the sixfold increase in Q is probably due to decreased damping as gas molecules evacuate the microwell[41]. This finding further validates the successful trapping of N_2 in the 2DPA-1-sealed microwell and represents a polymer resonator approaching molecular thinness (that is, sub-10 nm)—presenting myriad opportunities for scientific exploration.

The facile processability of a mechanically robust and transmembrane defect-free film is crucial to the practical scaling and implementation of 2DPA-1 as a molecular barrier. To demonstrate the potential for scale-up, we directly spin-coated methylammonium lead iodide (MAPbI_3) three-dimensional perovskite thin films (1 cm × 1 cm) with 60 nm of 2DPA-1 to encapsulate the perovskite and assess inhibition of O_2 and water vapour by 2DPA-1 (Fig. 4d–f). MAPbI_3 is extremely sensitive to O_2 and water[42], completely degrading to lead iodide (PbI_2) after only 3 d of air exposure. Conventional encapsulation methods for three-dimensional perovskites, such as UV-curable adhesives and single-layer hydrophobic encapsulation, typically require coatings over 100 μm thick[43]. By contrast, encapsulating the perovskite with only a 60-nm-thick protection layer of 2DPA-1 markedly retards the degradation of MAPbI_3—extending its stability to 21 d under ambient conditions, with negligible degradation observed over the first 7 d. We monitored the degradation kinetics of the 2DPA-1-coated and uncoated systems by comparing the relative intensities of the MAPbI_3 (110) and PbI_2 (001) X-ray diffraction (XRD) peaks over time (Extended Data Fig. 10). The retarded degradation kinetics of the 2DPA-1-coated perovskite begets an estimated O_2 permeability of 3.3 × 10−8 Barrer (1.1 × 10−23 mol m−1 s−1 Pa−1). This value is several orders of magnitude lower than existing crystalline polymers and implies that the lack of persistent bulge formation on O_2 pressurization is a result of inadequate sealing of the microwell interface, potentially induced by interactions between O_2 and 2DPA-1 chemical groups. More importantly, this demonstrates the capability of 2DPA-1 to be scaled to larger dimensions than the micrometre scale of the Si/SiO_2 wells without failing from defect formation, such as cracks or pinholes that commonly plague thin films made of inorganic 2D materials31,44.

Methods

Materials

TMC, melamine and N-Methyl-2-pyrrolidone (NMP) were open and stored in a N_2-environment glovebox. All other reagents, including acetone, isopropyl alcohol (IPA), TFA, deuterated TFA, polystyrene (280 kDa), anisole, chloroform, hexane, dimethyl formamide and DMSO were obtained from commercial vendors and used as received. CD-flat holey carbon (8/2-hole pattern) grids used for TEM were purchased from Electron Microscopy Sciences.

Synthesis of 2DPA-1 powder

For a typical 2DPA-1 powder with a high r value, we first mix 1 mmol of TMC with 1 mmol of melamine in a 40-ml glass vial. Using a magnetic stir bar, stir the contents in a N_2-environment glovebox with 18 ml of NMP followed by 2 ml of pyridine. Seal the glass vial with a solvent-resistant cap, and leave the reaction mixture to stir between 350 rpm and 400 rpm at room temperature inside the glovebox for 24 h. The r value can be manipulated by altering the duration the reaction mixture is left stirring. After the desired stir time has passed, add 20 ml of IPA into the reaction vial and let the vial continue stirring for 30 min to fully quench the reaction. Remove the vial from the glovebox, add 20 ml of deionized water, and filter the reaction mixture using a nylon membrane (Cytiva nylon membrane filters, 0.45-μm pore size, diameter 47 mm, product no. 7404-004) and then wash the solid residue repeatedly with IPA. Collect the IPA-rinsed solid residue from the filter paper and place in a clean 20-ml glass vial. Add 10 ml of deionized water and 10 ml of acetone (1:1 volume ratio) into the vial, and stir the solution with a magnetic stir bar for at least 8 h at room temperature. This helps dissolve any remaining impurities. After 8 h, filter the mixture again with a plastic filter funnel (Chemglass Life Sciences filter funnel, disposable, 110 ml, 10 μm polyethylene frit, product no. OP-6602-14), and wash the solid residue several times with acetone. Collect the solid from the filter and dry in a vacuum oven at 100 °C for 3 h. The resulting powder should possess a yellow to light orange hue.

Characterization methods for bulk 2DPA-1 and thin films

Characterizing 2DPA-1 samples with nuclear magnetic resonance (NMR) spectroscopy followed previously established protocol[38]. In brief, we first suspended 2DPA-1 powders in TFA at a concentration of 10 mg ml−1. We sonicated the solution in a Branson ultrasonic cleaner for 15 min before adding into a Wilmad NMR tube (5 mm diameter, economy). All the 1H NMR measurements were performed at room temperature on an Avance III HD 500 NMR Bruker spectrometer with D1 setting of 1 s. Characterization by means of 13C NMR is reported elsewhere[38].

TEM imaging was conducted using S/TEM (Titan Themis Z G3 Cs-Corrected S/TEM) in the TEM mode with an accelerating voltage of 200 kV. Images were taken at magnifications ranging from ×11,000 to ×4,000,000 using the fast 4k × 4k complementary metal-oxide-semiconductor camera at MIT.nano. For TEM sample preparation of nanoplatelets, a diluted solution of 2DPA-1 dissolved in DMSO was drop-cast onto a single-graphene-layer TEM grid. For TEM sample preparation of nanofilms, the as-synthesized 2DPA-1 platelets were initially dissolved in TFA at a concentration of 10 mg ml−1 and then spin-coated onto a TEM grid at 1,000 rpm for 1 min. Both sample types were placed in a vacuum oven at 100 °C overnight to remove TFA and DMSO before TEM analysis.

The chemical stability of 2DPA-1 is imparted by the irreversible nature of its amide bonds having a high dissociation energy. Transamidation, a potential side reaction common to many polyaramids45,46, is not observed for 2DPA-1 when exposed to TFA for 9 h (ref. [38]). Marginal changes in the chemical structure occur after sitting in TFA for 24 h, indicating that some transamidation can occur if the sample remains in highly acidic conditions for an extended period, but this is beyond the exposure that 2DPA-1 would experience before film processing.

One-dimensional polyaramids are notoriously hygroscopic[47]. Using 1H NMR, we find no notable effect of water on the chemical or molecular structure of 2DPA-1 after 539 d (about 1.5 years) of exposure (Supplementary Fig. 1). The data depict a slight increase in the r value from 5.15 to 5.45 after long-term exposure. The aromatic region and end group region also showed sharp peaks before water storage. Both observations indicate the presence of small particles before water storage, which may have disappeared because of particle aggregation or sedimentation. These data support the chemical stability of this material in protic polar solvents and the lack of hygroscopic degradation observed by the sustained gas barrier properties over 1,000 d in ambient humidity conditions.

Spectroscopy of bulk 2DPA-1 and thin films

Amide linkages for bulk 2DPA-1 and thin films were characterized by means of attenuated total reflectance Fourier transform infrared (ATR-FTIR) and Raman spectroscopy. ATR-FTIR spectra were measured using a Bruker ATR-FTIR Spectrometer with a reflection diamond ATR module. Bulk samples consisted of 2DPA-1 powder compressed against the diamond ATR module with a copper film backing. To obtain infrared signals from the 35-nm 2DPA-1 films used in this study, three films were transferred sequentially onto copper film substrates (thickness about 100 nm) to maximize both sample cross-section and reflectance.

Raman spectra (Supplementary Fig. 2a) were measured using a confocal micro-Raman spectrometer in backscattering geometry equipped with 532-nm and 785-nm laser lines (Horiba LabRAM HR Evolution with hole 500 μm, slit 150 μm, Olympus LMPLFLN ×50 air objective, numerical aperture = 0.5). Low laser powers (about 0.1 mW) were applied to collect spectra with minimal beam damage at 120-s acquisition time over two accumulations. The 785-nm laser was used to detect Raman scattering responses with minimized background photoluminescence. Bulk 2DPA-1 samples were prepared by drop-casting 5 mg ml−1 2DPA-1 dissolved in TFA onto a SiO_2 substrate. For thin films, we transferred the 35-nm 2DPA-1 film onto a gold-sputtered, surface-enhanced Raman scattering (SERS) substrate to maximize signal. Photoluminescence of 35-nm 2DPA-1 films suspended on the gold SERS substrate was measured using the polarized 532-nm laser line in the Horiba LabRAM. Photoluminescence spectra were collected in normal and parallel modes to assess anisotropy in the molecular ordering of 2DPA-1. In other words, photoluminescence of the film was collected with the excitation source orthogonal and parallel to the film surface by rotating the substrate 90° from lying flat to on-edge, respectively, as described previously[8].

We performed a normal mode analysis on the vibrational density of states for peak assignment of the simulated structure (Supplementary Table 1). Specifically, we computed the Fourier transforms of the autocorrelation functions of the polarizability tensor[1] and dipole moment vector[2] for Raman and infrared spectra, respectively. The underlying data were obtained from ab initio molecular dynamics simulations carried out at 300 K for 32.5 ps on a bilayer structure in vacuum. The simulations were performed in CP2K[3], where every eighth frame (4 fs) was selected to perform reruns for computing the volumetric electron densities required for computing infrared spectrum. For Raman, three more rerun simulations were performed with electric fields of magnitude 2.57 × 10^9 V m−1 applied along the X, Y and Z directions, respectively, to extract field-induced electron-density changes.

For infrared spectra, the dipole moment of the system was calculated at each frame by partitioning the electron density using the radical Voronoi tessellation method[4]. The molecular dipole moment μD is given by[5]

$${\mu }_{{\rm{D}}}=\sum _{{\rm{A}}}{Z}_{{\rm{A}}}{R}_{{\rm{A}}}-\int r\rho (r){\rm{d}}r$$

(1)

where ZA and RA denote the core charge and position of atom ‘A’ in a given molecule, and ρ(r) is the valence electron charge density partitioned to the molecule. The infrared absorption intensity is then obtained from the autocorrelation function of the time derivative of the dipole moment vector μD using[6]

$$A(\widetilde{v})=\frac{{N}_{{\rm{A}}}}{12{\varepsilon }_{0}c{k}_{{\rm{B}}}T}\int {\langle {{\boldsymbol{\mu }}}_{{\rm{D}}}(\tau ).\,{{\boldsymbol{\mu }}}_{{\rm{D}}}(t+\tau )\rangle }_{\tau }\,{e}^{-2{\pi }{ic}\widetilde{v}t}{\rm{d}}t$$

(2)

where \(\widetilde{v}\) is the wavenumber, T = 300 K, \({{\varepsilon }}_{0}\) is the vacuum permittivity, c is the speed of light, NA is Avogadro’s number, kB is the Boltzmann constant, and \({ < > }_{{\tau }}\) denotes time averaging over τ. The derivatives of μD were evaluated using a second-order finite difference scheme.

The polarizability tensor, α, used for Raman spectra was computed from finite-field simulations[7]:

$${\alpha }_{{ij}}=\frac{{\mu }_{{\rm{D}},i}({E}_{j})-{\mu }_{{\rm{D}},i}(0)}{{E}_{j}}$$

(3)

where μD,i is the ith component of the molecular dipole moment vector and Ej is the applied external electric-field in the jth direction. The isotropic and anisotropic (invariants) Raman scattering cross-sections were computed from derivatives of the polarizability tensor \(\dot{{\alpha }}\) using the expressions depicted in equations 4 and (5), respectively[8]:

$$\begin{array}{l}I(\widetilde{v})=\frac{h}{8{\varepsilon }_{0}^{2}{k}_{{\rm{B}}}T}\frac{{({\widetilde{v}}_{\mathrm{in}}-\widetilde{v})}^{4}}{\widetilde{v}\left(1-\exp \left(-\frac{{hc}\widetilde{v}}{{k}_{{\rm{B}}}T}\right)\right)}\\ \,\,\,\int \frac{1}{3}{\langle Tr(\dot{\alpha }(\tau )).{Tr}(\dot{\alpha }(t+\tau )))\rangle }_{\tau }{e}^{-2{\pi }{ic}\widetilde{v}t}{\rm{d}}t;\end{array}$$

(4)

$$\begin{array}{l}I(\mathop{v}\limits^{ \sim })\,=\,\frac{h}{8{{\varepsilon }}_{0}^{2}{k}_{{\rm{B}}}T}\frac{{({\mathop{v}\limits^{ \sim }}_{{\rm{i}}{\rm{n}}}-\mathop{v}\limits^{ \sim })}^{4}}{\mathop{v}\limits^{ \sim }\left(1-\exp \left(-\frac{hc\mathop{v}\limits^{ \sim }}{{k}_{{\rm{B}}}T}\right)\right)}\\ \,\,\,\int [\sum _{{\rm{p}}{\rm{a}}{\rm{i}}{\rm{r}}{\rm{s}}i < j}\frac{1}{2}{\langle ({\dot{\alpha }}_{ii}(\tau )-{\dot{\alpha }}_{jj}(\tau )).({\dot{\alpha }}_{ii}(t+\tau )-{\dot{\alpha }}_{jj}(t+\tau ))\rangle }_{\tau }\\ \,\,\,+\,\sum _{{\rm{p}}{\rm{a}}{\rm{i}}{\rm{r}}{\rm{s}}i < j}{\frac{3}{2}\langle {\dot{\alpha }}_{ij}(\tau ).{\dot{\alpha }}_{ij}(t+\tau )\rangle }_{\tau }]\,{e}^{-2\pi ic\mathop{v}\limits^{ \sim }t}{\rm{d}}t.\end{array}$$

(5)

Here, \({\widetilde{v}}_{\mathrm{in}}\left(\frac{1}{500}{\rm{n}}{{\rm{m}}}^{-1}\right)\) corresponds to the incident laser wavelength used for Raman scattering. All post-processing steps—including Voronoi tessellation, dipole moment and polarizability computation, autocorrelation function evaluation and spectral Fourier transforms—were carried out using TRAVIS5,6,7,9.

To understand the origin of the various bands in the infrared and Raman spectra, we performed a generalized normal mode analysis as implemented in TRAVIS[10]. The objective of this analysis is to extract the normal mode frequencies and the corresponding atomic displacement patterns. Briefly, we computed the Fourier transform of the mass-weighted velocity cross-correlation functions to yield a frequency-dependent matrix, \(P(\widetilde{v})\), where[10]

$${P}_{{ij}}(\widetilde{v})=\sqrt{{m}_{i}{m}_{j}}{\Re }\left(\int {\langle {\dot{x}}_{i}({\rm{\tau }}).{\dot{x}}_{j}(t+\tau )\rangle }_{\tau }{e}^{-2{\pi }{ic}\widetilde{v}t}{\rm{d}}t\right).$$

(6)

Here, \({\Re }\) denotes the real part (corresponds to symmetrization over time forwards and backwards correlations), mi and mj are the masses of the ith and jth degrees of freedom, and \({\dot{x}}_{i}({\tau })\) and \({\dot{x}}_{j}({\tau })\) are the velocities of the ith and jth degrees of freedom. To decouple the vibrational modes, the integrals of the off-diagonal elements of \(P(\widetilde{v})\) are minimized using a modified Jacobi algorithm[11]. This procedure determines an orthogonal transformation matrix, C, such that the transformed matrix \(\acute{P}(\widetilde{v})={CP}(\widetilde{v}){C}^{-1}\) has minimized off-diagonal elements. C is composed of new coordinate vectors—generalized coordinates—that define the normal modes of the system. The diagonal elements, \({\acute{P}}_{{ii}}(\widetilde{v})\), correspond to the power spectra of the decoupled normal modes. The frequency of each mode is determined by locating the maximum of its corresponding diagonal spectral component.

For the analysis, a monolayer trajectory was extracted from the bilayer ab initio molecular dynamics simulation, and translational motion was removed before post-processing. Once the normal mode frequencies were obtained, the modes corresponding to various infrared and Raman bands were identified and visualized using the MOLDEN[12] package.

We find generally good agreement between the bulk Raman spectrum and the simulated Raman spectrum. Amide I and II bands are assigned between roughly 1,450 cm−1 and 1,650 cm−1 to C–C aromatic stretching and C=O stretching of the amide groups48,49. Amide III bands are assigned between roughly 1,220 cm−1 and 1,280 cm−1 as C–N stretching and N–H in-plane bending48,49. Residual TFA is identified in both the bulk and 35-nm film samples at about 260 cm−1 and 2,150 cm−1 (ref. [50]) because of processing techniques. Although several of the amide bands are detected in the thin film, the signal is relatively weak over background photoluminescence because of the limited sample cross-section. However, a strong Raman band appears at 710 cm−1 for the 35-nm film, which we attribute to symmetric stretching of the triazine cores51,52,53. The total symmetric stretching vibration of triazine cores is typically observed between 680 cm−1 and 690 cm−1 but has been observed to shift to higher wavenumbers because of interaction with SERS substrates, along with notable signal enhancements due to preferential ordering at the SERS surface52,53.

ATR-FTIR spectra (Supplementary Fig. 2b) were measured using a Bruker ATR-FTIR spectrometer with a reflection diamond ATR module. We again find good agreement between the bulk and simulated FTIR spectra. Amide I and II bands were assigned to C=O and N–H bending as well as C=O stretching of the amide groups between roughly 1,500 cm−1 and 1,750 cm−1 (Supplementary Table 1). Amide III bands are assigned to strong C–N stretching between roughly 1,110 cm−1 and 1,350 cm−1 (Supplementary Table 1). Strong N–H stretching observed around 3,500 cm−1 in the simulated spectrum is found to be markedly broadened in the experimental data, a common occurrence due to intermolecular and intramolecular hydrogen bonding interactions that can weaken or shift the stretching frequency. The ATR-FTIR spectrum of the triple-stacked 35-nm film (about 100 nm total thickness) mirrors that of the bulk sample with the addition of signal from TFA (1,830 cm−1, 2,100 cm−1 and 2,300 cm−1)[50]. The most notable difference, however, is the relative suppression of the C–N stretching around 1,250 cm−1 from the bulk sample to the thin film. The mechanistic reasoning for this change requires further investigation beyond this work, but our initial hypothesis is that this is due to the sensitivity of ATR-FTIR to molecular ordering through longitudinal vibrational modes[54].

Numerous non-crystalline and paracrystalline materials possess orientational ordering that is not captured by conventional X-ray techniques, including ordered polyaramid and cellulose fibres55,56. Beyond XRD or direct imaging by electron microscopy, non-crystalline ordering can be detected using polarization-sensitive approaches[57]. We demonstrate orientational ordering of a 35-nm 2DPA-1 film using polarized photoluminescence (Supplementary Fig. 4). We find that when excited at 532 nm with a polarized laser, the photoluminescence emission of the 35-nm 2DPA-1 film depends on its orientation with respect to the beam path. Specifically, the thin film emission transitions from roughly 1,400 cm−1 (575 nm) when excited orthogonal to the beam path to 2,270 cm−1 (605 nm) when excited parallel to the beam path. This twofold symmetry is consistent with anisotropic platelets oriented parallel to the substrate with a transition dipole along the long axis of the platelet[8]. We note that the emission window reported is beyond that of SiO_2 to eliminate signal interference.

Gas adsorption measurements

N_2 adsorption isotherms were measured at 77 K up to 100 kPa (1 P/P_0) using a low-pressure manometric instrument (Autosorb 6100, Anton Paar). The temperature was controlled using a liquid nitrogen bath. Before each isotherm measurement, the sample (about 0.220 g) was outgassed under high vacuum, with ramping rate of 1 K min−1 from room temperature to 373 K and holding at that temperature for 12 h, before being cooled back down to room temperature and backfilled with N_2. The dry sample mass was determined after outgassing and before the isotherm measurement. The BET-specific surface area was calculated from a BET plot constructed in the classical BET range of 0.05 ≤ P/P_0 ≤ 0.3. The pore size distribution was determined using the Barrett, Joyner and Halenda method from the desorption branch (minus P/P_0 below 0.35) of the N_2 sorption isotherm at 77 K. The estimated 95% uncertainty was determined from two relative standard errors from measurements on two aliquots of the sample.

The N_2 adsorption isotherm of for 2DPA-1 platelets follows Type II isotherm behaviour (Supplementary Fig. 10a). It also possesses a type H3 hysteresis loop, which can be attributed to non-rigid aggregates of plate-like particles or pore network consisting of macropores that are not completely filled with condensate[58]. The BET-specific surface area from the average of two aliquots is (74 ± 10) m^2 g−1. The pore size distribution of powder 2DPA-1 consists of mostly mesopores, with an average mode pore width of about (11.1 ± 2.6) nm and some macro-porosity (Supplementary Fig. 10b). Bulk 2DPA-1 platelets have a total pore volume of (0.41 ± 0.07) cm^3 g−1. Given that the mode pore width is similar in size to the 2DPA-1 nanoplatelets (about 10–15 nm; Fig. 1b), this pseudo porosity is probably from interparticle space due to non-rigid aggregation of the 2DPA-1 platelets.

Unary He, H_2, CH_4 and CO_2 adsorption isotherms were measured at 298 K up to 1 MPa with a high-pressure manometric instrument (BELSORP-VC, Microtrac Bel). For measurements made in the high-pressure manometric system, the sample was outgassed ex situ in a tube furnace attached to a pumping station that is equipped with a turbomolecular pump (vacuum level of 10−7 Pa) backed by a scroll pump (vacuum level of 1 Pa). The following activation protocol was used: under high vacuum (0.1 Pa), the temperature was ramped up from room temperature to 373 K at a rate of 1 K min−1, held at 373 K for 12 h and then cooled ( about 2 h) to room temperature to a final vacuum level of 10–5 Pa. After activation, the sample was transferred under air- and moisture-free conditions from the sealed activator tube to an Ar glovebox for storage and transfer.

Inside the Ar glovebox, an aliquot (0.5391 g) of activated sample was loaded into the sample cell, which was then sealed, removed from the glovebox and connected to the instrument. The void volume was determined by means of helium expansion with 10 measurement points up to 3.5 MPa. The system contains one pressure transducer: 13.5 MPa with an accuracy of plus or minus 0.1% full scale. The temperature stability of the sample (plus or minus 0.1 K) was maintained by a water bath. The instrument air bath was maintained at (298 ± 0.1) K throughout the isotherm measurements. Before each isotherm measurement, the activated sample was reactivated at 373 K for 3 h to a vacuum level of 10−2 Pa using a turbomolecular pump. Surface excess uptake was determined by keeping track of the moles of free gas in the system from the pressure and temperature readings and volumes of reference and sample chambers. The density of the gas was determined from the NIST Reference Fluid Thermodynamic and Transport Properties Database. The measurement uncertainty is 3 ml at standard temperature (273.15 K) and pressure (101.3 kPa) as reported by instrument manufacturer, which equates to 0.25 mmol g−1.

The water vapour adsorption isotherm was measured using an automated dynamic vapour sorption analyser (VTI-SA+, TA Instruments) at ambient pressure with N_2 as the carrier gas. Bulk 2DPA-1 (about 15 mg) was outgassed for 12 h at 398 K under pure N_2 flow and then cooled down to 298 K. For the isotherm measurement, the relative humidity was determined with a dew point sensor and controlled by continuously mixing a dry N_2 (0% relative humidity) flow with a humid N_2 flow. The adsorbed amount is determined from sample mass changes during the vapour sorption experiment relative to the dry sample mass after outgassing. At each set relative humidity, equilibrium condition for the water uptake was set to sample mass change less than or equal to 0.001% in 99 min for a maximum time of 8 h. The instrument has a ±0.1 μg mass resolution and a relative humidity control standard uncertainty of plus or minus 1% based on manufacturer specifications. The adsorption of water on 2DPA-1 at low relative humidity implies an intrinsic hydrophilicity of the material (Supplementary Fig. 10d).

Preparation of 2DPA-1 thin films for bulge tests

We start by cleaning our Si/SiO_2 wafers with a 5-min sonication bath in acetone, followed by a 5-min sonication bath in IPA and finished by blow-drying with N_2. We then spin-coat a 10 wt% polystyrene in anisole solution onto the substrate at 2,000 rpm for 1 min and promptly transfer it to a hot plate at 110 °C, where it is annealed for 15 min to remove the anisole. We place a glass petri dish over the substrate during the annealing process to minimize the collection of debris on the polystyrene layer. We then spin-coat a solution of 2DPA-1 dissolved in TFA onto the polystyrene-coated substrate using the same spin settings. We can control the film thickness from roughly 60 nm down to 4 nm by adjusting the concentration of 2DPA-1 dissolved in TFA (Extended Data Fig. 1). The 2DPA-1 coated substrate is then quickly annealed on a hot plate at 50 °C for 5 min to evaporate any remaining TFA. We cut or trim away uneven areas of the film to ensure uniformity of the film thickness.

After our polystyrene-supported 2DPA-1 thin films have been prepared, we wet-transfer them to our etched substrates for bulge testing. To do so, we first clean the etched SiO_2 substrates using the same procedure for our SiO_2 wafers. We then use a razor blade to cut out the area of 2DPA-1/polystyrene film we would like to transfer from our wafer and bring the edge of this film to water so that it may lift the desired section of film off the wafer. With our section of 2DPA-1/polystyrene floating on the surface of the water (2DPA-1 facing the air), we quickly bring our etched substrate in contact with the 2DPA-1 side of the film and scoop it out of the water. The film-covered substrate is then left to dry overnight to remove any excess water. To form films that deflect downwards into the well, the substrates are annealed at 50 °C during the drying period.

After annealing, the films are placed in a glass petri dish with a 20-ml mixture of 25% v/v chloroform in hexane for 4 h to etch away the polystyrene support layer. We then transfer the substrates into a 20-ml mixture of 30% v/v chloroform in hexane as a polishing step for another 4 h. The substrates are then gently rinsed in hexane and air dried to remove any remaining solvents. 2DPA-1-coated substrates are stored at ambient conditions.

Relating change in bulge deflection to gas permeability

We account for the gas flux through the membrane with the ideal gas law:

$$\frac{{\rm{d}}n}{{\rm{d}}t}=\frac{V}{{RT}}\frac{{\rm{d}}p}{{\rm{d}}t},$$

(7)

where dn/dt is the change in the moles of gas inside the well over time, V is the volume of gas trapped under the 2DPA-1 film, R is the gas constant, T is temperature and dp/dt is the change in pressure over time. Because the volume contributed by the bulge’s deflection is much less than the volume of the 1-μm-deep etched wells, V approximates that of an etched well, πa^2h/4. The permeability, \(\hat{P}\), of the film is thus related to the gas leak rate as follows

$$\frac{{\rm{d}}n}{{\rm{d}}t}=-{\rm{\Delta }}p\frac{\hat{P}\pi {a}^{2}}{4d},$$

(8)

where Δp is the pressure difference across the film. The combination of equations 7 and (8) yields

$$\frac{{\rm{d}}p}{{\rm{d}}t}=-{\rm{\Delta }}p\frac{{RT}}{V}\left(\frac{\hat{P}\pi {a}^{2}}{4d}\right)=-{\rm{\Delta }}p\frac{{RT}\hat{P}}{{hd}}.$$

(9)

We can relate the film deflection, δ, measured with AFM to the pressure difference between the microwell (pint) and the external environment (pext) by

$${\rm{\Delta }}p={p}_{\mathrm{int}}-{p}_{{\rm{ext}}}=\frac{16{EdK}(\nu )}{{a}^{4}}{\delta }^{3}+\frac{4{S}_{0}}{{a}^{2}}\delta $$

(10)

where the first term on the right side of equation 10 invokes Hencky’s solution[59] and the second term on the right side of equation 10 describes the influence of the initial surface tension of the film, S_0 (ref. [6]). E and d are the film elastic modulus and thickness, respectively, the diameter of the microwell is represented as a, and K(ν) is a constant that depends on the film’s Poisson ratio, ν. We assign K(ν) = 3.09 for 2DPA-1, assuming it is equivalent to graphene in this case.

We solve the differential equation for p(t) and combine the result with equation 10, neglecting the initial-tension term, to obtain the relationship between the pressure differential and bulge deflection, δ, in logarithmic form:

$$\mathrm{ln}({\rm{\Delta }}p)=3\mathrm{ln}\delta (t)+\mathrm{ln}\left(\frac{16{EdK}(\nu )}{{a}^{4}}\right)=\mathrm{ln}C-\frac{{RT}\hat{P}}{{hd}}t,$$

(11)

where C is a constant of integration. Finally, rearranging and taking the derivative with respect to t yields the relationship between the film permeability and the rate of change for the bulge deflection:

$$\hat{P}=-\left(\frac{{\rm{d}}\mathrm{ln}\delta (t)}{{\rm{d}}t}\right)\frac{3{hd}}{{RT}}.$$

(12)

Thermal degradation of bulk 2DPA-1 and temperature dependence of films

TGA-MS was carried out to analyse gas evolution at elevated temperatures during thermal degradation of 2DPA-1 (r = 10.5) in an inert N_2 atmosphere. The ramp rate was 10 °C min−1 up to 1,000 °C. The off-gas was simultaneously analysed by a connected mass spectrometer. TGA-MS shows no trace of acetone (58 atomic mass units (AMU)), NMP (99 AMU), or pyridine (79 AMU) in low or high temperatures. This corroborates NMR results that show these chemicals are effectively removed during polymer purification[38]. Water vapour (18 AMU) evolved from the 2DPA-1 sample between 65 °C and 90 °C probably corresponds to surface-bound (that is, physisorbed) water molecules. This is reflected in the first peak of the derivative thermogravimetric curve (Supplementary Fig. 12a). Water absorbed in highly confined pores requires substantially more energy to remove, often leading to desorption temperatures in excess of 100 °C (refs. 60,61,62). Notably, we also detect water evolution from the 2DPA-1 sample between 300 °C and 450 °C. The release of water vapour observed at these elevated temperatures is beyond that expected from stronger confinement interactions and can instead be explained by the cleavage of the amide bonds during the thermal degradation of 2DPA-1. This is a well-studied mechanism in thermal degradation of similar polyaramids such as Kevlar and Nomex in which water is a common byproduct of thermal degradation63,64.

As a hydrophilic material, water sorbed in the unit-cell pores of 2DPA-1 thin films may influence molecular transport of other gases. We investigated the potential effect of sorbed water on the gas impermeability of 2DPA-1 by means of a temperature-controlled environmental AFM stage (Asylum Research, Cypher ES Environmental AFM) to capture real-time measurements of four bulge deflections with respect to temperature (Supplementary Fig. 13a–d). All four bulge deflections decreased on the initial heating ramp up to 120 °C but did not result in complete deflation. We can thus conclude that pores blocked by water or residual TFA are not the root cause for the observed molecular impermeability of 2DPA-1, as the counter case would result in complete deflation on vaporization up to 120 °C. We attribute the decrease in deflection to a temporary and reversible opening of the rim seal, probably owing to the influence of heating on thermal expansion of the 2DPA-1 thin film and adhesion between 2DPA-1 and the SiO_2 substrate. When subsequently oscillating the temperature between 80 °C and 120 °C for 2.5 cycles, the bulge deflections similarly oscillated on the order of 10 nm.

Assuming no gas transport is occurring, we can predict the average change in the bulge deflection from 80 °C to a theoretical height at 120 °C through the series of equations shown below originating with the ideal gas law: \({P}_{1}{V}_{1}/{T}_{1}={P}_{2}{V}_{2}/{T}_{2}\). Here, the subscripts refer to states 1 and 2 of the bulge, and P, V and T are the internal pressure, volume under the bulge and temperature, respectively. By accounting for the geometry of the microwell system and using Hencky’s solution to relate bulge deflection, δ, to a pressure difference[59] (equation (10)), we can solve for δ_2 given T_1, T_2 and δ_1.

We set T_1 = 80, T_2 = 120 and δ_1 as the average experimental value at 80 °C in the oscillation regime (Supplementary Fig. 13e–h), with theoretical upper and lower bounds defined by the range of elastic moduli reported for 2DPA-1[8]. We find that 17 of the 20 (85%) experimentally determined deflections at 120 °C fall within the predicted range. Hence, our results do not indicate any discernible gas loss while oscillating between 80 °C and 120 °C and support the notion that physisorbed water is not the mechanistic explanation for the film’s measured impermeability.

Optical analysis of bulge deflections

AFM is the preferred technique to measure bulge deflection because of its high spatial resolution. However, AFM allows for limited control over environmental conditions as AFM tip calibration and tuning become distorted at non-atmospheric pressures and temperatures. We have therefore developed an optical technique that leverages the semitransparency of the polymer film to extract bulge deflections, allowing us to analyse the behaviour of bulges under non-atmospheric pressures and temperatures.

Light reflected from both the membrane and the bottom of the well undergoes constructive and destructive interference when collected at the detector, depending on the difference in optical path length. We exploit the resulting interference pattern to determine the degree of bulging (Extended Data Figs. 3–4). We illuminated our sample with a broadband light source (Thorlabs MCWHL5), where the reflected light passed through one of three optical bandpass filters (Semrock FBP01) with bandwidths of less than or equal to 16 nm and centre wavelengths (λ) of 440 nm, 540 nm and 600 nm, before being recorded by a camera (Extended Data Fig. 3a). We use MATLAB image processing tools to find the average intensities I(λ, r) as a function of the radial position, r (Extended Data Fig. 3b). A local maximum corresponds to a constructive (that is, bright) interference fringe, whereas a local minimum corresponds to a destructive (that is, dark) fringe. The static out-of-plane displacement δ(r) can theoretically be determined at any position from I(λ, r) if the intensities at destructive and constructive interferences, Imin(λ) and Imax(λ), are known[65]. However, Imin(λ) and Imax(λ) cannot be easily extracted from local extrema because the amount of light that is reflected back along the optical axis depends on the local slope \({\rm{d}}\delta /{\rm{d}}r\) (Extended Data Fig. 3c).We therefore combine two different methodologies to determine δ(r) at all fringes as well as in the centre of the membrane, where \({\rm{d}}\delta /{\rm{d}}r=0\).

At each interference fringe with radius ρ, the deflection is a solution to

$$2\delta (r=\rho )=q\frac{\lambda }{2}-{\rm{\Delta }}L,$$

(13)

where the integer, q, is odd for minima and even for maxima. The optical path length difference for a flat membrane, ΔL, accounts for the gap, h, between the membrane and substrate, the phase shift of π at the substrate and the complex transmission and reflection coefficients of the membrane. To calculate the deflection in the centre of the membrane, we perform another calibration measurement. We measured the intensity, I(λ, r = 0), while incrementally decreasing the bulge deflection by manipulating the pressure inside the well. As long as the total change in optical path length exceeds one λ, we can define Imin(λ) and Imax(λ) as the lowest and highest measured values, respectively. Assuming that the centre point is aligned with the optical axis, the centre displacement is then a solution to

$$2\delta (r=0)=q\frac{\lambda }{2}\pm {\cos }^{-1}\left(1-2\frac{I(\lambda ,r=0)-{I}_{\min }(\lambda )}{{I}_{\max }(\lambda )-{I}_{\min }(\lambda )}\right)\frac{\lambda }{\pi }-{\rm{\Delta }}L,$$

(14)

where q is an odd integer.

We identify solutions to equations 13 and (14) that match the parabolic deflection profile δ(r) that we expect based on theory and AFM measurements. To that end, we analyse the interference pattern for each wavelength as follows. Among the solutions to equation 13 for the outermost fringe, we select the value for q that corresponds to the smallest absolute deflection. Working towards the centre, we increase q by 1 for each subsequent fringe. At the centre, we assume that the deflection is within the range of \(\delta (r={\rho }^{* }) < \delta (r=0) < \delta (r={\rho }^{* })+\frac{\lambda }{4}\), where \({\rho }^{* }\) is the smallest observable fringe. The estimated bulge deflection is the average of the \(\delta (r=0)\) values obtained for each λ, with a standard deviation of roughly 10 nm. We extrapolate \(\delta (r)\) as shown in Extended Data Fig. 3d to confirm the validity of our calculations.

To make this technique more accessible to the broader scientific community, we apply it to an incoherent broadband light source that would be found on a common optical microscope. Here, we relate the bulge deflection to RGB pixel values from the optical image (Extended Data Fig. 5a,b). To physically connect the bulge deflection with its optical profile, we must first determine the combination of λ reflected by the sample into the camera using the Fresnel equations[66] and then convert those combinations detected by the camera into RGB pixel values.

The combination of reflected light wavelengths and their relative intensities when passing through three distinct layers (Extended Data Fig. 5c) can be calculated by[66]

$$I={\left|\frac{{r}_{1}{e}^{i{\phi }_{{\rm{a}}}}+{{r}_{2}e}^{-i{\phi }_{{\rm{b}}}}+{r}_{3}{e}^{-i{\phi }_{{\rm{a}}}}+{r}_{1}{r}_{2}{r}_{3}{e}^{i{\phi }_{{\rm{b}}}}}{{e}^{i{\phi }_{{\rm{a}}}}+{r}_{1}{{r}_{2}e}^{-i{\phi }_{{\rm{b}}}}+{{r}_{1}r}_{3}{e}^{-i{\phi }_{{\boldsymbol{a}}}}+{r}_{2}{r}_{3}{e}^{i{\phi }_{{\rm{b}}}}}\right|}^{2},$$

(15)

where rj and \({\phi }_{j}\) are defined by the complex refractive indices (n) of layers j and j – 1:

$${r}_{j}=\frac{{n}_{j-1}-{n}_{j}}{{n}_{j-1}+{n}_{j}};$$

(16)

$${\phi }_{j}=\frac{2\pi {n}_{j}{d}_{j}}{\lambda }.$$

(17)

The thickness of the layers is represented by dj. Here, \({\phi }_{a}\) and \({\phi }_{b}\) are combinations of \({\phi }_{j}\) for the 2DPA-1 layer (layer 1) and layer of air trapped in the microwell (layer 2): \({\phi }_{a}={\phi }_{1}+{\phi }_{2}\) and \({\phi }_{b}={\phi }_{1}-{\phi }_{2}\). Layers 0 and 2 are air; hence, n_0 and n_2 are set to unity. The SiO_2 layer (layer 3) has a λ-dependent complex refractive index[67]. We measured the refractive index of 2DPA-1, n_1, using a Filmetrics F20 reflectometer (Extended Data Fig. 5d).

Using equation 15, we plot I as a function of λ and film depth (that is, bulge deflection), represented here as the thickness of layer d_2 (Extended Data Fig. 5e). We then weighted I with the spectral power distribution of a 100 W tungsten halogen bulb and combined it with an assumed sensitivity of the detector to each λ. Because of the lack of information on the detector sensitivity, we assumed the sensitivity of the detector matches that of the human eye. This enables us to convert the combination of λ throughout the visible spectrum and their relative intensities to XYZ colour space and, finally, to RGB. In Extended Data Fig. 5f, we overlay the theoretical RGB values from this calculation with the measured data taken from Extended Data Fig. 5a,b after removing deflection data points between −100 nm and 15 nm, where a sharp discontinuity occurs because of edge adhesion effects and renders our three-layer model unsuitable. We find the model appropriately captures the relationship between RGB values and measured bulge deflection, albeit with a noticeable offset in the x and y directions. This offset stems from the assumed sensitivity of the detector as the human eye and could be improved on with more accurate information about the detector. We also note that the intensities of the measured R values seem to reach a saturation point, a likely result of the lighting conditions used throughout this study.

To perform our experiments in a controlled pressure and temperature environment, the sample is separated from the camera by another glass cover layer. This introduces two more layers to the model: the glass cover and another layer of air between the 2DPA-1 film and glass cover. This system thus requires a more complex formulation akin to equation 15, wherein there are 16 terms—rather than only 4—in both the numerator and denominator[66]. Nonetheless, the overall form remains that of a linearly combined squared Fourier series and a discernible trend between the glass-covered RGB values and the AFM deflections remain evident.

To circumvent discrepancies between the existing theoretical model and measured data, we use a Fourier curve fitting function to produce practical relationships between the glass-covered RGB values and the bulge deflections measured in this study (Extended Data Fig. 6a). This enables us to extract valuable, dynamic information about the bulge deflections while they are in an environmental chamber. The sinusoidal relationship between R, G and B values are not one-to-one functions. Consequently, we cannot simply invert them to obtain a function of the form f(RGB) = δ. We instead obtain several deflection solutions for a single RGB triplet, necessitating the use of all three Fourier fit functions comparing the bulge deflection to independent R, G and B channels to minimize the number of solutions. Minimizing solutions aids in our ability to filter for the true deflection profile.

To eliminate extraneous solutions, we identify whether the local slope of a data point—when compared to its nearest neighbours—contributes to a bulge profile that is physically possible. Four quadrants are defined for physically possible bulge slopes. For a downwards deflected dimple, the slope is negative between the left-most edge and the centre (that is, pixels 0 to 50) and then positive between the centre and right-most edge (that is, pixels 50 to 100). For an upwards deflected bulge, the slope is positive between the left-most edge and the centre (that is, pixels 0 to 50) and then negative between the centre and right-most edge (that is, pixels 50 to 0). These physically expected slopes establish the constraints for viable solutions. Using a threshold of 35 nm to qualify two data points as neighbours, we keep only solutions that have at least one neighbour and obey the slope constraints expected for their quadrant. We display an example of solutions having passed this filter in Extended Data Fig. 6b.

We note that, even after filtering, remaining data can contribute to either negative or positive deflections. We thus introduce two more thresholds to determine the appropriate solution. The first threshold compares the number of data points contributing to either a positive (n+) or negative (n-) deflection, biasing the solution towards the larger dataset when n+/− > 6.67n−/+. If this condition is not met for either dataset, we introduce the second threshold. The second threshold compares the R^2 values of the positive \(({R}_{+}^{2})\) and negative \(({R}_{-}^{2})\) datasets, fit by a second-order polynomial in the form \(f(x)=a{x}^{2}+{bx}+c\), with f(x) = 0 at the bounds of the section profile. If \({R}_{-}^{2} > 0.7{R}_{+}^{2}\), then the negative dataset is selected and vice versa. These thresholds were manually determined by comparing the results for bulges with known negative and positive deflections. After selecting the negative or positive dataset, we perform two stepwise regressions to remove outliers from skewing the second-order polynomial fit. Data points that lie more than two standard errors away from the fit line are considered outliers and removed. The fit after these two stepwise regressions is the final prediction for the deflection profile based on the input RGB profile.

We extended this technique, which predicts the 2D deflection profile from a single diameter slice of an image, to predict the full three-dimensional deflection profile of a positively or negatively deflected film. We first isolated the image of the bulge from the substrate using standard image processing software and then created a mask to detect the edges of the bulge and calculate its centre and diameter. Next, we used polar coordinates to discretize the bulge into 1° increments, obtaining 180 radial diameter slices of the bulge at different degrees about its centre. These radial slices can be individually analysed with the parabolic fitting method, then reconstructed to yield a three-dimensional height profile of a bulge (Extended Data Fig. 6c,d). We note that each slice is not in complete agreement about the direction in which the bulge is deflected. To obtain agreement, we first identify the direction of the deflection (that is, positive or negative) based on the majority response of the 180 slices. We then fit a single second-order polynomial to all the relevant slices and perform two stepwise regressions, where outliers are again defined as more than two standard errors away from the fit. The final fit obtained from this processing is used to define the cross-sectional profile of the three-dimensional bulge and construct a three-dimensional deflection profile.

Resonance measurements

We measured the thermal displacement fluctuations of the fundamental resonance mode with a path-stabilized homodyne Michelson interferometer as shown in Extended Data Fig. 4. We used a stabilized HeNe laser with λ = 633 nm and a peak power of 3 mW. The spot size (full width at half maximum) on the sample is about 800 nm, and the incident power is about 600 µW. Two photodetectors are used in this setup, PD_1 (New Focus 1801) for displacement measurements and PD_2 (Thorlabs PDA8A) for path stabilization. After taking a measurement at the antinode (that is, the centre of the suspended film), we take a second measurement with the same parameters on the polymer film adjacent to the well to determine the background noise level in our measurements, such as low-frequency laser noise or cable resonances. For both measurements, we averaged 10^3 data traces. Based on the assumption that different noise sources are uncorrelated, we subtracted our background noise from the noise measured on the suspended film.

DFT and molecular dynamics simulations

DFT calculations were performed on bilayer configurations with various interlayer offsets to determine the most stable configurations (minimum energy). The interlayer offsets were generated on a 4 × 4 grid in the x–y plane with a maximum shift of 8.3475 Å along each axis. For each offset, the system was first relaxed by performing a geometric optimization in vacuum using DFT. The interlayer interaction energy, Eint, for the final optimized geometry was computed as

$${E}_{\mathrm{int}}={E}_{{\rm{bl}}}-{E}_{{\rm{ml}}-1}-{E}_{{\rm{ml}}-2},$$

(18)

where Ebl is the energy of the bilayer and Eml–1 and Eml–2 are the energies of the two monolayers.

To investigate the interaction of various gases with the 2DPA-1 membrane, a 3 × 2 × 1 supercell was constructed from the experimentally obtained crystal structure of a 2DPA-1 unit cell. This supercell was geometrically optimized with DFT, and a bilayer structure composed of two such layers stacked together was generated. Various configurations were generated by offsetting the adjacent layers relative to each other along the x axis (Supplementary Fig. 17).

To determine the translocation energy profiles for CO_2, N_2 and Ar, a single gas molecule was positioned at the centre of a pore in the bilayer membrane at varying distances along the z axis (translocation axis) from the membrane (Supplementary Fig. 17). The membrane-gas interaction energy, Eint, was computed as

$${E}_{\mathrm{int}}={E}_{{\rm{m}}+{\rm{g}}}-{E}_{{\rm{m}}}-{E}_{{\rm{g}}},$$

(19)

where Em+g is the energy of the combined membrane and gas system, Em is the energy of the membrane alone, and Eg is the energy of the isolated gas molecule. These energies were calculated using DFT, as implemented in the CP2K package[68]. Single-point energy calculations were performed with the Gaussian and Plane Wave scheme[69], using a plane-wave cutoff of 500 Ry. The Geodecker–Teter–Hutter pseudopotentials[70] and triple-zeta valence polarized basis sets with molecularly optimized functions[71] were used for all atoms. The Perdew–Burke–Ernzerhof exchange-correlation functional[72], combined with D3 dispersion correction[73], was used for these calculations.

To ensure physical validity, initial structures were energy minimized with pore atoms allowed to relax while keeping the gas molecules fixed. Because of the computational cost of DFT, energy minimizations were performed using the Optimized Potentials for Liquid Simulations classical interatomic potential in GROMACS74,75,76. The force-field parameters for 2DPA-1 were derived using the LIGPARGEN tool77,78,79 and BOSS[80] implemented by POLYPARGEN[81], and the parameters for CO_2, N_2 and Ar were sourced from previous studies82,83.

Energy profiles were plotted along a uniform grid with a spacing of 0.5 Å along the translocation axis. For each gas and membrane offset, the energy barriers, Ebarrier, were determined as

$${E}_{{\rm{barrier}}}={E}_{\max }-{E}_{\min },$$

(20)

where Emax and Emin are the maximum and minimum interaction energies along the z axis, respectively. The resultant barrier versus offset plots for CO_2, N_2 and Ar are shown in Supplementary Figs. 18 and 19.

To identify the interlayer offset that results in the maximum gas translocation energy barrier, we conducted further DFT energy calculations using the same protocols outlined above. In this series of calculations, gas molecules were held fixed at three distinct positions along the translocation axis (Extended Data Fig. 8a) while the pore-to-pore offset between the membrane layers was varied from 0% to 72%. For each offset, the interaction energy between the membrane and the gas molecule was computed using equation 19. Introducing offsets between the layers results in the splitting of the pores into two separate regions. Consequently, for each offset and gas position along the translocation axis, we considered two distinct configurations by positioning the gas molecule in each of the two resulting pores. The barrier for a given offset was determined as the minimum of the two interaction energies (Extended Data Fig. 8b).

We accounted for entropic effects on the free energy barrier during gas translocation across the 2DPA-1 bilayer by computing the potential of mean force (PMF). We focused on the most sterically restricted case—a 60% pore-to-pore offset (Supplementary Fig. 20). We note that the energy barriers determined from the PMF profiles should be interpreted more qualitatively than quantitatively. The PMF profiles for CO_2, N_2 and Ar were obtained using the umbrella sampling technique[84]. In this approach, gas molecules were constrained at various positions along the translocation axis relative to the membrane, with each position representing an umbrella window. A harmonic potential with a force constant of k = 50,00 kJ mol−1 nm−2 was applied to constrain the gas molecules in all three spatial directions. Each simulation window was run for 10 ns at 300 K using the Optimized Potentials for Liquid Simulations force field to ensure adequate sampling. Histograms of the gas molecule positions were generated from the simulation data, and the PMF profiles were computed using the Weighted Histogram Analysis Method[85], as implemented in GROMACS with the g_wham tool[86].

Monte Carlo simulations

Frameworks of stacked 2DPA-1 platelets were generated layer by layer, where the 2DPA-1 platelet is represented by an array of pores with a radius of 5 Å (rp) and centre-to-centre spacing of 2.42 Å. Each adjacent layer is separated by a predefined pore offset between 0% and 100% (that is, 0 to 2rp). The offset angle between adjacent layers is randomized between 0 and 2π for each additional layer. The transport area under consideration is 8 nm × 12 nm with periodic boundary conditions to approximate an infinite planar system without defects.

We probe the transport of gas molecules across the 2DPA-1 lamellar framework using an effusion-based hit-and-miss methodology[30]. This simplified approach treats gas molecules as hard spheres with a radius defined by their kinetic diameter. Impingement attempts by the gas molecules result in successful permeation events if they pass through an effective pore in the 2DPA-1 lamellae without interacting with the pore edge. In other words, gas molecules that contact any part of the impervious 2DPA-1 framework are considered sterically excluded. An effective pore in this case is considered vertically continuous free volume across the 2DPA-1 lamellae formed by the overlapping of in-plane pores by adjacent 2DPA-1 platelets. We probe the framework with 10^4 gas molecules of random location to determine the percentage of successful permeation events. This number was chosen based on a sensitivity analysis to minimize computational cost without significant loss of accuracy (Supplementary Fig. 15). Results from the Monte Carlo simulations were compared against analytical solutions in Supplementary Fig. 16. Deviation between the two results above an offset of 60% stems from the difference in offset angles. For the analytical solution, the offset angle is taken as 0, whereas the Monte Carlo framework is randomized.

Confined diffusion

A confined diffusion is characterized by a MSD of the form87,88,89

$$\langle {r}^{2}\rangle ={R}_{{\rm{C}}}^{2}(1-{A}_{1}{e}^{-{A}_{2}{Dt}/{R}_{{\rm{C}}}^{2}}),$$

(21)

where RC is the characteristic size of the region of confinement, D is the diffusion coefficient, t is time, and A_1 and A_2 are geometric constants. We fit equation 21 to the longitudinal data found in Fig. 3 and Supplementary Figs. 22 and 23 to extract the RC for each dataset.

Fabrication and characterization of 2DPA-1-coated perovskites

Three-dimensional MAPbI_3 perovskite was prepared in a two-step process: Si substrates were first cleaned with UV-Ozone (Jelight Company Inc.) for 20 min. In parallel, PbI_2 beads were added into a 9:1 solvent mixture of dimethyl formamide:DMSO with a concentration of 2 mmol l−1. Meanwhile, methylammonium iodide (MAI) powder was added into isopropanol with a concentration of 1 mmol l−1. Then, 30 µl of PbI_2 was spin-coated onto the cleaned Si substrate at 2,000 rpm for 1 min. The substrate was subsequently annealed at 70 °C for 1 min. The MAI solution was then spin-coated onto the PbI_2-coated substrate at 2,000 rpm for 1 min. The substrate was then annealed at 150 °C for 10 min to form a three-dimensional MAPbI_3 thin film. A 10 mg ml−1 solution (in TFA) was spin-coated onto the MAPbI_3 surface at 2,000 rpm for 1 min. The resulting composite were annealed at 100 °C for 5 min to remove any residual TFA. 2DPA-1-coated perovskite films were characterized using XRD with a Rigaku Smart Lab, using a Cu Kα source (λ = 1.54056 Å) in Bragg Brentano mode.

Relating bulge lifetime to 2DPA-1 permeability

To elucidate the inhibition of molecular transport across the 2DPA-1 thin film, we consider three viable transport pathways: (1) through the 2DPA-1 unit-cell pores, (2) through a Knudsen pore formed by interconnected voids in the film, or (3) through both the unit-cell pores and non-connected voids in the film (Supplementary Fig. 11). We assume no interlayer transport because of the absence of free volume between stacked platelets. For the second pathway, gas permeability across the film \((\hat{P})\) can be modelled using classical effusion[30]:

$$\hat{P}=\frac{\sigma d}{\sqrt{2\pi {M}_{{\rm{w}}}{RT}}}$$

(22)

where \(\hat{P}\) depends on the film porosity (σ), gas molecular weight (Mw), ideal gas constant (R) and temperature (T). \({\hat{P}}_{{{\rm{N}}}_{2}}=7\times {10}^{-2}\) Barrer is predicted by equation 22 for a 35-nm-thick film suspended across the Si/SiO_2 microwell in the limiting case of only a single 1-nm-diameter Knudsen pore. A permeability this high would lead to a 99% loss of overpressure (that is, pressure above atmospheric) in only a few seconds for a bulge of any thickness (Supplementary Fig. 11). We refer to this threshold for the overpressure loss as the bulge lifetime (t), which can be determined by treating the gas efflux from the sealed microwell as a first-order decay process:

$$t=-\tau \mathrm{ln}\left(\frac{\Delta p(t)}{\Delta {p}_{0}}\right)=\frac{-{\rm{d}}l}{\hat{P}{RT}}\mathrm{ln}\left(\frac{\Delta p(t)}{\Delta {p}_{0}}\right)$$

(23)

where τ is the time constant for the decay rate and l is the depth of the microwell. The short lifetime of bulges possessing any Knudsen pores, which would not survive long enough to be measured in AFM, disqualifies effusion-based mechanisms. We therefore postulate that gas transport, if occurring at all, must take place by means of passage through the 2DPA-1 unit-cell pore.

This approach can also be leveraged to determine the maximum permeability threshold for bulges that do not deflate over the observation period (tobs). If no significant loss of overpressure occurs, evidenced by a lack of negative change in the bulge deflection, we can safely assert that the bulge did not reach the half-life (t1/2) of its deflation over the duration of observation, where \({\rm{\Delta }}p(t)={\rm{\Delta }}{p}_{0}/2\). The maximum allowable permeability necessary for a bulge to maintain half of its overpressure (that is, stay inflated) over that observation period can be extracted from equation 23 by assigning t1/2 as tobs.

Positively deflected bulges that formed from the trapping of excess air molecules between the 2DPA-1 film (35-nm to 4-nm films) and Si/SiO_2 substrate during film transfer remained inflated for days, with some persisting up to the longest measurement period of 2 months (Extended Data Fig. 1). Using equation 12, we can relate the change in bulge deflection over time to the flux of air out of the sealed microwell. We find the deflection rates of the 2DPA-1 bulges span several orders of magnitude and lack a thickness dependence, illustrated by deviance from intrinsic permeability curves (Extended Data Fig. 1), that would otherwise demonstrate transport occurs solely across the accessible film area. This finding is akin to previous measurements of gas transport across graphene suspended on Si/SiO_2 substrates[6] and implies that gas leakage is occurring variably through the seal between the 2DPA-1 film and microwell interface. We term this interface the rim seal.

Importance of the rim seal in forming persistent bulges

Eliminating interfacial leakage is essential to measure the intrinsic permeability of a suspended film; hence, various efforts such as ultrahigh pressure sealing with a diamond AFM tip[24] and the fabrication of graphitic microwells5,34,90 have been pursued to improve the rim seal for suspended graphene. The effects of eliminating interfacial leakage can be substantial. By suspending graphene monolayers on graphitic rather than Si/SiO_2 microwells, gas efflux was eliminated entirely. To control the internal gas environment of a persistent bulge without interfacial leakage, the following must occur: the interfacial rim seal must open on external gas exposure, sufficient time must be given for pressure equilibration on both sides of the film, and the rim seal must close after removing the external gas.

Non-deflected films suspended over microwells often indicate a poorly sealed interface, leading to the quick deflation of bulges formed after pressurizing. By contrast, the existence of negatively deflected films (that is, downwards dimples) necessitates an adequate rim seal to prevent atmospheric air from equilibrating the negatively pressurized microwell. We therefore put emphasis on starting with downwards dimples to minimize effects from defective rim seals and maximize the success rate of pressurizing bulges with desired gases.

The ideal gas law dictates that ambient gas trapped in the microwell at an elevated temperature when the rim seal is formed will have a lower pressure than the atmosphere on cooling. We targeted the formation of negatively deflected films by annealing our sample overnight at 50 °C immediately after transferring the film to the Si/SiO_2 substrate. Pressurizing negatively deflected films led to a near-perfect rate of forming persistent bulges when using N_2.

Solution-phase properties that give rise to impermeable films

To gain deeper insight into the r-dependent size variations among 2DPA-1 samples, we used fluorescence measurements of 2DPA-1 in TFA (Supplementary Fig. 27). Solution-phase fluorescence measurements were measured with Duetta fluorescence and absorbance spectrometer from Horiba Canada Inc. To prepare the samples, we suspended the 2DPA-1 powders into TFA at a concentration of 1 mg ml−1 and then sonicated in a Branson ultrasonic cleaner for 15 min. The solution was then added into a quartz cuvette for measurement.

The 2DPA-1 with r = 2.27 shows three main features, with a predominant excitation feature around 350 nm that is not present in the r = 9.67 sample. The large associated Stokes shift (greater than 100 nm) for this feature compared to the smaller Stokes shift (less than 50 nm) observed around an excitation of 450 nm indicates that the lower-r sample possesses a greater abundance of small particles in solution—a phenomenon that is well-established for various nanomaterials91,92,93,94. We find a strong dependence on the ability of the 2DPA-1 films to suspend over the microwells with their associated platelet r parameter (Extended Data Fig. 9). This analysis was conducted by quantifying the yield of suspended films using optical microscopy (Supplementary Fig. 28). Below an r value of 4, all films collapsed into the microwell, whereas the yield for films with higher r values approach 100%. Previous work has alluded to the importance of interlayer interactions in the mechanical integrity of thin films made from 2D materials[95]. We therefore posit that the films made from 2DPA-1 with a high r parameter benefit from increased mechanical integrity due to an abundance of interlayer interactions between large areas of overlapping platelets.

Calculation of 2DPA-1 density

We derived the theoretical density of 2DPA-1 using a combination of experimental data and literature sources. We begin with the following expression:

$${\rho }_{{\rm{V}}}=\frac{{\rho }_{{\rm{A}}}}{{d}_{{\rm{p}}}}=\frac{{m}_{{\rm{p}}}}{{A}_{{\rm{p}}}{d}_{{\rm{p}}}},$$

(24)

where \({\rho }_{{\rm{A}}}\) is the areal density of a single 2D platelet, and mp, Ap and dp are the molecular mass, area and thickness of a platelet, respectively. For this calculation, we analysed an infinite hexagonal platelet and treated it as a hexagonal cylinder. We normalized mp and Ap to be per monomer. In an infinite platelet, mp corresponds to two monomer types (Supplementary Fig. 26a,b). We thus averaged the molecular mass of both monomers, yielding 141.1 g mol−1 for mp.

We then determined the area of the unit-cell six-membered ring, A_6 (Supplementary Fig. 26c), to calculate Am. Each monomer is part of three six-membered rings; therefore, one third of each monomer’s area is in the single-unit-cell ring. Given the six monomers per ring, Am = A_6/2. We used literature values of melamine, terephthaloyl chloride, a bifunctional proxy for trimesoyl chloride and amide peptides (Supplementary Table 2 and Supplementary Fig. 26d) for the bond lengths and angles of the six-monomer ring of 2DPA-1. Consequently, we obtain a side length of 6.562 Å for the unit-cell ring and Am = 55.93 Å as a result. Pairing these calculations with previous measurements of dp (ref. [8]) gives us a volumetric platelet density, \({\rho }_{{\rm{V}}}\), of 1.14 ± 0.09 g cm−3.

Platelet defectiveness

A perfect hexagonal platelet has unreacted groups only along the periphery of the platelet, whereas a defective platelet with branches or missing interior monomers would have unreacted groups along the periphery and along the defects. It was previously found that 2DPA-1 platelets have 0.28 acyl-group nm−2 in excess of a perfect platelet with the same corresponding diameter[8]. Thus, we find it necessary to consider this amount of defectiveness in our comparisons.

We define the defectiveness of a platelet by comparing how close a platelet is to the perfect hexagonal case using

$$D=\frac{{M}_{{\rm{perf}}}/{U}_{{\rm{perf}}}}{{M}_{{\rm{defect}}}/{U}_{{\rm{defect}}}},$$

(25)

where D is the defectiveness, U is the number of unreacted sites on the platelet, and M is the number of monomers that the platelet consists of. This differs from a ratio of r values as r only accounts for the trimesoyl chloride monomer. When calculating the defectiveness, we use the Mperf/Uperf ratio of the perfect platelet with corresponding total diameter. For a fourth-generation perfect platelet, Mperf/Uperf = 3.

Analysis of O_2/H_2O permeability in 2DPA-1 thin films by means of perovskite degradation kinetics

The degradation of MAPbI_3 perovskites has been intensively studied, and it is generally accepted that ambient oxygen (O_2) and water (H_2O) play essential roles in the degradation process96,97,98,99. XRD is the most common technique to monitor MAPbI_3 film degradation because the (110) peak of MAPbI_3 at 2θ = 14.1° and signature (001) peak of the degradation product PbI2 at 2θ = 12.7° can be easily discerned and tracked. More importantly, given that the sample is fixed in position, the two diffraction patterns are fully comparable in both shape and intensity[96]. The colorimetric changes of MAPbI_3 films in ambient conditions can be another sign of degradation[100]. Specifically, freshly made MAPbI_3 films exhibit a dark black colour (that is, black phase), whereas degraded MAPbI_3 films containing mostly PbI_2 and MAI exhibit a yellow colour (that is, yellow phase). The precise role of O_2 versus H_2O in MAPbI_3 perovskite degradation remains unclear, and many different design strategies and barrier materials have been explored to slow down the degradation[101]. When designing barrier materials for three-dimensional perovskite optoelectronic devices, it is optimal to have the materials that serve both as encapsulation layers and as electron or hole conduction layers, and this property is not explored in this work.

We analysed the time-dependent XRD data from Fig. 4g,h to extract the kinetics of solid-state reaction with ambient O_2 and H_2O, both with and without 2DPA-1 barrier protection. By comparing the relative rates of PBI_2 formation from these reactants for these two perovskite systems (protected and unprotected), we can estimate the permeability of these two reactants. The chemical kinetic and mass transfer model for this system are derived below. The 650-nm perovskite film was left exposed to ambient humidity (25% relative humidity) and temperature (298 K) with or without a protective 60-nm 2DPA-1 coating. XRD spectra were collected at varied time intervals along with images documenting the colorimetric change, consistent with the rate of degradation.

Our simplified kinetic model considers the degradation of Phase A (MAPbI_3) to Phase B (PbI_2) driven by the ambient reactant gas driving the gas (labelled G):

$${\rm{A}}+{\rm{G}}\to {\rm{B}}.$$

(26)

The overall kinetics are modelled using the second-order reaction rate: \({r}_{{\rm{A}}\to {\rm{B}}}={k}_{{\rm{in}}}{C}_{{\rm{A}}}{C}_{{\rm{G}}}\), where kin is the intrinsic rate constant, CA is the concentration of Phase A, and CG is the concentration of the reactant gas. The cases of unprotected and protected perovskite systems reduce to first-order reactions. For the unprotected system, the reactant G is necessarily in excess. The reaction of Phase A to Phase B thus reduces to

$${\rm{A}}\to {\rm{B}},$$

(27)

where the reaction rate is defined as \({r}_{{\rm{A}}\to {\rm{B}}}=k{C}_{{\rm{A}}}\), with k = kinCG. For the protected system, the concentration of reactant G at the perovskite surface is reduced significantly but assumed to be kept at a reduced steady state such that the first-order reaction above applies to this case.

The coordinate system for the 2DPA-1-coated perovskite is depicted in Extended Data Fig. 10. Diffusion of reactant G into the perovskite lattice is considered fast compared to all other rates in the system such that its concentration is considered uniform throughout the perovskite lattice. In other words, the concentration of reactant G throughout the perovskite lattice is equivalent to the concentration at the perovskite surface, CGδ. The decay of Phase A to Phase B is first order with rate constant k = kinCGδ. For the unprotected perovskite, the ambient concentration of the reactant G, CG0, is equivalent to CGδ. For Phases A and B with starting compositions in the perovskite lattice of CA0 and CB0, respectively, the kinetics of the conversion are

$${C}_{{\rm{A}}}(t)={C}_{{\rm{A}}0}\exp (-{kt});$$

(28)

$${C}_{{\rm{B}}}(t)=({C}_{{\rm{A}}0}+{C}_{{\rm{B}}0})-{C}_{{\rm{A}}0}\exp (-{kt}).$$

(29)

The ratio of the XRD peak areas for Phases B and A, PA/PB, is proportional to the composition ratio CB(t)/CA(t) through constant α assumed as unity for CB0/CA0, the initial ratio at t = 0 (PA0/PB0). The kinetics of the ratio should thus be described by

$$\mathrm{ln}\left(\frac{\frac{{P}_{{\rm{B}}}}{{P}_{{\rm{A}}}}+1}{\frac{{P}_{{\rm{B}}0}}{{P}_{{\rm{A}}0}}+1}\right)=\mathrm{ln}\left(\frac{\frac{{C}_{{\rm{B}}}}{{C}_{{\rm{A}}}}+1}{\frac{{C}_{{\rm{B}}0}}{{C}_{{\rm{A}}0}}+1}\right)=kt={k}_{{\rm{i}}{\rm{n}}}{C}_{{\rm{G}}}t$$

(30)

and, hence, a plot of peak ratios versus time should be linear with zero intercept and a slope equal to k, the pseudo-first-order rate constant of perovskite decay (Extended Data Fig. 10b).

For the case of the 2DPA-1-coated perovskite, we treat the transport of reactant G in the polymer layer as classic Fickian diffusion:

$${D}_{{\rm{G}}}\frac{{d}^{2}{C}_{{\rm{G}}}}{d{x}^{2}}=0.$$

(31)

The boundary conditions are CG = CG0 at x = 0 and CG = CGδ at x = δ. The concentration profile of reactant G in the 2DPA-1 film is linear

$${C}_{{\rm{G}}}(x)=\frac{{C}_{\delta }-{C}_{{\rm{G}}0}}{\delta }x+{C}_{{\rm{G}}0},$$

(32)

and the diffusive flux at the perovskite interface (x = δ) must equal the rate of the reaction for G in the well-mixed perovskite lattice; hence

$$-{AD}\frac{d{C}_{{\rm{G}}{\rm{\delta }}}}{{dx}}=V{k}_{{\rm{in}}}{C}_{{\rm{A}}}{C}_{{\rm{G}}{\rm{\delta }}}.$$

(33)

The concentration of reactant G at the perovskite interface (x = δ) is then

$${C}_{{\rm{G}}{\rm{\delta }}}=\frac{{C}_{{\rm{G}}0}}{1+\frac{{k}_{{\rm{in}}}{C}_{{\rm{A}}}\gamma \delta }{{D}_{{\rm{G}}}}}=\frac{{C}_{{\rm{G}}0}}{1+{\phi }^{2}},$$

(34)

where ϕ is the Thiele modulus—or ratio of reaction to diffusion rate—applicable for this system. The analysis of the perovskite lattice above applies with the pseudo-first-order rate constant modified by ϕ:

$$\mathrm{ln}\left(\frac{\frac{{C}_{{\rm{B}}}}{{C}_{{\rm{A}}}}+1}{\frac{{C}_{{\rm{B}}0}}{{C}_{{\rm{A}}0}}+1}\right)=kt=\frac{{k}_{{\rm{i}}{\rm{n}}}{C}_{{\rm{G}}0}}{1+{\phi }^{2}}t.$$

(35)

We display the analogous plot of the XRD peaks for the case of the 2DPA-1-protected perovskite in Extended Data Fig. 10b. The linear dependence yields a first-order reaction rate constant of 7 × 10−7 s−1. The square of the Thiele modulus, \({\phi }^{2}\), is found from the rate constant ratio of the unprotected and protected cases: \({\phi }^{2}={k}_{{\rm{uncoated}}}/\)\({k}_{{\rm{coated}}}-1\) = 14.3. The diffusivity of G in 2DPA-1 thus found to be 2.93 × 10−20 m^2 s−1. In standard permeability units (DG/RT) at 298 K, the value is 1.1 × 10−23 mol m−1 Pa−1 s−1 or 3.3 × 10−8 Barrer.

Data availability

All data needed to support the conclusions of this manuscript are included in the paper or Supplementary Information. Other data are available on request from the corresponding authors. Source data are provided with this paper.

Code availability

Source codes are available on request from the corresponding authors.

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Acknowledgements

This work was primarily supported as part of the Center for Enhanced Nanofluidic Transport–Phase 2 (CENT^2), an Energy frontier Research Center funded by the US Department of Energy, Office of Science, Basic Energy Sciences under grant no. DE-SC0019112. Resonator work performed by H.G. and K.L.E. was supported by the US NSF (grant nos. CMMI-2001403 and CMMI-2337507). K.A. was supported by funding from the Distinguished Summer Research Fellowship (DSRF) and the Undergraduate Research Opportunities Program (UROP) at Boston University. The authors acknowledge the use of the parallel computing resource Lonestar6 provided by the Texas Advanced Computing Center (TACC) at The University of Texas at Austin. Synthetic work was carried out in part under auspices of the Institute for Soldier Nanotechnologies (ISN), and material characterization was carried out in part through the use of MIT.nano’s facilities. Gas adsorption measurements were performed at the National Institute of Standards and Technology (NIST). Certain commercial equipment, instruments or materials are identified in this paper to foster understanding. Such identification does not imply recommendation or endorsement by NIST, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose. We thank J. Qin and S. Eppley for their assistance with deflection measurements for gas-pressurized bulges, D. Lloyd and S. Yang for their help fabricating and preparing the etched substrates used for gas permeation measurements and D. Lundberg for his intellectual contributions.

Author information

Author notes

1. These authors contributed equally: Cody L. Ritt, Michelle Quien, Zitang Wei

Authors and Affiliations

1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Cody L. Ritt, Michelle Quien, Zitang Wei, Yu-Ming Tu, Sanjay S. Garimella, Shahab Amirabadi, Michael Gadaloff & Michael S. Strano

2. Department of Chemical and Biological Engineering, University of Colorado–Boulder, Boulder, CO, USA

Cody L. Ritt

3. Department of Mechanical Engineering, Division of Materials Science and Engineering, and the Photonics Center, Boston University, Boston, MA, USA

Hagen Gress, Kaan Altmisdort, Kamil L. Ekinci & J. Scott Bunch

4. Oden Institute for Computational Engineering and Sciences, Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA

Mohan T. Dronadula & Narayana R. Aluru

5. Chemical Sciences Division, National Institute of Standards and Technology, Gaithersburg, MD, USA

Huong Giang T. Nguyen & Christopher D. Zangmeister

6. Department of Chemical Engineering, National Taiwan University, Taipei, Taiwan

Yu-Ming Tu

7. Department of Polymer Science & Engineering, University of Massachusetts–Amherst, Amherst, MA, USA

Weiguo Hu

Contributions

C.L.R. and Z.W. carried out material synthesis. Z.W., M.Q. and W.H. characterized solution-phase properties of material. C.L.R., M.Q., Z.W. and S.S.G. prepared thin film devices for gas permeability measurements. C.L.R., M.Q., H.G., K.A., S.S.G. and J.S.B. measured bulge deflections with AFM and developed the interferometric protocol for tracking bulge deflection optically. M.T.D., N.R.A. and C.L.R. simulated material properties and transport across thin films. C.L.R., M.Q. and M.S.S. modelled mean square displacement of bulges. Z.W. and M.S.S. performed and modelled perovskite degradation. H.G., K.L.E. and J.S.B. performed resonator experiments. C.L.R. and S.S.G. measured vibrational and photoluminescence spectra of material. Z.W. and C.L.R. collected and modelled diffraction patterns of material. H.G.T.N. and C.D.Z. measured gas adsorption isotherms. Y-.M.T. performed TEM. S.A. measured thermal degradation. M.Q. and M.G. estimated material density and defectiveness. C.L.R., M.Q., Z.W. and M.S.S. wrote the manuscript. M.S.S. conceived and designed the project. All authors contributed to the discussion of the results and revision of the manuscript.

Corresponding authors

Correspondence to J. Scott Bunch or Michael S. Strano.

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Nature thanks Timur Ashirov, Xiansong Shi 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 Thickness-independent, stochastic deflation of air-trapped bulges.

(A) Thickness of spin coated films with respect to 2DPA-1 concentration in TFA solution. Error bars represent the standard deviation of measurements from six separate film sections. (B) Bulge deflection, measured by AFM, with respect to time for 4 nm (top left), 8 nm (bottom left), 15 nm (top right), and 35 nm (bottom right) thick 2DPA-1 films. (C) Rate of change of the log-normal bulge deflection over time with respect to film thickness. Purple curves display the intrinsic relationship between deflection rate and film thickness for specified film permeabilities. The detection limit (DL), taken as 3× the noise of the measurements from panel B, is displayed as the black dashed line. Data beneath this line in the grey shaded region did not deflate at rates above the noise of the measurement. Error bars represent the standard error of the slope for the bulge deflection over time taken from data in panel B.

Extended Data Fig. 2 Negative deflection and adhesion of films into microwells.

(A-B) Optical micrographs of 35-nm 2DPA-1 film transferred onto silicon oxide substrate with a 50 °C annealing step implemented prior to etching away the polystyrene support layer. Wells with brown coloration indicate films that collapsed into the bottom of the well rather than suspend. (C) Corresponding atomic force microscopy (AFM) profiles, taken horizontally, of the microwells highlighted with the dashed white circles in panels A and B. (D) Comparison of bulge profiles for a completely dimpled film (red), representing a negative pressure differential, against a positively deflected film that is adhered into the microwell (blue), representing a positive pressure differential. The adhesion length is assigned as the distance between the top of the microwell and the minimum value of the positively deflected film (dashed line). (E) Histogram of the film depth into well attributed to the total deflection (solid bars) and adhesion to the inner wall of the well (hatched bars).

Extended Data Fig. 3 Interferometric analysis of bulge deflection.

(A) Optical top-view images of a bulged-up 35-nm film after passing through three different bandpass filters. (B) Radial intensity profiles of the images shown in panel A. The position is normalized by the radius, a, of the membrane. Local minima correspond to fringes with destructive interference, local maxima to fringes with constructive interference. (C) Cross-sectional illustration of a microwell with a bulged-up film. The yellow arrows represent how the direction of the reflected light depends on the local slope, dδ/dr, of the film. The green shading underneath the film corresponds to the interference pattern for λ = 540 nm. Each fringe with radius ri corresponds to a displacement δi. The vertical distance between consecutive fringes is always λ/4. (D) All solutions to eqs. M13 and M14. Unphysical values are grayed out. For the fringes, bright colors correspond to maxima, dark colors to minima. A representation of the intensity as a function of deflection is shown on the right. The black line represents the extrapolated deflection profile δ(r) of the bulge.

Extended Data Fig. 4 Illustration of homodyne optical interferometer.

PBS: polarizing beam splitter; λ/2: half wave plate; λ/4: quarter wave plate; PD: photodetector; PID: proportional-integral-derivative controller; PAM: piezo-actuated mirror; SA: spectrum analyzer. The displacement fluctuations are measured with the spectrum analyzer connected to PD_1 while PD_2 is used for feedback stabilization.

Extended Data Fig. 5 Experimental and theoretical translation of colorimetric values to bulge deflection.

(A-B) Deflection profiles [top] compared against R, G, and B pixel values [bottom] for dimpled down (A) and bulged up (B) films. (C) Schematic illustration of three-layer model used to explain the behavior of reflected light. (D) Refractive index, n [top], and absorption coefficient, k [bottom], of 35-nm 2DPA-1 film measured by a Filmetrics F20 reflectometer. (E) Light intensity with respect to wavelength, λ, at various film deflections. (F) Pixel values for R [top], G [middle], and B [bottom] channels with respect to film deflection. Solid curve represents the calculated theoretical value, whereas scatter points are measured experimental values.

Extended Data Fig. 6 Reconstruction of bulge deflection profile using optical measurements.

(A) Pixel values for R- (red square), G- (green circle), and B-channels (blue triangles) with respect to bulge deflection when viewed optically within the glass-covered environmental chamber. Data are fit with a Fourier curve fitting function (solid curves). (B) Predicted deflection values (light blue circles) from the Fourier functions in panel A for a single slice of pixel values extracted from the optical image of a bulge confined within the environmental chamber (inset). The red curve represents the bulge profile fit after filtering extraneous solutions. (C) Predicted bulge profiles for 180 slices extracted from a 180°-rotation around the bulge optical image. Light blue symbols represent profiles that were eliminated via stepwise regression to minimize outliers. The red line represents the final fitting curve used to estimate the bulge deflection. (D) Representation of outlier filtering process to produce final 3D bulge configurations. 2D contour of bulge deflections before [left] and after [middle] stepwise regression.

Extended Data Fig. 7 Pressurization of negatively deflected films.

(A) Change in bulge deflection with respect to the overpressure applied during the N_2 pressurization process. Error bars represent standard deviations from n = 17, 10, 10, 8, and 12 samples for applied pressures of 20, 50, 100, 150, and 200 kPa, respectively. The blue line is cubic fit intended to guide the reader’s eye. (B) Change in the absolute deflection of bulges with respect to time after being pressurized with 150 kPa N_2 over atmosphere. (C-D) Optical images of the 10-µm diameter Si/SiO_2 wells sealed with a 35-nm 2DPA-1 film. Red circles mark the wells that were tracked via AFM measurements. (C) Image taken before pressurization with 150 kPa N_2. (D) Image taken after pressurization with 150 kPa N_2.

Extended Data Fig. 8 N[2]–2DPA-1 interaction energies calculated by density functional theory, DFT.

(A) Top-down schematic of offset 2DPA-1 bilayer [top image] along with cross-sectional images of the three fixed positions [P1-3, bottom images] within bilayer used to perform calculations. Yellow-shaded area highlights the corresponding location of gases in bilayer framework from top and cross-sectional views. (B) Energy profile with respect to the pore-to-pore offset in the bilayer.

Extended Data Fig. 9 Platelet characteristics with respect to r value.

Comparison of 4th-generation hexagonal platelets that are perfect (A) and defective (B-D). In panel B, two types of defects are shown: periphery (shaded grey) and interior (shaded green). Panels C and D are platelets with 50% defectiveness. (E) Scaling of platelet diameter with respect to their r value for platelets that are ideal, 50% defective, and dendrimers. (F) Transmission electron microscopy (TEM) images of 2DPA-1 platelets with r values of 2.27 (top) and 9.67 (bottom). Scale bars represent 20 nm. (G) Yield of mechanically stable films suspended over the 10-μm-diameter Si/SiO_2 wells with respect to the r value of the 2DPA-1 platelets. Error bars represent standard deviations from 440 film-covered microwells analyzed across three separate film transfers. The transition from red to blue shaded regions represents the threshold r value necessary to achieve mechanically robust films at 35 nm thickness.

Extended Data Fig. 10 Estimating O[2] permeability using Thiele modulus.

(A) Schematic of 2DPA-1-coated MAPbI_3 perovskite with defined coordinate system. The top of the 2DPA-1 surface (x = 0) is exposed to ambient O_2 and H_2O, conflated as reactant G in the model at an ambient concentration of CG0. The perovskite volume, V, has an interface with the polymer at x = δ, with pristine (Phase A – left) and degraded (Phase B – right) phases. The interfacial concentration of reactant G is CGδ. The thickness of 2DPA-1 and perovskite phases are δ and γ, respectively. (B) Perovskite degradation kinetics from the XRD peak ratio of the intrinsic MAPbI_3 and PbI_2 phases. The left panel is the uncoated perovskite with pseudo-first order rate constant k = 10−5 s−1 while the right panel shows the 2DPA-1-coated perovskite kinetic rate constant k = 7 × 10−7 s−1.

Supplementary information

Supplementary Information

Supplementary Figs. 1–29, Tables 1 and 2 and Video 1 detailing further aspects of the methodology.

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Supplementary Video 1

Optical response of bulges during pressurization. Optical micrograph (50× objective) of four suspended bulges as they are subjected to increasing N_2 pressure (50 kPa to 150 kPa) over time within the Linkam environmental stage. The change in bulge height during pressurization results in a hue transition from green to pink. The five microwells with suspended films that do not change colour are partially collapsed within the well.

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Ritt, C.L., Quien, M., Wei, Z. et al. A molecularly impermeable polymer from two-dimensional polyaramids. Nature 647, 383–389 (2025). https://doi.org/10.1038/s41586-025-09674-9

- Received: 06 December 2024

- Accepted: 25 September 2025

- Published: 12 November 2025

- Version of record: 12 November 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09674-9

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Global satellite survey reveals uncertainty in landfill methane emissions

Matthieu Dogniaux,

Joannes D. Maasakkers,

Marianne Girard,

Dylan Jervis,

Jason McKeever,

Berend J. Schuit,

Shubham Sharma,

Ana Lopez-Noreña,

Daniel J. Varon &

…

Ilse Aben

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Abstract

Methane is a potent but short-lived greenhouse gas and rapid reductions of its anthropogenic emissions could help decrease near-term warming[1]. Solid waste emits methane through the decay of organic material, which amounts to about 10% of total anthropogenic methane emissions[2]. Satellite instruments[3] enable monitoring of strong methane hotspots[4], including many strongly emitting urban areas that include solid waste disposal sites as most prominent sources[5]. Here we present a survey of methane emissions from 151 individual waste disposal sites across six continents using high-resolution satellite observations that can detect localized methane emissions above 100 kg h–1. Within this dataset, we find that our satellite-based estimates generally show no correlation with reported or modelled emission estimates at facility scale. This reveals major uncertainties in the current understanding of methane emissions from waste disposal sites, warranting further investigations to reconcile bottom-up and top-down approaches. We also observe that managed landfills show lower emission per area than dumping sites, and that detected emission sources often align with the open non-covered parts of the facility where waste is added. Our results highlight the potential of high-resolution satellite observations to detect and monitor methane emissions from the waste sector globally, providing actionable insights to help improve emission estimates and focus mitigation efforts.

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Main

Global waste production has nearly tripled since 1965, reaching 2 billion tonnes per year in 2016 and, with a growing population[6], is expected to further increase by 70% by 2050[7]. Close to 70% of waste currently ends up in landfills or dumping sites[7], in which anaerobic decomposition of organic material produces methane. Methane is a short-lived (with about a nine-year lifetime[8]) but potent greenhouse gas and its anthropogenic emissions make it the second most important contributor to human-induced climate change after anthropogenic carbon dioxide emissions, accounting for ~30% of current positive warming relative to pre-industrial temperatures (1850–1900 average)[9]. Deep and rapid reductions in global anthropogenic methane emissions are essential to keep net warming below 1.5 °C by 21001,10. Methane emissions from solid waste currently amount to 38 million tonnes per year, roughly 10% of total anthropogenic methane emissions[2], and could reach 60 million tonnes annually by 2050[11]. However, some mitigation options are available, for example, banning organic waste in landfills, source separation, reuse, recycling or treatment with an anaerobic digester[11]. If these are implemented to their fullest potential, 2050 methane emissions from solid waste could be as low as 11 million tonnes per year[11].

Solid waste emission estimates are based on widely used first-order decay models[12] that are used in country-level reporting of methane emissions[13] as well as at facility scale. Different variants of such models exist and can yield very different results for similar facilities[14]. The parameters (for example, methane generation potential of the waste) that drive these models are also uncertain and specific to each facility15,16. Finally, waste disposal management practices can greatly impact methane emissions, from unmanaged dumping sites to managed sanitary landfills that include linings, covers and gas capture systems of variable efficiency[17]. Considering all of these uncertainties, independent observations of methane emitted from waste disposal sites are critical and can be obtained through various on-ground and/or aerial-measurement methods[18] that are deployable at the site level and that can provide emission estimates at high continuous temporal resolution. Complementarily to these site-specific approaches, satellites offer extensive global coverage, providing consistent observation sets across a large number of sites. We present here a global-scale survey of methane emissions from waste disposal sites using 1,447 high-resolution satellite observations.

Satellite remote sensing of atmospheric methane can have an active role in methane emission mitigation by locating emission hotspots and identifying the super-emitting sources they contain[19]. Over the past decade, a range of spaceborne instruments have been transformative for methane imaging from space3,20,21,22,23,24. They provide spatial images of atmospheric methane concentrations that enable the detection of anthropogenic emission plumes. These consist of strong enhancements in methane concentration that extend downwind from localized emission sources, as illustrated in Fig. 1. Calibrated mass-balance approaches are employed to translate these instantaneous snapshots into emission rates (Methods), validated by single-blind controlled release[25]. Our study focuses on measurements from GHGSat’s high-resolution (~25 × 25 m^2) methane imaging satellites, which capture targeted 12 × 15 km² scenes and detect facility-scale plumes arising from localized sources at emission rates as low as 100 kg h–1. They can be attributed to individual sources across oil and gas facilities (onshore and offshore), coal mines and waste disposal sites5,26,27,28. Many of these individual sources were first coarsely spatially identified5,29,30,31,32,33 with the Tropospheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite[34]. It maps the atmospheric methane concentration with daily global coverage and a resolution of up to 7 × 5.5 km^2, enabling the detection[4] of methane plumes that can be followed-up—in a so-called tip-and-cue strategy—by targeted GHGSat observations to pinpoint their exact sources. This approach has been demonstrated for four urban areas with strongly emitting waste disposal sites[5]. Here we present a global GHGSat-based survey of methane emissions from waste disposal sites across 130 urban areas in 47 countries during 2021–2022.

Fig. 1: Examples of urban- and facility-scale satellite observations of methane plumes.

a–o, Examples of GHGSat facility-scale (b,d,f,g,h,j,k,m) and TROPOMI-detected urban-area (a,c,e,l) methane emission plumes for urban areas in Charlotte (USA, a,b), Bucharest (Romania, c,d), Hyderabad (India, e,f), Guadalajara (Mexico, g), Córdoba (Argentina, h), Hong Kong (China, j), Bangkok (Thailand, k) and Casablanca (Morocco, l,m). The spatio-temporal distributions of all GHGSat plume origins and Sentinel-2-detected surface activity (structural changes between visual Sentinel-2 images; Methods) for the Casablanca landfill are shown in n and o, respectively. Black crosses mark site locations, whereas white dots represent the GHGSat plume origins and thick black contours demarcate landfill site boundaries. White arrows (a–h, j–m) illustrate the wind direction sampled from the ERA5 reanalysis[48], with the associated labels indicating the wind speed. Plumes overall follow the reanalysis wind direction, with some exceptions at low wind speeds. Background imagery relies on non-concurrent Sentinel-2 data (2022). Copernicus Sentinel-2 data in parts a–h,j–o are adapted from Google Earth Engine49,50. Scale bars, 50 km (a,c,e,l), 1 km (b,d,f,g,h,j,k,m,n,o).

One-third of methane emission plumes detected in TROPOMI data from the year 2021 is related to urban areas[4]. In 2021 and 2022, we detect 897 plumes with TROPOMI across 46 urban areas among the 130 covered by GHGSat (Extended Data Fig. 1 and Supplementary Notes 1 and 2). These detections—which depend on observational coverage and the magnitude of emissions (Extended Data Fig. 2 and Supplementary Note 3)—are located on six different continents, with the majority coming from Asia (Fig. 2). TROPOMI plumes illustrate the mitigation potential concentrated in urban areas, which harbour a range of sources such as wastewater treatment, natural gas distribution and incomplete combustion[35]. Waste disposal sites, however, are some of the most concentrated and mitigable sources in urban areas and are therefore the facilities that we focus on in our GHGSat analysis.

Fig. 2: Distribution of GHGSat-observed waste disposal sites and their urban areas.

Location of the 151 waste disposal sites observed by GHGSat satellites, and the 46 out of 130 corresponding urban areas for which methane emission plumes have been detected in TROPOMI data (grey). GHGSat methane emission rate distributions over logarithmically spaced bins are given for all sites (black line), and separately for managed landfills (orange) and dumping sites (purple). This site classification has been manually determined using satellite and aerial imagery from Google Earth (see main text). The red line shows cumulative emissions. The site-level and urban-area-level data supporting this figure are provided in the Supplementary Information.

A global facility-scale satellite survey

We use 1,447 clear-sky observations acquired by GHGSat’s C1–C5 satellites in 2021 and 2022. These were targeted at 151 different waste disposal sites located in 130 urban areas scattered over six continents, as shown in Fig. 2. Only sites for which at least one methane emission plume has been detected by GHGSat are included, meaning that our sample is on the upper end of the global waste disposal site emission rate distribution. The median number of GHGSat observations per site is 5, with 23 sites that have been observed at least 20 times (Extended Data Fig. 3). These are opportunistic observations that could be made in parallel to regular GHGSat activities—a substantial fraction (51%) of which intersect with TROPOMI-detected urban methane hotspots.

Out of the 1,447 observations, 1,013 show at least one emission plume above GHGSat’s detection threshold (1,085 plumes in total, Fig. 1; quantified as described in Methods). We conservatively consider the emission rate of the 434 site-level null detections to be zero even though we may miss (possibly diffuse) emissions that are lower than the GHGSat detection threshold. The positive plume detection rate per site ranges from 7% (two plumes among 30 observations at Icheon, South Korea) to 100% (which we find for 74 sites). The median of the plumes’ detected methane emission rates is 2.4 t h–1, with 5th and 95th percentiles of 0.5 t h–1 and 15.1 t h–1, respectively. The median relative uncertainty of these emission rates is ~45% (Supplementary Note 4).

Recurrent observations allow us to investigate the potential drivers of the detected emission variability. We compared site-wise emission variability against several meteorological variables (10 m wind speed, 2 m temperature, surface pressure, surface pressure change and accumulated precipitation over two weeks) as well as the hemisphere-corrected day in the year, but we did not find any significant link between them (Extended Data Fig. 4 and Supplementary Note 5); however, past on-site studies have indicated that surface pressure change drives landfill methane emissions36,37,38. Our findings based on satellite observations of high-emitting active sites are consistent with recent airborne-based results[39]. This finding could be explained by meteorological driving producing too small emission changes compared to single observation uncertainty for the sites included in our dataset.

The median site-wise averaged emission rate is 1.2 t h–1 (including null detections), with 5th and 95th percentiles of 0.1 t h–1 and 6.8 t h–1, respectively (Methods, Extended Data Fig. 5 and Supplementary Note 4). The lowest three site-averaged detected emission rates are found at a Canadian landfill in British Columbia (0.03 ± 0.04 t h–1), an Italian landfill near Rome (0.04 ± 0.03 t h–1) and at a South-African landfill near Gqeberha (0.06 ± 0.04 t h–1). The highest three site-averaged detected emission rates are found at the Norte III landfill in Buenos Aires, Argentina (22.0 ± 1.9 t h–1), at a landfill near Hong Kong, China (10.0 ± 2.7 t h–1) and at a landfill near Tehran, Iran (9.4 ± 4.9 t h–1). Averaged emission rates have a median relative uncertainty of 45%, which accounts for both single observation and sampling uncertainties, calculated consistently across all sites (Methods). Using satellite and aerial imagery from Google Earth, we manually classify the 151 waste disposal sites into two categories: 108 managed landfills (sites with organized structures for burying waste, for example, featuring covers) and 43 dumping sites (with informal gathering of waste). Within this dataset, managed landfills and dumping sites do not show a statistically significant differennce in the total detected emission rate distributions. However, when normalized by the total site area, managed landfills show significantly lower area-normalized emission rates compared with dumping sites, thus demonstrating the expected effects of emissions mitigation by closing and covering modules of the landfill (Extended Data Fig. 6 and Supplementary Note 6). Overall, the distribution of site-wise averaged detected emissions is heavy-tailed, with the 60 (40%) strongest-emitting sites (47 managed landfills and 13 dumping sites) accounting for 80% of total emissions (Supplementary Note 4). This estimated skewness is probably conservative as the 100 kg h–1 detection threshold and selective targeting of GHGSat would limit the inclusion of low-emitting sites. This detection threshold enables to cover 54% of the sites (assuming sufficiently localized emissions sources) included in the facility-scale waste disposal site emission database compiled by Climate Tracking Real-Time Atmospheric Carbon Emissions (Climate TRACE)[40], but these 54% of sites amount to 96% of total emissions (Extended Data Fig. 7 and Supplementary Note 4). Overall, the 151 waste disposal sites observed here represent a small fraction of the global total number of landfills (over 10,000 are included in the Climate TRACE datasets[40]) but, assuming constant emissions, their collective instantaneous emission rate scales up to a yearly total of 2.8 million tonnes. This corresponds to 7.4% of 2022 global solid waste emissions in version 8 of the Emissions Database for Global Atmospheric Research (EDGAR) inventory[2].

Modelled and GHGSat-based rates disagree

Figure 3 compares facility-level GHGSat-detected methane emission rates against national site-level reporting programs41,42,43 (which are based on process-based models or gas capture efficiency assumptions) and emissions obtained from data-driven models developed by the non-profit coalition, Climate TRACE[40]. National reporting data exclusively cover managed landfills in the United States, Canada and some EU countries, whereas Climate TRACE has more global coverage and includes dumping sites (Extended Data Fig. 8 and Supplementary Note 7). Overall, we find no correlation between satellite-based and reported or modelled estimates (r = 0.03 for reported emissions, and r = 0.18 for Climate TRACE), with differences showing an insignificant bias and a large scatter, exceeding the averaged emission rates (Supplementary Note 8). Analysing managed landfills and dumping sites separately does not change this conclusion. Although no overall bias is found between reported and GHGSat-based estimates, emissions from 14 (out of 37) landfills are at least twice as large as what is reported to national programmes. As the US Greenhouse Gas Reporting Program includes reports based on two different methodologies—one based on gas capture efficiency and the other based on waste decay modelling—we can compare our results for the US to both (Fig. 3 separates US sites depending on which reporting method was chosen by the facilities). Comparing both estimates for all US landfills to GHGSat-detected emissions (Extended Data Fig. 9 and Supplementary Note 9), we observe that the approach based on gas capture efficiency tends to underestimate emissions (by a factor two), whereas the approach based on waste decay modelling tends to overestimate them (by a factor 1.5). These US results are consistent with a recent investigation of landfill emission models used for reporting[44] and with aerial-based observations[39]. Neither method shows a strong correlation with our results. These findings highlight the critical importance of coordinating bottom-up modelling efforts with independent observations of landfill emissions to improve the understanding of facility-scale waste emissions.

Fig. 3: Discrepancy between satellite-based and bottom-up emission estimates.

Comparison of site-wise methane emission rates observed by GHGSat against data included in reporting programmes (left) and emissions calculated by the Climate TRACE non-profit (right), both averaged over the corresponding GHGSat observation years. Reported and Climate TRACE data are provided as annual totals and have been converted to hourly rates assuming constant emission. Error bars show the site-wise-averaged GHGSat emission uncertainty. The one-to-one line is shown in black. The sites marked by dashed-circles in both panels drive the high correlations for EU site reports (r = 0.76) and Climate TRACE dumping sites (r = 0.54). If removed, these correlations drop to r = −0.62 and r = −0.15, respectively.

Plume sources relate to site activity

Figure 1 shows that high-resolution observations also allow us to pinpoint where detected emissions originate from within a solid waste disposal facility. To understand these origins, we compare manually verified GHGSat emission plume origins with surface activity detected from clear-sky Sentinel-2 10-m resolution RGB observations (Methods and Supplementary Note 10). A landfill near Casablanca (Morocco; Fig. 1l–o) is a clear example, as both GHGSat plume sources and landfill surface activity show north-to-south migration as time progresses and a new section of the landfill is developed in the southwest. Across 107 facilities that have a sufficient number of clear-sky Sentinel-2 images and high-quality surface activity detection results, we find that 44 (41%) show a statistically significant proximity (P-value < 0.05) between surface landfill activity and GHGSat plume source location. When considering only the 21 sites with at least 16 identifiable plume origins in GHGSat observations, we find statistically significant proximity for 18 (86%) of them (Extended Data Fig. 10). Upon revisiting our dataset showing that total site area-normalized emission rates are significantly lower for managed landfills compared with dumping sites (Extended Data Fig. 6 and above), we conclude that the small fraction of open active areas in managed landfill accounts for almost all of the emissions detected by GHGSat. This highlights the predominant role open modules have in managed landfill emissions. This result is consistent with reports of methane emissions being observed originating from landfill work faces in on-ground and satellite-based studies for a limited number of sites5,45, and with an extensive aerial survey covering the United States that showed the prevalence of work faces in total landfill emissions39,46. Our dataset shows the active surface is the dominant emission source across management and economic development levels. This emphasizes the need to quantify emissions from the active surface, underscoring the importance of repeated observations to both reliably estimate mean emissions and to narrow down on (potentially migrating) source locations within a landfill. This spatial information can for example help site operators focus mitigation efforts more effectively. Our dataset also includes two example plumes originating from adjacent facilities: a biogas plant near the Las Dehesas landfill near Madrid and from a wastewater treatment plant near Shanghai (both filtered from the analysis; Supplementary Note 11). They illustrate the mitigation potential that satellite observation can detect in facilities related to and neighbouring waste disposal sites.

Our survey has extensive spatial coverage that brings top-down observation-based estimates of methane emissions for 151 waste disposal sites across six continents. It sheds new light on the mitigation potential of urban methane emissions and on the ability of high-resolution satellites to monitor methane emissions from waste disposal sites and support mitigation activities by pinpointing emission sources within the facility, highlighting the importance of the active surface. The availability of such high-resolution methane-sensitive satellite observations is currently increasing, with the expanding GHGSat constellation and new initiatives such as Carbon Mapper’s Tanager-1 satellite, as well as public hyperspectral satellite missions[3]. Across the 151 surveyed sites—assuming constant emissions to scale up the snapshot averages provided by satellites—we find that bottom-up and top-down satellite-based solid waste emission estimates cannot currently be reconciled at facility scale. This disagreement is consistent with past facility-scale studies using aerial measurements[39] and country-scale studies using TROPOMI data[47]. These discrepancies highlight the importance of site-level data and practices, and call for further efforts that focus on both managed landfills and dumping sites, aiming to close the gap between current bottom-up and top-down understandings of methane emissions from solid waste. Ideally, such studies would involve partners operating waste disposal sites, bottom-up modellers, aerial and satellite-based methane observations augmented by complementary on-ground observations that can provide continuous measurements, including at night. An improved understanding of site-level solid waste methane emissions can support more effective emission mitigation strategies contributing to the worldwide efforts against climate change.

Methods

Automatic methane plume detection in TROPOMI data

TROPOMI[51] on board the European satellite Sentinel-5 Precursor was launched in 2017. It observes backscattered sunlight in the near- and shortwave infrared around the 0.76 µm O_2 and 2.3 µm methane bands, at approximately 1:30 pm local time. Total columns (vertically integrated concentrations) of methane with near vertically uniform sensitivity down to the surface are retrieved from these observations using a full-physics approach that accounts for the interfering impact of surface reflectance, aerosol and other geophysical variables on the shortwave infrared signal (v.2.6.0)[34]. TROPOMI is a methane flux mapper that offers daily global coverage with a 7 × 5.5 km^2 spatial resolution at nadir. In addition to being used in long-term inverse analyses, its imaging capabilities enable the detection of anthropogenic methane emission plumes that arise from the world’s largest emitters[29]. We employ a two-step machine learning approach to explore TROPOMI data for methane emission plumes automatically[4]. We analyse and manually verify all plumes detected in 2021 and 2022 with estimated sources within 50 km from any of the landfills targeted by GHGSat. We apply the Integrated Mass Enhancement (IME) method[20], calibrated specifically for TROPOMI using atmospheric transport simulations, to quantify the methane emission rate and its uncertainty for each TROPOMI-detected plume[4].

Given TROPOMI’s spatial resolution (7 × 5.5 km^2) compared with GHGSat’s (25 × 25 m^2), we cluster the 151 landfills observed by GHGSat into 130 TROPOMI-relevant urban areas. For each urban area, we first apply a 2σ filter to remove outlier estimates that can be hampered by an unrepresentative plume mask due to variable meteorology or surface effects (for example, plume masks truncated by clouds for lower estimates). Then, relying on the remaining TROPOMI plume detections, we report their mean detected urban-scale methane emissions and their standard deviation. These averages only cover emissions detected as strong plumes and are not representative of mean urban emissions but do provide an indication of urban mitigation potential. Not detecting a plume does not imply that there are no emissions: it means that concentrated emissions are lower than the ~8 t h–1 TROPOMI plume detection threshold, or that the observational or geographical conditions did not allow for TROPOMI detection[4]. The discrepancy with mean emissions is verified for four different (above-average emitting and often detected) cities (Buenos Aires, Delhi, Mumbai and Lahore) where IME-based rates show a 7–47% overestimation (while agreeing within uncertainties) compared with urban-level methane emission estimates based on atmospheric inversions and TROPOMI data[5] (Supplementary Note 1).

GHGSat observations and emission quantification

GHGSat-C1 to -C5 instruments were launched between 2020 and 2022. Satellites C1-2-3 perform measurements in the morning around 10 am (local time), while satellites C4-5 perform measurements in the afternoon around 2 pm (local time). These instruments estimate the total column (vertically integrated content) of methane at ~25 × 25 m^2 resolution over targeted 12 ×15 km^2 domains[52] from backscattered sunlight measurements in the shortwave infrared near 1.65 µm, that provide near-surface sensitivity. The GHGSat instruments have an empirically measured methane column precision range of 1.4–2.9%[53], which allows them to observe emission plumes from point (for example, a gas pipeline leak) or very localized sources (for example, active faces of landfills) emitting more than ~100 kg h–1 (this detection threshold increases with wind speed)[28]. Pixels exhibiting local spatially correlated methane column enhancements above background are clustered together and considered to belong to a plume[26]. We apply the IME method[20] to estimate an emission rate Q based on a delineated plume and the local wind speed sampled from a meteorological model. We have:

$$Q=\frac{{U}_{\mathrm{eff}}}{L}\sum _{i}{\rm{\Delta }}{{X}_{\mathrm{CH}}}_{4,i}{a}_{i}$$

where Ueff is the effective wind speed, calibrated against the 10-m wind speed based on a set of large Eddy simulations (LES)[5]; \(L=\sqrt{\sum _{i}{a}_{i}}\) is the plume length computed as the square-root of the plume total area, where ai is the area of the ith pixel included in the plume; and \({\rm{\Delta }}{{X}_{\mathrm{CH}}}_{4,i}\) is the local enhancement above the background of the methane total column for this ith pixel. Here we use an effective wind speed calibration specific to landfills, based on LES of area sources: \({U}_{{\rm{e}}{\rm{f}}{\rm{f}}}=0.34\times {U}_{10\,{\rm{m}}}+0.66\) (ref. [5]), where \({U}_{10\,{\rm{m}}}\) is the 10-m wind speed sampled from the GEOS-FP meteorological reanalysis[54]. The emission rate uncertainty calculation includes contributions from (1) wind speed error; (2) methane column retrieval error; and (3) IME calibration error[26].

The calibration of this mass-balance approach against LES of known synthetic emission rates ensures that the estimated rates correctly account for the different advective transport conditions explored within the set of LES. Beyond this calibration on simulations, numerous real-life validation efforts have been organized, including controlled-release experiments, which are the validation gold standard. Notably, GHGSat participated and showed excellent agreement with metered emission rates in internal controlled releases, as well as in two single-blind controlled-release campaigns, where the true emission rates (and wind speeds) are not known to the satellite data providers and the comparisons are done by a third party (in this case a research group from Stanford University)25,55. Beyond controlled releases, landfill emission rates obtained through aerial methane imagery with an instrument that can detect plumes down to 10 kg h–1 have been validated against traditional aerial mass-balance results18,39. Besides, an in-depth study of two landfills near Madrid that included both similar airborne observations and GHGSat satellite observations showed that GHGSat satellite-based estimates match the total of airborne-detected plumes for same day observations within uncertainties[56]. Combined, these results show that GHGSat satellite-based observations can provide accurate estimates of methane emissions from waste disposal sites.

Estimating site-level GHGSat averages

Three outcomes are possible for any individual waste disposal site observation during a single overpass by GHGSat: (1) no plume is detected; (2) only one plume is detected; and (3) several plumes (arising from the same site) are detected. In the first case, we conservatively consider the emission rate to be equal to zero, with no uncertainty. In the second and third cases, we apply the IME method to each plume separately to quantify its emission rate and uncertainty. In the third case, we sum together all of the detected plume emission rates (and sum their respective uncertainties quadratically) to obtain an emission rate for the whole site.

Given a set of observations for a waste disposal site, we employ a two-step random sampling approach to evaluate the site-level averaged emission rate and its uncertainty, accounting for both single-observation and sampling uncertainties. First, in a bootstrapping approach, we randomly (N = 100,000) resample our set of observations by randomly picking single observations with replacement. This enables us to generate an ensemble of averaged emission rates for which we also compute corresponding uncertainties assuming that single observations are independent Gaussian variables. We then sample a Gaussian distribution (N = 1,000) for all these ensemble elements relying on their respective rates and uncertainties. Finally, we report the mean and standard deviation across this two-step random sampling approach as averaged emission rate and its uncertainty. This method accounts for the single-observation uncertainties and is especially useful to handle bi- or multi-modal site-wise emission rate distributions that can have a peak at zero (all of the observations without any detection) and one or several peaks for positive emission rate values (all of the observations with detected plumes).

Comparison of GHGSat and reported or calculated emissions

For site-wise GHGSat-based methane emission rate comparison against site-wise reported values included within national reporting programs, we manually match sites based on addresses (no distance threshold is used). To compare site-wise GHGSat-based methane emission rates against values modelled by Climate TRACE, we only select sites for which we find matches within a 2 km distance of GHGSat targets, and then only consider the facilities within these 2 km that show the minimum distance from GHGSat targets (Supplementary Note 7 details the other data selection criteria specific to each dataset we compare with). Reported and Climate TRACE data are provided as annual totals and have been converted to hourly rates assuming constant emissions.

Landfill surface activity detection from Sentinel-2 imagery

Managed landfills and dumping sites are active and constantly evolving as they accept new waste: they expand and their active surface(s) move(s) to accommodate the incoming waste. High-resolution visual imagery can be used to track the surface activities at waste disposal sites. To compare the spatio-temporal distributions of GHGSat-detected methane plumes origins and landfill activities, we devise an image analysis scheme to automatically detect surface activity from time series of clear-sky 10-m resolution Sentinel-2 satellite images of waste disposal sites.

For each of the 151 waste disposal sites observed by GHGSat, we convert the time series of Sentinel-2 clear-sky visual RGB images to greyscale by using the National Television Standard Committee’s formula[57]:

$$\mathrm{Greyscale}=0.299\times R+0.587\times G+0.114\times B$$

We then apply a three-image moving filter (over time) based on local structural analysis58,59, which determines surface activity in a given image by detecting overlapping structural changes that occur between this image and the previous one, and between this image and the next one. Using manually outlined landfill masks based on the latest Google Earth imagery, we only consider surface activity that is detected within landfill boundaries, and use filters to ignore pixels associated with water, clouds or cloud shadows. We smooth the raw activity map with a median filter to remove spatially inconsistent noise and only keep spatially consistent activity clusters. Individual activity clusters are then identified, outlined with convex hulls and stored as surface activity results. For each landfill, surface activity results are manually verified before being included in the analysis (Supplementary Methods 1; illustrations are provided in Supplementary Note 10).

Comparison of landfill surface activity results and GHGSat methane plume origins

The wind direction allows to estimate the plume origin as the most upwind highly enhanced pixel included in the plume mask. We also manually verify this result and pinpoint the approximate source(s) of all GHGSat plumes, allowing one to select multiple sources for overlapping plumes originating from the disposal site where appropriate. We use these source locations to compare to the Sentinel-2 based surface activity analysis.

For a given plume, we use the minimum distance between the manually determined plume origin and the nearest outline of a surface activity cluster detected in the closest-in-time Sentinel-2 image as the proximity metric. We set the metric to zero if a plume origin falls inside a detected activity cluster. Consequently, the lower the metric value, the closer the source is to a detected surface activity cluster. We also compute the same metric for N = 10,000 points randomly drawn within the landfill boundaries. This comparison is conservative because it is possible that GHGSat plumes show sources outside of landfill boundaries (their metric values have no upper boundary) whereas these random points can only be located inside (their metric values have an upper boundary).

For each site, we compute the averaged metric across all GHGSat-detected methane emission plumes and compare this result with the distribution of averaged metric values obtained for the N = 10,000 randomly drawn points. We then evaluate the P-value probability of randomly obtaining averaged metric values that are lower than the GHGSat-based result. We consider that GHGSat plume origins show a statistically significant proximity with detected landfill surface activity if we obtain a P-value lower than 0.05 (Supplementary Methods 2).

Supplementary Note 10 showcases examples from different landfills and present an overview of P-value results for all landfills where surface activity could be detected.

Reporting summary

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

Data availability

The Sentinel-5P TROPOMI data and Sentinel-2 data are available at the Copernicus Data Hub via https://dataspace.copernicus.eu. GEOS-FP wind data can be downloaded from https://gmao.gsfc.nasa.gov/GMAO_products/. ERA5 and GEOS-CF meteorological data were sampled using Google Earth Engine. The GHGSat-detected methane plumes are available on Zenodo via https://doi.org/10.5281/zenodo.16641834 (ref. [60]). Tables summarizing site-level results for GHGSat, and urban-area-level results for TROPOMI, are available in Supplementary Notes 1–11.

Code availability

The codes that have been developed to perform the analysis of detected plume statistics and landfill surface activity are available via Code Ocean at https://codeocean.com/capsule/2078268/tree. They enable the production of Figs. 1, 2 and 3 and the data instrumental to these figures.

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Acknowledgements

We thank the Global Methane Hub for funding the ‘Targeting Waste Emissions Observed from Space—Phase 1’ project. M.D. acknowledges funding from the GALES project (grant no. 15597) of the Dutch Technology Foundation STW-NWO. M.D. and A.L.N. acknowledge the NSO TROPOMI national programme. S.S. acknowledges funding from the IMEO Studies programme contract DTIE22-EN5036. We thank the team that realized the TROPOMI instrument and its data products, consisting of the partnership between Airbus Defence and Space Netherlands, KNMI, SRON and TNO and commissioned by NSO and ESA. The Sentinel-5 Precursor and Sentinel-2 are part of the EU Copernicus programme, and Copernicus (modified) Sentinel-5P data (2021–2022) and Sentinel-2 data (2020–2023) have been used.

Author information

Authors and Affiliations

1. SRON Space Research Organisation Netherlands, Leiden, The Netherlands

Matthieu Dogniaux, Joannes D. Maasakkers, Berend J. Schuit, Shubham Sharma, Ana Lopez-Noreña & Ilse Aben

2. GHGSat Inc., Montreal, Canada

Marianne Girard, Dylan Jervis, Jason McKeever & Berend J. Schuit

3. Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA

Daniel J. Varon

4. Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA

Daniel J. Varon

5. Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Ilse Aben

Contributions

M.D., J.D.M. and I.A. designed the study. M.D. performed the analysis and interpretation of TROPOMI and GHGSat methane plumes, as well as the Sentinel-2 activity detection analysis, under the supervision of J.D.M. and I.A. M.G., D.J. and J.M.K. selected the GHGSat methane plumes and contributed to their analysis and interpretation. D.J.V. performed and provided the effective wind speed calibration for landfills, and contributed to the result interpretation. B.J.S. performed the plume detection in TROPOMI data and contributed to their analysis. S.S. and A.L.N. contributed to the analysis of TROPOMI data. M.D. wrote this article with feedback from all co-authors.

Corresponding author

Correspondence to Matthieu Dogniaux.

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Competing interests

The authors declare no competing interests.

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Nature thanks Ke Yin, Rona Thompson 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 Map of the number of urban-scale methane emission plumes detected in TROPOMI data.

Spatial distribution of urban-scale methane emission plume numbers detected in TROPOMI data for the 46 urban areas targeted by GHGSat that show methane emission plumes in 2021–2022 TROPOMI data.

Extended Data Fig. 2 Prevailing explanation for the absence of plume detections in TROPOMI data.

Spatial distribution of all 130 urban areas observed by GHGSat. Urban areas that show methane emission plumes detected in TROPOMI data are depicted in black. Colored urban areas do not show methane emission plumes detected in TROPOMI data, and their color depicts the prevailing explanation of why that is the case. Overall, we find that the absence of a detected plume can be explained by observational-coverage-related issues for 62 urban areas. Expected urban-scale methane emissions lower than the TROPOMI plume detection threshold explain the absence of plume detections for 19 urban areas and, finally, strong TROPOMI methane column correlation with surface reflectance features hampering plume detection explains the absence of plume detections for 3 urban areas.

Extended Data Fig. 3 Map of the number of GHGSat observations per site.

Spatial and site-wise observation count distributions of the 151 waste disposal sites observed by GHGSat satellites. All sites have at least one plume detection.

Extended Data Fig. 4 Comparison of methane emission rate variations against meteorological variables.

Methane emission rate deviations from site-wise medians (computed excluding null detections) against deviations from site-wise medians (computed excluding null detections) for wind speed (a), 2 m air temperature (b), surface pressure (c), change in surface pressure over 1 h (d), accumulated precipitations over two weeks (e), and month of the year (corrected for hemisphere). All meteorological data are sampled from ERA5. Smoothed mean curves are shown (thick black lines) and are used to compute first-order sensitivity indices Si, providing the dataset variance fraction explained by the considered meteorological variable. Smoothed mean curves ± local standard deviations are also shown (thin dashed lines). Overall, no meteorological variable significantly explains site-wise temporal emission variability in our dataset.

Extended Data Fig. 5 GHGSat site-wise emission rate and uncertainty distributions.

GHGSat site-wise averaged methane emission rate (a) and relative uncertainty (c) distributions, with their relationship (b). Relative uncertainties above 100% are related to sites showing one positive detection and a larger number of negative detections.

Extended Data Fig. 6 Comparison of managed landfill and dumping site total and area-normalized emission distributions.

Distributions of total (full colors) and area-normalized (lines) methane emission rates for managed landfills (orange) and dumping sites (purple). Two-sided two-sample Kolmogorov-Smirnov (K-S) and a two-sample Anderson-Darling (A-D) tests are performed to test whether distributions are significantly different between managed landfills and dumping sites. Total site-wise emission distributions are not significantly different, but area-normalized emission distributions are, with lower (on average) area-normalized emissions in managed landfills compared to dumping sites. This may be explained by managed landfills including closed inactive modules that generally show no emissions above the GHGSat detection threshold but still add to the total site area whereas, by definition, dumping sites do not show these closed inactive modules. This reflects the expected effect of definitively covering and closing some parts of managed landfills, thus confirming the efficiency of this mitigation strategy.

Extended Data Fig. 7 Comparison of cumulative distribution functions between this work and bottom-up datasets.

Distribution of emission rates for the 151 sites included in this study (left y-axes) compared to the cumulative distribution functions (right y-axes) for total emissions (a) and total number of sites (b) computed for all the sites included in the bottom-up facility-scale datasets considered in this work (see Supplementary Note 7) and for our sample of sites. The sites studied in this work and the GHGSat detection threshold cover the emission rate range that contributes most to total solid waste methane emissions. This highlights the potential of high-resolution methane imaging satellites to monitor solid waste methane emissions globally.

Extended Data Fig. 8 Spatial distribution of waste disposal sites for which bottom-up estimates are available.

Spatial distribution of site-wise comparison between GHGSat methane emission rates and reported (a) or calculated (b, Climate TRACE) emissions. These are the sites included in Fig. 3.

Extended Data Fig. 9 Impact of US GHGRP reporting method on the comparison against GHGSat-based emission rates.

GHGSat-based emission rates compared to annual US GHGRP reported emissions rates averaged over the corresponding GHGSat observation years, and obtained from the official GHGRP dataset (a), GHGRP results of the gas-capture efficiency method for all landfills (b), and GHGRP results of the waste-decay modelling for all landfills (c). Reported data are provided as annual totals and have been converted to hourly rates assuming constant emissions. Black lines show the 1:1 line. Waste decay modelling provides higher emission estimates compared to GHGSat-based emission rates, while the gas-capture efficiency method provides lower estimates. Regardless of the method, reported estimates do not correlate with GHGSat-based emission rates.

Extended Data Fig. 10 Summary of GHGSat plume source comparison to Sentinel-2 detected surface activity.

Proximity significance p-value for the 107 sites that passed all Sentinel-2 detected surface activity filtering criteria against the number of plume sources identified per site (a, left y-axis), and total number of sites that show at least a given number of plume sources (a, right y-axis, blue line) and that also show a p-value < 0.05 (a, right y-axis, red line). Distribution of proximity significance p-value values (b). We find that 44 sites show statistically significant proximity between Sentinel-2 detected surface activity and GHGSat plume sources, with an increasing fraction of sites showing such proximity for an increasing number of GHGSat observations.

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Supplementary Methods 1 and 2, Notes 1–11, Figs. 1–22 and Tables 1–10.

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Dogniaux, M., Maasakkers, J.D., Girard, M. et al. Global satellite survey reveals uncertainty in landfill methane emissions. Nature 647, 397–402 (2025). https://doi.org/10.1038/s41586-025-09683-8

- Received: 10 July 2024

- Accepted: 29 September 2025

- Published: 05 November 2025

- Version of record: 05 November 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09683-8

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Somatic mutation and selection at population scale

Andrew R. J. Lawson,

Federico Abascal,

Pantelis A. Nicola,

Stefanie V. Lensing,

Amy L. Roberts,

Georgios Kalantzis,

Adrian Baez-Ortega,

Natalia Brzozowska,

Julia S. El-Sayed Moustafa,

Dovile Vaitkute,

Belma Jakupovic,

Ayrun Nessa,

Samuel Wadge,

Marc F. Österdahl,

Anna L. Paterson,

Doris M. Rassl,

Raul E. Alcantara,

Laura O’Neill,

Sara Widaa,

Siobhan Austin-Guest,

Matthew D. C. Neville,

Moritz J. Przybilla,

Wei Cheng,

Maria Morra,

Lucy Sykes,

Matthew Mayho,

Nicole Müller-Sienerth,

Nicholas Williams,

Diana Alexander,

Luke M. R. Harvey,

Thomas Clarke,

Alex Byrne,

Jamie R. Blundell,

Matthew D. Young,

Krishnaa T. A. Mahbubani,

Kourosh Saeb-Parsy,

Hilary C. Martin,

Michael R. Stratton,

Peter J. Campbell,

Raheleh Rahbari,

Kerrin S. Small &

…

Iñigo Martincorena

Nature volume 647, pages 411–420 (2025)

Abstract

As we age, many tissues become colonized by microscopic clones carrying somatic driver mutations1,2,3,4,5,6,7. Some of these clones represent a first step towards cancer whereas others may contribute to ageing and other diseases. However, our understanding of this phenomenon remains limited due to the challenge of detecting mutations in small clones. Here we introduce a new version of nanorate sequencing (NanoSeq)[8], a duplex sequencing method with an error rate lower than five errors per billion base pairs, which is compatible with whole-exome and targeted capture. Deep sequencing of polyclonal samples with single-molecule sensitivity simultaneously profiles large numbers of clones, providing accurate mutation rates, signatures and driver frequencies in any tissue. Applying targeted NanoSeq to 1,042 non-invasive samples of oral epithelium and 371 blood samples from a twin cohort, we report an extremely rich selection landscape, with 46 genes under positive selection in oral epithelium, more than 62,000 driver mutations and evidence of negative selection in essential genes. High-resolution maps of selection across coding and non-coding sites are obtained for many genes: a form of in vivo saturation mutagenesis. Multivariate regression models enable mutational epidemiology studies on how exposures and cancer risk factors, such as age, tobacco or alcohol, alter the acquisition or selection of somatic mutations. Accurate single-molecule sequencing provides a powerful tool to study early carcinogenesis, cancer prevention and the role of somatic mutations in ageing and disease.

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Main

In the past decade, increasingly sensitive sequencing methods have begun to unravel the somatic mutation landscapes of human tissues. They have revealed that mutations accumulate linearly with age in a tissue-specific manner2,9,10, largely due to endogenous mutational processes but also influenced by mutagen exposures, germline variation and disease states. These studies have also revealed that as we age our tissues are colonized by myriad clones carrying positively selected driver mutations1,2,3,4,5,6,7. These clones provide a window into early carcinogenesis and may contribute to other diseases. However, most clones are microscopic and methods to detect them, such as laser microdissection[11] or single-cell cultures[12], are low throughput, which has limited our understanding to a few tissues and small donor cohorts.

An alternative approach is error-corrected bulk sequencing[13], such as duplex sequencing, which combines information from both strands of each original DNA molecule to eliminate sequencing and amplification errors14,15,16,17. Theoretically, duplex error rates should approximate the polymerase error rate squared (fewer than 10−8 errors per base pair (bp)). However, they are typically higher (around 10−7) due to interstrand error copying during library preparation[8]. We have previously described NanoSeq, a protocol that avoids error transfer by using restriction enzyme fragmentation without end repair, and dideoxynucleotides during A-tailing, achieving error rates below 5 × 10−9 errors per bp in single DNA molecules[8]. As this rate is two orders of magnitude lower than the mutation burden of normal adult cells (around 10−7)2,9,12, mutations are accurately detected from single DNA molecules, enabling the quantification of mutation rates and signatures in any tissue. However, this protocol is unsuitable for driver discovery, as restriction enzymes only provide partial coverage of the human genome.

Full-genome nanorate sequencing

To achieve full-genome representation while retaining ultra-low error rates, here we introduce two alternative fragmentation methods: (1) sonication followed by exonuclease blunting; and (2) enzymatic fragmentation in a buffer optimized to eliminate error transfer between strands. As in the original NanoSeq protocol[8], dideoxynucleotides prevent the extension of single-stranded nicks and quantitative PCR followed by a library bottleneck is used to optimize duplicate rates to maximize cost efficiency. After extensive optimization (Supplementary Note 1), we achieved full-genome coverage (Extended Data Fig. 1a,b) with similar efficiency and error rates as the original NanoSeq protocol.

To demonstrate their accuracy, we used cord blood DNA as a negative control, as neonatal blood cells carry just 60–80 somatic mutations (roughly 10−8 mutations per bp). Both new versions of NanoSeq (sonication -MB-NanoSeq- and enzymatic -US-NanoSeq-) yielded mutation loads and spectra consistent with previous knowledge[8] (Fig. 1a,b). By contrast, standard duplex sequencing (with end repair and without dideoxynucleotides), using sonication or enzymatic fragmentation, showed error rates around 1.5 × 10−7 errors per bp and 4 × 10−8 errors per bp, respectively. We then tested these protocols on samples with high levels of DNA damage (pancreas biopsies fixed in formalin for 3 days or 17 days). Standard duplex sequencing error rates increased roughly tenfold due to error transfer at damaged sites, whereas both versions of NanoSeq yielded comparable mutation loads to a control formalin-free biopsy (Extended Data Fig. 1c). This raises the possibility of using NanoSeq on more heavily damaged sources of DNA. The clean fragmentation protocols introduced here may also be useful upstream of other error-corrected sequencing methods beyond duplex sequencing, such as CODEC, SMM-seq or HiDEF-seq18,19,20, to lower error rates while providing full-genome coverage.

Fig. 1: Technical and biological validation of targeted NanoSeq.

a, Genome-wide SNV burden estimates, as mutations per base pair, for cord blood granulocytes, sequenced using four different fragmentation and library preparation protocols. Error bars show 95% Poisson CIs. Horizontal lines denote the observed burden (solid) and 95% Poisson CIs (dashed) for cord blood granulocytes sequenced by restriction enzyme NanoSeq[8]. Duplex sequencing and NanoSeq burdens are corrected for missed embryonic mutations, as described in ref. [8]. Enz, enzymatic fragmentation; sonic, sonication; rep, replicate. b, Trinucleotide mutational spectra of single-cell derived cord blood colonies from a previous study[56] (top), and cord blood granulocytes sequenced using standard duplex sequencing (middle) and whole-genome NanoSeq (bottom). Duplex sequencing and NanoSeq spectra are corrected by the ratio of genomic to observed trinucleotide frequencies. Cosine similarity 95% CIs are calculated by drawing 1,000 random samples from each observed profile, as described in ref. [8]. c, Linear regression of genome-wide SNV burdens (estimated using targeted NanoSeq) for whole-blood samples from 371 donors against donor age. Points and their associated error bars represent the point estimates and 95% Poisson bootstrapping CIs of passenger mutation burdens for each sample. Slope and intercept of the fitted model (point estimates and 95% CIs) are indicated. One sample was excluded due to the ratio between upper and lower confidence limits being greater than five. d, Mutation counts for each coding mutation consequence (top) and estimated mutant cell fractions (bottom) for 14 genes under significant positive selection in blood. Mutant cell fractions are shown for individuals aged 65–85 whose blood samples were not selected on the basis of their oral epithelium results.

Mutation detection in polyclonal tissues

Combining these new protocols with bait capture[21], targeted NanoSeq can accurately quantify somatic mutation rates, signatures and driver landscapes in any tissue. Unlike traditional bulk sequencing, which only detects mutations over a certain variant allele fraction (VAF) (typically more than 1–5%), single-molecule sequencing detects mutations present at any cell fraction, even in single cells, with a detection probability proportional to the mutation frequency in the cell population. In highly polyclonal samples in which the number of clones is larger than the duplex depth achieved, most mutations are seen in just one molecule, providing an efficient way to profile driver mutations in hundreds of clones simultaneously with a single sequencing library.

Our first somatic mutation studies in skin and oesophagus revealed a rich clonal landscape but were limited to a few individuals due to technical limitations1,2. To investigate how mutation landscapes vary across the population, we chose oral epithelium, a tissue with varied mutagenic exposures and amenable to large-scale non-invasive collection using buccal swabs. Here we describe its mutation landscape across 1,042 individuals, applying targeted NanoSeq to buccal swabs using a panel of 239 genes (0.9 Mb) (Methods, Extended Data Fig. 2 and Supplementary Table 1). Samples were sequenced to an average depth of 665 duplex coverage (dx), achieving 693,208 dx coverage across all samples. We also applied targeted NanoSeq to 371 blood samples from these donors (cumulative 250,947 dx).

Targeted NanoSeq of blood

Analysis of the blood data demonstrates that targeted NanoSeq recapitulates the known mutation rates, signatures and drivers of a well-studied tissue. Mutation rates and trinucleotide spectra were consistent with previous whole-genome sequencing of haematopoietic stem cell colonies (Fig. 1c and Extended Data Fig. 3a,b). Using dNdScv to detect genes under positive selection[22] (Methods), we identified 14 genes (Fig. 1d, Extended Data Fig. 3e and Supplementary Table 3), all known clonal haematopoiesis drivers23,24. Hotspot dN/dS (the ratio of non-synonymous (N) to synonymous (S) substitutions) analyses also identified evidence of selection on several extra drivers, including JAK2, MYD88, SF3B1, SRSF2, GNB1 and STAT3 (Supplementary Table 4 and Supplementary Notes 2 and 4).

Despite the modest size of the dataset (371 samples, mean 676 dx), we found 4,406 non-synonymous mutations in these 14 driver genes (11.9 mutations per donor), including 1,904 mutations in DNMT3A and TET2 (Fig. 1d). Of the mutations detected, 95% were called by just one molecule, 99% had unbiased VAFs under 1% and 90% had under 0.1% (Extended Data Fig. 3c and Methods). For comparison, a recent study of clonal haematopoiesis in more than 200,000 individuals using standard sequencing (only sensitive to clones with more than 1% VAF) found 0.029 and 0.012 DNMT3A and TET2 mutations per donor[25], roughly a 100–200-fold lower yield of driver mutations per sample. Overall, these results confirm the power of targeted NanoSeq to measure mutation rates, spectra and selection in highly polyclonal samples.

Driver landscape in oral epithelium

Self-collected buccal swabs were received by post from 1,042 volunteers from TwinsUK[26]. The cohort had a median age of 68 years (range 21–91), 79% women, 37% smokers and 332 pairs of twins (214 identical or monozygotic, 118 non-identical or dizygotic) (Extended Data Fig. 2a–g). A protocol designed to reduce saliva and blood contamination was used, with methylation and mutation analyses confirming a mean epithelial fraction of more than 90% (Extended Data Fig. 2h, Methods and Supplementary Note 4). Across donors, we found 341,682 somatic mutations, including 160,708 coding single-nucleotide variants (SNVs) and 29,333 coding indels (Extended Data Fig. 4g). We found that mutations in oral epithelium accumulate linearly with age, with rates roughly 18.0 SNVs per cell per year (95% confidence interval (CI) 16.7–19.4) and roughly 2.0 indels per cell per year (95% CI 1.7–2.4) (Fig. 2a,b). Because these rates are extrapolated from genic regions, which often have lower mutation rates, we applied RE-NanoSeq on 16 samples, revealing a genome-wide rate for oral epithelium of roughly 23 SNVs per cell per year (Extended Data Fig. 4a and Supplementary Note 3).

Fig. 2: Driver landscape of oral epithelium in 1,042 donors.

a,b, Linear regressions of the extrapolated genome-wide SNV (a) and indel burdens (b) in oral epithelium (estimated using targeted NanoSeq) against donor age. Points represent the point estimates of passenger mutation burdens for each sample. Slope and intercept of the fitted model (point estimates and 95% CIs) are indicated. c–f, For the top 20 significant driver genes based on driver mutation frequency, panels show mutation counts per mutation consequence category (c), dN/dS ratios per mutation consequence category (horizontal line indicates neutral dN/dS = 1, only categories with significant dN/dS ratios are shown for each gene) (d), estimated mutant cell percentages (upper and lower bounds for the mean across donors aged 65–85) (e) and percentage of tumours carrying a non-synonymous mutation (with error bars denoting 95% binomial CIs) (f). g, dN/dS ratios for missense and nonsense mutations in genes under significant negative selection. Error bars denote 95% CIs; horizontal line indicates neutral dN/dS = 1. h, Global dN/dS ratios for missense and nonsense mutations across non-driver genes (n = 18,767), targeted driver genes (n = 49) and 17 targeted essential genes. Error bars denote 95% CIs; horizontal line indicates neutral dN/dS = 1. i, Numbers of amino acid changes under significant positive selection based on site-level dN/dS (site-wide or under restricted hypothesis testing of known cancer hotspots), grouped by gene and mutation consequence category. Counts of significant amino acid changes per gene are shown above each bar.

The data also revealed an unprecedentedly rich landscape of selection. We found 49 genes under positive selection by dNdScv, with over 90,000 non-synonymous mutations in them across clones, of which around 62,000 are estimated to be drivers (Fig. 2c–f, Extended Data Figs. 4h and 5a, Supplementary Table 3 and Supplementary Note 2). Comparison to matched blood suggests that selection in three of the genes (DNMT3A, TET2 and FOXP1) results from low-level blood contamination in the buccal swabs (Extended Data Fig. 3f, Methods and Supplementary Note 4). Several other genes, including PPM1D and ASXL1, are genuine drivers in both tissues. Detailed information on the drivers uncovered is available in Supplementary Note 4 and Extended Data Figs. 5 and 6.

The commonest oral drivers match those in skin and oesophagus1,2,3. However, 31 of the oral drivers have not been previously reported in skin or oesophagus, including several drivers of head and neck squamous cell carcinomas (HNSC) (Supplementary Note 4). The density of driver mutations is considerably lower in oral epithelium than oesophagus (three to four times lower for NOTCH1 or TP53)[2], and a few strong drivers in normal oesophagus seem neutral or weakly selected in oral epithelium (KMT2D, NFE2L2 and PIK3CA). The absence of some important oral cancer drivers from the 46 genes under selection is also interesting. CDKN2A, NFE2L2, PTEN, HLA-A, SMAD4, B2M and RB1 seem neutral or weakly selected in normal oral epithelium despite being common drivers in HNSC (Extended Data Fig. 5e), suggesting that selection on these genes may be a later event in HNSC development. This includes HLA-A and B2M, which may facilitate immune escape later in carcinogenesis.

Although duplex sequencing is normally applied to small gene panels14,21, the use of a quantitative PCR step followed by a library bottleneck simplifies the use of panels of any size, including whole-exome panels. To ensure that major drivers are not being missed by our 239-gene panel, we performed exome-wide NanoSeq on 12 samples to a total duplex coverage of 1,024 dx. This reidentified NOTCH1, TP53, PPM1D, RAC1 and ZFP36L2, suggesting that our panel includes the commonest drivers in oral epithelium. We also found a significant excess of indels in the keratin gene KRT15, which is probably the result of a hypermutation process known to affect highly expressed lineage-defining genes[27].

Our data reveal that the oral epithelium is composed of large numbers of small clones, with 10–20% of all buccal cells carrying driver mutations in older individuals (Extended Data Fig. 5d). We found 95.5% of oral mutations in only one duplex molecule, and around 90% had unbiased VAFs less than 0.1%. These VAFs are consistent with the submillimetric size of most clones reported in other epithelia1,2,5, emphasizing the importance of single-molecule sensitivity to study solid tissues in bulk.

Aggregating duplex VAFs (Methods), we estimate that, in donors aged 65–85 years, the average fraction of cells carrying a driver mutation is approximately 10% for NOTCH1, 3% for TP53, 1% for NOTCH2, CHEK2 and ATM, and less than 1% for other driver genes (Fig. 2e and Extended Data Fig. 5d). The frequency of NOTCH1 and TP53 mutations in oral epithelium contrasts with their frequencies in HNSC (The Cancer Genome Atlas, TCGA) of 16% and 69%, respectively (Fig. 2f and Methods). The similar frequency of NOTCH1 driver mutations in oral cancer and normal oral epithelium suggests that NOTCH1 mutations lead to benign clonal expansions at similar risk of transformation than NOTCH1-wild-type cells. By contrast, TP53 and most other driver genes found under selection in oral epithelium seem enriched in squamous carcinomas consistent with a genuine tumorigenic role of these mutations. We note, however, that comparisons for most genes are limited by the number of cancers sequenced so far, compared with the thousands of normal clones assayed in this study.

Negative selection on essential genes

The high number of mutations detected per gene also provides unprecedented power to detect negative selection, manifested as genes with a depletion of non-synonymous mutations (dN/dS < 1). Previous studies have shown that, exome-wide, most coding somatic mutations are tolerated and not negatively selected during somatic evolution, in contrast to long-term germline evolution22,28. However, strong negative selection in a small fraction of genes remains possible, particularly in essential haploinsufficient genes, but requires larger sequencing studies to be detectable[29].

Powered by the very high duplex depth and using new one-sided negative selection tests (Methods), we found nine genes under significant negative selection in our panel, mostly driven by selection against truncating SNVs (dN/dS < 1 for nonsense and essential splice site mutations) (Fig. 2g and Supplementary Table 3). This includes three essential genes from CRISPR screens (SF3B1, CHD4 and CDK4) (Methods). PIK3CA, SF3B1 and TERT showed negative selection against truncating mutations and positive selection on activating hotspot mutations (coding in PIK3CA and SF3B1, and promoter in TERT), suggesting that these genes are both essential genes in wild-type oral cells, and drivers upon acquiring activating mutations. Aggregating mutations from the 17 panel genes known to be essential in CRISPR screens revealed clear negative selection against truncating mutations in them (dN/dS = 0.69, 95% CI 0.61–0.78) (Fig. 2h). By contrast, dN/dS ratios for not-significantly selected genes in the panel as well as exome-wide dN/dS ratios, excluding selected genes, were consistent with a largely neutral accumulation of coding somatic mutations (Fig. 2h and Extended Data Fig. 5f).

In vivo saturation mutagenesis

The high number of mutations per gene provides an opportunity to start building high-resolution maps of selection across sites for the main driver genes1,2,21,30 (Fig. 3a–c), a form of in vivo saturation mutagenesis. To formalize the analysis of recurrent mutation hotspots, we used site-level dN/dS models (‘sitednds’, Methods). Powered by the high number of mutations, we found 1,220 amino acid changes under significant positive selection (q-value less than 0.01), including 599 in NOTCH1 and 268 in TP53 (Fig. 2i and Methods). Restricting hypothesis testing to known cancer hotspots added several oncogenes to the list of positively selected genes in buccal swabs, including PIK3CA, ERBB2, KRAS and HRAS (Supplementary Table 4 and Methods).

Fig. 3: In vivo saturation mutagenesis in oral epithelium.

a, Mutation bar plot for TP53. The x axis represents coordinates along the coding sequence. Exons and protein domains are indicated along the x axis. The y axis represents number of mutations, either in the 1,042 TwinsUK oral epithelium samples used in this study (top) or in squamous cell carcinoma from the COSMIC database (bottom). Mutations are coloured according to mutation consequence category. Grey shading indicates cumulative duplex coverage across TwinsUK buccal swab samples. b, Numbers of mutations per gene found in this study and in the COSMIC catalogue (obtained from across all whole-genome sequencing (WGS) and whole-exome sequencing (WES) studies or only squamous cell carcinoma (SCC) WGS and WES studies), for a selection of driver genes. c, Mutation bar plots for NOTCH1, PPM1D, TP63 and RAC1. Elements are as indicated in a; COSMIC mutations not shown. d, Diagrams of the three-dimensional structure of RAC1, showing the clustering of sites under significant positive selection around the GDP/GTP binding pocket. Residues with site-level dN/dS q < 0.01 are coloured. Shading intensity denotes degree of significance. e, dN/dS ratios for driver sites under significant positive selection based on the withingenednds method. Driver sites are classified into six groups according to mutation consequence. Labels in grey indicate genes not identified as significant by gene-level dN/dS analyses. f, Mutation bar plot for TP53, including all mutations (top) and synonymous or non-coding mutations only (bottom). The x axis represents genomic coordinates along the gene body, with coding exons (red) and untranslated regions (UTRs) (blue) indicated by the gene diagram on top and the shading within each histogram. The grey line denotes cumulative duplex coverage across TwinsUK buccal swab samples. Coding mutation counts are coloured according to mutation consequence as indicated in a. TSS, transcription start site.

The distribution of coding mutations in TP53 mirrors that observed across thousands of cancers in the COSMIC database[31] (Fig. 3a and Methods). In TP53, we found nearly as many mutations as in 44,000 cancer exomes and genomes, and for several other driver genes, the number of mutations reported here far outweighs all previously observed mutations from cancer studies (Fig. 3b). For comparison, analysis of more than 7,500 cancer exomes from 29 cancer types from TCGA yielded around 15,000 driver mutations in known cancer genes[22], a quarter of the driver mutations observed in the current study.

Studying the distribution of mutations within genes revealed a diversity of selection patterns (Fig. 3a,c) (see Supplementary Note 4 and Extended Data Fig. 6 for detailed descriptions). TP53 shows strong selection on missense mutations in the DNA binding domain and on truncating mutations across the gene. NOTCH1 shows a characteristic clustering of missense mutations in EGF repeats 8–12, predicted to disrupt binding to NOTCH1 ligands Jagged and Delta[2]. Truncating mutations are subject to much weaker selection in the last exon of NOTCH1 (dN/dS for nonsense mutations was 68.4 across the gene and 6.9 in the last exon), which probably reflects their inability to trigger nonsense-mediated decay. RAC1 shows a classical oncogene pattern of strong selection on activating hotspots, with site dN/dS identifying 58 missense sites under significant selection (q < 0.01). Although these sites are scattered along the gene, they cluster around the GDP/GTP binding pocket in the three-dimensional structure of RAC1 (Fig. 3d). PPM1D encodes a known negative regulator of p53 and shows a characteristic pattern of recurrent nonsense SNVs and indels in the last exon, which results in the loss of a C-terminal degradation domain leading to a more stable isoform of the protein, hence increasing p53 suppression[32]. Finally, TP63 shows an unusual selection pattern with a highly recurrent essential splice hotspot predicted to lead to an alternative isoform of p63 (ref. [33]). Extra mutation maps are shown in Extended Data Fig. 6a–i.

Beyond coding mutations, we obtained high duplex coverage in exon-flanking sequences, and we targeted the promoters of many genes (Methods). To test for selection on specific subsets of coding and non-coding sites within a gene, we implemented a new function in dNdScv (‘withingenednds’, Supplementary Note 2). This identified several underappreciated driver sites, including strong positive selection on mutations causing stop codon loss in TP53 and PPM1D, on intronic mutations near essential splice sites in TP53, NOTCH1, CHEK2 and NOTCH2, and on some synonymous sites in TP53 and NOTCH1 predicted to affect splicing by SpliceAI[34] (Fig. 3e and Supplementary Note 4). In addition, we observed suggestive clustering of mutations at the TP53 transcription start site, the TP53 polyadenylation signal, and at splice sites in the first non-coding exon of TP53 (Fig. 3f), as well as hotspots in non-canonical but previously reported 5′ untranslated region sites in TERT. These analyses also revealed a general inflation of mutations in the core promoters of many genes, suggestive of a higher background mutation rate in promoters rather than selection (Extended Data Fig. 6m), consistent with previous reports[35]. Despite our panel not being designed to search for non-coding cis-regulatory driver mutations, these examples show the potential of deep somatic mutation scanning to exhaustively discover coding and non-coding driver sites.

Variants of uncertain significance are germline or somatic variants identified by genetic testing whose clinical relevance is unknown. Evidence of selection in cancer is starting to be used for the classification of variants in some genes[36] but is limited by the sparsity of cancer genomic datasets. To investigate whether selection in normal tissues could contribute to these efforts, we compared the distribution of site-level dN/dS ratios for sites annotated in ClinVar as pathogenic, benign or of uncertain significance. Nearly all known pathogenic sites in TP53, NOTCH1 and PPM1D had high dN/dS ratios, and nearly all known benign sites had low dN/dS ratios (Extended Data Fig. 6n). Looking at sites reaching or approaching significance (q < 0.20), we find many variants of uncertain significance (and zero benign variants) with comparable evidence of selection to known pathogenic variants (including 86 in TP53, 35 in NOTCH1 and 5 in PPM1D) (Supplementary Table 4). Although deeper sequencing will be required to achieve true saturation (Supplementary Note 4), these results show that ultra-deep single-molecule sequencing of polyclonal tissues has the potential to provide in vivo saturation mutagenesis information for genes under somatic selection.

Mutational epidemiology

The discovery of many clones carrying cancer-driver mutations in normal tissues has caused some confusion about their role in carcinogenesis. However, these clones are entirely compatible with a multistage model of carcinogenesis, and were in fact anticipated by some classical mathematical models (see Supplementary Note 5 for an extended description). In the 1950s, Armitage and Doll[37] proposed that the rapid increase in cancer incidence with age could be explained by a model in which cells acquire mutations linearly with age and 6–7 driver events are required for transformation. Lesser-known models with clonal expansions were proposed soon after and showed that the size and type of clonal expansion had large effects on cancer incidence. The current model of carcinogenesis is that cancers emerge by somatic evolution. Both mutation and selection (clonal expansion) increase the likelihood of a cell acquiring the complement of driver changes needed for transformation. Carcinogens may thus act by inducing mutations (mutagens) or by altering selection (promoters38,39 or selectogens[40]) (see Supplementary Note 5 for an extended explanation). By studying the variation in mutation and selection across 1,042 individuals, we can begin to quantify these processes.

To investigate the mode of clonal growth in oral epithelium, we first studied how the frequency of driver mutations increases with age in our cohort. This showed that the estimated fraction of cells carrying driver mutations increases roughly linearly with age, through the accumulation of many small clones, with the VAF of the largest clone per individual growing slowly or plateauing with age (Fig. 4a). As new driver mutations occur continuously, this observation is inconsistent with models of continued clonal growth, including exponential growth, quadratic growth (expected if clones grow only at their edges)[41] and models predicting an acceleration of selection during ageing[42]. Instead, the pattern seems more consistent with a plateauing model of clonal expansion, in which clone sizes are constrained (by cell-intrinsic or cell-extrinsic mechanisms) (Supplementary Notes 5 and 6 and Extended Data Fig. 7). This contrasts with the pattern observed in blood in which both the driver density summed across clones and the size of the largest clone increase almost exponentially with age, consistent with previous clonal haematopoiesis studies[43]. Models suggest that the slower-than-expected increase in driver density with age and the small size of epithelial clones must be major barriers to carcinogenesis (Supplementary Note 5).

Fig. 4: Mutational epidemiology in oral epithelium.

a, Mutant cell percentages for the largest clone (orange) and for all mutant clones (grey) for NOTCH1, TP53 and CHEK2 in oral epithelium, and DNMT3A and TET2 in blood, as a function of age. Error bars denote 95% CIs. b, Trinucleotide mutational spectra for (top to bottom) inferred signatures A and B, and mutations in oral epithelium from heavy-smoking heavy-drinking donors (n = 17) and non-smoking non-drinking donors (n = 224). Mutational spectra are corrected by the ratio of genomic to observed trinucleotide frequencies. c,d, Linear regressions of genome-wide signature A (c) and signature B (d) burdens in oral epithelium against donor age. e, Heatmap of associations between different measures of mutation burden, signature burden or driver density (y axis) and relevant donor metadata (x axis), inferred using linear mixed-effects regression models. The likelihood-ratio test P value of each association is indicated by both colour shading (red and blue for positive and negative associations, respectively) and asterisk labels (****q < 10−4; ***q < 10−3; **q < 0.01; *q < 0.05; dot, P < 0.05; q-values are calculated using the Benjamini and Hochberg false discovery rate method). BMI; body mass index; IPAQ, International Physical Activity Questionnaire; T2D, type 2 diabetes. f, Change in SNV burden and in dN/dS ratios for missense and truncating mutations in NOTCH1 and TP53, as a function of smoking status (never, 0 pack-years, n = 632; moderate, 0–20 pack-years, n = 283; heavy, more than 20 pack-years, n = 84). Error bars denote 95% CIs. g, Non-mechanistic (top) and mechanistic (bottom) risk models connecting predictor variables to cancer risk. Mechanistic risk models can offer insight into the impact of risk factors on mutational or clonal landscapes and may be used to predict cancer risk.

Targeted NanoSeq also provides information on mutation rates and signatures across individuals. Performing signature decomposition on all 1,042 donors, we found two dominant mutational signatures (Fig. 4b). Signature A resembles a combination of COSMIC single-base substitution (SBS) signatures SBS5 and SBS1 (94% and 6%, respectively, cosine similarity 0.90, Methods). SBS5 is a ubiquitous clock-like signature observed across tissues, believed to result from the occasional misrepair of the continuous DNA damage suffered by all cells8,9,44, whereas SBS1 results from the deamination of 5-methylcytosine. Signature A is largely responsible for the life-long accumulation of mutations in oral epithelium, with a slope of roughly 15.3 mutations per cell per year (Fig. 4c, R^2 = 0.65, P < 2.2 × 10−16). Signature B resembles COSMIC SBS16 (cosine similarity 0.97), a common signature in oesophagus and liver, associated with alcohol consumption and aldehyde metabolism3,45,46 (Extended Data Fig. 8). Signature B showed extreme variation across donors, contributing low numbers to most individuals but more than 1,000 mutations per cell in some heavy drinkers (Fig. 4d and Supplementary Note 3).

The paucity of smoking-associated signatures (SBS4 and SBS92) and APOBEC mutagenesis (SBS2 and SBS13) in oral epithelium is remarkable given their frequency in HNSC tumours, but seems compatible with a recent study of oral cancer evolution[47]. Further analyses supported their paucity, including alternative deconvolution methods, the absence of smoking-associated indel and double-base substitution (DBS) signatures (Extended Data Fig. 8d,e), and non-significant likelihood-ratio tests comparing models with and without these signatures (Supplementary Note 3). The absence of the classical smoking signature SBS4 may be explained by the low expression of CYP1A1 in oral epithelium[47], the main metabolizer of benzo(a)pyrene and perhaps by a lower exposure to tobacco mutagens of the basal stem cells in the oral squamous epithelium compared with respiratory epithelium.

Oral cancer risk factors could act by increasing mutation rates or inducing clonal expansions. To test for such mutagenic and selectogenic effects while accounting for confounders, we used multivariate mixed-effect regressions using risk factors and other metadata as covariates, and different measures of mutation rates, signatures or driver densities as outcome variables (Fig. 4e, Supplementary Note 7 and Extended Data Fig. 9). As expected, SNVs, indels, dinucleotides, signature A (but not signature B) burden and the density of all major drivers increased strongly with age. Sex was not significantly associated with differences in any of these outcome variables when correcting for confounders, despite our power to detect differences greater than 5% (Extended Data Fig. 9). This suggests that the higher incidence of HNSC in men may be mostly explained by lifestyle factors, as predicted by some epidemiological studies[48].

Tobacco smoking is a major oral cancer risk factor, and we found pack-years to be strongly associated with total SNVs, signature A and signature B, dinucleotide substitutions (but not indels), driver density across genes, NOTCH1 driver density and nominally significantly associated with three other drivers. Alcohol consumption is another major oral cancer risk factor, and we found estimated drink-years to be strongly associated with SNV and signature B burden, but not signature A burden, consistent with the known aetiology of signature B/SBS16 (Methods). Poor oral health is also an oral cancer risk factor[49], and we found that the number of missing teeth correlated with signature A burden and overall driver density. We were unable to study the effect of oral human papillomavirus (HPV) infection in our dataset, an increasingly important risk factor for oral cancer particularly in younger individuals[50], as HPV history was unavailable and sequencing-based detection of HPV yielded limited information (Methods).

The association of alcohol consumption with signature B is believed to result from DNA damage by alcohol-derived aldehydes[45]. However, the mechanistic basis for the association of smoking with signature B is less clear. Analyses in our dataset suggest that smoking increases signature B by exacerbating the mutagenic effects of alcohol consumption, consistent with epidemiological studies[51] (Supplementary Note 7). However, we cannot rule out the possibility that the association is partially caused by inaccurate self-reporting of alcohol consumption. Whereas our models also suggest that smoking and poor oral health are significantly associated with an increase in signature A/SBS5, the mechanistic bases for these associations remain unclear. Notably, these regressions suggest that 1 additional year of life causes as many mutations in the oral epithelium as roughly 2.8 pack-years or 19.1 drink-years (95% CI 2.03–3.63 and 13.9–24.3, respectively, see Supplementary Note 7 for caveats and interpretation).

More regression models can further disentangle the mutagenic or selectogenic effects of some risk factors. If a carcinogen acts solely as a mutagen without altering selection, driver density should increase proportionally to the increase in mutation burden, at least under some assumptions. Pure promoters or selectogens should alter clonal selection without changes in mutation rates, whereas dual carcinogens may alter both (Supplementary Note 7). Thus, to test for selectogenic effects, we used different regression models correcting driver density for changes in mutation rates (Supplementary Note 7). Putative selectogenic associations included an increase in NOTCH1 clones with smoking, CHEK2 with poor oral health and trends for other genes (Fig. 4e and Extended Data Fig. 9d). These associations are suggestive of promoter or selectogenic effects, but they are only correlative and further studies are needed to confirm them. We also note that we have lower statistical power to detect changes in selection per gene than changes in mutation rates (Extended Data Fig. 9e), and that our detection of selectogenesis is limited to effects on clones observed in normal oral epithelium (for example, studying selectogenesis on more advanced precancerous lesions will require other cohorts). Nevertheless, these analyses show the potential of mutational epidemiology studies to illuminate the mechanisms of action of major cancer risk factors.

Altogether, these results indicate that tobacco may contribute to early oral carcinogenesis not through classical SBS4 or SBS92 mutagenesis, but through an acceleration of SBS5 and SBS16 and changes in clonal selection. Alcohol consumption seemed to cause fewer driver mutations than expected from its increase in mutation rates. Analysis of the distribution of signature B mutations revealed that this is due to a low driver-generation potential of signature B/SBS16, as ATA>ACA or ATT>ACT signature B mutations are heavily biased towards intronic sequences (Extended Data Fig. 10a,b).

Finally, this dataset offers an opportunity to start investigating germline influences on somatic mutation rates. First, we leveraged our twin design to test for heritability. We compared the difference in mutation rates and driver frequencies between identical (monozygotic), non-identical (dizygotic) and unrelated same-age pairs of donors, while accounting for confounders (Supplementary Note 8). This provided some evidence of heritability for signature A (monozygotic versus dizygotic P = 0.004), NOTCH1 (P = 0.023) and TP53 (P = 0.018) (Extended Data Fig. 10c). Similar signals were found with more formal ACE (A, additive genetic effects; C, common/shared environment effects; E, unshared environment effects) and genomic-relatedness tests (Supplementary Table 5 and Supplementary Note 8). Second, we took advantage of having genome-wide single-nucleotide polymorphism (SNP) genotyping and complete metadata from 590 donors to evaluate the effect on the mutation landscape of 52 SNPs associated with oral cancer or clonal haematopoiesis risk. This revealed a significant association between a SNP near ALDH2 (rs4767364), a key enzyme in alcohol metabolism and the rate of signature B/SBS16 (P = 9.4 × 10−5, q = 0.02) (Supplementary Table 6 and Supplementary Note 8). This suggests that the known association between rs4767364 and HNSC risk[52] is driven by a higher mutagenicity of alcohol in these donors, consistent with the known effects of a different ALDH2 SNP (rs671) common in East Asian individuals. Finally, although the statistical power was low, we performed genome-wide association studies (GWAS) of mutation rates and driver densities for completeness, not finding convincing genome-wide significant associations (Supplementary Note 8). Altogether, these analyses suggest that germline factors can influence somatic mutation rates and clonal selection, although larger cohorts are needed to comprehensively identify these associations. We note that discovering germline mutations influencing somatic mutation rates could illuminate the mechanistic bases of SBS5 and enable Mendelian randomization for causal inference on the role of somatic mutations across common diseases.

Discussion

Building on duplex sequencing, we have developed a new version of NanoSeq that achieves accurate somatic mutation detection on single DNA molecules (with fewer than five errors per gigabase) while being compatible with whole-genome, whole-exome and deep targeted sequencing. This method greatly simplifies the study of somatic mutation rates, signatures and driver landscapes in any tissue, regardless of clonality.

Applying targeted NanoSeq to oral epithelium, we have unveiled an unprecedentedly rich landscape of selection in a normal solid tissue, with 46 genes under positive selection, more than 62,000 driver mutations and several genes under negative selection. These data also exemplify how deep single-molecule sequencing of highly polyclonal tissues can yield high-resolution maps of selection within genes. This could complement in vitro saturation mutagenesis efforts to help variant annotation for genetic diagnosis. Whereas this approach is limited to genes under selection in a tissue, a wider range of disease-relevant genes can be assayed across tissues[28].

The ability to study somatic landscapes in large sample cohorts offers several opportunities to augment traditional cancer epidemiology. First, systematic studies of the mutation landscape across individuals, case–control studies16,17 and intervention studies could help build mechanistic models connecting risk factors to mutation and clonal landscapes, and these landscapes to cancer risk (Fig. 4g). Such studies could provide insights into the mode of action of poorly understood risk factors (for example, obesity), as well as enable risk prediction or stratification. Second, studies of the mutation landscapes of normal tissues in populations with unusually high rates of certain cancers could shed light on unknown exposures, potentially helping develop prevention strategies. Third, mutation and clonal landscapes may be informative as surrogate risk markers in cancer prevention and molecular prevention trials. Although molecular prevention of cancer in the general population is rarely discussed, the discovery of simple markers of cardiovascular disease risk, such as low-density lipoprotein cholesterol and hypertension, enabled the development of statins and antihypertensive medications, which have transformed the management of cardiovascular disease[53].

Beyond cancer, somatic mutations have long been speculated to contribute to ageing and other diseases. Suggestive associations have now been found between somatic mutations in certain genes and many diseases54,55. However, systematic studies in polyclonal conditions have not been possible with available technologies. Accurate whole-exome single-molecule sequencing has the potential to enable sensitive and unbiased discovery of somatic driver mutations in any tissue and across diseases.

Methods

Cohort selection

The TwinsUK study contains around 16,000 participants. From a preselection of 4,800 donors, we invited 1,796 to participate based on several criteria, receiving buccal swabs from 1,236 donors (Extended Data Fig. 2a). The use of these samples was approved initially by the North West Research Ethics Committee (REC 19/NW/0187 and REC 24/NW/0106), and informed consent was provided by participants. To increase our statistical power to study associations with exposures, risk factors and germline factors, we included all available donors of age 80 or higher (n = 230), as many complete twin pairs as possible, smokers, individuals with obesity (BMI > 30), and individuals with available genome-wide genotyping information. We also favoured the selection of men and people of colour to reduce some of the demographic biases in the TwinsUK registry compared with the general population. To test for associations between the mutational landscape and medications or clinical histories, we favoured the inclusion of individuals with a history of cancer (including all donors with a history of oral cancer, n = 12) or a self-reported treatment history including tamoxifen, immunosuppressants, metformin, aspirin or ibuprofen. 194 samples were excluded from analysis based on several sequencing quality metrics, leaving a total of 1,042 samples in the study. Exclusion criteria included: removal of contaminated samples with either human (n = 17) or non-human (n = 132) DNA, exclusion of samples with mean duplex coverage lower than 50 dx (n = 79) and exclusion of swabs with genotyping information not matching the pre-existing genotyping information from TwinsUK (n = 7) (Extended Data Fig. 2a).

From the final 1,042 donors in the study, we also selected 380 individuals with archival whole-blood DNA available for sequencing in the TwinsUK BioBank. In total, 371 samples passed quality controls for study inclusion (Extended Data Fig. 2a). The selection of blood donors was based on several criteria: 12 donors (and their twins) treated with metformin, 30 donors (and their twins) with the highest mutation burden per year in the buccal swabs, 25 donors (and their twins) with the highest driver fractions, 25 donors (and their twins) with the lowest driver fractions, 25 donors with high driver fractions in the buccals for known clonal haematopoiesis drivers (TET2, SF3B1, DNMT3A) and 5 donors with high driver fractions in the buccal swabs for each of the following drivers: PPM1D, ASXL1 and NOTCH3. The remaining twin pairs were sampled randomly.

Metadata

Metadata were provided by TwinsUK, obtained through periodical questionnaires that were collected longitudinally for most donors. For each participant, TwinsUK provided age, sex, height, weight, BMI, twin zygosity and ethnicity. A few self-reported zygosities were corrected based on genotyping information. Self-reported medication histories were also obtained from questionnaires, however, these are expected to be incomplete. Further information on history of herpes labialis and a short list of prespecified treatments was provided by TwinsUK from anonymized medical records: metformin, tamoxifen, rapamycin, aspirin, non-steroidal anti-inflammatories and immunosuppressants. Cancer history was provided and coded as: 0 (no cancer), 1 (non-melanomatous skin cancer), 2 (other cancer) and 3 (oral cancer).

For major oral cancer risk factors and other relevant variables, we processed available questionnaires further to obtain summary metrics, including: tobacco smoking, alcohol consumption, physical activity, weight, height, BMI, oral hygiene, gastro-oesophageal reflux, diabetes, history of cancer and medication histories.

Smoking and alcohol consumption

Self-reported smoking and alcohol consumption was collated from 14 periodical questionnaires. We focused on the most recent questionnaires due to the relevance of the questions asked in them and the coverage of answers across individuals. For smoking, we kept the maximum value of reported pack-years per donor across questionnaires. As standard, 1 pack-year was defined as 365 packs of cigarettes (7,300 cigarettes). For alcohol intake, self-reported current weekly consumption was available for most donors, but self-reported information on lifetime consumption was only available for a few donors. An estimate of drink-years was calculated by multiplying the average current weekly alcohol consumption, across several questionnaires if available, by the duration of adult life (age minus 18). We note that this estimate is an extrapolation and should be used with caution, but regression models suggest that this estimate was more explanatory than self-reported lifetime consumption (see Supplementary Note 7 for analyses on alternative metrics).

Oral health

Self-reported information on gingivitis, periodontitis and gum bleeding was only available for a few donors. By contrast, the number of natural teeth remaining was available for most donors, recorded as an ordinal variable. For ease of interpretation in the regression models, we inverted this variable to reflect the number of missing teeth, as follows: 0, 20 or more natural teeth; 1, 10–19 natural teeth; 2, 1–9 natural teeth and 3, no natural teeth. Where several answers were available from questionnaires on different years, the lowest number of natural teeth left was used.

BMI, weight and height

Weight and height were provided by TwinsUK for most donors. Both metrics were averaged across questionnaires for each donor. BMI was calculated using the standard formula: weight/(height^2).

Buccal swab processing and sequencing

Puritan buccal swab kits with instructions for self-collection were posted to the homes of voluntary donors by TwinsUK (CamBio, CA-1723-H100). Kits contained a primary and secondary plastic container, an outer rigid container (Alpha Laboratories, RF95-LL1) and a prepaid return envelope. Participants mailed their buccal swabs directly to the Wellcome Sanger Institute. Swabs were refrigerated at 4 °C on arrival.

To extract DNA, buccal cells were dissociated into 1 ml of PBS solution in an Eppendorf tube through manual agitation for 1 min. The swab tip was then cut with scissors and left in the tube for 30 min before removal. The solution was then centrifuged at 1,000g for 1 min. The supernatant was removed leaving a cell pellet with minimal residual PBS (less than 100 μl). The QIAamp DNA Micro Kit (QIAGEN, 56304) was used for cell lysis and DNA extraction. First, 180 μl of buffer ATL and 20 μl of proteinase K were added to the resuspended cell pellet, followed by overnight incubation on a thermomixer at 56 °C and 800 rpm. DNA extraction followed the manufacturer’s protocol with several modifications: centrifugation steps were all performed at 20,000g, DNA was eluted in 50 μl if buffer EB (10 mM Tris-Cl, pH 8.5) (QIAGEN, 19086), incubation with the first elution step was for 5 min, and the eluent was passed through the spin column for a repeat elution into a DNA LoBind 1.5 ml tube (Eppendorf, 0030108051). The extracted DNA was quantified using a Qubit High Sensitivity and then stored at −20 °C before 40 μl of the thawed sample being diluted to a final volume of 120 μl with buffer EB (QIAGEN, 19086) and submitted for NanoSeq library preparation on an Abgene AB0800G plate (Thermo Fisher Scientific, AB0800G).

A detailed description of the targeted NanoSeq and standard duplex sequencing library preparation protocols is provided in Supplementary Note 1.

Mutation calling

Sequencing data were mapped to the human genome (GRCh37, hs37d5 build) with BWA-mem[57] as described before[8]. Bases were called when there was duplex consensus with at least two reads per original strand, requiring a minimum consensus base quality score of 60, a VAF lower than 0.1 in the matched normal, a minimum AS-XS of 10 (below), no more than an average of 3 mismatches per read (or 4 if a variant is called), a minimum coverage of 25× in the matched normal and trimming 8 bp from each read end. We note that by counting all mutant bases and all reference bases in each duplex molecule, NanoSeq implicitly considers the VAF of each mutation to calculate mutation burdens. This makes NanoSeq robust to differences in clonal composition across samples. Compared with ref. [8], instead of sequencing independent matched normals to filter out germline variation, we took advantage of the high coverage and polyclonality of the buccal swab samples to remove germline SNPs by filtering out variants with VAF ≥ 10%. We note that this is adequate as long as the samples are highly polyclonal. Relaxing this cut-off to VAF ≥ 30% did not seem to recover genuine mutations in the buccal swabs but led to an increase in mapping artefacts. Because all blood samples had matching buccal swab data, somatic mutations in blood were called using their buccal swabs as matched normals, excluding as probably germline any variants with VAF ≥ 10% in the buccal swabs.

A significant modification in the targeted NanoSeq calling pipeline compared with our published RE-NanoSeq pipeline is the relaxation of the AS-XS threshold from 50 to 10. AS-XS measures the difference in mapping quality between the primary and secondary alignments, excluding regions with ambiguous mapping from analysis. For mutation burden and signature analyses with whole-genome NanoSeq, we previously recommended a strict AS-XS cut-off to minimize the impact of mapping artefacts[8]. However, for driver discovery it is important to preserve regions with less unique mapping qualities. Using a list of 1,152 oncogenic hotspots from TCGA and MSKCC provided by the dNdScv package[22], we noticed that the original AS-XS cut-off would have filtered out a significant number of them. Reducing the AS-XS cut-off from 50 to 10 ensured the retention of duplex coverage on nearly all canonical cancer hotspots while still ensuring accurate mutation rates and signatures in control cord blood samples (Extended Data Fig. 2l–o).

Two extra filters are important to avoid recurrent mapping artefacts and to minimize the effect of inter-individual contamination. First, a ‘SNP+noise’ mask containing common germline SNP sites and recurrent mapping artefacts was generated for targeted NanoSeq as described before[8]. Second, we noticed that mapping errors not captured by this mask can manifest as recurrent artefacts where the mutant base is often seen at specific positions within a read. This can be caused, for example, by mismapping of reads from polymorphic segmental duplications. A Kolmogorov–Smirnov test on the position of the mutant bases within reads was applied to remove recurrent artefacts after mutation calling. Indels were also filtered out if their overlap with the ‘SNP+noise’ mask was 50% or greater, if they occurred at sites without a base called, if they had a VAF > 0.1 or if they were seen in more than 50 samples. This only removed a small number of artefactual indel sites, which also had a strong read positional bias.

Duplex VAFs and unbiased VAFs

The VAF represents the proportion of reads at a specific site carrying a variant, relative to the total reads at that site. When working with standard duplex sequencing or targeted NanoSeq data, only a fraction of read bundles reach the ‘2 + 2’ requirement for duplex calling (that is, read families with at least two reads from both strands). We can then calculate three separate VAFs: (1) the ‘duplex VAF’, defined as the fraction of callable (2 + 2) read bundles supporting a given mutation, (2) the ‘BAM VAF’, calculated using the deduplicated BAM file containing one representative read per read bundle (and including calling and non-calling read bundles) and (3) the ‘unbiased BAM VAF’, calculated using the deduplicated BAM file but excluding calling read bundles.

These VAFs can be used for different purposes. (1) Estimation of the fraction of cells in a sample carrying a specific mutation. If a mutation was discovered in a sample using duplex (2 + 2) reads, duplex VAFs or BAM VAFs tend to overestimate the fraction of cells carrying the mutation in the sample due to the discovery bias resulting from the inclusion of reads used for mutation calling. For this purpose, ‘unbiased BAM VAFs’ provide an unbiased estimate of the VAF of a mutation in the sample as they are calculated from reads not used for duplex calling. (2) Estimation of the fraction of cells carrying somatic mutations in a given gene. The molecules that reach duplex calling (2 + 2) in a targeted NanoSeq experiment represent a random sample of all copies of a gene in a population of cells. The duplex VAF for a given site represents the fraction of mutant molecules at the site. If we assume that all (or nearly all) cells are diploid and that cells carry at most one driver mutation per gene (heterozygous), then we can estimate the fraction of cells with mutations in a given gene by summing the duplex VAF (vd) of mutations across all sites in the gene (F = 2Σvd). If we assume that cells may carry up to two mutant copies of the gene per cell or if we are looking at a haploid region of the genome (for example, the X chromosome in male individuals), we can estimate the fraction of mutant cells in the sample using the sum of duplex VAFs across all sites in the gene (F = Σvd). Some genes, such as NOTCH1 in squamous epithelia can show biallelic loss by one mutation in each allele (SNVs or indels) or by one mutation and a copy number change (either a deletion or a copy-neutral loss of heterozygosity). We have previously shown that for these conditions, as well as for populations with mixtures of heterozygous and homozygous mutant cells, the fraction of mutant cells in the population falls within the range [Σvd, 2Σvd] (ref. [2]). Unless described otherwise, other references to the fraction of mutant cells for a given gene assume a maximum of one driver mutation per cell and a largely diploid population.

As not all non-synonymous mutations in a driver gene are driver mutations[22], to estimate the fractions of cells with driver mutations (Figs. 1d and 2e), we multiplied the estimated fraction of cells with non-synonymous mutations by the estimated fraction of mutations that are drivers for each class. We estimated the fraction of mutations that are drivers using (ω − 1)/ω, for mutation classes with ω ≥ 1 (where ω is the dN/dS ratio per mutation type per gene). To account for potential differences in clone sizes for driver mutations, we used dN/dS ratios calculated without collapsing mutations reported by many molecules into single entries to dNdScv (Supplementary Code).

Epithelial purity and targeted methylation

To quantify the epithelial fraction of a representative set of buccal swabs, we used two approaches: (1) targeted enzymatic methylation sequencing on 187 buccal swabs, and (2) comparing the VAFs of clonal haematopoiesis mutations in the buccal swabs of donors with blood and buccal swab data.

From 187 swabs, we generated low-input enzymatic methylation libraries and then undertook targeted capture with a panel of informative CpG sites, using the NEBNext Enzymatic Methyl-seq Kit (NEB, E7120L). We used a custom Twist Bioscience hybridization panel targeting 1,162 CpGs selected from the centEpiFibFatIC.m, centDHSbloodDMC.m and centEpiFibIC.m matrices in the EpiDISH R package[58], to deconvolute epithelial, fibroblast, fat and blood cell types. We also targeted 353 CpG from the original Horvath clock[59] and 50 CpGs in the promoters of 25 driver genes. The design is available in Supplementary Table 2.

For each sample, DNA was quantified and normalized to roughly 1 ng μl−1. Normalized DNA samples were then sheared with the NEBNext UltraShear fragmentation mix (NEB, M7634L), end-repaired, A-tailed, adapter-ligated with a methylated TruSeq-compatible adapter stub (all using NEB Ultra II reagents) and, after a SPRI (solid-phase reversible immobilization) clean-up, the resulting libraries were oxidized using TET2 (converting methylcytosines to carboxylcytosines) and deaminated using APOBEC (converting bare cytosines to uracils but retaining the carboxylcytosines, thus preserving the locations of methylation marks). The deaminated libraries were amplified, and sequencing indexes (and the rest of the adapter sequence) were introduced using NEB Q5U and the Sanger Institute’s UDI primers. After a further SPRI clean-up, libraries were requantified and mixed in an equimolar pool with a cumulative DNA mass of 1–4 μg. Twist Bioscience probes targeting the sequences of interest were then added. After evaporating all the liquid, the probes hybridized to the DNA and the targets were pulled down and cleaned up (using Twist fast hybridization reagents and Thermo DynaBeads MyOne streptavidin-coupled beads). After a final PCR amplification (KAPA HiFi) and SPRI clean-up, a pool of all samples underwent quality control by Agilent Bioanalyser and sequenced in a single S4 lane of Illumina NovaSeq 6000.

Epithelial, fibroblast and blood cell fractions were estimated using EpiDISH and hEpiDISH[58]. The latter allows hierarchical deconvolution, first relying on centEpiFibIC to estimate epithelial, fibroblast and blood fractions, and applying centDHSbloodDMC to deconvolute the different types of blood cell. The median epithelial fraction across all 187 swabs was 95.1% (Extended Data Fig. 2h). Most of the non-epithelial cells were neutrophils, probably a result of saliva contamination of the buccal swabs.

As a complementary analysis of blood contamination in the buccal swab samples, we compared the VAF of blood mutations in buccal swabs. To do so, we used 43 pairs of buccal and archival blood samples in which the date of collection of the blood sample was within 3 years of the buccal swab, and which contained at least 1 large clone in blood (VAF ≥ 1%). The median of the ratio of buccal VAF to blood VAF for 58 blood mutations that met these criteria was 0.076, which provides an alternative estimate of the median blood contamination in these samples around 7–8%.

Removal of DNA contamination

The ability of NanoSeq to detect somatic mutations in single molecules of DNA makes it particularly sensitive to DNA contamination, either from other humans (calling germline SNPs from the contaminant individual as somatic mutations in the affected sample) or from other species with sufficient conservation to map to the human genome (which is more likely in targeted NanoSeq due to the higher conservation of coding regions).

Human DNA contamination

We have previously shown[8] that when analysing whole-genome NanoSeq data, the percentage of contaminating DNA can be estimated using verifybamID[60]. However, we found verifybamID to be unreliable for targeted NanoSeq data. To qualitatively detect human DNA contamination on targeted NanoSeq data, a useful metric is the fraction of all substitutions filtered by the ‘SNP+noise’ mask. Although useful, this metric may not be reliable for samples with low duplex coverage and few mutations. As a complementary approach, we genotyped common SNPs in targeted regions to identify homozygous alternative (non-reference) SNPs. Presence of reference bases at these sites is indicative of contamination. Although this is not a direct estimate of the percentage of contamination given the difficulty of determining the genotype of the contaminant at those alternative homozygous SNPs, it can serve as a sensitive indicator of inter-individual DNA contamination.

We called SNPs with bcftools[61] using the following commands: bcftools mpileup --max-depth 20000 -Ou -f $genome $bam | bcftools call --ploidy GRCh37 -mv -Ob -o BCFTOOLS/$OUT_PREFIX.calls.bcf; bcftools view -i ‘%QUAL > = 100’ BCFTOOLS/$OUT_PREFIX.calls.bcf > BCFTOOLS/$OUT_PREFIX.calls.filtered.vcf.

For the assessment of contamination, we restricted the analysis to SNPs overlapping both our SNP mask and our targeted panel. We used bam2R (from the deepSNV R package)[62] to obtain the number of reads supporting the alternative and reference alleles, and kept SNPs with a mean coverage across samples greater than 200×. For each SNP in each sample, the genotype was set to ‘NA’ if the coverage was less than 20×, to alternative homozygous (1/1) if the VAF was greater than 0.8, to heterozygous (0/1) if the VAF was between 0.3 and 0.7, and to reference homozygous (0/0) if the VAF was less than 0.1. Finally, we only kept SNPs seen in 2 or more samples and in fewer than 1,000 samples. For each homozygous SNP, we calculated the reference fraction and we report the median across all homozygous SNPs in the sample. We considered 17 samples with a median reference base VAF > 0.01 at non-reference homozygous SNP sites to be contaminated and excluded them from all further analyses (Extended Data Fig. 2d).

Cross-species contamination

Donors were requested to rinse their mouths before buccal swab collection to minimize non-human DNA contamination from food or bacteria. However, some samples showed evidence of non-human DNA contamination, which resulted in mismapping of non-human DNA reads to the human genome, detectable as an excess of clustered synonymous mutations. To systematically identify these samples, we used Kraken v.2 (ref. [63]), using 1 million unmapped reads per swab and a database of potential sources of contamination able to map to the human genome: Mus musculus, Bos taurus, Ovis aries, Sus scrofa, Equus caballus, Oryctolagus cuniculus, Meleagris gallopavo and Gallus gallus. Bacterial contamination should not be a problem given their sequence divergence from the human genome. In addition, for each sample we calculated the global dN/dS ratio across passenger genes, and compared the contamination fractions estimated with Kraken with the observed dN/dS ratios. dN/dS ratios decrease with non-human contamination because of evolutionary conservation of non-synonymous sites. On the basis of the impact of contamination on dN/dS ratios (Extended Data Fig. 2e), we excluded from further analyses 132 samples with more than 0.25% of non-human unmapped reads.

HPV detection and characterization

The genome sequence of 19 HPV types considered high-risk[64] were retrieved from GenBank. We built a multiple sequence alignment of these genomes with MAFFT[65] using Jalview[66]. Based on conservation across these highly divergent HPV strains, we retained roughly 3,000 bp for each of the strains to design HPV-specific probes that we included in our Twist target gene panel. The GenBank accession numbers of the 19 selected HPV types were: KU298887.1, KU298893.1, KU298928.1, KX514417.1, KX514421.1, KX514431.1, KY225967.1, LR862061.1, LR862064.1, LR862079.1, MT218010.1, MT783412.1, MT783416.1, MT783417.1, MZ374448.1, MZ509108.1, NC_001357.1, NC_001526.4 and NC_001583.

Once our targeted sequencing data were mapped to the human genome we retrieved the unmapped reads and remapped them to the genomes of the 19 HPV strains using BWA-mem[57]. Mapping results were reviewed manually to distinguish between unreliable mappings (very repetitive, low-complexity, soft-clipped reads) and probably true HPV sequences. For ambiguous cases, we searched the mapped read with BLAST against the National Center for Biotechnology Information’s non-redundant nucleotide database. This allowed us to identify some hits to HPV strains not originally covered in our panel.

We detected HPV in 12 samples, in some cases supported by thousands of reads while in others by as little as one single read. Several HPV strains were detected. The following (anonymized) list of donors show the results: X1 donor (HPV 16, 44 reads), X2 (HPV 53, 6 reads), X3 (HPV 33 and HPV 58, 20 and 5 reads), X4 (HPV 33, 115 reads), X5 (HPV 53, 6667 reads), X6 (HPV 59, 12 reads), X6 (HPV 56, 707 reads), X7 (HPV 51, 236 reads), X8 (HPV 56, 1 read), X9 (HPV 21 not in panel, 2 reads), X10 (HPV 24 not in panel, 1 read), X11 (HPV 30, not in panel, 1 read) and X12 (HPV 33, 6 reads).

Given that only 12 out of 1,042 samples had detectable HPV presence using the targeted capture and that this is not a validated assay for HPV detection, we were unable to study the impact of HPV on the mutation and selection landscape in the oral epithelium, which remains an important question for future studies. Instead, we excluded these 12 samples from the epidemiological regression analyses to reduce the risk of confounding effects.

Germline genotyping

Genotyping array data

Pre-existing array genotyping data from TwinsUK were used for GWAS and other analyses. The samples had been genotyped with the following arrays: HumanHap300, HumanHap610Q, 1M-Duo and 1.2MDuo 1M. Following genotype calling, some samples were excluded from analyses involving genotyping data based on different criteria: a sample call rate less than 98%, heterozygosity across all SNPs that were 2 or more standard deviations from the sample mean, evidence of non-European ancestry as assessed by principal components analysis comparison with HapMap3 populations, observed pairwise identity by descent probabilities suggestive of sample identity errors. We also used identity by descent probabilities to correct misclassified zygosity. We then excluded SNPs using the following criteria: Hardy–Weinberg P < 10−6, assessed in a set of unrelated samples; minor allele frequency of 1%, assessed in a set of unrelated samples; SNP call rate less than 97% (SNPs with a minor allele frequency of 5% or more) or less than 99% (for 1% less than or equal to minor allele frequency of less than 5%). Following genotype and sample filtering, the data were imputed using the Haplotype Reference Consortium reference panel and SNPs with an imputation R^2 < 0.5 were excluded.

Germline genotyping from sequencing data

For analyses relying on common SNPs, we called SNPs using bcftools as described in the DNA contamination section. For analyses relying on both common and rare SNPs we run GATK’s HaplotypeCaller (v.4.0.1.2)[67], using default options, setting ploidy to 2 except for the male chromosome X (haploid) and providing dbSNP v.141 (ncbi.nlm.nih.gov/snp, ref. [68]) for annotation of the calls. The resulting VCF files were intersected with our panel regions using bedtools[69] and missing genotypes were annotated as REF with bcftools +missing2 (ref. [61]), on the basis of the high coverage available.

Selection analyses

A detailed description of the methods used to analyse positive and negative selection in this study is provided in Supplementary Note 2. This includes a description of the new one-sided tests in dNdScv, the use of duplex coverage correction in dNdScv, estimates of the number of driver mutations in the dataset and a description of dN/dS analyses at the level of single sites and groups of functionally related sites within genes.

Mutation burdens and signatures

Mutation burden is defined as the number of mutations per base pairs in a given region, and it is calculated in NanoSeq data as the number of mutant bases divided by the total number of bases sequenced with duplex information. Estimating mutation rates from targeted data can have several challenges. First, the mutation burden of a given region of the genome will be affected by its sequence composition. We can remove this confounding effect by correcting mutation burdens by the trinucleotide frequencies of a targeted region (relative to the whole genome) and the mutability of each trinucleotide, as described before[8]. Whereas this corrects for the effect of different sequence composition, it does not correct for a systematic difference in the mutability of different regions, such as genic and intergenic sequences, as explained in the text. RE-NanoSeq (and the full whole-genome NanoSeq protocols introduced in this study) can be used for an unbiased genome-wide measurement of mutation burdens (for example, Extended Data Fig. 4a–f and Supplementary Note 3). Second, when estimating mutation rates from gene sequences, particularly from panels of positively selected genes, positive selection can lead to an inflation of the apparent mutation rate. To avoid this, the mutation burdens described in this paper were estimated only from passenger genes. Synonymous sites can also be used as a proxy for the neutral mutation rate, as described before[2]. Finally, mutation burdens estimated from targeted regions can be inflated or deflated by the undue influence of one or a few large clones. For example, if a sample is dominated by a large clone, the presence of a passenger mutation in the clone overlapping the target region would lead to an overestimation of the mutation burden, whereas the absence of any mutation in the clone in the target region would lead to a modest underestimate of the burden. This is apparent in the targeted NanoSeq data for blood (Fig. 1c), in which a few samples show inflated burden estimates due to high VAF passenger mutations. Some duplex sequencing studies avoid this by counting each mutant site only once, but this leads to a systematic underestimation of mutation burdens, leading to lower bound estimates of the mutation burden. Instead, for the targeted NanoSeq blood data, we calculated the CIs for the mutation burden using Poisson bootstrapping of the mutant sites, resulting in wider CIs when one or a few sites had an undue influence in the burden estimate. In general, burden estimates from targeted NanoSeq are expected to be most reliable when working with highly polyclonal samples or when the size of the panel is considerably larger than the inverse of the mutation rate per base pair.

We inferred mutational signatures of SBS using the sigfit (v.2.1.0) R package[70]. Genome strand information for each target gene was used to produce transcriptional strand-wise (TSW) trinucleotide mutation catalogues (192 mutation categories) for mutations within genes, using the build_catalogues function in sigfit. Inference was performed for a range of signature numbers (N = 2,…,5), using the TSW mutation counts from 92 oral epithelium samples having 500 or more mutations each. To account for variation in sequence composition, observed mutation opportunities (trinucleotide frequencies based on the NanoSeq coverage per site for each sample) were supplied to the extract_signatures function. Mutation opportunities were assumed to be equal between the transcribed and untranscribed strands. The best-supported number of signatures, on the basis of overall goodness-of-fit and consistency with known COSMIC signatures (v.3.0; cancer.sanger.ac.uk/signatures), was found to be N = 2. Of the two inferred signatures, signature A corresponded to a combination of COSMIC signatures SBS1 (6%) and SBS5 (94%) (cosine similarity 0.90), whereas signature B was highly similar to COSMIC SBS16 (cosine similarity 0.97). To estimate the contribution of both signatures to all oral epithelium samples, these two signatures were fitted to the TSW mutation counts for each sample using the fit_signatures function. Signature burdens (mutations per diploid genome attributed to each signature) were calculated by multiplying the signature exposure estimates by the whole-genome passenger mutation burden estimates for each sample. Before plotting using the plot_spectrum function, signatures were transformed to a genome-relative representation by scaling their probability values according to the corresponding whole-genome human trinucleotide frequencies, using the convert_signatures function.

A high rate of T>C mutations at ApT dinucleotides is common to the COSMIC SBS5 and SBS16 signatures. To explore whether these T>C mutations are caused by similar underlying processes, we studied the extended (pentanucleotide) sequence context of T>C mutations in several datasets. To do so, we obtained TSW pentanucleotide counts for T>C substitutions (256 mutation categories) applying a custom R function to mutations in the following sample sets: (1) matched blood samples (n = 371); (2) hepatocellular carcinoma (Liver HCC) samples from the Pan-Cancer Analysis of Whole Genomes study[71] (downloaded from dcc.icgc.org/pcawg) for which signature fitting estimated a COSMIC SBS16 exposure greater than 0.2 (n = 4); (3) oral epithelium samples with signature B exposure greater than 0.25 (n = 121) and (4) oral epithelium samples with signature B exposure less than 0.25 (n = 921). Before plotting using custom R functions, pentanucleotide catalogues were transformed to a genome-relative representation by scaling mutation counts according to the corresponding whole-genome human pentanucleotide frequencies. The results of this analysis are described in Extended Data Fig. 8 and Supplementary Note 3.

Mutation catalogues of DBSs (78 mutation categories) and indels (83 mutation categories) were produced for mutations in the following sample sets: (1) all oral epithelium samples (n = 1,042); (2) oral epithelium samples from heavy-smoking non-drinking donors (n = 27) and (3) oral epithelium samples from non-smoking non-drinking donors (n = 224). DBS catalogues were produced using a custom R function, whereas indel catalogues were produced using the indel.spectrum function in the Indelwald tool (24 September 2021 version; github.com/MaximilianStammnitz/Indelwald). Although we attempted both de novo extraction and fitting of mutational signatures to the DBS and indel catalogues, mutation numbers were not large enough to allow inference of informative signatures or exposures. Mutation spectra for DBS and indels were plotted using the plot_spectrum function in sigfit. The results of this analysis are described in Extended Data Fig. 8 and Supplementary Note 3.

Regression analyses

To test for associations between epidemiological variables and rates of mutational signatures or driver mutation frequencies, we used mixed-effect regression models (lmer function in the lme4 R package[72]) as described below.

Outcome variables

For the analyses shown in the main text, we ran a separate regression model for each outcome variable: SNV burden, signature A burden, signature B burden, indel burden, dinucleotide burden, the sum of all driver frequencies in a sample and the driver density per sample for ten major driver genes. To avoid excessive loss of statistical power due to multiple testing correction across all outcome variables and predictors, and to focus on the genes with the highest information content, we restricted the regression analyses to 10 driver genes with ≥1,000 or more coding mutations across mutation types (missense, truncating or no-SNVs) in the dataset, and with dN/dS ≥ 5 (that is, with an estimated driver fraction of 80% or more).

Predictor variables

For the analyses in the main text, we selected nine variables as predictors in multiple regression models, including major oral cancer risk factors as well as other potentially relevant variables: age, sex (female individuals or male individuals), pack-years, drink-years, type 2 diabetes (T2D, Y/N), body mass index, missing teeth, physical activity score (International Physical Activity Questionnaire (IPAQ)) and cancer history (Y/N). The twin structure was modelled with a random effect. The R code used for these and supplementary regressions is provided in the Supplementary Code, but for illustrative purposes the structure was as follows:

$$\begin{array}{c}{\rm{lmer}}({\rm{SNVburden}} \sim {\rm{age}}+{\rm{sex}}+{\rm{packyears}}+{\rm{drinkyears}}+{\rm{T2D}}\\ \,+{\rm{BMI}}+{\rm{missingteeth}}+{\rm{IPAQ}}+{\rm{cancer}}\\ \,+(1| {\rm{familyID}}),{\rm{REML}}=F)\end{array}$$

Only samples with a mean duplex coverage across genes ≥200 dx and available metadata for all the predictor variables and the outcome variable in each multiple regression model were included for analyses. Twelve samples with potential evidence of HPV reads (above) and six samples with a self-reported history of chemotherapy were excluded from the regression. These variables may be expected to have mutagenic and/or selectogenic effects on the oral epithelium, but the number of affected donors was too low for a robust analysis in the current study.

P values were calculated for each covariate in each multivariate regression model using a likelihood-ratio test by comparing the likelihood of the full model with a model without each variable, using the drop1 function in R. Multiple testing adjustment using the Benjamini–Hochberg procedure was then applied to all P values in the main text analyses (126 tests: 14 predictors × 9 outcome variables).

Extra regression models, GWAS and heritability analyses

Extra regression analyses, including using extended medication data as predictors, interaction analyses between smoking and alcohol, and measures of selection (corrected for mutation rates) as outcome variables for the detection of selectogenic influences, are described in Supplementary Note 7. Methods and supplementary results for GWAS analyses and heritability tests are described in Supplementary Note 8.

Reporting summary

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

Data availability

Sequencing data have been deposited in the European Genome-Phenome Archive (EGA) under accession numbers EGAD00001015618 (TwinsUK_TargetedNanoSeq_Buccal), EGAD00001015619 (TwinsUK_TargetedNanoSeq_Blood), EGAD00001015620 (TwinsUK_ExomeNanoSeq_Buccal), EGAD00001015621 (TwinsUK_RENanoSeq_Buccal), EGAD00001015622 (TwinsUK_TargetedEMSeq_Buccal), EGAD00001015623 (TwinsUK_TargetedEMSeq_Blood) and EGAD00001015624 (Sanger_NanoSeq_RandD). Data access for EGAD00001015618, EGAD00001015619, EGAD00001015620, EGAD00001015621, EGAD00001015622 and EGAD00001015623 is managed by TwinsUK (EGAC00001000274) (Supplementary Table 7). Patient metadata are managed by TwinsUK. Anonymized mutational data are available in Supplementary Tables 8 and 9.

Code availability

Supporting code can be found at GitHub through https://github.com/cancerit/NanoSeq/ and https://github.com/im3sanger/dndscv, and as an accompanying R HTML MarkDown file. All analyses have been done using the human genome assembly GRCh37.

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Acknowledgements

We are grateful to members of the TwinsUK cohort for volunteering samples and metadata for this study. We thank G. Davey Smith and P. M. Visscher for advice on epidemiology and heritability analyses; B. Sexton, A. Peltan and New England BioLabs for advice during protocol development; A. Teschendorff for advice on methylation; A. P. Butler and V. Offord for assistance with establishing the bioinformatic pipeline; Y. Hooks for sample processing; K. Roberts, T. Baxter, K. Smith, N. Yilmaz, V. Uksaite, E. Ferla, H. Savin, L. Allen and C. Latimer for helping with sample shipments; and the CASM support and DNA pipeline teams at the Wellcome Sanger Institute for their essential role in data generation. This research was financed in whole, or in part, by the Wellcome Trust 220540/Z/20/A. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. I.M. is funded by Cancer Research UK (C57387/A21777), the Dr Josef Steiner Cancer Research Foundation and the Wellcome Trust. TwinsUK is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation, Zoe Ltd, the National Institute for Health and Care Research Clinical Research Network and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London.

Author information

Author notes

1. These authors contributed equally: Andrew R. J. Lawson, Federico Abascal, Pantelis A. Nicola

Authors and Affiliations

1. Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, UK

Andrew R. J. Lawson, Federico Abascal, Pantelis A. Nicola, Stefanie V. Lensing, Adrian Baez-Ortega, Natalia Brzozowska, Raul E. Alcantara, Laura O’Neill, Matthew D. C. Neville, Moritz J. Przybilla, Nicholas Williams, Diana Alexander, Luke M. R. Harvey, Thomas Clarke, Alex Byrne, Michael R. Stratton, Peter J. Campbell, Raheleh Rahbari & Iñigo Martincorena

2. Sequencing Operations, Wellcome Sanger Institute, Hinxton, UK

Stefanie V. Lensing, Sara Widaa, Siobhan Austin-Guest, Wei Cheng, Maria Morra, Lucy Sykes, Matthew Mayho & Nicole Müller-Sienerth

3. Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK

Amy L. Roberts, Julia S. El-Sayed Moustafa, Dovile Vaitkute, Belma Jakupovic, Ayrun Nessa, Samuel Wadge, Marc F. Österdahl & Kerrin S. Small

4. Human Genetics Programme, Wellcome Sanger Institute, Hinxton, UK

Georgios Kalantzis & Hilary C. Martin

5. Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK

Anna L. Paterson

6. Department of Pathology, Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK

Doris M. Rassl

7. Quotient Therapeutics Limited, Saffron Walden, UK

Raul E. Alcantara, Matthew D. Young & Peter J. Campbell

8. Early Cancer Institute, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK

Jamie R. Blundell

9. Department of Surgery, University of Cambridge, Cambridge, UK

Krishnaa T. A. Mahbubani & Kourosh Saeb-Parsy

10. NIHR Cambridge Biomedical Research Centre, Cambridge Biomedical Campus, Cambridge, UK

Krishnaa T. A. Mahbubani & Kourosh Saeb-Parsy

Contributions

A.R.J.L., F.A., P.A.N. and I.M. conceptualized the project with support from M.R.S., P.J.C., R.R. and K.S.S. A.R.J.L., F.A., G.K., A.B.-O. and I.M. led the data analysis with support from P.A.N., A.L.R., M.F.Ö., M.D.C.N., M.J.P., N.W., D.A., L.M.R.H., J.R.B. and M.D.Y. A.R.J.L., P.A.N. and S.V.L. led the experimental work with support from N.B., S. Widaa, W.C., M. Morra, L.S., M. Mayho and N.M.-S. F.A., R.E.A., T.C., A.B. and I.M. developed algorithms and software. A.R.J.L., F.A., S.V.L., S. Widaa and I.M. contributed to method development. A.L.R., J.S.E.-S.M., D.V., B.J., A.N., S. Wadge, K.T.A.M. and K.S.-P. collected samples. L.O. and S.A.-G. helped with sample and project administration. A.L.P. and D.M.R. provided histology support. K.S.-P., H.C.M., M.R.S., P.J.C., R.R., K.S.S. and I.M. provided supervision. A.R.J.L., F.A., P.A.N., A.B.-O. and I.M. wrote the paper. All authors contributed to reviewing and editing it.

Corresponding author

Correspondence to Iñigo Martincorena.

Ethics declarations

Competing interests

I.M., M.R.S. and P.J.C are cofounders, R.E.A., M.D.Y. and P.J.C. are employees and I.M., M.R.S., N.B. and F.A. have consulted for Quotient Therapeutics Ltd. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Ruoyan Li, Pradeep Natarajan 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 Sequencing coverage and description of the cohort.

a, Integrative Genomics Viewer image[73] showing sequencing reads and coverage for three NanoSeq protocols at the TP53 locus: sonication followed by mung bean nuclease treatment NanoSeq (MB-NanoSeq, top), Ultrashear enzymatic fragmentation NanoSeq (US-NanoSeq, middle), and restriction enzyme NanoSeq (RE-NanoSeq, bottom). Reads are coloured by mapping orientation. MB- and US-NanoSeq show the coverage in merged bams containing all the cord blood samples analysed in this study, whereas RE-NanoSeq shows the coverage in 12 buccal samples. b, Evenness of coverage for the three protocols described in a. Lorenz coverage curves for three million sites sampled from the corresponding bams (locus chr17:6000000-9000000), resulting in Gini coefficients of 0.66 for RE-NanoSeq, 0.18 for MB-NanoSeq and 0.22 for US-NanoSeq. c, Genome-wide mutation burden estimates, as mutations per base pair, for adult pancreas samples formalin-fixed for 3 days (3 d) or 17 days (17 d), and sequenced using four different protocols (sonic refers to sonication and enz to enzymatic fragmentation). Error bars show Poisson 95% CIs. Horizontal lines denote the burden and the associated Poisson 95% CIs in a matching fresh-frozen sample.

Extended Data Fig. 2 Targeted NanoSeq study design and quality metrics.

a, Flow diagram describing the selection of the donor cohort used in this study. b, c, Distribution of zygosity, sex, age, smoking (pack-years) and drinking (drink-years) values for (b) buccal swab (n = 1,042) and (c) archival blood sample donors (n = 371). d, Identification of samples contaminated with human DNA from another individual, comparing the proportion of mutation calls falling in the ‘SNP+noise’ mask versus the median fraction of reference bases at alternative homozygous SNPs; point size is proportional to the duplex coverage. The vertical dashed line indicates our exclusion criterion for human contaminated samples (>0.01). e, Identification of samples contaminated with non-human DNA, comparing the dN/dS values in passenger genes versus the percentage of unmapped reads mapping to a set of potential contaminant species. Horizontal dashed line indicates neutral dN/dS=1. Red points indicate samples with upper bound 95% CI dN/dS ratio <1. Vertical dashed line shows our exclusion criterion for non-human contaminated samples (>0.25). f, Histogram of duplex coverage (dx) in the buccal swab cohort, at on-target and near-target regions. Vertical dashed line shows our exclusion criterion for low coverage samples (<50dx). g, Distribution of the mean deduplicated coverage (×) in the buccal swab cohort, at on-target and near-target regions. Raw sequencing coverage is ~6.6 times higher due to the average 85% duplicate rate required for duplex consensus calling. h, Estimation of epithelial fraction in buccal swab samples by targeted enzymatic methylation sequencing. Vertical dashed line shows the median epithelial fraction of 0.95. i, Sequencing quality metrics including the on-target capture fractions, estimated excess in strand drop-out (SDO), and the achieved duplicate rates for the buccal swab cohort. Random binomial sampling is expected to cause lack of coverage in one of the DNA strands in a proportion of cases. We estimated the excess in SDO by subtracting the observed and expected SDOs. Box plots show the interquartile range, median, 95% confidence intervals and outliers as dots for the buccal cohort (n = 1,042). j, Relationship between duplicate rates and sequencing efficiency, measured as the number of bases with duplex support divided by the total number of bases sequenced. k, Relationship between duplicate rates and sequencing efficiency after factoring in the on-target fraction (t) and the excess in strand drop-out (SDO). l, Number of duplex calls as a function of the primary alignment score minus secondary alignment score (AS-XS) threshold. m, Substitution burdens calculated within each AS-XS threshold corrected for trinucleotide context. Error bars for substitution burdens indicate Poisson 95% CIs. n, o, Hotspots covered by different AS-XS thresholds, shown as (n) total number of hotspots and (o) their aggregated frequency in TCGA. Horizontal dashed line indicates the detection of all studied hotspots.

Extended Data Fig. 3 Further description of the blood driver landscape.

a, Numbers of total mutations, coding SNVs and coding indels identified in whole blood samples from 371 donors using targeted NanoSeq. b, Trinucleotide mutational spectra of adult haematopoietic stem and progenitor cell (HSPC) colonies from a previous study[56] and whole blood samples sequenced using targeted NanoSeq (corrected by the ratio of genomic to observed trinucleotide frequencies). c, Distribution of (log_10-scaled) unbiased VAFs for mutations with sequencing depth ≥2000× identified in 371 whole blood samples using targeted NanoSeq. Unbiased VAFs are calculated from read bundles not used for duplex variant calling. d, Relationship between donor ages (years) for matched buccal swab samples and archival blood samples (n = 371). The diagonal line represents the identity function, y = x. e, Venn diagram summarising the overlaps between three approaches for identifying genes under significant positive selection by dNdScv in the archival blood targeted NanoSeq data. f, Ratio of estimated driver densities between buccal swab samples and archival blood samples, for 10 genes identified as being under gene-level significant positive selection in both blood and buccal swab samples. g-j, Mutation barplots for DNMT3A, TET2, CBL and MYD88. The x-axis represents coordinates along the coding sequence. Exons and protein domains are indicated along the x-axis. The y-axis represents number of mutations, either in the 371 TwinsUK archival blood samples used in this study (top) or in lymphoid and haematopoietic neoplasm samples (whole-exome and whole-genome) from the COSMIC database (bottom). Mutations are coloured according to mutation consequence category. Grey shading indicates cumulative duplex coverage across TwinsUK archival blood samples.

Extended Data Fig. 4 Restriction enzyme NanoSeq and targeted NanoSeq on buccal swabs.

a, Regression of SNV mutation burden with age for 12 samples selected from across the age range (black dots), three samples with high SigB signature contribution (grey), and one sample with a high mutation burden from a donor with a history of CHOP chemotherapy treatment (orange). The regression results listed within the plot were generated using only the 12 samples randomly selected for their age range. Error bars show Poisson 95% CIs for the estimated burdens (substitutions per cell). P-values calculated with t-test and 2 degrees of freedom. b, Transcription-coupled repair and damage in three donors with high contribution of SigB. Estimated substitution burdens plus their associated 95% CIs (error bars) across upstream, transcribed and downstream regions, showing T > C and A > G in the coding strand separately. c, Number of substitutions per Mbp per year in 12 age range donors for each of 10 major ENCODE chromatin states. Reference chromatin states were obtained from ENCODE E057 foreskin keratinocytes. Chromatin states BivFlnk, EnhBiv, TssBiv, TxFlnk, and ZNF/Rpts were removed given their smaller footprint and too large confidence intervals. Burdens were normalised to whole genome trinucleotide frequencies. Error bars show Poisson 95% CIs. d, Number of substitutions per Mbp per year in 3 donors with strong SigB exposure for each of 10 major ENCODE chromatin states. Only T > C rates are shown, calculated as the number of T > C substitutions observed and divided by the number of [TA] bps. Error bars show Poisson 95% CIs. e, Cosine similarities between the observed and reconstructed substitution profiles as a function of the number of mutations in each sample, highlighting the outlier donor with a history of CHOP chemotherapy treatment (brown). f, Transcriptional strand-wise trinucleotide SBS spectrum for the outlier CHOP donor. g, Numbers of total mutations, coding SNVs and coding indels identified in oral epithelium samples from 1,042 donors using targeted NanoSeq. h, Numbers of non-synonymous mutations identified by targeted NanoSeq per donor in oral epithelium for genes NOTCH1, TP53, FAT1 and NOTCH2. Mutation counts are ordered independently for each gene.

Extended Data Fig. 5 Full driver landscape in oral epithelium.

a, For the 49 significant driver genes in oral epithelium (of which DNMT3A, TET2 and FOXP1 are likely attributable to low-level blood contamination, as shown in Extended Data Fig. 3f), panels show (top to bottom) mutation counts per mutation consequence category, dN/dS ratios per mutation consequence category (horizontal line indicates neutral dN/dS=1), estimated mutant cell percentages in donors aged 65-85, and the distribution of unbiased VAFs. b, Spearman’s correlation and associated P-values between the generalised linear model residuals for driver burden across top driver genes (defined as genes with >1,000 driver mutations across mutational classes with >80% estimated driver fraction). c, Venn diagrams summarising (top) the overlaps between four approaches for identifying genes under significant positive selection genes in TwinsUK oral epithelium by dNdScv, and (bottom) driver genes in TwinsUK oral epithelium samples and head and neck squamous cell carcinomas (HNSC) in The Cancer Genome Atlas. d, Left. Estimated mutant cell fraction in oral epithelium per donor for 12 genes. Each dot represents one donor in the dataset (restricted to 65-85 year old donors, n = 583). The estimated mutant cell fraction represents an upper bound estimate using the sum of duplex VAFs multiplied by 2 for genes in diploid chromosomes (see Methods for an explanation of the assumptions and rationale). This analysis takes into account all non-synonymous mutations observed in each gene in each donor. The red line represents the median estimated cell fraction across donors. Right. Observed VAFs (shown as percentages) for all non-synonymous SNVs in each gene in each donor, restricted to sites with ≥1000× coverage (most values < 0.01% had unbiased VAFs = 0). Each dot represents one mutation. This highlights that the vast majority of mutations observed across genes have very low VAFs, with only a small number of mutations having VAFs>2% (largely in clonal haematopoiesis genes and a few in TP53). e, Comparison of dN/dS ratios per gene between healthy oral epithelium (top) and HNSC (bottom). f, Observed missense (top) and truncating (bottom) dN/dS ratios per gene for the 239 genes in the dataset, with 95% confidence intervals, showing that dN/dS ratios are close to 1 for the vast majority of genes, with only a small minority of genes showing clear negative selection.

Extended Data Fig. 6 Distribution of mutations within selected buccal driver genes.

a-i, Mutation distribution within nine selected genes. The x-axis represents coordinates along the coding sequence. Exons and protein domains are indicated along the x-axis. The y-axis represents number of mutations, either in the 1,042 TwinsUK oral epithelium samples used in this study (top) or across whole-exome and whole-genome samples of any cancer type in the COSMIC database (bottom). Mutations are coloured according to mutation consequence category. Grey shading indicates cumulative duplex coverage across TwinsUK samples. j, Diagrams of the 3-dimensional structure of CHK2 (encoded by CHEK2), showing the clustering of sites under significant positive selection on one side of the forkhead-association domain. Residues with site-level dN/dS q-value < 0.01 are coloured. Shading intensity denotes degree of significance. k, Fraction of significant genes (top) and fraction of mutations contributed by genes identified as significant (bottom) identified by gene-level dN/dS across subsamples of the buccal swab cohort. l, Fraction of significant sites (top) and fraction of mutations contributed by sites identified as significant (bottom) by site-level dN/dS for five genes across subsamples of the buccal swab cohort. m, Observed and expected (withingenednds) density of mutations as a function of position relative to the transcription start site (TSS), aggregated across all targeted genes. n, Distribution of site-level dN/dS ratios for sites annotated in ClinVar as benign, pathogenic or of uncertain significance, in NOTCH1, TP53 and PPM1D. Significant sites are shown in red. Box plots show the interquartile range, median, 95% confidence intervals and outliers as grey/red dots.

Extended Data Fig. 7 Models of clonal growth.

a-c, Graphs illustrating the increase in the fraction of cells carrying a driver mutation as a function of age, as predicted by (a) exponential, (b) quadratic and (c) logistic models of clonal growth. The dotted line in (c) represents the prediction from a linear growth model. In all three cases, we used a driver mutation rate per cell per year of μ = 4 × 10−6 (1000 driver sites per genome, 4 × 10−9 mutations per cell per year). Other parameters for the three growth models were chosen to obtain a fraction of cells with a driver mutation around 10-15% by age 80, to facilitate comparison to the buccal swab data in Fig. 4a. The parameters used are the following: (a) exponential model using r = 0.1, (b) quadratic model using r = 0.2, (c) logistic model using L = 500 and r = 0.5.

Extended Data Fig. 8 Mutational spectra of somatic single-base substitutions (SBSs), double-base substitutions (DBSs) and indels.

a, Transcriptional strand-wise versions of the trinucleotide SBS spectra shown in Fig. 4b. b, Transcriptional strand-wise pentanucleotide spectra of T > C SBSs in (top to bottom): blood samples (n = 371), hepatocellular carcinoma samples (Liver HCC; Supplementary Note 3) with >20% SBS16 exposure (n = 4), oral epithelium samples with >25% Signature B exposure (n = 121), and oral epithelium samples with <25% Signature B exposure (n = 921). c, Trinucleotide substitution spectra for non-drinking, never-smoking (left) and non-drinking, heavy smoking (right) donors. d, DBS spectra in (top to bottom): all oral epithelium samples (n = 1,042), oral epithelium from heavy smoking, non-drinking donors (n = 27), and oral epithelium from non-smoking, non-drinking donors (n = 224). e, Spectra of indels in the same sample sets shown in d.

Extended Data Fig. 9 Additional regression models.

a, Heatmap of associations between different measures of mutation burden, signature burden or driver density (y-axis) and relevant donor metadata (x-axis), inferred using linear mixed-effects regression (LMER) models with (top) inverse-normal transformation (INT) or (bottom) outlier removal, where outliers are defined as those values larger than 3 × IQR + Q_3 (where IQR is the interquartile range and Q_3 is the third quartile for each outcome variable). The P-value of each association is calculated with a Likelihood ratio test and is indicated by both colour shading (red and blue for positive and negative associations, respectively) and asterisk labels (****: q < 10−4; ***: q < 10−3; **: q < 0.01; *: q < 0.05; •: P < 0.05; q-values were calculated with the Benjamini and Hochberg false discovery method). b, Additional SNV burden obtained after regressing out confounders using an LMER model for (left) smoking history across pack-year bins and (right) alcohol consumption history across drink-year bins. Error bars show 95% confindence intervals. Smoking pack-year bins: (−1,0] n = 632 (not shown), (0,5] n = 139, (5,10] n = 63, (10,15] n = 41, (15,20] n = 40, (20,25] n = 21, (25,30] n = 20, (30,35] n = 11, (35,40] n = 16, (40,45] n = 6, and (45,50] n = 4; Drink-year bins: (−1,0] n = 305 (not shown), (0,70] n = 378, (70,140] n = 200, (140,210] n = 92, (210,280] n = 31, and (280,350] n = 13. c, Heatmap (as described in a) with medication history included as predictors. BMI stands for body mass index; IPAQ score for International Physical Activity Questionnaire; ACEIs for angiotensin-converting enzyme inhibitors; ARBs for angiotensin receptor blockers; NSAIDs for non-steroidal anti-inflammatory drugs; PPIs for proton pump inhibitors; SSRIs for selective serotonin reuptake inhibitors. d, Heatmap (as described in a) using a multivariate LMER model with the driver density per gene per donor normalised by the exonic mutation burden in passenger genes per donor. Normalisation was achieved by either (left) using the ratio of driver density and mutation burden as a new outcome variable for each driver gene or (right) including the mutation burden as a covariate. e, Heatmaps showing the fraction of significant tests (P < 0.05 or 5e-8, as indicated above each heatmap) for simulations of different variables, for different effect sizes (y-axes), and different fractions of affected individuals (x-axes). The contour line in each heatmap shows the conditions that provide 80% power (i.e. 80% of significant tests).

Extended Data Fig. 10 Functional impact of mutational signatures and heritability.

a, Fraction of ATA > ACA or ATT > ACT SNVs in samples presenting high (top 5%, red) or low (bottom 5%, blue) exposure to Signature B (SigB). Fractions are presented for non-coding SNVs, coding SNVs in all genes, and coding SNVs in top driver genes (defined as genes with >1,000 driver mutations across mutational classes with >80% estimated driver fraction). Error bars denote 95% CIs. b, Distribution of predicted mutation consequences for substitutions in any trinucleotide context (dark grey) and for ATA > ACA or ATT > ACT substitutions only (light grey). c, Distributions of pairwise differences in generalised linear model residuals for Signature A and B mutation burdens and NOTCH1 and TP53 driver fractions, between pairs of unrelated (age-matched) individuals (n = 305), pairs of dizygotic (DZ) twins (n = 104), and pairs of monozygotic (MZ) twins (n = 211). The P-values for significantly different pairs of distributions (two-sided Mann–Whitney–Wilcoxon tests) are highlighted in bold. Box plots show the interquartile range, median, and 95% confidence interval for the median.

Supplementary information

Supplementary Information

This file contains Supplementary Notes 1–8 and References.

Reporting Summary

Supplementary Table 1

Targeted panel design for targeted NanoSeq: genes and coordinates.

Supplementary Table 2

Targeted methylation panel design: coordinates of CpGs and promoters.

Supplementary Table 3

Output of the dNdScv method showing genes under positive and negative selection in oral epithelium and blood.

Supplementary Table 4

Output of the sitednds method showing amino acid replacements under positive selection in oral epithelium and blood.

Supplementary Table 5

Heritability estimates with the GREML and ACE models.

Supplementary Table 6

GWAS replication study for SNPs associated with clonal haematopoiesis and cancer, and de novo GWAS.

Supplementary Table 7

Data deposited in EGA.

Supplementary Table 8

Mutations in oral epithelium.

Supplementary Table 9

Mutations in blood.

Supplementary Data

This zipped file contains the Supplementary Code.

Peer Review File

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Lawson, A.R.J., Abascal, F., Nicola, P.A. et al. Somatic mutation and selection at population scale. Nature 647, 411–420 (2025). https://doi.org/10.1038/s41586-025-09584-w

- Received: 18 October 2024

- Accepted: 29 August 2025

- Published: 08 October 2025

- Version of record: 08 October 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09584-w

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Sperm sequencing reveals extensive positive selection in the male germline

Matthew D. C. Neville,

Andrew R. J. Lawson,

Rashesh Sanghvi,

Federico Abascal,

My H. Pham,

Alex Cagan,

Pantelis A. Nicola,

Tetyana Bayzetinova,

Adrian Baez-Ortega,

Kirsty Roberts,

Stefanie V. Lensing,

Sara Widaa,

Raul E. Alcantara,

María Paz García,

Sam Wadge,

Michael R. Stratton,

Peter J. Campbell,

Kerrin Small,

Iñigo Martincorena,

Matthew E. Hurles &

…

Raheleh Rahbari

Nature volume 647, pages 421–428 (2025)

Abstract

Mutations that occur in the cell lineages of sperm or eggs can be transmitted to offspring. In humans, positive selection of driver mutations during spermatogenesis can increase the birth prevalence of certain developmental disorders1,2,3. Until recently, characterizing the extent of this selection in sperm has been limited by the error rates of sequencing technologies. Here we used the duplex sequencing method NanoSeq[4] to sequence 81 bulk sperm samples from individuals aged 24–75 years. Our findings revealed a linear accumulation of 1.67 (95% confidence interval of 1.41–1.92) mutations per year per haploid genome driven by two mutational signatures associated with human ageing. Deep targeted and exome NanoSeq[5] of sperm samples identified more than 35,000 germline coding mutations. We detected 40 genes (31 newly identified) under significant positive selection in the male germline that have activating or loss-of-function mechanisms and are involved in diverse cellular pathways. Most of the positively selected genes are associated with developmental or cancer predisposition disorders in children, whereas four of the genes exhibited increased frequencies of protein-truncating variants in healthy populations. We show that positive selection during spermatogenesis drives a 2–3-fold increased risk of known disease-causing mutations, which results in 3–5% of sperm from middle-aged to older individuals with a pathogenic mutation across the exome. These findings shed light on germline selection dynamics and highlight a broader increased disease risk for children born to fathers of advanced age than previously appreciated.

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Main

All human cells accumulate mutations throughout life. In proliferating tissues, acquired driver mutations that confer a selective advantage can promote the expansion of individual clones in competing stem and progenitor cell populations. Although patterns of selection and clonal expansion have been extensively studied in cancers, recent research has also highlighted their occurrence in normal tissues during ageing6,7,8,9,10,11,12,13,14.

Spermatogonial stem cells of the testis occupy a distinct niche relative to other studied normal tissues. Among replicating cells, these stem cells have the lowest mutation rate, which is around 5–20-fold lower than other studied somatic cell types[12]. They are also the only adult proliferating cells with the potential to transmit mutations to offspring, balancing self-renewal and spermatogenesis to produce 150–275 million sperm per day15,16. Targeted sequencing studies have revealed that driver mutations are acquired in spermatogonial stem cells and that these cell populations expand along seminiferous tubules, which results in increased fractions of mutant clones that are detectable in sperm1,17,18,19,20. Notably, all the male germline drivers identified so far are activating missense hotspot mutations, which contrasts with the broader range of activating and inactivating drivers observed in cancers and somatic tissues. These germline driver mutations can have substantial implications for offspring, as such mutations are found in a set of 13 genes all known to cause severe developmental disorders[3]. This effect leads to a significant increase, up to 1,000-fold, in the sporadic birth prevalence of these disorders, with a strong correlation between the age of the father and prevalence[2].

Technical limitations, related to the polyclonality and low mutation rate of testis and sperm, have prevented extensive characterization of this selection beyond a limited set of genes. Recent advances in error-corrected duplex DNA sequencing approaches use information from both DNA strands to detect mutations at single-molecule resolution21,22,23,24. These new methods have proven successful for the accurate estimation of mutation burden in sperm, with an error rate of <5 × 10−9 per base pair[4]. Here we combine the duplex approaches of whole-genome NanoSeq[4] with deep whole-exome and targeted NanoSeq[5] to characterize positive selection in the male germline and to quantify its consequences for the accumulation of disease mutations in sperm.

Cohort and sequencing coverage

We performed whole-genome NanoSeq[4] of bulk semen samples (n = 81, 1–2 time points per donor, 57 donors with an age range of 24–75 years) and matched blood (n = 119, 1–3 time points, 63 donors with an age range of 22–83 years) from men in the TwinsUK cohort[25] (including 8 monozygotic and 3 dizygotic twin pairs; Methods and Supplementary Table 1). The analysed semen samples had sperm counts >1 million per ml, as samples below this threshold showed evidence of somatic cell contamination (Extended Data Fig. 1 and Supplementary Note 1). The mean number of unique DNA molecules per site where a mutation was callable (duplex coverage (dx)) was 3.7 dx in sperm and 4.3 dx in blood (Extended Data Fig. 2a). For sperm (a haploid cell), 1 dx is equivalent to one cell, whereas for blood (a diploid cell), 2 dx is equivalent to one cell.

Mutational burden and signatures

We identified single nucleotide variants (SNVs) and small insertion–deletion mutations (indels) in whole-genome NanoSeq data from sperm and blood. Inherited germline variants were excluded using matched blood (Methods). From the 6,653 SNVs detected in sperm (Supplementary Table 2), we estimated an accumulation of 1.67 substitutions per year per haploid genome (95% confidence interval (CI) = 1.41–1.92, linear mixed-effect regression). This result is comparable with estimates from paternal de novo mutations (DNMs) in family pedigrees[26] of 1.44 substitutions per year (95% CI = 1.00–1.87) and seminiferous tubules of the testes[12] of 1.40 substitutions per year (95% CI = 1.02–1.76; Fig. 1a). Indels accumulated in sperm at a rate of 0.10 per year per haploid genome (95% CI = 0.06–0.15), again similar to the rate observed in DNMs[26] of 0.08 haploid indels per year (95% CI = −0.02 to 0.17) and testes[12] of 0.08 haploid indels per year (95% CI = 0.02–0.13; Fig. 1b and Extended Data Fig. 3a).

Fig. 1: Mutational burden and signature analysis in sperm and matched blood.

a,b, Substitutions (a) and indels (b) per haploid cell from whole-genome NanoSeq of sperm, trio paternal DNMs[26] called with standard sequencing and clonal variants from seminiferous tubules of testis[12] called with standard sequencing. Dots indicate single donors, except for testis with 1–15 samples per donor. c,d, Substitutions (c) and indels (d) per diploid cell for different ages from blood NanoSeq samples. e, Ratio of blood to sperm substitutions and indels per diploid cell per year. Each dot corresponds to an individual with both a blood and sperm sample. For individuals who had multiple time points, the mean value of all time points in that tissue was used. Box plots show the median as the centre line, the 25th and 75th percentiles as box limits and whiskers extending to the largest and smallest values within 1.5× the interquartile range from the limits from n = 57 biologically independent samples. f, Trinucleotide mutation counts in all sperm and blood samples. g, Contributions of the signatures SBS1, SBS5 and SBS19 in sperm and blood samples ordered by age. For a–d, models are linear mixed regressions, with the central line showing the model fit and the shaded bands indicating 95% CIs calculated using parametric bootstrapping.

From 92,035 SNVs and 4,641 indels detected in whole blood (Supplementary Table 3), we estimated an accumulation of 19.9 substitutions per year per diploid genome (95% CI = 17.3–22.5; Fig. 1c) and 0.9 indels per year (95% CI = 0.7–1.1; Fig. 1d and Extended Data Fig. 3b). Both estimates are within the range of mutation rates observed for specific cell types in blood[13] and are consistent with measuring a weighted average of these cell types in whole blood (Extended Data Fig. 4a,b). Individuals had a mean of 7.6-fold more substitutions per base pair per year (range of 4.2–11.5; Fig. 1e) and 6.3-fold more indels per base pair per year (range of 2.2–18.7; Fig. 1e) in blood than in sperm. Accounting for twin status or multiple time points from the same individuals had a significant predictive effect for mutation burden in blood but not in sperm (Supplementary Note 2).

The SNV mutational signatures in sperm were inferred to be SBS1 (mean 16%) and SBS5 (mean 84%), which were the expected clock-like ageing signatures[27] (Fig. 1f,g). In blood, SBS1 (mean 15%) and SBS5 (mean 75%) were also the main mutational signatures, with an additional contribution of SBS19 (mean 10%), which has been linked to persistent DNA lesions in haematopoietic stem cells[27] (Fig. 1f,g). We observed that all signatures were correlated with age (Extended Data Fig. 4c,d). SBS1 and SBS5 accumulated in individuals at a mean of 8.9-fold (range of 2.3–39.1) and 6.8-fold (range of 3.7–10.9) higher rate in blood than in sperm, respectively (Extended Data Fig. 4e). This finding indicates that SBS19 does not explain a substantial fraction of the mutation burden gap between the two tissues.

Selective pressure dynamics in sperm

We next investigated positive selection in protein-coding regions in sperm. We used a capture-based modification to NanoSeq[5] to deeply sequence coding regions from the same set of semen samples. Specifically, we sequenced 38 samples using whole-exome NanoSeq to a mean depth of 551 dx per sample (20,923 cumulative dx). We also sequenced 81 samples using targeted NanoSeq to a mean depth of 985 dx per sample (79,811 cumulative dx) with a target panel that consisted of 263 canonical cancer driver genes, 107 of which are also associated with developmental disorders (Methods, Extended Data Fig. 2a and Supplementary Table 4). We detected 56,503 (58% in coding regions) SNV and indel mutations from the exome panel and 5,059 (58% in coding regions) from the targeted cancer panel (Methods and Supplementary Table 5). The mutation burdens for exome and targeted samples were consistent with whole-genome NanoSeq after correcting for the relative trinucleotide composition of sequencing coverage (Extended Data Fig. 2b).

The majority of variants (99.5%) were detected only in a single duplex molecule of a sample. Similarly, in the 23 samples with 2 time points (mean of a 12.1-year gap), 99.3% of the 5,143 variant calls from the first time point were not called in the second time point. These results are consistent with sperm being a highly polyclonal collection of cells derived from a large population of spermatogonial stem cell progenitors in the testis.

The exome-wide strength of positive selection in sperm was quantified by estimating the rate of nonsynonymous (N) relative to neutral synonymous (S) mutations (dN/dS ratio, where dN/dS = 1.0 indicates neutrality). We used the dNdScv algorithm[28] with modifications to account for duplex sequencing coverage per base, CpG methylation levels in testis samples and pentanucleotide context. These modifications refined exome-wide dN/dS ratios by resolving specific mutation rate biases but had minor effects on gene-level dN/dS ratios (Extended Data Fig. 5 and Supplementary Note 3).

Using this model, the dN/dS ratio in the exome-sequenced samples were estimated to be 1.07 (95% CI = 1.04–1.10). This ratio implies that 6.5% (95% CI = 3.8%–9.1%) of the observed nonsynonymous substitutions in sperm conferred a clonal advantage during spermatogenesis in this cohort. Splitting the cohort into thirds by age, the exome-wide dN/dS ratio was 1.01 (95% CI = 0.93–1.09) in 26–42 year olds, 1.03 (95% CI = 0.97–1.10) in 43–58 year olds and 1.09 (95% CI = 1.06–1.13) in 59–74 year olds (Fig. 2f). A linear regression of dN/dS ratio against age group showed a positive trend, although significance was not reached (P = 0.18). This result suggests that the dN/dS ratio may increase over the male lifespan. If so, then the cohort-wide dN/dS ratios presented here partially reflect the age distribution of samples (age range of 26–74 years, mean of 53 years).

Fig. 2: Germline positive selection.

a–c, Genes with significant dN/dS ratios from exome-wide and restricted hypothesis tests. a, Mutation count split by mutation class. b, Enrichment over expectation of mutation classes. c, Mutation type driving dN/dS enrichment, COSMIC cancer gene tier, developmental disorder (DD) gene status in DDG2P and potential germline selection gene set. d,e, Observed sperm mutations across the cohort for CUL3 and SMAD4. The height of each lollipop represents the number of biologically independent samples with a mutation at that position, and the colour indicates mutation type. Mutations are labelled with their amino acid consequence or, for insertions (ins) and deletions (del), as in-frame (IF) or frameshift (FS). A ‘P’ indicates pathogenic/likely pathogenic classification in ClinVar. Exons are shown as purple rectangles and the blue background shows the total duplex coverage across the cohort. Lines below the gene indicate somatic mutations in cancer from COSMIC. f,g, dN/dS ratios for sperm SNVs across sets of individuals or genes, where the dotted black line indicates neutrality and the dotted orange line represents the cohort average across all genes. f, Exome-wide dN/dS ratios for younger, middle and older age groups of n = 11, n = 9 and n = 18 biologically independent sperm samples, respectively. g, Expression levels as log_2 of unique molecular identifier (UMI) counts and cell-type clusters from single-cell sequencing of germ cells[29]. Germ-cell types include undifferentiated and differentiated spermatogonial stem cells (SSCs), spermatocytes, round spermatids (spermatid 1) and elongating spermatids (spermatid 2). Data are from mutations detected in n = 38 biologically independent sperm samples. h, Observed/expected mutation rates in sperm for variant recurrence bins (data from COSMIC and DDD databases) using mutations from n = 38 biologically independent sperm samples. Data in f–h show ratio point estimates, error bars indicate 95% CIs.

We next compared the dN/dS ratios across gene sets related to spermatogenesis expression[29] (Fig. 2g). Genes with the highest dN/dS ratios were those that were highly expressed during spermatogenesis (1.25, 95% CI = 1.13–1.38) and most specific to differentiated spermatogonial stem cells (1.11, 95% CI = 1.05–1.17). By contrast, the genes that were not expressed in spermatogenesis (0.98, 95% CI = 0.88–1.11) and the genes most specific to elongating spermatids (1.01, 95% CI = 0.94–1.08) showed dN/dS ratios close to neutrality. These results are consistent with the understanding that excess nonsynonymous mutations observed in sperm confer a competitive advantage earlier in their cell lineage, specifically in the spermatogonial stem cells of the testis[18].

Driver gene discovery

We then combined exome and targeted panel datasets to investigate which genes were driving the signal of positive selection (Methods). We applied dN/dS tests for excess nonsynonymous mutations at gene-wide and SNV hotspot levels, which together identified 40 genes under significant positive selection. Of these, 35 genes reached exome-wide significance at the gene level (false discovery rate q < 0.1; Supplementary Table 6) and/or contained 1 of 17 exome-wide significant hotspots (q < 0.1; Extended Data Table 1). The genes PTPN11, MIB1, RIT1, FGFR3, EP300 and FGFR2 were significant in both the gene and hotspot tests. The genes KDM5B, NF1, SMAD6, CUL3, RASA2, PRRC2A, PTEN, ROBO1, DDX3X, CSNK2B, KRAS, PPM1D, ARID1A, BRAF, HRAS, KMT2E, SCAF4, BMPR2, TCF12, CCAR2, DHX9, NSD1, LZTR1, ARHGAP35, CBL, SSX1 and RBM12 were significant in only the gene test. The genes SMAD4 and FAM222B were significant in only the hotspot test (Fig. 2a,b). We excluded SEMG1 despite it reaching gene-level significance, as its extreme expression in seminal vesicles[30], lack of expression in spermatogenesis and indel-specific enrichment indicate indel hypermutation31,32 from minor seminal vesicle DNA contamination rather than germline selection.

Subsequently, we carried out restricted hypothesis dN/dS tests at the per-gene and per-site level. The gene-level test examined only the set of 263 canonical cancer driver genes on our target panel. The site-level test used a set of 1,963 sites consisting of known cancer hotspots and recurrent DNM sites from the Deciphering Developmental Disorders (DDD) cohort[33]. This analysis identified five additional genes—KDM5C, KMT2D, AR, CTNNB1 and RAF1—and seven hotspots not already significant at the exome-wide level (q < 0.1; Fig. 2a,b, Extended Data Table 1 and Supplementary Table 7).

The genes previously implicated in germline positive selection all operate through activating missense mutations, with 12 linked to the RAS–MAPK signalling pathway[3] and one (SMAD4) linked to TGFβ–BMP signalling[34]. Our findings replicated SMAD4 and 8 out of the 12 RAS–MAPK pathway genes as under significant positive selection in this dataset. The four genes that did not reach significance (MAP2K1, MAP2K2, SOS1 and RET) each had between twofold and fourfold enrichment of missense mutations, which corresponded to nominally significant missense enrichment in all four genes (P < 0.1). Given the direct evidence for these genes driving clonal selection in testis and nominal enrichment from sperm sequencing, we anticipate that each will reach exome-wide significance with deeper sequencing.

We estimate that together, the 44 genes linked to germline selection here or in previous studies contain 357 (95% CI = 319–387) excess nonsynonymous variants in exome sequenced sperm samples. This result would account for 23% (95% CI = 14%–43%) of the total estimated driver variants across the exome. The wide CIs and the sensitivity of this estimate to the mutation model used (Supplementary Note 3) suggest that small uncertainties in mutation rates, when propagated across the exome, make it difficult to precisely estimate the fraction of drivers explained. Nevertheless, the findings indicate that additional driver genes remain to be discovered.

The 31 newly identified genes demonstrate that germline positive selection is not restricted to activating mutations or to the RAS–MAPK pathway. For instance, 30 out of the 31 genes are enriched for loss-of-function (LOF) mutations such as nonsense, splice and indel variants, which are indicative of protein-inactivating mechanisms of selection (Fig. 2c,d). Splitting the germline-selection genes and known cancer genes on the basis of ten canonical cancer pathways[35], we found that the top gene groups enriched in dN/dS are RAS–MAPK, WNT and TGFβ–BMP signalling (Extended Data Fig. 6a). Indeed, many of these selection genes are linked to the RAS–MAPK pathway (such as NF1, CUL3 and LZTR1), WNT signalling (CSNK2B, MIB1 and CCAR2) and TGFβ–BMP signalling (SMAD6, TCF12 and BMPR2) (Fig. 2c). We also identified several genes that encode epigenetic modifiers (KDM5B, KDM5C, ARID1A, KMT2D, KMT2E, EP300 and NSD1) and genes encoding RNA metabolism proteins (DHX9, DDX3X and SCAF4). These findings highlight a new diversity of genes, mutational mechanisms and pathways that drive germline selection.

It has been observed that cancers and germline developmental disorders share causal pathways and genes36,37. Notably, the 13 genes previously linked to germline positive selection are all known cancer and known developmental disorders genes3,34. This pattern holds, but to a lesser extent, in the new germline selection genes identified here: 16 out of 31 genes are tier 1 or 2 cancer census genes[38] and 27 out of 31 are linked to monogenic disorders in the Development Disorder Genotype–Phenotype (DDG2P) or Online Mendelian Inheritance in Man (OMIM) databases[39] (Fig. 2c and Supplementary Table 8).

The overlap between germline positive selection and cancer or developmental disorders is also apparent at the variant level. Somatic mutations that are most frequently observed (>50 times) in the Catalogue Of Somatic Mutations In Cancer (COSMIC) database are enriched 11-fold (95% CI = 6–20) among our sperm mutation dataset after adjusting for the expected mutation rate (Methods). Similarly, germline mutations that are most frequently observed (>5 times) in a large cohort of children with developmental disorders are enriched 66-fold (95% CI = 41–100) in our sperm mutation dataset (Fig. 2h). Moreover, the mutation types enriched in sperm for a given gene largely matched those enriched in cancer and linked to developmental disorders (Extended Data Figs. 6a and 7). These results show a clear overlap between genes, hotspots and mutation mechanisms that drive germline positive selection, cancer and developmental disorders. A notable exception to this pattern is SMAD4, which has two distinct missense hotspots in sperm that are developmental disorder hotspots causal for Myhre syndrome[40] but are not often seen in cancers, a result that replicates recent findings[34] (Fig. 2e).

Enrichment of disease-causing mutations in sperm

Given the association of many positively selected genes to disease, it is of interest to assess to what degree positive selection may increase the fraction of sperm with potential disease-causing mutations, and thus the birth prevalence of the associated disease. To estimate the fraction of sperm with specific classes of variants, we aggregated the variant allele frequencies (VAFs) of different mutation types and compared this to expected values from our dNdScv mutation model (Methods).

The fraction of sperm carrying noncoding or synonymous mutations increased linearly with age, as predicted by the neutral model (Extended Data Fig. 8). By contrast, missense, truncating and coding indel variants deviated above expected in older individuals, an observation consistent with the dN/dS results and indicative of age-related positive selection.

To assess potential disease burden, we compiled a conservative list of probable monoallelic disease-causing mutations, including pathogenic or likely pathogenic variants from ClinVar[41] and highly damaging variants (combined annotation dependent depletion (CADD)[42] value of >30) in high-confidence monoallelic developmental disorder genes from the DDG2P cohort[39]. Across all ages, the observed frequency of disease mutations exceeded expectation from the germline mutation model. The expected fraction ranged from 0.73% in 30 year olds to 1.6% in 70 year olds. When fitting a quasibinomial regression, the observed values ranged from 2% (95% CI = 1.6%–2.5%) in 30 year olds to 4.5% (95% CI = 3.9%–5.2%) in 70 year olds, with a significant relationship between the observed fraction and age (P = 1.75 × 10–5; Fig. 3c). These differences represent similar enrichments of 2.8-fold (95% CI = 2.2 to 3.5) and 2.9-fold (95% CI = 2.5 to 3.3) in 30 year olds and 70 year olds, respectively.

Fig. 3: Pathogenic burden.

a, Estimated mean percentage of sperm in the cohort with a likely monoallelic disease mutation (left) or a driver mutation in a germline-selection gene (right). Disease mutations are divided into the fraction that was expected from the mutation model, the portion explained by driver variants and the portion unexplained. Driver mutations are split by those contributing to the disease mutations and the remainder, ‘ageing drivers’. b, Estimated percentage of sperm per individual with a driver mutation by age. c, Observed and expected percentages of sperm with a likely disease mutation by age. For b and c, the central line represents the model-predicted mean from quasibinomial regression, and shaded bands indicate 95% CIs. d, Cohort means from a split by gene and ordered by estimated mutation percentage. Per-gene contributions are shown above each gene; the summed contributions of all genes are shown below. Genes with four or fewer variants are grouped on the left with a condensed x axis for clarity.

Notably, the disease cell-fraction estimates were made up of many low-frequency variants rather than being driven by individual high VAF mutations. The estimates in exome samples are made up of a mean of 18.3 distinct variants (range of 4–62) per individual. Furthermore, 692 out of 696 (99.4%) of all those variants were only observed in a single sperm, similar to the average of all variants (99.5%).

We next investigated to what degree the observed enrichment of disease mutations can be attributed to driver mutations in positively selected genes. Fitting a quasibinomial regression, we observed a strong positive correlation between age and driver rate (P = 7.95 × 10–6) with an estimated 0.5% (95% CI = 0.3%–0.8%) of sperm from individuals at age 30 years and 2.6% (95% CI = 2.0%–3.3%) of sperm from individuals aged 70 years with a known driver mutation (Fig. 3b). However, only about two-thirds (65.6%) of those driver mutations met our criteria of likely disease-causing.

Mutations in sperm that are likely disease-causing and those classified as known driver mutations represented overlapping, yet distinct, annotations (Fig. 3a). Across the cohort, an estimated 3.3% of sperm carried a likely disease-causing mutation. Of this, approximately one-third (1.2%) was expected by the neutral mutation model, another third (1.1%) was explained by known driver mutations and the remaining third (1.0%) was unexplained by either source. These findings indicate that the increase in likely disease-causing mutations is largely driven by germline positive selection on known genes, but also indicate that additional driver genes with disease associations remain to be identified.

After examination of the driver mutations that did not meet our criteria for likely disease-causing, we estimated that they affected 0.6% of sperm across the cohort. The inheritance consequences of these mutations are unclear. For instance, SMAD6 variants, which are linked to variably penetrant congenital phenotypes[43], may be disease-causing in some cases but not others. Other possibilities include mutations that are unannotated disease-causing, impair fertilization, are embryonic lethal or act through biallelic disruption.

A large fraction of sperm with disease and/or driver mutations could be attributed to a small number of genes. Of the 374 genes with at least one such variant, 33 with ≥5 independent mutations, most under significant positive selection (26 out of 33), accounted for 42.8% of the disease or selection burden (Fig. 3d). Notably, six of those genes, all under significant positive selection (KDM5B, MIB1, SMAD6, PRRC2A, NF1 and PTPN11) alone explained over 20% of the disease or selection fraction. This result suggests that although all individual driver mutations were observed at low allele frequencies, they were disproportionately concentrated in specific driver genes.

We next examined whether risk factors beyond age contribute to the accumulation of disease or driver mutations in sperm. Known germline mutagens include chemotherapy, inherited DNA repair defects33,44 and weaker influences from genetic ancestry and smoking[45]. Although our cohort did not include individuals with chemotherapy exposure or DNA repair defects, phenotype data on body mass index (BMI), smoking and alcohol consumption, all known to affect mutation burden in some somatic tissues[46], were available. Using multivariate generalized linear models (Methods and Extended Data Fig. 9), we tested associations between these factors and measures of mutation burden, signatures and driver cell fractions, correcting for multiple testing. Across sperm datasets (targeted, exome and whole genome) no significant effects were found for BMI, smoking pack-years or alcohol drink-years. Notably, in blood samples, smoking and alcohol consumption showed significant effects on SNV and SBS5 mutational signature burdens. These findings indicate that the male germline may be largely protected from these exposures, although further investigation in larger and more diverse cohorts is needed.

Selection across germline variant sources

Mutations in sperm account for about 80% of DNMs and are therefore also the primary origin of population-level variants. Comparisons of these germline mutation sources offer insight into how positive selection in spermatogonia manifests over time.

Control DNMs from unaffected offspring[47] showed a dN/dS ratio near neutrality (0.98, 95% CI = 0.90–1.08; Fig. 4a), whereas DNMs from children with developmental disorders showed a marked nonsynonymous enrichment (1.36, 95% CI = 1.33–1.39; Fig. 4a), a result consistent with previous reports[33]. However, ascertainment biases in these cohorts probably distort the observed ratios, and larger, unbiased birth cohorts will be required to resolve the baseline of DNMs entering the population.

Fig. 4: Comparison with population variation.

a, Exome-wide dN/dS ratio point estimates across different variant sets, including n = 38 biologically independent sperm samples, DNMs from n = 1,886 healthy trios[47] and n = 22,742 trios from the DDD cohort[33], and population variants from n = 125,748 individuals in gnomAD[48], split by allele frequency (AF). Error bars indicate 95% CIs. b, Observed/expected enrichment of missense and LOF (essential splice, nonsense or indel) variants in positively selected genes in sperm (x axis) from dN/dS models versus gnomAD (v.2) LOF z scores. Positive z scores indicate LOF depletion, whereas negative scores indicate excess over expected.

To assess how selection shapes germline variants in the population, we analysed dN/dS ratios across allele frequencies in gnomAD[48] (Fig. 4a). This analysis revealed a decay in dN/dS ratios at higher allele frequencies, a result consistent with purifying selection across generations. Together, these results suggest that positive selection is the greater force that acts on germline mutations during spermatogenesis, whereas negative selection predominates over generations. This finding mirrors the well-established contrast between selection on cancer and population-level mutations[28].

Most genes in gnomAD have positive z scores, which indicates LOF depletion due to negative selection. Of the 31 selection genes with LOF enrichment in sperm (range of 3-fold to 50-fold), 27 showed LOF depletion in gnomAD, a result consistent with positive selection during spermatogenesis but purging in the population by negative selection. Notably, four genes, SMAD6, MIB1, LZTR1 and SSX1, had more LOFs than expected in gnomAD (Fig. 4b). The latter three are among the strongest LOF-enriched outliers in gnomAD (v.2) and are flagged by gnomAD for unexplained LOF enrichment. Our results suggest that their increased LOF frequency in gnomAD reflects increased input from germline positive selection, with insufficient negative selection to remove them from the population.

Discussion

We sequenced sperm and blood from healthy men spanning a wide age range to quantify mutation rates and positive selection in the male germline. Mutation rates and signatures in sperm matched those from family trio and testis studies12,26,49,50,51,52. Despite sharing SBS1 and SBS5 signatures, mutations accumulated around eightfold more slowly in sperm than blood, a result that supports our previous observations of the protected nature of the germline relative to the soma[12].

By analysing more than 35,000 coding mutations from sperm exome-wide, we corroborated key findings from previous studies of spermatogonial selection1,17,18,19,20 and significantly advanced our understanding of its scope and impact. Our results replicated 9 out of 13 known germline-selection genes and confirmed that the impact of selection increases with age, leading to an increased fraction of sperm with pathogenic variants. We also identified 31 new positively selected genes, thereby broadening the range of pathways and mechanisms implicated in this process. The discovery of diverse genes and the identification of LOF as a common selection mechanism highlight the power of NanoSeq to enable exome-wide searches and to detect mutations in single cells, overcoming the limitations of previous methods reliant on high-frequency gain-of-function missense mutations. These findings reveal that germline selection operates in the broader framework of cellular selection, driven by many of the same genes and mechanisms that shape clonal dynamics in somatic tissues. However, unlike somatic selection, germline selection affects offspring phenotypes and influences evolutionary trajectories.

Perhaps unsurprisingly then, driver mutation burden in sperm is low compared with many somatic proliferative tissues. In middle-aged to older men, we showed that only 1–3% of sperm carry a known driver mutation. Low levels have also been reported in colon (about 1%) and liver (1–5%)8,9. In blood, many individuals fell within this range, although some showed large expansions of single clones[14]. Much higher burdens are seen in epithelial tissues such as endometrium, oesophagus and skin, where 30–50% of cells often have driver mutations6,7,10. These differences are probably shaped in part by both tissue architecture and mutation rate. In the testis, the low mutation rate combined with the tubular structure may together be crucial to constrain the generation and spread of driver clones. This low driver rate also aligns with the rarity and biology of tumours that originate from spermatogonia, which are typically driven by aneuploidy rather than sequential driver mutations[53].

Our findings have significant implications for disease and evolutionary studies that rely on germline mutation models, as they do not currently account for positive selection. We showed that this selection can distort estimates of selective constraint based on population-level data. Germline selection will also affect the discovery of disease-causing genes from DNM enrichment tests. For example, excess LOF mutations in MIB1 found in developmental disorder trios[33] probably reflects germline selection rather than disease association, as they are more common in population cohorts than expected and do not show phenotype correlation[54]. In principle, case–control tests for DNM enrichment would avoid this bias, but require large cohorts of age-matched controls for sufficient statistical power. Until such resources become available, DNM enrichment studies should consider evidence for germline selection influencing individual genes presented here (Supplementary Table 5).

Unlike MIB1, most genes under positive selection during spermatogenesis are linked to severe monogenic disorders. We demonstrated that positive selection in spermatogenesis led to a substantial twofold to threefold enrichment in sperm with likely disease-causing mutations across the age range studied. As a result, we estimate that 3–5% of sperm from men aged over 50 years carry a likely disease-causing mutation, a value that exceeds previous estimates based on germline mutation models[55].

Although these risks are substantial, typical paternal age at conception is lower than in our cohort, which suggests that the impact on birth outcomes may be more modest. Moreover, the relationship between sperm mutations and birth prevalence remains uncertain. Many pathogenic variants in sperm may not result in live births owing to impaired fertilization, embryonic lethality or pregnancy loss (Supplementary Note 4). Nevertheless, growing awareness of these risks may prompt interest in reproductive planning, genetic counselling or clinical interventions. Translating these findings into specific clinical recommendations, however, presents challenges. Unlike inherited variants, which are present in every cell, or aneuploidies, for which clinical focus is limited to a few recurrent events, pathogenic mutations in sperm are both highly diverse and individually rare, which makes them difficult to target with standard screening approaches. Nonetheless, targeted risk assessment may be valuable in specific contexts, such as individuals with an increased risk of sperm hypermutation due to impaired DNA repair or chemotherapy exposure52,56.

Our results focused on SNVs and indels, without capturing other variant classes. Although long-read sequencing studies have begun to explore these in sperm[57] and trios[58], further work is needed to capture the full range of human germline variation and its impact on offspring.

This study provides important insights into the historically underexplored reproductive risks of ageing in men. Unlike the well-established effects of maternal ageing on oocyte quality and aneuploidy[59], we are only beginning to understand the full scope of male-specific risk, including the clonal expansion of driver mutations. As trends towards delayed reproduction continue[60], it is essential to recognize that the age of both parents contributes to risk of disorders in offspring. Future research will refine our understanding of selective pressures and disease risk associated with germline mutation, clarifying their implications for human reproduction and health.

Methods

Ethics

This study was carried out under TwinsUK BioBank ethics, approved by the North West–Liverpool Central Research Ethics Committee (REC reference 19/NW/0187), IRAS ID 258513 and earlier approvals granted to TwinsUK by the St Thomas’ Hospital Research Ethics Committee, later the London–Westminster Research Ethics Committee (REC reference EC04/015).

Sample collection

Semen samples were collected or obtained from archival samples with informed consent from 75 research participants in the TwinsUK cohort[25]. Archival whole-blood samples were also obtained from 67 of those men from the TwinsUK BioBank. A total of 104 semen samples spanned an age range of 24–75 years and included 29 men with 2 time points separated by a mean of 12.1 years (range of 12–13 years) and the remaining 46 men with a single time point. A total of 133 blood samples were collected at an age range of 22–83 years from men with 1–4 time points. The mean interval between blood time points was 8.1 years (range of 1–13 years). In the cohort, there were a total of nine monozygotic twins and three dizygotic twin pairs. Counts of samples, time points and twin pairs that were successfully sequenced and passed analysis quality control thresholds are summarized in Supplementary Table 1.

Metadata

Self-reported age, height, weight, ethnicity, twin zygosity, smoking and alcohol consumption were obtained from questionnaires provided by TwinsUK and periodically taken. All individuals who provided ethnicity information indicated white. BMI was calculated as weight/height^2. A smoking pack-years was defined as 365 packs of cigarettes and a total pack-year was calculated using the highest estimate across all questionnaires from cigarettes per day or week and total years smoked. Alcohol drink-years was calculated using average weekly alcohol consumption extrapolated to the duration of adult life before sampling (age – 18).

DNA extraction

DNA was extracted from sperm samples using a Qiagen QIAamp DNA Blood Mini kit. Isolation of genomic DNA from sperm protocol 1 (QA03 Jul-10) was followed, but with the exceptions of substituting DTT in place of β-mercaptoethanol for buffer 2 and substituting buffer EB in place of buffer AE for the elution of DNA.

DNA was extracted from whole blood using a Gentra Puregene Blood kit using the protocol for 10 ml of compromised whole blood from the Gentra Puregene Handbook (v.06/2011).

Targeted gene panels

Three separate Twist Bioscience gene panels were used for targeted NanoSeq sequencing in this study: (1) a custom pilot panel of 210 genes; (2) a similar but extended custom panel of 263 genes (Supplementary Table 4); (3) and a default exome-wide panel of 18,800 genes. The two custom gene panels were highly similar, with the extended panel being almost exclusively regions added to the pilot panel. From the 84 samples that underwent targeted sequencing, 13 were sequenced using a pilot panel of 210 canonical cancer or somatic driver genes, and all 84 were sequenced using the extended panel of 263 genes. Sequencing coverage and mutations were merged from samples sequenced on both targeted panels and they were treated as ‘targeted’ samples in the Article. The custom panels were designed by gathering sets of published lists of genes implicated as drivers in cancers61,62,63,64 and somatic tissues6,65 as previously described[5].

Sequencing and library preparation

Restriction-enzyme whole-genome NanoSeq libraries were prepared as previously described[4] and subjected to whole-genome sequencing (WGS) at target 20–30× coverage on NovaSeq (Illumina) platforms to generate 150-bp paired-end reads with 9–10 samples per lane. Standard WGS of blood (31.7× median coverage) was used to generate matched-normal libraries for both restriction-enzyme NanoSeq blood and sperm samples.

Targeted and exome NanoSeq libraries were prepared by sonication and one to two rounds of pull down of target sequences. They were then PCR amplified and sequenced with NovaSeq (Illumina) platforms to generate 150-bp paired-end reads with 7–8 samples per lane for the targeted panel and 2 lanes per sample for the exome panel. These steps are described in detail in ‘Sonication NanoSeq, Library amplification and sequencing, and Hybridization Capture’ of supplementary note 1 of ref. [5].

Base calling and filtering

All samples were processed using a Nextflow implementation of the NanoSeq calling pipeline (https://github.com/cancerit/NanoSeq). BWA-MEM[66] was used to align all sequences to the human reference genome (NCBI build37). Whole-genome NanoSeq samples were called with their matched WGS normal and default parameters except for var_b (minimum matched normal reads per strand) of 5 as needed for WGS normals, cov_Q (minimum mapQ to include a duplex read) of 15 and var_n (maximum number of mismatches) of 2.

For targeted and exome samples, we leveraged the high sequencing depth and high polyclonality to exclude variants with VAF > 10% instead of using a matched normal. Default parameters of the calling pipeline except for cov_Q of 30, var_n of 2, var_z (minimum normal coverage) of 25, var_a (minimum AS-XS) of 10, var_v (maximum normal VAF of 0.1) and indel_v (maximum normal VAF) of 0.1. After variant calling, we further filtered variants to those below 1% VAF. The few variants observed between 1% and 10% duplex VAF as variants were highly enriched for mapping artefacts, particularly for indels. No excluded variant from the additional 1% VAF threshold was found to be either a likely driver or a ClinVar pathogenic variant.

A set of common germline variants from dbsnp[67] and a custom set of known artefactual call sites in NanoSeq datasets were masked for coverage and variant calls as previously described[4].

Assessing DNA contamination

The single-molecule accuracy of the duplex sequencing method NanoSeq allows sequencing of polyclonal cell types such as sperm, but also renders mutation calls sensitive to nontarget cell-type contamination and to contamination of foreign DNA. Nontarget cell-type contamination was evaluated using manual cell counting of semen samples, which resulted in the exclusion of six samples with a sperm count of <1 million sperm per ml. Sperm counting methods and analysis are detailed in Supplementary Note 1.

Foreign DNA contamination in whole-genome NanoSeq samples was assessed using verifyBamID[68], which checks whether reads in a BAM file match previous genotypes for a specific sample, with higher values indicating more contamination. Three blood whole-genome NanoSeq samples were excluded on the basis of a verifyBamID alpha value above the suggested[4] cut-off value of 0.005. In sperm, we found that several samples had outlier mutation burdens with verifyBamID values just below the 0.005 cut-off. This result is logical, as sperm has a much lower mutation rate compared with somatic tissues, for which the recommended cut-off was designed. Consequently, sperm samples are more sensitive to low levels of contamination. To account for this, we adjusted the verifyBamID alpha threshold for sperm to a more stringent level of 0.002, which resulted in the exclusion of three samples on the basis of this criterion.

When assessing foreign DNA contamination in targeted and exome samples, we found that 9 targeted and 3 exome samples had verifyBamID values above >0.002, slight outlier mutation burdens and a high ratio of SNP masked variants to passed variants (4-fold to 16-fold more masked variants). After further investigation, we found that all samples that exceeded verifyBamID thresholds were processed in the same sequencing batch and that this contamination could be explained by inherited germline variants of other samples in that same batch. This result suggested that a small amount of cross-contamination may have occurred during sample preparation or sequencing steps. To remove contaminant germline mutations, we performed an in silico decontamination step as previously described[4]. This step involved calling germline variants from all targeted and exome samples using bcftools mpileup[69] at sites where there were >10 reads and a mutation call with VAF > 0.3. All such sites were subsequently masked across all samples for both mutation calls and coverage, which essentially extended the default common SNP mask to also include rare inherited variants across the cohort. This approach resulted in all samples previously identified as contaminated with mutation burdens consistent with their age and all with a ratio of masked to passed variants of <0.1, and were therefore retained for analysis.

Corrected mutation burdens

Given that mutation rates are strongly influenced by trinucleotide composition, it is important to consider differences in sequence composition when comparing mutation rates in datasets that target different regions of the genome[70]. The coding region target panels and the restriction enzyme used in whole-genome NanoSeq for instance, each will systematically bias sequencing coverage to specific genomic regions that may not reflect the full genome. To correct for these effects, in each sperm NanoSeq dataset, we generated a corrected mutation burden relative to the full genome trinucleotide frequencies as previously described[4].

Comparison of burden estimates

To compare whole-genome NanoSeq mutation burdens to mutation burdens from standard WGS, we multiplied the corrected mutation burden estimates described in the previous section by the genome size per cell type. We assumed 2,861,326,455 mappable base pairs in a haploid cell for germline datasets and the diploid equivalent for blood.

External datasets for comparison to NanoSeq results were processed to achieve comparable burden estimates. For testis WGS samples[71], we implemented a previously described method[4] that restricts analysis to regions with high coverage (20+ reads) that overlap with NanoSeq covered regions and corrected for differences in trinucleotide background frequencies relative to the full genome. For trio paternally phased DNMs from standard sequencing, as a callable genome size per sample following thorough filtering was available, we generated the mutation per cell estimate by multiplying the paternally phased DNM count by the ratio of the total genome size to the callable genome size of the sample as follows: DNMs paternal × (total genome/callable genome). For comparison with cell types in blood, we directly compared results to published mutation burden regressions[13].

Mutation burden regressions

To investigate the relationship between age and mutation burdens, we performed linear mixed-effects regression analyses. For each tissue and mutation type for which a regression was performed, the model was constructed using the lmer function from the lme4 package[72] in R. Each model included age at sampling as a fixed effect and a random slope for each individual to account for multiple time point samples, with restricted maximum likelihood (REML) set to false, specified as follows:

$${\rm{l}}{\rm{m}}{\rm{e}}{\rm{r}}({\rm{b}}{\rm{u}}{\rm{r}}{\rm{d}}{\rm{e}}{\rm{n}}\sim {\rm{a}}{\rm{g}}{\rm{e}}+({\rm{a}}{\rm{g}}{\rm{e}}-1|{\rm{i}}{\rm{n}}{\rm{d}}{\rm{i}}{\rm{v}}{\rm{i}}{\rm{d}}{\rm{u}}{\rm{a}}{\rm{l}}),\,{\rm{R}}{\rm{E}}{\rm{M}}{\rm{L}}={\rm{F}}{\rm{A}}{\rm{L}}{\rm{S}}{\rm{E}})$$

The 95% CIs for regression lines were calculated through bootstrapping by simulating prediction intervals. For each model, we generated 1,000 bootstrap samples. Predictions and their associated standard errors were calculated for a sequence of ages from 14 to 84 years. The 95% CIs were then derived by determining the range within which 95% of the bootstrap sample predictions fell.

Mutational signature analysis

We extracted DNM signatures using hierarchical dirichlet process (HDP; https://github.com/nicolaroberts/hdp), which is based on the Bayesian hierarchical dirichlet process. HDP was run with double hierarchy: (1) individual identifier (ID) and (2) tissue types (either blood or sperm), without the COSMIC reference signatures[73] (v.3.3) as priors, on the mutation matrices. The number of mutations were normalized for the trinucleotide context abundance specific for each sample relative to the full genome. Both clustering hyperparameters, beta and alpha, were set to one. The Gibbs samples ran for 30,000 burn-in iterations (parameter ‘burnin’), with a spacing of 200 iterations (parameter ‘space’), from which 100 iterations were collected (parameter ‘n’). After each Gibbs iteration, three iterations of concentration parameters were conducted (parameter ‘cpiter’). Two components were extracted as DNM signatures, which were further reconstructed and decomposed into known COSMIC (v.3.3) SBS signatures using SigProfilerAssignment (https://github.com/AlexandrovLab/SigProfilerAssignment).

Quantifying selection with dN/dS

To examine genes under positive selection and to quantify global selection, we used the dNdScv algorithm[28]. This algorithm was extended using the base-pair-level duplex coverage, the methylation level and the pentanucleotide context to capture more complex context-dependent mutational biases and to achieve higher accuracy for our selection analysis. Detailed methods for input mutations, model selection and evaluation, site dN/dS tests, driver mutation estimation and gene set enrichment are described in Supplementary Note 3.

Gene disease and mechanism annotation

Positively selected genes were annotated with monoallelic disease consequences using the 29 February 2024 release of the DDG2P database[39] and the 21 June 2024 release of the OMIM database[74]. OMIM annotations related to somatic disease, complex disease or tentative disease associations were excluded.

Genes were also annotated for their potential mutation mechanism observed in sperm and cancer or developmental disorders when available. In sperm, genes were labelled as LOF if they had nominal enrichment of nonsense + splice variants and/or indel variants (ptrunc_cv <0.1 | pind_cv <0.1) and 2+ LOF mutations. There were two exceptions to this, whereby genes met these thresholds but were labelled as activating owing to having a restricted repertoire of LOF mutations that are known to be oncogenic in cancers: CBL (LOFs in and downstream of the RING zinc finger domain)[75] and PPM1D (LOFs in final two exons)[76]. All other genes had missense enrichment only and were labelled as activating. The mechanism in cancer was defined by using the ‘role in cancer’ field of the COSMIC cancer gene census (v.99)[38], where ‘oncogene’ was labelled as activating and ‘tumour suppressor gene’ as LOF. Annotations of a fusion mechanism were not displayed, except for genes that had neither an oncogene nor a tumour suppressor annotation, which were labelled as ‘fusion only’. The developmental disorder mechanism was defined by using the variation consequence field of DDG2P, for which ‘restricted repertoire of mutations;activating’ was labelled as activating and ‘loss_of_function_variant’ was labelled as LOF.

Gene mutation plots

The lollipop gene mutation plots were created using a coordinate system, whereby position 1 was defined as the first coding base of the GRCh37-GencodeV18+Appris[77] transcript of the gene. The data sources included protein domains from UniProt[78], somatic mutations from the exome and genome-wide screens of the COSMIC (v.99)[38], ClinVar (release 2024.07.01)[41] pathogenic annotation, per base pair cohort-wide (targeted + exome) NanoSeq coverage, sperm mutation count (number of independent individuals with a mutation) and mutation consequence and amino acid change annotated by the dNdScv algorithm[28]. These data were plotted using code modified from the lolliplot function in the trackViewer R package[79].

Variant annotation

Variants were annotated using Ensembl’s variant effect predictor (VEP)[80] with added custom annotations of mutation context, ClinVar (release 2024.07.01)[41], CADD (v.GRCh37-v1.6)[42] and the average methylation level in testis. Methylation data were obtained from whole-genome shotgun bisulfite sequencing methylation data from male testis samples from a 37-year-old (ENCFF638QVP) and 54-year-old (ENCFF715DMX) from the ENCODE project[71]. The average methylation level was calculated by selecting CpG sites with coverage of three or more and averaging the per cent of sites methylated between the two samples.

Variants were annotated as likely monoallelic disease-causing mutations if they met at least one of two criteria: (1) reported in ClinVar as pathogenic, likely pathogenic or if they were reported as ‘conflicting classifications of pathogenicity’, where the conflict was between reports of pathogenic/likely pathogenic and ‘uncertain significance’ with no reports of benign or likely benign and not specified as a recessive condition; or (2) were a highly damaging variant in high-confidence monoallelic developmental disorder genes from DDG2P[39]. Genes met the following criteria: (1) allelic requirement being monoallelic_autosomal, monoallelic_X_hem, monoallelic_X_het or mitochondrial; (2) confidence in strong, definitive or moderate; and (3) a mutation consequence of ‘absent gene product’. Highly damaging was defined as being a ‘high’ impact variant in the VEP annotation (frameshift splice_acceptor, splice_donor, start_lost, stop_gained or stop_lost) or a missense variant with CADD[42] score >30 (top 0.1% damaging).

Variants were defined as a likely driver if they met the ‘highly damaging’ criteria defined above in a significant germline-selection gene with LOF mutation enrichment or if they were in 1 of the 24 significant mutation hotspots. This resulted in 320 variants being labelled as likely drivers in exome samples.

Cell fraction mutation estimates

To calculate the mean count of synonymous, missense or LOF or pathogenic mutations per sperm cell, we summed the duplex VAFs of all variants in that class. For example, if an individual had 3 synonymous mutations each observed once with a duplex coverage of 100 at each of those sites, each of those variants would have a duplex VAF of 1/100 = 0.01. The sum of VAFs in this example would then be 0.03 and this would then be reported as the estimate for the mean count of synonymous variants per sperm cell for that individual. At low fractions such as 0.03, the mean count per cell is approximately equivalent to the percentage of sperm with this mutation class (3%); therefore, the driver and disease mutations are reported as percentage estimates. At higher fractions (for example, mean count > 1), the fractions are not equivalent to percentage, as many cells will have multiple variants of that class; therefore, the estimates are reported as mean count.

Expected mean counts for SNVs were generated by annotating each possible substitution at each covered site with an expected number of mutations per sample as given by expCountSNV = context_mut_rate × duplex_coverage × age_correction. The context_mut_rate was given by the 208 basePairCov + cpgMeth trinucleotide mutation model estimates for that trinucleotide + methylation mutation context (Supplementary Note 3 and Supplementary Table 9). Duplex coverage is the exact duplex coverage at that site for that sample. The age_correction parameter was given to normalize the mutation model estimates (derived from all exome samples) to the mutation rate of that sample based on age. Specifically, we fit a linear model to the mutation burden versus age of the exome samples and used this to generate a predicted mutation rate for each sample based on age. The per sample correction parameter was calculated as the age predicted mutation burden divided by the mean mutation rate of all exome samples (3.89 × 10–8). The resulting corrections spanned from 0.50 (youngest sample) to 1.42 (oldest samples). The expected indel mutation rate was calculated in the same way, except with a single mutation rate parameter (indels/bp) expCountIndel = indel_mut_rate × duplex_coverage × age_correction. The expected mean count was then calculated for each category (for example, synonymous or likely disease) by summing the expected values for each SNV and/or indel base pair matching the relevant annotation. As background for possible ClinVar pathogenic variants, we only considered indels of size 21 bp or less, the largest detected indel in the dataset. Regressions were fit with either linear models or generalized linear models (glm in R) with family = quasibinomial.

Regression analysis

We tested for associations between mutation outcome variables from sperm genome, sperm exome, sperm targeted and blood genome NanoSeq data and the phenotype predictor variables of BMI, smoking pack-years and alcohol drink-years. These tests were performed using a Gaussian family generalized linear regression in R. For each mutation outcome variable the test took the following form: glm(mutationOutcome ~ age_at_sampling + BMI + pack_years + drinkYears, family = “gaussian”).

The mutation outcome variables examined were SNV and indel burden from all four sequencing datasets, SBS1 and SBS5 count from sperm genomes, SBS1, SBS5 and SBS19 from blood genomes, and likely disease cell fraction and likely driver cell fraction from sperm targeted and sperm exomes. The significance of each predictor was assessed from the summary output coefficients of the model, and P values were adjusted for 68 total tests using the false discovery rate method.

Reporting summary

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

Data availability

Sequencing data are available through managed access at the European Genome-Phenome Archive (EGA) under accession numbers EGAD00001015591 (targeted and exome NanoSeq), EGAD00001015590 (whole-genome NanoSeq) and EGAD00001015592 (WGS matched normals). All non-TwinsUK files necessary to reproduce the results are available from GitHub (https://github.com/mattnev17/spermPositiveSelectionManuscript). Individual-level data from the TwinsUK cohort cannot be publicly shared or deposited owing to restrictions in the original participant consent. Access to these data is governed by the TwinsUK Resource Executive Committee (TREC), which reviews requests on a monthly basis. For information on how to apply for access to TwinsUK genotype and phenotype data, please see the website: https://twinsuk.ac.uk/researchers/access-data-and-samples/request-access/. This study also made use of the following publicly available resources: DDG2P (https://www.deciphergenomics.org/ddd/ddgenes); OMIM (https://omim.org); COSMIC Cancer Gene Census (https://cancer.sanger.ac.uk/census); gnomAD (v.2.1.1; https://gnomad.broadinstitute.org); ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/); and bisulfite methylation data from the ENCODE Project (https://www.encodeproject.org).

Code availability

The NanoSeq variant calling pipeline, available from GitHub (https://github.com/cancerit/NanoSeq; v.3.3), was used to process duplex sequencing data. R (v.4.3.1) was used for statistical analyses and visualization. Code modified from trackViewer (R/Bioconductor package v.1.38.0) was used to generate gene mutation lollipop plots. lme4 (v.1.1-33) was used for linear mixed-effects models. ggplot2 (v.3.4.4) was used for plotting. dNdScv (https://github.com/im3sanger/dndscv; version as of commit on 29 September 2023) was used for selection analysis. HDP (https://github.com/nicolaroberts/hdp) was used for DNM mutational signature extraction. SigProfilerAssignment (v.0.0.27; https://github.com/AlexandrovLab/SigProfilerAssignment) was used for COSMIC signature fitting. All custom analysis code and scripts are available from GitHub (https://github.com/mattnev17/spermPositiveSelectionManuscript).

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Acknowledgements

We thank the TwinsUK research volunteers for participating in the study; L. O’Neill, C. Latimer and all of the CASM support team for their assistance; and C. Seymour, E. Ferraro and C. White for training and guidance on sperm counting. This research was funded by Wellcome Trust Grant 108413/A/15/D and 220540/Z/20/A. R.R. is funded by Cancer Research UK (C66259/A27114) and the Medical Research Council (MR/W025353/1). TwinsUK is funded by the Wellcome Trust, the Medical Research Council, Versus Arthritis, the European Union Horizon 2020, the Chronic Disease Research Foundation (CDRF), Zoe Ltd, the National Institute for Health and Care Research (NIHR) Clinical Research Network (CRN) and the Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London.

Author information

Authors and Affiliations

1. Cancer, Ageing and Somatic Mutation, Wellcome Sanger Institute, Hinxton, UK

Matthew D. C. Neville, Andrew R. J. Lawson, Rashesh Sanghvi, Federico Abascal, My H. Pham, Alex Cagan, Pantelis A. Nicola, Tetyana Bayzetinova, Adrian Baez-Ortega, Kirsty Roberts, Stefanie V. Lensing, Raul E. Alcantara, Michael R. Stratton, Peter J. Campbell, Iñigo Martincorena, Matthew E. Hurles & Raheleh Rahbari

2. Sequencing Operations, Wellcome Sanger Institute, Hinxton, UK

Stefanie V. Lensing & Sara Widaa

3. Quotient Therapeutics, Saffron Walden, UK

Raul E. Alcantara

4. Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK

María Paz García, Sam Wadge & Kerrin Small

Contributions

M.D.C.N., M.E.H. and R.R. wrote the manuscript. All authors reviewed and edited the manuscript. M.E.H. and R.R. supervised the project. M.P.G., S. Wadge, K.S. and R.R. led sample procurement. M.D.C.N., T.B. and P.A.N. conducted sperm counting. A.R.J.L., P.J.C., K.R., S.V.L., S. Widaa and I.M. contributed to sample sequencing implementation. M.D.C.N. led the analysis of the data with help from A.R.J.L., R.S., F.A., M.H.P., A.C., P.A.N., A.B.-O., R.E.A., M.R.S., P.J.C., I.M., M.E.H. and R.R.

Corresponding author

Correspondence to Raheleh Rahbari.

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Competing interests

I.M., M.R.S. and P.J.C. are co-founders, shareholders and consultants for Quotient Therapeutics. R.E.A. is an employee of Quotient Therapeutics. M.E.H. is a co-founder of, consultant to and holds shares in Congenica, a genetics diagnostic company. The remaining authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Sperm counting.

a,b,c, Slides of Papanicolaou stained semen samples for (a) an azoospermic sample where no sperm cells are visible, (b) an oligozoospermic sample where a small number of sperm samples are visible and (c) a normozoospermic sample where many sperm cells are visible. Sperm concentrations are given for each sample in millions of sperm per ml (M/ml). The black band in the bottom left of each slide photo corresponds to 100 µm. Staining was independently repeated once with similar results. d, The distribution of sperm counts on a log scale among semen samples analysed with colour bands indicating the concentration bin of the sample. All samples below 1 million/mL were subsequently excluded. e, The distributions of mutation burden per year from blood samples and three categories of sperm samples broken down by sperm concentration.

Extended Data Fig. 2 Sequencing method and coverage summary.

a, Graphical relationship between NanoSeq methods applied in manuscript. Both sperm and blood samples underwent genome NanoSeq, used for mutation burden and mutational signature analyses. Only sperm samples were used for targeted NanoSeq applied for selection, driver, and pathogenic variant analyses. Targeted NanoSeq is adaptable to different target panels, and we have named the sample sets as “targeted” for the samples using the 263 gene cancer panel and “exome” for samples using the exome wide panel. b, Mean duplex coverage (log scale) and percentage of genome covered (log scale) per sample. Panels summarise the mean duplex coverage (dx) and mean percentage of genome covered per NanoSeq type and tissue. c, Mutation burden of targeted (dark orange), exome (yellow), and genome (blue) sperm sequenced samples that were observed without correction (left), corrected for trinucleotide composition of covered base pairs relative to the whole genome (middle) or corrected and masked for mutations and coverage in the 44 genes linked to germline positive selection (right). Models are linear regressions, with the central line showing the model fit and the shaded bands indicating 95% confidence intervals.

Extended Data Fig. 3 Insertion deletion mutation profiles.

a,b, Distribution of indel types observed in whole genome (a) sperm and (b) blood.

Extended Data Fig. 4 Mutation rates relative to blood cell types and split by signatures.

a,b, Substitutions (a) and indels (b) per diploid cell from blood NanoSeq relative to specific blood cell types[13]. c,d, Substitutions per haploid cell for sperm (c) and diploid cell for blood (d) split by signature contributions of SBS1, SBS5, and SBS19. a,b,c,d, Models are linear mixed regressions, with the central line showing the model fit and the shaded bands indicating 95% confidence intervals calculated by parametric bootstrapping. e, Ratio of age-corrected blood to sperm substitutions per diploid cell per year for mutations assigned to SBS1 and SBS5. Each dot corresponds to an individual with both a blood and sperm sample and where individuals had multiple timepoints the mean value of all timepoints in that tissue was used. Box plots show the median as center line, the 25th and 75th percentiles as box limits, and whiskers extending to the largest and smallest values within 1.5× the interquartile range from the limits from n = 57 biologically independent samples.

Extended Data Fig. 5 Model selection dN/dS.

a,b, Mean duplex coverage (a) and methylation percentage (b) of all base pairs with exome sequencing coverage split by mutation consequence. c, C > T mutation rate point estimates at CpG sites in n = 38 biologically independent exome-sequenced sperm samples split by methylation bin based on percentage methylated from testis bisulfite sequencing. d, Comparison of global dN/dS ratio point estimates from mutations of n = 38 exome-sequenced sperm samples using different modifications to the dNdScv algorithm. Categories include all nonsynonymous mutations, missense, nonsense or essential splice. The basic model excludes genes with no coverage and uses default parameters. Additional models show the impact of adding corrections for duplex coverage per base pair (BasePairCov), CpG methylation level (CpGmeth), and pentanucleotide context (Penta). e, Comparison of per-gene significance in exome-wide (blue) or restricted hypothesis (orange) dN/dS tests using the different models. Genes that did not reach significance in either test are shown in grey. Error bars indicate 95% confidence intervals.

Extended Data Fig. 6 Gene mutation mechanisms.

a, dN/dS ratios for sperm SNVs across germline selection genes and cancer gene census genes split by ten canonical cancer pathways, where the dotted black line indicates neutrality and the dotted orange line represents the cohort average across all genes. b, The mutation mechanism assigned to each gene based on the mutation pattern in sperm, developmental disorders, and cancer (Methods).

Extended Data Fig. 7 Gene mutation patterns.

a,b,c,d,e,f, Observed sperm mutations across the cohort for six illustrative genes where the height of the “lollipop” represents the number of unique samples with a mutation at that location and the colour represents its mutation type. Mutations are labelled with their amino acid consequence for point substitutions or their insertion (ins)/deletion (del) consequence of in frame (IF) or frameshift (FS). A “P” indicates that the variant is classified as pathogenic/likely pathogenic in ClinVar. Exons are shown as purple rectangles and the blue background represents the total duplex coverage across the cohort. Lines below the gene indicate COSMIC somatic mutations in cancer within that gene.

Extended Data Fig. 8 Mean variant class count per individual by age.

The relationship between age and the mean count of SNVs (non-coding, synonymous, missense, and loss-of-function (nonsense or essential splice)) and indels (non-coding indel and coding indel) per sperm cell. The red points represent the observed values for each individual. The grey line represents the expected mutation count per sperm based on the germline mutation rate model. Error bands indicate 95% confidence intervals of linear regressions.

Extended Data Fig. 9 Phenotype correlations.

Correlation of cohort phenotypes with mutation outcomes across sequencing datasets. Associations were tested using two-sided generalised linear models (family = gaussian). P values were adjusted for multiple comparisons using the false discovery rate (FDR) method. Asterisks indicate FDR-corrected P value ranges: (*P value > 0.01 to <0.05, **P value > 0.001 to <0.01, ***P value < 0.001).

Extended Data Table 1 Significant SNV hotspots from dN/dS exome-wide and restricted hypothesis tests (RHT)

Supplementary information

Supplementary Information

Supplementary Notes 1–4 and supplementary references.

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Supplementary Tables 1–11.

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Neville, M.D.C., Lawson, A.R.J., Sanghvi, R. et al. Sperm sequencing reveals extensive positive selection in the male germline. Nature 647, 421–428 (2025). https://doi.org/10.1038/s41586-025-09448-3

- Received: 18 October 2024

- Accepted: 23 July 2025

- Published: 08 October 2025

- Version of record: 08 October 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09448-3

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Abstract

Men are at higher risk of several cancer types than women[1]. For bladder cancer the risk is four times higher for reasons that are not clear[2]. Smoking is also a principal risk factor for several tumour types, including bladder cancer[3]. As tumourigenesis is driven by somatic mutations, we wondered whether the landscape of clones in the normal bladder differs by sex and smoking history. Using ultradeep duplex DNA sequencing (approximately 5,000×), we identified thousands of clonal driver mutations in 16 genes across 79 normal bladder samples from 45 people. Men had significantly more truncating driver mutations in RBM10, CDKN1A and ARID1A than women, despite similar levels of non-protein-affecting mutations. This result indicates stronger positive selection on driver truncating mutations in these genes in the male urothelium. We also found activating TERT promoter mutations driving clonal expansions in the normal bladder that were associated strongly with age and smoking. These findings indicate that bladder cancer risk factors, such as sex and smoking, shape the clonal landscape of the normal urothelium. The high number of mutations identified by this approach offers a new strategy to study the functional effect of thousands of mutations in vivo—natural saturation mutagenesis—that can be extended to other human tissues.

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In the interplay between mutagenesis and selection, human somatic tissues evolve as mosaics of competing clones driven by mutations, many of which affect cancer genes4,5,6,7,8,9,10,11,12. Although most of these clones do not result in cancer, in some instances they constitute the first step of the evolutionary trajectory towards malignant tumours. Therefore, characterizing the clonal landscape of human tissues can provide a path to understanding the mechanisms of cancer formation and cancer risk. In the case of the bladder, men and people with smoking history have an increased risk of developing cancer, independently of other risk factors1,2 (Supplementary Note 1). The reasons for the sex bias, and whether it is also present in the clonal landscape of the normal urothelium, are not known. The role of smoking—whether purely mutagenic or also as promoter of mutant clones—is not well understood either.

The detection of mutant clones in normal urothelium, as in other normal tissues, is challenging because clones are small and escape detection by conventional bulk sequencing methods. Here we directly probed large mixtures of clones from epithelial brushes of normal bladder using ultradeep DNA duplex sequencing (around 5,000× per sample, amounting to approximately 400,000 haploid genomes in aggregate)13,14. We focused on the detection of clones driven by mutations in 16 genes known to be under positive selection in bladder urothelium10,15, and/or mutated frequently in bladder carcinomas16,17, including the telomerase gene (TERT) promoter[18]. We then used the magnitude of positive selection for each gene at the sample level to regress out the effect of sex and smoking on their clonal landscape.

We also considered that the capacity to identify the driver mutations of thousands of clones in a human tissue could increase markedly our ability to uncover the functional effect of all mutations in genes relevant in tumourigenesis. Identifying all potential driver mutations in a cancer gene is key to advancing personalized cancer medicine. The selective advantage provided by spontaneous mutations in millions of cells is tested in the interplay between mutagenesis and selection in normal human tissues. We should then be able to probe the results of these natural experiments by sequencing a large enough number of cells in a somatic tissue. We explore this postulate with data from ultradeep sequencing of normal bladder. As a result, we have uncovered a powerful tool with which to quantify positive selection at site resolution.

Mutation landscape of normal urothelium

We collected cells from either one or two regions—the upper (dome) and lower (trigone)—of the bladder urothelium from 45 deceased donors (79 samples) through a brushing of relatively large surface areas (roughly 2 cm^2) at autopsy (Fig. 1a and Supplementary Note 2). Clinical data, including exposure to known or suspected bladder cancer risk factors such as tobacco smoking or chemotherapy, was available for most donors (Supplementary Table 1). We used ultradeep DNA duplex sequencing13,14 (average depth ranging between 1,236 and 9,303 across samples, with a median of 5,164; Supplementary Note 2) and a newly developed and carefully calibrated computational pipeline to identify somatic mutations with high sensitivity and specificity in these polyclonal samples (Extended Data Fig. 1a–c and Supplementary Note 3). The accuracy of this technology allows the reliable detection of mutations that are present in a single DNA molecule (Extended Data Fig. 2a–c and Supplementary Note 4).

Fig. 1: Ultradeep DNA sequencing of normal urothelium targeting driver genes shows thousands of mutations.

a, Schematic representation of sampling and duplex DNA sequencing of polyclonal epithelial brushes from normal bladders. b, Number of SNVs detected in a panel of selected genes in the normal bladder in this study and comparison with the number of mutations detected in 892 tumours from bladder cancer genomics studies, obtained from intOGen[24]. c, Number and density of somatic mutations (SNVs, MNVs and indels up to 100 base pairs) identified in 79 samples of normal urothelium obtained from 45 donors. Bladder location (dome or trigone), age, sex and smoking history of donors are shown below the bar plots. d, Trinucleotide substitution profiles of the mutational signatures identified across this cohort of normal urothelium samples through de novo extraction. e, Top, scatter plot representing the relationship between the activity (number of mutations contributed) of SBS-ageing in samples and the age of donors; effect size, regression line and P value of a univariate mixed-effects linear model. Bottom, box plot representing the activity of SBS-chemo in donors exposed or not exposed to chemo/radiotherapy; effect size and P value of a univariate mixed-effects linear model. The boxplots show the quartiles with whiskers extending to the highest and lowest data points within 1.5 times the interquartile range; N = 79 samples for all plots in the panel. Mb, megabase. BioRender was used to create panel a (https://BioRender.com/fgnnet9).

To investigate how bladder cancer risk factors might change the clonal landscape of the urothelium, we focused on 15 genes identified previously to be under positive selection in normal bladder10,15 and/or known to frequently drive bladder tumours16,17 and the promoter of the gene encoding TERT, which is mutated in around 70% of bladder tumours18,19,20,21,22,23. This amounted to 111,876 base pairs of genomic DNA comprising the exonic regions and neighbouring intronic sites of the 15 genes (in full or selected fragments) and a region of the TERT promoter (hereinafter for simplicity, genes; Supplementary Tables 2, 3 and 4). Across samples, we identified a total of 64,278 mutations, between 54 and 1,785 (median 774) mutations per sample, including single nucleotide variants (SNVs), multiple nucleotide variants (MNVs) and indels up to 100 base pairs (Supplementary Table 5). This total number of mutations is approximately 16-fold higher than the number identified in the same genes across 892 bladder tumours (intOGen)[24] sequenced over a decade of cancer genomics (Fig. 1b,c).

The overall non-protein-affecting mutation density—mutations per megabase sequenced—was associated, as expected, with age and the exposure to certain mutagenic factors, such as several chemotherapies/radiotherapy (Supplementary Note 5). Using two orthogonal signature extraction methods25,26,27, and supported by the detection of a large number of non-protein-affecting mutations, we discerned the activity of three main mutational signatures (Supplementary Note 5 and Extended Data Fig. 3a–d). These captured the activity of the APOBEC family of cytidine deaminases[28] (SBS-APOBEC; Fig. 1d), age-related mutagenesis[29] (SBS-ageing; Fig. 1d,e) and an unknown process (harder to reduce to a linear combination of mutational signatures in the COSMIC catalogue[30]) that seemed to be associated with exposure to chemotherapy (SBS-chemo; Fig. 1d,e and Supplementary Note 5). Overall, the landscape of mutational signatures and the number of mutations per megabase sequenced was similar to that identified in a previous study of normal urothelium[10] (Extended Data Fig. 2b,c), with the exception of a signature reported to be associated with tobacco smoking. Mutational signatures were correlated highly between the dome and trigone of the same donor (Supplementary Note 5).

Driver mutations in normal bladder

The 16 genes included in the panel had been demonstrated previously to be under positive selection in normal urothelium or mutated frequently in bladder tumours. Thus, we first examined whether their mutations were positively selected in the samples included in this cohort. To this end, we used four complementary computational methods (Supplementary Note 6) on the basis of comparing features of the pattern of mutations observed in a gene with that expected assuming neutral evolution31,32.

First, we estimated the expected number of protein-affecting mutations with different impact, that is, missense or truncating (including nonsense and essential splice-site mutations) on the basis of the number of observed synonymous mutations in each gene across samples and in each sample using a newly developed method called Omega (Fig. 2a and Supplementary Note 6). This calculation takes into account the number of sites allowing amino acid changes of each type in the protein, the frequency of trinucleotide changes observed across samples and the depth at which each individual genomic site has been sequenced. Then, using a dN/dS approach[33], Omega computes the degree of positive selection on missense (dN/dS missense) or truncating (dN/dS truncating) mutations. These values represent the excess of missense or truncating mutations—driver mutations—among those observed in the gene in the sample (Fig. 2b). In addition to the excess of truncating and missense mutations, we quantified three other signals of selection: clustering of mutations in the three-dimensional structure of the protein, functional impact bias and excess of frameshift indels (Supplementary Note 6 and Extended Data Fig. 4a). These metrics indicated that RBM10, KDM6A, STAG2, KMT2D, ARID1A, CDKN1A, TP53, EP300, NOTCH2, CREBBP, FOXQ1, KMT2C and RB1—13 of the genes included in this study—are under positive selection, driving clonal expansion across the 79 normal urothelium samples (Extended Data Fig. 4b,c). We verified that the same is true for PIK3CA through analysis of the mutations in a hotspot affecting the histidine at position 1,047 (Extended Data Fig. 4d).

Fig. 2: Computing positive selection in the normal urothelium.

a, Distribution of truncating, missense and synonymous somatic mutations along the coding regions of RBM10 and TP53. Each needle represents the number of samples with mutations with one of the three consequences occurring on an amino acid residue. b, Magnitude of positive selection on truncating and missense mutations in RBM10 and TP53. Left, magnitude of positive selection indicated as the dN/dS ratio on the basis of the number of observed and expected mutations of each type. Right, percentage of driver mutations (observed in excess of neutrality) among truncating and missense in RBM10 and TP53. The number of observed, expected and estimated driver mutations are indicated for each gene. c, Magnitude of selection on truncating and missense mutations in FGFR3. dN/dS below 1 indicates negative selection. d, Magnitude of positive selection of activating mutations in the TERT promoter. Left, distribution of somatic mutations along the sequence of the TERT promoter colour coded according to whether they are activating (seen in tumours). Right, bar representing the magnitude of positive selection on activating TERT promoter mutations. In dN/dS bar plots (b–d), the shaded segment in each bar represents the number of truncating or missense (or activating in the case of TERT) mutations expected under neutrality. The numbers above the bar detail the value of dN/dS for each type of mutation. Numbers inside bars represent the mutations in excess over the expectation, that is, the drivers. The P values in b–d were calculated using a dN/dS approach (Omega) described in Supplementary Note 6.

This analysis produced two additional findings. First, we discovered that truncating (both SNVs and frameshift indels) mutations of FGFR3 seem to be under negative selection (Fig. 2c and Extended Data Fig. 4b,c). Second, we observed 85 SNVs affecting the TERT promoter (across 42 samples; Fig. 2d). The TERT gene encodes telomerase, and its promoter includes two hotspots that result in the creation of two de novo ETS transcription factor binding sites[34] and are mutated frequently in human tumours18,19,22,23. To formally compute the strength of positive selection on the TERT promoter in our cohort, we dichotomized the mutations on the basis of whether or not they have been observed several times across 8,136 tumour whole genomes (Supplementary Note 6; Methods), that is, using their presence in tumours as a surrogate of their capacity to activate the expression of TERT. We could thus calculate a modified dN/dS value for the TERT promoter (dN/dS pTERT; Supplementary Note 6). Although we expected fewer than one activating mutation, we actually observed 56, implying that at least 55 (98.2%) of these mutations detected in the TERT promoter are drivers of clonal expansion, probably by activating the expression of telomerase in normal urothelial cells.

We next computed positive selection separately for each sample in the cohort (Extended Data Fig. 5a). The high depth of sequencing in the study guarantees that these sample-level dN/dS values can be estimated accurately and robustly (Supplementary Notes 7 and 8). The number of clones driven by mutations detected in each of the 13 genes under positive selection varies between zero and several hundreds across the samples analysed from each donor (Fig. 3a). Despite the abundance of driver mutations, the relative size of the clones is very small and thus the fraction of the urothelium covered by mutant clones of these genes is modest (less than 1%) in most cases (Extended Data Fig. 5b). Some activating TERT promoter mutations reach relatively high values of variant allele frequency (VAF), probably due to both clonal expansion and convergent evolution.

Fig. 3: Sex bias of the clonal landscape of normal urothelium.

a, Heatmaps with number of driver SNVs (top) and the total number of protein-affecting indels (bottom) in 13 genes across female and male donors. For donors with dome and trigone samples (marked with a dot), the number of driver SNVs in both samples has been added. Donors are sorted by age in ascending order. b, Relationship between age and the density (per Mb) of protein-affecting and non-protein-affecting mutations. c, Association of age with the protein-affecting (top coefficient) and non-protein-affecting (bottom coefficient) mutation density (left), and the magnitude of positive selection on missense (centre) and truncating (right) mutations of 13 genes (gene–mutation consequence combinations for which we could calculate dN/dS at the level of sample for at least 80% of samples were included in the calculation). d, Distribution of dN/dS truncating values of RBM10, CDKN1A, ARID1A and STAG2 and dN/dS missense values of TP53 among male and female donors. Horizontal lines denote the median. e, Association of sex with the protein-affecting (top coefficient) and non-protein-affecting (bottom coefficient) mutation density (left), and the magnitude of positive selection on truncating mutations in RBM10, CDKN1A, ARID1A and STAG2 (right). The plots represent the results of multivariate regressions accounting for age, smoking history, alcohol drinking history, BMI and exposure to chemotherapy. In all regressions (c and e), multivariate linear mixed-effects models (accounting for sex, smoking history, alcohol drinking history, BMI and exposure to chemotherapy) were used to account for the presence of several samples of the same donor, and these models yielded the effect size and P values. Circles represent the point estimate of the effect size of the regressions; horizontal lines represent 95% confidence intervals. Circles with dark outer circumference denote significant associations (false discovery rate (FDR) threshold of 0.2). N = 79 samples in b, c and e. All corrected P values for these regressions appear in Supplementary Table 7. F, female donors; M, male donors.

We also found that the patterns of positive selection of the dome and trigone from the same donor were strikingly similar across the entire cohort (Extended Data Figs. 5c and 6a–c and Supplementary Note 5), indicating that the evolutionary dynamics and selective forces in the two regions are equivalent. Thus, a brushing of cells from approximately 2 cm^2 of the urothelium provides a good representation of the clonal structure of the entire tissue.

In summary, applying a dN/dS-based approach to mutations detected across urothelial samples through ultradeep sequencing, we demonstrated that a large percentage of mutations in the profiled genes are drivers, indicating pervasive positive selection; we uncovered negative selection for truncating mutations in FGFR3; we showed positive selection for activating mutations in the TERT promoter and we made robust estimations of positive selection and number of driver mutations per gene in each sample, thus enabling the study of inter-individual differences in clonal selection (Fig. 3a, Extended Data Fig. 7a,b and Supplementary Table 6).

Sex bias in the bladder clonal landscape

To identify a potential sex bias of the urothelial clonal landscape, we resorted to regressions that incorporated meaningful covariates, in particular age and history of smoking1,35,36,37 (Supplementary Note 1). Indeed, part of this heterogeneity across donors is explained by age. The density of protein-affecting mutations increases at a higher pace than that of non-protein-affecting mutations, indicating that clones driven by mutations of the 13 genes under positive selection expand with age, as reported for other normal tissues7,8,9,11,12,38,39 (Fig. 3b,c and Supplementary Note 9). In agreement with this, the dN/dS values of missense and truncating mutations of these genes are also associated significantly with age (Fig. 3c and Supplementary Table 7).

Using multivariate regressions, we determined that the values of dN/dS truncating of RBM10, CDKN1A and ARID1A are increased significantly in the urothelium of men compared with women, independently of age, smoking history, alcohol drinking history, body mass index (BMI) and exposure to chemotherapy (Fig. 3d,e, Extended Data Fig. 8a,b, Supplementary Table 7 and Supplementary Notes 9 and 10). In addition, the dN/dS truncating of STAG2 shows a tendency, although not significant, towards higher values in men. Two of these genes (RBM10 and STAG2) are in the X chromosome, whereas CDKN1A and ARID1A are in autosomes. KDM6A is another X chromosome gene that has been described previously with a higher number of mutations in female bladder tumours[40]. A closer inspection of X chromosome genes confirms the expectation of higher coverage across women for all three, although only RBM10 (and STAG2 to a lesser extent) shows a sex bias of dN/dS truncating (Supplementary Note 9). This implies that the growth of clones driven by mutations under positive selection in these genes is different in the urothelium of men and women (Supplementary Notes 9 and 10). The number of bladder tumours with truncating mutations in RBM10 and CDKN1A is also significantly higher across men than women (Extended Data Fig. 8c,d and Supplementary Note 11). In summary, clonal selection in normal bladder differs between men and women, mirrored by an increased number of mutations in RBM10 and CDKN1A in male bladder tumours.

TERT clones associate with age and smoking

We discovered that activating mutations in the promoter of TERT (that is, those observed at least twice in tumours18,19,20,21,22,23,34), were much more recurrent among older donors in the cohort. Specifically, all TERT promoter-activating mutations in the cohort, except one, occurred across donors older than 55 years (Fig. 4a,b). Furthermore, in this group, those with a history of smoking exhibited a significantly higher rate of TERT promoter mutations (Fig. 4a,b). Some of these mutations showed relatively large VAF, probably caused by several expanding clones with these mutations (Extended Data Fig. 8e,f and Supplementary Note 10). We found that the association between smoking history in older donors and the rate of TERT promoter-activating mutations is independent of sex, BMI and exposure to chemotherapy (Fig. 4c), as well as the depth of sequencing. The significant increase of activating TERT promoter mutations in smokers contrasts with the absence of association between smoking history and overall mutation density across samples (Supplementary Note 5 and Supplementary Table 7). This result indicates that tobacco carcinogens have a role in promoting the growth of clones with TERT promoter mutations. In summary, the rate of TERT promoter-activating mutations in the normal urothelium is associated significantly with the interaction of age and a history of smoking. More than 70% of bladder tumours have TERT promoter mutations, with a comparable frequency in smokers and non-smokers. In lung tumours, where the frequency of TERT promoter mutations is much lower, we find a significantly higher frequency in smokers (2.1%) compared with non-smokers (0.9%) (Supplementary Note 11). Collectively, these results indicate that exposure of the normal urothelium to tobacco carcinogens may increase the risk of bladder cancer through the promotion of clones bearing mutations of the TERT promoter.

Fig. 4: Association of activating mutations in the TERT promoter with smoking.

a, Number of activating (observed in tumours) and other (not observed in tumours) mutations in the TERT promoter across donors younger than 55 years (top), older than 55 years with no history of smoking (middle) or older than 55 years with a history of smoking (bottom). b, Density (mutations per Mb) of activating TERT promoter mutations observed in the three groups of donors. The horizontal line denotes the median of the distribution of the frequency of mutations in the group of donors older than 55 years with a history of smoking. The sample size of the three groups was 25 (samples from donors younger than 55), 14 (samples from donors older than 55 with no smoking history) and 38 (samples from donors older than 55 with smoking history), respectively. The two samples from a donor with unknown smoking history were excluded. c, Association between the frequency of activating mutations in the TERT promoter and the interaction between age and smoking history. A linear mixed-effects model was used to compute the P value. The circle represents the point estimate of the effect size of a multivariate linear mixed-effects regression, and the horizontal line represents 95% confidence intervals. The dark outer circumference denotes a significant association (FDR threshold of 0.2). The corrected P value appears in Supplementary Table 7. Number of samples for this analysis is the same as in a and b.

Natural human saturation mutagenesis

Experimental saturation mutagenesis (that is, the insertion of all possible mutations in a genomic element in experimental systems) has been used to identify sites or regions in proteins that are key for their structure, function or regulation, to understand their role in disease and improve the design of drugs to target them41,42,43,44,45. Recently, we introduced the concept of in silico saturation mutagenesis, exploiting more than 20 years of cancer genomics research on the natural experiments that are human tumours (or clonal expansions in normal blood) to build machine learning models that identify all their potential driver mutations46,47. In contrast to tumours, whose evolutionary history entails the expansion of a single clone, with only one or two mutations observed in a cancer driver gene in most cases, in normal polyclonal samples, several mutations affecting a given gene are detected as drivers of the expansion of several clones. Therefore, sequencing normal human tissues, which can also be considered natural experiments of clonal evolution, can provide a pathway towards natural saturation mutagenesis.

To understand how far along the ultradeep sequencing of roughly 400,000 haploid genomes takes us in the path towards natural saturation mutagenesis, we first calculated the number of mutations observed in each amino acid residue of the protein-coding genes (Fig. 5a). For example, we detected mutations (missense or nonsense) on 230 (58%) of the 394 amino acid residues of TP53 protein product covered by duplex sequencing (Fig. 5a). In normal urothelium, the frequency of mutations by gene differed from those in bladder cancers (Extended Data Fig. 9a) and the proportion of mutated residues was larger in all genes tested (Extended Data Fig. 9b), demonstrating different selective pressures and the advantage of studying mutations in normal tissue to reach saturation mutagenesis.

Fig. 5: Natural saturation mutagenesis.

a, Percentage of amino acid residues in each gene with zero, one, two or three or more mutations across the 79 samples. b, Theoretical and observed kinetic of natural saturation mutagenesis for TP53, EP300 and FGFR3 as cumulative depth of sequencing increases. Grey line, theoretical kinetic of saturation mutagenesis (assuming no selection). Red circle, saturation achieved across the cohort. Red dashed line, observed kinetic (obtained by downsampling). c, Natural saturation mutagenesis of TP53 in normal bladder urothelium. From top to bottom, distribution of truncating, missense and synonymous mutations along the coding sequence of the gene; site selection computed for each amino acid residue of TP53 protein product; solvent accessibility along the protein sequence; TP53 protein product domains; duplex sequencing depth per amino acid residue. Left top, TP53 protein product three-dimensional structure with significant site selection of residues highlighted in blue. d, Experimental functional impact[51] of TP53 mutations not observed, observed, or observed with significant site selection across the 79 samples. Only mutations with experimental functional impact reported in ref. [51] are included. e, dN/dS truncating and dN/dS missense values for each domain of TP53 protein product. The vertical lines represent the 95% confidence intervals of the dN/dS estimate. Solid border represents significant dN/dS values (P value < 0.05) according to Omega (Supplementary Note 6); N = 79 samples. f, Natural saturation mutagenesis of the TERT promoter. From top to bottom, distribution of mutations; site selection computed for observed mutations; experimental functional impact values of mutations in the TERT promoter according to ref. [55]; distribution of mutations observed in the TERT promoter across 8,136 tumours (Supplementary Note 6). g, Experimental functional impact[55] of mutations not observed, observed or observed in the TERT promoter with significant site selection across the 79 samples. h, Relationship between site selection and experimental functional impact value[55] of all mutations observed in the TERT promoter.

Next, we asked how many more genomes would need to be sequenced to observe mutations on all amino acid residues of each gene. To estimate this, we first calculated the probability to observe each mutation in a gene under neutrality, on the basis of the number of synonymous mutations and the trinucleotide mutational probabilities observed in our cohort. Thus, we obtained a theoretical curve describing the fraction of all possible mutations that are expected to be observed at different sequencing depths (theoretical kinetic of natural saturation mutagenesis; Fig. 5b (continuous curve), Extended Data Fig. 9c and Supplementary Note 12). The shape of this curve is determined by the difference in probability across mutations, governed by their trinucleotide contexts, with some mutations much more likely to occur than others. For example, for TP53, with the accumulated depth provided by the entire cohort (approximately 500,000×), only on the basis of neutral mutagenesis, we expected to have observed around 26% of all possible mutations. In theory, being able to detect every possible mutation in the gene would require an accumulated depth of around 10^7, assuming an aggregated mutation density and mutational signatures as the samples in the study.

Nevertheless, the observation of mutations in normal urothelium is not neutral, but instead is strongly affected by selection. To obtain the actual curve of the fraction of observed mutations depending on the number of sequenced genomes (observed kinetic of natural saturation mutagenesis), we sub-sampled the number of mutations observed in each gene across the cohort, respecting their VAF but reducing the depth of sequencing at each position (dots in Fig. 5b and Extended Data Fig. 9c). The divergence in the shape of the two curves shows the importance of selection in shaping the probability to observe a given fraction of mutations in a gene. For example, as missense and truncating TP53 mutations are under positive selection, we actually observed more mutations than predicted by the theoretical kinetic. For several genes, we observed this faster accumulation of mutations than expected under neutral mutagenesis due to positive selection (Extended Data Fig. 9c). The curve for FGFR3 falls below the theoretical curve constructed under purely neutral mutagenesis (Fig. 5b), which supports the notion that FGFR3 mutations are under negative selection in the normal urothelium.

Approaching natural saturation mutagenesis provides an opportunity to calculate the strength of positive selection on mutations affecting each amino acid residue (Supplementary Note 6), and for other within-gene structures such as exons or protein domains (Fig. 5c, Extended Data Figs. 9d,e, 10a and Supplementary Fig. 1). In the case of TP53, most sites under significant selection are in the p53 DNA-binding domain[48] (Fig. 5c), and are observed more frequently across tumours24,49,50 (Extended Data Fig. 10b), more likely to be identified as cancer drivers[46] (Extended Data Fig. 10b) and tend to score higher on an experimental saturation mutagenesis assay[51] (Fig. 5d), as described previously in duplex sequencing analysis of TP53 in other normal tissues52,53,54. In this and other genes that act as tumour suppressors, the mutations with significant site selection appear in buried areas of the protein (Fig. 5c, Extended Data Fig. 10c,d and Supplementary Fig. 2).

The calculation of dN/dS at the level of domains shows that only the p53 DNA-binding domain exhibits a significant excess of missense mutations (Fig. 5e). By contrast, TP53 dN/dS truncating values present a more homogeneous distribution across exons and domains (Fig. 5e and Supplementary Fig. 2). The same is true for other genes such as EP300 and CREBBP where only the histone acetyltransferase domain (HAT-KAT11) shows significant dN/dS missense values (Extended Data Fig. 9d and Supplementary Figs. 1 and 2). For RBM10, with a much stronger signal of truncating mutations, residues with the highest site selection are distributed more uniformly than in TP53 (Supplementary Fig. 2). Genes affected mostly by truncating driver mutations, such as STAG2 and RBM10, show a higher dN/dS truncating than dN/dS missense values for virtually all exons, except the first and last (Supplementary Fig. 2). Despite the fact that almost all genes sequenced are tumour suppressors, in all cases, a few SNVs exhibit much stronger site selection than the rest (Extended Data Fig. 10a and Supplementary Fig. 2). Whereas FGFR3 mutations showed negative selection overall (Fig. 2c), mutations affecting a single residue (G380) were positively selected in normal urothelium. This residue did not correspond to the three most common FGFR3 hotspots observed in bladder cancer (Supplementary Fig. 3).

TERT promoter mutations with significant site selection in normal urothelium appear more frequently across tumours (Extended Data Fig. 10e), and present overall higher experimental functional impact values[55] (Fig. 5f–h). In summary, natural saturation mutagenesis, brought about by the possibility of identifying mutations at extremely low VAF through ultradeep sequencing of normal tissues, opens up the possibility of directly assessing the functional impact of mutations affecting different genes in human tissues.

Discussion

The reasons behind the wide heterogeneity in clonal landscape observed across normal human tissues from different people7,8,9,10,11,12,38,39 and the potential role of known cancer risk factors are not understood. In this study we demonstrated the suitability of ultradeep DNA duplex sequencing13,14 (approximately 400,000 haploid genomes) to accurately measure the magnitude of positive selection on each of 16 genes in 79 individual samples (Supplementary Note 6). The high number of mutations identified proved key to showing associations between clonal expansions and bladder cancer risk factors. Specifically, we discovered that the magnitude of positive selection on truncating mutations in RBM10, ARID1A and CDKN1A is significantly higher among men even when adjusting for age, smoking history, alcohol drinking history, and BMI. Importantly, mutations in RBM10 and CDKN1A are also significantly more abundant in bladder cancers of men than of women (Supplementary Note 11). We can speculate that the observed differences between men and women could be related to differences in exposure to internal factors (for example, sex hormones) or extrinsic exposures that disproportionately affect men. Discerning whether the bias towards the expansion of clones bearing mutations in these and other genes is somehow related with the observed increased risk of bladder cancer across men requires further investigation. We also found a high increase of TERT promoter mutations among older people with a history of smoking, which may explain at least part of the link between smoking and increased bladder cancer risk35,36,37, given the high frequency of TERT promoter mutations across bladder tumours18,19,20,21,22,23,34. In this cohort, we do not find a specific smoking-associated mutational signature. The high increase of activating TERT promoter mutations in smokers, rather than a general increase in mutation density, points to a non-mutagenic—acting rather as promoter56,57,58—role of tobacco smoking in bladder tumourigenesis. Scaling up these analyses to probe the influence of other bladder cancer risk factors requires the possibility of obtaining samples in a minimally invasive manner, which could be conceivably achieved through urine or lavages[59].

The proposal of a pathway towards the study of natural saturation mutagenesis through ultradeep error-correcting duplex DNA sequencing of large mixtures of clones from normal tissues is a very compelling one. In this study we have provided a strong argument for the plausibility of this approach, paving the way for future studies. We have shown that it is possible to obtain a fine map of the degree of selection acting on mutations at individual sites or regions of a protein throughout the normal development of tissues of people exposed to different external factors. Saturation mutagenesis—a key development towards personalized cancer medicine[60]—through ultradeep sequencing in human tissues, thus provides a cost-effective and direct alternative to complement experimental and in silico approaches. In addition, characterization of the patterns of positive (and negative) selection in different genes and human tissues will be critical to understanding how somatic evolution influences cancer risk.

Methods

Sample collection

Two epithelial brushes (2–3 cm^2) from the bladder top (dome) and the bladder floor (trigone) were obtained from 53 people without known bladder pathology and no history of bladder cancer upon autopsy (average 4 days post-mortem) at the University of Washington. Next of kin consented to autopsies and research on leftover specimens. The study of de-identified collected specimens and linked clinical history from the deceased donors was reviewed and deemed not human subjects research by the University of Washington Institutional Review Board (STUDY00016707; IRB Federal Wide Assurance number, FWA 00006878). We obtained the following relevant clinical information for all donors: age, sex, BMI, tobacco smoking history, alcohol use, previous cancer and chemotherapy exposure (Supplementary Table 1). Three donors with active or chronic inflammation and four donors with insufficient DNA were discarded. The two samples from another donor were also discarded from the study upon visual inspection of their mutational profile, resulting in a total of 45 deceased donors in this study (Supplementary Notes 2 and 3).

Duplex DNA sequencing

Capture panel design

We designed a panel including ten genes identified in a previous study as being under positive selection in the normal urothelium and with more than ten mutations across the 1,647 microbiopsies analysed[10]. We added five genes that are mutated frequently in bladder tumours[30] and the TERT promoter—also known to be under positive selection in bladder carcinomas22,23. This resulted in a panel containing the entire (or almost entire) coding region of 12 genes (ARID1A, NOTCH2, FOXQ1, CDKN1A, KMT2D, RB1, CREBBP, TP53, EP300, KDM6A, RBM10 and STAG2), three genes for which only selected regions were targeted because of clustering of cancer mutations in those regions and/or difficulties for capturing the full gene (PIK3CA, FGFR3 and KMT2C) and the TERT promoter10,61. The panel was constructed by TwinStrand Biosciences and covered 111,876 base pairs, including 65,086 base pairs in coding regions and 46,790 base pairs in non-coding regions (Supplementary Tables 2 and 3 and Supplementary Note 2).

DNA extraction, duplex library preparation and sequencing

After centrifugation of epithelial brushes, DNA was extracted using the DNeasy Blood and Tissue (Qiagen) kit, following manufacturer’s instructions with some variations (Supplementary Note 2). The DNA integrity number (DIN) was measured using Agilent 4200 TapeStation Genomic tapes. Duplex sequencing libraries were prepared using commercially available kits (TwinStrand Biosciences)13,14,62,63,64 and 250 ng of genomic DNA. The DNA fragmentation step was carried out taking into account the starting DIN of samples and monitored using TapeStation. Fragmented DNA was subject to end-repair, A-tailing, ligation to duplex sequencing adaptors, library conditioning and PCR amplification, according to protocol. The PCR product was captured at 65 °C for 16–20 h. Upon PCR amplification, libraries were pooled for sequencing. Sequencing was performed with a NovaSeq 6000 at the Department of Laboratory Medicine and Pathology at University of Washington or a NovaSeq X Plus at Novogene or the Fred Hutchinson Cancer Center using 2 × 150 base-pair paired-end reads (around 115 million reads per sample; Supplementary Note 2).

Duplex DNA sequencing was successful for 34 donors on both samples. For 11 other donors, we produced duplex DNA sequencing data only for dome or trigone because of insufficient DNA or too fragmented DNA (DIN < 1.4) in the paired sample. Thus, the final number of donors with available data for at least one sample was 45 (Supplementary Table 1) and the final number of samples processed was 79 (Supplementary Table 4).

Somatic mutation calling

We constructed a computational pipeline (deepUMIcaller) in Nextflow[65] to call mutations from duplex sequencing data on the basis of an early version of nf-core/fastquorum pipeline[66], which implements the fgbio Best Practices FASTQ to Consensus Pipeline (https://github.com/fulcrumgenomics/fgbio/blob/main/docs/best-practice-consensus-pipeline.md) and downstream variant calling by VarDictJava (https://github.com/AstraZeneca-NGS/VarDictJava). A series of filters to discard potential artefacts are included in the pipeline. Code implementing deepUMIcaller is available at (github.com/bbglab/deepumicaller). For a detailed description of the pipeline, see Supplementary Note 3.

To estimate the error rate of TwinStrand DNA duplex sequencing, we compared the density of mutations and mutational profile identified across three cord blood samples (purchased from StemCell) with that expected on the basis of colonies obtained from human haematopoietic stem cells67,68. The estimated error rate resulting from this analysis was around 4 × 10−8, which is two orders of magnitude lower than the mutation density across samples in this study (Extended Data Fig. 2a and Supplementary Notes 4 and 8).

Mutational signatures

De novo signature extraction

We extracted mutational signatures de novo with a Bayesian hierarchical Dirichlet process using HDP_sigExtraction pipeline (https://github.com/McGranahanLab/HDP_sigExtraction) and the R-package hdp developed by N. Roberts (https://github.com/nicolaroberts/hdp)[9] and SigProfilerExtractor (https://github.com/AlexandrovLab/SigProfilerExtractor)[26]. The HDP_sigExtraction pipeline was run with the default parameters with no previous signatures assigned. Five signatures in addition to the null signature were extracted. De novo signatures were extracted using SigProfiler using the nonnegative matrix factorization approach. The same input data were used. The upper bound for the number of signatures was set to ten; however, the most robust solution was three signatures that were similar to the three most active signatures extracted by HDP. These two sets of mutational signatures were decomposed into known COSMIC signatures when possible (Supplementary Note 5).

Assessing biases in trinucleotide composition of the panel

To account for biases due to the trinucleotide composition of the panel, the number of substitutions of each class was re-calculated. The rate of each substitution was calculated as the number of observed substitutions with the consequence in question divided by the number of corresponding sequenced trinucleotide sites (number of trinucleotides with this consequence in the panel weighted by sequencing depth). This mutational probability was multiplied by the number of the corresponding trinucleotide in the genome to get the expected number of substitutions of this type in the whole genome. The mutational probability of each substitution for the whole genome was calculated by dividing the expected number of substitutions with this consequence per genome by the sum of all expected substitutions per genome. Finally, the expected number of substitutions with each consequence was obtained by multiplying the probability by the total number of substitutions observed in the sample to keep the absolute number of observed mutations.

Statistical association between mutational signatures and clinical variables

We tested for possible associations of de novo extracted signatures with age, sex and bladder location. To do this we used linear mixed-effect regression models (Supplementary Note 5). We tested both the number of mutations attributed to the signature (counts) and the relative contribution (proportion) of each signature as a response variable. Age, sex and bladder location were used as fixed effects (separated regression was prepared for each fixed effect variable) and donors were used as random effects to control for non-independence between dome and trigone samples from the same person. P values for the association were obtained using likelihood-ratio tests (ANOVA function) comparing models including and excluding the variable of interest. Associations with all other clinical variables (smoking history, drinking history, chemotherapy history, and so on) were tested in the same way but always including age, sex and bladder location in the regression together with the variable of interest.

Positive selection

We used four methods to compute positive selection on the mutations observed across genes. One method (Omega)—a dN/dS approach to assess the strength of selection on the mutational pattern of genes—was developed de novo for this study and is described at length in Supplementary Note 6 (https://github.com/bbglab/omega). Two others, OncodriveFML and Oncodrive3D, which compute the deviation in the average functional impact and clustering in the three-dimensional structure of proteins, respectively, from those expected under neutrality, had been developed previously, and were adapted here to work on duplex sequencing data. These are also described in Supplementary Note 6. A fourth method, assessing the relative enrichment for frameshift indels observed across genes was also developed de novo for this study and is described thoroughly in Supplementary Note 6. For the TERT promoter, all mutations that were observed at least twice across 8,136 whole-genome sequencing tumour samples sequenced by the Hartwig Medical Foundation and the Pan-Cancer Analysis of Whole Genomes consortium (Supplementary Note 6) were considered activating. The remaining mutations were used to calculate the expected number of activating mutations under neutrality.

Fraction of the urothelium covered by clones with driver mutations

The number of missense and truncating driver mutations of each gene in a sample was obtained from the dN/dS missense and dN/dS truncating values. From these values the number of missense and truncating mutations in excess in each sample were calculated. Specifically, the 95% confidence intervals of both dN/dS values were used to compute two extreme values of driver mutations in each sample, whereas the mean dN/dS value was used to compute an expected number of driver mutations.

This mean number was thus used to select the most likely driver mutations in the sample. To this end, mutations were ranked in descending order according to their VAF, and the top number of mutations corresponding to the expected number of driver mutations were selected. Then, for a gene G, we computed the fraction of genomes bearing driver mutations out of those sequenced at each genomic position covered by the DNA duplex sequencing as:

$$P(G)=\mathop{\sum }\limits_{i=1}^{n}{(-1)}^{i-1}\sum _{| S| =i}\prod _{x\in S}{p}_{x}=\sum _{x}{p}_{x}-\sum _{x\ne y}{p}_{x}{p}_{y}+\cdots +{(-1)}^{n-1}{p}_{{x}_{1}}{p}_{{x}_{2}}\cdots {p}_{{x}_{n}}$$

where px is the all-molecules VAF of a driver mutation x (considering both duplex and non-duplex reads; Supplementary Note 3) and n is the number of driver mutations from the previously selected set in the gene. The formula is the realization of the probabilistic principle of inclusion-exclusion assuming independence across mutations[69].

The fraction of genomes with driver mutations is equal to the fraction of cells with driver mutations under the assumption that two mutations, one in each homologous copy of the gene, are required to drive the clonal expansion. This extreme would be true in the case that all studied genes behave as classical tumour suppressors and bear deleterious mutations in both alleles. An exception to this rule are the mutations in genes in the X chromosome in men, whereby one mutation would suffice to drive the clonal expansion. In general, it is possible for some genes to be capable of driving the clonal expansion (or mutations at specific positions in a gene) with only one mutated allele. It is also possible that the other allele is affected by a large deletion or methylation event not seen by DNA duplex sequencing. In the extreme that only one mutation per cell per gene is required, the fraction of cells with driver mutations will be double the fraction of genomes. In the plot in Extended Data Fig. 5b, we use the two-hit assumption.

Association of the urothelium clonal structure with bladder cancer risk factors

We designed a two-step strategy to assess the influence of known bladder cancer risk factors on features of the urothelium clonal structure across samples from donors in the cohort. As dependent variables representing the urothelium clonal structure, we selected the (protein-affecting and non-protein-affecting) mutation density, and the magnitude of positive selection on genic mutations (dN/dS missense and dN/dS truncating). As the dependent variables are continuous, we chose linear regressions, specifically mixed-effects linear models, to account for two samples from the same donor and use all available samples. As independent variables, we included a list of available clinical features: age (in decades), sex, smoking history (binarized as ever or never smoker), alcohol consumption history (also binarized), BMI (re-scaled within the 0–1 interval), and exposure to chemo/radiotherapy (binarized). For the case of TERT promoter mutation density association, we used the interaction of age and smoking history instead of age and smoking history separately. In the first step, we applied univariate mixed-effects linear models to identify any clinical feature with a significant association with any of the dependent variables for any gene or for all genes. In the second step, we applied multivariate mixed-effects linear models to rule out confounding effects for the associations that seemed significant in the first step. Associations with FDR below 0.2 were deemed significant. We also carried out a binomial test to rule out a spurious dependence of the mutation density on group differences in terms of sequencing depth. For details, see Supplementary Notes 9 and 10.

Tumour data

Mutations identified across 622 muscle-invasive and 105 non-muscle-invasive bladder cancer (MIBC and NMBIC, respectively) cohorts were downloaded from cBioPortal49,70,71,72,73 together with the clinical data of the combined study. From the MIBC Beijing Genomics Institute cohort[74] only samples labelled as invasive were considered MIBC and added to the MIBC dataset. Mutations identified in a further cohort of 79 NMIBCs were obtained from the literature[40] and included as part of the NMIBC dataset. Only non-silent protein-affecting mutations in 14 of the genes included in the panel were analysed. In case of duplicated samples across MIBC and NMBIC cohorts, the mutations from the most recent published study were kept. In total, mutations across 806 bladder tumours were obtained (622 MIBC and 184 NMIBC). The mutation density per gene across the two datasets was calculated by dividing the number of observed mutations per gene by the length of its coding region. To compute a mutation density metric comparable with that used in the normal urothelium that accounts for the number of megabases sequenced, we multiplied the coding length by two times the number of samples in which that gene was sequenced, to take into account the two copies in a diploid genome. Under the assumption that, in tumours each mutation belongs to a single clonal expansion and in normal tissues we have a mixture of clones, we reasoned that the number of different tumour genomes sequenced would be equivalent to the number of genomes sequenced in normal urothelium. The mutation density per megabase was calculated multiplying the mutation density by 10^6.

Mutations identified across 33,218 tumours (892 bladder tumours, mostly MIBC) in intOGen[24] were downloaded from intogen.org. These mutations were used to obtain the total number of mutations observed in each gene, their distribution along the sequence of the genes in the study and the percentage of sites affected by different numbers of mutations. The same data of 109,017 tumours (3,909 bladder tumours) were obtained from the GENIE project[50] to calculate the frequency of mutations in each of the genes and the TERT promoter. Mutations in the TERT promoter were also obtained from two cohorts of tumours (included in intOGen) sequenced at the whole-genome level (Hartwig Medical Foundation[21]: N = 5,582 and Pan-Cancer Analysis of Whole Genomes[19]: N = 2,554). The classification (and score) of all possible mutations in TP53 into drivers and passengers through in silico saturation mutagenesis was obtained from boostDM (intogen.org/boostDM). Details of analyses involving tumour mutations appear in Supplementary Note 11.

Natural saturation mutagenesis

To compare the distribution of somatic mutations along the sequence of each of the genes in normal urothelium and bladder tumours, we first downloaded somatic mutations identified across 892 bladder tumours from the intOGen (intogen.org) platform. Although this cohort is larger than the 806 muscle-invasive and non-muscle-invasive tumours used to explore differences in mutation density, it is composed mostly of muscle-invasive carcinomas.

For each gene, we started the analysis with all genomic sites in the coding sequences and splicing sites that passed the pipeline’s filtering criteria of sufficient duplex coverage across samples (Supplementary Note 3). We then mapped these DNA sites to protein positions using the Ensembl REST API[75], allowing us to determine the total number of protein positions (including amino acid residues and stop codon) covered by the panel. Then, we computed the number of mutations affecting each protein residue of every gene studied here across the 79 normal urothelium samples and the 892 bladder tumours. Next, we obtained the consequence type (missense, synonymous, nonsense or splice-affecting) of these mutations on the MANE transcript[76] of each gene from the output of the Variant Effect Predictor v.111 (ref. [77]), run within the intOGen pipeline[24] (https://github.com/bbglab/intogen-plus). The method to compute the natural saturation mutagenesis kinetics in Fig. 5b is described in Supplementary Note 12. The residue-level sequencing depth shown in Fig. 5c, Extended Data Fig. 9d and Supplementary Figs. 2 and 3 was calculated as the average depth across all sites corresponding to each codon.

Calculation of site selection

We developed a metric to measure selection per site by comparing the observed with the expected number of mutations at each site or residue. The expected number of mutations per site was obtained by distributing the expected number of mutations in a given gene under neutrality along all the possible changes in that same genomic region, with the assumption that only the mutation probability of each trinucleotide and the sequencing depth are responsible for the within-gene differences of mutation probability. We computed this value for each possible mutation in the positively selected genes including the TERT promoter, and for the protein-coding genes we also obtained a value per residue. A more complete explanation on this metric (and the detection of positive selection in sub-genic structures such as exons and domains) can be found in Supplementary Note 6.

Structural representation and features

Structural models for all proteins used to run Oncodrive3D were obtained from the AlphaFold database (AlphaFold 2 v.4)78,79. Three-dimensional protein structures and protein structural features represented across figures were also obtained from the AlphaFold database. Solvent accessibility and secondary structure information were extracted from the AlphaFold-predicted PDB structures using PDB_Tool (https://github.com/realbigws/PDB_Tool). Structural visualizations of proteins were produced using UCSF ChimeraX[80].

Comparison with experimental saturation mutagenesis

The results of two saturation mutagenesis experiments estimating the functional impact of mutations in the TP53 DNA-binding domain[51] and along the sequence of the TERT promoter[55] were obtained directly from supplementary tables from both papers. In the case of TP53, we used the transformed score. In the case of the TERT promoter, values calculated for the glioblastoma SF7996 cells system were used. In these comparisons, the site selection of individual mutations was used.

Comparison of orthogonal studies of normal bladder

We obtained the mutations identified through whole-genome sequencing in clonal or quasi-clonal samples obtained from the normal bladder of donors by laser capture microdissection in a previous study[10]. We used these samples to construct the mutational profile of normal urothelium, which we compared with that reconstructed in the present study through ultradeep sequencing (Extended Data Fig. 2b). From the same previous study, we obtained the mutations identified through whole-exome sequencing. Mutations obtained from samples sequenced at depth lower than 80× were discarded. From these data, we calculated the number of mutations per megabase observed in each sample, and averaged this value across donors. A linear regression was then constructed between these values and the age of donors, and a trend line calculated. The number of mutations per megabase calculated across the samples in our cohort was overlaid upon the obtained regression. The results of this comparison are presented in Extended Data Fig. 2c.

Reporting summary

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

Data availability

Raw sequencing data for this study were deposited in dbGaP under accession number phs004105.v1.p1. The set of mutations used in all analyses presented in the paper is available at Zenodo (https://doi.org/10.5281/zenodo.15836679)[81]. Executing the code provided in the third repository mentioned below, all figures in the paper can be reproduced. Reference mutational signatures were obtained from https://cancer.sanger.ac.uk/signatures/sbs/. Tumour mutations were obtained through cBioPortal (datasets from refs. [16],17,74 at https://www.cbioportal.org/), intogen (intogen.org) and the GENIE synapse data portal (https://genie.synapse.org/) as described in Methods. Protein structural models for the entire human proteome were obtained from the AlphaFold database (https://alphafold.ebi.ac.uk/). The results of two experimental saturation mutagenesis studies on TP53 and the TERT promoter were obtained from refs. [51] and [55], respectively.

Code availability

The code of the mutation calling (deepUMIcaller) pipeline is available at (https://github.com/bbglab/deepUMIcaller). The code of a pipeline containing all analyses described in this manuscript (deepCSA) is available at (https://github.com/bbglab/deepCSA). Code needed to reproduce paper figures using the data in the Zenodo repository mentioned above is available at (https://github.com/bbglab/normal_bladder_paper).

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Acknowledgements

We acknowledge the donors and their families for generously contributing their samples. We thank the University of Washington autopsy service staff including B. McGing and J. Grillo, who were instrumental in sample collection, and N. Jackson and K. Scherpelz for facilitating and supporting autopsy collection. We also thank C. Coombes for revising the manuscript, E. Jonlin for regulatory support and F. Brando for technical support in the pipeline for data analysis. We would also like to thank the nf-core community for developing the nf-core infrastructure and resources for Nextflow pipelines. Institute for Research in Biomedicine Barcelona is a recipient of a Severo Ochoa Centre of Excellence Award from the Spanish Ministry of Economy and Competitiveness (MINECO; Government of Spain) and an Excellence Institutional grant by the Asociacion Española contra el Cancer, and is supported by CERCA (Generalitat de Catalunya). R.B.M.-I. acknowledges support from a fellowship from the Ministerio de Universidades para la Formación de Profesorado Universitario (FPU21/05649). F.C. acknowledges support of fellowship PRE2022-101268, funded by MICIU/AEI /10.13039/501100011033 and by FSE+. A.R.H. is supported by an EMBO long-term fellowship (ALTF 1058-2022). J.E.R.-Z. was supported by a Postdoctoral AECC 2023 fellowship from Fundación Científica Asociación Española Contra el Cáncer (POSTD234814RAMI). This work was delivered as part of the PROMINENT team supported by the Cancer Grand Challenges partnership funded by Cancer Research UK (CGCATF-2021/100008), the NCI (OT2CA278668) and the Scientific Foundation of the Spanish Association Against Cancer, AECC. This research has been conducted using the UK Biobank Resource under application number 69794. This research was funded with two Research Grants (2020, 2023) from the Department of Laboratory Medicine and Pathology, University of Washington and support from NCI R01 CA259384 (R.A.R.). Components of Fig. 1a and Supplementary Fig. 10 were created using BioRender (https://BioRender.com/fgnnet9).

Author information

Author notes

1. These authors contributed equally: Ferriol Calvet, Raquel Blanco Martinez-Illescas

2. These authors jointly supervised this work: Abel Gonzalez-Perez, Nuria Lopez-Bigas, Rosa Ana Risques

Authors and Affiliations

1. Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain

Ferriol Calvet, Raquel Blanco Martinez-Illescas, Ferran Muiños, Maria Andrianova, Stefano Pellegrini, Axel Rosendahl Huber, Joan Enric Ramis-Zaldivar, Miguel L. Grau, Abel Gonzalez-Perez & Nuria Lopez-Bigas

2. Centro de Investigación Biomédica en Red en Cáncer (CIBERONC), Instituto de Salud Carlos III, Madrid, Spain

Ferriol Calvet, Raquel Blanco Martinez-Illescas, Ferran Muiños, Stefano Pellegrini, Joan Enric Ramis-Zaldivar, Abel Gonzalez-Perez & Nuria Lopez-Bigas

3. Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain

Ferriol Calvet, Raquel Blanco Martinez-Illescas, Stefano Pellegrini, Abel Gonzalez-Perez & Nuria Lopez-Bigas

4. Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA

Maria Tretiakova, Elena S. Latorre-Esteves, Jeanne Fredrickson, Shuyi Charlotte An, Elana Thieme, Brendan F. Kohrn & Rosa Ana Risques

5. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

Nuria Lopez-Bigas

Contributions

F.C. designed, coded and validated the DNA duplex calling and analysis pipelines, designed and carried out data analysis, participated in data interpretation (including duplex mutation calling, computation of mutation density, study of variability between samples and individuals, and association of these metrics with bladder cancer risk factors and saturation mutagenesis), and participated in drafting and revising the manuscript. R.B.M.-I. participated in coding specific parts of the DNA duplex analysis pipeline, designed and carried out data analysis (including duplex mutation calling, computation of mutation density, estimation of positive selection, study of variability between samples and individuals, and association of these metrics with bladder cancer risk factors) and participated in data interpretation, drafting and revising the manuscript. F.M. participated in coding specific parts of the DNA duplex analysis pipeline, designed and carried out specific data analyses (including estimation of positive selection, association of these metrics with bladder cancer risk factors and saturation mutagenesis) and participated in data interpretation and in revising the manuscript and drafting supplementary information. M.T. designed, performed and supervised sample collection, retrieved clinical information, reviewed pathological findings, participated in project design and obtained funding. E.S.L.-E. performed optimization of enzymatic fragmentation, sample processing, and patient and sample data curation. J.F. performed optimization of enzymatic fragmentation, sample processing, and patient and samples data curation, contributed to the design and optimization of the capture panel, and supervised sequencing and data quality control. M.A. performed the mutational signature analysis and participated in data interpretation and in revising the manuscript and drafting supplementary information. S.P. participated in the analysis of positive selection signals, prepared plots (including saturation mutagenesis plots), and participated in data interpretation and in revising the manuscript and drafting supplementary information. A.R.H. carried out the analysis of the expected fraction of mutated urothelium and participated in data interpretation and in revising the manuscript and drafting supplementary information. J.E.R.-Z. did the epidemiological analyses with UK BioBank data and participated in data interpretation and in revising the manuscript and drafting supplementary information. S.C.A. contributed to sample processing. E.T. contributed to sample processing and data analysis. B.F.K. performed data processing and quality control data analysis, contributed to the design and optimization of the capture panel and supervised sequencing and data quality control. M.L.G. contributed to the development of the DNA duplex calling pipeline. A.G.-P. conceptualized and supervised the project, participated in the interpretation of results and led the drafting of the manuscript. N.L.-B. conceptualized and supervised the project, obtained funding, participated in the interpretation of results and led the drafting of the manuscript. R.A.R. designed the project, obtained funding, supervised sample collection, contributed to the design and optimization of the capture panel, supervised sequencing and data quality control, conceptualized and supervised the project, participated in the interpretation of results and led the drafting of the manuscript. All authors have read and approved the manuscript. F.C. and R.B.M-I. contributed equally to the manuscript and author order was decided randomly.

Corresponding authors

Correspondence to Nuria Lopez-Bigas or Rosa Ana Risques.

Ethics declarations

Competing interests

R.A.R. is an equity holder at TwinStrand Biosciences Inc. and NanoString Technologies Inc. R.A.R. is named inventor on patent no. 11,479,807 (Methods for targeted nucleic acid sequence enrichment with applications to error corrected nucleic acid sequencing) owned by the University of Washington and licensed to TwinStrand Biosciences Inc. R.A.R. was a consultant at TwinStrand Biosciences Inc. and received research funding from a joint research grant with TwinStrand Biosciences Inc. and Ovartec GmbH. B.F.K. is an equity holder at NanoString Technologies Inc. The other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Ultradeep sequencing of a mixture of urothelial clones.

a) Depth of DNA duplex sequencing across the 79 samples. The boxplots represent the distribution of sequencing depth across all genes included in the panel, while the dots represent the average sequencing depth obtained for each gene in each sample. b) Distribution of DNA duplex sequencing depth across the 16 genes. The boxplots represent the distribution of sequencing depth across all samples, and the dots represent the average sequencing depth per gene in each sample. c) Average DNA duplex sequencing depth obtained for each exon included in the panel, represented by the color of each tile according to the color scale in the colorbar on the right. White color represents the exon is not covered. Box plots in a and b display the quartiles with whiskers extending to the highest and lowest data points within 1.5 times the interquartile range. Details can be found in Supplementary Note 3.

Extended Data Fig. 2 Error rate of the DNA duplex sequencing technology.

a) Left panel, orange bars, mutations per sequenced nucleotide detected by the DNA duplex sequencing technology in this study using the same panel of bladder genes in cord blood DNA samples from three donors. Blue bars (for comparison), mutations per sequenced nucleotide detected by a similar technology (NanoSeq; data taken from ref. [82]) in two cord blood samples (Supplementary Note 4). Each bar presents the mutation density computed for a separate cord blood sample, with vertical lines representing the Poisson 95% confidence intervals. The comparison of the number of observed mutations per sequenced nucleotide with those expected in cord blood DNA based on prior studies (dashed red line, Supplementary Note 4) yields an estimate of the error rate of the technology of ~4 × 10−8 per sequenced nucleotide. Right panel, comparison of the estimated error rate by both technologies with the mutations per sequence nucleotide across the 79 normal urothelial samples included in this study (rightmost red bar). It shows that the rate of errors of the DNA duplex sequencing technology used in the study is approximately 25 times smaller than the mutation density detected in the normal urothelium. (N = 3 for cord blood DNA duplex sequencing). b) Mutational profile of normal urothelium obtained through two orthogonal approaches. Top panel, profile constructed using mutations detected through laser capture microdissection (LCM) of clonal or quasi-clonal samples followed by regular shallow whole-genome sequencing (data taken from ref. [10]). Bottom panel, profile constructed using mutations detected in this study from ~2 cm^2 brushes followed by ultradeep DNA duplex sequencing. c) Relationship between the mutation density calculated in normal bladder urothelium using two orthogonal approaches. The red dots correspond to the rate of mutations detected through laser capture microdissection of clonal or quasi-clonal samples followed by regular shallow whole-exome sequencing (WES LCMs; data taken from ref. [10]). The blue dots correspond to the rate of mutations computed for the 79 samples in this study from ~2 cm^2 brushes followed by ultradeep DNA duplex sequencing. The trend line represented in the plot was calculated from the WES LCMs samples. For more details on the error rate of the technology, see Supplementary Note 4.

Extended Data Fig. 3 Mutational signatures active across the cohort.

a) Mutational profile of the signatures identified using SigProfiler. b) Mutational profile of the signatures identified using HDP. c) Activity of the signatures identified using SigProfiler across the 79 samples. d) Activity of the signatures identified using HDP across the 79 samples. For more details on the identification of these mutational signatures and the decipherment of their etiology, see Supplementary Note 5.

Extended Data Fig. 4 Calculation of positive selection.

a) Five signals of positive selection on the mutations observed in TP53 in all samples. The first three rows show the distribution of truncating (nonsense and splice-site affecting), synonymous, and missense mutation across the coding region of TP53. On the right side, dN/dS truncating (top row) and dN/dS missense (third row) represent the estimation of the excess of truncating and missense mutations, respectively, over the neutral expectation calculated from the observed synonymous variants (second row). The magnitude of the neutral expectation is indicated inside of the horizontal bars with shaded diagonal lines and the p-value corresponds to the Omega implementation of dN/dS (Supplementary Note 6). Fourth row, 3D clustering score of missense mutations (blue line) compared to the neutral expectation (gray line), along with detected 3D clusters of missense mutations (filled light blue line). The right-hand panel represents the distance between the distribution of expected 3D clustering scores of the residue with the highest observed score (gray) and the observed score itself (vertical dashed lines), used to compute an empirical p-value (Supplementary Note 6). Fifth row, functional impact score of SNVs (synonymous, missense and truncating) observed in the protein. The right-hand panel represents the distance between mean expected functional impact scores (gray areas) and the observed average functional impact score (vertical dashed lines), used to compute an empirical p-value (Supplementary Note 6). Sixth row, deviation in the ratio of frameshift (purple) to inframe (brown) indels in the gene compared to the ratio of non-3n to 3n (a length multiple of three nucleotides) indels in neighboring non-coding regions (excess of frameshift indels). The right-hand bars represent the numbers of coding frameshift and inframe indels and non-coding non-3n to 3n indels. Analytical or empirical tests used to calculate the p-values shown in the different panels are described in Supplementary Note 6. P-values for all genes computed using all methods appear in Supplementary Table 6. b) Magnitude of all signals of positive selection calculated for 14 genes on the pooled mutations of the 79 samples. Dashed lines for truncating, missense and indels indicate an equal number of observed and expected mutations of each type (dN/dS =1, that is, no selection). In 3D clustering the size of the circles is proportional to the difference between observed and expected scores. In functional impact bias, the size of the circles is proportional to the Z-score. c) Comparison of the excess of truncating mutations (dN/dS truncating, top) and the excess of missense mutations (dN/dS missense) calculated taking into account every observed mutation only once (as used in the manuscript, and represented by the unshaded bars in each plot) and taking into account the number of DNA duplex reads supporting each mutation (bars shaded with diagonal line pattern). d) Positive selection on mutations in PIK3CA. Left panel, needleplot representing the distribution of missense, truncating and synonymous mutations in the region of PIK3CA covered by sequencing reads. Right panel, magnitude of positive selection on missense mutations across the 79 samples calculated using the Omega dN/dS approach. The p-value is calculated using the Omega implementation of the dN/dS approach described in Supplementary Note 6. The p-value appears in Supplementary Table 6.

Extended Data Fig. 5 Calculation of positive selection at the sample level.

a) dN/dS truncating values for RBM10 and TP53 in the dome of donors 04 and 09. All legends as defined in Fig. 2. The p-values are calculated using the Omega implementation of the dN/dS approach described in Supplementary Note 6, and appear in Supplementary Table 6. b) Landscape of the fraction of urothelium covered by driver mutations of each gene in the panel. The values of covered urothelium have been discretized. In most cases, the percentage of urothelium covered by driver mutations of each gene falls in the lowest categories. The right-hand graph shows stacked barplots with the distribution of samples in different categories of covered urothelium across genes. c) Agreement of the magnitude of dN/dS missense (top) and dN/dS truncating (bottom) calculated for all genes in the dome and trigone of donors 14, 04 and 23. R-squared (R²), Pearson’s correlation coefficient of the dN/dS values calculated for the dome and trigone samples from each donor. The p-values corresponding to the Pearson’s correlation coefficients are shown.

Extended Data Fig. 6 Similarity between dome and trigone.

a) Top, number of SNVs that are shared between the dome and trigone samples (or unique to each of them) of donors for which both areas were brushed. Bottom, percentage of SNVs found in the trigone sample of each individual that are shared with the dome sample of the same individual (top), of SNVs found in the dome that are shared with the trigone (middle), and Jaccard index measuring the overlap of the SNVs identified within both samples (bottom). b) Distribution of Jaccard Index values of SNVs (first at the left), missense mutations (second), truncating mutations (third), and non-protein affecting mutations (last to the right) shared between the dome and trigone samples of the same individual and pairs of samples from different donors. The Jaccard index obtained for any subset of mutations is significantly higher for the dome and trigone samples of the same donor (p-values from one-tailed Wilcoxon-Mann–Whitney test). N indicates the number of sample pairs. c) Comparison of the distribution of Pearson’s correlation coefficients comparing dN/dS values between dome and trigone samples of the same donor (as done in Extended Data Fig. 5c) or from different donors. In the first boxplot, all mutations are included in the calculation of dN/dS values, while in the second, mutations shared between dome and trigone of the same donor are excluded. The correlation is significantly higher between dome-trigone pairs of samples of the same donor than of different donors (p-values from one-tailed Wilcoxon-Mann–Whitney test). Only pairs of samples for which Omega values of at least two genes could be computed are included in the boxplots. N indicates the number of sample pairs. Box plots in b and c display the quartiles with whiskers extending to the highest and lowest data points within 1.5 times the interquartile range.

Extended Data Fig. 7 Tolerance of dN/dS values to errors.

a) Measurement of the tolerance of RBM10 dN/dS truncating to artifactual mutations (artifacts) following the BotSeq mutational profile[82]. The boxplots represent the distribution of dN/dS values calculated from 100 synthetic samples with increasing rates of injected artifacts between 0 and 1 × 10−7 (one order of magnitude higher than estimated for the technology), and for increasing values of ground truth dN/dS (between 1 and 50). The boxplots display the quartiles with whiskers extending to the highest and lowest data points within 1.5 times the interquartile range. N = 100 synthetic samples. b) Average percentage of RBM10 dN/dS truncating reconstructed value across 100 synthetic samples, calculated by computing which fraction of the ground truth dN/dS in a sample is obtained upon calculation. c) Summary of the results of the experiment of error tolerance. Left panel, average percentage of reconstructed ground truth dN/dS across synthetic samples (for all genes and all ground truth dN/dS explored altogether) that is calculated upon injection of increasing rates (x-axis) of different types of artifacts (color legend). Center plot, average percentage of reconstructed ground truth dN/dS across synthetic samples (for all genes and all artifacts altogether) that is calculated upon injection of increasing rates (x-axis) for different values of ground truth dN/dS (color legend). Right panel, average percentage of reconstructed ground truth dN/dS across synthetic samples (for all ground truth dN/dS explored and all artifacts altogether) for different genes (color legend), that is calculated upon injection of increasing rates (x-axis) of artifacts.

Extended Data Fig. 8 Heterogeneous clonal landscape and power calculation.

a) From simulated datasets reflecting the same distributional features and data dependencies found in the study cohort, we computed the statistical power as the proportion of times the variable of interest (sex) came out significant in the univariate linear mixed-effects regression against truncating dN/dS. In this analysis the female group was picked as the baseline group. We simulated data with different ground truth female baselines (expected truncating dN/dS among females) and between-group differences (effect size). For each baseline-effect combination we can draw a power value, which are represented collectively in the form of these power profiles (see Supplementary Note 10). For the two exemplary genes RBM10 and ARID1A we highlight the profile curves corresponding to their observed baselines in the cohort and the projected power given the inferred effect in the cohort. b) Table presenting a summary of the five associations found between dN/dS and sex in the study. Here we briefly define the meaning of each column. See also Supplementary Note 10 for a more in-depth account on the methodology. CSQN: Either missense or truncating, represents the specific dN/dS used as response variable in the association analysis. ESTIMATE: Coefficient of the binary variable of interest (“is_male”) inferred via linear-mixed effects regression against dN/dS using the donor as a random intercept. CI_LOW, CI_HIGH: Lower and upper 95% CI bounds of ESTIMATE. PVAL: p-value associated with the variable of interest in the regression analysis. INTERCEPT: Inferred intercept in the regression analysis. BASELINE: Average CSQN-specific dN/dS value in the baseline group of samples (female). INTERCEPT and BASELINE are expected to follow closely one another. COVARIATE: The (binary) explanatory variable representing sex. POWER: Statistical power corresponding to the BASELINE and ESTIMATE in the power profile. EFFECT_PVAL: The “effect p-value” is an ad-hoc metric that we defined as the proportion of times the sex coefficient attains a value at least as high as ESTIMATE upon regression with a dataset corresponding to BASELINE and zero ground-truth effect. It can be thought of as an effect-aware false positive rate. c) Frequency of tumor samples with missense or truncating mutations of 6 genes in males and females across a cohort of 2,965 bladder carcinomas from the GENIE cohort. d) Multivariate logistic regression (including age) of sex on mutations in the 6 genes. Circles represent the point estimate of the effect size of the linear regression, and the horizontal line, the 95% confidence intervals. Circles with dark outer circumference denote significant associations (FDR threshold of 0.2). e) Distribution of expected number of mutations in the two TERT promoter mutational hotspots (chr5:1295113 and chr5:1295135) across donors younger than 55 years old or never smokers assuming a mutation rate equal to that observed across ever smokers older than 55 years old. The red dashed vertical line represents the actual observed number of mutations in the two hotspots across donors younger than 55 years or never smokers. The p-value was calculated empirically based on 10,000 randomizations, as described in Supplementary Note 10. f) Maximum variant allele frequency detected for activating TERT promoter mutations in a sample vs the number of activating TERT promoter mutations (i.e. mutations observed in tumors, see main text) identified in a sample. The observation of different activating TERT promoter mutations in the same sample indicates the existence of convergent evolution of TERT promoter mutations. This, in turn, suggests that the observation of mutations with large variant allele frequency may also represent multiple mutated clones with the exact mutations (convergent evolution) rather than very large clones.

Extended Data Fig. 9 Calculations of natural selection mutagenesis.

a) Comparison of the density of protein affecting mutations in 14 genes across two cohorts of bladder tumors (muscle invasive and non-muscle invasive) and in the normal urothelium of the 45 donors. Mutation density in tumors is calculated by dividing the number of observed mutations (normally 1) by the gene length in megabases (Mb). b) Percentage of amino acid residues in each gene with zero, one, two, or three or more mutations observed across 892 bladder tumor samples from the intOGen cohort. The order of the genes is as in Fig. 5a to facilitate visual comparison. c) Theoretical and observed curves of saturation mutagenesis for genes not shown in Fig. 5b. The grey dashed line represents the kinetic of saturation mutagenesis under the theoretical assumption of no selection, in which mutations are observed based only on their neutral probability of occurrence. The red circle denotes the degree of saturation achieved by probing the 79 samples in the cohort. The red dashed line is constructed through successive depth down-samples of the current observation and represents the observed kinetic of natural saturation mutagenesis (see details in Supplementary Note 12). d) Natural saturation mutagenesis of EP300 in normal bladder urothelium. Besides the tracks described in Fig. 5c for TP53, 3D clusters obtained via Oncodrive3D (second), dN/dS truncating and dN/dS missense values for each exon (fourth), and the distribution of tumor mutations (from intOGen; see Methods) along the sequence of the gene (last) have been added. These same types of plots are presented for the rest of genes in the study in Supplementary Figs. 2 and 3. Right plot, EP300 3D structure with residues with significant site selection highlighted in blue. e) dN/dS truncating and dN/dS missense values for each domain of EP300. The vertical lines represent the 95% confidence intervals of the dN/dS estimate. Solid border represents significant dN/dS values (p-value < 0.05) according to Omega (Supplementary Note 6). N = 79 samples.

Extended Data Fig. 10 Application of natural saturation mutagenesis.

a) Manhattan plot illustrating the strength of site selection for all genomic sites included in the sequencing panel. Some of the mutations in the sites with strongest selection are indicated. b) Application of site selection values to TP53 mutations. In all plots, TP53 tumor mutations observed in two large cohorts (intOGen, N = 33,218 and GENIE, N = 109,017) are grouped depending on whether they have been observed across bladder normal samples in this study and their site selection (i.e., not observed, observed with non-significant site selection and observed with significant site selection). Left top panel, distribution of the frequency across intOGen tumors of the three groups of mutations. Right top panel, distribution of the frequency across GENIE tumors. Left bottom panel, boostDM (machine learning models for in silico saturation mutagenesis)[46] scores of the three groups of mutations. Right bottom panel, proportion of mutations annotated or not annotated as oncogenic in ClinVar[83] or OncoKB[84]. c,d) Distribution of the solvent accessibility of sites with mutations in each group for TP53 (c) and EP300 (d). e) Application of site selection values to TERT promoter mutations. Mutations observed in two large cohorts of tumors (or the subset of bladder tumors in GENIE) are grouped depending on whether they have been observed across normal samples and their site selection into not observed, observed with non-significant site selection and observed with significant site selection. Left, distribution of the frequency across tumors in a large cohort of whole-genome sequenced samples (N = 8,136) of the three groups of mutations. Center, distribution of the frequency across all GENIE tumors (N = 109,017) of the three groups of mutations. Right, distribution of the frequency across GENIE bladder tumors (N = 3,909) of the three groups of mutations.

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Supplementary Figs. 1–3, Notes 1–12, Tables 8–10 and References.

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Calvet, F., Blanco Martinez-Illescas, R., Muiños, F. et al. Sex and smoking bias in the selection of somatic mutations in human bladder. Nature 647, 436–444 (2025). https://doi.org/10.1038/s41586-025-09521-x

- Received: 29 November 2024

- Accepted: 12 August 2025

- Published: 08 October 2025

- Version of record: 08 October 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09521-x

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Arousal as a universal embedding for spatiotemporal brain dynamics

Ryan V. Raut,

Zachary P. Rosenthal,

Xiaodan Wang,

Hanyang Miao,

Zhanqi Zhang,

Jin-Moo Lee,

Marcus E. Raichle,

Adam Q. Bauer,

Steven L. Brunton,

Bingni W. Brunton &

…

J. Nathan Kutz

Nature volume 647, pages 454–461 (2025)

Abstract

Neural activity in awake organisms shows widespread, spatiotemporally diverse correlations with behavioural and physiological measurements1,2,3,4. We propose that this covariation reflects in part the structured, nonlinear dynamics of an underlying arousal-related process that organizes brain-wide and body-wide physiology on the timescale of seconds. By framing this interpretation within dynamical systems theory, we arrive at a surprising prediction: a single, scalar measurement of arousal (for example, pupil diameter) should suffice to reconstruct the continuous evolution of multidimensional, spatiotemporal measurements of large-scale brain physiology. Here, to test this hypothesis, we perform multimodal cortex-wide optical imaging[5] and behavioural monitoring in awake mice. We demonstrate that the seconds-scale spatiotemporal dynamics of neuronal calcium, metabolism and brain blood oxygen can be accurately and parsimoniously modelled from a low-dimensional, nonlinear manifold reconstructed from a time delay embedding6,7 of pupil diameter. Extending this framework to behavioural and electrophysiological measurements from the Allen Brain Observatory[8], we demonstrate the ability to integrate diverse experimental data into a unified generative model via mappings from a shared arousal manifold. Our results support the hypothesis[9] that spontaneous, spatially structured fluctuations in brain-wide physiology on timescales of seconds—widely interpreted to reflect regionally specific neural communication10,11—are in large part expressions of a low-dimensional, organism-wide dynamical system. In turn, reframing arousal itself as a latent dynamical system offers a new perspective on fluctuations in brain, body and behaviour observed across modalities, contexts and scales.

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Main

The past decade has seen a proliferation of research into the organizing principles, physiology and function of ongoing brain activity and brain ‘states’ as observed across various recording modalities, spatiotemporal scales and species12,13,14. Ultimately, it is of interest to understand how such wide-ranging phenomena are coordinated within a functioning brain. However, as different domains of neuroscience have evolved around unique subsets of observables, the integration of this research into a unified framework remains an outstanding challenge[15].

Interpreting ongoing neural activity is further complicated by its widespread, spatiotemporally heterogeneous relationships with behavioural and physiological variables1,2,3,4. For instance, in awake mice, individual cells and brain regions show reliable temporal offsets and multiphasic patterns in relation to spontaneous whisker movements or running bouts1,2,16,17. Such findings have spurred widespread interest in disambiguating neural fluctuations interwoven with state changes and movements18,19. Notwithstanding efforts to statistically2,3 or mechanistically12,20,21 decompose this covariance, there is lingering mystery surrounding the endogenous processes that generate and continuously orchestrate the underlying variation in time[22].

In this work, we propose a parsimonious explanation that unifies these observations: neural activity, organismal physiology and behaviour are jointly regulated through an intrinsic, arousal-related process that continuously evolves over a latent, nonlinear manifold on the timescale of seconds. Of note, nominally arousal-related fluctuations—indexed by scalar quantities derived from pupillometry, locomotion, cardiorespiratory physiology or electroencephalography—have been implicated across virtually all domains of (neuro-)physiology and behaviour4,23,24,25,26,27,28, most recently emerging as a principal statistical factor of global neural variance2,12. Yet the interpretation and integration of these findings remain hindered by the longstanding challenge of precisely defining what ‘arousal’ actually is25,29. Rather than reducing this construct to individual biological mechanisms—which fails to account for the organism-wide coordination implicit in arousal[23]—we strengthen and extend previous findings by linking variation in common arousal indices to the structured dynamics of an underlying process. Through a data-driven approach, we show that this process—which we refer to as arousal dynamics—ultimately accounts for even greater spatiotemporal structure in brain measurements than is conventionally attributed to arousal (for example, via linear regression with scalar arousal indices).

Our framework seeks to unify brain-state research across two largely separate paradigms. The first, primarily involving invasive recordings in awake mice, has shown that cortical activity (especially above 1 Hz) is modulated in a region-dependent manner according to ongoing fluctuations in arousal and behavioural state1,3,12; however, general principles have remained elusive12,15. The second, originating in non-invasive human neuroimaging, has established general principles of spontaneous fluctuations in large-scale brain activity (particularly below 0.1 Hz)14,30, characterized by topographically organized covariance structure. This structure is widely thought to emerge from regionally specific neuronal interactions10,11; as such, arousal is not conventionally understood as fundamental to the physiology or phenomenology of interest. Instead, in this paradigm, arousal enters as either a slow or intermittent modulator of interregional covariance28,31,32,33, or as a distinct (namely, physiologically and/or linearly separable) global component34,35. Yet, growing evidence indicates that global spatiotemporal processes (1) predominate at frequencies below 0.1 Hz, (2) can account for topographical structure36,37,38,39, (3) covary with arousal indices on the timescale of seconds32,35,37,40, and, crucially, (4) remain dynamically interrelated regardless of how they are decomposed30,38. Together, we believe that these themes invite a radical reinterpretation of this paradigm[9] that ultimately aligns it with the first. In short, we propose that (1) regional variation in the arousal-related modulation of fast neural activity arises from a complex, non-decomposable spatiotemporal process, and that (2) this process constitutes the primary generative mechanism underlying topographically organized covariance.

Here we tested a central prediction of this unified account: that a scalar index of arousal suffices to accurately reconstruct multidimensional measurements of large-scale spatiotemporal brain dynamics unfolding on the timescale of seconds. Inherent in this prediction is the hypothesis that ‘arousal’, as typically operationalized, is underpinned by a specific process that is sufficiently complex—namely, multidimensional[41]—to sustain non-equilibrium behaviour, such as persistent fluctuations and travelling waves. To recover the latent dimensions of this hypothesized process, we used a time delay embedding6,7, a powerful technique in data-driven dynamical systems that, under certain conditions, theoretically enables reconstruction of dynamics on a manifold from incomplete, even scalar, measurements. This theory is further strengthened by the Koopman perspective of modern dynamical systems[42], which shows that time delay coordinates provide universal embeddings across a range of observables[43].

We support our predictions through simultaneous widefield recordings of neuronal calcium, metabolism and haemodynamics, demonstrating that their spatiotemporal evolution reflects a shared trajectory along a latent manifold, defined via a time delay embedding of pupil diameter. Time-invariant mappings from this ‘arousal manifold’ predicted structured variation across modalities and individuals, suggesting a low-dimensional dynamical system that continuously and robustly shapes brain and organismal dynamics.

Shared dynamics across components

We performed simultaneous multimodal widefield optical imaging[5] and face videography in awake, head-fixed mice. Data were acquired in 10-min sessions from n = 7 transgenic mice expressing the red-shifted calcium indicator jRGECO1a primarily in excitatory neurons.

We observed prominent, spontaneous fluctuations in pupil diameter and whisker motion throughout each recording session (Fig. 1b). Consistent with previous work2,44, these fluctuations were closely tracked by the leading linear dimension of the neural data, obtained here via principal component analysis of the widefield calcium time series (Fig. 1b). Of note, this leading component accounted for 70–90% of the variance in all seven mice (Supplementary Fig. 1).

Fig. 1: Study overview.

a, We performed simultaneous widefield optical imaging and face videography in awake mice. We interpreted these measurements as observations of dynamics on an organism-intrinsic arousal manifold \({\mathcal{M}}\) (that is, a low-dimensional surface within the state-space). Trajectories of the system state z along \({\mathcal{M}}\) reflect intrinsic arousal dynamics and stochastic (that is, arousal independent) perturbations ω(t) arising from within and outside the organism, such that \(\mathop{z}\limits^{.}=f(z,\omega )\). The silhouette of the mouse was adapted from ref. [71] and obtained from SciDraw (https://SciDraw.io) under a Creative Commons licence CC BY 4.0. b, We extracted three observables: scalar indices of pupil diameter and whisker motion, and high-dimensional images of widefield calcium fluorescence. Here widefield images are represented via the first two principal components (PCs). The first brain PC (PC1; dark purple) closely tracked pupil diameter and whisker motion and accounted for 70–90% of the variance in each mouse (Supplementary Fig. 1). However, remaining PCs often retained clear temporal relationships with the first; here, for instance, the temporal derivative of PC2 (red dashed line) closely resembles PC1 (Pearson r = −0.73), implying that nonlinear relationships with arousal dynamics extend beyond the dominant ‘arousal component’2,44. c, We developed an approach to model the full manifestations of arousal dynamics. Each observable yi is interpreted as resulting from an observation function of the latent arousal dynamics plus observable-specific variability ηi (that is, yi = gi(z) + ηi). By Takens’ theorem[6], the full-state latent dynamics can, in principle, be recovered from a time delay embedding of a scalar observable. Accordingly, we learned an encoder ψ that maps from an embedding comprising d time-lagged copies of y_1 (that is, pupil diameter) to an m-dimensional latent space (m < d), as well as a decoder (ϕ) implementing a time-invariant mapping from this latent space to widefield measurements. Figure adapted from ref. [72], Oxford University Press. d, An example epoch contrasting original and pupil-reconstructed widefield images (test data from one mouse). The black trace indicates pupil diameter. The blue traces denote delay coordinates, where increasing lightness indicates increasing order (Methods).

Such a dominant explanatory factor for global covariance has been widely interpreted to reflect ‘arousal’, conventionally understood as a unidimensional continuum indexing the overall ‘wakefulness’ or ‘activation’ level of an organism23,26. Crucially, this framing tacitly assumes that brain activity naturally decomposes into linearly independent factors. This, in turn, compels ad hoc explanations for the remaining, orthogonal dimensions of neural data—that is, by construction, ‘not arousal’ (for example, see refs. 2,3,4).

A key caveat of this framing is that linear dimensions need not correspond to physically meaningful variables. More fundamentally, linear independence—although mathematically convenient—is biologically arbitrary and does not imply physical or mechanistic separability45,46 (also see ref. [47], chapter 6). Consistent with this, we observed that higher principal components typically retained clear temporal relationships to the first principal component, often closely approximating its successive time derivatives (Fig. 1b). These dynamical relationships support our alternative view: that scalar indices of ‘arousal’, such as pupil size or a single principal component, are best understood as one-dimensional projections of an underlying process (Fig. 1a), one whose full manifestations span and fundamentally entangle multiple (Euclidean) dimensions. Below, we elaborate on this perspective before introducing a dynamical systems framework enabling its empirical validation.

Studying arousal dynamics

We interpret nominally arousal-related observables (for example, pupil size and whisking) as reflections of an organism-wide regulatory process (Fig. 1a). This dynamic regulation[48] may be viewed as a low-dimensional process whose evolution induces stereotyped variations in the high-dimensional state of the organism. Physiologically, this abstraction captures the familiar ability of arousal shifts to elicit multifaceted, coordinated changes throughout the organism (for example, ‘fight-or-flight’ versus ‘rest-and-digest’ modes)[49]. Operationally, this framing motivates a separation between the effective dynamics of this system, which evolve along a latent, low-dimensional manifold50,51, and the high-dimensional ‘observation’ space, which we express via time-invariant mappings from the latent manifold. In this way, rather than assuming high-dimensional, pairwise interactions among the measured variables, we attempted to parsimoniously attribute widespread variance to a common, latent dynamical system[52].

To specify the instantaneous latent arousal state, we draw on Takens’ embedding theorem[6] and connections with modern Koopman operator theory[42], which together provide general conditions under which the embedding of a scalar time series in time delay coordinates can faithfully represent latent dynamics on the original, full-state attractor. As we hypothesized that brain-wide physiology is spatiotemporally regulated in accordance with these arousal dynamics[9], this theory yields a surprising empirical prediction: a scalar observable of arousal dynamics (for example, pupil diameter) can suffice to reconstruct the states of a high-dimensional observable of brain physiology (for example, optical images of cortex-wide activity), insofar as both reflect the same underlying dynamics7,53.

To test this hypothesis, we developed a data-driven framework to relate observables evolving under a shared dynamical system (Fig. 1c). For each mouse, we embedded pupil diameter in a high-dimensional space defined by its past values (that is, time delay coordinates). We learned a map (or encoder) ψ from this space to a continuous, low-dimensional latent space in which the instantaneous arousal state z is represented as a single point. We also learned a map (decoder) ϕ from this latent space to the widefield calcium observation space. These mappings, estimated with linear or nonlinear methods, together enable each calcium image to be expressed as a function of a multidimensional state inferred from pupil delay coordinates, to the extent that both observables are deterministically linked to the same latent state.

Spatiotemporal dynamics from a scalar

For all analyses, we focused on (‘infra-slow’[54]) fluctuations below 0.2 Hz to distinguish arousal-related dynamics from physiologically distinct processes manifesting at higher frequencies (for example, delta waves)55,56. To model the hypothesized mappings, we implemented an iterative, leave-one-out strategy in which models were trained using 10-min recordings concatenated across six mice, then tested on the held-out 10-min recording from the seventh mouse (Methods). Results from a separate, within-mouse train–test pipeline are shown in Supplementary Fig. 2.

For both pipelines, candidate models included a linear regression model based on a single lag-optimized copy of pupil diameter (‘no embedding’), as well as a model incorporating multiple delay coordinates and nonlinear mappings (‘latent model’). Both models allowed us to assess the hypothesis that the widefield measurements primarily reflect arousal-related variation. However, the latter model enables stronger validation of our framework by allowing detection of the hypothesized nonlinear, dynamical aspects of pupil-widefield covariation. Nonlinear mappings were learned through feedforward neural networks modelled after a variational autoencoder (Methods). See Extended Data Figs. 1 and 2 and Supplementary Text for validation on a toy (stochastic Lorenz) system.

Modelling results from the leave-one-out pipeline are shown in Fig. 2. We first considered total variance explained, summarized across time points and pixels. In each of n = 7 mice, our models reconstructed 60–85% of the total widefield variance below 0.2 Hz based on simultaneously recorded pupil diameter (Fig. 2a). This total variance was dominated by the first principal component (Supplementary Fig. 1), which showed a tight, linear relationship with pupil diameter, allowing much of it to be captured by a one-dimensional pupil regressor (Fig. 2a). These results thus support the notion that widefield calcium measurements are dominated by nominally arousal-related fluctuations. Nonetheless, as the total variance metric collapses across pixels and time points, we next examined whether model reconstructions faithfully captured geometric and dynamical structure within the original widefield data.

Fig. 2: Pupil diameter enables reconstruction of spatiotemporal brain dynamics.

a,b, Variance explained in held-out widefield data from each of n = 7 mice (for each prediction, the model was trained on data from the other 6 mice). A simple linear regression model based on one (lag-optimized) pupil regressor (no embedding) accurately predicted approximately 70–85% of total variance in six of the seven mice (a). Delay embedding and non-linearities (latent model) further improved widefield prediction (a), crucially, doing so by reliably capturing variance orthogonal to PC1 (that is, PCs 2-N) that was not tracked by the scalar pupil regressor (b). See Extended Data Fig. 5 for pixelwise maps of variance explained. c, Reconstructing propagation dynamics. Phase maps were obtained from dynamic mode decomposition applied to the original and reconstructed data from n = 7 mice, and subsequently compared via (circular) spatial correlation. Phase maps from one example mouse are shown on the left (phase maps from the one-dimensional model are not shown, as they lack phase structure due to the absence of complex-valued modes). The Allen Mouse Common Coordinate Framework atlas is overlaid in white. d, Example state trajectories (held-out data) visualized in coordinates defined by pupil diameter (top), the first three widefield PCs (middle left) or three latent variables obtained from the delay-embedded pupil time series (middle right). Trajectories are colour coded by the cluster assignment of each time point as determined by a GMM applied to the original widefield image frames (middle left) or the same GMM applied to reconstructed frames (middle right) (k = 6 clusters; bottom). Cluster means are shown underneath. Similar colour arrangement within pupil delay coordinates indicates that pupil dynamics encode spatially specific information about cortex-wide activity. e, Decoding brain states from pupil dynamics. Comparison of decoder accuracy in properly identifying ground-truth GMM cluster assignments for each frame based on the most frequent assignment (‘null’ decoder) or on data reconstructed via either the no embedding or latent model.

Crucially—and in contrast to the no embedding model—the latent model captured substantial variance orthogonal to the first PC (Fig. 2b, Extended Data Figs. 3 and 4 and Supplementary Fig. 2). Spatially, the topography of total variance explained closely mirrored the first PC and was heavily weighted towards posteromedial cortices (Extended Data Fig. 5), resembling previous arousal-related maps1,3,12,57. By contrast, the latent model additionally explained variance in anterolateral (‘orofacial’) cortices (Extended Data Fig. 5). These regions were more strongly correlated with pupil derivative and amplitude envelope (Extended Data Fig. 6), two features implicitly represented in the delay embedding and known to track neuromodulator dynamics20,58. These modelling improvements did not trivially result from increased model complexity, as confirmed by shuffling pupil and widefield measurements across mice (Extended Data Fig. 7). We conclude that multiple widefield dimensions maintain lawful, time-invariant relationships with pupil dynamics.

These relationships imply that pupil and widefield measurements share latent, nonlinear geometric structure, consistent with both reflecting the structured flow of an underlying process. This interpretation would further predict that pupil dynamics encode the spatiotemporal dynamics of widefield calcium signals. To test this hypothesis, we fitted linear dynamical systems models (via dynamic mode decomposition[42]; Methods) to the original and reconstructed datasets and examined the leading spatial modes. In all seven mice, the original widefield data yielded a complex-valued leading mode that indicated a cortex-wide propagation pattern resembling previous results55,59. Crucially, the latent model consistently preserved this phase structure (Fig. 2c). By contrast, the no embedding model lacked spatiotemporal dynamics altogether (Supplementary Videos 1 and 2). Indeed, bona fide spatiotemporal dynamics are inherently multidimensional, as they entail interregional phase shifts that reside in the complex plane, thus requiring at least two (Euclidean) dimensions; by contrast, a one-dimensional system cannot generate or represent rich phase structure. Hidden Markov model analyses similarly supported the unique ability of delay embeddings to preserve widefield dynamical structure (Extended Data Fig. 8). Collectively, these points suggest that infra-slow widefield spatiotemporal dynamics reflect a multidimensional process that is also tracked by pupil dynamics.

Next, we sought to verify the continuous nature of this coupling by evaluating frame-by-frame spatial correspondence between the original and reconstructed datasets. To do this, we clustered variance-normalized image frames based on spatial similarity using a Gaussian mixture model (GMM), then quantified the percentage of ‘ground truth’ GMM cluster assignments (from the original data) matching those decoded by applying the same GMM to the reconstructed frames. Figure 2e shows the resulting accuracy as a function of the number of clusters.

Spatial patterns largely alternated between two main topographies, consistent with infra-slow spatiotemporal dynamics across modalities and species9,30,57. With a two-cluster solution, the latent model enabled accurate decoding of more than 80% of image frames. Although accuracy gradually declined with increasing cluster number—reflecting reduced distinctiveness among clusters—it consistently improved from the null to the no embedding decoder, and, finally, to the latent model decoder. The latter uniquely tracked spatial variation along multiple dimensions; accordingly, its improvements became even more pronounced when clustering based on equally weighted principal components (Extended Data Fig. 9). Decoding improvements were also more apparent when labelling states according to an hidden Markov model (which also considers dynamics) rather than a GMM (Extended Data Fig. 8). These results confirm that pupil dynamics reliably and continuously index the instantaneous spatial topography of widefield calcium.

Latent dynamics link multimodal observables

Beyond neuronal activity per se, spatially structured fluctuations are also prominent in metabolic and haemodynamic measurements. Such fluctuations are typically attributed to state-dependent, region-dependent and context-dependent responses to local neuronal signalling. By contrast, our account posits that multiple aspects of physiology are jointly regulated according to arousal dynamics (see also, for example, refs. 27,44). This suggests the possibility of more parsimoniously relating multiple spatiotemporal readouts of brain physiology.

To test this prediction, we leveraged two readouts acquired alongside neuronal calcium using the multispectral optical imaging platform[5]: oxidative metabolism (indexed by flavoprotein autofluorescence (FAF)[5]) and haemodynamic signals (which approximate the physiological basis of fMRI). We attempted to reconstruct all three observables from delay embedded pupil measurements. To do this, we froze the encoder ψ trained to predict calcium, and simply trained two new decoders to map from its latent space to FAF and oxygenated haemoglobin (ϕ_2 and ϕ_3 in Fig. 3a, respectively).

Fig. 3: Multimodal measurements reconstructed from a shared latent manifold.

a, Delay embedding framework extended to multimodal observables. We coupled the existing encoder—trained to predict calcium images—to decoders trained separately to predict FAF and haemoglobin. ox., oxidative. b, Example reconstructions of widefield calcium (jRGECO fluorescence), metabolism (FAF) and blood oxygen (concentration of oxygenated haemoglobin). c, Reconstruction performance (as in Fig. 2a,b). See Extended Data Fig. 10 for pixelwise maps of variance explained (n = 7 mice).

Modelling results are shown in Fig. 3b. As with calcium, we successfully modelled the majority of variance in these two modalities across all n = 7 mice. Both modalities were dominated by the first principal component, but slightly less so than calcium (Supplementary Figs. 3 and 4; compare with Supplementary Fig. 1). Correspondingly, delay embeddings and nonlinearities made even larger contributions to the total explained variance in these two modalities, even after adjusting for a modality-specific, globally optimized delay (Fig. 3c and Extended Data Fig. 10). We conclude that a far larger portion of metabolic and haemodynamic fluctuations is attributable to a common, arousal-related mechanism than conventionally appreciated (also see refs. 9,37). For context, predictions based on pixelwise calcium signals are shown in Supplementary Fig. 5. We return to the physiological implications of these relationships in the Discussion.

Arousal manifold as an intrinsic reference frame

A key motivation for our framework is the growing array of observables linked to arousal indices15,27, despite persistent ambiguity surrounding this construct[29]. Accordingly, we next considered how our framing of arousal could quantitatively integrate such findings. We extended our approach to publicly available behavioural and electrophysiological recordings from the Allen Institute Brain Observatory[8]. We examined 30-min recordings from mice in a task-free context, deriving eight common indices of brain state (Methods): pupil diameter, running speed, the hippocampal θ:δ ratio, instantaneous rate of hippocampal sharp-wave ripples (SWRs), band-limited power (BLP) derived from the local field potential across visual cortical regions, and mean firing rate across several hundred neurons per mouse. BLP was analysed within three canonical frequency ranges: 0.5–4 Hz (‘delta’), 3–6 Hz (‘alpha’[60]) and 40–100 Hz (‘gamma’).

We began by concatenating measurements across mice, then trained neural networks to predict each observable from a latent space constructed from delay embedded pupil measurements. Next, we applied the learned encoding to the delay embedded pupil measurements for each individual, training decoders to map from this shared latent space to the observables of the individual (see Supplementary Fig. 6 for quantitative validation). Finally, we repeated this procedure for the primary widefield dataset, thus leveraging pupil diameter as a common reference to align observables across individuals and datasets to a shared manifold.

To visualize the resulting, multidataset generative model (Fig. 4), we defined the arousal manifold \({\mathcal{M}}\) as a two-dimensional latent space and evaluated each observable’s mapping ϕi over a grid of sample points within it. We also computed a vector field f(z) over this grid using a data-driven analytic representation of the latent dynamics[61] (Methods). Thus, at each point along \({\mathcal{M}}\), Fig. 4 depicts the conditional expectation of both the local direction of arousal dynamics (via f) and the state of each observable yi (via ϕi). \({\mathcal{M}}\), f and the mappings \({\{{\phi }_{i}\}}_{i=1}^{N}\) together constitute our dynamical, relational interpretation of arousal.

Fig. 4: Unifying observables through arousal dynamics.

The arousal manifold provides an organism-intrinsic reference frame to align and aggregate measurements across datasets. To visualize the discovered relations, we define the manifold as a two-dimensional latent space parameterized by abstract coordinates z_1 and z_2 (that is, \({\bf{z}}\in {\mathcal{M}}\subset {{\mathbb{R}}}^{2}\); top middle). The learned decoders ϕ map each point z in this manifold (that is, each latent arousal state) to an expected value of a particular observable (for example, a single widefield calcium image or a single value of pupil size), enabling representations of the manifold from the perspective of various observables (left and right columns). Here widefield observables are evaluated at 3 × 3 evenly spaced points within the latent space, whereas each scalar observable is evaluated over a 100 × 100 grid. The white diagonals indicate iso-contours of pupil size in either the WashU or Allen Brain Observatory datasets, providing a shared visual reference across observables within the same dataset. Meanwhile, f(z), which we approximate via a data-driven analytic representation[61], maps each point z to an expected direction of movement (that is, \(\mathop{{\bf{z}}}\limits^{.}=f({\bf{z}})\)), enabling representation of the vector field over the manifold (bottom middle). Together, these representations convey how a typical trajectory on the arousal manifold, governed by intrinsic dynamics, maps onto observable changes. a.u., arbitrary units; HC, hippocampus; VIS, visual cortex. The silhouette of the mouse was adapted from ref. [71] and obtained from SciDraw (https://scidraw.io) under a Creative Commons licence CC BY 4.0.

This representation unifies and clarifies themes across the experimental literature, which we briefly summarize here. As expected, running speed, the hippocampal θ:δ ratio, cortical gamma BLP and mean firing rate are all positively correlated with pupil size along a single, principal dimension, z_1 (that is, the horizontal coordinate in each plot; Fig. 4, ‘Allen Brain observables’). These observables are inversely correlated with delta BLP, alpha BLP and hippocampal SWR rate along the same dimension, each of which indexes more internally oriented processes that are enhanced during sensory disengagement16,26.

Including a second dimension (z_2, vertical coordinate in each plot) reveals a rotational flow that recapitulates observations relating to brain and behavioural state transitions20,58,62,63. Thus, the ‘arousal onset’ (Fig. 4) begins with decreasing alpha BLP[63], followed by increases in firing rates and gamma BLP and, subsequently, pupil diameter44,62. This phase is accompanied by the onset of whisking and cortical propagation towards midline and posterior regions55,57 (widefield optical imaging (WashU) observables). Continuing clockwise, pupil diameter, locomotion and the hippocampal θ:δ ratio peak around the time that alpha BLP begins to increase from its nadir, consistent with its appearance at the offset of locomotion[60] (Fig. 4, ‘arousal offset’). Increases in delta BLP and hippocampal SWR rate ensue, in parallel with cortical propagation from posteromedial to anterolateral regions (WashU observables). This embedding of cortex-wide topographical maps within a canonical ‘arousal cycle’ resembles previous findings in humans and monkeys, which also describe topographically parallel waves in subcortical structures[9].

In summary, this framework parsimoniously and quantitatively represents a globally coordinated pattern that is registered only piecemeal through diverse observables. Although verification would ideally require simultaneous measurements of all observables—which remains practically infeasible—the ability to integrate partial observations has a key role in contextualizing results and supporting experiment–theory iteration.

Discussion

The concept of ‘brain state’ has gained prominence in systems neuroscience as experimental advances have made studies in awake, behaving organisms increasingly routine[15]. Although brain state is typically viewed as comprising several biological or behavioural factors—one of which is often presumed to be ‘arousal’4,12—principled criteria for defining and disentangling such components are lacking. Alternatively, construing organisms as dynamical systems52,64, our approach seeks to factorize brain (or rather, organismal) state in terms of constituent endogenous processes, one of which, examined herein, we identified with the arousal construct. Despite grounding arousal in a specific process, our results reveal this process to be multidimensionally structured and nonlinearly coupled to various observables, ultimately implying even broader relevance for arousal than presently recognized.

This generalized view of arousal resonates with two emerging themes. First, brain or arousal states often correspond to qualitatively distinct (neuro-)physiological regimes27,65, limiting the utility of simply orthogonalizing observations with respect to an arousal index (for example, via linear regression or removal of the leading principal component). Second, numerous studies have reported non-random transitions among brain states[12], as well as functional correlates of dynamical indices of arousal, such as the phase or derivative of pupil size44,58,62, often with surprising timescale invariance9,17,56. These observations imply an underlying nonlinear dynamic system[66]. Together, these two themes portray arousal as a dynamic, pervasive and persistent ‘background’; this complicates the ability to interpret spontaneous or task-related3,12 variation observed even over seconds without reference to this evolving internal context13,65.

Indeed, by more rigorously capturing the scope of arousal dynamics, our models reveal surprisingly rich spatiotemporal structure across the cortex, challenging conventional views of the underlying mechanisms. Thus, for decades, large-scale brain dynamics have been overwhelmingly interpreted as summations of diverse interactions among discrete regions10,11,40, fuelling a proliferation of decomposition techniques. Although distinctions among modalities and species should not be overlooked, evidence across these domains has increasingly converged towards a small set of core spatiotemporal components30,36,38, alongside growing appreciation for arousal-related contributions28,31,32,35,40. We extend these themes by advocating a more radically unified view: that these core components are themselves projections of a non-decomposable dynamical system—the same system that effectively underpins arousal. In this sense, arousal dynamics do not merely modulate interregional interactions, but actively generate spatiotemporal structure through the synchronized[53] propagation of topographically organized rotating waves[9].

This unified model specifically concerns infra-slow dynamics. Although similar spatial patterns occur at timescales of greater than 1 Hz (ref. [67])—probably reflecting shared anatomical constraints—dynamical behaviour more clearly distinguishes these two timescales55,59, suggesting distinct generative mechanisms[56]. Nonetheless, the expression of faster spatiotemporal dynamics is strongly modulated by infra-slow (and/or arousal) dynamics54,55,67. Generalizing this theme, we suggest that the infra-slow evolution of arousal state modulates parameters governing faster processes throughout the brain and body (for example, Fig. 4).

This theoretical framing suggests a simplified view of the interrelations among electrical, metabolic and vascular cerebral activity. Tight coupling across these domains—essential for sustaining brain function—is typically attributed to complex neurometabolic and neurovascular cascades operating locally, thereby posing an enormous regulatory challenge. By contrast, longstanding evidence shows that neuromodulatory processes can influence these domains in parallel68,69, suggesting mechanisms enabling their coordinated regulation. The strong performance of our model (Fig. 3) suggests that such mechanisms may be actively harnessed to effectively streamline cerebral regulation in accordance with arousal dynamics, thereby reducing reliance on conventional feedforward processes (for example, ‘the haemodynamic response’). This interpretation could help to explain observations that are difficult to reconcile with local response models (for example, ref. [70]), although its validation requires targeted experiments.

Finally, although we have adopted the term ‘arousal’ given its widespread use for the observables analysed here, we used it simply as a label for the latent process inferred from the observables in a data-driven manner. For the same reason, our results probably relate to a range of cognitive and behavioural phenomena traditionally distinguished from arousal (for example, ‘attention’), but potentially coupled to or overlapping with the dynamical process formalized here[13].

Methods

Datasets and preprocessing

Dataset 1: WashU

Animal preparation. All procedures described below were approved by the Washington University Animal Studies Committee in compliance with the American Association for Accreditation of Laboratory Animal Care guidelines. Mice were raised in standard cages in a double-barrier mouse facility with a 12–12-h light–dark cycle and ad libitum access to food and water (22 °C ambient temperature and 40–60% humidity). Experiments used n = 7 12-week-old mice hemizygous for Thy1-jRGECO1a (JAX 030525) on a C57BL/6J background, enabling optical imaging of the jRGECO1a fluorescent calcium sensor protein primarily expressed in excitatory neurons of cortical layers 2/3 and 5 (ref. [73]). Before imaging, a cranial window was secured to the intact skull of each mouse with dental cement under isoflurane anaesthesia according to previously published protocols[72].

Data acquisition. Widefield imaging was conducted on a dual fluorophore optical imaging system; details of this system have been presented elsewhere5,74. In brief, sequential illumination was provided at 470 nm, 530 nm and 625 nm; reflected light in each channel was collected by a lens (focal length = 75 mm; NMV-75M1, Navitar), split by a 580-nm dichroic (FF580-FDi02-t3-25 × 36, Semrock) into two channels, one filtered by 500-nm long pass (FF01-500/LP-25, Semrock; blocks FAF excitation, passes FAF emission and 530-nm Hb reflectance), and the other by a 593-nm long pass (FF01-593/LP-25, Semrock; blocks jRGECO1a excitation, passes jRGECO1a emission and 625-nm Hb reflectance). The two channels were detected separately via two CMOS cameras (Zyla 5.5, Andor). Data were cropped to 1,024 × 1,024 pixels, and binned to 512 × 512 to achieve a frame rate of 100 Hz in each camera, with all contrasts imaged at 25 Hz. The light-emitting diodes (LEDs) and camera exposures were synchronized and triggered via a data acquisition card (PCI-6733, National Instruments) using MATLAB R2019a (MathWorks).

Mice were head fixed under the imaging system objective using an aluminium bracket attached to a skull-mounted Plexiglas window. Before data acquisition in the awake state, mice were acclimated to head fixation over several days.

The body of the mouse was supported by a felt pouch suspended by optical posts (Thorlabs). Resting-state imaging was performed for 10 min in each mouse. Before each imaging run, dark counts were imaged for each mouse for 1 s with all LEDs off to remove background sensor noise.

Preprocessing. Images were spatially normalized, downsampled to 128 × 128 pixels, co-registered and affine transformed to the Paxinos atlas, temporally detrended (fifth order polynomial fit) and spatially smoothed (5 × 5 pixel Gaussian filter with standard deviation of 1.3 pixels)[75]. Changes in 530-nm and 625-nm reflectance were interpreted using the modified Beer–Lambert law to calculate changes in oxy-haemoglobin and deoxy-haemoglobin concentration[5].

Image sequences of fluorescence emission detected by CMOS1 (that is, uncorrected FAF) and CMOS2 (that is, uncorrected jRGECO1a) were converted to percent change (dF/F) by dividing the time trace of each pixel by its average fluorescence over each imaging run. Absorption of excitation and emission light for each fluorophore due to haemoglobin was corrected as outlined in ref. [76]. From the face videography, we derived scalar indices of pupil size via DeepLabCut software[77] and whisker motion via the Lucas–Kanade optical flow method[78] applied to and subsequently averaged across five manually selected data points on the whiskers. The resulting pupil diameter, whisker motion and widefield time series were bandpass filtered between 0.01 < f < 0.2 Hz to distinguish the hypothesized spatiotemporal process from distinct phenomena occurring at higher frequencies (for example, slow waves)[55], and to accommodate finite scan duration for 10 min. For visualization only—namely, to view the Allen atlas boundaries in register with cortical maps—we manually aligned the Paxinos-registered data from one mouse to a 2D projection of the Allen Mouse Common Coordinate Framework (v3)[79] using four anatomical landmarks: the left, right and midline points at which the anterior cortex meets the olfactory bulbs, and an additional midline point at the base of the retrosplenial cortex3,80. Coordinates obtained from this transformation were used to overlay the Common Coordinate Framework boundaries onto the Paxinos-registered cortical maps for all mice.

Dataset 2: Allen Brain Observatory

We additionally analysed recordings obtained from awake mice and publicly released via the Allen Brain Observatory Neuropixels Visual Coding project[8]. These recordings include eye-tracking, running speed (estimated from running wheel velocity), and high-dimensional electrophysiological recordings obtained with Neuropixels probes[81]. We restricted analyses to the ‘functional connectivity’ stimulus set, which included a 30-min ‘spontaneous’ session with no overt experimental stimulation. Data were accessed via the Allen Software Development Kit (https://allensdk.readthedocs.io/en/latest/). From 26 available recording sessions, we excluded four with missing or compromised eye-tracking data, one lacking local field potential (LFP) data, and five with locomotion data indicating a lack of immobile periods or appearing otherwise anomalous, thus leaving a total of n = 16 recordings for downstream analyses.

Preprocessing. From the electrophysiological data, we derived estimates of population firing rates and several quantities based on the LFP. We accessed spiking activity as already extracted by Kilosort2 (ref. [2]). Spikes were binned in 1.2-s bins as previously described[2], and a mean firing rate was computed across all available units from neocortical regions surpassing the default quality control criteria. Neocortical LFPs were used to compute BLP within three canonical frequency bands: gamma (40−100 Hz), ‘alpha’ (3−6 Hz) and delta (0.5−4 Hz). Specifically, we downsampled and low passed the signals with a 100-Hz cutoff, computed the spectrogram (scipy.signal.spectrogram[82]) at each channel in sliding windows of 0.5 s with 80% overlap, and averaged spectrograms across all channels falling within ‘VIS’ regions (including primary and secondary visual cortical areas). BLP was then computed by integrating the spectrogram within the frequency bands of interest and normalizing by the total power. A similar procedure was applied to LFP recordings from the hippocampal CA1 region to derive estimates of hippocampal theta (5−8 Hz) and delta (0.5−4 Hz) BLP. Hippocampal SWRs were detected on the basis of the hippocampal CA1 LFP via an automated algorithm[83] following previously described procedures[84]. In general, extreme outlying values (generally artefact) were largely mitigated through median filters applied to all observables, whereas an additional thresholding procedure was used to interpolate over large negative transients in running speed. Finally, all time series were downsampled to a sampling rate of 20 Hz and filtered between 0.01 < f < 0.2 Hz to facilitate integration with dataset 1.

Data analysis

State-space framework

Formally, we considered the generic state-space model:

$$\mathop{{\bf{z}}}\limits^{.}={\bf{f}}({\bf{z}},\omega ),$$

(1)

$${{\bf{y}}}_{i}={{\bf{g}}}_{i}({\bf{z}})+{\eta }_{i},$$

(2)

where the nonlinear function f determines the (nonautonomous) flow of the latent (that is, unobserved), vector-valued arousal state \({\bf{z}}={[{z}_{1},{z}_{2},\ldots ,{z}_{m}]}^{{\rm{T}}}\) along a low-dimensional attractor manifold \({\mathcal{M}}\), whereas ω(t) reflects random (external or internal) perturbations decoupled from f that nonetheless influence the evolution of z (that is, ‘dynamical noise’). We consider our N observables \({\{{{\bf{y}}}_{i}\}}_{i=1}^{N}\) as measurements of the arousal dynamics, each resulting from an observation equation gi, along with other contributions ηi(t) that are decoupled from the dynamics of z (and in general are unique to each observable). Thus, in this framing, samples of the observable yi at consecutive time points t and t + 1 are linked through the evolution of the latent variable z as determined by f. Note that f is presumed to embody various causal influences spanning both brain and body (and the feedback between them), whereas measurements (of either brain or body) yi do not contribute to these dynamics. This framework provides a formal, data-driven approach to parsimoniously capture the diverse manifestations of arousal dynamics, represented by the proportion of variance in each observable yi that can be modelled purely as a time-invariant mapping from the state-space of z.

State-space reconstruction via delays

Our principal task was to learn a mapping from arousal-related observables to the multidimensional space where the hypothesized arousal process evolves in time. To do this, we took advantage of Takens’ embedding theorem from dynamical systems theory, which has been widely used for the purpose of nonlinear state-space reconstruction from an observable. Given p snapshots of the scalar observable y in time, we began by constructing the following Hankel matrix \({\bf{H}}\in {{\mathbb{R}}}^{d\times (p-d+1)}\):

$${\bf{H}}=\left[\begin{array}{cccc}y({t}_{1}) & y({t}_{2}) & \ldots & y({t}_{p-d+1})\\ y({t}_{2}) & y({t}_{3}) & \ldots & y({t}_{p-d+2})\\ \vdots & \vdots & \ddots & \vdots \\ y({t}_{d}) & y({t}_{d+1}) & \ldots & y({t}_{p})\end{array}\right]$$

(3)

for p time points and d time delays. Each column of H represents a short trajectory of the scalar observable y(t) over d time points, which we refer to as the delay vector h(t). These delay vectors represent the evolution of the observable within an augmented, d − dimensional state-space.

We initially constructed H as a high-dimensional (and rank-deficient) matrix to ensure it covered a sufficiently large span to embed the manifold. We subsequently reduced the dimensionality of this matrix to improve conditioning and reduce noise. Dimensionality reduction is carried out in two steps: (1), through projection onto an orthogonal set of basis vectors (below), and (2) through nonlinear dimensionality reduction via a neural network (detailed in the next section).

For the initial projection, we note that the leading left eigenvectors of H converge to Legendre polynomials in the limit of short delay windows[85]. Accordingly, we used the first r = 10 discrete Legendre polynomials as the basis vectors of an orthogonal projection matrix:

$${{\bf{P}}}^{(r)}=[{{\bf{p}}}_{0},{{\bf{p}}}_{1},\ldots ,{{\bf{p}}}_{r-1}]\in {{\mathbb{R}}}^{d\times r}$$

(4)

(polynomials obtained from the special.legendre function in SciPy[82]). We applied this projection to the Hankel matrix constructed from a pupil diameter timecourse. The resulting matrix \(\mathop{{\bf{Y}}}\limits^{ \sim }\in {{\mathbb{R}}}^{r\times (p-d+1)}\) is given by:

$$\widetilde{{\bf{Y}}}={{\bf{P}}}^{(r){\rm{T}}}{\bf{H}}.$$

(5)

Each column of this matrix, denoted \(\widetilde{{\bf{y}}}={{\bf{P}}}^{(r){\rm{T}}}{\bf{h}}\), represents the projection of a delay vector h(t) onto the leading r Legendre polynomials. The components of this state vector, \(\widetilde{{\bf{y}}}(t)={[{y}_{0}(t),\ldots ,{y}_{r-1}(t)]}^{{\rm{T}}}\), are the individual r Legendre coordinates[85], which form the input to the neural network encoder.

The dimensionality of H is commonly reduced through its singular value decomposition (SVD; for example, see refs. 43,86). In practice, we found that projection onto Legendre polynomials yields marginal improvements over SVD of H, particularly in the low-data limit[85]. More importantly, the Legendre polynomials additionally provided a universal, analytic basis in which to represent the dynamics; this property was exploited for comparisons across mice and datasets.

Choice of delay embedding parameters was guided on the basis of autocorrelation time and attractor reconstruction quality (that is, unfolding the attractor while maximally preserving geometry), following decomposition of H. The number of delay coordinates was guided based on asymptoting reconstruction performance using a linear regression model. For all main text analyses, the Hankel matrix was constructed with d = 100 time delays, with each row separated by Δt = 3 time steps (equivalently, adopting H as defined in equation (3), we set d = 300 and subsampled every third row). As all time series were analysed at a sampling frequency of 20 Hz, this amounts to a maximum delay window of 100 × 3 × 0.05 = 15 s.

For all modelling, pupil diameter was first shifted in time to accommodate any physiological delay between the widefield signals and the pupil. For each widefield modality, this time shift was selected as the abscissa corresponding to the peak of the cross-correlation function between the pupil and the mean widefield signal. For the leave-one-out pipeline, we used the median time shift across the six mice in the training set.

State-space mappings via VAE

To interrelate observables through this state-space framework (and thus, through arousal dynamics), we ultimately sought to approximate the target functions

$${{\boldsymbol{\psi }}}_{i}:{{\bf{h}}}_{i}\to {\bf{z}},$$

(6)

$${{\boldsymbol{\phi }}}_{j}:{\bf{z}}\to {{\bf{y}}}_{j},$$

(7)

where hi(t) represents the delay vectors (columns of the Hankel matrix constructed from a scalar observable yi), \({\bf{z}}(t)\in {{\mathbb{R}}}^{m}\) is the low-dimensional representation of the latent arousal state, and yj(t) is a second observable. Various linear and nonlinear methods can be used to approximate these functions (for example, see ref. [87]). Our primary results were obtained through a probabilistic modelling architecture based on the variational autoencoder (VAE)88,89.

In brief, a VAE is a deep generative model comprising a pair of neural networks (that is, an encoder and decoder) that are jointly trained to map data observations to the mean and variance of a latent distribution (via the encoder), and to map random samples from this latent distribution back to the observation space (decoder). Thus, the VAE assumes that data observations y are taken from a distribution over some latent variable z, such that each data point is treated as a sample \(\widehat{z}\) from the prior distribution pθ(z), typically initialized according to the standard diagonal Gaussian prior, that is, \({p}_{\theta }(z)={\mathcal{N}}(z| 0,{\bf{I}})\). A variational distribution qφ(z∣y) with trainable weights φ is introduced as an approximation to the true (but intractable) posterior distribution p(z∣y).

As an autoencoder, qφ(z∣y) and pθ(y∣z) encode observations into a stochastic latent space, and decode from this latent space back to the original observation space. The training goal is to maximize the marginal likelihood of the observables given the latent states z. This problem is made tractable by instead maximizing an evidence lower bound, such that the loss is defined in terms of the negative evidence lower bound:

$${{\mathcal{L}}}_{\varphi ,\theta }(y)=-{{\mathbb{E}}}_{z \sim {q}_{\varphi }(z| y)}[\log {p}_{\theta }(y| z)]+\beta \cdot {\mathbb{KL}}({q}_{\varphi }(z| y)\parallel {p}_{\theta }(z)),$$

(8)

where the first term corresponds to the log-likelihood of the data (reconstruction error), and the Kullback–Leibler (KL) divergence term regularizes the distribution of the latent states qφ(z∣y) to be close to that of the prior pθ(z). Under Gaussian assumptions, the first term is simply obtained as the mean squared error, that is, \(\parallel y-\widehat{y}{\parallel }_{2}^{2}\). The KL term is weighted according to an additional hyperparameter β, which controls the balance between these two losses90,91. Model parameters (φ and θ) can be jointly optimized via stochastic gradient descent through the reparameterization trick[88].

In the present framework, rather than mapping back to the original observation space, we used the decoder pθ(y∣z) to map from z to a new observation space. In this way, we used a probabilistic encoder and decoder, respectively, to approximate our target functions ψ(h) (which is partly constituted by the intermediate projection P(r), via equation (5)) and ϕ(z). Of note, VAE training incorporates stochastic perturbations to the latent representation, thus promoting discovery of a smooth and continuous manifold. Such a representation is desirable in the present framework, such that changes within the latent space (which is based on the delay coordinates) are smoothly mapped to changes within the observation spaces[92]. After training, to generate predicted trajectories, we simply fed the mean output from the encoder (that is, q(μz∣y)) through the decoder to (deterministically) generate the maximum a posteriori estimate.

Model architecture and hyperparameters

VAE models were trained with the Adam optimizer[93] (see Supplementary Table 1 for details specific to each dataset). Across datasets, the encoder comprised a single-layer feedforward neural network with \(\tanh \) activations, which transformed the vector of leading Legendre coordinates \(\widetilde{{\bf{y}}}\) (equation (5)) to (the mean and log variance of) an m-dimensional latent space (m = 4 unless otherwise stated). The latent space Gaussian was initialized with a noise scale of σ = 0.1 to stabilize training. The decoder from this latent space included a single-layer feedforward neural network consisting of 10 hidden units with \(\tanh \) activations, followed by a linear readout layer matching the dimensionality of the target signal. During training, the KL divergence weight was gradually annealed to a factor of β over the first approximately 15% of training epochs to mitigate posterior collapse90,94. Hyperparameters pertaining to delay embedding and the VAE were primarily selected by examining expressivity and robustness within the training set of a subset of mice. Within each modelling pipeline or dataset, hyperparameters were kept fixed across all mice and modalities to mitigate overfitting.

Model training pipeline

For all main analyses, we implemented an iterative leave-one-out strategy in which candidate models were trained using 10-min recordings concatenated across six of the seven mice, and subsequently tested on the held-out 10-min recording obtained from the seventh mouse. To make model training efficient, we used a randomized SVD approach95,96. Thus, for each iteration, we began by pre-standardizing (to zero mean and unit variance) all datasets (‘standardization 1’) and aligning in time following delay embedding of the pupil measurements (thus removing the initial time points of the widefield data for which no delay vector of pupil values was available). Next, we lag adjusted the (delay embedded) pupil and widefield time series from each mouse according to the median lag (across the six training mice) derived from the cross-correlation function of each training mouse between their pupil time series and their mean widefield signal. Next, we concatenated the six training datasets and computed the SVD of the concatenated widefield time series (\({{\bf{Y}}}_{{\rm{cat}}}={{\bf{U}}}_{{\rm{cat}}}{{\bf{S}}}_{{\rm{cat}}}{{\bf{V}}}_{\,{\rm{cat}}}^{{\rm{T}}}\)) to obtain group-level spatial components Vcat. Data were projected onto the leading ten components (that is, the first ten columns of Vcat) and standardized once more (‘standardization 2’), and training proceeded within this reduced subspace. Note that this group-level subspace excluded data from the test mouse, such that Vcat differed for each iteration of the leave-one-out pipeline.

For the held-out mouse, the pre-standardized pupil diameter (that is, having undergone ‘standardization 1’ above) was standardized once more using the training set parameters (‘standardization 2’), and used to generate a prediction within the group-level 10-dimensional subspace. This prediction was then inverse transformed and unprojected back into the high-dimensional image space using the standardization and projection parameters of the training set. All model evaluations were performed following one final inverse transformation to undo the initial pre-standardization (standardization 1), enabling evaluation in the original dF/F units. The pre-standardization parameters were not predicted from the training data; including the pre-standardization as part of the model would make model performance sensitive to the robustness of the relationship between dF/F values and raw pupil units, which is not scientifically relevant in this context. Finally, any subsequent projections used to evaluate model performance in terms of principal components (as in Fig. 2b) utilized mouse-specific SVD modes (denoted simply as V), rather than the group modes (Vcat) used during training.

In addition to the leave-one-out pipeline, we implemented a separate, within-subject pipeline in which we trained models on the first 5 min of data for each mouse and report model performance on the final 3.5 min of the 10-min session. This training pipeline involved prediction of the full-dimensional images, without the intermediate SVD used in the leave-one-out pipeline. All results pertaining to this modelling pipeline are contained in Supplementary Fig. 2.

Model evaluation

For analyses reporting ‘total variance’ explained (Figs. 2a and 3c, top), we report the R^2 value over the full 128 × 128 image, computed across all held-out time points. R^2 was computed as the coefficient of determination, evaluated over all pixels n and temporal samples t (that is, the total variance):

$${R}^{2}=1-\frac{{\sum }_{n}{\sum }_{t}{({y}_{n,t}-{\widehat{y}}_{n,t})}^{2}}{{\sum }_{n}{\sum }_{t}{({y}_{n,t}-\bar{y})}^{2}}$$

(9)

where yn,t and \({\widehat{y}}_{n,t}\) are, respectively, the true and predicted values of y at the n-th pixel and t-th time point, and \(\bar{y}\) is the global mean. In practice, this value was computed as the pixel-wise weighted average of R^2 scores, with weights determined by pixel variance (computed using the built-in function sklearn.metrics.r2_score, with ‘multioutput’ set to ‘variance weighted’).

To examine variance explained beyond the first principal component (Figs. 2b and 3c, bottom), we computed the (randomized) SVD of the widefield data for each mouse (Y = USVT) and, after training, projected the original and reconstructed widefield data onto the top N = 200 spatial components excluding the first (that is, YV2:N) before computing R^2 values. The matrices from this SVD were used for all analyses involving projection onto the leading principal components.

For the shuffled control analysis (Extended Data Fig. 7), we examined total variance explained in widefield data from the test set (as in Fig. 2a), except that for each mouse, we swapped the original pupil diameter timecourse with one from each of the other six mice (that is, we shuffled pupil diameter and widefield calcium data series across mice).

Dynamic mode decomposition

Dynamic mode decomposition (DMD)97,98,99 is a data-driven method to dimensionally reduce time series measurements into a superposition of spatiotemporal modes. Given the time series matrix Y = {y(t_1), y(t_2), …, y(tp)}, where y(ti) represents the system state vector at the i-th time point, the data are split into two matrices, Y(1) = {y(t_1), …, y(tp−1)} and Y(2) = {y(t_2), …, y(tp)}. DMD seeks an approximate linear mapping A such that

$${{\bf{Y}}}^{(2)}\approx {\bf{A}}{{\bf{Y}}}^{(1)}.$$

(10)

Theoretically, the operator A represents a low-rank approximation to an infinite-dimensional linear operator—namely, the Koopman operator—associated with nonlinear systems42,99, motivating the use of DMD even in the context of nonlinear dynamics. This low-rank approximation is obtained via SVD of Y(1) and Y(2). The measurement data can thus be approximated in terms of the spectral decomposition of A:

$${\bf{y}}(t)\approx \mathop{\sum }\limits_{k}^{r}{b}_{k}{{\bf{v}}}_{k}{e}^{{\omega }_{k}t}$$

(11)

where the eigenvectors vk give the dominant spatial modes, the eigenvalues ωk give the dominant temporal frequencies and the coefficients bk determine the amplitudes.

For each mouse, we applied DMD separately to three data matrices: the original widefield calcium images, and the predictions generated through both the no embedding model and the latent model. Here again, for numerical efficiency, we incorporated randomized SVD as part of the randomized DMD algorithm[100] implemented in pyDMD101,102.

Following application of DMD, we constructed spatial phase maps from the leading eigenvector of each A matrix. Specifically, given the leading eigenvector \({{\bf{v}}}_{1}={[{v}_{1},{v}_{2},\ldots ,{v}_{n}]}^{{\rm{T}}}\in {{\mathbb{C}}}^{n}\), we computed the phase at each pixel i as \({\theta }_{i}=\arg ({v}_{i})\), where \(\arg (\,\cdot \,)\) denotes the complex argument of each entry, and vi represents the value of the eigenvector v_1 at the i-th pixel. Note that the no embedding model predictions yielded exclusively real-valued eigenvectors, as these model predictions were fundamentally rank-one (that is, they reflect linear transformations of the scalar pupil diameter). Accordingly, as the complex argument maps negative real numbers to 0 and positive real numbers to π, phase maps obtained from the no embedding model predictions were restricted to these two values.

We used the circular correlation coefficient to quantify the similarity between the phase map obtained from the original dataset and the phase map obtained from either of the two model predictions. Specifically, let θi and \({\widehat{\theta }}_{i}\) represent the phase at spatial location i in the phase maps of the original and reconstructed datasets. The circular correlation ρcirc between the two phase maps was computed as[103]:

$${\rho }_{{\rm{circ}}}=\frac{{\sum }_{i=1}^{n}\sin ({\theta }_{i}-\bar{\theta })\sin ({\widehat{\theta }}_{i}-\bar{\widehat{\theta }})}{\sqrt{{\sum }_{i=1}^{n}{\sin }^{2}({\theta }_{i}-\bar{\theta }){\sum }_{i=1}^{n}{\sin }^{2}({\widehat{\theta }}_{i}-\bar{\widehat{\theta }})}},$$

(12)

where \(\bar{\theta }\) and \(\bar{\widehat{\theta }}\) are the mean phase angles (that is, the mean directions) of the respective maps. The circular correlation is bounded between −1 and 1, where 1 indicates perfect spatial phase alignment between the datasets, and −1 indicates an inverted pattern. However, because the eigenvectors are only determined up to a complex sign, Fig. 2c reports the absolute value this quantity, that is, ∣ρcirc∣.

Clustering and decoding analysis

We used GMMs to assign each (variance-normalized) image frame to one of k clusters (parameterized by the mean and covariance of a corresponding multivariate Gaussian) in an unsupervised manner. This procedure enables assessment of the spatial specificity of cortical patterns predicted by arousal dynamics, with increasing number of clusters reflecting increased spatial specificity.

In brief, a GMM models the observed distribution of feature values x as coming from some combination of k Gaussian distributions:

$$p(x| {\rm{\pi }},\mu ,\Sigma )=\mathop{\sum }\limits_{i=1}^{k}{{\rm{\pi }}}_{i}\cdot {\mathcal{N}}(x| {\mu }_{i},{\Sigma }_{i}),$$

(13)

where μi and Σi are the mean and covariance matrix of the i-th Gaussian, respectively, and πi is the probability of x belonging to the i-th Gaussian (that is, the Gaussian weight). We fit the mean, covariance and weight parameters through the standard expectation-maximization algorithm as implemented in the GMM package available in scikit-learn[104]. This procedure results in a posterior probability for each data point’s membership to each of the k Gaussians (also referred to as the ‘responsibility’ of Gaussian i for the data point); (hard) clustering can then be performed by simply assigning each data point to the Gaussian cluster with maximal responsibility.

Images were spatially normalized such that the (brain-masked) image at each time point was set to unit variance (thus yielding a ‘spatial sign’[105]), followed by projection onto the top three principal components to regularize the feature space. Then, for each mouse and each number of clusters k, a GMM with full covariance prior was fit to the normalized, dimensionally reduced image frames to obtain a set of ‘ground-truth’ cluster assignments. After training, this GMM was subsequently applied to the reconstructed rather than the original image frames to obtain maximum a posteriori cluster assignments associated with the no embedding or latent model predictions. These cluster assignments were compared with the ground-truth assignments, with accuracy computed as the proportion of correctly identified cluster labels.

The preceding algorithm effectively clusters the dimensionally reduced image frames on the basis of their cosine distance (technically, their L_2-normalized Euclidean distance). Because the first principal component tends to explain the vast majority of the variance, this Euclidean-based distance is largely determined by distance along the leading dimension. This analysis choice suffices for the primary purpose of the analysis—namely, to assess correspondence between the original and reconstructed data at the level of individual time points. However, in the context of multivariate data, it is common to also normalize the data features before clustering, thus equally weighting each (spatial) dimension of the data. To examine this clustering alternative, we performed feature-wise normalization of each spatial dimension following projection of the (sample-wise) normalized image frames onto the top three principal components. This essentially constitutes a whitening transformation of the image frames, with Euclidean distance in this transformed space effectively approximating the Mahalanobis distance between image frames. Clustering results from this procedure are shown in Extended Data Fig. 9. For these supplementary analyses, which we also extend to higher cluster numbers, we used a spherical (rather than full) covariance prior as additional regularization.

Hidden Markov model

We additionally used hidden Markov models (HMMs) to capture the temporal dynamics within the original and reconstructed widefield calcium measurements. In brief, under a Gaussian HMM, the observations {y(t_1), y(t_2), …, y(tp)} are assumed to be generated by a sequence of latent discrete states zt ∈ {1, …, K}, with the full generative model including:

1. (1)
A transition matrix \({\bf{A}}\in {{\mathbb{R}}}^{K\times K}\), where Aij = P(zt + 1 = j∣zt = i) represents the probability of transitioning from state i to state j.

2. (2)
An initial state distribution, represented as a vector of state probabilities \({\boldsymbol{\pi }}\in {{\mathbb{R}}}^{K}\) (with πi = P(z_1 = i)).

3. (3)
Emission distributions, such that each observation yt is modelled as a multivariate Gaussian conditioned on the latent state zt—that is, \({{\bf{y}}}_{t} \sim {\mathcal{N}}({{\boldsymbol{\mu }}}_{z},{{\boldsymbol{\Sigma }}}_{z})\), where μz and Σz denote the state-specific mean and covariance, respectively.

The model is thus parameterized by \({\boldsymbol{\theta }}=\{{\bf{A}},{\boldsymbol{\pi }},{\{{{\boldsymbol{\mu }}}_{z},{{\boldsymbol{\Sigma }}}_{z}\}}_{z=1}^{K}\}\). We estimated these parameters via an expectation-maximization algorithm (Baum-Welch), as implemented in hmmlearn[106].

Before HMM fitting, the widefield images were projected onto the three leading principal components and normalized along each dimension (similar to preprocessing for the GMM with whitening above, but without the initial normalization of each individual image frame). HMMs were fit to these data for a range of state numbers (K = 2, 3, 4, 5, 6).

To ensure a robust fit, we initialized the state probabilities to be uniform, \({{\rm{\pi }}}_{i}=\frac{1}{K}\), and initialized the transition matrix with probabilities of 0.8 along the diagonal (that is, within-state transitions or ‘stay’ probabilities) and 0.2/K for all off-diagonal elements (that is, between-state transitions).

An HMM was first fit to the original data (yielding a ground-truth model), and then separate HMMs were fit to the reconstructions. In the latter cases, the emission parameters \({\{{{\boldsymbol{\mu }}}_{z},{{\boldsymbol{\Sigma }}}_{z}\}}_{z=1}^{K}\) were fixed to the values obtained from the ground-truth model, so that only the transition matrix A and the initial state probabilities π were updated.

For each mouse and each choice of K states, we computed three evaluation metrics:

1. (1)
The log-likelihood of the observed data under the fitted HMMs.

2. (2)
Decoding accuracy, computed as the agreement between the latent states inferred from the ground-truth HMM and those predicted by the HMMs fit to the reconstructed datasets.

3. (3)
Transition matrix similarity, which we computed as the row-wise KL divergence between the ground-truth and the reconstructed transition matrices:

$${\mathbb{KL}}({{\bf{A}}}_{{\rm{true}}}\parallel {{\bf{A}}}_{{\rm{model}}})=\frac{1}{K}\mathop{\sum }\limits_{i=1}^{K}\mathop{\sum }\limits_{j=1}^{K}{A}_{ij}^{{\rm{true}}}\log \frac{{A}_{ij}^{{\rm{true}}}}{{A}_{ij}^{{\rm{model}}}}.$$

(14)

For numerical stability in computing KL divergence, transition probabilities below a threshold of ϵ = 10−6 were set to zero and each row was renormalized to maintain stochasticity, that is,

$$\mathop{\sum }\limits_{j=1}^{K}{A}_{ij}=1\quad \,{\rm{for\; all}}\,i.$$

(15)

Multi-dataset integration

To extend our framework to an independent set of observables, we trained our architecture on concatenated time series from the Allen Institute Brain Observatory dataset (n = 16 mice). For visualization purposes, we sought to learn a group-level two-dimensional embedding (that is, \({\bf{z}}\in {{\mathbb{R}}}^{2}\)) based on delay embedded pupil measurements. After group-level training, we froze the weights of the encoder and retrained the decoders independently for each mouse, enabling individual-specific predictions for the Allen Institute mice from a common (that is, group-level) latent space. We additionally applied these encoder weights to delay embedded pupil observations from the original ‘WashU’ dataset, once again retraining mouse-specific decoders to reconstruct observables from the common latent space.

Once trained, this procedure results in the generative model p(yi, z), which we used to express the posterior probability of each observable yi as a function of position within a common 2D latent space (note that this formulation does not seek a complete generative model capturing all joint probabilities among the observables). This allowed us to systematically evaluate the conditional expectation (\({\mathbb{E}}({{\bf{y}}}_{i}| {\bf{z}})\)) for each observable over a grid of points in the latent space, which was regularized to be continuous (roughly, an isotropic Gaussian[89]). Figure 4 represents this expectation, averaged over decoder models trained for each mouse.

To visualize a vector field over the manifold, we applied a data-driven nonlinear equation discovery method—sparse identification of nonlinear dynamics (SINDy)61,107—to the dynamics represented in the 2D latent coordinates, treating data from each mouse as an independent trajectory. We obtained a simple nonlinear oscillator:

$${\mathop{z}\limits^{.}}_{1}=\alpha {z}_{2},$$

(16)

$${\mathop{z}\limits^{.}}_{2}=\beta {z}_{1}+\gamma {z}_{1}^{2}{z}_{2},$$

(17)

with α = 0.384, β = −0.251 and γ = −0.193. This system was evaluated over a grid of evenly spaced sample points within the latent space, enabling a visual approximation of the true underlying vector field.

Reporting summary

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

Data availability

The widefield and pupillometry data collected for this study are available on Zenodo[108] (https://doi.org/10.5281/zenodo.15777664). Data from the Allen Brain Observatory, including experimental recordings and Common Coordinate Framework reference files, can be accessed via the Allen Software Development Kit (https://allensdk.readthedocs.io/en/latest/).

Code availability

All analysis code for this study is publicly available on GitHub (https://github.com/ryraut/arousal_dynamics).

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Acknowledgements

We thank the Allen Institute for the publicly available data used in this study; and T. Voss for assistance with the optical set-up. The mouse illustration in Figs. 1 and 4 is adapted from ref. [71], obtained from SciDraw (https://SciDraw.io). R.V.R. was supported by the Shanahan Family Foundation Fellowship at the Interface of Data and Neuroscience at the Allen Institute and the University of Washington, supported in part by the Allen Institute. This research was additionally supported by the American Heart Association grant 20PRE34990003 (to Z.P.R.); the US National Institutes of Health grants R01NS126326, R01NS102870 and RF1AG07950301 (to A.Q.B.), and R37NS110699, R01NS084028 and R01NS094692 (to J.-M.L.); and the National Science Foundation AI Institute in Dynamic Systems grant 2112085 (to J.N.K. and S.L.B.).

Author information

Author notes

1. Ryan V. Raut

Present address: Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA

Authors and Affiliations

1. Allen Institute, Seattle, WA, USA

Ryan V. Raut

2. Department of Physiology & Biophysics, University of Washington, Seattle, WA, USA

Ryan V. Raut

3. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Zachary P. Rosenthal

4. Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA

Xiaodan Wang, Marcus E. Raichle & Adam Q. Bauer

5. Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA

Hanyang Miao, Jin-Moo Lee & Marcus E. Raichle

6. Department of Computer Science & Engineering, University of California San Diego, La Jolla, CA, USA

Zhanqi Zhang

7. Department of Mechanical Engineering, University of Washington, Seattle, WA, USA

Steven L. Brunton

8. Department of Biology, University of Washington, Seattle, WA, USA

Bingni W. Brunton

9. Department of Applied Mathematics, University of Washington, Seattle, WA, USA

J. Nathan Kutz

Contributions

R.V.R., Z.P.R., S.L.B., B.W.B. and J.N.K. conceptualized the study. Z.P.R. acquired the data. Z.P.R., H.M. and Z.Z. processed the data. R.V.R. analysed the data and undertook the modelling. R.V.R., Z.P.R., X.W., J.-M.L., M.E.R., A.Q.B., S.L.B. and J.N.K. provided resources and funding. R.V.R. wrote and revised the manuscript with input from all authors.

Corresponding author

Correspondence to Ryan V. Raut.

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Extended data figures and tables

Extended Data Fig. 1 Illustration of shadow manifold reconstruction for the 3D Lorenz system.

The white dot in each plot represents the same point in time (i.e., the same dynamical state) from several different views (or embeddings). Here, the measurements y_1 and y_2 are simply taken to be the state variables z_1 and z_2 from the original system z(t). Each of these observations can be stacked with time-shifted copies of itself to obtain a (high-dimensional) time delay embedding that is diffeomorphic to the original attractor. These embeddings can be dimensionally reduced to obtain low-dimensional ‘shadow manifolds’ that (approximately) preserve the topology of the original system. Pairs of black arrows indicate relation via a diffeomorphic mapping. These relations clarify the motivation for modeling one observable from a dynamical system (e.g., y_2) as a function of a time delay embedding of a second observable (e.g., the augmented observable \({\widetilde{{\bf{y}}}}_{1}\)). See Extended Data Fig. 2 for demonstration of our computational framework applied to the Lorenz system.

Extended Data Fig. 2 Toy demonstration of delay embedding for multimodal cross-prediction in the presence of dynamical and observation noise.

A Delay embedding framework applied to a stochastic version of the 3D Lorenz system (compare to Fig. 1c). Here, the observation functions g_1 and g_2 are defined as identity functions. Across simulations, y_1 is taken to be a function of z_2, whereas y_2 is a 2D function of z_1 and z_3. We aim to model y_2 according to y_1 (that is, a noisy measurement of a single state variable, z_2), across different levels of dynamical noise and observation noise. f corresponds to the stochastic Lorenz system defined in Eq. 18. B-C Reconstruction accuracy across multiple levels of dynamical noise, scaled by the parameter α. B illustrates sample reconstructions of the state variables z_1 and z_3 from the observable y_1 at the highest dynamical noise level tested (α = 21). D-E Same as B-C, but across multiple levels of observation noise, scaled by the parameter β. D illustrates sample reconstructions for the state variable time series at the highest noise level of observation noise tested (β = 21). See Supplementary Text for further details and interpretation.

Extended Data Fig. 3 Contributions of delays and nonlinearities to predictive performance.

A Total variance explained in test data for four model categories: ‘No embedding’ (linear pupil regressor), ‘No embedding, Nonlinear’ (nonlinear mapping from pupil regressor), ‘Linear embedding’ (linear mapping from delay embedded pupil), and ‘Latent model’ (nonlinear mapping from delay embedded pupil). B Same as in A, but for variance explained along PCs 2-N. Compare also with simulation in Extended Data Fig. 2c,e.

Extended Data Fig. 4 Pupil delay embeddings explain multidimensional variance in widefield calcium images.

A Variance explained along the dimensions spanned by each of the first four principal components from each mouse. B-C Principal component (PC) time series and their prediction from pupil delay embeddings in test data, shown for two example mice. PC spatial maps corresponding to each time series are shown underneath. Higher-order PCs (here, 2 and 3) retain clear temporal relationships to PC1, enabling some of this multidimensional variance to be captured from a time-invariant mapping from the pupil-derived latent space.

Extended Data Fig. 5 Topographies of widefield calcium variance explained.

R^2 maps in held-out data corresponding to the ‘No embedding’ and ‘Latent model’ predictions, shown for each mouse (A) and averaged across mice (B). Dynamical information consistently improved explanatory power, particularly within a bilateral set of premotor cortical regions. This pattern recalls prior results in humans (cf. refs. 109,110).

Extended Data Fig. 6 R[2] maps for widefield calcium images as linearly modeled by dynamical features of pupil size (no cross-validation).

A R^2 maps derived from the pupil delay embedding (i.e., the Hankel matrix) projected onto successive Legendre polynomials. Projections onto Legendre polynomials (i.e., the Legendre coordinates \(\widetilde{{\bf{y}}}\) from equation (5)) essentially define convolutions of pupil diameter with increasingly high-frequency information from the recent past. The 0th-order polynomial effectively captures the amplitude envelope of pupil diameter (cf. widefield maps obtained from cholinergic indicators[21], which also correlate with pupil amplitude envelope[58]). B R^2 maps derived from successive temporal derivatives of pupil diameter (0th order derivative corresponds to the original pupil diameter time series).

Extended Data Fig. 7 Shuffled control analysis for prediction of widefield calcium data from pupil dynamics.

Models were trained according to the standard leave-one-out pipeline, similar to Fig. 2a. However, at test time, widefield data from the held-out mouse are here predicted from not only the held-out mouse’s own pupil data (filled circles), but also, separately, from the pupil data from each of the other mice (empty circles). For all mice, explained variance is high when using the mouse’s own pupil timecourse (filled circles). In contrast, explained variance is invariably poor when attempting to predict widefield data from pupil dynamics obtained from another mouse (empty circles; negative R^2 values occur when the prediction performs worse than simply predicting the mean value of each pixel). This result clarifies that good performance in held-out data does not trivially result from a more complex model.

Extended Data Fig. 8 Hidden Markov models (HMMs) reveal that the latent model recapitulates spatially structured dynamics not captured by instantaneous pupil diameter.

A Six HMM ‘states’ for a sample mouse (same as in Fig. 2d), computed as the mean of the Gaussian emission associated with each hidden state. B HMM transition matrices involving the HMM states in A for the sample mouse, computed from the original data (left), the data reconstructed through the Latent model (middle), and the data reconstructed through the No embedding model (right). See Methods for model fitting details. Note high probabilities along the superdiagonal (elements immediately to the right of the principal diagonal) indicating a canonical cycle among states in this mouse (cf. Fig. 2d). This structure is less apparent in the No embedding reconstruction (right). C-E Evaluation metrics computed over a range of latent states (2-6) for all seven mice. C Decoding accuracy, as in Fig. 2e. Null predictions are based on repeatedly choosing the most common state in the ground truth HMM for each time point. Greater accuracy indicates greater agreement of latent state labels with labels obtained from the original HMM. D Total negative log-likelihood (NLL) of the ground truth or reconstructed datasets based on the HMM fit to the original data. Greater NLL indicates data are less probable according to ground truth generative model. E KL divergence of the ground truth and reconstructed transition matrices from the original HMM transition matrix. Greater KL divergence indicates greater difference from the ground truth transition matrix.

Extended Data Fig. 9 Decoding ‘brain states’ from pupil dynamics.

Comparison of decoder accuracy in properly identifying GMM cluster assignments for each image frame based on the most frequent assignment in the training set (‘Null’ decoder), the ‘No embedding’ model, or the ‘Latent model’. A Same as in Fig. 2e, but for up to 10 clusters and using a spherical rather than full covariance prior (Methods). B Same as in (A) but after whitening transformation (i.e., after projection onto the top 3 PCs, each dimension was set to unit variance). The whitening transformation highlights the ability of the delay embedding to capture genuinely multidimensional information, which is otherwise masked by the dominant PC1.

Extended Data Fig. 10 Topographies of variance explained.

R^2 maps in held-out data corresponding to the ‘No embedding’ and ‘Latent model’ predictions, shown for FAF (A, C) and hemoglobin (B, D), for each mouse (A, B) and averaged across mice (C, D). See Extended Data Fig. 5 for calcium results.

Supplementary information

Supplementary Information

This file contains Supplementary Text, Supplementary Table 1, Supplementary Figs 1–6 and Supplementary references.

Reporting Summary

Supplementary Video 1

Widefield calcium epoch from a sample mouse, shown for the original data (left) and pupil predictions generated via the ‘Latent model’ (middle) and the ‘No embedding’ model (right).

Supplementary Video 2

Widefield calcium epoch from a second sample mouse, shown for the original data (left) and pupil predictions generated via the ‘Latent model’ (middle) and the ‘No embedding’ model (right).

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Raut, R.V., Rosenthal, Z.P., Wang, X. et al. Arousal as a universal embedding for spatiotemporal brain dynamics. Nature 647, 454–461 (2025). https://doi.org/10.1038/s41586-025-09544-4

- Received: 15 February 2024

- Accepted: 18 August 2025

- Published: 24 September 2025

- Version of record: 24 September 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09544-4

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ABCA7 variants impact phosphatidylcholine and mitochondria in neurons

Djuna von Maydell,

Shannon E. Wright,

Ping-Chieh Pao,

Colin Staab,

Oisín King,

Andrea Spitaleri,

Julia Maeve Bonner,

Liwang Liu,

Chung Jong Yu,

Ching-Chi Chiu,

Daniel Leible,

Aine Ni Scannail,

Mingpei Li,

Carles A. Boix,

Hansruedi Mathys,

Guillaume Leclerc,

Gloria Suella Menchaca,

Gwyneth Welch,

Agnese Graziosi,

Noelle Leary,

George Samaan,

Manolis Kellis &

…

Li-Huei Tsai

Nature volume 647, pages 462–471 (2025)

Abstract

Loss-of-function variants in the lipid transporter ABCA7 substantially increase the risk of Alzheimer’s disease1,2, yet how they impact cellular states to drive disease remains unclear. Here, using single-nucleus RNA-sequencing analysis of human brain samples, we identified widespread gene expression changes across multiple neural cell types associated with rare ABCA7 loss-of-function variants. Excitatory neurons, which expressed the highest levels of ABCA7, showed disrupted lipid metabolism, mitochondrial function, DNA repair and synaptic signalling pathways. Similar transcriptional disruptions occurred in neurons carrying the common Alzheimer’s-associated variant ABCA7 p.Ala1527Gly[3], predicted by molecular dynamics simulations to alter the ABCA7 structure. Induced pluripotent stem (iPS)-cell-derived neurons with ABCA7 loss-of-function variants recapitulated these transcriptional changes, displaying impaired mitochondrial function, increased oxidative stress and disrupted phosphatidylcholine metabolism. Supplementation with CDP-choline increased phosphatidylcholine synthesis, reversed these abnormalities and normalized amyloid-β secretion and neuronal hyperexcitability—key Alzheimer’s features that are exacerbated by ABCA7 dysfunction. Our results implicate disrupted phosphatidylcholine metabolism in ABCA7-related Alzheimer’s risk and highlight a possible therapeutic approach.

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Main

After APOE4, rare loss-of-function (LoF) mutations in ABCA7 caused by premature-termination codons (PTCs) are among the strongest genetic risk factors for Alzheimer’s disease (AD), with an odds ratio of approximately 2 (refs. 1,2). Common single-nucleotide polymorphisms in ABCA7 also moderately increase AD risk2,3, suggesting that ABCA7 dysfunction contributes substantially to disease risk in the broader population. Despite their significance, the precise cellular mechanisms through which ABCA7 LoF variants affect AD risk remain poorly defined.

ABCA7 functions by transporting phospholipids across cell membranes, thereby maintaining membrane asymmetry and facilitating lipid transport within the brain4,5,6. Mouse model studies suggest that ABCA7 dysfunction promotes amyloid deposition, impairs amyloid clearance by astrocytes and microglia and increases glial inflammatory responses7,8,9,10. Moreover, recent research in human cell lines and tissues has identified disturbances in lipid metabolism as potential mechanisms linking ABCA7 dysfunction to AD risk11,12,13. However, systematic analyses of ABCA7 LoF effects across various human brain cell types have not yet been performed. Investigations specifically addressing known ABCA7 PTC variants found in patients have been limited, with most existing studies focusing broadly on complete ABCA7 knockouts8,11,14.

Single-nucleus RNA sequencing (snRNA-seq) has effectively identified cell-type-specific transcriptional changes linked to other AD-associated genes, such as APOE and TREM215,16,17, and provided insights into disease mechanisms and therapeutic targets. Here we generated a cell-type-specific transcriptomic snRNA-seq atlas of ABCA7 LoF variants in the post-mortem human prefrontal cortex (PFC). Using this resource, we identified the cell-type-specific correlates of ABCA7 LoF variants in the human brain—particularly in neurons—and experimentally investigated these predictions in human neurons harbouring ABCA7 PTC variants.

Single-cell atlas of ABCA7 LoF variants

To investigate the cell-type-specific impacts of ABCA7 LoF variants in AD, we selected 12 carriers of rare ABCA7 PTC variants, including splice (c.4416+2T>G and c.5570+5G>C), frameshift (p.Leu1403fs, p.Glu709fs) and nonsense (p.Trp1245*, p.Trp1085*) variants, from the ROSMAP cohort (Fig. 1a,b and Supplementary Tables 1 and 2). ABCA7 PTC variants are presumed to cause ABCA7 haploinsufficiency, and were associated with lower ABCA7 protein levels in the PFC compared with in matched non-carriers, in a previously available dataset in which a subset overlapped with our sequencing patients (Extended Data Fig. 1a and Supplementary Table 3). Twenty-four matched controls without ABCA7 PTC variants were selected on the basis of AD pathology, age, sex, APOE genotype and cognitive status (Fig. 1c, Extended Data Fig. 1b,c, Supplementary Table 4 and Supplementary Note 1). The absence of rare damaging variants in other AD-associated genes[2] was confirmed and a subset of genotypes was verified using Sanger sequencing (Methods and Extended Data Fig. 1d).

Fig. 1: snRNA-seq atlas of post-mortem PFC from ABCA7 LoF variant carriers.

a, ABCA7 gene structure indicating studied variant locations. Exons are indicated by rectangles; introns are indicated by lines. The pie chart shows the frequency of ABCA7 PTC-variant carriers in the ROSMAP cohort. b, Human snRNA-seq cohort overview. The diagram was created using BioRender. c, Metadata summary of the snRNA-seq cohort. n = 36 individuals. This snRNA-seq experiment was performed once. d, 2D UMAP of ABCA7 LoF gene perturbation scores (S = −log_10[P] × sign(log_2[fold change (FC)]); unadjusted Limma-Voom P values; n = 12 (LoF) and n = 24 (control) individuals), restricted to genes with |S| > 1.3. Red, S > 1.3; blue, S < −1.3; point size ∝ |S|. The top 10 genes are labelled. e, 2D UMAP coloured by gene cluster assignment (Gaussian mixture model). f, For each cluster, the top two enriched pathways (GO BP; hypergeometric enrichment, one-sided, unadjusted P < 0.01; relative to all genes in UMAP) and top five genes (highest absolute mean S across cell types among genes in enriched pathways, P < 0.01) are shown. g, Cell-type-specific scores per gene cluster (SC), calculated as the mean perturbation score (S) of all genes in each cluster. *FDR-adjusted P < 0.01, |SC| > 0.25. Ast, astrocytes; Ex, excitatory neurons; In, inhibitory neurons; Mic, microglia; Oli, oligodendrocytes.

Raw snRNA-seq data from the BA10 region of the PFC were available for ten non-carrier samples from a previous study[18]. Fresh-frozen PFC BA10 tissues from the remaining individuals underwent snRNA-seq analysis using the 10x Genomics Chromium platform. After extensive quality control—including genotype–transcriptome matching to confirm sample identities and rule out potential sample swaps and correcting for batch effects (Extended Data Fig. 1e, Supplementary Fig. 1 and Supplementary Table 5)—our final dataset consisted of 102,710 high-quality cells from an initial total of 150,456 cells, representing inhibitory neurons, excitatory neurons, astrocytes, microglia, oligodendrocytes and oligodendrocyte precursor cells (OPCs; Supplementary Fig. 2). A small putative vascular cell cluster did not meet our quality thresholds and was excluded from further analysis.

We next analysed cell-type-specific transcriptional changes associated with ABCA7 LoF variants. We identified 2,389 genes with nominal evidence of perturbation (P < 0.05), suggesting possible transcriptional changes across six major neural cell types after controlling for covariates and focusing on genes detected in more than 10% of cells per type (Supplementary Table 6). We visualized these perturbations in two dimensions, revealing clear transcriptional patterns across cell types (Fig. 1d and Extended Data Fig. 2a). To nominate biological pathways that may be affected by ABCA7 LoF, we clustered genes on the basis of their proximity in this two-dimensional (2D) visualization, as closer genes exhibited similar perturbation patterns (Fig. 1e). Each gene cluster was then analysed for enrichment of biological pathways using the Gene Ontology Biological Process database, highlighting candidate functional themes disrupted by ABCA7 LoF, including cellular stress and apoptosis, synaptic function, DNA repair and metabolism (Fig. 1f and Supplementary Table 7).

Specifically, microglia exhibited marked downregulation of stress-response genes (such as HSPH1, cluster 11), a trend also observed, although less prominently, in neurons and OPCs (Fig. 1g). Microglia and astrocytes showed increased expression of transcriptional regulatory genes (clusters 9 and 10, respectively) (Fig. 1g). OPCs and oligodendrocytes displayed changes in inflammatory signalling pathways (for example, IL10RB, cluster 0; STAT2, cluster 8) (Fig. 1g). Neurons demonstrated increased expression of DNA-repair genes (such as FANCC, cluster 12) and reduced expression of synaptic transmission genes (for example, NLGN1 and SHISA6, cluster 1) (Fig. 1g). Excitatory neurons uniquely exhibited enhanced expression of cellular respiration genes (such as NDUFV2, cluster 7) and decreased expression of triglyceride biosynthesis genes (such as PPARD, cluster 5) (Fig. 1g). Overlaps in gene perturbations across cell types are summarized in Extended Data Fig. 3.

Together, these results highlight extensive cell-type-specific transcriptional disruptions associated with ABCA7 LoF in the human PFC. This single-cell atlas serves as a valuable resource to nominate pathways and genes for future investigation, and is accessible through the Single Cell Portal and Synapse (accession IDs: SCP3182, syn53461705).

ABCA7 LoF profiles in excitatory neurons

Our snRNA-seq data revealed that excitatory neurons express the highest levels of ABCA7 among major neural cell types in the brain (Extended Data Fig. 4a,b). We validated these expression patterns using an independent dataset[19] (Supplementary Table 3), confirming significantly higher ABCA7 expression in neuronal versus glial populations from the human temporal cortex. Expression profiles of control genes that are known to be neuron or glia specific matched expectations (Extended Data Fig. 4c).

Given this expression profile, we hypothesized that excitatory neurons may be particularly impacted by ABCA7 LoF variants. To identify transcriptional correlates in excitatory neurons, we performed gene set enrichment analysis (GSEA) using WikiPathways (472 pathways), and we identified 34 candidate ABCA7 LoF-perturbed pathways (P < 0.05) involving 268 unique genes (Supplementary Table 8). To minimize redundancy and clearly identify biological themes, we grouped these genes into non-overlapping clusters through graph partitioning (Fig. 2a,b, Supplementary Fig. 3 and Supplementary Note 2). This analysis revealed eight biologically meaningful clusters highlighting three major themes: (1) energy metabolism and lipid homeostasis (clusters PM.0 and PM.1); (2) DNA damage and cellular stress responses (clusters PM.2, PM.3, PM.4 and PM.5); and (3) synaptic signalling (cluster PM.7) (Fig. 2a). Layer-specific analysis indicated consistent transcriptional perturbation patterns across cortical layers (Extended Data Fig. 5).

Fig. 2: Transcriptional changes in ABCA7 LoF and ABCA7 p.Ala1527Gly excitatory neurons.

a, Kernel density plots of gene perturbation scores (S = −log_10[P] × sign(log_2[FC]); unadjusted Limma-Voom P values; n = 12 (LoF), n = 24 (control) individuals) per Kernighan–Lin cluster. Positive S indicates increased expression in ABCA7 LoF. The solid lines show the cluster means; the top pathways are indicated. Kernighan–Lin clustering performed on leading-edge genes from perturbed pathways (fGSEA, WikiPathways; unadjusted P < 0.05; Methods). b, Kernighan–Lin gene–pathway graph related to a: genes (circles) and pathways (squares) are indicated. c, Schematic of the ABCA7 gene, highlighting the p.Ala1527Gly variant (purple arrow). Cohort overview for snRNA-seq data from post-mortem PFC, comparing carriers of ABCA7 Gly1527 (≥1 allele) with Gly1527 non-carriers, that is, Ala1527 carriers (data from ref. [21]). The diagram was created using BioRender. d, Perturbation of ABCA7 LoF-associated gene clusters (from a) in excitatory neurons from Ala1527 (n = 227 individuals) versus Gly1527 (n = 133 individuals) carriers (fGSEA analysis of ABCA7 LoF clusters 0–7). Normalized enrichment scores (NES) are shown. The top unadjusted P values are indicated. Positive scores indicate upregulation in Gly1527 variant carriers. e, The closed-conformation ABCA7 structure, highlighting the simulated domain (residues 1517–1756, yellow) and the lipid bilayer (orange). The inset shows Ala1527 (grey) and Gly1527 (purple). f, The root mean squared deviation (r.m.s.d.) of the ABCA7 domain (from e) with the Ala1527 (grey) or Gly1527 (purple) variant, relative to the closed-conformation reference during simulations. Inset: the average positional fluctuations of Cα atoms. Statistical analysis was performed using a two-sided Mann–Whitney U-test. g, Projection of Cα atom positional fluctuations onto first two principal components for the Ala1527 (top, grey) and Gly1527 (bottom, purple) variants during simulations.

Clusters PM.0 and PM.1 were enriched for genes involved in lipid metabolism, mitochondrial function and oxidative phosphorylation (OXPHOS). Specifically, cluster PM.0, including genes associated with lipid homeostasis (such as NR1H3, ACLY and PPARD), was downregulated, whereas cluster PM.1, comprising mitochondrial complex genes (such as COX7A2 and NDUFV2), was upregulated. Clusters PM.2, PM.3 and PM.6 contained upregulated DNA damage response and replication genes (such as RECQL, TLK2 and BARD1). Clusters PM.4 and PM.5 encompassed genes associated with proteasomal degradation, ciliogenesis, apoptosis and inflammation, exhibiting mixed directional regulation. Similarly, cluster PM.7, linked to synaptic and developmental pathways, contained both upregulated and downregulated genes (Fig. 2a).

ABCA7 LoF and p.Ala1527Gly overlap

ABCA7 LoF variants significantly increase AD risk but are rare, and therefore contribute to a small fraction of AD cases1,13. To assess whether transcriptional patterns associated with ABCA7 LoF extend to more common variants, we analysed carriers of the prevalent ABCA7 missense variant p.Ala1527Gly (rs3752246; minor allele frequency ≈ 0.18) within the ROSMAP cohort (Fig. 2c and Supplementary Table 3). Although annotated as the reference allele, Gly1527 represents the less-frequent allele associated with moderately increased AD risk (odds ratio = 1.15 (95% confidence interval 1.11–1.18))2,3,20. We analysed existing snRNA-seq data[21] from human PFC samples of 133 Gly1527 carriers and 227 non-carriers, ensuring no overlap with the previous ABCA7 LoF cohort.

We observed directional transcriptional perturbations in excitatory neurons from Gly1527 carriers that were consistent with those previously associated with ABCA7 LoF variants across all identified clusters (PM.0–7) (Fig. 2a,d). Notably, clusters related to DNA damage (PM.3) and proteasomal function (PM.4) demonstrated evidence of upregulation, suggesting similar cellular stress and genomic instability signatures associated with ABCA7 LoF (Fig. 2a,d). Moreover, we found evidence for downregulation in lipid metabolism (PM.0) and modest upregulation in mitochondrial function (PM.1), aligning with changes seen in ABCA7 LoF neurons (Fig. 2a,d).

To explore structural explanations for these shared transcriptional patterns, we conducted molecular dynamics simulations, comparing Ala1527 and Gly1527 variants in the two extreme ABCA7 conformations—the fully closed, ATP-bound state (Fig. 2e–g) and the fully open, ATP-unbound state (Extended Data Fig. 6). Both conformations were embedded within a lipid bilayer and simulated over a 300 ns timescale to evaluate the local structural impact of the Gly1527 variant. The Gly1527 variant exhibited increased structural flexibility specifically in the ATP-bound closed state, characterized by pronounced conformational fluctuations compared with the Ala1527 variant (Fig. 2f,g). Given that the ATP-bound closed conformation is proposed to facilitate lipid presentation to apolipoproteins22,23, the increased flexibility of the Gly1527 variant may reduce lipid extrusion efficiency, consistent with recent experimental findings[23]. Both variants remained structurally stable in the ATP-unbound open state (Extended Data Fig. 6c–e). These results are further supported by analyses of ϕ/ψ dihedral angle distributions and secondary structure persistence, as described in Supplementary Note 3 and Extended Data Fig. 7. These structural insights, together with our transcriptomics data, suggest that both rare, high-effect ABCA7 LoF variants and common, moderate-risk variants may influence AD risk through similar ABCA7-dependent mechanisms, indicating broader relevance of ABCA7 dysfunction in AD.

Conserved signatures in ABCA7 LoF iNs

To experimentally validate the effects of ABCA7 LoF predicted by our single-cell dataset, we generated two isogenic iPS cell lines homozygous for distinct ABCA7 LoF variants using CRISPR–Cas9 editing (Fig. 3a and Supplementary Fig. 4). One variant, p.Glu50fs*3, introduces an early frameshift mutation, while the other, p.Tyr622*, represents a clinically relevant AD-associated variant[1]. Both variants likely produce severely truncated ABCA7 proteins or trigger nonsense-mediated decay; however, transcript rescue mechanisms such as exon skipping cannot be excluded[24]. We differentiated these iPS cells into induced neurons (iNs) through doxycycline-inducible NGN2 expression[25] (Supplementary Fig. 5a). Wild-type (WT) and ABCA7 LoF neurons both expressed neuronal markers, formed robust neuronal processes within 2–4 weeks (Supplementary Fig. 5b,c), and exhibited electrophysiological activity (Extended Data Fig. 8a–e). ABCA7 LoF neurons demonstrated increased excitability, firing action potentials at lower thresholds than WT neurons (Extended Data Fig. 8f,g), consistent with neuronal hyperexcitability observed in AD.

Fig. 3: ABCA7 LoF variants impact mitochondrial function in neurons.

a, iPS cell (iPSC)-derived isogenic iNs with ABCA7 PTC variants (exon 3: p.Glu50fs*3; exon 15: p.Tyr622*). The gene schematic shows exons (rectangles) and introns (lines). The diagram was created using BioRender. Confocal MAP2 staining is shown. b, Correlation of gene perturbation scores (S = −log_10[P] × sign(log_2[FC]); unadjusted P values were computed using Limma-Voom) from bulk mRNA-seq data. n = 2 (WT) and n = 5 (for each LoF line) wells. c, Kernighan–Lin clustering of leading-edge genes from perturbed pathways in WT versus p.Tyr622* iNs (fGSEA, WikiPathways; FDR-adjusted P < 0.05). For the gene–pathway graph, genes (circles) and pathways (squares) are indicated. d, Heat map (Jaccard index) comparing Kernighan–Lin (K–L) clusters from p.Tyr622* iNs and post-mortem neurons (from Fig. 2a,b). Upregulated (red) and downregulated (blue) clusters in LoF neurons are indicated. FDR-adjusted P was calculated by permutation (1,000 iterations, one-sided). e, Kernel density plots of gene perturbation scores per cluster. Positive S indicates increased expression in p.Tyr622* iNs. The solid lines show the cluster means. The top pathways are indicated. f, Volcano plot of genes encoding mitochondrial proteins (MitoCarta); genes with FDR-adjusted P < 0.05 (Limma-Voom) in WT versus p.Tyr622* iNs are coloured. The top ten upregulated and downregulated genes are labelled. g, Seahorse mitochondrial uncoupled OCR (%). n = 18 (WT), n = 17 (p.Tyr622*) and n = 13 (p.Glu50fs*3) wells; two experiments. h, MitoHealth intensity. n = 8 (WT), n = 11 (p.Tyr622*) and n = 9 (p.Glu50fs*3) wells; around 3 × 10^3 cells per condition; three experiments. Statistical analysis was performed using a linear mixed-effects model. Maximum-intensity projections are shown with NeuN/GFP clipped at the 90th percentile, γ-corrected (γ = 0.5). i, The average TMRM intensity per 75th percentile mask (n = 4 (WT) and n = 5 (p.Tyr622*) wells; mean projection over time). j, The average CellROX intensity per 75th percentile mask. n = 10 wells per genotype. k, Differentially abundant lipid species in WT versus p.Tyr622* iNs (coloured by class); species are labelled if FDR-adjusted P < 0.05, |log[FC]| > 1, two-sided t-test, unequal variances assumed. n = 10 (WT) and n = 8 (p.Tyr622*) wells. For a–k, experiments were carried out after 4 weeks of differentiation; wells represent technical replicates. For g–j, analysis was performed using two-sided t-tests following Shapiro–Levene tests; the box plots show the median (centre line), interquartile range (IQR) (box limits) and 1.5 × IQR (whiskers). For g–j, datapoints represent the per-well mean. Experiments were performed once (a–f, j and k) or at least twice (g–i). Scale bars, 62 μm (a) and 125 μm (h–j).

We next examined whether transcriptional signatures identified in ABCA7 LoF post-mortem neurons were recapitulated in iNs using bulk mRNA-seq after 4 weeks of differentiation (Supplementary Table 9). Transcriptional perturbations between the p.Glu50fs*3 and p.Tyr622* variants were strongly correlated (Pearson correlation = 0.84; Fig. 3b). GSEA revealed 15 significantly perturbed pathways for each variant (false-discovery rate (FDR)-adjusted P < 0.05; WikiPathways; Supplementary Table 10). Kernighan–Lin partitioning of these pathways identified nine transcriptional clusters perturbed in WT versus p.Tyr622* and ten clusters in WT versus p.Glu50fs*3 (Fig. 3c, Extended Data Fig. 9a and Supplementary Table 10). These clusters showed substantial overlap between the two variants, with eight out of nine clusters from p.Tyr622* significantly overlapping with eight out of ten clusters from p.Glu50fs*3 (FDR-adjusted P < 0.05; Extended Data Fig. 9b). Moreover, we observed significant concordance with transcriptional signatures from post-mortem excitatory neurons, with five out of nine p.Tyr622*-associated clusters and seven out of ten p.Glu50fs*3-associated clusters overlapping significantly with post-mortem ones, predominantly with concordant directional changes (Fig. 3d and Extended Data Fig. 9c). For example, consistent with post-mortem findings, p.Tyr622* iNs exhibited downregulated clusters associated with lipid metabolism (T.9 and T.13) and upregulated clusters related to cell cycle regulation and proteasomal activity (T.8 and T.14) compared with WT neurons (Fig. 3e). A mitochondrial cluster (T.10) showed the strongest overlap with post-mortem neurons (cluster PM.1), being consistently upregulated in both variant lines (Fig. 3d and Extended Data Fig. 9c). Together, these data support a causal relationship between ABCA7 LoF variants and multiple transcriptional disruptions in excitatory neurons, particularly affecting proteostasis, cell cycle, lipid metabolism and mitochondrial function.

ABCA7 LoF variants impact mitochondria

To investigate mitochondrial alterations in ABCA7 LoF iNs in more depth, we analysed the expression of 1,136 genes encoding mitochondrial proteins from the MitoCarta database (Supplementary Table 3). Upregulated genes in p.Tyr622* neurons included mitochondrial apoptosis pathway genes (such as CASP3 and BID) and OXPHOS subunits, previously identified in clusters PM.1 and T.10 (Fig. 3f and Supplementary Table 11). Conversely, downregulated genes were enriched for β-oxidation (ACAD and CPT), mitochondrial metabolite transport (SLC25) and oxidative stress detoxification (CAT) (Fig. 3f and Supplementary Table 11). Similar gene expression profiles were observed in the p.Glu50fs*3 variant (Extended Data Fig. 9e).

We directly assessed mitochondrial function by measuring the oxygen-consumption rates (OCRs) using the Seahorse assay (Extended Data Fig. 10a,b). During OXPHOS, OCR-driven proton movement across the inner mitochondrial membrane maintains mitochondrial membrane potential (ΔΨm) and supports ATP production (Extended Data Fig. 10c). To account for differences in cell viability and mitochondrial abundance, we analysed internally normalized OCR ratios. Spare respiratory capacity—representing the mitochondria’s ability to increase respiration in response to demand—was comparable between WT and ABCA7 LoF neurons (Extended Data Fig. 10d,e). However, ABCA7 LoF neurons showed significantly reduced uncoupled mitochondrial respiration, defined as the proportion of basal oxygen consumption dedicated to maintaining membrane potential lost due to proton leakage rather than ATP synthesis[26], compared with WT neurons (Fig. 3g and Extended Data Fig. 10f). The uncoupled mitochondrial OCR in WT neurons (approximately 20%; Fig. 3g) aligns with previous reports for neurons and other cell types27,28, indicating that ABCA7 LoF neurons exhibited abnormally low mitochondrial uncoupling. Consistent with this finding, expression of UCP2—a mitochondrial uncoupling protein expressed in the brain[29]—was reduced in ABCA7 LoF neurons (Extended Data Fig. 10g).

As reduced mitochondrial uncoupling can result in an elevated ΔΨm, we assessed ΔΨm using MitoHealth and TMRM dyes, both of which accumulate in mitochondria proportionally to the membrane potential. Both dyes showed higher fluorescence—indicative of elevated ΔΨm—in ABCA7 LoF neurons compared with in WT neurons (Fig. 3h,i). Signal specificity was confirmed by decreased fluorescence after FCCP-induced depolarization (Extended Data Fig. 10h). As mitochondrial uncoupling regulates reactive oxygen species (ROS) generation[27], we next measured oxidative stress using CellROX dye. ABCA7 LoF neurons showed significantly increased fluorescent CellROX signal compared with WT neurons (Fig. 3j). Together, these data indicate that ABCA7 LoF variants decrease mitochondrial uncoupling, resulting in elevated membrane potential and increased oxidative stress.

ABCA7 LoF alters PC balance

As ABCA7 functions as a lipid transporter, we used liquid chromatography coupled with mass spectrometry (LC–MS) to examine lipid profiles in WT and ABCA7 LoF iNs (Supplementary Table 12). Comparing WT and p.Glu50fs*3 iNs revealed significant changes across several lipid classes, including neutral lipids, phospholipids, sphingolipids and steroids (Extended Data Fig. 11a,b). Notably, triglycerides (TGs), particularly long-chain polyunsaturated species, were frequently elevated in p.Glu50fs*3 iNs (Extended Data Fig. 11b,c).

Consistent with ABCA7’s known transport of phospholipids5,23, phosphatidylcholines (PCs)—key structural membrane components and potential ABCA7 substrates[6]—were prominently affected, with approximately 22% of detected PC species perturbed in p.Glu50fs*3 iNs (Extended Data Fig. 11b). Analysis by fatty acid saturation showed significant enrichment of saturated PCs among upregulated species (hypergeometric P = 0.026; Extended Data Fig. 11d). By contrast, several highly unsaturated polyunsaturated fatty acid (PUFA)-containing PCs showed decreased abundance (for example, PC(44:7) and PC(38:7); Extended Data Fig. 11e,f).

Lipidomics analysis in p.Tyr622* iNs (conducted in positive-ionization mode) revealed similarly increased saturated PCs (hypergeometric P = 0.044; Fig. 3k and Extended Data Fig. 11g,h). However, PUFA-containing PCs and long-chain TGs were not reliably detected in this analysis (Extended Data Fig. 11i,j).

De novo PC synthesis occurs through the Kennedy pathway, followed by fatty acyl chain remodelling through the Lands cycle, mediated by LPCAT enzymes, with LPCAT3 specifically introducing PUFA chains[30]. LPCAT3 expression was reduced in both p.Tyr622* and p.Glu50fs*3 iNs compared with in WT iNs (Extended Data Fig. 11k,l), consistent with elevated saturated PC levels. Overall, these results indicate that ABCA7 LoF neurons accumulate neutral lipids, including long-chain polyunsaturated TGs and sterol lipids (zymosteryl), and show enriched saturated PC content.

CDP-choline reverses ABCA7 LoF effects

Previous studies showed that inhibition of de novo PC synthesis through the Kennedy pathway increased PC saturation in yeast, whereas boosting this pathway using exogenous choline reversed APOE4-induced lipid defects, including saturation changes, in human astrocytes31,32. We therefore tested whether choline supplementation could similarly mitigate ABCA7 LoF-induced phenotypes in iNs. To test this, we first treated p.Tyr622* iNs with CDP-choline for 2 weeks and performed targeted LC–MS analysis. CDP-choline treatment elevated extracellular CDP-choline from undetectable to detectable levels (Extended Data Fig. 12a and Supplementary Table 13). Furthermore, CDP and choline specifically accumulated in medium conditioned by treated p.Tyr622* cells (Extended Data Fig. 12a), indicating extracellular hydrolysis. Intracellular choline was significantly increased after treatment (Extended Data Fig. 12b), along with elevated expression of choline transporters (Extended Data Fig. 12c), confirming successful choline uptake by p.Tyr622* iNs.

We hypothesized that increased intracellular choline would enhance PC synthesis. Indeed, lipidomic analysis showed elevated levels of choline-containing phospholipids—particularly PCs, lysophosphatidylcholines (LPCs) and sphingomyelins—alongside a reduction in a single TG species, with other neutral lipid species showing a similar downward trend (Fig. 4a and Supplementary Table 12). Correspondingly, PCYT1B, the rate-limiting enzyme of Kennedy pathway-mediated PC synthesis, showed increased expression (Extended Data Fig. 12c). Moreover, LPCAT enzymes, including LPCAT3, exhibited higher expression after treatment (Extended Data Fig. 12d), consistent with increases to several unsaturated PC species (Fig. 4a and Extended Data Fig. 12e). These findings suggest that CDP-choline enhances the synthesis and remodelling of choline-containing lipids in ABCA7 LoF iNs.

Fig. 4: CDP-choline reverses ABCA7 LoF impacts in neurons.

a, Differentially abundant lipid species in p.Tyr622* iNs with or without CDP-choline. Species are labelled if unadjusted P < 0.05, |log[FC]| > 1 (two-sided t-test, equal variances assumed). n = 5 wells per condition. b, Correlation of gene scores comparing WT versus p.Tyr622* iNs and p.Tyr622* iNs with or without CDP-choline (n = 2 (WT), n = 5 (p.Tyr622* + H_2O) and n = 5 (p.Tyr622* + CDP-choline); experiment from Fig. 3). c, Kernighan–Lin clustering of leading-edge genes in p.Tyr622* iNs with or without CDP-choline (fGSEA, WikiPathways; FDR-adjusted P < 0.05). Gene–pathway graph: genes (circles) and pathways (squares) are indicated. d, Heat map (Jaccard index) comparing Kernighan–Lin clusters in p.Tyr622* iNs with or without CDP-choline and WT versus p.Tyr622* iNs (Fig. 3c,e). Upregulated (red) and downregulated (blue) clusters in p.Tyr622* iNs + CDP-choline relative to p.Tyr622* iNs or in p.Tyr622* iNs relative to WT are indicated; FDR-adjusted permutation P values (1,000 iterations, one-sided). e, Kernel density plots of gene scores per cluster. Positive S represents an increase in the p.Tyr622*+CDP-choline condition. The solid lines show the cluster means. The top pathways are indicated. f, Volcano plot of genes encoding mitochondrial proteins. Genes with FDR-corrected P < 0.05 (Limma-voom) in p.Tyr622* iNs with or without CDP-choline are indicated in colour. Bold font indicates shared top genes with Fig. 3f. g, Seahorse mitochondrial uncoupled OCR (%). n = 6 (p.Tyr622* + H_2O) and 8 (p.Tyr622* + CDP-choline) wells. h, The average TMRM intensity per masked region (75th percentile threshold; n = 8 wells per condition). i, The average CellROX intensity per masked region (75th percentile threshold; n = 10 wells per condition; same experiment as in Fig. 3j). j, Secreted Aβ, cortical organoids (182-day culture, with or without 1 mM CDP-choline for 4 weeks). n = 20 (WT), n = 19 (p.Tyr622*) and n = 14 (p.Tyr622* + CDP-choline) organoids. k, Spontaneous action potentials in dissociated cortical organoids (150 day culture, with or without 100 µM CDP-choline for 2 weeks). n = 7 (WT), n = 13 (p.Tyr622*) and n = 9 (p.Tyr622* + CDP-choline) cells. Statistical analysis was performed using two-sided Mann–Whitney U-tests following a Shapiro test. For a–i, 4-week differentiation was performed. +CDP-choline indicates treatment with 100 µM CDP-choline during the last 2 weeks; wells represent technical replicates. For g–k, the box plots show the median (centre line), IQR (box limits) and 1.5 × IQR (whiskers). For g–j, statistical analysis was performed using two-sided t-tests following Shapiro–Levene tests. Experiments were performed once (g, i and k) and at least twice (a–f, h and j). Scale bars, 125 μm (h and i).

Next, we characterized changes induced by CDP-choline treatment using LC–MS-based metabolomics and bulk RNA-seq. Although many metabolites altered by treatment could not be annotated, principal component analysis indicated that CDP-choline treatment reversed the separation between WT and p.Tyr622* iNs along the first principal component (PC1; Extended Data Fig. 12f). Transcriptomic analysis further demonstrated clear separation between treated and untreated samples (Extended Data Fig. 12g). Notably, the transcriptional signature of CDP-choline treatment negatively correlated with the p.Tyr622* signature (Fig. 4b), indicating partial restoration toward the WT state. Kernighan–Lin cluster analysis comparing untreated versus treated p.Tyr622* samples revealed significant overlap in seven out of nine clusters identified between p.Tyr622* and WT, with five clusters showing reversed directional changes after treatment (Fig. 4c–e and Supplementary Table 10).

Specifically, clusters associated with proteasomal and ribosomal functions (T+C.25, T+C.31), previously upregulated in p.Tyr622* iNs, were downregulated by CDP-choline treatment (Fig. 4e). Importantly, mitochondrial cluster T+C.26, strongly overlapping with mitochondrial cluster T.10 observed in post-mortem data (PM.1), also reversed after treatment (Fig. 4e). Analysis of MitoCarta genes confirmed a significant reversal in the expression of genes encoding mitochondrial proteins (Extended Data Fig. 12h), including reduced apoptosis-related genes (BID and CASP3; Fig. 3f), restored mitochondrial metabolic signatures (Supplementary Table 14) and elevated mitochondrial fusion regulators (MFN2 and OPA1), which support mitochondrial biogenesis and function[33]. Overall, CDP-choline treatment significantly reversed gene expression changes associated with ABCA7 LoF.

To assess whether CDP-choline treatment could restore mitochondrial uncoupling to WT levels, we repeated the Seahorse assay on p.Tyr622* iNs with and without treatment (Extended Data Fig. 12i,j). CDP-choline treatment significantly increased uncoupled respiration in p.Tyr622* iNs to WT levels (Fig. 4g), without altering the spare respiratory capacity (Extended Data Fig. 12k). Supporting this result, both TMRM and MitoHealth dyes showed lower fluorescence—indicative of decreased ΔΨm—in treated cells compared with in untreated cells (Fig. 4h and Extended Data Fig. 12l). Moreover, CDP-choline significantly reduced oxidative stress, as indicated by decreased CellROX fluorescence (Fig. 4i).

CDP-choline reduces AD phenotypes

Finally, we evaluated whether CDP-choline treatment could ameliorate key AD-associated phenotypes, as ABCA7 dysfunction has been linked to altered amyloid-β (Aβ) processing[13]. p.Tyr622* iNs secreted significantly higher Aβ40 and showed a trend toward increased Aβ42 secretion, although the absolute levels remained relatively low (Extended Data Fig. 13a). To examine the effects in a model with stronger pathology, we differentiated p.Tyr622* and WT lines into cortical organoids matured for approximately 6 months, a stage at which robust Aβ secretion was observed (approximately twofold to fourfold higher than iNs; Extended Data Fig. 13b,c). Treatment for 4 weeks with 1 mM CDP-choline normalized Aβ40 and Aβ42 secretion in p.Tyr622* organoids to WT levels (Fig. 4j); this effect was concentration and duration dependent (Extended Data Fig. 13c). Furthermore, CDP-choline treatment at 100 µM significantly reduced neuronal hyperexcitability in dissociated cortical organoids, as assessed by electrophysiology (Fig. 4k).

Discussion

Here we generated a transcriptional atlas to identify potential cell-type-specific effects of ABCA7 LoF variants in the human PFC. Excitatory neurons expressed the highest levels of ABCA7 and showed transcriptional alterations in pathways related to lipid biosynthesis, mitochondrial respiration and cellular stress, including upregulation of DNA-damage-response genes, as well as changes in inflammatory and synaptic genes. Experimental validation of predictions from this atlas revealed that ABCA7 LoF variants impaired mitochondrial uncoupling, elevated mitochondrial membrane potential and increased ROS levels in human iNs. Consistent with ABCA7’s function as a phospholipid transporter, ABCA7 LoF altered PC composition in these neurons, characterized by increased saturated PCs and reduced highly polyunsaturated PCs. CDP-choline treatment increased PC synthesis, elevated expression of PC-remodelling enzymes, and corrected mitochondrial uncoupling deficits, mitochondrial membrane potential and oxidative stress. Furthermore, CDP-choline supplementation reduced neuronal hyperexcitability and amyloid-β secretion.

Our findings indicate that ABCA7 LoF neurons accumulate saturated PC species, consistent with recent reports of phospholipid saturation imbalance in human amyotrophic lateral sclerosis and frontotemporal dementia neurons[34]. Treatment with CDP-choline, which boosts de novo PC synthesis, effectively reduced the downstream effects of ABCA7 LoF but did not fully normalize the lipid profile. Enhancing de novo PC synthesis with CDP-choline may broaden the diversity of PC species by providing additional substrates for the Lands cycle. This cycle rapidly remodels the PC pool, generating diverse saturated and unsaturated PCs35,36, potentially counteracting an excess of saturated species. Although further studies are needed to fully characterize these lipid alterations and their functional implications, and investigate the specificity of CDP-choline treatment, our data suggest that disrupted PC metabolism may contribute to neuronal dysfunction associated with ABCA7 LoF variants.

PCs are abundant components of biological membranes, including mitochondrial membranes. Changes in their fatty acyl chain composition impact mitochondrial bioenergetics, dynamics and membrane potential[37]. Mitochondrial dysfunction, including impaired mitochondrial uncoupling, is increasingly linked to ageing and neurodegeneration, although its specific role in AD remains unclear[38]. Neurons heavily depend on mitochondrial OXPHOS to meet their energy demands[39], and regulated mitochondrial uncoupling supports neuronal health by controlling mitochondrial membrane potential, reducing ROS and promoting mitochondrial biogenesis40,41. Elevated oxidative stress resulting from impaired uncoupling could contribute to neuronal DNA damage and inflammation observed in AD brains[42], both of which are transcriptionally evident in ABCA7 LoF carriers.

Further studies are needed to clarify how ABCA7 regulates PC composition. Previous research suggests that ABCA7 transports PC and its Lands cycle derivatives, LPCs[6]. We speculate that impaired ABCA7 floppase activity, which moves phospholipids between membrane leaflets, might initially cause PC accumulation at the inner leaflet. This buildup could trigger compensatory downregulation of PC synthesis. Supporting this idea, the Kennedy pathway’s rate-limiting enzyme, CTP:phosphocholine cytidylyltransferase, is activated after binding to PC-deficient membranes[43]. Such conditions could shift the existing PC pool toward more saturated species, as previously shown in yeast[31]. Impaired floppase activity involving PCs, LPCs or other phospholipids transported by ABCA7, such as phosphatidylserines[23], might also indirectly alter PC composition by changing membrane fluidity and curvature[44]. These membrane changes could then affect lipid metabolism by modulating enzymes such as LPCAT3, which helps to maintain levels of unsaturated phospholipids45,46.

Consistent with our findings linking PC imbalance to mitochondrial dysfunction in ABCA7 LoF neurons, a recent independent study reported mitochondrial impairment associated with phosphatidylglycerol deficiency in ABCA7-deficient neurospheroids[11], further emphasizing lipid metabolism as a therapeutic target. Here we demonstrate that CDP-choline—a safe and widely available dietary supplement[47]—reverses key aspects of ABCA7 LoF-induced neuronal dysfunction, including AD pathology and neuronal hyperexcitability. Recent studies from our laboratory similarly linked PC and fatty acid saturation imbalances to APOE4-associated dysfunction[32] and cognitive resilience to AD pathology[48], highlighting broad relevance for PC disruptions in AD risk.

Supporting the therapeutic potential of targeting phospholipid metabolism in AD, dietary choline supplementation in APP/PS1 mouse models significantly reduced amyloid pathology[49], aligning with our findings in cortical organoids. Moreover, recent studies in Drosophila amyotrophic lateral sclerosis/frontotemporal dementia models demonstrated that in vivo overexpression of fatty acid desaturases improved survival, probably through correction of phospholipid saturation imbalances[34]. Notably, a recent epidemiological study also linked higher dietary choline intake to reduced AD risk in humans[50], further highlighting the promise of targeting PC pathways therapeutically in AD.

Our data also suggest that the common missense variant p.Ala1527Gly may produce effects convergent with ABCA7 LoF. Genetic interactions between mild ABCA7 dysfunction and other AD risk factors, such as APOE4, could substantially amplify AD risk[51]. Collectively, these findings align with a growing body of literature, including recent work on APOE416,52,53, highlighting that lipid metabolic disruptions are central to AD pathogenesis and identifying additional genotypes that may benefit from modifying phospholipid metabolism.

Methods

Isolation of nuclei from post-mortem brain tissue

Batch 1 nuclei (BA10 region, frozen tissue) were isolated according to a protocol adapted from a previous study[18], performed entirely at 4 °C or on ice. In brief, tissue was homogenized (700 µl homogenization buffer: 320 mM sucrose, 5 mM CaCl_2, 3 mM Mg(CH_3COO)2, 10 mM Tris-HCl pH 7.8, 0.1 mM EDTA pH 8.0, 0.1% IGEPAL CA-630, 1 mM β-mercaptoethanol, 0.4 U µl−1 recombinant RNase inhibitor (Clontech)) using a Wheaton Dounce tissue grinder (15 strokes, loose pestle), filtered (40 µm cell strainer), then mixed 1:1 with working solution (diluent (30 mM CaCl_2, 18 mM Mg(CH_3COO)2, 60 mM Tris pH 7.8, 0.6 mM EDTA, 6 mM β-mercaptoethanol) and OptiPrep density-gradient solution (Sigma-Aldrich, D1556-250ML), 1:5). The sample was layered onto an OptiPrep density gradient consisting of 750 µl of 30% OptiPrep (1.5:1 ratio of working solution:homogenization buffer) above 300 µl of 40% OptiPrep (4:1 ratio of working solution:homogenization buffer), centrifuged (10,000g, 5 min, 4 °C) and nuclei were collected from the 30/40% interface (100 µl). Nuclei were washed twice (1 ml PBS, 0.04% BSA, 300 g, 3 min), resuspended (100 µl PBS, 0.04% BSA), counted (C-Chip hemocytometer) and diluted to 1,000 nuclei per µl (PBS 0.04% BSA).

Batch 2 nuclei (fresh post-mortem PFC BA10 tissue) were prepared as part of a previous study[18].

Informed consent and Anatomical Gift Act consent were obtained, including repository consent to allow sharing of data and biospecimens. Rush University Medical Center IRB approved protocols (Religious Orders Study, Rush Memory and Aging Project).

Droplet-based snRNA-seq

Batch 1 libraries were prepared using Chromium Single Cell 3′ Reagent Kits v3 (10x Genomics) and sequenced on the NovaSeq 6000 S2 (paired-end, 28 + 91 bp, 8-nt index) system. Each sample was sequenced twice across two lanes to increase the depth. Batch 2 libraries were prepared using the Chromium Single Cell 3′ Reagent Kits v2 and sequenced using the NextSeq 500/550 High Output v2 kits (150 cycles), as previously described[18]. All raw reads were processed together for alignment and gene counting.

Variant calling and ROSMAP participant selection

We selected 36 individuals from the ROSMAP cohort, a longitudinal study of ageing and dementia[54]. Whole-genome sequencing (WGS) variant calls (n = 1,249 available ROSMAP samples) were downloaded from Synapse (syn11724057) for genes with rare damaging variants linked to AD: SORL1, TREM2, ABCA7, ATP8B4, ABCA1 and ADAM10[2]. For participants with multiple WGS samples, the highest-quality sample was chosen (Genomic Quality Score). Samples with sex mismatches or genotype inconsistencies were excluded (see Synapse accession syn12178037). Only variants passing quality control (FILTER_PASS) were used.

PTC variants flagged as splice, frameshift, nonsense, missense or premature stop variants annotated as loss-of-function (LOF) were identified. For ABCA7, known LoF variants from the literature were captured, except for c.5570+5G>C, which was manually added. Additional WGS details (library preparation, quality control, annotations, impact predictions) can be viewed at Synapse (syn10901595).

We selected 12 individuals (LoF samples) who carried ABCA7 PTC variants, had no PTC variants in the other candidate genes listed above and had fresh-frozen tissue available from Rush University. Moreover, we chose 24 matched controls without any PTC variants in ABCA7 or the other listed genes. Controls were matched by age, sex and pathology.

Read counting and alignment

Libraries were demultiplexed using the MIT BioMicroCenter BMC/BCC 1.8 pipeline (updated 9 December 2020; https://openwetware.org/wiki/BioMicroCenter:Software#BMC-BCC_Pipeline). Fastq reads were aligned to the human reference genome (GRCh38) and counted using Cell Ranger (v.6.1.2; 10x Genomics) with intron counting enabled and an expected cell count of 5,000 per sample. The default parameters were otherwise used. Counts from all samples were aggregated using a custom script, yielding a total of 150,456 cells.

Sample-swap analysis

Sample-swap analysis was performed using an established pipeline (MBV; QTLtools v1.1)[55], comparing allelic concordance between genomic (VCF) and transcriptomic (BAM, generated by Cell Ranger) data. We specifically analysed chromosome 19 variants (location of ABCA7). Each single-cell sample matched the expected WGS sample clearly, showing higher concordance (fewer mismatches) compared with all other ROSMAP WGS samples (examples are shown in Extended Data Fig. 1e).

Cell filtering metrics

Aggregated counts underwent quality control before cell annotation. Cells with fewer than 500 or more than 10,000 detected genes (count > 0) were removed. Next, we filtered cells by mitochondrial fraction (total mitochondrial counts divided by total gene counts), a measure of nuclear integrity. We log-transformed mitochondrial fractions and fitted a Gaussian mixture model (GMM, sklearn GaussianMixture) to identify and remove cells assigned to the GMM component with the highest mean mitochondrial fraction. This step removed approximately 20,000 low-quality cells.

We next considered cells in marker-gene expression space defined by known major cell-type markers for human PFC: astrocytes (159 markers), excitatory neurons (113), inhibitory neurons (83), microglia (97), oligodendrocytes (179), OPCs (143) and vascular cells (124)[1] (Supplementary Table 3). Marker counts were normalized to total library size, mean-centred and scaled to unit variance. Incremental principal component analysis (sklearn IncrementalPCA) reduced dimensionality (top 50 principal components). Visually, cells projected onto the first two principal components formed distinct Gaussian-like clusters. Assuming each Gaussian cluster corresponded to a distinct brain cell type, we fitted another GMM to the projected data. The resulting ten clusters aligned clearly with known brain cell types.

Cells poorly modelled by this GMM (log-probability < −100) and two clearly outlying clusters were removed. These excluded cells had lower total counts and higher mitochondrial fractions, suggesting low quality. This step removed approximately 12,000 cells, leaving a final dataset of 118,668 cells.

Gene filtering metrics

Downstream analyses included only nuclear-encoded, protein-coding genes (total 19,384) based on Ensembl GRCh38p12 annotations.

Cell type annotations

We first corrected variance due to sequencing batch and individual-of-origin by applying Harmony[56] to the top 50 principal components from the quality-controlled data. Using Harmony-corrected principal components, we computed a neighbourhood graph (default Scanpy parameters)[57] and clustered cells with the Leiden algorithm (Scanpy implementation)[58].

Major cell types (excitatory neurons, inhibitory neurons, astrocytes, microglia, oligodendrocytes, OPCs, vascular cells) were assigned to Leiden clusters by computing cell-type-specific marker gene enrichment. Specifically, we calculated enrichment scores as the average log-ratio of expression for marker genes inside versus outside each cluster and assigned labels based on the highest enrichment.

We then subclustered each major cell type using the Leiden algorithm and removed subclusters with excessively high mitochondrial fraction or extreme total counts. Thresholds were set at two s.d. above the mean for these metrics within each major cell type. Removed clusters were small, poorly represented across individuals and weakly connected on manual inspection.

Individual-level filtering

After all of the previous quality-control steps, six individuals with fewer than 500 cells were excluded from further analyses, leaving 24 control individuals and 12 individuals with ABCA7 LoF. None of these excluded individuals carried ABCA7 PTC variants, and their removal did not substantially affect clinical variable distributions across genotypes.

Differential gene expression

Pseudo-bulk gene expression values were generated by summing cell-level counts per gene per individual (matrix multiplication). For each major cell type, we considered genes detected in >10% of cells. Counts were normalized by TMM (edgeR), and residual mean-variance trends were removed using Limma-Voom. Unknown variance was modelled through surrogate variable analysis (SVA). Differential expression analysis (Limma: lmFit, eBayes, topTable) was performed separately for each major cell type using the following linear model for each gene (Gi):

$$\begin{array}{c}{G}_{i}={\beta }_{0}+{\beta }_{1}\times ABCA{7}\,\text{LoF}+{\beta }_{2}\times \text{msex}+{\beta }_{3}\,\times \,\text{nft}\\ \,+\,{\beta }_{4}\times \text{amyloid}+{\beta }_{5}\times \text{age}{\rm{\_}}\text{death}+{\beta }_{6}\,\times \,\text{PMI}\\ \,+\,{\beta }_{7}\times \text{batch}+{\beta }_{8}\times APOE{4}+\mathop{\sum }\limits_{j=1}^{n}{\beta }_{{\text{SV}}_{j}}\times {\text{SV}}_{j}\end{array}$$

where n is the number of surrogate variables determined by num.sv() per cell type and ABCA7 LoF indicates individuals carrying ABCA7 LoF variants. Additional covariates (defined in Supplementary Note 1) included sex, NFT, amyloid burden, age at death, PMI, sequencing batch and APOE4 status.

Gene perturbation projections across cell types

We computed cell-type-specific gene perturbation scores summarizing differential expression significance and direction associated with ABCA7 LoF as S = sign(log_2[FC]) × −log_10(P), where positive log_2[FC] indicates upregulation in ABCA7 LoF. Scores for genes not detected in >10% of cells per cell type were set to zero. Genes with \(| S| > 1.3\) in at least one of six major cell types (excitatory neurons, inhibitory neurons, astrocytes, microglia, oligodendrocytes and OPCs) were projected from 6D perturbation-score space into 2D using UMAP (Python umap).

Genes were clustered in the resulting 2D embedding using Gaussian mixture modelling (Python sklearn). Clusters were annotated by hypergeometric enrichment (Python gseapy) for Gene Ontology Biological Process pathways (Supplementary Table 3), using all genes in the embedding as background. Pathways with enrichment P < 0.01 were selected for naming each cluster. Per-cell-type perturbation scores for each cluster were calculated as the mean gene score within clusters. Statistical significance was assessed by permuting cluster assignments (100,000 permutations).

Gene-set enrichment and Kernighan–Lin pathway clustering

Genes were ranked by perturbation scores S (see the ‘Gene perturbation projections across cell types’ section). Fast GSEA (fGSEA; R implementation[59]) with 10,000 permutations tested enrichment of WikiPathways gene sets (Supplementary Table 3) among differentially expressed genes. Only gene sets with 5–1,000 genes were considered.

To simplify gene–pathway associations, we constructed a bipartite graph using genes from the fGSEA leading-edge (LE) subset (268 genes, enriched at P < 0.05 in ABCA7 LoF excitatory neurons) and WikiPathways associated with ≥4 LE genes. We treated gene–pathway grouping as a graph partitioning problem (Supplementary Note 2). Among three graph-partitioning algorithms tested (Supplementary Note 2), the METIS and the Kernighan–Lin algorithms showed the lowest loss and highly comparable performance (within 1.8% loss; Rand index = 0.98 after 5.0 × 10^4). We selected the Kernighan–Lin algorithm because it consistently outperformed the METIS algorithm across a wider range of graph sizes. The Kernighan–Lin algorithm was implemented in Python as described previously[60] with the parameters C=0, KL_modified=True, random_labels=True, unweighted=True, and K=50 to partition the graph into eight groups. We performed 5.0 × 10^4 random initiations and selected the lowest-loss solution.

Graph layouts were computed using the spring layout algorithm (networkx, 10,000 iterations) and visualized using matplotlib. Representative pathways for each cluster were identified by averaging ABCA7 LoF perturbation scores (S) of genes in the cluster connected directly to each pathway. Pathways with ≥5 intracluster gene connections are highlighted in the figures.

Excitatory neuronal layer annotation

Excitatory neurons were annotated by cortical layer using published marker gene sets[61] (Supplementary Table 3) according to the procedures described in the ‘Cell type annotations’ section. In brief, the normalized expression matrix was filtered to include only layer-specific marker genes and cells expressing ≥15% of these genes. Dimensionality was reduced using iterative principal component analysis, followed by batch-effect correction using Harmony. A neighbourhood graph was constructed, and cells were clustered using the Leiden algorithm. Clusters enriched for layer-specific markers (average log-transformed FC > 0.1) were labelled accordingly, while ambiguous clusters were excluded. Layers 5 and 6 were combined into a single ‘L5/6’ category. Annotations were validated using independent marker genes[62] (Supplementary Table 3). Layer-specific differential expression analysis was performed as described in the ‘Differential gene expression’ section, followed by gene-set enrichment analysis (fGSEA, as described in the ‘Gene-set enrichment and Kernighan–Lin pathway clustering’ section) testing enrichment of ABCA7 LoF-associated gene clusters identified by Kernighan–Lin clustering (as described in the ‘Gene-set enrichment and Kernighan–Lin pathway clustering’ section).

ABCA7 p.Ala1527Gly variant calling and gene–pathway clustering comparisons

Participants carrying the ABCA7 p.Ala1527Gly variant with available PFC snRNA-seq data from a previous study (Supplementary Table 3) were identified using methods described in the ‘Variant calling and ROSMAP participant selection’ section. Differential expression was computed as described in the ‘Differential gene expression’ section, followed by fGSEA to test enrichment of ABCA7 LoF-associated gene clusters identified by Kernighan–Lin clustering (see the ‘Gene-set enrichment and Kernighan–Lin pathway clustering’ section).

Culture and generation of human isogenic iPS cells

A control parental iPS cell line (AG09173; 75-year-old female individual, APOE3/3 genotype) was generated previously by the Picower Institute iPSC Facility[63]. Two ABCA7 LoF isogenic lines were derived from AG09173: ABCA7 p.Glu50fs*3, containing a novel premature stop codon in exon 3 (generated by Synthego), and ABCA7 p.Tyr622*, containing a patient-derived mutation (Y622*)[64] generated in-house by CRISPR–Cas9 editing.

For the ABCA7 p.Tyr622* line, an sgRNA targeting ABCA7 (oligos: forward, 5′-CACCGCCCCTACAGCCACCCGGGCG-3′; reverse, 5′-AAACCGCCCGGGTGGCTGTAGGGGC-3′; designed at http://crispr.mit.edu) was cloned into pSpCas9-2A-GFP (PX458, Addgene, 48138) as previously described[65]. The plasmid was confirmed by Sanger sequencing, then nucleofected (Amaxa, Lonza Human Stem Cell Nucleofector Kit I, program A-23) along with 15 μg of a single-stranded oligodeoxynucleotide template into dissociated AG09173 iPS cells (Accutase, Thermo Fisher Scientific; 10 μM ROCK inhibitor, Tocris). Cells (around 5 × 10^6) were sorted (BD FACS Aria IIU, Whitehead Institute), plated at single-cell density in medium supplemented with penicillin–streptomycin (Gemini Bio-products) and ROCK inhibitor. Colonies were expanded, screened by genomic DNA extraction (DNeasy Blood & Tissue Kit, Qiagen, 69504) and Sanger sequencing to confirm the Y622* mutation (Supplementary Table 16).

All iPS cell lines were regularly tested for karyotypic normality (Cell Line Genetics) and cultured at 37 °C, 5% CO_2, in feeder-free conditions using mTeSR-1 medium (StemCell Technologies, 85850) on Matrigel-coated plates (Corning; hES-cell-qualified, 354277). Cells were passaged at 60–80% confluence using ReLeSR (StemCell Technologies, 05872) onto Matrigel-coated plates at a 1:6 to 1:24 split ratio.

rTTA and NGN2 virus production

HEK293T cells were seeded at 5 × 10^6 cells per 10 cm plate and transfected using a third-generation lentiviral system. Per plate, transfection mixtures contained 10 µg plasmid DNA (EF1a-rtTA-Hygro, Addgene 66810, or pLV-TetO-hNGN2-eGFP-Puro, Addgene, 79823), 5 µg pMDLg/pRRE, 2.5 µg pRSV-Rev, 2.5 µg MD2.G and 48 µl polyethyleneimine (1 mg ml−1) diluted in 600 µl OptiMEM (Thermo Fisher Scientific, 51-985-034). Mixtures were incubated 20 min at room temperature, added dropwise to cells and replaced with fresh medium after 16 h. Virus-containing supernatant collected 72 h after transfection was clarified (3,000g, 5 min, 4 °C) and the supernatant was ultracentrifuged (Beckman Optima L-90K Ultracentrifuge, SW32Ti rotor, 25,000 rpm, 2 h), resuspended in 1 ml PBS per 10 cm plate and stored at −80 °C.

Lentivirus-mediated NGN2 induction in iPS cells and drug treatments

iPS cells were dissociated into single-cell suspensions (Cell Dissociation Buffer, Life Technologies, 13151-014), resuspended in mTeSR1 medium with ROCK inhibitor (Rockout; Abcam, ab285418), and plated onto Matrigel-coated six-well plates at 50–60% confluence after 24 h. After 1 day, cells were co-transduced overnight with 80 µl each of pLV-TetO-hNGN2-eGFP-Puro and EF1a-rtTA-Hygro lentivirus per well. NGN2 expression was induced 24 h later with doxycycline (1 µg ml−1) and ROCK inhibitor. Puromycin selection was performed 24 h after viral transduction. Immature neurons were replated on PDL/laminin-coated plates (1 × 10^6 cells per well in six-well plates, or 5 × 10^4 cells per well in 96-well plates), and maintained in BrainPhys neuronal medium (StemCell Technologies, 05793) with Neurocult SM1 neuronal supplement (StemCell Technologies, 05711), (N2-supplement-A StemCell Technologies, 07152), laminin (1 µg ml−1) and doxycycline (1 µg ml−1). Half-medium changes were performed every 3–4 days, and cultures were matured for 28 days before experiments.

Neurons were treated with cytidine 5′-diphosphocholine (CDP-choline, Millipore Sigma-Aldrich, 30290) at a final concentration of 100 µM starting at day 14, continuing with each medium change until day 28. Choice of treatment concentration and duration was based on a previous study by our laboratory[32].

Cortical organoid generation

Dorsal cortical organoids were generated as previously described[66]. In brief, iPS cells at 80–90% confluence were dissociated into single-cell suspensions (1 × 10^5 cells per ml) in mTeSR with 10 µM ROCK inhibitor, seeded at 100 µl per well in PrimeSurface 96 Slit-well plates (S-Bio, MS9096SZ) and induced to differentiate using neural induction medium consisting of DMEM/F12 (Life Technologies, 11330-032), 100 mM GlutaMAX (Life Technologies, 35050-061), 0.1 mM 2-mercaptoethanol (Sigma-Aldrich, M3148), 1% penicillin–streptomycin (Life Technologies, 15070-063) and 10 µM SB-431542 (R&D Systems, 1614), and 2.5 µM dorsomorphin (Sigma-Aldrich, P5499-CONF) with daily medium changes (days 0–5). The medium was then switched (days 6–16) to neural differentiation medium (Neurobasal A, B27 supplement, GlutaMAX, penicillin–streptomycin, human recombinant EGF and FGF2, 20 ng ml−1 each), with daily changes until day 16, then every other day until day 25. From day 25 onwards, EGF and FGF2 were replaced with 20 ng ml−1 each of BDNF and NT3, with medium changes twice weekly after day 45.

Confocal imaging experiments

All confocal images were acquired on a Zeiss LSM900 microscope using ZEN software.

For mitochondrial health staining, live cells were incubated with MitoHealth dye (Thermo Fisher Scientific, H10295) according to the manufacturer’s protocols for 30 min at 37 °C, fixed (4% paraformaldehyde/4% sucrose, 15 min, room temperature), permeabilized (0.1% Triton-X, 5 min), blocked (2% BSA, Fisher Bioreagents, BP9703) and incubated overnight at 4 °C with NeuN antibody (1:500), followed by incubation with secondary antibodies (1:1,000) for 2 h and Hoechst (1:2,000, Invitrogen, H3570) for 10 min (Supplementary Table 17). Images were captured as z stacks (1 µm intervals).

Live imaging of mitochondrial membrane potential used TMRM (0.1 µM, 30 min at 37 °C; Thermo Fisher Scientific, I34361), followed by imaging before and immediately after adding the mitochondrial uncoupler FCCP (1 µM; Cayman Chemical, 15218). ROS were assessed by live staining with CellROX Orange (5 µM, 30 min at 37 °C; Thermo Fisher Scientific, C10443). TMRM and CellROX images were acquired as single optical sections.

For immunostaining, iNs cultured on coverslips and cortical organoid cryosections (20 µm) were fixed (4% formaldehyde, 10 min), permeabilized (0.2% Triton X-100) and blocked (10% BSA, 1 h), and incubated overnight at 4 °C with primary antibodies (MAP2 and NeuN, both 1:1,000). Alexa-Fluor-conjugated secondary antibodies (1:500) and Hoechst (1:1,000) were used for visualization. Coverslips were mounted with Fluoromount-G, and images were captured as single optical sections.

For visualization, confocal images were pseudocoloured to enhance the signal contrast; representative unprocessed images are provided in Supplementary Fig. 6.

Confocal image quantification

Confocal images (.czi format; 8 or 16 bits; voxel size: 1 × 0.62 × 0.62 µm) were loaded into Python (aicsimageio) and normalized to floating-point format [0,1]. Acquisition settings were consistent within each imaging batch.

For fixed z-stack images, NeuN-positive cell bodies were segmented in 3D using the pre-trained cyto2 model (Cellpose[67]). The segmentation quality was manually verified (blinded), and low-quality images were excluded. Cell-level fluorescence intensities were computed as probability-weighted sums of voxel intensities, using segmentation-derived voxel probabilities. Measurements from multiple differentiation batches (independent staining and imaging experiments) were combined by uniformly sampling cells per condition per batch, batch-wise z-scaling fluorescence values, and including batch and well-of-origin indicator variables in downstream analyses. Clipping was minimal (<0.1%), and the confocal microscope response was assumed linear. A linear mixed-effects model (mixedlm() from statsmodels) tested cell-level fluorescence intensities, modelling genotype or treatment as a fixed effect and well of origin as a random effect.

For single-plane live imaging (TMRM, CellROX), images were binarized at the 75th percentile intensity threshold per channel to identify regions occupied by neuronal soma or processes, according to established methodology[68]. Mean fluorescence intensities were quantified within these masked areas. For time-course imaging, images were spatially aligned by Fourier-based registration (phase cross-correlation), with alignment accuracy confirmed manually. A mask from the baseline (pre-FCCP) TMRM image (75th percentile threshold) was consistently applied across timepoints. For all live-imaging experiments, masked regions (wells) were treated as individual observations in statistical tests. Batch-wise z-scaling was not required here, as data were not combined across batches for these experiments.

One outlier (p.Tyr622*+H20; value 0.34) was identified and removed in the TMRM p.Tyr622* (with or without CDP-choline) experiment using the interquartile range (IQR) method (values outside Q1−2 × IQR or Q3 + 2 × IQR) and removed for plotting convenience. This did not affect the statistical significance of the results.

Aβ ELISA assays

Culture media were collected and analysed for Aβ40 and Aβ42 levels using enzyme-linked immunosorbent assay (ELISA) kits (Thermo Fisher Scientific, KHB3481 and KHB3441, respectively) according to the manufacturer’s protocols. For 4-week-old iNs, media were flash-frozen before analysis. For cortical organoids (aged 5–6 months; days 176–182), media were analysed immediately after collection following 3–4 weeks of treatment with 500 µM or 1 mM CDP-choline.

Electrophysiology recordings

Electrophysiology recordings were performed using the Axon Multiclamp 700B amplifier and Clampex 11.2 software (Molecular Devices). Cells were visualized using infrared differential interference contrast imaging (Olympus BX-50WI microscope), placed in a recording chamber and perfused continuously at 2 ml min−1 (32 °C) with oxygenated artificial cerebrospinal fluid (containing 125 mM NaCl, 2.5 mM KCl, 1.2 mM NaH_2PO_4·H_2O, 2.4 mM CaCl_2·2H_2O, 1.2 mM MgCl_2·6H_2O, 26 mM NaHCO_3, and 11 mM d-glucose).

Action potentials were elicited by injecting current steps in current-clamp mode. Whole-cell currents were recorded from a holding potential of −80 mV by stepping to various voltages in voltage-clamp mode. Spontaneous firing was recorded in cell-attached configuration. Recordings were filtered at 1 kHz (four-pole Bessel filter), digitized at 10 kHz with a Digidata 1550B interface (Molecular Devices). Pipette solution contained 120 mM K-gluconate, 5 mM KCl, 2 mM MgCl_2·6H_2O, 10 mM HEPES, 4 mM ATP and 0.2 mM GTP. Data were analysed using pClamp 11.2 and GraphPad Prism 10.

For electrophysiology recordings from cortical organoids, day 150 organoids were dissociated using Accutase (StemCell Technologies, 07920, 40 min, 37 °C), plated onto #1 glass coverslips (Thermo Fisher Scientific, 50-194-4702) coated with PDL, laminin and Matrigel, and maintained in 2D culture with or without 100 µM CDP-choline for 2 weeks before recordings.

Spontaneous action potential outliers were identified using the IQR method (values outside Q1–Q2 × IQR or Q3 +Q2 × IQR) and removed, resulting in the exclusion of two datapoints (9.38 in p.Tyr622*; 6.15 in p.Tyr622* + CDP-choline). Cells recording zero spontaneous potentials (likely glial) were also excluded.

Seahorse metabolic assays and OCR analysis

iPS-cell-derived neurons were differentiated directly in Seahorse XFe96/XF Pro microplates for 28 days before metabolic assays on a Seahorse XFe96 Analyzer. Seahorse XF cell mito stress and oxidation stress tests were conducted according to manufacturer’s protocols using final drug concentrations of 2.5 µM oligomycin, 1 µM FCCP and 0.5 µM rotenone/antimycin. Data were analysed using XFe Assay v.2.6.3.5 software.

OCRs were monitored over time, with curves visually inspected (blinded) to exclude wells not responsive to drug injections. The following OCR metrics were computed from integrals of OCR curves between specific experimental intervals: (1) basal respiration (before oligomycin injection); (2) proton leak (after oligomycin, before FCCP); (3) maximal respiration (after FCCP, before rotenone/antimycin); (4) relative uncoupling (proton leak divided by basal respiration); and (5) spare respiratory capacity (maximal respiration divided by basal respiration).

mRNA-seq and analysis of iNs

Total RNA was extracted from iNs using the RNeasy Mini Kit (Qiagen). RNA quality was assessed (Fragment Analyzer, Agilent), and only samples with RNA quality number > 9.5 were selected. Full-length cDNA libraries were generated (SMART-seq v4 kit, Takara Bio), and sequencing libraries prepared (Nextera XT DNA Library Preparation Kit, Illumina) for sequencing on the Element AVITI platform (Element Biosciences; 75 bp paired-end reads with dual 8-nucleotide indexes) at the MIT BioMicro Center.

Sequencing data were processed through the MIT BioMicro Center BMC/BCC pipeline v1.8 (updated 6 June 2023; https://openwetware.org/wiki/BioMicroCenter:Software#BMC-BCC_Pipeline). Reads were adapter-trimmed (Trim Galore, Nextera-specific settings, minimum overlap 3 bases), aligned to the human reference genome (GRCh38.p14, GENCODE release 47; STAR aligner), and counted (featureCounts, paired-end settings). Read counts were summarized at the exon level and aggregated by gene identifier.

Differential expression analysis (edgeR, limma-voom) retained protein-coding genes expressed at ≥1 CPM in ≥1 sample, normalized counts and used linear modelling with empirical Bayes moderation with contrasts based on experimental conditions (treatment/genotype). fGSEA (10,000 permutations) of WikiPathways gene sets (Supplementary Table 3) was performed using ranked differentially expressed genes (score: sign(log[FC]) × −log_10[P]), as described above (see the ‘Gene-set enrichment and Kernighan–Lin pathway clustering’ section). Significant pathways (adjusted P < 0.05) were identified, and leading-edge genes underwent gene–pathway clustering (Kernighan–Lin heuristic, described above). Gene–pathway cluster similarity was assessed by computing Jaccard indices based on pathways and genes assigned to each Kernighan–Lin cluster. The significance of observed overlaps was determined empirically through comparison to 1,000 random permutations, with P values adjusted using the Benjamini–Hochberg method to control the FDR.

LC–MS lipidomics on iNs

iPS-cell-derived neurons were washed in cold PBS, scraped, centrifuged (2,000g, 5 min), counted, pelleted to equal number and resuspended in cold methanol (2 ml). Biphasic extraction was performed by sequentially adding cold chloroform (4 ml) and cold water (2 ml), vortexing after each addition, then centrifuging (3,000 rcf, 10 min) for phase separation. Samples prepared at the Harvard Center for Mass Spectrometry were similarly processed from provided pellets (in 500 µl methanol), supplemented with additional methanol (1.5 ml) and chloroform (4 ml), sonicated (10 min), mixed with water (2 ml) and centrifuged (800 rcf, 10 min, 4 °C). Upper aqueous phases were collected for metabolomics, while chloroform phases were reserved for lipidomics. At least one blank control (no cells) was included in each extraction run. All LC–MS analyses were performed by the Harvard Center for Mass Spectrometry.

Extracted samples were dried under nitrogen, fully evaporated, resuspended in chloroform (scaled by biomass (cell count); ≥60 µl), and split equally for positive and negative ionization analyses (or unsplit if only positive mode). After centrifugation (18,000 rcf, 20 min, 4 °C), the supernatants were transferred into microinserts for LC–MS.

LC–MS analyses were performed on an Orbitrap Exactive plus MS (Thermo Fisher Scientific) consistent with an Ultimate 3000 LC (Thermo Fisher Scientific) in positive- and negative-ionization modes (in WT versus p.Tyr622* only in positive mode), in top five automatic data-dependent MS/MS mode. Chromatography separation was performed on the Biobond C4 column (4.6 × 50 mm, 5 µm particle size; Dikma Technologies). The flow rate began at 100 µl min−1 with 0% mobile phase B (MB) for the initial 5 min, followed by an increase to 400 µl min−1 over the next 50 min with a linear gradient of MB from 20% to 100%. The column was subsequently washed at 500 µl min−1 for 8 min with 100% MB, then re-equilibrated for 7 min at 500 µl min−1 using 0% MB. For positive-ion mode, mobile phases consisted of buffer A (MA: 5 mM ammonium formate, 0.1% formic acid and 5% methanol in water) and buffer B (MB: 5 mM ammonium formate, 0.1% formic acid, 5% water and 35% methanol in isopropanol). For negative-ion mode, buffer A (MA) contained 0.03% ammonium hydroxide and 5% methanol in water, and buffer B (MB) contained 0.03% ammonium hydroxide, 5% water and 35% methanol in isopropanol.

Lipids were identified, and their signals integrated using the Lipidsearch software (v.4.2.27, Mitsui Knowledge Industry, University of Tokyo). Integrations and peak quality were curated manually. Peak areas were background-corrected (subtracting 3× median blank peak areas; negative values set to zero). Statistical analyses were performed using Welch’s t-tests (unequal variance) to compare different cell lines, and Student’s t-tests (equal variance) for treatment comparisons within identical genetic backgrounds.

LC–MS metabolomics on iNs

Samples were dried under nitrogen, evaporated completely and resuspended in biomass-scaled volumes (≥20 µl) of 50% acetonitrile in water. After centrifugation (maximum speed, 10 min), consistent volumes (12 or 15 µl, depending on batch) of supernatants were transferred to microinserts. The remainder of the sample volumes was combined to create a pool sample used for MS2/MS3 data acquisition.

LC–MS metabolomics analyses were performed at the Harvard Center for Mass Spectrometry using a Vanquish LC system coupled with an ID-X mass spectrometer (Thermo Fisher Scientific). Samples (5 µl injection) were analysed on a ZIC-pHILIC peek-coated column (150 mm × 2.1 mm, 5 µm particle size; Sigma-Aldrich) held at 40 °C. Mobile phases comprised buffer A (20 mM ammonium carbonate and 0.1% ammonium hydroxide in water) and buffer B (97% acetonitrile in water). The gradient initiated at 93% B, decreasing linearly to 40% B over 19 min, further decreasing to 0% B over the subsequent 9 min, held at 0% B for 5 min, returned to 93% B within 3 min and finally was re-equilibrated at 93% B for 9 min. The flow rate was held constant at 0.15 ml min−1, except for an initial 30 s ramp from 0.05 to 0.15 ml min−1. MS data were acquired in polarity-switching mode at 120,000 resolution, with an AGC target of 1 × 10^5, covering an m/z range from 65 to 1,000. MS1 acquisition used polarity switching for all samples. MS2 and MS3 analyses were performed on pooled samples using the AcquireX DeepScan method, with five reinjections each in positive- and negative-ion modes separately. A mixture containing standards of targeted metabolites was prepared and analysed immediately after the sample runs for targeted metabolite analysis.

Data were analysed using Compound Discoverer 3.2 (Thermo Fisher Scientific). Metabolite identification was based either on MS2/MS3 spectral matching against a local mzVault library and corresponding retention times from pure standards (level 1), or spectral matching using mzCloud (level 2). Each metabolite identification was manually inspected. Blank samples were used to exclude background compounds (compounds for which the area in at least one sample was not higher than three times the area in the blanks). Median-centred peak areas were scaled to zero-mean and unit variance before principal component analysis. The Harvard Center for Mass Spectrometry identified three samples with notably low overall metabolite intensities, which were subsequently excluded from downstream analyses.

LC–MS metabolomics on medium

Medium samples (100 µl each) were transferred into microcentrifuge tubes containing 1 ml of methanol and incubated at −20 °C for 2 h. After incubation, the samples were centrifuged at 18,000 rcf for 20 min at −9 °C, and the supernatants were transferred into new tubes and evaporated to dryness under nitrogen flow. The dried samples were resuspended in 50 µl of 30% acetonitrile in water containing 2 mM medronic acid, centrifuged again at 18,000 rcf for 20 min at 4 °C and the resulting supernatants were transferred into glass microinserts for LC–MS analysis.

Peak areas from targeted metabolite analysis of media samples were compared for CDP, CDP-choline and choline. To ensure accurate detection, solvent blanks were analysed: CDP and CDP-choline were not detected in these blanks, while choline was detected at levels several orders of magnitude lower than in medium samples.

Molecular dynamics simulations

ABCA7 structures (unbound-open and bound-closed conformations; Protein Data Bank (PDB): 8EE6, 8EOP) containing the G1527 variant were retrieved from the PDB. The A1527 variant was generated by mutation (Gly to Ala) using PyMOL v.2.0. ABCA7 residues 1517–1756 were embedded in a DPPC membrane (CHARMM-GUI) and oriented according to the OPM database. Four simulations were performed (GROMACS 2022.3; CHARMM36M force field; Supplementary Table 15).

The protein–membrane system was solvated in a cubic box with a minimum distance of 1.0 nm between the protein and the box edge, using the TIP3P water model. Energy minimization was performed using the steepest descent algorithm with a maximum force threshold of 1,000 kJ mol−1 nm−1 to relieve any steric clashes or bad contacts. The system was equilibrated in six phases, each 125 ps long, to equilibrate volume (NVT) and pressure (NPT). The production run, 300 ns long, was performed in the NPT ensemble at 323 K using a V-rescale thermostat and 1 bar using the Parrinello–Rahman barostat. A 2 fs time step with H-bond constraints was used with periodic boundary conditions applied in all directions. Long-range electrostatics were handled using the particle mesh Ewald method with a cut-off of 1.0 nm for non-bonded interactions.

The r.m.s.d. was calculated to monitor the conformational stability of a given structure over the course of the simulation by comparing the position of Cα at time t under simulation to its reference position (in 8EOP or 8EE6). The ϕ and ψ dihedral angles were calculated using the gmx rama tool, followed by post-processing. Secondary structure analysis was performed using gmx dssp -hmode dssp, with subsequent post-processing using custom Python scripts. Visualization of the trajectories was carried out using VMD v.1.94 software. Principal component analysis was conducted on Cα atom positional fluctuations to identify the major conformational changes during the simulation.

Eukaryotic cell lines

Human iPS cell lines used in this study were generated by the Picower Institute for Learning and Memory iPSC core. The initial parental cell line (AG09173) was obtained from the Coriell Institute. HEK293T cells (ATCC, CRL-3216) were sourced from ATCC. iPS cell lines were confirmed by cell marker staining, RNA-seq and karyotyping. No further authentication of HEK293T cells was performed. All cell lines used here tested negative for mycoplasma contamination.

Use of large-language models

ChatGPT (OpenAI) was used to edit portions of the manuscript text for brevity and clarity, and to assist in generating selected plotting code.

Ethics statement

The study protocol involving the use of human stem cells was approved by the Coriell Institutional Review Board (Coriell IRB) in compliance with DHHS regulations (45 CFR Part 46). The initial cell lines were obtained from the Coriell Institute, which ensured that informed consent was received from all donors. Donors were informed that their tissue donations would be used for the creation of cell lines intended for educational and research purposes, and that all biological materials would be anonymized. For post-mortem human brain samples, informed consent was obtained from each participant, and the Religious Orders Study and Rush Memory and Aging Project were approved by an Institutional Review Board (IRB) of Rush University Medical Center.

Reporting summary

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

Data availability

The primary post-mortem human omics dataset generated in this study was deposited in the AD Knowledge Portal under Synapse ID syn53461705. As the data contain potentially re-identifiable human information, access is controlled. Researchers with a Synapse account can request the dataset by submitting a data use certificate (DUC) at https://help.adknowledgeportal.org/apd/Data-Use-Certificates.2623373330.html; requests are reviewed within approximately 1 week, and approved users may download the data for 12 months under the terms of the DUC. Redacted data can also be explored through the Single Cell Portal (https://singlecell.broadinstitute.org/single_cell/study/SCP3182). Bulk mRNA-seq fastq files and count matrices are publicly available in the Gene Expression Omnibus: GSE299277. All other data are deposited through the Open Science Framework (https://osf.io/pqr9m/). External datasets analysed (but not generated) in this work are listed in Supplementary Table 3 with URLs and accession IDs.

Code availability

All code used in this study is available on GitHub (https://github.com/djunamay/ABCA7lof2) and deposited at Zenodo[69] (https://doi.org/10.5281/zenodo.15722817).

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Acknowledgements

The results published here are in whole or in part based on data obtained from the AD Knowledge Portal. We thank the individuals who donated post-mortem brain samples, and their families, for enabling this research; Y. Zhou, E. McNamara and T. V. Garvey for administrative support; U. Geigenmüller for reviewing and editing the manuscript and for discussions; R. Pinals, M. Victor, J. Davila-Velderrain and R. Firenze for discussions and input on the manuscript; D. A. Bennett and G. Klein for providing the post-mortem samples; the staff at the MIT SuperCloud and Lincoln Laboratory Supercomputing Center for providing HPC and consultation resources that have contributed to the research results reported within this paper; the staff at the Harvard Center for Mass Spectrometry (HCMS) for lipidomic and metabolomic sample runs and quantifications and C. Vidoudez for consultation on the lipidomic data analysis and experimental procedures; the members of the MIT BioMicro Center for bulk and single-nucleus RNA-seq runs; and S. Levine and his team for consultations on data processing. This work was supported in part by the Cure Alzheimer’s Fund, The Freedom Together Foundation, the Carol and Gene Ludwig Family Foundation, James D. Cook and NIH grants R56-AG081376,RF1-AG062377, RF1-AG054321, RO1-AG054012 (L.-H.T.) and RO1-AG075901 (principal investigator, E. Fraenkel). Graphic illustrations were generated using BioRender under agreements PA28FD1SCF, IW28ETKEWW, CA28ETIAJQ, DU28ETMKEJ and RS28ETN9LG.

Author information

Author notes

1. Hansruedi Mathys

Present address: Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA

Authors and Affiliations

1. Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA

Djuna von Maydell, Shannon E. Wright, Ping-Chieh Pao, Colin Staab, Oisín King, Julia Maeve Bonner, Liwang Liu, Chung Jong Yu, Ching-Chi Chiu, Daniel Leible, Aine Ni Scannail, Mingpei Li, Hansruedi Mathys, Gloria Suella Menchaca, Gwyneth Welch, Agnese Graziosi, Noelle Leary, George Samaan & Li-Huei Tsai

2. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

Djuna von Maydell, Shannon E. Wright, Ping-Chieh Pao, Colin Staab, Oisín King, Julia Maeve Bonner, Liwang Liu, Chung Jong Yu, Ching-Chi Chiu, Daniel Leible, Aine Ni Scannail, Mingpei Li, Hansruedi Mathys, Gloria Suella Menchaca, Gwyneth Welch, Agnese Graziosi, Noelle Leary, George Samaan & Li-Huei Tsai

3. Department of Medical Biotechnologies and Translational Medicine, University of Milano, Milan, Italy

Andrea Spitaleri

4. MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA

Carles A. Boix, Guillaume Leclerc & Manolis Kellis

5. Broad Institute of MIT and Harvard, Cambridge, MA, USA

Carles A. Boix & Manolis Kellis

Contributions

D.v.M. and L.-H.T. designed the study, with L.-H.T. supervising the overall project and acquiring funding alongside M.K. Experimental work included snRNA-seq experiments performed by D.v.M., J.M.B., H.M. and L.L. Seahorse assays conducted by S.E.W. Differentiation and maintenance of iPS cell lines, iNs and cortical organoids were conducted by S.E.W., C.S., P.-C.P. and O.K.; S.E.W. and P.-C.P. prepared samples for LC–MS analysis and performed amyloid ELISA experiments and neuronal marker staining. P.-C.P. conducted TMRM and CellRox assays and S.E.W. performed MitoHealth assays. P.-C.P., C.S. and O.K. prepared bulk RNA samples. L.L. conducted electrophysiological recordings and analysis, and C.J.Y. generated the p.Tyr622* cell line. A.S. performed molecular dynamics simulations, analyses and visualizations and D.v.M. performed formal data analysis and visualization, with contributions from C.A.B., G.L. and A.S. Experimental and technical support was provided by A.N.S., M.L., G.S.M., G.W., A.G., N.L. and G.S.; C.-C.C. and D.L. helped with revision experiments. D.v.M., S.E.W. and L.-H.T. wrote the first draft of the manuscript. D.v.M. and L.-H.T. wrote the revised draft of the manuscript.

Corresponding author

Correspondence to Li-Huei Tsai.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Johan Auwerx, Konstantin Khodosevich and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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 Overview of human snRNA-seq cohort.

a, ABCA7 and RBFOX3 (NeuN) protein levels from postmortem human PFC (Supplementary Table 3) comparing control (N = 180 individuals) vs. ABCA7 LoF carriers (N = 5, “All”) and subset overlapping with snRNA-seq cohort (N = 6 control, N = 4 ABCA7 LoF, “Subset”). b, Distribution of continuous clinical variables (Supplementary Note 1) comparing control (N = 24) vs. ABCA7 LoF carriers (N = 12). c, Distribution of discrete metadata variables comparing control (N = 24) vs. ABCA7 LoF carriers (N = 12); Fisher’s exact tests (R stats::fisher.test, extended to r × c tables). d, Sanger sequencing validating three ABCA7 LoF variants in genomic DNA from ABCA7 LoF carriers and controls. Variant location indicated by black box; WGS sample IDs shown. e, Validation plots demonstrating concordance between SNP calls from WGS and snRNA-seq libraries per individual. Extreme outlier points (dark blue) indicate correct matches. f, 2D UMAP projection of single-cell gene expression coloured by transcriptionally defined cell type. a,b: boxplots show median, IQR (box), whiskers=1.5×IQR; two-sided Mann–Whitney U tests. a–f: experiments performed once.

Extended Data Fig. 2 Expanded view of ABCA7 LoF gene perturbation landscape.

a, 2D UMAP of ABCA7 LoF gene perturbation scores (S = −log10(p) × sign(log2(FC)); unadjusted Limma-Voom p-values, N subjects=12 LoF, 24 control), restricted to genes with |S | > 1.3. Red: S > 1.3, blue: S < −1.3; point size ∝ |S|. Top 50 genes labelled.

Extended Data Fig. 3 Shared differentially expressed genes across cell types.

a, Heatmap indicating overlap of differentially expressed genes (unadjusted Limma-Voom p < 0.05 in ≥3 cell types). b, Functional annotations of genes shown in the heatmap (same gene order as panel a).

Extended Data Fig. 4 Neuronal expression of ABCA7 in postmortem human brain.

a, ABCA7 detection rate (counts > 0) per major cell type in postmortem PFC (snRNA-seq; N cells=Ex 42,014, In 14,806, Ast 7,158, Mic 5,441, Oli 28,078, Opc 5,213). b, Normalized ABCA7 expression comparing glial cells (mean per individual across Oli, Opc, Ast, Mic) vs. neuronal cells (mean per individual across Ex, In) from snRNA-seq (N = 24 control, 12 LoF). Two-sided paired Wilcoxon test following Shapiro test. c, Normalized expression of indicated genes comparing NeuN− vs. NeuN+ cells (N = 6 individuals; 3 control, 3 AD, γH2AX− cells; data from ref. [19], Supplementary Table 3). Two-sided paired t-test following Shapiro test. a–b: boxplots show median, IQR (box), whiskers=1.5×IQR.

Extended Data Fig. 5 Annotation of excitatory neurons from postmortem snRNA-seq dataset by cortical layer.

a, UMAP visualization of excitatory neurons annotated by cortical layers (Leiden clustering; N cells=42,014 from ABCA7 LoF snRNA-seq cohort). b, Heatmap showing enrichment of cortical layer-specific marker genes (from ref. [61]) across annotated layers. Colours indicate average marker gene expression (log2(fold-change)) of each layer’s marker genes relative to all other clusters. c, Heatmap validating layer annotations using an independent set of cortical layer marker genes (from ref. [62]). Colours represent average marker gene expression (log2(fold-change)) relative to all other clusters. d, Perturbation of ABCA7 LoF-associated gene clusters identified in all excitatory neurons (Fig. 2a), stratified by cortical layer (N subjects=24 control, 12 LoF; fGSEA analysis of clusters 0–7). Normalized enrichment scores (NES) and unadjusted p-values shown.

Extended Data Fig. 6 Molecular dynamics simulations of ABCA7 open conformations with p.Ala1527Gly substitution.

a, Open-conformation ABCA7 protein structure highlighting simulation domain (residues 1517–1756, yellow). Inset shows expanded view of structures with Ala1527 (grey) and Gly1527 (purple) variants. b, Further expanded inset from panel a. c, Root mean squared deviations (RMSD) of the open-conformation ABCA7 domain (panel b) carrying Ala1527 (grey) or Gly1527 (purple), measured relative to open-reference structure during simulations. d, Projection of Cα atom positional fluctuations onto first two principal components during simulations for Ala1527 (top, grey) and Gly1527 (bottom, purple) variants. e, Violin plot showing average positional fluctuations of Cα atoms; Mann-Whitney test, two-sided; **** = p < =1e-4.

Extended Data Fig. 7 Local conformational fluctuations and secondary structure changes induced by the p.Ala1527Gly substitution in ABCA7 open and closed conformations.

a, Phi vs. Psi dihedral angle distribution of residue 1527 over simulation time in open and closed ABCA7 conformations. b, Overall Phi vs. Psi angle distributions of residue 1527 across the entire simulation, comparing open and closed conformations. c, Time-resolved secondary structure assignments for residues 1517–1537. Alpha-helical regions highlighted in red; other colours indicate distinct secondary structures. d, Fraction of alpha-helical content for residues 1517–1537 during simulations. A value of 1 indicates continuous alpha-helical structure throughout duration of the simulation. e, Structural alignment of closed-conformation ABCA7 (purple; PDB ID: 8EOP) with ABCA1 (cyan; PDB ID: 7TBW). Gly1527 (ABCA7) and corresponding residue Val1646 (ABCA1) indicated as spheres. f, Structural alignment of closed-conformation ABCA7 (purple; PDB ID: 8EOP) with ABCA4 (green; PDB ID: 7LKZ). Gly1527 (ABCA7) and corresponding residue Ile1671 (ABCA4) indicated as spheres. a,d: G1527 refers to the ABCA7 structure with the risk variant (as present in the reference structures; Supplementary Table 15); G1527A refers to the ABCA7 structure with the mutated Gly→Ala change made to the reference structure in PyMOL.

Extended Data Fig. 8 Electrophysiological characterization of iPSC-derived neurons harbouring ABCA7 PTC variants.

a, Representative sweeps showing action potentials elicited by 800 ms current injections in patched iNs. b, Action potential frequency (mean ± s.e.m.) elicited by varying injected currents in 4-week-old iNs; N cells = 10 WT, 13 p.Tyr622*, 23 p.Glu50fs*3. c, Representative sweeps of inward (top) and outward (bottom) currents recorded in 4-week-old WT neurons (N cells = 23). d, Quantification of currents from panel c. e, Resting membrane potential (mV) in 4-week-old WT, ABCA7 p.Tyr622*, and ABCA7 p.Glu50fs*3 iNs. f, Rheobase (pA) in 4-week-old WT, ABCA7 p.Tyr622*, and ABCA7 p.Glu50fs*3 iNs; WT vs p.Tyr622* p = 0.0424; WT vs p.Glu50fs*3 p = 0.0200. g, Action potential frequency elicited by indicated current injections in 4-week-old WT, ABCA7 p.Tyr622*, and ABCA7 p.Glu50fs*3 iNs; 10 pA: WT vs p.Glu50fs*3 p = 0.0491; 15 pA: WT vs p.Tyr622* p = 0.0003 and WT vs p.Glu50fs*3 p = 0.0109; 20 pA: WT vs p.Tyr622* p = 0.0007 and WT vs p.Glu50fs*3 p = 0.0160. e–g: n = 24 WT, 13 p.Tyr622*, 23 p.Glu50fs*3 4-week-old iNs. Bar plots indicate mean ± s.e.m. P values by two-way ANOVA indicated as: P < 0.05 (*), P < 0.001 (***).

Extended Data Fig. 9 mRNA-seq analysis of p.Glu50fs*3 iNs.

a, K/L clustering of leading-edge genes from perturbed pathways in WT vs. p.Glu50fs*3 iNs (fGSEA, Wikipathways; FDR-adjusted p < 0.05; N wells=2 WT, 5 p.Glu50fs*3). Gene-pathway graph: genes (circles), pathways (squares). b, Heatmap (Jaccard index) comparing K/L clusters from p.Glu50fs*3 iNs vs. p.Tyr622* iNs (from Fig. 3c). Average per-cluster gene score S (S = −log10(p)*sign(log2(FC))) indicated; \(\bar{S} > 0\) (red); \(\bar{S} < 0\) (blue); FDR-adjusted permutation p-values (1000 iterations, one-sided). c, Heatmap (Jaccard index) comparing K/L clusters from p.Glu50fs*3 iNs vs. postmortem neurons (Fig. 2a,b). Red/blue indicates direction as described in c; FDR-adjusted permutation p-values (1000 iterations, one-sided). d, Kernel density plots of gene perturbation scores S; unadjusted Limma-Voom p-values; N wells=2 WT, 5 per LoF) per cluster. Positive S indicates increased expression in p.Glu50fs*3. Solid lines show cluster means; top enriched pathways indicated. e, Correlation of gene perturbation scores S for genes encoding mitochondrial proteins (MitoCarta database, Supplementary Table 3; N wells=2 WT, N = 5 per LoF line). a–e: 4-week differentiation; wells=technical replicates; same mRNAseq experiment as Fig. 3b; experiment performed once.

Extended Data Fig. 10 Analysis of oxygen consumption rates (OCR) in WT vs ABCA7 LoF iNs.

a, Seahorse OCR curves (one representative experiment from Fig. 3g). Lines indicate per-condition mean; error bars represent 95% confidence intervals (N wells=10 WT, 7 per LoF line). b, Representative per-well OCR traces from panel a. c, Simplified schematic of mitochondrial bioenergetics in electrical terms: oxygen consumption by the respiratory chain generates a proton current (I) that builds the proton-motive force (Δp, voltage V); proton return through ATP synthase (cyan) for ATP production or through uncoupling protein (red) provides parallel resistances (R) that dissipate Δp; BioRender agreement # RS28ETN9LG; based on information in[27]. d, Schematic depicting measurements of maximal and basal OCR used to calculate Spare Respiratory Capacity (SRC). e, Schematic showing measurement of uncoupled OCR (%). f, SRC computed for WT, ABCA7 p.Glu50fs*3, and ABCA7 p.Tyr622* iNs (N wells=18 WT, 17 p.Tyr622*, 13 p.Glu50fs*3; two experiments, same as Fig. 3g). Two-sided Mann–Whitney U test (WT vs. p.Tyr622*) and two-sided t-test (WT vs. p.Glu50fs*3, unequal variances), following Shapiro/Levine tests. g, UCP2 mRNA expression by genotype (N wells=2 WT, 5 per LoF; unadjusted Limma-Voom p-values). h, Average TMRM intensity per masked region (75th percentile threshold; N wells=4 WT, 5 p.Tyr622*; mean projection over time), under baseline conditions and after FCCP addition. Same experiment and images as Fig. 3i. Two-sided t-test assuming equal variance, following Shapiro/Levine tests for both comparisons. f, h: boxplots show median, IQR (box), whiskers=1.5×IQR. a–h: 4-week iN differentiation; g: experiment performed once, a, b, f, h ≥twice.

Extended Data Fig. 11 LC-MS lipidomics of ABCA7 LoF iNs.

a, Volcano plot of WT vs p.Glu50fs*3 showing differentially abundant lipid species, coloured by class. b, Table summarizing the significant species by lipid subclass. c, Fold-change distribution for triglycerides (TG) grouped by fatty-acid chain length and saturation (WT vs p.Glu50fs*3). d, Volcano plot highlighting perturbed phosphatidylcholines (PCs) that contain saturated or monounsaturated fatty acids (SFA/MUFA). e, As d but for PCs with polyunsaturated fatty acids (PUFA). f, Fold-change distribution for PCs grouped by chain length and saturation (WT vs p.Glu50fs*3). g, Table of significantly altered lipid species in WT vs p.Tyr622*, grouped by subclass (same experiment as Fig. 3k). h, Volcano plot comparing WT vs p.Tyr622* with saturated PCs highlighted in blue. i, TG fold-change distribution for WT vs p.Tyr622*. j, PC fold-change distribution for WT vs p.Tyr622*. k,l, mRNA expression changes of LPCAT genes in p.Tyr622* and p.Glu50fs*3 iNs relative to WT (unadjusted p by Limma-Voom; N wells=2 WT, 5 per LoF line). a-l: iNs differentiated for 4 weeks. a-j: Two-sided t-test assuming unequal variance; FDR-adjusted p < 0.05 and |log2(FC)| > 1 used to define significance. N wells= 10 WT, 8 p.Tyr622*, 6 p.Glu50fs*. Wells are technical replicates. g-l: experiment performed once, a-f ≥twice.

Extended Data Fig. 12 Treatment of p.Tyr622* iNs with CDP-choline.

a, Choline metabolites in media (targeted LC-MS; N wells=2 media-only, 4 cell-conditioned); “N/F”=not detected. b, Intracellular choline metabolites (targeted LC-MS; N = 8 wells/condition, 4 blanks). c, Differentially expressed choline synthesis and transport genes in p.Tyr622* ± CDP-choline iNs. d, Differentially expressed LPCAT genes in p.Tyr622* ± CDP-choline iNs. e, Phosphatidylcholine species log2 fold-changes by fatty acid chain length and saturation in p.Tyr622* ± CDP-choline (lipidomics; N = 5 wells/condition). f, PCA of untargeted LC-MS metabolite profiles from WT and p.Tyr622* ± CDP-choline iNs (N wells=9 WT, 7 per p.Tyr622* condition). g, PCA of mRNA-seq data from p.Tyr622* ± CDP-choline iNs. h, Correlation of gene perturbation scores (S = −log10(p) × sign(log2(FC)); unadjusted Limma-Voom p-values) for genes encoding mitochondrial proteins in WT vs p.Tyr622* and p.Tyr622* ± CDP-choline iNs (MitoCarta; Supplementary Table 3; N = 2 WT, 5 per p.Tyr622* condition). i, Seahorse OCR curves; lines=mean, error bars=95% CI. j, Representative OCR traces from panel i. k, SRC in p.Tyr622* ± CDP-choline (two-sided t-test, equal variances after Shapiro/Levene tests). l, MitoHealth intensity per NeuN+ volume (N = 11 wells p.Tyr622*+H_2O, 12 p.Tyr622*+CDP-choline; ~3 × 10³ cells/condition; three experiments; linear mixed-effects model). Visualization: maximum projections, NeuN/GFP clipped at maximum, γ-corrected (γ = 0.5). Individual points=well averages; same experiment as Fig. 3h. a,b: two-sided t-tests (equal variances). c,d,g: mRNA-seq; unadjusted Limma-Voom p-values; N = 5 wells/condition. i–k: N wells=8 p.Tyr622*+CDP-choline, 6 p.Tyr622*+H_2O (same experiment as Fig. 4g). a,b,k,l: boxplots=median, IQR (box), whiskers=1.5×IQR. a–l: 4-week iN differentiation; last 2 weeks, 100 µM CDP-choline. a-l: wells=technical replicates. a,b,e,f,i–k: experiments repeated once, c,d,g,h,l ≥twice.

Extended Data Fig. 13 CDP-choline treatment in cortical organoids.

a, Amyloid-β (Aβ40, Aβ42) levels quantified by ELISA from media of 4-week-old iNs (N wells=12 WT, 8 p.Tyr622*). Aβ40: two-sided t-test (equal variances); Aβ42: two-sided Mann–Whitney U test, following Shapiro–Levene tests. b, Representative images of cortical organoid slices by genotype; 5.5 months. c, Amyloid-β (Aβ40, Aβ42) levels quantified by ELISA from cortical organoid media (176-day-old), grouped by genotype and treatment (500 µM or 1 mM CDP-choline for 3 weeks). Samples correspond to organoids in Fig. 4k, analysed one week prior to assays presented there (N organoids=20 WT, 20 p.Tyr622*+H_2O, 15 p.Tyr622*+500 µM CDP-choline, 14 p.Tyr622*+1 mM CDP-choline). Statistical comparisons (two-sided t-tests): Aβ40: WT vs p.Tyr622* (unequal variances), p.Tyr622*±500 µM (unequal variances), p.Tyr622*±1 mM (equal variances). Aβ42: all comparisons (equal variances). All tests followed Shapiro–Levene tests. a,c: boxplots=median, IQR (box), whiskers=1.5×IQR. a-c: experiments performed once.

Supplementary information

Supplementary Information

Supplementary Notes 1–3, Supplementary Tables 1–3, 11 and 14–17, and Supplementary Figs. 1–6.

Reporting Summary

Supplementary Table 4

Metadata for ROSMAP cohort individuals analysed. Sample_Id: snRNA-seq sample identifier. ABCA7LoF: indicates the presence (1) or absence (0) of ABCA7 premature termination variant (Methods). Passed_QC: snRNA-seq quality control status (Methods). Additional variables are described in Supplementary Note 1.

Supplementary Table 5

Technical parameters from Cell Ranger analysis for snRNA-seq samples.

Supplementary Table 6

Differential expression analysis results on snRNA-seq data for genes comparing ABCA7 LoF variant carriers to controls within each cell type. The columns report the log[FC], average expression (AveExpr) and statistical significance (t-statistic, P value, adjusted P value).

Supplementary Table 7

Summary of gene clusters identified from the UMAP projection of ABCA7 LoF-associated differential expression scores across cell types. Each cluster lists enriched Gene Ontology pathways, the number of overlapping genes, hypergeometric enrichment P values and associated genes contributing to each pathway enrichment.

Supplementary Table 8

Enriched pathways identified by fGSEA (WikiPathways database; cutoff P < 0.05) comparing ABCA7 LoF to control excitatory neurons. The columns include pathway, raw and adjusted P values, NES, leading-edge genes, cluster assignments (Kernighan–Lin partitioning) and cluster annotations.

Supplementary Table 9

Differential expression analysis results from mRNA-seq comparing WT, p.Tyr622* with or without CDP-choline treatment and p.Glu50fs*3 iNs. The columns report the log[FC], average expression, t-statistic, P value and adjusted P value.

Supplementary Table 10

Enriched pathways identified by fGSEA (WikiPathways database; cut-off Padj < 0.05) comparing WT, p.Tyr622* with or without CDP-choline treatment, and p.Glu50fs*3 iNs. The columns include pathway, raw and adjusted P values, NES, leading-edge genes, cluster assignments (Kernighan–Lin partitioning) and cluster annotations.

Supplementary Table 12

Differential lipid abundance results comparing WT, p.Tyr622* with or without CDP-choline treatment, and p.Glu50fs*3 iNs. The columns include lipid species (name), lipid group (grp), lipid class, log_2[FC], raw and FDR-corrected P values, and group comparisons. Undefined indicates a signal below the blank threshold.

Supplementary Table 13

Targeted metabolomics of p.Tyr622* iNs and culture medium comparing the CDP-choline-treated and untreated conditions. The columns show peak areas for CDP, CDP-choline and choline in samples and blanks.

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von Maydell, D., Wright, S.E., Pao, PC. et al. ABCA7 variants impact phosphatidylcholine and mitochondria in neurons. Nature 647, 462–471 (2025). https://doi.org/10.1038/s41586-025-09520-y

- Received: 11 June 2024

- Accepted: 12 August 2025

- Published: 10 September 2025

- Version of record: 10 September 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09520-y

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Assessing phylogenetic confidence at pandemic scales

Nicola De Maio,

Nhan Ly-Trong,

Samuel Martin,

Bui Quang Minh &

…

Nick Goldman

Nature volume 647, pages 472–478 (2025)

Abstract

Phylogenetics has a central role in evolutionary biology and genomic epidemiology[1]. Assessing phylogenetic confidence and reliability is therefore crucial and the methods that do this, such as those derived from Felsenstein’s bootstrap[2], are among the most widely used in modern science. However, these methods require enormous computational capacity, and are unsuitable for large datasets. Furthermore, most of these methods emerge from a focus on the membership of clades (groupings of taxa), which makes their results difficult to interpret in the context of genomic epidemiology. Here we propose subtree pruning and regrafting-based tree assessment (SPRTA), an efficient and interpretable approach to assess confidence in phylogenetic trees. SPRTA shifts the paradigm of phylogenetic support measurement from evaluating the confidence in clades to evolution histories and phylogenetic placement—for example, assessing whether a lineage evolved from another considered lineage, which is particularly valuable in genomic epidemiology. We use SPRTA to investigate a global public SARS-CoV-2 phylogenetic tree relating more than two million genomes, highlighting plausible alternative evolutionary origins of many SARS-CoV-2 variants, assessing reliability in the Pango outbreak lineage classification system[3], and demonstrating the effect of phylogenetic uncertainty on inferred mutation rates. Our results show that SPRTA enables pandemic-scale and detailed probabilistic assessment of transmission and mutational histories. Our method introduces a new approach to assessing phylogenetic confidence, enhancing the interpretability of pandemic-scale phylogenetic analyses and improving our ability to prepare for and respond to future pandemics.

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Main

Phylogenetics is central to evolutionary biology[1]. Phylogenetic trees are graphs representing evolutionary histories and ancestry, and are typically inferred from the genomic data. In genomic epidemiology, for example, DNA sequences of the same pathogen are collected from different hosts (for example, SARS-CoV-2 genomes collected from different patients) and compared with one another. Phylogenetic trees inferred from these genomes can reveal the emergence of drug resistance and new variants of concern, transmission between individuals and countries, and many other details of the evolution and spread of the pathogen[4].

Most phylogenetic methods that are scalable to large datasets—such as maximum-likelihood, parsimony-based and heuristic approaches—typically estimate a single phylogenetic tree without intrinsically assessing the reliability or uncertainty of inferences. This issue is typically addressed with additional methods such as Felsenstein’s bootstrap[2]. For a given dataset, Felsenstein’s bootstrap typically creates 100 or 1,000 replicates by randomly resampling the genetic data with replacement. Phylogenetic inference is performed on each to estimate replicate trees, and the support score of a clade (the set of taxa inferred to be all the descendants of one ancestor in the tree) is defined as the proportion of replicate trees containing that clade. This is also considered to be the support of the phylogenetic branch that separates the clade from all other taxa in the tree.

Felsenstein’s bootstrap has been developed in the context of inter-species evolutionary biology. Consequently, it has a number of drawbacks when applied to genomic epidemiological datasets of closely related genetic sequences. First, repeatedly performing phylogenetic estimation on all replicate datasets can be excessively computationally demanding. Although Felsenstein’s bootstrap approximations5,6,7 are more efficient, these are still not feasibly applicable to pandemic-scale phylogenetic analyses involving millions of genomes8,9.

Second, even a small number of ‘rogue taxa’—that is, sequences whose placement in the inferred phylogenetic tree is highly uncertain (such as incomplete sequences or recombinants)—can substantially lower the Felsenstein’s bootstrap support of many internal branches throughout phylogenetic trees[10]. Third, Felsenstein’s bootstrap does not measure posterior probability, but rather measures repeatability[11]. In genomic epidemiology, a single mutation is typically sufficient to define a clade with negligible uncertainty. However, in this scenario, Felsenstein’s bootstrap usually requires three mutations supporting any one clade to be able to assign 95% support to it, making it excessively conservative2,12.

Finally, Felsenstein’s bootstrap and most other phylogenetic branch support measures have a ‘topological’ focus, in that they aim at assessing the reliability of the inferred tree topology via its constituent clades. Although clades are important in taxonomy, they are not as relevant in genomic epidemiology, where the focus is typically on mutation and transmission histories and lineage assignment3,13.

Existing local branch support measures14,15,16,17 are considerably more computationally efficient than Felsenstein’s bootstrap, but also rely on a topological interpretation. They usually compare the likelihood of the inferred phylogenetic tree against the likelihoods of similar alternative trees (Methods, ‘SPRTA and aBayes’). Local branch support measures are particularly appealing because of their computational efficiency and their robustness to rogue taxa.

Here, drawing on concepts from local branch support measures and the approximate Bayes approach (aBayes)[15], we present a new measure of branch support that is robust to rogue taxa and can scale to pandemic-scale trees. Subtree pruning and regrafting-based tree assessment (SPRTA) shifts the focus from a topological point of view to one centred around the evolutionary origin of lineages (‘mutational’ or ‘placement’ focus).

Principles of SPRTA

As typical in molecular phylogenetics, we assume that genetic data are represented as a multiple sequence alignment D: a matrix in which each row corresponds to the genetic sequence of a considered taxon (in genomic epidemiology this is often the genome sequence of one of the considered samples, aligned to the reference genome) and each column contains all homologous nucleotides (those descending from the same ancestral nucleotide). We also assume we have a rooted phylogenetic tree T (Methods, ‘SPRTA and aBayes’) inferred from D. Our aim is to assign confidence scores to branches b of T (see Fig. 1). Most branch support methods assign scores that represent the confidence that the sequences contained in subtree Sb (black triangle in Fig. 1), containing all descendants of b, indeed form a clade within T—we refer to this as the topological focus. Instead, we want to assess the probability that b correctly represents the evolutionary origin of Sb—we call this a mutational focus. To clarify this, we describe branch b as having immediate ancestor A and descendant B (the root of subtree Sb), and dividing T into Sb and its complement T\Sb (Fig. 1). A and B might correspond to individual genomes if, as typical in genomic epidemiology, uncertainty regarding ancestral genomes is low; otherwise, they might represent sets of probable genomes. Our aim is to efficiently approximate the probability that B evolved from A through mutations along branch b (that is, B and Sb are ‘placed’ at A), as opposed to the alternative hypothesis that B descended from some other node of T\Sb (‘alternative placements’ of Sb (blue arrows in Fig. 1)). In other words, SPRTA(b) represents the confidence that branch b is the evolutionary origin of B and Sb.

Fig. 1: Graphical representation of SPRTA branch support measurement.

Here we represent SPRTA branch support calculation for an example tree branch b (highlighted as a solid red line). The subtree Sb below b is represented with a black triangle and is not affected by any of the SPR moves considered to evaluate the support of b. A and B represent the ancestors of b and Sb, respectively. Solid black lines represent the branches of T\Sb, which are assessed as alternative placements for Sb, and the dashed black line represents the rest of T (which is an arbitrarily large tree), not shown here. Some possible SPR moves are highlighted with dotted blue arrows, and cause hypothetical new branches (shown as shaded red dashed lines), leading to copies of Sb (grey triangles) descending from nodes A_2–A_4. The relative likelihood of the original tree is \(\Pr (D| {T}_{1}^{b})\) while the likelihoods of alternative topologies are represented by \(\Pr (D| {T}_{2}^{b})\), \(\Pr (D| {T}_{3}^{b})\) and \(\Pr (D| {T}_{4}^{b})\). In this case, the support for branch b will be \(\Pr (D| {T}_{1}^{b})/{\sum }_{i=1}^{4}\Pr (D| {T}_{i}^{b})\). In practice, a large number Ib of alternative topologies might be considered for each branch b (Methods, ‘SPRTA and aBayes’).

To achieve this, for each branch b, SPRTA considers a number Ib of alternative topologies \({T}_{i}^{b}\) (1 ⩽ i ⩽ Ib) of T obtained by performing single subtree pruning and regrafting (SPR) moves—changes to T that relocate Sb as a descendant of other parts of T\Sb (Fig. 1; Ib is defined in Methods, ‘SPRTA and aBayes’). Each such SPR move represents a possible different origin of B, as a direct descendant of a node Ai other than A. For notational convenience, we assume that \({T}_{1}^{b}=T\) is the original tree topology. The likelihood \(\Pr (D| {T}_{i}^{b})\) of such topologies is routinely and efficiently calculated by MAPLE[9] (up to a constant common multiplicative factor) and we use it to define the SPRTA support score:

$${\rm{SPRTA}}(b)=\mbox{Pr}(b| D,T\backslash b)=\frac{\mbox{Pr}(D| T)}{{\sum }_{1\leqslant i\leqslant {I}_{b}}\mbox{Pr}(D| {T}_{i}^{b})}$$

(1)

where T\b represents the union of subtree Sb and its complement subtree T\Sb. SPRTA is described in more detail in Methods ‘SPRTA and aBayes’.

SPRTA mutational branch support scores need to be interpreted differently from those of existing topological branch support methods. SPR moves involving branch b preserve the clade defined by b (Fig. 1), and so SPRTA scores do not represent an assessment of this clade. Rather, SPRTA scores are to be interpreted as an approximate probability that B evolved directly from A. These scores are a particularly useful assessment of tree reliability in the context of genomic epidemiology, where the placement of individual incomplete sequences (rogue taxa) can be highly uncertain. SPRTA scores are expected to be robust to rogue taxa, since their placement is expected to have negligible effect on relative likelihood scores at internal tree nodes.

SPRTA scores for the terminal branches of a tree evaluate the placement probability of individual observed sequences, and in fact correspond closely to the probabilistic support measure used by some tools that map query sequences onto a pre-estimated phylogeny18,19. By contrast, topological support methods cannot assess terminal branches and sequence placements.

Computational demand

The SPR search required by SPRTA is typically performed as part of the phylogenetic tree search in many maximum-likelihood phylogenetic methods such as RaxML[20] and MAPLE[9], and so it is not expected to lead to significant additional runtime when executed in conjunction with phylogenetic inference. For comparison, we assessed the computational demand of SPRTA against the main existing measures of branch support (Felsenstein’s bootstrap[2], local bootstrap probability (LBP)[14], approximate likelihood ratio test (aLRT)[15], aLRT with Shimodaira–Hasegawa test (aLRT-SH)[16], Bayesian-like transformation of aLRT (aBayes)[17], transfer bootstrap expectation (TBE)[10] and ultrafast bootstrap approximation (UFBoot)[7]). SPRTA reduces runtime and memory demands by at least two orders of magnitude compared with all these methods, with the difference growing as dataset size increases (Fig. 2 and Extended Data Fig. 1a,b). Note also the premature termination of the lines in Fig. 2 and Extended Data Fig. 1 for the other methods, indicating cases where they could not be run successfully. Among these other methods, those based on Felsenstein’s bootstrap (Felsenstein’s bootstrap, UFBoot and TBE) have substantially higher computational demand than local branch support measures. This comparison shows that SPRTA can assess much larger phylogenetic trees than existing approaches.

Fig. 2: Computational demand.

a,b, Time (a) and maximum memory usage (b) of different branch support methods. The x axis shows the number of simulated SARS-CoV-2 genomes (Methods, ‘Simulated genomes’) included in each replicate. For each dataset size considered, we ran 20 replicates. Dots show the mean across replicates and violin plots show variation between replicates. All analyses were run on one core of an Intel Xeon Gold 6252 processor at 2.10 GHz. Each method was run on the same replicates for the results in a,b. In b, the lines for all methods other than UFBoot and SPRTA overlap. FB, Felsenstein’s bootstrap.

Accuracy

To benchmark different branch support methods, we simulated SARS-CoV-2-like genome data (Methods, ‘Simulated genomes’) for which we know the true tree and the true mutational history along the tree. Because genomes in genomic epidemiological datasets are typically very similar to each other, we usually infer ancestral genomes and mutation events conditional on an inferred phylogenetic tree T with negligible uncertainty. In this case the task of assessing whether B evolved from A along branch b is equivalent to assessing the correctness of the mutation events implied by T on b, since alternative placements of Sb often result in alternative mutational histories leading to B. Therefore, in this benchmark we consider a mutational interpretation of branch support, and interpret branch support scores as an estimate of the posterior probability of the mutation events implied by T on the considered branch b. Each individual branch of a tree inferred from simulated data is considered correct if the mutations inferred on that branch actually happened in our simulations, and so if B at the lower end of b actually evolved from A. An advantage of this approach is that, whereas Sb or the clade defined by it are often uncertain and might not exist in the real tree, B will typically not be affected by rogue taxa or other topological uncertainty within Sb. More details on how we define accuracy of branch support are provided in Methods, ‘Benchmarking of SPRTA support’. This is not the typical interpretation of branch support measures other than SPRTA, and so results here are not meant as an evaluation of other branch support measures in their classical topological interpretation. Below, all branch support measures are evaluated based on their ability to assign higher support to correctly inferred mutation events, and lower support to erroneously inferred mutation events.

Generally, all methods give high support scores (>80%) to all mutations, both correctly and wrongly inferred ones (Fig. 3 and Extended Data Fig. 1c,d). This is to be expected given the low level of divergence in the considered dataset, and that these mutations have been inferred by maximum likelihood and so by definition will typically have higher likelihood than alternative mutation histories. However, SPRTA is the only method that reliably assigns higher support to correctly inferred mutation events (typically a mean of 98–99%) and lower support scores to wrongly inferred ones (typically a mean of 85–90%) (Fig. 3 and Extended Data Fig. 1e,f). Although some methods like UFBoot, aLRT, and aBayes give higher support to correctly inferred mutations than does SPRTA, they also similarly support wrongly inferred mutations, and so do not discriminate between the two. This is also reflected in higher AUROC and AUPRC values for SPRTA (Extended Data Figs. 2–5). It should be remembered, however, that topological approaches have been developed for a different interpretation of branch support scores, and so these results are not indicative of their performance for that task. What our analysis does show is that SPRTA can usefully and effectively assess the confidence in mutational histories implied by inferred phylogenetic trees.

Fig. 3: Benchmark of branch support methods.

a, Mean support of correctly inferred mutations (supp. correct mut.) within each replicate. b, Mean support of wrongly inferred mutations (supp. wrong mut.) within each replicate. Note that values shown are distributions of mean support values across all mutations in a replicate simulation, and not distributions of support scores of individual branches or mutations. Other details are as in Fig. 2. Branch support scores for methods other than SPRTA were again only calculated when computationally feasible.

Uncertainty of SARS-CoV-2 evolution

We applied SPRTA to a global public dataset of 2,072,111 SARS-CoV-2 genomes (Methods, ‘Viridian genome dataset’) that is too large for the feasible application of existing topological branch support measures. Phylogenetic tree estimation with MAPLE, parallelized over 14 cores of an Intel Xeon Gold 6252 processor at 2.10 GHz, took around 10 days (ref. [21]). Post hoc SPRTA mutational support assessment, re-performing SPR move evaluations that had already been performed during tree inference, required 7 h 27 m on a single core and maximum memory 26.93 Gb. This branch support assessment can be also performed simultaneously with tree inference, at negligible additional computational cost compared to the tree inference itself. During tree inference, however, we do not evaluate possible alternative placements of less informative genomes (a sequence is not informative at a position if it contains the ‘N’ character at that position, and a sequence s_1 is less informative than s_2 if it coincides with s_2 except at positions where s_1 is not informative), since alternative placements of these genomes do not increase the likelihood of the considered tree[21]. To evaluate alternative placements of all genomes—for example, to identify a larger number of rogue taxa in our dataset—we also performed a more in-depth and computationally demanding SPRTA assessment that did not collapse genomes less informative than other genomes in the dataset; this required 22 h 42 m on a single core and maximum memory 27.75 Gb.

Phylogenetic uncertainty

Among the 2,072,111 genomes considered here, 636,022 are mutationally informative (the branch separating them from the tree has inferred mutations on it), but for many of these the mutational history is uncertain: 87,406 have SPRTA placement below 90% and 53,365 have SPRTA placement below 50%. From the remaining 1,436,089 non-mutationally informative genomes (those that are separated from the tree by a terminal branch of length 0 and so, conditional on the rest of the tree, are not informative of mutation events), 162,100 have SPRTA placement support below 90% and 115,358 have SPRTA placement below 50%.

Phylogenetic uncertainty also affects many internal branches of the phylogenetic tree, highlighting substantial uncertainty in the ancestral mutational history, and not only in terminal branches: from 453,976 internal branches with inferred mutations, 59,523 have SPRTA support below 90% and 29,641 have SPRTA support below 50%. In terms of inferred mutations (which are assigned the same SPRTA score as the branch they are inferred to have occurred on), out of a total of 1,827,786 there are 249,092 with SPRTA support below 90% and 126,308 with SPRTA support less than 50%.

Although in our simulations we model site-specific genome evolution and incomplete sequence data to closely match patterns observed in real SARS-CoV-2 data (Methods, 'Simulated genomes’), we still find lower SPRTA scores in real data than in simulations (Extended Data Fig. 1g). This suggests further complexities in real data such as site- and nucleotide-specific mutational patterns[22] that are not accounted for in our models of genome evolution.

A prominent examples of uncertainty in the SARS-CoV-2 mutational history is the evolution of the AY.4 Delta sub-lineage, one of the most abundant SARS-CoV-2 lineages, represented here by more than 480,000 genomes. Two mutations appear to be ancestral to most AY.4 genomes: T17040C and C21846T (Fig. 4, middle). After the appearance of mutation T17040C, the reversion C17040T appears to have occurred many times: we infer around 650 reversions. Position 17040 is inferred by MAPLE to have a substitution rate 31.9 times higher than average[21], mostly as a result of these reversions within AY.4. Although inferred reversions can be due to reference biases in consensus genome calling methods, our consensus genomes were called with Viridian[23], which is not affected by this issue21,23. Furthermore, we did not observe any issues with read data or substitution distribution along the tree that would suggest the presence of recurrent sequence errors at position 17040[21]. This suggests that 17040 might be a genuinely hyper-mutable genome position in SARS-CoV-2, but only when in the mutated C nucleotide state, and not in the ancestral T. This is in line with the observation that mutation patterns in SARS-CoV-2 are highly position- and nucleotide-specific[22].

Fig. 4: Uncertain evolutionary history of SARS-CoV-2 lineage AY.4.

Left, the global SARS-CoV-2 phylogenetic tree inferred by MAPLE and visualized in Taxonium[25]. Tips are coloured according to the Pango[3] lineage assigned by Pangolin[35] v.4.3 (with Pangolin-data v.1.21) to the corresponding genomes. We also show the names of some of these lineages. Middle, magnified view of lineage AY.4, showing the locations of inferred T17040C (green arrow) and C21846T (blue arrow) mutations that define the inferred early evolution of the lineage. We highlight with red circles the approximately 650 C17040T reversions within AY.4. One of these, highlighted with a red arrow, is ancestral to more than 163,000 genomes and has only 7.6% SPRTA support. Right, further magnification of the location of an alternative origin of this last sub-lineage (with corresponding SPR move represented by the dark blue arrow) that has 5.0% SPRTA support and entails re-placing the aforementioned sub-lineage as a direct descendant of the phylogenetic node with genome containing the T27484C and C17040T mutations.

The largest inferred sub-lineage within AY.4 descending from a C17040T reversion contains more than 163,000 genomes (Fig. 4, middle). However, the branch defining this lineage (Fig. 4, middle, red arrow) has only 7.6% SPRTA support. The reason is that, owing to the hyper-mutability of C17040, there are many alternative plausible mutational histories within AY.4. The most likely alternative origin of this sub-lineage has SPRTA support of 5.0% and involves replacing the C17040T reversion defining this lineage with a C27484T reversion within a genomic background that already contains the C17040T reversion (Fig. 4, right, dark blue arrow). Although many equally parsimonious alternative mutation histories exist, this one is inferred by SPRTA to be the most likely alternative origin of this sub-lineage because the background mutation rate from C to T is very high in SARS-CoV-2, and because position 27484 also has an inferred substitution rate 20.8 times above average[21]. These results show that the evolutionary history of lineage AY.4 is highly uncertain, and illustrate how SPRTA scores can not only highlight uncertain parts of an inferred phylogenetic tree and mutational history, but also identify and probabilistically assess alternative evolutionary origins of considered pathogen variants.

Regarding individual samples with uncertain placement, we show two examples (of many) in Fig. 5. In the top example, uncertainty is caused by the sample having an incomplete genome sequence; in the bottom example, uncertainty is caused by the existence of two mutually plausible mutational histories. This shows how SPRTA can effectively identify the uncertainty related to the placement of rogue taxa, which by definition will have many possible placements but all with similarly low SPRTA support. Other branch support methods, by contrast, cannot provide an evaluation of sample placement, as they only measure support for internal branches of the phylogenetic tree. Also, unlike for Felsenstein’s bootstrap and most other topological branch support measures, rogue taxa mostly do not affect the SPRTA support scores of ancestral nodes, as their placement typically only affects the inference of mutation events on phylogenetic branches near the placement itself.

Fig. 5: Sample placement uncertainty.

Left, the global SARS-CoV-2 tree as in Fig. 4. Right, magnified view of two uncertain sample placements. Top right, within lineage Alpha, the considered sample (marked by the black arrow) has no sequence information at genome position 27281 (ambiguity character N), the same position where a G-to-T mutation occurs nearby in the phylogenetic tree (marked by the blue arrow). This sample has 50% SPRTA support both at the current placement and at the other side of G27281T (red arrow). Bottom right, within the AY.44 lineage, the placement of a sample has 80.5% SPRTA support, since the mutation C10748T implied by the current placement of the sample (top green arrow) also occurs on a nearby branch (bottom green arrow), permitting an alternative placement with 19.5% SPRTA support (red arrow). This alternative placement would require one fewer C10748T mutation on the tree but one additional G5743T mutation.

These are only some examples of all mutations and placements in the SARS-CoV-2 tree that are substantially uncertain (126,308 out of all 1,827,786 inferred mutations have SPRTA support below 50%; see above). Our full maximum-likelihood tree annotated throughout with SPRTA support scores concisely represents the evolution of SARS-CoV-2 during the COVID-19 pandemic and presents the SPRTA probabilistic assessment of each sampled genome placement and each inferred mutation. It also offers a concise summary of plausible alternative placements and mutational histories. Such pandemic-scale probabilistic phylogenetic assessment—and in general the assessment of mutation events, placements and alternative mutational histories—is not possible with existing popular branch support methods. This annotated tree is available on Zenodo[24] together with the considered SARS-CoV-2 genome alignment, and can be visualized easily within Taxonium[25].

Effect on inferred mutation rates

In phylogenetic investigations of mutation rates and selective pressures, mutations are usually estimated on a fixed phylogenetic tree, without accounting for phylogenetic uncertainty[22]. We find that ignoring phylogenetic uncertainty does not substantially affect the inference of genome-wide mean nucleotide substitution rates for SARS-CoV-2 (Extended Data Fig. 6a). However, it can substantially affect the numbers of inferred mutations at individual sites (Extended Data Fig. 6b). One of the sites strongly affected is 17040, owing to previously described uncertain reversions (Fig. 4). Other significantly affected sites also show similar patterns, with many reversions following a substitution (for example, see Extended Data Fig. 7 for position 7926 and Extended Data Fig. 8 for position 21595). The presence of several genome positions with high reversion rate and therefore highly uncertain mutational history might explain why we find lower SPRTA scores in real data than in simulations (Extended Data Fig. 1g). Details of these methods are provided in Methods, ‘Assessing the impact on mutation rates’.

Effect on Pango lineages

The definition of pathogen outbreak lineages (for example, with the Pango[3] or Autolin[26] systems) and the assignment of samples to these lineages (for example, with the Pangolin tool[27]) often rely on a fixed phylogenetic tree. Uncertainty in this tree is typically ignored for these tasks. We investigated how much phylogenetic uncertainty might affect the definition of and assignment to Pango lineages, a globally important system for classifying and naming SARS-CoV-2 lineages including variants of concern (detailed methods in ‘Assessing the impact on Pango lineages’).

We found that out of 1,127 Pango lineages in our tree, 26 had more than 5% probability (and 39 had more than 1% probability) of origin from a different Pango lineage than the one implied by the maximum-likelihood tree. Among these, the most noticeable (due to its size and uncertainty) was Pango lineage BA.2.13. This lineage contains more than 500 genomes and is inferred to have descended from Pango lineage BA.2.56 in our maximum-likelihood tree (Fig. 6). However, a likely alternative placement of this lineage suggests that it might have evolved directly from BA.2. The uncertainty appears to be caused by the joint occurrence of the pair of substitutions A23767G and C22792T, both within lineages BA.2 and BA.2.56, giving rise to two almost equally probable evolutionary origins (with approximately 54% and 46% total SPRTA support, respectively) of BA.2.13 (Fig. 6).

Fig. 6: Uncertain origin of Pango lineage BA.2.13.

Left, the global SARS-CoV-2 tree as in Fig. 4. Middle, magnified view of lineage BA.2. Right, magnified view of the two plausible origins of Pango lineage BA.2.13 within BA.2 and its descendants. Top right, the maximum-likelihood placement of BA.2.13 as a descendant of sub-lineage BA.2.56. BA.2.13 has three substitutions that are not part of BA.2: C22916A (red, shared with BA.2.56 and BA.2.9), C22792T (purple, shared with BA.2.9) and A23767G (green). The inferred maximum-likelihood tree suggests that BA.2.13 evolved through a A23767G mutation within a BA.2.56 background already containing substitutions C22916A and C22792T. Bottom right, the distinct occurrence of A23767G and C22792T within the BA.2 lineage. SPRTA shows that lineage BA.2.13 might plausibly have evolved from an additional C22916A substitution within this BA.2 background already containing substitutions A23767G and C22792T, corresponding to a reversed ordering of the three substitutions that define BA.2.13.

We also found that, out of the 2,072,111 genomes in our tree, 3,036 had less than 95% total SPRTA support for being assigned to the Pango lineage onto which they are placed in the maximum-likelihood tree; 754 of these had less than 70% support. We illustrate these findings using sample ERR10476226. This sample has 6 possible placements in the tree, all with the same likelihood and SPRTA support (approximately 16.7%). This is owing to the incomplete consensus sequence of the sample, causing it to be potentially identical to six different genomes in the tree (Extended Data Fig. 9), and so possibly being considered a ‘rogue taxon’. Although the maximum-likelihood placement in the tree of this sample is in Pango lineage BQ.1, all five other placements are on lineage BQ.1.1, which can therefore be interpreted as the most probable lineage assignment for this sample.

Discussion

With the increasing use of genomic epidemiology, pandemic-scale phylogenetics is set to become an essential tool for pandemic preparedness and epidemiology in general. New approaches such as MAPLE[9] and UShER[8] can be used to infer the huge phylogenetic trees necessary at this scale, but there is considerable uncertainty inherent in these estimates due to complications including low divergence, recurrent mutations and errors, and incomplete genome sequences21,28,29.

Traditional methods to quantify and represent this uncertainty cannot be used with pandemic-scale datasets. To overcome this problem, we present a new approach, SPRTA. In addition to addressing the limitations of existing methods in terms of computational demand, SPRTA also offers a new mutational (or ‘lineage evolution’) interpretation of branch support scores that is particularly useful in genomic epidemiology, replacing the topological focus of previous approaches.

Owing to the excessive computational demand of applying Bayesian phylogenetic methods or Felsenstein’s bootstrap to large genome datasets, pandemic-scale phylogenies are usually taken at face value in downstream analyses such as inference of viral geographic spread[30], mutational patterns[22], lineage assignment[26], recombination[31] and variant fitness advantage[32]. Unchecked errors and uncertainty in phylogenetic trees can therefore propagate in these analyses and affect their accuracy and measures of uncertainty. Our approach allows us to efficiently distinguish between reliable and unreliable parts of an inferred phylogenetic tree, so that downstream analyses can focus on reliable phylogenetic signals or integrate over phylogenetic uncertainty.

Although SPRTA is currently implemented in MAPLE and optimized for applications to datasets with a low level of divergence, it would be possible to implement the same approach within any likelihood-based phylogenetic inference tool. Its mutationally focused scores could therefore be applied to any phylogenetic analysis, without constraints on the number of sequences considered, their divergence or their length. The only limitation to be considered is that at higher divergence, mutational histories conditional on a tree T become more uncertain, and so the mutation-based simulation benchmark that we used here might not be readily interpretable.

SPRTA could also be used to create a phylogenetic network based on a backbone maximum-likelihood phylogenetic tree that is extended to include additional branches with lower but substantial support. In this way, SPRTA could be used to efficiently summarize vast numbers of possible phylogenetic trees (see for example, ref. [33]). In future, this might serve as a foundation for developing an efficient and complementary approach to Bayesian phylogenetics, helping to account for tree uncertainty in applications such as phylodynamics[34].

In conclusion, SPRTA not only addresses a fundamental outstanding problem in genomic epidemiology, but also offers a new paradigm in evolutionary biology for the interpretation and representation of phylogenetic information and uncertainty.

Methods

SPRTA and aBayes

Given an estimated rooted phylogenetic tree T and data D in the form of a multiple sequence alignment, our aim is to assign confidence scores to branches b of T.

We take inspiration from the approximate Bayes (aBayes) approach[17]. aBayes assigns to b a probability score Pr(b∣D, T\b) based on the ratio of the likelihood Pr(D∣T) of the estimated binary tree T versus the likelihoods of the trees \({T}_{i}^{b}\) obtained using nearest neighbour interchange[36] (NNI) moves centred around b (which comprise \(T={T}_{1}^{b}\) itself in addition to two tree topologies not containing the clade in T descending from b):

$${\rm{aBayes}}(b)=\Pr (b| D,T\backslash b)=\frac{\Pr (D| T)}{{\sum }_{i=1}^{3}\Pr (D| {T}_{i}^{b})}.$$

(2)

These NNI moves perform small changes to T adjacent to branch b. One of the appeals of aBayes is that it can score not only T, but also the two alternative topologies considered, \({T}_{2}^{b}\) and \({T}_{3}^{b}\), not containing the clade defined by b:

$$\Pr ({T}_{j}^{b}| D,T\backslash b)=\frac{\Pr (D| {T}_{j}^{b})}{{\sum }_{i=1}^{3}\Pr (D| {T}_{i}^{b})}.$$

(3)

aBayes has a topological focus, with score aBayes(b) for branch b interpreted as the support for the existence of the clade of T containing all descendants of b. aBayes(b) is in effect an approximate Bayesian posterior score for this clade, where a flat tree prior is assumed. In contrast to typical Bayesian phylogenetics, however, instead of integrating over branch lengths we define \(\Pr (D| {T}_{i}^{b})\) as the maximum-likelihood score of topology \({T}_{i}^{b}\) over all possible branch lengths, and we only consider the alternative topologies obtainable through a single NNI move[17] starting from T. Although computationally much faster than Felsenstein’s bootstrap, aBayes can still be too demanding for large genomic epidemiological datasets if implemented within classical maximum-likelihood phylogenetic methods (for example, see Fig. 2). Also, aBayes is defined based only on NNI moves, an approach insufficiently comprehensive for pandemic-scale data[9]: owing to the existence of many phylogenetic topologies with similar likelihood[29], the two alternative topologies obtainable through NNI moves, \({T}_{2}^{b}\) and \({T}_{3}^{b}\), might represent only a very small subset of plausible alternative topologies not containing the clade defined by b.

Here we address these limitations and define a new measure of branch support, SPRTA, that is particularly useful in the context of large-scale genomic epidemiology, but is also applicable more generally in phylogenetics. First, to address the problem of computational demand, we consider trees estimated using methods suitable for pandemic-scale datasets, such as MAPLE[9]. In the following, we assume that the likelihood of alternative tree topologies is also calculated with MAPLE or a similar method.

We define the SPRTA support of branch b by considering a certain number Ib of possible alternative topologies \({T}_{i}^{b}\) (1 ⩽ i ⩽ Ib), obtained by performing single subtree prune and regraft[36] (SPR) moves that relocate Sb, the subtree of T containing all descendants of b, as a descendant of other parts of T (Fig. 1). Again, we assume for simplicity that \({T}_{1}^{b}=T\) corresponds to the null SPR move. Compared to NNI moves, SPR moves are much more numerous, and can cause long-range changes to the tree; as such, SPR moves create a much more comprehensive set of alternative evolutionary histories than NNI moves. For each branch b, the number of possible SPR moves is linear in the number of sequences in the considered dataset, which means if we do not select which of these SPR moves we focus on, Ib can become too large, and the calculation of SPRTA scores too computationally demanding. However, to obtain an accurate evaluation we only need to consider topologies that have non-negligible likelihood scores compared to T.

For this reason, we first perform an initial, approximate evaluation of alternative tree topologies obtained through SPR relocations of Sb using fixed branch lengths (we leave the length of the placement branch equal to the length of b, and we assess placements only halfway along branches). From this, we retain only topologies with an initial log-likelihood score difference from T within a threshold corresponding approximately to one extra mutation event (the natural logarithm of the genome length, which for SARS-CoV-2 is about 10.3). The likelihoods of all Ib topologies passing this threshold are then more deeply evaluated by optimizing branch lengths. This two-step approach is similar to the ‘baseball’ heuristic of the metagenomic query mapper pplacer[19]. More precisely, when we consider an alternative placement of Sb on branch \({b}^{^{\prime} }\), let us use \({n}^{^{\prime} }\) to denote the new node at the conjunction of \({b}^{^{\prime} }\) with Sb. For this placement, the three branches whose length we optimize are the one connecting \({n}^{^{\prime} }\) with Sb (length l_1), the one within \({b}^{^{\prime} }\) below \({n}^{^{\prime} }\) (length l_2), and the one within \({b}^{^{\prime} }\) above \({n}^{^{\prime} }\) (length l_3), similarly to the ‘lazy subtree rearrangement’ approach of RAxML[37] (see Extended Data Fig. 10a). \(\Pr (D| {T}_{i}^{b})\) is defined as the maximum likelihood obtained by optimizing these three branches, while leaving all other model parameters and branch lengths unaltered. This approximation of \(\Pr (D| {T}_{i}^{b})\) is the same one made by MAPLE, and is justified by the fact that, at the low levels of divergence typically considered in genomic epidemiology, changes in topology and branch lengths usually only affect nodes near those directly affected by the changes[9].

The likelihood score \(\Pr (D| {T}_{i}^{b})\) of an SPR move is calculated by MAPLE (up to a normalizing factor) by comparing the partial likelihoods at node B (the root of Sb), informed by sequence data within Sb, against the partial likelihoods at the new placement nodes Ai, informed by the sequence data within T\Sb (ref. [9]). The partial likelihoods at node B represent genetic sequence uncertainty for the ancestor represented by this node. In genomic epidemiology, due to dense sampling and short divergence, this uncertainty is often negligible, and these partial likelihoods will often support only one genome. Likewise, partial likelihoods at Ai will often also only support a single genome. In these cases, the score \(\Pr (D| {T}_{i}^{b})\) (up to a normalizing factor) is used as an approximation of the probability that the genome in B evolved from the genome in Ai. In case uncertainty of the ancestral genetic sequence at these nodes is not negligible, we consider all possible ancestral genomes, weighted according to their likelihood, and \(\Pr (D| {T}_{i}^{b})\) will approximate the probability that any of the possible genomes in B evolved from any of the possible genomes in Ai.

Due to the initial filtering of plausible alternative topologies, Ib depends on branch b. These topologies are the same ones typically considered during the final stage of standard tree search in MAPLE.

SPRTA defines the support probability of b as

$${\rm{SPRTA}}(b)=\Pr (b| D,T\backslash b)=\frac{\Pr (D| T)}{\sum _{1\leqslant i\leqslant {I}_{b}}\Pr (D| {T}_{i}^{b})}$$

(4)

where T \ b represents the two subtrees obtained by removing b from T. We can similarly define \(\Pr ({T}_{i}^{b}| D,T\backslash b)\) for the alternative placements \({T}_{i}^{b}\ne T\) (2 ⩽ i ⩽ Ib) for any subtree Sb considered. Note that SPRTA typically evaluates a much larger number of alternative topologies than aBayes (equation (2)): up to quadratic in the number of genomes analysed for SPRTA, but only linear for other local support measures such as aBayes or aLRT.

When evaluating possible SPR placements, placements resulting in the same topology are equivalent, and so we only consider them once. In particular, we take into account multifurcations caused by branches of length 0, and consider placements into points of the tree with a null distance between them (based on considering optimized placement-specific branch lengths as just described) as equivalent.

Because relative likelihood calculations of alternative topologies are performed as a standard component of tree inference in MAPLE, assessing SPRTA support probabilities adds negligible computational overhead.

MAPLE infers rooted phylogenetic trees, and as such we have defined and implemented SPRTA to assess rooted tree inference. The same principles could however also be applied to the evaluation of unrooted trees. In this case, to calculate the score of a branch b, we would not only consider SPR moves representing alternative placements of subtree Sb within T \ Sb, but also SPR moves representing alternative placements of T \ Sb within Sb, so increasing the number of SPR moves to be considered for each branch b, but leaving the rest of the approach unaltered.

Benchmarking of SPRTA support

Branch support measures are often used to assess the expected accuracy of phylogenetic inference. However, how do we assess the accuracy of these measures of accuracy? We approach this problem using simulations, for which we have a ground truth against which to compare estimates. First, we simulate a tree and a set of genomes (Extended Data Fig. 10b); second, we estimate a tree from these simulated genomes (Extended Data Fig. 10c). Some branches of the inferred tree will be correct and some will be wrong: therefore, third, we assess branch support measures according to their ability to give higher support scores to correctly estimated branches, and lower support scores to wrongly estimated branches (Extended Data Fig. 10d).

We benchmark branch support methods on simulated SARS-CoV-2 genome data (Methods, 'Simulated genomes’) using our ‘mutational’ focus rather than a traditional ‘topological’ focus. We define phylogenetic correctness in terms of the genome evolutionary history implied by a phylogenetic tree. From each individual simulated dataset (see graphical example in Extended Data Fig. 10b), we first infer in MAPLE a maximum-likelihood phylogenetic tree T and mutation events by marginal posterior mutation mapping[38] conditional on T (Extended Data Fig. 10c). Only mutation events inferred with  >0.5 probability by MAPLE are considered here. We define estimated mutation events as pairs (G, m) where G is a whole genome sequence and m = (n_1, p, n_2) is a single-nucleotide substitution at position p of G from nucleotide n_1 (contained in G at position p) to nucleotide n_2 (Extended Data Fig. 10d). An inferred mutation event (G, m) is considered correct if it is also present in the simulated tree, otherwise it is considered as an estimation error (Extended Data Fig. 10d). Finally, we assign the support score of branch b (estimated by SPRTA or any other method) to all the mutations inferred to have occurred on b. We consider a branch support measure as more accurate if in our simulations it assigns higher support to correctly estimated mutation events, and lower support to erroneously inferred ones.

Evaluating the accuracy of SPRTA using inferred mutations might seem counter-intuitive, since our definition of SPRTA scores does not consider explicit mutational histories. However, by analysing the levels of support for different placements of subtree Sb associated with branch b, different possible mutation histories immediately ancestral to Sb are implicitly considered and evaluated via the likelihood of alternative subtree placements. We thus interpret SPRTA scores as the support for the hypothesis that the profile B at the lower end of b evolved from profile A at the upper end of b through mutations on branch b (see ‘Accuracy’).

In the scenario of short branches considered here, there is extremely low uncertainty in most mutation events and ancestral genomes implied by a given topology, and so we can often interpret the correctness of the mutational history inferred on b as the correctness of b itself. This does not mean that in this case b will be topologically correct—for example, the placement of rogue taxa within or outside the subtree Sb defined by b can make b topologically uncertain without causing uncertainty in the inferred mutational history or the placement of Sb.

Implementation and usage of SPRTA

We ran SPRTA as implemented within MAPLE v.0.6.8 (https://github.com/NicolaDM/MAPLE). While SPRTA can be run in MAPLE at the same time as tree inference with minimal additional computational cost (Methods ‘SPRTA and aBayes’), to aid comparability of computational performance with other approaches here we have considered its use to assess a pre-estimated input phylogenetic tree provided with the option –inputTree. We used options –numTopologyImprovements 0 –doNotImproveTopology to perform a shallow SPR search in MAPLE. We also used options –model UNREST –rateVariation to use an UNREST model[39] with rate variation[21], and option –estimateMAT to infer mutation events.

Other branch support methods

All other branch support measures considered here were calculated using IQ-TREE v.2.1.3[40] with options –seqtype DNA –seed 1 -m GTR+F+G4 –quiet -nt 1. As with SPRTA, we always use the tree estimated by MAPLE as a starting tree (supplied via the option -t) since on these datasets IQ-TREE will typically not converge to a tree with likelihood as high as MAPLE[9]. We used the following additional IQ-TREE options:

- -B 1000 (1,000 bootstrap replicates) for UFBoot2[7]

- –fast -b 100 (100 bootstrap replicates and fast tree search) for Felsenstein’s bootstrap[2]

- –fast -b 100 –tbe (100 bootstrap replicates and fast tree search) for TBE[10]

- –fast –alrt 1000 (1,000 bootstrap replicates and fast tree search) for aLRT-SH[16]

- –fast –alrt 0 (fast tree search) for aLRT[15]

- –fast –abayes (fast tree search) for aBayes[17]

- –fast –lbp 1000 (1,000 bootstrap replicates and fast tree search) for LBP[14].

The number of bootstrap replicates has very limited impact on the computational demand of UFBoot2 and LBP, hence these were set to 1,000 for reduced stochasticity with minimal computational cost.

SARS-CoV-2 genome datasets

Viridian genome dataset

We applied SPRTA to a SARS-CoV-2 dataset containing 2,072,111 genomes collected up to February 2023. The consensus sequences of these genomes were consistently called with Viridian, a tool that prevents common reference biases in genomic regions of low sequencing coverage[23]. Furthermore, we filtered out potentially contaminated samples, and masked alignment columns affected by recurrent sequence errors[21]. We estimated a phylogenetic tree using MAPLE v.0.6.8 with an UNREST substitution model, rate variation, and deep SPR phylogenetic search. For a full description of data preparation and phylogenetic inference, see ref. [21]. We ran SPRTA on this alignment and tree using MAPLE v.0.6.9, with the options described in ‘Implementation and usage of SPRTA’, and additionally with option –supportFor0Branches to evaluate the support of all genome placements, even those not involving mutations (see ‘Uncertainty of SARS-CoV-2 evolution’).

Simulated genomes

For benchmarking, we simulated SARS-CoV-2 genomes evolving along a known (‘true’) background phylogeny. The background tree we used was the publicly available 26 October 2021 global SARS-CoV-2 phylogenetic tree from http://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/UShER_SARS-CoV-2/[41] representing the evolutionary relationship of 2,250,054 SARS-CoV-2 genomes, as inferred using UShER[8].

We used phastSim v.0.0.3[42] with options –treeFile public-latest.all.nwk –scale 0.00003344 –reference MN908947.3.fasta –alpha 0.2 –createNewick to simulate sequence evolution along this tree according to SARS-CoV-2 non-stationary neutral mutation rates[22], using the SARS-CoV-2 Wuhan-Hu-1 genome[43] as root sequence, and with gamma-distributed (α = 0.2) rate variation[44] (similar to that estimated from real data[21]). These simulations of complete genomes were used for Figs. 2 and 3, and Extended Data Figs. 1e, 2 and 3.

We also created a second set of simulations mimicking the distribution of genome incompleteness from real data as in ref. [9]. In each simulated sequence we included N and gap (–) characters copied in number and location from a randomly selected paired sequence from the real SARS-CoV-2 genome dataset considered in ref. [9]. This step simulates the distribution of missing sequence data due to low sequencing depth at certain specific genome regions. Additionally, in each simulated sequence we masked a number of randomly selected SNPs (differences with respect to the reference genome) equal in number to the isolated ambiguous characters in the paired randomly sampled real sequence. This additional step mimics the pattern caused by mixed infections and contamination, in which phylogenetically informative positions are selectively masked in consensus genomes due to within-sample heterozygosity (see ref. [9] for more detail). This second set of simulations was used to create Extended Data Figs. 1a–d,f, 4 and 5.

Assessing the impact on mutation rates

To assess the impact of phylogenetic uncertainty on estimates of mutation patterns, we mimicked typical studies of mutation rate inference in SARS-CoV-222,45,46. We consider real SARS-CoV-2 data and the corresponding inferred tree and mutation events as described in ‘Viridian genome dataset’. We then created three datasets: one containing all inferred mutations, one containing only mutations on branches with at least 50% SPRTA support (representing a mildly conservative approach, discarding highly uncertain mutations), and one with only mutations on branches with at least 90% SPRTA support (representing a highly conservative approach, removing any moderately uncertain mutation).

Equilibrium frequencies (Extended Data Fig. 6a) represent the equilibrium nucleotide distribution of the Markov chain defined by the genome-wide mutation rate matrix[47] inferred from the mutation counts as

$${q}_{ij}\propto \frac{{n}_{ij}}{{{\rm{\pi }}}_{i}},$$

(5)

where nij is the count of mutations from nucleotide i to nucleotide j and πi is the frequency of nucleotide i in the reference genome.

We assess the impact of phylogenetic uncertainty on site-specific mutation counts by calculating, for every genome position, the ratio of the most conservative mutation counts (those on branches with at least 90% support) to the least conservative mutation counts (those on all branches) (Extended Data Fig. 6b). To avoid high variance in values of this ratio at sites with low numbers of substitutions, only sites with at least 50 mutations of the given type over all branches were included.

Assessing the impact on Pango lineages

To assess the impact of phylogenetic uncertainty on the definition and the inference of the origin of Pango lineages, we mapped 1,542 Pango lineage consensus genomes (as of 28 February 2023; https://github.com/corneliusroemer/pango-sequences) onto our SARS-CoV-2 phylogenetic tree (‘Viridian genome dataset’) using MAPLE v.0.7.3. This mapping associates Pango lineages with nodes in our tree. Similarly to our SARS-CoV-2 alignment, we masked regions of recurrent sequence errors from these consensus genomes (see ref. [21]). Of these genomes, 1,127 mapped onto our tree with ≤1 mutation separating them from the tree, and with ≥95% SPRTA placement support score. We discarded consensus genomes that were further removed (>1 mutation separating them from the tree), as they typically represent more-recent lineages that did not exist at the time our dataset was collected. Consensus genomes with low SPRTA placement support were discarded since they could not be uniquely associated with a single node in our tree (this uncertainty can be caused, for example, by incomplete genome sequences in our dataset). In total, these 1,127 genomes mapped onto 1,117 distinct tree branches, and represent the Pango lineages that we can place on the tree with high confidence.

We used these 1,127 consensus genome placements to assign a Pango lineage to each branch and sample in our tree: each was assigned to the lineage represented by the closest ancestral consensus genome placement (the first one met moving from the considered node towards the tree root). We used this assignment of Pango lineages to assess uncertainty in the lineage assignment of the genomes in our dataset. For each of the 2,072,111 genomes in our alignment, we considered the SPRTA scores of their current and alternative placements. To each considered placement, we assigned the Pango lineage of the placement branch (alternative placements on the same branch as the placement of a Pango consensus genome were ignored).

Reporting summary

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

Data availability

The real data alignment, metadata, inferred tree and SPRTA support scores are available on Zenodo (https://doi.org/10.5281/zenodo.14974813)[24]. The tree used as input in our simulations was downloaded from http://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/UShER_SARS-CoV-2/. Pango lineage consensus sequences were downloaded from https://github.com/corneliusroemer/pango-sequences.

Code availability

We ran SPRTA as implemented within MAPLE v.0.6.8, v.0.6.9 and v.0.7.3 (https://github.com/NicolaDM/MAPLE), but we also provide an implementation within CMAPLE[48] which is incorporated into IQ-TREE v.3[49]. All other branch support methods considered were run as implemented within IQ-TREE v.2.1.3[40]. Code for the simulation-based method benchmark analysis is available at https://github.com/NicolaDM/MAPLE/blob/main/scripts/benchmarking_branchSupport.py.

References

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Acknowledgements

N.D.M., N.G. and S.M. were supported by the European Molecular Biology Laboratory and by MRC grant MR/Z503769/1. N.L.-T. and B.Q.M. were supported by a Chan Zuckerberg Initiative grant (EOSS4-0000000312) for Essential Open Source Software for Science. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors thank Z. Iqbal, M. Hunt and all groups that publicly shared SARS-CoV-2 sequencing data that made the analyses in this manuscript possible.

Funding

Open access funding provided by European Molecular Biology Laboratory (EMBL).

Author information

Authors and Affiliations

1. European Bioinformatics Institute, European Molecular Biology Laboratory, Hinxton, UK

Nicola De Maio, Samuel Martin & Nick Goldman

2. School of Computing, College of Systems and Society, Australian National University, Canberra, Australian Capital Territory, Australia

Nhan Ly-Trong & Bui Quang Minh

Contributions

N.D.M. conceived and implemented the methods in MAPLE, performed simulation tests, benchmarks and real-data phylogenetic uncertainty analyses, and wrote the manuscript. N.L.-T. performed the analysis on the impact of phylogenetic uncertainty on Pango lineages and implemented SPRTA in CMAPLE. S.M. performed the analysis on the impact of phylogenetic uncertainty on inferred mutation rates. B.Q.M., N.L.-T. and S.M. provided support during the analyses, methods implementation and drafting of the manuscript. N.G. supervised the work and wrote the manuscript.

Corresponding author

Correspondence to Nicola De Maio.

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Nature thanks Frederick Matsen 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 Other simulation results.

A Time and B maximum RAM usage of different branch support methods on simulated incomplete genomes. In A, the lines for FB and TBE coincide very closely, as do those for the pairs LBP and aLRT-SH, and aBayes and aLRT. In B, all methods other than UFBoot and SPRTA overlap. C Mean support of correctly inferred mutations within each replicate in simulations with incomplete genomes. The lines for aBayes and aLRT nearly coincide, as do those for FB and aLRT-SH but with FB unable to be run for more than 2,000 genomes. D Mean support of wrongly inferred mutations within each replicate in simulations with incomplete genomes. E Difference between the mean support of correctly inferred mutation events (Fig. 3A), and the mean support of wrongly inferred mutation events (Fig. 3B) in simulations with complete genomes. F Same as E but in simulations with incomplete genomes. Other details in A–F are as in Figs. 2 and 3. G Cumulative distributions of SPRTA support scores of mutations in real SARS-CoV-2 data (blue, see Section 4.5.1), simulated data with incomplete sequences (red, see Section 4.5.2), and simulated data with complete sequences (green, see Section 4.5.2). For the simulated data distributions we merged the mutation support scores for all 20 replicates of the largest (200,000 sequence) simulations into a single distribution.

Extended Data Fig. 2 Receiver operating characteristic (ROC) curves.

ROC curves for all considered methods on simulated data, from (A) 1,000 to (H) 200,000 samples. On the Y-axis we show the true positive rate of branch support scores when used to distinguish between correct and wrong mutation events. On the X-axis is the false positive rate. For each curve we combine all branches of all replicates of the same size. In the legend, next to the name of each method that could be run for this data size, we show the area under the curve (AUROC). Worse than random performance of some methods (e.g. FB and TBE) can be attributed to the fact that these support scores measure the confidence in the clade defined by the considered branch, and not in the mutation events implied by that branch (compare to Extended Data Fig. 1E).

Extended Data Fig. 3 Precision-recall curves (PRC).

Precision-recall curves for all considered methods on simulated data. On the Y-axis we show the precision of branch support scores when used to distinguish between correct and wrong mutation events. On the X-axis is the recall rate. Legends indicate methods that could be run for each data size and areas under their curves (AUPRC). Other details are as in Extended Data Fig. 2.

Extended Data Fig. 4 Receiver operating characteristic (ROC) curves for simulations with incomplete genomes.

ROC curves for all considered methods on simulated data, from (A) 1,000 to (H) 200,000 samples, on simulations with incomplete genomes. Other details are as in Extended Data Fig. 2.

Extended Data Fig. 5 Precision-recall curves (PRC) on simulated incomplete genomes.

Precision-recall curves for all considered methods on simulated incomplete genomes. Other details are as in Extended Data Fig. 3.

Extended Data Fig. 6 Impact of phylogenetic uncertainty on the inference of substitution rates.

A (upper) normalized substitution counts and (lower) equilibrium base frequencies calculated from inferred rates over (left) all branches, (middle) branches with > 50% support, and (right) branches with > 90% support. Note that there are only very minor differences between the results based on mutations on branches at the three different support levels. B Top: ratios, along the SARS-CoV-2 genome, of site-specific A to G mutation counts on branches with > 90% SPRTA support vs. the same counts from all branches. A lower ratio means that more inferred A to G mutations at the considered site occur on phylogenetically uncertain branches. To avoid extreme ratios due to high variance at low counts, we only include sites with at least 50 substitutions of the considered type across all branches. Second, third and fourth rows: same for G to A, C to T, and T to C substitutions, respectively. Sites that have been most impacted by uncertainty are annotated with position and count information. Note that points are drawn with size inversely proportional to Y-axis values.

Extended Data Fig. 7 Complex mutational patterns at position 7926 of the SARS-CoV-2 genome.

Far left: SARS-CoV-2 phylogeny as in Fig. 4, showing C to T substitutions at position 7926 as yellow circles (on branches with > 90% SPRTA support, n = 617) and red circles (on branches with < 90% SPRTA support, n = 716). Center left: Zoom-in to Omicron lineage BA.1.1, with n = 178 C to T substitutions on branches with > 90% SPRTA support, and n = 661 C to T substitutions on branches with < 90% SPRTA support. Far right and center right: The same phylogeny and zoom-in, now showing T to C reversions at the same position (full tree has 22 yellow circles and 43 red; zoom-in has 18 yellow circles and 42 red). The right-hand tree and zoom-in are in reverse orientation for ease of comparison. Low support substitutions (red circles) are prevalent in the zoom-in, showing elevated evolutionary uncertainty in this lineage and genome position.

Extended Data Fig. 8 Complex mutational patterns at position 21595 of the SARS-CoV-2 genome.

Far left and left: SARS-CoV-2 phylogeny as in Fig. 4 and zoom-in to Omicron lineage BA.1.1. Yellow and red circles are as in Fig. 7, this time showing C to T substitutions at position 21595 (874 yellow and 1129 red on the full tree; 429 yellow and 1024 red on the zoom-in). Far right and center right: The same phylogeny and zoom-in, now showing T to C reversions at the same position (full tree has 75 yellow circles and 180 red; zoom-in has 71 yellow circles and 175 red). Low support substitutions (red circles) are prevalent in the zoom-in, showing elevated evolutionary uncertainty in this lineage and genome position.

Extended Data Fig. 9 Uncertain placement and lineage assignment of sample ERR10476226.

To left is the global SARS-CoV-2 tree as in Fig. 4. To right we zoom-in on the location in lineage BQ.1 of sample ERR10476226 (highlighted inside the red circle), and the five alternative placements of this sample (with blue circles, two of which overlap fully, highlighting the tree nodes descending from the branch of each alternative placement), all with the same likelihood and SPRTA score (≈ 16.7%) as the placement in the maximum likelihood tree. In red text after the sample name we list the regions of the consensus sequence that contain “N” characters (i.e. that are not informative). These positions overlap with the mutations (blue text) on branches of alternative placements of the sample. In particular, mutation G22599C separates BQ.1 and its descendant lineage BQ.1.1, and the “N” of the sample sequence at this position is the reason why it could be assigned to either lineage BQ.1 or BQ.1.1.

Extended Data Fig. 10 Graphical representation of branch length optimization and branch support assessment.

A Given a branch b (highlighted in red), we show an example re-placement (dotted shaded blue arrow) of its subtree Sb (black triangle). \({n}^{^{\prime} }\) is the node at which Sb is re-attached, within branch \({b}^{^{\prime} }\) (green). To evaluate this SPR move, we optimize the branch lengths l_1, l_2 and l_3 in the figure, while the lengths of the remaining branches in the tree are kept constant. Other details are as in Fig. 1. B–D To assess SPRTA in our simulation-based benchmark, we consider support values given to correctly and wrongly inferred mutational events. Here we give a graphical example. B Part of an example true, simulated tree (the dashed branch represents the remainder of the tree) annotated with simulated ancestral genome sequences. For simplicity, we consider a genome of length 4. C Example tree estimated from genomes in B, annotated with inferred ancestral genomes. D Mutation events inferred in C. The first three mutation events are also present in B, and so are classified as correctly inferred. The final mutation event, absent in B, is instead classified as wrongly inferred. We evaluate our branch support method based on the score given to branches with correctly inferred mutations (where higher scores are considered better) vs. branches with wrongly inferred mutations (lower scores considered better).

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De Maio, N., Ly-Trong, N., Martin, S. et al. Assessing phylogenetic confidence at pandemic scales. Nature 647, 472–478 (2025). https://doi.org/10.1038/s41586-025-09567-x

- Received: 04 November 2024

- Accepted: 22 August 2025

- Published: 05 November 2025

- Version of record: 05 November 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09567-x

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Ultrafast Sample placement on Existing tRees (UShER) enables real-time phylogenetics for the SARS-CoV-2 pandemic

Article 10 May 2021

Maximum likelihood pandemic-scale phylogenetics

Article Open access 10 April 2023

Pandemic-scale phylogenomics reveals the SARS-CoV-2 recombination landscape

Article Open access 11 August 2022

Article Open access Published: 01 October 2025

A new paradigm for outer membrane protein biogenesis in the Bacteroidota

Xiaolong Liu,

Luis Orenday Tapia,

Justin C. Deme,

Susan M. Lea &

…

Ben C. Berks

Nature volume 647, pages 479–487 (2025)

Abstract

In Gram-negative bacteria, the outer membrane is the first line of defence against antimicrobial agents and immunological attacks[1]. A key part of outer membrane biogenesis is the insertion of outer membrane proteins by the β-barrel-assembly machinery (BAM)2,3,4. Here we report the cryo-electron microscopy structure of a BAM complex isolated from Flavobacterium johnsoniae, a member of the Bacteroidota, a phylum that includes key human commensals and major anaerobic pathogens. This BAM complex is extensively modified from the canonical Escherichia coli system and includes an extracellular canopy that overhangs the substrate folding site and a subunit that inserts into the BAM pore. The novel BamG and BamH subunits that are involved in forming the extracellular canopy are required for BAM function and are conserved across the Bacteroidota, suggesting that they form an essential extension to the canonical BAM core in this phylum. For BamH, isolation of a suppressor mutation enables the separation of its essential and non-essential functions. The need for a highly remodelled and enhanced BAM complex reflects the unusually complex membrane proteins found in the outer membrane of the Bacteroidota.

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Main

The well-characterized E. coli BAM complex (BAMEc) is composed of the outer membrane protein (OMP) BamA together with four periplasmic lipoprotein subunits[5]. Only BamA and the lipoprotein BamD are individually essential for BAM function, and the roles of the remaining subunits remain poorly defined[2]. BamA is a 16-stranded OMP[2] that is related to the central subunit of the machinery that inserts β-barrel proteins into the mitochondrial outer membrane (OM)[6]. The BamA barrel has a periplasmic extension composed of five polypeptide transport-associated (POTRA) domains to which the lipoprotein subunits bind7,8,9. Within the BamA barrel the seam between the first and last strands is unusually short and can open7,9, exposing the N-terminal strand of the BamA barrel to pair with the C-terminal strand of an incoming substrate OMP10,11. This structure in turn acts as a template for insertion and folding of successive strands of the nascent OMP through β-augmentation. The result is the formation of a hybrid barrel between BamA and the client OMP that is resolved when the substrate barrel is completed and closes to release it from BamA3,4.

The Bacteroidota are a phylum of abundant Gram-negative commensals found in the human gut and other human microbiomes[12] that includes major opportunistic anaerobic pathogens that are responsible for sepsis (for example, Prevotella species and Bacteroides fragilis) and severe dental disease (Porphyromonas gingivalis). OM proteins in the Bacteroidota exhibit considerably greater structural diversity than the OM proteome of E. coli, raising the possibility that the Bacteroidota BAM machinery might be functionally augmented relative to BAMEc. Bacteroidota OMPs commonly possess much larger extracellular domains than E. coli OMPs13,14,15 and Bacteroidota BAM must be capable of assembling these. Furthermore, and unlike E. coli, the Bacteroidota possess abundant cell surface lipoproteins (SLPs), which the BAM complex has been speculated to export13,16. Notably, both of these biosynthetic requirements are involved in the assembly of starch utilization system (SUS) nutrient uptake systems, a characteristic and highly abundant feature of the Bacteroidota OM, which consist of a SLP (SusD) and an OMP with large extracellular regions (SusC)13,17. A further intriguing aspect of Bacteroidota BAM is a possible functional connection with the Bacteroidota-specific type 9 secretion system (T9SS)[18] which has two essential components encoded at the bamA locus[19].

To investigate the nature of the Bacteroidota BAM system we isolated and characterized the BAM complex from the T9SS-possessing bacterium F. johnsoniae.

F. johnsoniae BAM complex structure

We isolated the native F. johnsoniae BAM complex (BAMFj) using an affinity tag fused to BamA (Fjoh_1690). Biochemical (Fig. 1a) and structural (Fig. 1b Extended Data Figs. 1 and 2a and Extended Data Table 1) analysis of the BAMFj complex revealed that it contains five proteins in addition to BamA, one of which could be assigned as BamD (Fjoh_3469). The remaining co-purifying proteins were unrelated to known BAM subunits from other organisms, and did not include T9SS components. We named these novel BAMFj subunits BamG (Fjoh_1412), BamH (Fjoh_0823), BamM (metal ion-containing; Fjoh_0050) and BamP (periplasmic; Fjoh_1771). Smaller BamA-containing complexes present in the sample appear to be fragmentation products (Extended Data Fig. 1).

Fig. 1: Structure of the BAMFj complex.

a, Size-exclusion chromatography profile of the purified BAMFj preparation and Coomassie-stained SDS–PAGE gel of the indicated fractions. Bands were identified by peptide fingerprinting. Fraction A was used to determine the structure of the full BAMFj complex and fraction B was used for the structure of the BamAP complex. Similar data were obtained for three independent preparations. b, Cryo-electron microscopy (cryo-EM) volume for the BAMFj complex overlaid on the hybrid model shown in d. The volume is shown at a high contour level (coloured) and at a low contour level (semi-transparent). c,d, Comparison of the most similar E. coli BAM complex structure (darobactin 9-bound complex; PDB: 8ADI) (c) with the BAMFj complex (d). Structures are shown in cartoon representation with lipids and metal ions in space-filling atom representation coloured by element. For BAMFj, the poorly resolved BamA POTRA 1–3 domains and BamP C-terminal domain are modelled by placing AlphaFold[20] structures in the electron microscopy density (lighter coloured domains). e–i, The BAMFj hybrid model (Supplementary Data 1) with protein components in space filling representation and lipids shown as atom spheres coloured by element. e, View in the same orientation as d, left. f, The N-acyl and S-diacylglyceryl groups attached to the N-terminal cysteine of BamH. g, The resolved portion of a lipopolysaccharide (LPS) molecule in the outer leaflet of the OM and two ordered phospholipid molecules on the inner leaflet of the OM. h, View from the periplasm with the periplasmic side of the complex cut away to the membrane midpoint. i, View from the exterior with the extracellular side of the complex cut away to the membrane midpoint.

Source data

As in BAMEc, BamA forms the core of BAMFj, to which the other subunits are directly or indirectly attached (Fig. 1c). However, whereas all the accessory subunits of BAMEc are located in the periplasm (Fig. 1c), BAMFj has a remarkably different organization in which only BamD and BamP are periplasmic or part periplasmic proteins (Fig. 1b,d,e). Uniquely, the BamG subunit is a transmembrane OMP, whereas BamH and BamM are SLPs that together form an extensive extracellular structure. BamG is bound to the ‘rear’ of the BamA barrel relative to the lateral seam. The interaction between BamA and BamG is reinforced by lipid binding on either side of the protein interface. On one side, these interactions are provided by the phospholipid tail of BamH (Fig. 1f) and on the other, they are provided by the ordered lipid portion of a lipopolysaccharide molecule in the outer leaflet of the membrane and two ordered phospholipid molecules in the inner leaflet (Fig. 1g). BamH and BamM interact with each other to form a long canopy structure on the extracellular side of the OM that extends from the rear of BamA across the BamA barrel and out beyond the lateral seam to cover the position in the membrane where client OMPs assemble on BamA (Fig. 1b,d,e,h). The canopy is positioned at an approximately constant height of 40 Å above the inferred position of the membrane bilayer and delineates an approximately 3,000 Å^3 space above the membrane surface. The canopy is anchored to the BAMFj complex through binding to extracellular ‘pillars’ provided by the BamA and BamG subunits. At the periplasmic side of the membrane the folded domains of the novel BamP subunit are bound to BamD and to the POTRA domains of BamA. These domains are linked by a loop that enters the interior of the BamA barrel and exits at the periplasmic end of the lateral seam (Fig. 1b,d,e,h,i). The more membrane-distal POTRA 1–3 domains of BamA, together with the C-terminal portion of BamP, are poorly resolved in the structure and are modelled in all figures by placement of AlphaFold[20] structures into the electron microscopy map (Fig. 1d and Supplementary Data 1).

In structurally characterized BAM complexes, the lateral seam of BamA has been observed to be either open or closed3,4. In our BAMFj structure, BamA is in the closed state with the lateral seam sealed by a two-residue overlap between the N- and C-terminal strands (Fig. 1d,e). Multiple interstrand loops of the F. johnsoniae BamA barrel are extended relative to the canonical E. coli protein (Fig. 2a,b). First, 15 additional residues in periplasmic turn 1 (T1) form a short amphipathic structure along the periplasmic face of the OM that extends away from the BamA barrel. Second, 76 additional residues in extracellular loop 5 (L5) fold into a β-sheet domain that provides the binding platform for BamM. Finally, as in other BamA proteins, extracellular loop 6 (L6) enters the barrel lumen, where it contacts the barrel wall through a conserved VRGF/Y motif8,21,22 (the actual sequence being L^779RGY^782 in BamAFj). However, in BamAFj an additional 16 residues form a β-strand-containing loop that extends across and fills the extracellular end of the pore, notably contacting the most deeply inserted piece of BamP. AlphaFold 3 modelling[23] indicates that all three of these extended loop structures are highly conserved across Bacteroidota BamA proteins, although only the proteins from Flavobacteria include the BamM-binding domain at the tip of L5 (Supplementary Fig. 1).

Fig. 2: Structural features of the BAMFj subunits.

a,b, Comparison of the F. johnsoniae (a) and E. coli (PDB: 8ADI) (b) BamA barrels. The strands closest to the viewer have been removed, revealing BamP within the F. johnsoniae barrel. The structure in a also highlights the hydrogen-bonding interaction between the side chain of Gln801 (substituted in the bamH suppressor mutant) and the main chain of Gly591 (both in ball and stick representation). c, Cartoon representation of BamP (orange). The C-terminal domain (pale yellow) is an AlphaFold model docked into the electron microscopy density. Portions of the BamA barrel (blue) are shown for orientation. d, Sequence conservation and intra-chain interactions of the inter-domain loop of BamP (cartoon with ball and stick side chains) within the BamA barrel (surface representation). Min, minimum; max, maximum. e,f, Superimposition of BamG (chainbows colouring) and E. coli FadL (grey; PDB: 3DWO). A proposed substrate-mimicking C_8E_9 detergent molecule in FadL is shown in grey spheres. In f, the front walls of the barrels, oriented as in e, left, are cut away and the N-terminal amino acid of FadL together with the equivalent sequence position residue in BamG are shown as spheres. g, View from outside the cell showing how the N-terminal region of BamH is bound by BamG. BamG is in surface representation with the N tail (residues 1–32) coloured blue. Partial structures of BamA and BamH are shown in cartoon representation with the N-terminal cysteine of BamH and its attached lipid groups shown as atomic spheres and coloured by atom. h, The BAMFj extracellular canopy viewed from BamA. Bound calcium ions and their coordinating side chains in BamM and glycosylation of BamH are shown in ball and stick representation. i, Surface conservation (left) and electrostatics (right, kcal (mol.e)–1 at 298 K) of the extracellular canopy in the same orientation as in h.

The novel BamP subunit has a tripartite structure in which the N-terminal and C-terminal structured domains are joined by an extended linker (Fig. 2c). The N-terminal domain binds to POTRA 4 and POTRA 5 of BamA (Figs. 1d,e and 2c). The linker then extends up into the BamA barrel, which it penetrates as far as loop L6 (Fig. 2a) while making conserved contacts with the interior of the barrel (Fig. 2d), then exits the open periplasmic end of the lateral seam running back into the periplasm (Figs. 1d,e and 2a,c). BamP ends in a three-helix C-terminal domain that is sandwiched between, and thus links, BamA POTRA 1 and BamD (Fig. 1d,e,i).

BamG is a member of the FadL family of 14-stranded OMPs, which are characterized by a lateral opening in the transmembrane barrel and a long N-terminal tail that threads through the barrel pore to reach the extracellular side of the membrane[24] (Fig. 2e,f). Canonical FadL proteins function as transporters for hydrophobic molecules. In these proteins, the lateral opening acts as a conduit to move hydrophobic substrate molecules between the protein interior and the membrane bilayer[25]. However, in BamG the N-terminal tail is extended and threads through the lateral opening with the N-terminal residue of the tail touching BamA (Fig. 2e–g). Many additional contacts between BamG and BamA are present and span the entire width of the bilayer. BamG also makes limited contact with BamD through the final three amino acids of its C-tail (Fig. 1d, right). BamG is O-glycosylated on the periplasmic portion of the N tail.

The extracellular portions of BamG anchor BamH to the BAMFj complex through extensive contacts. Strands 3 to 7 of the BamG barrel extend into the extracellular space to form the pillar onto which the proximal folded end of BamH docks (Fig. 2e–g). The lipidated N-terminal tail of BamH extends around the pillar in a deep groove on the BamG surface before exiting towards BamA (Fig. 2g), packing the three acyl chains between the BamA and BamG barrels (Figs. 1g and 2g).

BamH and BamM are both elongated two-domain proteins (Fig. 2h). The BamG-proximal end of BamH adopts a chondroitin sulfate-binding carbohydrate binding fold and is O-glycosylated facing BamA (Extended Data Fig. 2b,d). The N-terminal domain of BamM has a peptidyl-prolyl isomerase (PPI) fold (Extended Data Fig. 2c), whereas the C-terminal domain contains no well-defined secondary structural elements but is structured in part by the presence of seven metal ions (Fig. 2h and Extended Data Fig. 2e) assigned by their co-ordination chemistry as calcium ions. The phospholipid tail of BamM is not resolved. However, the N terminus of BamM is positioned to allow it to insert in the OM (Fig. 1b,d,e).

BamH and BamM pack along their long axes, where each interdigitates a β-hairpin into the other protein (Fig. 2h). The membrane-proximal side of the BamHM unit is likely to face substrate proteins and has a deep central valley (Fig. 2i). This surface is hydrophilic and highly acidic in the BamM portion, and shows little amino acid conservation (Fig. 2i), suggesting that it does not make highly specific interactions with substrates.

Subunit conservation and essentiality

BamA, BamD, BamG and BamH are universally conserved across the Bacteroidota, whereas homologues of the full-length BamM protein are only found in the genus Flavobacterium and detectable homologues of BamP are restricted to the family Flavobacteriaceae (Extended Data Table 2 and Supplementary Table 1). This suggests that BamADGH constitute the core of the Bacteroidota BAM system. The BamG and BamH subunits are also conserved across six of the seven phyla that together with the Bacteroidota comprise the wider Fibrobacterota–Chlorobiota–Bacteroidota (FCB) superphylum (Extended Data Table 2), indicating that these phyla also possess a Bacteroidota-like BAM complex.

F. johnsoniae possesses homologues of BamG (BamG2), BamH (BamH2) and three additional homologues of BamP (BamP2, BamP3 and PamP4) (Extended Data Fig. 3a and Extended Data Table 1). With the exception of BamP4, none of these homologues is expressed at an appreciable level in cells cultured on rich medium[26]. Pull-down experiments confirm that BamP4 binds to BAMFj in vivo (Extended Data Fig. 3b–d).

We were unable to delete the genes for BamG or BamH (or in control experiments BamA and BamD) (Extended Data Fig. 4a), suggesting that these core BAMFj proteins are essential (confirmed below). By contrast, the genes encoding the accessory proteins BamM and BamP, as well as the various BAMFj subunit homologues could all be deleted (Extended Data Fig. 4a,b). Strains lacking either BamM or BamP, or all BamP homologues, exhibit no defects in growth on rich medium (Extended Data Fig. 4c) or on carbon sources (galactomannan or xyloglucan) that require SusCD systems to metabolize[27] (Extended Data Fig. 4d), or in the T9SS-dependent process of gliding motility[28] (Extended Data Fig. 4e). They also show no defect in the canonical BAM function of OMP insertion as assessed through standard chemical challenges for loss of OM integrity[5] (Extended Data Fig. 4f,g), with the exception that loss of BamP4 results in a modest increase in sensitivity to vancomycin, which can be reversed by BamP overexpression, showing a functional equivalence between BamP4 and BamP (Extended Data Fig. 4h).

Structural consequences of BamP removal

The central loop of BamP is bound at the lateral seam of BamA in a way that would sterically impede hybrid barrel formation with the substrate protein. This suggests that our BAMFj structure represents an inhibited or inactive state and that for catalysis to occur the BamP loop must be displaced. In an attempt to mimic the loop-displaced state we deleted the BamP subunit and structurally characterized the resulting BamA complex. Following grid preparation, only BamAD complexes were identified, even though the preparation also contained BamGHM proteins (Fig. 3a, Extended Data Fig. 5 and Extended Data Table 1). The loss of BamGHM does not in itself affect the conformation of the BamA barrel, because the barrel conformer does not change between the full BAMFj complex and a BamAP sub-complex that is present in the original BAMFj preparation (Figs. 1a and 3a,b Extended Data Fig. 6 and Extended Data Table 1).

Fig. 3: Structural and functional consequences of losing BamP.

a, Comparison of the structure of the BamAD complex from a BamP-deleted (ΔbamP) background and a BamAP complex from the wild-type (WT) background. The proposed phenylalanine molecule is shown in orange space-filling representation. b, Overlay of the structures shown in a aligned on the N-terminal 100 residues of the BamA barrel. c, Detail from b showing the enlargement and register shift of the sheet between the BamA barrel N and C terminal strands upon BamP removal and the incompatible binding modes of BamP and the putative phenylalanine (orange space-filling representation). Spheres show the Cα atom of Gly897 in each model. d, Cryo-EM volume for the BamAD complex from a BamP-deleted background reveals a partially occupied second barrel (silver). Inset shows the putative phenylalanine density. e, Superposition of the complex in d with an E. coli BamA–EspP complex[29] (PDB: 8BO2; yellow) aligning on the blue BamAFj. The view is from the cell exterior but truncated in the periplasm for clarity. A second copy of BamAFj (silver) has been docked into the second barrel density in d and occupies the same position as the EspP substrate (yellow barrel, right). f, Removal of BamP homologues sensitizes F. johnsoniae to darobactin. The ΔporV Δplug background permeabilizes the OM by opening the T9SS translocon channel[15]. g, BamP overexpression restores darobactin resistance to a strain lacking all BamP homologues. Strains contain plasmids overexpressing Twin-Strep-tagged BamP (pTSBamP) or BamP4 (pTSBamP4). f,g, Similar data were obtained for three biological repeats.

In the absence of BamP, the lateral seam of the BamA barrel remains closed (Fig. 3a). However, the sheet between the barrel N and C termini shifts register and is enlarged through the formation of an additional hydrogen bond (Fig. 3b,c). The structure contains partial density for a second β-barrel next to the lateral seam (Fig. 3d) that is likely to represent a second copy of BamA (Fig. 3e and Extended Data Fig. 5e), as well as unconnected density at the periplasmic side of the BamA lateral seam that we model as a phenylalanine side chain of unknown origin (Fig. 3a–d).

The BamP-deleted state closely and uniquely resembles a complex between E. coli BAM and the substrate protein EspP (BAM–pair3-EspP in ref. [29]; Protein Data Bank (PDB): 8BO2), which exhibits the same register shift between the first and last strands of BamA (Fig. 3e). Notably the position of the folded EspP substrate in the E. coli complex is very similar to the position of the second barrel in our BamP-deleted structure (Fig. 3e) suggesting a correlation between the register shift at the lateral seam and the presence of a barrel interacting at this position. The E. coli complex has been interpreted as representing the end state in OMP insertion[29] and so our BamP-deleted complex may be an analogue of this state.

The presence of the BamP loop within the lateral seam of BAMFj would be expected to block binding of the BAM-specific antibiotic darobactin that also binds at this position[30] (Fig. 1c), potentially explaining the insensitivity of Bacteroidota to this antibiotic30,31. Consistent with this hypothesis, we found that removing all four BamP homologues renders F. johnsoniae sensitive to darobactin (Fig. 3f). Deletion of BamP4 had the largest effect on darobactin sensitivity, but this was additive with removal of the other BamP homologues (Fig. 3f), and overexpression of BamP alone suppressed the effect of deleting all four BamP homologues (Fig. 3g and Extended Data Fig. 4h). Thus, all four BamP homologues are likely to interact with the same site on BamA to prevent darobactin binding, and the BamP proteins must be interacting with BamA during normal cell growth in order to provide their protective effect. The restricted phylogenetic distribution of BamP proteins within the Bacteroidota (Extended Data Table 2) suggests that other organisms within the phylum either have other mechanisms for darobactin resistance or possess unrecognized, mechanistically analogous proteins.

Depletion of essential BAMFj subunits

To gain insight into the roles of the essential BamG and BamH proteins we developed a genetic system to enable gene depletion in F. johnsoniae. In this system, a duplicate copy of the gene of interest is expressed ectopically on the chromosome under the control of a TetR-repressible promoter (Extended Data Fig. 7a–d). Provided expression of this second copy of the gene is maintained by the inclusion of the inducer anhydrotetracyline (aTC) in the growth medium, the native copy of the gene can be deleted. Omission of aTC in the resultant strain prevents further synthesis of the target protein leading to its depletion as the cells grow and divide. Using this strategy, we confirmed that BamG, BamH and BamA are essential for growth under standard laboratory conditions (Fig. 4a). In all three cases, full depletion of the target protein is apparent by 6 h after removal of the inducer (Fig. 4b), at which point cell growth slows (Fig. 4a). Within a further 2 h, the cells become misshapen (Extended Data Fig. 7e) and start to lose periplasmic contents (Fig. 4b, SkpA lanes). More detailed analysis of the depleted cells by transmission electron microscopy shows that all three depletion strains exhibit a similar perturbed morphology in which the OM no longer buds OM vesicles[32], but is deformed by massive blebbing, while the inner membrane remains intact (Fig. 4c). Thus, depletion of any of the three essential BAMFj subunits leads to gross defects in OM biogenesis, similar to those reported in E. coli following BamA depletion[33].

Fig. 4: Depletion analysis of the essential BAMFj subunits.

Strains are the wild-type and depletion strains for BamA (bamAdep), BamG (bamGdep) and BamH with either a strong (bamHdep) or weak (bamHlow) inducible promoter. SkpA is a periplasmic protein to control for OM integrity; GroEL is a loading control. a–d, Strains were cultured in rich (Casitone yeast extract, CYE) medium. The aTC inducer of the target gene was removed at 0 h where indicated (−aTC) to initiate subunit depletion. Samples in b–d were taken at the indicated time points in a. a, Growth curves. Data are mean ± s.d. b, Immunoblots of whole-cell lysates. c, Representative transmission electron microscopy images showing OM defects in the depletion strains. Yellow arrows highlight budding OM vesicles; black arrows highlight OM blebbing and rupture. Scale bar, 500 nm. d, Depletion of BAMFj subunits for 6 h does not change the surface exposure of the SLP SusE as assessed by proteinase K accessibility. Triton X-100 permeabilizes the OM. Reactions were stopped immediately (t_0) or after 20 min (t_20) and analysed by immunoblotting. e, Comparative whole-membrane proteome analysis of depleted (−aTC) versus induced (+aTC) strains collected at the 6 h time point in a. Data points for OMPs and SLPs are coloured as indicated and the most highly expressed OM proteins are labelled. A significance threshold is drawn according to a two-tailed t-test with a false discovery rate (FDR) of 0.1 and a variance correction constant S_0 of 0.1. Data are averaged over three biological repeats. FC, fold change. f,g, Analysis of chronic BamH depletion in an induced strain (bamHlow + aTC) in which a weak promoter results in the incomplete restoration of wild-type BamH levels. f, Whole-cell immunoblots. Arrow indicates BamH; asterisk indicates a non-specific band. g, As e but comparing chronic BamH depletion (bamHlow strain + aTC) relative to the wild type. a–d,f, Similar data were obtained for three biological repeats.

Source data

We used immunoblotting to assess the effects of depletion of BAM subunits on the cellular levels of the remaining BAMFj components and of representative OMPs and SLPs (Fig. 4b). The analysed proteins include the two most abundant F. johnsoniae OM components26,34,35, OmpA (Fjoh_0697), an 8-strand OMP that anchors the OM to the cell wall, and a SUS complex of unknown function that we show here to be composed of a 22-strand SusC OMP (Fjoh_0403) together with its SusD SLP partner (Fjoh_0404) and a structurally unrelated SLP SusE (Fjoh_0405) (Extended Data Fig. 7f). We also assessed the levels of SprF, a 14-strand OMP involved in gliding motility[36]. The effects of depleting all three BAMFj subunits were broadly similar. Levels of OMPs decreased after depletion of the target subunit, although at differing rates. OmpA is notably slow to deplete, and it is possible that other BamA homologues present in F. johnsoniae may also be able to insert this simple OMP, as has recently been demonstrated for the E. coli translocation assembly module (TAM) complex[37]. The levels of the SLPs (SusD and SusE) also decreased, with the exception of BamH, which instead increased.

Because F. johnsoniae releases OM vesicles (Fig. 2c and ref. [32]), we investigated whether the reduced OM protein levels in the depletion strains were a consequence of OM loss through vesicle shedding. However, we detected no increase in OM protein in the vesicle fraction of the culture supernatant (Extended Data Fig. 7g). Thus, as in E. coli[38], the OM is not lost through vesicle production when BAM is depleted. The observed reduction in OMP levels therefore reflects defects in their biogenesis.

Analysis of the surface exposure of an overexpressed tagged version of the SLP SusE provides no evidence that SLPs are accumulating inside the depletion strains, and thus no evidence that their export is blocked (Fig. 4d and Extended Data Fig. 7h).

We extended our analysis of the effects of the BAM subunit depletions to the whole OM proteome (Fig. 4e and Extended Data Fig. 7i). We analysed membranes collected 6 h after removal of the inducer, at which point depletion of the target subunit is complete but the other BAMFj subunits are still present and the OM is still intact (Fig. 4a–c). The overall pattern of OM proteome changes in all three depletions is similar, with marked decreases in the levels of many OMPs and some SLPs (Fig. 4e and Extended Data Fig. 8a). Thus, removal of the essential BAMFj subunits has the general effect of reducing the levels of OM proteins.

As an alternative to fully depleting the BAMFj subunits, we also investigated the effects of chronically reducing the steady-state concentration of BamH to a level at which there is a marked effect on cell growth (Fig. 4a,f). Cells of this strain had less severe defects in OM morphology than after full BamH depletion, although the budding of OM vesicles seen in the parental strain was almost fully suppressed (Fig. 4c). The differences in the steady-state OM proteome in this strain relative to that in wild-type cells followed the same trends as the proteome changes seen in the total depletion experiments in showing a general reduction in OMPs and SLPs (Fig. 4f,g and Extended Data Figs. 7i and 8a).

In summary, the loss of either BamG or BamH results in changes in the OM proteome and cellular morphology that closely match those associated with the total loss of BAM function that occurs when BamA is removed. Thus, BamG and BamH are both essential for the core BAMFj function of OMP insertion.

Isolation of a bamH suppressor mutant

The requirement for BamG and BamH in BAMFj function could reflect a direct involvement of these subunits in the general OMP biogenesis function of the BAM complex. However, the same phenotype could also arise indirectly if BamG and BamH have a specialized role in the maturation of a subset of BAMFj clients such that in their absence these clients accumulate on BamA and interfere with its ability to carry out general OMP biogenesis. Although no additional proteins corresponding to trapped substrates were co-purified with BamA complexes isolated from strains depleted for BamG or BamH (Extended Data Fig. 9a,b), the hypothesis that BamG and BamH have client-specific roles in BAMFj function suggests that it might be possible to identify suppressor mutations that relieve the secondary effects on general BAM function of BamG or BamH removal.

We were able to select a spontaneous mutant of the BamH depletion strain that allowed growth in the absence of the inducer aTC. Genome sequencing identified a Q801K substitution in BamA as most probably responsible for the suppressor phenotype. Re-creation of the BamA Q801K substitution in a clean background permitted deletion of both bamH and its orthologue bamH2, confirming that this single amino acid substitution was responsible for the bamH suppressor phenotype and that it did not operate through upregulating bamH2 expression. The resultant bamAQ801K ΔbamH ΔbamH2 strain (hereafter bamHsup) grew as rapidly as the wild-type strain on rich medium (Fig. 5a), even though BamH was absent (Fig. 5b). Thus, although bamH behaves as an essential component of BAMFj in the native context, it is dispensable in an experimentally modified genetic background. This has parallels to the way that E. coli BamD can be deleted in a bamA suppressor background[39]. Of note, the bamAQ801K mutation did not allow deletion of bamG, indicating that BamH and BamG have non-identical functions (Extended Data Fig. 4a).

Fig. 5: Characterization of a bamH suppressor mutant.

Comparative characterization of the recreated bamHsup mutant (bamAQ801K ΔbamH ΔbamH2) and wild-type strains. a, Growth on rich (CYE) medium in the absence of aTc. Data are mean ± s.d. b, Whole-cell immunoblots. SkpA is a periplasmic protein to control for OM integrity. GroEL is a cytoplasmic protein as loading control. BamA and BamG are detected via epitope tags. Asterisk indicates non-specific bands. c, Representative transmission electron microscopy images of the wild type and bamHsup mutant. Yellow arrows highlight budding OM vesicles. Scale bar, 500 nm. d, Comparative whole-membrane proteome analysis of the bamHsup strain relative to a BamH-induced strain (bamHdep + aTC). Data points for OMPs and SLPs are coloured as indicated and the most highly expressed OM proteins are labelled. Proteins that show poor recovery in the bamHsup strain in a post hoc ANOVA with BamH-induced and depleted strains are numbered as in Extended Data Fig. 8b. A significance threshold is drawn according to a two-tailed t-test with a FDR of 0.1 and a S_0 of 0.1. Data are averaged over three biological repeats. a–c, Similar data were obtained for three biological repeats. Cells were analysed (b,c) and membranes prepared (d) at the 6 h time point in a. e, Size comparison between BAMFj and a typical SusCD complex and OmpA. SusCD and OmpA are illustrated using homologous proteins of known structure from other organisms (labelled with their PDB accession numbers).

Source data

The bamHsup strain had normal cellular morphology (Fig. 5c) and no defect in OM integrity, SLP export or gliding motility (Extended Data Fig. 9c–e). The SusC, SusD and SusE proteins were restored to wild-type levels (Fig. 5b) and the cell was able to assemble these proteins into SusCDE complexes (Extended Data Fig. 9f). Thus, the most abundant F. johnsoniae SUS system does not depend on BamH for its biogenesis.

Analysis of the OM proteome of the suppressor strain showed strong restoration of the levels of many OMPs and SLPs relative to BamH-depleted conditions (Fig. 5d and Extended Data Fig. 8b). However, the levels of other OM proteins recovered poorly, suggesting that these proteins were particularly sensitive to the loss of BamH. These sensitive proteins were almost all SusCD pairs and their SLP partners (Extended Data Fig. 8b). Thus, BamH may be particularly important in the biogenesis of a subset of SUS systems.

Discussion

Our phylogenetic and experimental analyses indicate that BamADGH constitute the essential core of the Bacteroidota BAM complex with which a species-variable complement of accessory subunits associate. This pattern is consistent with a contemporaneous characterization of BAM complexes from Bacteroides thetaiotaomicron and P. gingivalis[40], which conserve only BamADGH from the BAMFj complex but have three distinct and non-essential SLP subunits (though two of these have PPI folds resembling one of the BamMFJ domains). Note that the subunit nomenclature in that study matches that used here. Variation in accessory subunit composition has previously been observed between the BAM complexes of Proteobacteria41,42.

The Bacteroidota BAM complex has previously been proposed to be involved in SLP export13,16. Consistent with this suggestion, we find that blocking core BAMFj function through depletion of BamA reduces SLP levels (Fig. 4a–c). However, assigning causality is complex because any OM protein involved in SLP export will indirectly depend on BAMFj for their own biogenesis. Furthermore, our biochemical analysis provides no evidence that loss of BAMFj leads to the accumulation of non-exported SLPs inside the cell (Fig. 4d).

The BAMFj canopy provides a protected extracellular cavity above the position in the membrane where client OMPs assemble on BamA, suggesting that it functions as an extracellular folding vestibule. A possible precedent for a BAM-like machine providing trans-side folding assistance comes from the mitochondrial SAM complex, which contains subunits that contact client OMPs from the cytoplasmic (external) side of the membrane[43]. The BAMFj canopy might protect folding intermediates on the BAM complex from proteolysis by sterically blocking the access of proteases in the extracellular environment. Similarly, the presence of the canopy should sterically exclude lipopolysaccharide molecules (which have large head groups and form rigid arrays in the OM1,44) from the region of the membrane next to the lateral seam. This would provide a patch of phospholipid bilayer in the OM for client OMPs to fold into.

The novel BamG, BamH and BamM subunits of BAMFj are likely to expand the range of OMPs inserted relative to the canonical BAMEc complex and therefore act on specific structural classes of proteins that are found only in the Bacteroidota. In addition, their cell surface location implies that the novel subunits act on the extracellular portions of BAM substrates. Given these expectations, it is likely that these components are involved in one or more of the following processes: biogenesis of OMPs with large extracellular regions; assisting BamA to transport and fold SLPs (but see comments above); or allowing the assembly of the abundant SusCD family complexes that characterize the Bacteroidota OM. In this context, it may be important that the only FCB phylum that lacks BamG and BamH proteins (the Chlorobiota) also lacks SusCD systems and the FCB-specific T9SS translocon channel SprA, which has more than 150 kDa of polypeptide on the extracellular side of the membrane[15] (Extended Data Table 2). Thus, there is a correlation between having a BAMFj-like BAM complex and being able to build the complex OMPs that characterize the FCB superphylum. Our observation that a subset of SusCD proteins are only minimally recovered by a bamH suppressor mutation (Fig. 5d) supports the idea that at least BamH is involved in the assembly of some SusCD systems.

In Fig. 5e, we compare the proportions of BAMFj with those of the SusCD unit that it may assemble as well as the more classical E. coli OMP substrate OmpA that does not have an extensive extracellular domain. Although SusC could be accommodated under the BAMFj canopy, the full SusCD complex cannot do so without the canopy being raised. This appears unlikely owing to the tethering of the canopy to BamA and BamG at one end, and to the membrane by the lipid anchor of BamM at the other. Thus, SusC is likely to fold on BAMFj and be at least partially released before forming a complex with its SusD partner.

We were able to select a suppressor mutation in bamA that compensates for the loss of the BamH subunit. This single amino acid substitution in BamA is sufficient to restore OMP biogenesis and OM morphology (Fig. 5b,c), showing that general OMP insertion in F. johnsoniae does not physically require the presence of BamH. It is unlikely that the suppressing amino acid substitution in BamA functions by replicating the role of BamH, as it is difficult to see how alterations in BamA could create a similar structural environment to the BamH-containing extracellular canopy. Instead, it is most plausible that the suppressor substitution acts by compensating for the toxic consequences of loss of BamH function. Since removal of BamH closely phenocopies the loss of BamA (Fig. 4b,c,e–g), the most probable suppression scenario is that loss of BamH blocks BamA function through the accumulation of stalled BamH-requiring substrates and that this blockage is relieved by a structural change in BamA that corrects the problem, for example, by accelerating substrate release. Gln801, the BamA residue that is substituted in the bamH suppressor, is located in extracellular loop L6, which lies over the extracellular end of the BamA pore (Fig. 2a). Gln801 is hydrogen-bonded through its side-chain oxygen atom to the main chain amine of Gly591 in adjacent loop L5 (Fig. 2a), so it is likely that its substitution disrupts the packing of the BamA extracellular cap. We speculate that this may marginally destabilize BAM–substrate interactions, allowing the release of misfolded substrates.

Although analysis of the bamH suppressor allowed us to identify certain SusCD proteins that are heavily dependent on BamH for their biogenesis, many other SusCD systems, including the most abundant SusCDE complex, were well-restored in the same background (Fig. 5b,d Extended Data Fig. 9d). We interpret this as indicating that most BamH clients are able to fold without BamH during the vast majority of BAMFj turnovers and that BamH is only required to correct a small proportion of insertion events that go wrong. In this model, BamH has a role in quality control that prevents infrequent errors in folding blocking the BAMFj complex. Alternatively, BamH may have a more critical role in the biogenesis of these proteins under specific conditions, such as stress or under conditions that are not readily replicated in the laboratory.

Methods

Bacterial strains and growth conditions

All strains and plasmids used in this work are listed in Supplementary Tables 2 and 3. F. johnsoniae was routinely cultured aerobically in Casitone yeast extract (CYE) medium[45] at 30 °C with shaking. For some physiological studies the cells were cultured in PY2 medium[46] as indicated below. For experiments testing growth on complex sugars cells were cultured in a 96-well plate in a CLARIOstarPlus plate reader using modified minimal A medium[27] and containing 0.25% (w/v) of either carob galactomannan (Megazyme, 11078-30-1) or tamarind xyloglucan (Megazyme, 37294-28-3) as the sole carbon source. E. coli strains were routinely grown aerobically in LB medium at 37 °C with shaking, or on LB agar plates. Where required, 100 µg ml−1 erythromycin was used in the growth medium for F. Johnsoniae. 100 µg ml−1 ampicillin or 50 µg ml−1 kanamycin were used in the growth medium for E. coli. aTC (CAY10009542-50 mg, Cambridge Bioscience) was used as a final concentration of 0.2 µg ml−1 (liquid culture) and 2 µg ml−1 (agar plates).

Genetic constructs

Plasmids were constructed by Gibson cloning[47] using the primers and target DNA in Supplementary Table 4. Suicide and expression plasmids were introduced into the appropriate F. johnsoniae background strain by triparental mating as previously described[46]. Chromosomal modifications were introduced using the suicide vector pYT313 harbouring the counter-selectable sacB gene as previously described[48]. All plasmid constructs and chromosomal modifications were confirmed by sequencing.

Construction of a tightly regulated gene expression system for F. johnsoniae

The aTC-inducible systems for the depletion of essential BamFJ components (Extended Data Fig. 7a) were based on the native F. johnsoniae ompA and fjoh_0824 promoters and contain the 100 bp upstream of ompA or fjoh_0824. Guided by the observations of Lim et al.[49], a tetO2 site (TetR binding site) was inserted upstream of the conserved −33 motif in these promoters and another tetO2 site downstream of the conserved −7 motif generating the synthetic promoters PompAinduc and Pfjoh_0824induc (Extended Data Fig. 7b). The constructs also contain tetR under the control of an additional copy of the constitutive F. johnsoniae ompA promoter. The final inducible systems containing the gene to be induced were integrated into the chromosome at an assumed phenotypically neutral site26,36 by replacing fjoh_4538 to fjoh_4540.

The designed inducible systems were validated using strains in which a NanoLuc reporter gene[50] was placed under the control of the chromosomally integrated aTC-inducible systems (Extended Data Fig. 7c). Overnight cultures of these strains were diluted 1:100 into fresh CYE medium in the absence or presence of 0.2 µg ml−1 aTC and cultured for 6 h to mid-exponential phase (OD_600 ~ 0.6). Cells were collected and resuspended in PY2 medium to OD_600 = 0.6. A volume of 50 µl of cell resuspension was mixed with 50 µl of reaction solution (48 µl PY2 medium supplemented with 2 µl of furimazine (Promega)) in a 96-well plate and the luminescence signal measured in a CLARIOstarPlus plate reader.

Strains to enable the depletion of the essential BAMFj subunits were constructed by introducing a copy of the target gene under the control of the designed inducible system into the chromosome at the phenotypically neutral site. The native copy of the target gene was then deleted in the presence of aTC to allow expression of the introduced copy of the gene.

Purification of BAMFj and SusCDE complexes

To purify complexes containing Twin-Strep tagged BamA, the relevant strain was cultured for 22 h in CYE medium using 1 l culture volume in 2.5 l flasks. A total culture volume of 12 l was used for sample preparations for structure determination, and 4 l of culture was used for analytical purifications of BAMFj variants. Cells were collected by centrifugation at 12,000g for 30 min and stored at −20 °C until further use. All purification steps were carried out at 4 °C. Cell pellets were resuspended in buffer W (100 mM Tris-HCl pH 8.0, 150 mM NaCl, 1 mM EDTA) containing 30 μg ml−1 DNase I, 400 μg ml−1 lysozyme and 1 mM phenylmethylsulfonyl fluoride (PMSF) at a ratio of 5 ml of buffer to 1 g of cell pellet. Cells were incubated on ice for 30 min with constant stirring before being lysed by two passages through a TS series 1.1 kW cell disruptor (Constant Systems) at 30,000 PSI. Unbroken cells were removed by centrifugation at 20,000g for 20 min. The supernatant was recovered and total membranes were collected by centrifugation at 230,000g for 75 min. Membranes were resuspended in buffer W to a protein concentration of 6.5 mg ml−1 and solubilized by incubation with 1% (w/v) lauryl maltose neopentyl glycol (LMNG, Anatrace) for 2 h. Insoluble material was removed by centrifugation at 230,000g for 75 min. Endogenous biotin-containing proteins were masked by addition of 1 ml BioLock solution (IBA Lifesciences) per 100 ml of supernatant and incubation for 20 min with constant stirring. The solution was then circulated through a Strep-TactinXT 4Flow High Capacity column (IBA Lifesciences) overnight. The column was washed with 10 column volumes of buffer W containing 0.01% LMNG (buffer WD) and bound proteins were eluted with 6 column volumes Strep-TactinXT BXT buffer (IBA Lifesciences) containing 0.01% LMNG. The eluate was concentrated to 500 μl using a 100-kDa molecular weight cut-off (MWCO) Amicon ultra-15 centrifugal filter unit (Merck) and then injected onto a Superose 6 Increase 10/300 GL column (Cytiva) previously equilibrated in buffer WD. Peak fractions were collected and concentrated using a 100-kDa MWCO Vivaspin 500 column (Sartorius).

Purification of SusCDE complexes with a N-terminal Twin-Strep tag on SusC was carried out by the same protocol.

Peptide mass fingerprinting

Samples were excised from Coomassie-stained gels. For whole sample proteomic analysis, SDS–PAGE was carried out only until the sample had fully entered the gel and the protein smear at the top of the gel was excised. Samples were subject to in-gel trypsin digestion and electrospray mass spectrometry at the Advanced Proteomics Facility (University of Oxford, UK).

Immunoblotting

Immunoblotting was carried out as previously described[19]. Antibodies against BAMFj subunits, Sus proteins and SkpA were raised in rabbits against His-tagged recombinant proteins produced using the plasmids listed in Supplementary Table 3. Antiserum against OmpA[34] was provided by S. Shibata and antiserum against SprF[36] by M. McBride. The following commercial antisera were used: anti-Strep-tag (34850 Qiagen), anti-GroEL (G6532 Merck), anti-ALFA-Tag (N1582 Synaptic Systems GmbH), anti-His-tag (H1029-100UL Merck Life Science), anti-HA-tag (26183 Thermo Fisher Scientific), anti-mouse IgG peroxidase conjugate (A4416 Merck) and anti-rabbit IgG peroxidase conjugate (31462 Pierce). Antibodies were used at the following dilutions: anti-His-tag and anti-HA-tag, 1:1,000; anti-SprF, 1:2,500; anti-BamH, anti-BamM, anti-BamP, anti-SusC, anti-SusD, anti-SusE, anti-SkpA, anti-Strep-tag and anti-ALFA-tag, 1:3,000; anti-OmpA and anti-GroEL, 1:50,000.

Original uncropped gels and immunoblots are shown in Supplementary Fig. 2.

Darobactin inhibition experiments

E. coli or F. johnsoniae strains were cultured, respectively, in LB and CYE medium (supplemented with erythromycin if carrying pTSBamP or pTSBamP4 plasmids). Five-millilitre starter cultures were grown aerobically overnight at 30 °C, then diluted into 5 ml fresh medium to OD_600 = 0.02 and then grown to OD_600 between 0.6 to 0.8. The cultures were then diluted with fresh medium to OD_600 = 0.006. Fifty microlitre aliquots were transferred into a 96-well plate and mixed with 50 µl of the required concentration of darobactin solution in growth medium. The minimum inhibitory concentration (MIC) for darobactin was assessed after overnight incubation at 30 °C in a CLARIOstarPlus plate reader.

BamP pull-downs

Strains with pCP11-derived plasmids expressing N-terminal Twin-Strep-tagged BamP or BamP4 under the control of a remA promoter were grown aerobically overnight at 30 °C in erythromycin-supplemented CYE medium. The culture was diluted into 100 ml fresh medium to OD_600 = 0.02 and grown to an OD_600 = 0.8–1.0. Cells were then collected by centrifugation at 8,000g for 10 min and resuspended in 3 ml of buffer W containing 30 µg ml−1 DNase I, 400 µg ml−1 lysosome and 1 mM PMSF. The cells were incubated for 30 min at 4 °C, and then lysed by sonication for 3 min on ice using a Sonics Vibra Cell Ultrasonic Processor VCX 130 with a 6 mm probe at 40% amplitude, with a 10 s on to 10 s off cycle. Unbroken cells were removed by centrifugation at 20,000g for 20 min. The supernatant fraction was then centrifuged at 200,000g for 1 h to pellet total membranes. The membrane pellets were resuspended to a protein concentration of 6.5 mg ml−1 with buffer W and solubilized by incubation with 1% (w/v) LMNG for 2 h. Insoluble material was removed by centrifugation at 230,000g for 1 h, and the recovered supernatant supplemented with 1% BioLock solution before mixing with 50 µl Strep-TactinXT 4Flow lbeads (IBA Lifesciences) that had been equilibrated in buffer WD. Samples were rotated slowly at 4 °C for 2 h and then transferred into Mini Bio-Spin Chromatography columns (Bio-Rad, 7326207), and centrifugation at 100g for 1 min. The beads were washed 3 times with 250 µl buffer WD and bound proteins then eluted with 150 µl of 1× Strep-TactinXT BXT buffer containing 0.01% LMNG. The elute was concentrated to 30 µl using a 10 kDa MWCO Vivispin500 centrifugal concentrator (VS0102, Sartorius).

BAMFj subunit depletion experiments

The desired depletion strain was grown overnight in CYE medium supplemented with 0.2 µg ml−1 aTC. Cells from 1 ml of the overnight culture were collected, washed once in 1 ml CYE, and resuspended in 1 ml of CYE medium. Cells from this sample were then used to inoculate 15 ml of CYE medium, either with or without 0.2 µg ml−1 aTC, to OD_600 = 0.02. The cells were then cultured aerobically at 30 °C and cell samples collected into SDS sample buffer every 2 h for subsequent analysis by immunoblotting. Samples for imaging or membrane preparation were collected and analysed as detailed below.

To purify BamA complexes after depleting the essential BamG or BamH subunits, a 200 ml overnight culture of the appropriate strain grown in the presence of 0.2 µg ml−1 aTC was collected and resuspended in the same volume of fresh CYE medium without aTC. This sample was used to inoculation 8 l of CYE without aTC to OD_600 = 0.1 which was then cultured aerobically at 30 °C for 6 h. Cells were collected and BamA complexes processed for purification as described above.

Microscopic analysis of cells during BAM subunit depletions

Live cells were imaged directly in growth medium by spotting samples taken from depletion cultures onto a 1% agarose pad prepared in PY2 medium. Phase contrast images were acquired on an inverted fluorescence microscope (Ti-E, Nikon) equipped with a perfect focus system, a 100× NA 1.4 oil immersion objective, a motorized stage, and a sCMOS camera (Orca Flash 4, Hamamatsu).

For transmission electron microscopy, cells were collected at the required time points during depletion by centrifugation at 8,000g for 5 min. After carefully removing the supernatant, cell pellets were gently resuspended in 1 ml of fixative solution (2.5% glutaraldehyde, 4% formaldehyde in 0.1 M PIPES buffer, pH 7.4) and incubated at room temperature for 1 h. Following fixation cells were washed with TEM buffer (100 mM PIPES NaOH pH 7.2), treated with TEM buffer containing 50 mM glycine, washed again in TEM buffer, and then subjected to secondary fixation with TEM buffer containing 1% (w/v) osmium tetroxide and 1.5% (w/v) potassium ferrocyanide. Samples were then washed extensively with Milli-Q water, stained with aqueous 0.5% (w/v) uranyl acetate overnight, then washed again with Milli-Q water. The samples were dehydrated through an ethanol series and infiltrated with and embedded in TAAB low viscosity epoxy resin ahead of polymerization at 60 °C for 24 h. Sections of 90 nm were cut from the resin blocks using a Leica UC7 Ultramicrotome and collected onto 3 mm copper grids. The sections were then post-stained with lead citrate and imaged using a JEOL Flash 120 kV TEM equipped with a Gatan Rio camera.

Whole-membrane proteomics

Fifteen millilitres of cells at the 6 h time point of the standard depletion experiment were collected by centrifugation at 8,000g for 5 min at 4 °C. The cells were resuspended in 1 ml of buffer W and lysed on ice using a probe sonicator (Sonics Vibra Cell, probe 630-0422) at 40% power by 12 repeats of a 10 s on/10 s off pulse cycle. After lysis, the samples were centrifuged at 20,000g for 20 min at 4 °C to remove cell debris. The supernatant was then centrifuged at 135,000g for 45 min at 4 °C to pellet the membranes. The membranes were resuspended in buffer W and the protein contents of the samples normalized by A280 nm. The samples were run together on SDS–PAGE gels and stained with Coomassie Blue (Extended Data Fig. 7i) to confirm that normalization had been correctly implemented. Statistical methods were not used to determine sample size. Randomization and blinding were not used.

Membrane fractions were resuspended in lysis buffer containing 1% SDS, 0.1 M ammonium bicarbonate pH 8.0. Samples were sonicated for 5× 15 s in a water bath with 15 s incubations on ice between each pulse cycle. The samples were clarified by centrifugation at 17,500g for 30 min and 50 µg of total protein lysate was taken for analysis. Samples were reduced for 30 min using 10 mM tris(2-carboxyethyl)phosphine (TCEP) followed by alkylation for 30 min in the dark using 2-chloroacetamide. SpeedBeads Magnetic Carboxylate Modified Particles (GE Healthcare) were mixed with the sample in a 10 volumes beads: 1 volume sample ratio and the samples shaken for 10 min at 1,000 rpm. The beads were then washed twice with 70% ethanol followed by 100% acetonitrile. This procedure was repeated 8 times. 100 mM ammonium bicarbonate was added to the washed beads and pre-digestion with endoprotease LysC (Wako; 1:100) was carried out at 37 °C for 2 h. This was followed by 16 h digestion with trypsin (Promega, 1:40) at 37 °C. The supernatant was collected and any remaining bound peptides were eluted from the beads using 2% dimethyl sulfoxide (DMSO). Digested peptides were loaded onto C18 stage tips, pre-activated with 100% acetonitrile and 0.1% formic acid and centrifuged at 4000 rpm. The tips were then washed with 0.1% formic acid and eluted in 50% acetonitrile/0.1% formic acid. Eluted peptides were dried in a speed-vac.

Peptide analysis employed a Thermofisher Scientific Ultimate RSLC 3000 nano liquid chromatography system coupled in-line to a Q Exactive mass spectrometer equipped with an Easy-Spray source (Thermofisher Scientific). Peptides were separated using an Easy-Spray RSLC C18 column (75 µm internal diameter, 50 cm length, Thermofisher Scientific) using a 60 min linear 15% to 35% solvent B (0.1% formic acid in acetonitrile) gradient at a flow rate 200 nl min−1. The raw data were acquired on the mass spectrometer in a data-dependent acquisition (DDA) mode. Full-scan mass spectra were acquired in the Orbitrap (Scan range 350–1,500 m/z, resolution 70,000, AGC target 3 × 10^6, maximum injection time 50 ms). The 10 most intense peaks were selected for higher-energy collision dissociation (HCD) fragmentation at 30% of normalized collision energy. HCD spectra were acquired in the Orbitrap at resolution 17,500, AGC target 5 × 10^4, maximum injection time 120 ms with fixed mass at 180 m/z.

Mass spectrometry data were analysed using MaxQuant 2.5.1.0 as previously described[51] to obtain label-free quantification values that were then used for data processing in Perseus 2.1.3.0[52]. Label-free quantification values were log_2-transformed and categorically grouped by replicates. Rows were filtered based on two valid values in each group and then missing values were replaced using a normal distribution with a width of 0.3 and down shift of 1.8 (default values). Then, dataset was normalized by subtracting the medians of each sample. After visually verifying a normal distribution and a linear correlation, sample pairs were subjected to a two-tailed t-test using a false discovery rate (FDR) of 0.1 and a S_0 of 0.1 to define a threshold of statistical significance. Proteins were represented in a volcano plot, according to the log_2 of their enrichment and the −log_10 of the t-test P value.

An ANOVA test was carried out for indicated groups of proteins using the Benjamini–Hochberg method with a FDR of 0.05 for truncation. Then, a post hoc Tukey’s honest significant difference test for one-way ANOVA using a FDR of 0.05 was carried out. Proteins were then filtered by ANOVA significance and by category to represent in a heat map their honest significant difference scores, as indicated.

A batch normalization using empirical Bayes method was carried out with the ComBat script[53] for PerseusR package 0.3.4[54] to make the heat map for all depletions (Extended Data Fig. 8). Then, samples were subjected to the statistical test previously described.

The proteins obtained from the mass spectrometry experiments were categorized as follows. Proteins with signal peptides or lipoprotein signal peptides were first extracted using SignalP 6.0[55] to obtain datasets containing only OM plus periplasmic proteins, or lipoproteins, respectively. Proteins were then manually sorted to the categories OMP or SLP. This sorting was carried out using Uniprot entry data that included AlphaFold[23] models. Lipoproteins were only classified as SLPs if they were either SusD homologues or if they were found at a locus coding SusCD systems.

Determination of cell surface exposure of SusE

The strain for analysis was transformed with plasmid pXL184 which expresses His-tagged SusE. The cells were then grown overnight in CYE supplemented with erythromycin, and for BAM subunit depletion strains with 0.2 µg ml−1 aTC. Cells were collected, resuspended in CYE medium, and then used to inoculate 10 ml of erythromycin-containing CYE medium to OD_600 = 0.02, supplementing with 0.2 µg ml−1 aTC as required. The cells were cultured for 6 h before being collected by centrifugation and resuspended in phosphate buffered saline (PBS) containing 10 mM MgCl_2 to a total volume of 80 µl and OD_600 = 1. Samples were supplemented as appropriate with 200 μg ml−1 proteinase K (Thermo Fisher) and 1% (v/v) Triton X-100 (Merck) and incubated for 20 min at room temperature. Reactions were stopped by the addition of 5 mM PMSF (ITW Reagents) followed by incubation at 100 °C for 5 min, addition of SDS–PAGE sample buffer, and further incubation at 100 °C for 5 min before analysis by immunoblotting.

Isolation of outer membrane vesicle fraction

The isolation of outer membrane vesicles (OMVs) was performed essentially as in ref. [38]. In brief, cells were separated from culture supernatant by centrifugation at 8,000g for 5 min and the pellets reserved as the whole-cell fraction. Culture supernatant from the equivalent of 2 ml of culture at OD_600 = 1 was filtered through a 0.2 µm filter (MilliporeSigma, SLGPR33RB) and concentrated using a 100 kDa molecular weight cut-off Amicon Ultra-4 centrifugal filter (MilliporeSigma, UFC810096) to produce the OMV fraction. Samples were adjusted to equal volume before analysis by immunoblotting.

Isolation of a spontaneous suppressor of BamH depletion

The BamH depletion strain XLFJ_1140 was grown overnight in CYE medium supplied with aTC. One millilitre of cells was collected by centrifugation at 8,000g for 3 min, washed once with CYE and then diluted to a starting OD_600 = 0.2 in 10 ml fresh CYE medium without aTC. After culturing for 6 h, cells were diluted 1:200 into fresh CYE medium without aTC and cultured for a further 2 days before plating on CYE agar to obtain single colonies. Individual clones were cultured in parallel with and without aTC in CYE and the expression of BamH analysed by whole-cell immunoblotting. Clones that grew without aTC but still expressed BamH only following aTC induction (showing that they were not constitutively de-repressed for BamH synthesis) were subjected to genome sequencing (Plasmidsaurus). This identified the potential suppressor mutation bamAQ801K, which was introduced into a BAM wild-type background, followed by successive deletions of bamH and bamH2 to produce the bamHsup strain XLFJ_1198.

Cryo-EM sample preparation and imaging

Four microlitres of either fraction A (for the BAMFj complex, 1.3 mg ml−1) or fraction B (for the BamAP complex, 1.3 mg ml−1) of the BAMFj preparation (Fig. 1a), or of the BamP-deleted BAM complex (ΔBamP complex, 1.2 mg ml−1) was adsorbed onto glow-discharged holey carbon-coated grids (Quantifoil 300 mesh, Au R1.2/1.3) for 10 s. Grids were blotted for 2 s at 10 °C, 100% humidity and frozen in liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific).

Movies were collected in counted mode, in Electron Event Representation (EER) format, on a CFEG-equipped Titan Krios G4 (Thermo Fisher Scientific) operating at 300 kV with a Selectris X imaging filter (Thermo Fisher Scientific) and slit width of 10 eV, at ×165,000 magnification on a Falcon 4i direct detection camera (Thermo Fisher Scientific), corresponding to a calibrated pixel size of 0.732 Å. Movies were collected at a total dose ranging between 52.0–60.3 e− Å−2 (Extended Data Table 1), fractionated to ~1.0 e− Å−2 per fraction for motion correction.

Cryo-EM data processing

Patched motion correction, contrast transfer function (CTF) parameter estimation, particle picking, extraction and initial 2D classification were performed in SIMPLE 3.01[56]. All downstream processing was carried out in cryoSPARC 4.5.3[57] or RELION 4.03[58], using the csparc2star.py script within UCSF pyem 0.5[59] to convert between formats. Global resolution was estimated from gold-standard Fourier shell correlations (FSCs) using the 0.143 criterion and local resolution estimation was calculated within cryoSPARC.

The cryo-EM processing workflow for the BAMFj complex is outlined in Extended Data Fig. 1. In brief, particles were subjected to one round of reference-free 2D classification (k = 200) using a 240 Å soft circular mask within cryoSPARC resulting in the selection of 2,153,927 clean particles. A subset of these particles (180,179) was subjected to multi-class ab initio reconstruction using a maximum resolution cut-off of 7 Å, generating 4 volumes. These volumes were lowpass-filtered to 20 Å and used as references in a heterogeneous refinement against the full 2D-cleaned particle set. Particles (903,299) from the most populated and structured class were selected and non-uniform refined against their corresponding volume lowpass-filtered to 15 Å, generating a 3.0 Å map. Bayesian polishing in RELION followed by duplicate particle removal generated a 2.5 Å map after non-uniform refinement, which could be further improved to 2.3 Å after local and global CTF refinement (fitting beam tilt and trefoil only). These particles were then subjected to heterogeneous refinement against four compositionally distinct volumes previously generated by RELION 3D classification (k = 8, 3.75° sampling) of a particle subset of pre-polished particles. Particles (274,708) belonging to the class with strong BamD and POTRA densities were selected and non-uniform refined against their corresponding volume, generating a 2.4 Å map. Additional alignment-free 3D classification in RELION was performed (k = 6) using a soft mask covering BamD and the BamA POTRA domains yielding a class with stronger density. Particles (55,795) from this class were selected and non-uniform refined against a previous volume lowpass-filtered to 15 Å, generating a consensus 2.7 Å volume. Local refinements were performed against the consensus volume (lowpass-filtered to 7 Å) using soft masks covering the BamD/POTRA domains or extracellular density, yielding 3.2 Å and 2.7 Å volumes, respectively. ChimeraX[60] was used to generate a composite map from the consensus and individual focused maps.

The cryo-EM processing workflow for the BamAP complex is outlined in Extended Data Fig. 6. Two datasets were collected for this sample. In the first dataset particles were subjected to two rounds of reference-free 2D classification (k = 200) using a 200 Å soft circular mask resulting in the selection of 979,474 clean particles. These particles were then subjected to multi-class ab initio reconstruction (k = 4) using a maximum resolution cut-off of 8 Å, generating 4 volumes. Particles (514,326) belonging to the 2 most prominent volumes were combined and non-uniform refined against one of their corresponding volumes, lowpass-filtered to 15 Å, generating a 3.7 Å volume. The second particle dataset underwent four rounds of 2D classification (k = 200, 200 Å soft circular mask) followed by multi-class ab initio reconstruction using a maximum resolution cut-off of 7 Å, generating 6 volumes. Particles (438,412) from the most populated class were selected and refined against their corresponding volume lowpass-filtered to 15 Å, generating a 3.7 Å volume. Particles from both datasets were independently polished within RELION, combined, and non-uniform refined, fitting per-particle CTF parameters, yielding a 3.5 Å map. Alignment-free 3D classification was subsequently performed within cryoSPARC (k = 6), using a soft mask covering the full protein density of the complex. Particles (96,076) from the class demonstrating strong density for the N-terminal domain of BamP were selected and non-uniform refined against their corresponding volume, lowpass-filtered to 15 Å, generating a 3.7 Å map.

The cryo-EM processing workflow for the ΔBamP complex is outlined in Extended Data Fig. 5. In brief, particles were subjected to two rounds of reference-free 2D classification (k = 200) using a 180 Å soft circular mask within cryoSPARC resulting in the selection of 1,177,554 clean particles. These particles were then subjected to multi-class ab initio reconstruction using a maximum resolution cut-off of 6 Å, generating 6 volumes. Particles from volume classes containing BamA barrels were independently non-uniform refined against their corresponding volume, lowpass-filtered to 15 Å. These particles were subsequently combined and refined against a volume (lowpass-filtered to 15 Å) from the most populated class, generating a 3.6 Å consensus volume. Bayesian polishing in RELION followed by non-uniform refinement and fitting of per-particle CTF parameters plus beam tilt and trefoil generated a 3.5 Å map. Map quality was further improved by non-uniform refinement of a cleaner particle set (534,368 particles) generated by an additional round of 2D classification (k = 100, 180 Å soft circular mask), despite no increase in nominal resolution. A second β-barrel could be resolved in map density at low contour level (0.08). Attempts to improve map quality for this partner β-barrel, through extensive 3D classification and local refinement schemes, did not improve map quality for this region.

Model building, structure refinement and figure preparation

Iterative model building and real-space refinement using secondary structure, rotamer, and Ramachandran restraints was performed in Coot v0.9[61] and Phenix 1.21[62], respectively. Validation was performed in Molprobity 4.5.2[63] within Phenix. Cryo-EM data collection, image processing and structure refinement statistics are listed in Extended Data Table 1. Figures were prepared using UCSF ChimeraX v.1.9[60].

Reporting summary

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

Data availability

Cryo-EM density maps and atomic coordinates have been deposited in the Electron Microscopy Data Bank (EMDB) with the following accession numbers: EMD-48835 (BAMFj composite map), EMD-48832 (BAMFj consensus map), EMD-48833 (BAMFj BamHM-focused map), EMD-48834 (BAMFj BamADP-focused map), EMD-48836 (BamAP complex) and EMD-48837 (BamAD complex). Atomic coordinates have been deposited in the Protein Data Bank (PDB) with the following accession numbers: 9N2D (BAMFj complex), 9N2E (BamAP complex) and 9N2F (BamAD complex). The hybrid model of the BAMFj complex is provided in Supplementary Data 1. Raw proteomics data have been deposited in the PRIDE database with the accession PXD065907. Processed proteomics source data and peptide fingerprinting source data are provided with this paper. Uncropped gels and immunoblots are in Supplementary Fig. 2. Requests for materials should be addressed to B.C.B. Source data are provided with this paper.

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Acknowledgements

The authors thank M. McBride and S. Shibata for providing antibodies used in this study; F. Lauber for producing the SkpA antiserum; K. Lewis and B. Hawking for providing darobactin; V. Ravikumar for collecting the proteomics data; M. Fournier, F. Mardakheh, S. Mohammed and J. Holder for advice on interpreting this data; R. Dhaliwal and C. Melia for carrying out the TEM analysis; and I. Lidbury for advice on sugar usage by F. johnsoniae. We acknowledge the use of the University of Oxford Department of Biochemistry Advanced Proteomics Facility, the Oxford Micron Advanced Imaging Facility, and the Sir William Dunn School of Pathology Electron Microscopy Facility. This work was supported by European Research Council Advanced Award 833713 (B.C.B.). This research was supported in part by the Intramural Research Program of the NIH.

Author information

Author notes

1. These authors contributed equally: Xiaolong Liu, Luis Orenday Tapia, Justin C. Deme

Authors and Affiliations

1. Department of Biochemistry, University of Oxford, Oxford, UK

Xiaolong Liu, Luis Orenday Tapia & Ben C. Berks

2. Center for Structural Biology, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA

Justin C. Deme & Susan M. Lea

3. Structural Biology, St Jude Children’s Research Hospital, Memphis, TN, USA

Susan M. Lea

Contributions

X.L. and L.O.T. carried out all genetic and biochemical experiments. J.C.D. and S.M.L. collected electron microscopy data and determined all structures. X.L., B.C.B. and S.M.L. conceived the project. B.C.B. and S.M.L. supervised the project and secured funding. All authors interpreted data and wrote the manuscript.

Corresponding authors

Correspondence to Xiaolong Liu, Susan M. Lea or Ben C. Berks.

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Nature thanks Han Remaut 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 Workflow for the cryoEM analysis of the F. johnsoniae BAMFJ complex and map quality metrics.

a, Twin-Strep-tagged BamA complexes were purified by Streptactin affinity chromatography and size exclusion chromatography and the major (highest molecular size) peak was analyzed. See Fig. 1a for corresponding SDS-PAGE analysis of this material. Image processing workflow for the BamA complexes. b,c, Gold-standard Fourier Shell Correlation (FSC) curves used for global resolution estimation (b) and local resolution estimate (c) of consensus (left), extracellular (middle), or periplasmic (right) volumes from the BAMFJ complex.

Extended Data Fig. 2 Further structural analysis of the BAMFJ complex.

a, Chain ordering. The indicated subunit in each panel is rainbow-coloured from the N- (blue) to C-terminus (red). b, BamH (green) in cartoon representation with ligands (glycosylation and lipidation) as space fill representation. The protein is viewed from the direction indicated in (a) and overlaid with the closest structural homologue as judged by PDBeFold 2.58[64], the chondroitin sulfate-binding carbohydrate binding module of a chondroitinase (dark grey, PDB 8wab, RMSD 2.5 Å across 64 equivalent residues), which is defined as a DNRLRE domain-containing protein by UniProtKB. c, BamM (tan) in cartoon representation with bound metals shown as purple spheres and coordinating residues in ball-and-stick representation. The protein is viewed from the direction indicated in (a) and overlaid with the closest structural homologue as judged by PDBeFold 2.58[64], the peptidyl-prolyl isomerases (PPI) subunit (dark grey) from the Type 9 Secretion System translocon complex (PDB: 6h3i chain B, RMSD 0.75 Å across 74 equivalent residues). d, Glycosylation and lipidation of BamH shown in ball-and-stick representation within the cryoEM volume in the context of the full chain (left) and in closeup (right). The modelled glycosylation was assigned on the basis of the EM density informed by prior studies of O-glycosylation in Bacteroidota[65] but without biochemical identification. e, Bound metals within BamM modelled as calcium ions (purple spheres) with coordinating residues shown in ball-and-stick representation. The model is displayed within the EM density (insets) or showing just the EM density for the metal ions (full structure). The metals were assigned as Ca ions based on their co-ordination chemistry (O-only ligation, variable co-ordination number and geometry, and appropriate bond lengths) and refining to thermal mobility (B) factors that matched those of the ligating protein atoms.

Extended Data Fig. 3 Genomic organisation of F. johnsoniae bam genes and biochemical evidence that BamP4 interacts with the BAMFJ complex.

a, Genomic organisation of F. johnsoniae bam genes. porG[66] and skpA[19] at the bamA locus code for components of the Type 9 Secretion system. BamH2 would be unlikely to interact with BamM as it lacks the protruding β-hairpin that BamH uses for this purpose. BamP homologues have related folded domains but markedly diverge in the interdomain loop. b-d, BamP interacts with the BAMFJ complex. Strains in which the native BamA protein was fused to a HA tag (HA-bamA allele) were transformed with plasmids overproducing N-terminally Twin-Strep-tagged BamP (pTSBamP) or BamP4 (pTSBamP4). wt, wild type. Similar data were obtained for three biological repeats. b, Immunoblots of whole cells showing overproduction of BamP or BamP4 relative to native BamP levels. The cytoplasmic protein GroEL was used as a loading control. c,d, Affinity purification of Twin-Strep-tagged BamP and BamP4 complexes. c, Coomassie-stained SDS–PAGE gel of the wash and elution fractions. BAM subunits were assigned by comparison with (d) and Fig. 1a. d, Immunoblotting analysis of the elution fractions with anti-HA (to identify BamA), anti-BamH, and anti-Twin-Strep (to identify BamP and BamP4) antibodies.

Extended Data Fig. 4 Phenotypic characterisation of strains with deletions in BAMFJ subunits or BAMFJ subunit homologues.

a, Results of attempts to delete BamFJ subunits and their homologues in different genetic backgrounds. Mutations and their combinations that were viable are indicated by green dots, while those that could not be constructed are indicated by red dots and are assumed to disrupt essential cell functions. b, Immunoblots of whole cells and isolated membranes of strains containing in-frame deletions of bamM or bamP. The cytoplasmic protein GroEL and OM protein OmpA were used as loading controls. *, non-specific band. Similar results were obtained from 3 biological repeats. c, Growth curves on rich CYE medium. Shown are the means ± 1 SD from three biological repeats. d, Growth curves on minimal medium containing either galactomannan or xyloglucan as carbon source. Shown are the means ± 1 SD from three biological repeats. e, Spreading (gliding) morphology of colonies on agar. Scale bar, 5 mm. Similar data were obtained for three biological repeats. f,g, OM integrity assays. Cells were grown on CYE agar with the indicated additions. wt, wild type; ΔbamP-P3, strain deleted for BamP, BamP2, and BamP3; ΔbamP-P4, strain deleted for all BamP homologues (ΔbamP-P3 ΔbamP4). The ΔporV Δplug background permeabilizes the OM through opening the T9SS translocon channel[15]. Similar data were obtained for three biological repeats. h, BamP overproduction restores vancomycin resistance to a strain lacking all BamP homologues (strain ΔbamP-P4). Where indicated strains were transformed with plasmids overproducing N-terminally Twin-Strep-tagged BamP (pTSBamP) or BamP4 (pTSBamP4). wt, wild type. Similar data were obtained for three biological repeats.

Extended Data Fig. 5 Workflow for the cryoEM analysis of the BamA complex isolated from a ΔbamP background.

a, Size exclusion chromatography profile of BamA complexes purified from a BamP-deleted background together with a Coomassie-stained SDS–PAGE gel of the indicated peak fraction that was used for structure determination. BamA* indicates a proteolysis product of BamA. Similar results were obtained from 2 biological repeats. b, Image processing workflow for the ΔBamP complex. c, Gold-standard Fourier Shell Correlation (FSC) curves used for global resolution estimation. d, Local resolution estimate of the volume, displayed at two contour levels. e, Density for the unassigned second barrel taken from the focused 3.7 Å volume is shown with either a second copy of BamA or BamG docked. Two views are shown from the side (left) or the cell exterior (right). The shape and size of the volume is clearly more consistent with this being a second copy of BamA than BamG.

Source data

Extended Data Fig. 6 Workflow for the cryoEM analysis of the BamAP complex.

The sample used was fraction B from Fig. 1a. a, Image processing workflow for the BamAP complexes. b, Gold-standard Fourier Shell Correlation (FSC) curves used for global resolution estimation. c, Local resolution estimate of the volume.

Extended Data Fig. 7 Depletion analysis of the essential F. johnsoniae BAM complex subunits.

a, Design of an anhydrotetracycline (aTC)-inducible system for the depletion of essential target genes in F. johnsoniae. The TetR repressor is constitutively expressed under the control of the F. johnsoniae ompA promoter (PompA) and the target gene is regulated by a designed TetR-repressed promoter (Pinduc). In the presence of the inducer aTC repression of the target gene by TetR will be released. The genetic system is integrated into the F. johnsoniae chromosome at a neutral locus. b, Sequences of the designed inducible PompA-induc and Pfjoh_0824-induc promoters. tetO2 arrays are placed upstream and downstream of the conserved −33 and −7 RNA polymerase binding sites (boxed) of the selected promoters. c, Tight regulation of protein expression by the designed inducible systems. Strains expressing NanoLuc under the control of either the PompAindc promoter (XLFJ_1095) or the Pfjoh_0824induc promoter (XLFJ_1100) were grown to mid-exponential phase (OD_600 = 0.6) in the presence or absence of aTC and the luminescence signal measured. Error bars represent the mean ± 1 SD from three biological repeats. P values were determined with a two-sided paired Student’s t-test. RLU, relative luminescence units. d, Comparison of the expression levels of Bam subunits in the wild type strain (wt, XLFJ_1078) and corresponding depletion strains grown in the presence of the inducer aTc (bamAdep, XLFJ_1129; bamGdep, XLFJ_1115; bamHdep, XLFJ_1140). Whole cell immunoblotting of cells grown to mid-exponential phase (OD_600 = 0.6). The blots for the depleted subunit are boxed in red. The BamH blot for the BamH depletion comparison is overexposed (OE) relative to the other BamH blots in order to detect the low levels of BamH in the depletion strain. BamA and BamG are detected via epitope tags. *, non-specific band. Similar results were obtained for 3 biological repeats. e, Phase contrast images of cells sampled at the indicated time points in the BAMFJ subunit depletion experiments shown in Fig. 4a. Scale bar, 10 µm. Similar results were obtained for 3 biological repeats. f, The major F. johnsoniae SUS complex is composed of SusC (Fjoh_0403), SusD (Fjoh_0404), and SusE (Fjoh_0405). The native SUS complex was purified via a Twin-Strep tag on the N-terminus of SusC followed by size exclusion chromatography and analysed on a Coomassie-stained SDS–PAGE gel. Proteins were identified by peptide mass fingerprinting. Similar data were obtained for two biological repeats. g, Outer membrane vesicle (OMV) production does not increase upon BAM depletion. Immunoblotting of the OM protein SusC in whole cells or the OMV fraction at the 6 h time point in Fig. 4a. GroEL serves as loading control. Similar results were obtained for 3 biological repeats. h, An exogenously-expressed His-tagged variant of SusE (SusEHis) is incorporated into the native SusCDE complex. SusC-containing complexes were purified as described in f from cells expressing SusEHis from a plasmid. The purified material was separated by SDS-PAGE and characterized by Coomassie-staining (Left) and anti-His tag immunoblotting (Right). Similar data were obtained for two biological repeats. i, Exemplar Coomassie-stained SDS-PAGE gel of the whole membrane samples used for the comparative proteome analysis (Fig. 4e,g) of induced/non-induced (i.e. undepleted/depleted) BAM subunit depletion strains harvested at the 6 h time point in Fig. 4a. Proteins present in the two obviously depleting bands were assigned by peptide mass fingerprinting. The data are representative of the three repeats used for the proteomics analysis.

Source data

Extended Data Fig. 8 OM proteomics data comparisons.

Heat maps of the indicated strains after hierarchical protein clustering of the entire datasets and post hoc ANOVA testing. Only proteins classified as OMPs or SLPs are displayed. Colours indicate HSD (honestly significant difference) values according to the intensity panel. a, Comparison of the datasets used in Fig. 4e and g. b, Comparison of the bamHsup mutant dataset with the induced and non-induced bamHdep datasets. The non-recovered proteins are numbered as in Fig. 5d and assigned to SusC, SusD, other SUS SLP, or TonB-dependent transporter (TBDT) protein families. TBDTs are 22-strand OMPs that are related to the SusC family.

Source data

Extended Data Fig. 9 Isolation of BamA complexes from subunit depleted backgrounds and phenotypic characterisation of the bamHsup strain.

a,b, Isolation of BamA complexes either (a) in the absence of BamM or (b) after 6 h of depletion of the essential BamG or BamH subunits. Size exclusion chromatography profile of Twin-Strep-tagged BamA complexes purified by Streptactin affinity chromatography (Left) and a Coomassie-stained SDS–PAGE gel of the indicated peak fractions (Right). BamA* indicates a proteolysis product of BamA. The identities of the BamA* and BamD + BamP bands were assigned by peptide fingerprinting. Similar results were obtained from 2 biological repeats. c-f, Characterization of the recreated bamHsup mutant (bamAQ801K ΔbamH ΔbamH2). wt, wild type. Similar results were obtained for three biological repeats. c, OM integrity assays. Cells were grown on CYE agar with the indicated additions. d, Surface exposure of the SLP SusE. Strains expressing a protease-sensitive His-tagged variant of SusE (SusEHis) were treated as indicated with Proteinase K and the detergent Triton X-100 (to permeabilise the OM). Reactions were stopped immediately (t_0) or after 20 min (t_20) and analysed by immunoblotting with His tag antibodies. The periplasmic protein SkpA serves as an OM integrity control. e, Spreading (gliding) morphology of colonies on agar. Scale bar, 5 mm. f, Purification of the native SusCDE complex via a Twin-Strep tag on the N-terminus of SusC followed by size exclusion chromatography. Analysed on a Coomassie-stained SDS–PAGE gel.

Source data

Extended Data Table 1 Cryo-EM data collection, refinement and validation statistics

Extended Data Table 2 Phylogenetic distribution of BAMFJ subunits

Supplementary information

Supplementary Information

Supplementary Fig. 1, Supplementary Tables 1–4 and references

Reporting Summary

Supplementary Figure 2

Uncropped gels and immunoblots. See separate source data file Supplementary Fig. 1.

Supplementary Data 1

Hybrid model of the BAMFj complex. The experimental model deposited as PDB: 9N2D is extended by AlphaFold structures for the parts of the complex that are insufficiently resolved in the electron microscopy density to allow confident model building (the POTRA 1–3 domains of BamA and C-terminal portion of BamP)

Source data

Source Data Fig. 1

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Source Data Fig. 5

Source Data Extended Data Fig. 5

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Source Data Extended Data Fig. 9

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Liu, X., Orenday Tapia, L., Deme, J.C. et al. A new paradigm for outer membrane protein biogenesis in the Bacteroidota. Nature 647, 479–487 (2025). https://doi.org/10.1038/s41586-025-09532-8

- Received: 03 February 2025

- Accepted: 14 August 2025

- Published: 01 October 2025

- Version of record: 01 October 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09532-8

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Although ICIs substantially improve survival in some patients, most patients do not benefit from these therapies1,2,3,4,5,6,7,8,9. These poor responses are attributed to immunosuppressive tumour microenvironments (TMEs) characterized by tolerogenic dendritic cells (DCs), myeloid suppressor cells and regulatory T cells, and may be predicted in some histologies by low intratumoural PD-L1 before immunotherapy initiation. However, there are currently no clinically available methods to improve responses to ICI by modifying the TME. We recently reported that systemic administration of highly immunogenic mRNA nanoparticles induces a viraemia-like cytokine/chemokine response that resets the systemic and intratumoural immune milieu, sensitizing resistant tumours to ICIs10,11,12,14. Although the personalized mRNA vaccines that we and others are developing remain in clinical evaluation (NCT04573140)[12], COVID-19 mRNA vaccines also induce robust stimulation of cytokine secretion[15], and there are now multiple case reports of patients whose tumours spontaneously resolved after COVID-19 mRNA vaccines16,17. However, the impact of COVID-19 mRNA vaccines on immune therapy is unknown.

Here we report that the innate immune response to SARS-CoV-2 spike mRNA vaccination resets the cancer immunotherapy cycle and primes adaptive immunity for synergy with ICIs. We found that receipt of a SARS-CoV-2 mRNA vaccine within 100 days of ICI initiation was associated with substantial improvements in overall survival (OS) in patients with non-small cell lung cancer (NSCLC) and melanoma. In preclinical models, we found that this effect required a surge in type-I interferon (IFN) that enhanced antigen-presenting cell (APC) priming of T cells in lymphoid organs. Although tumour cells subvert these primed responses by increasing PD-L1 expression, co-administration of ICIs sustains T cell responses and elicits epitope spreading against tumour-associated antigens. We revealed analogous response correlates for humans receiving COVID-19 mRNA vaccines, including heightened IFNα production, innate/adaptive immune activation and increases in tumour PD-L1 expression. Together, our results demonstrate that clinically available mRNA vaccines targeting non-tumour antigens are potent immune modulators capable of sensitizing tumours to ICIs.

Improved survival with COVID-19 vaccination

To determine whether COVID-19 mRNA vaccines were associated with improved responses to immune checkpoint blockade, we first compared the OS among a cohort of patients with stage III/IV NSCLC treated at The University of Texas MD Anderson Cancer Center (MDACC) between January 2015 and September 2022 (Supplementary Table 1a). We identified 180 patients who received a COVID mRNA vaccine within 100 days of ICI initiation, and 704 patients who were treated with ICI and did not receive a COVID vaccine (Supplementary Table 1a). Of the 180 patients who received an mRNA vaccine within 100 days of ICI initiation, 117 received the BNT162b2 vaccine and 63 received mRNA-1273 (Extended Data Fig. 1a); 24 received a priming dose only, 57 received a booster only, 93 received both a prime and a boost dose, 5 received 2 booster doses, and 1 received a priming dose and 2 booster doses within 100 days of ICI initiation (Extended Data Fig. 1b); 81 received 1 dose of COVID-19 mRNA vaccination within 100 days, 98 received 2 doses, and 1 received 3 doses (Extended Data Fig. 1c). After controlling for 39 covariables with Cox proportional hazards regression, including clinical stage, histology, steroid use, performance status, mutation status, comorbidities and treatment year, we found that receipt of a COVID-19 mRNA vaccine within 100 days of initiation of ICI was associated with significantly improved median OS (20.6 months versus 37.3 months) and 3 year OS (30.8% versus 55.7%, adjusted hazard ratio (HRadj = 0.51, 95% confidence interval (CI) = 0.37–0.71, P < 0.0001) (Fig. 1a and Supplementary Tables 2 and 3). This survival advantage was similar for patients with stage III unresectable NSCLC (HRadj = 0.37, 95% CI = 0.16–0.89, P = 0.0268) (Fig. 1b and Supplementary Tables 4 and 5) and stage IV NSCLC (HRadj = 0.52, 95% CI = 0.37–0.74, P = 0.0002) (Fig. 1c and Supplementary Tables 6 and 7); patients who received mRNA vaccines from either vaccine manufacturer (Extended Data Fig. 1a); and patients who had or had not received a previous COVID-19 mRNA vaccine (Extended Data Fig. 1b). Patients who received two vaccines in the 100 days surrounding initiation of ICI experienced similar OS compared with those who received only one vaccine (Extended Data Fig. 1c). These results were also consistent when considering only those patients whose closest mRNA vaccine was within 100 days before their first ICI (Extended Data Fig. 1d), when narrowing the vaccination window to 50 instead of 100 days (Extended Data Fig. 1e), when restricting to only those patients treated during the pandemic (Extended Data Fig. 1f), after correcting for immortal time bias (Extended Data Fig. 1g) and with propensity score matching (PSM; Extended Data Fig. 1h,i). Patients who received a COVID-19 vaccine within 100 days of chemotherapy (a group that did not include targeted therapies owing to significant heterogeneity and limited patient numbers within different drug cohorts) but did not receive ICI had no detectable survival benefit (Extended Data Fig. 2a). Likewise, patients who received a pneumonia or influenza vaccine within 100 days of initiating ICI (Extended Data Fig. 2b–e) and those with resectable stage III tumours (Extended Data Fig. 2f,g) experienced no improvement in survival.

Fig. 1: COVID-19 mRNA vaccines are associated with improved survival in patients with NSCLC or metastatic melanoma receiving immunotherapy.

a–e, Survival for patients with NSCLC (a–c) or metastatic melanoma (d,e) treated with ICI who received a COVID-19 mRNA vaccine within 100 days of initiating ICI or did not receive a COVID-19 mRNA vaccine. Survival is shown for all patients with NSCLC (a), patients with unresectable stage III NSCLC (b), patients with stage IV NSCLC (c) and patients with metastatic melanoma (d and e). P values and HRadj were calculated using two-sided Cox proportional hazards regression (Supplementary Tables 3, 5, 7, 9 and 11), including all variables that were significantly associated with survival on univariable analysis (Supplementary Tables 2, 4, 6, 8 and 10). The number of patients at risk at each timepoint is indicated below each graph.

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We then repeated this analysis in a separate cohort of patients treated with their first round of ICI for metastatic melanoma, including 43 patients who received a COVID-19 mRNA vaccine within 100 days of initiating ICI and 167 who did not receive a COVID-19 vaccine (Supplementary Table 1b). Of the patients who received mRNA vaccines, 21 received BNT162b2 and 22 received mRNA-1273 (Extended Data Fig. 3a); 6 received a priming dose only, 8 received a booster only, 16 received both a prime and a boost dose, and 13 received 2 booster doses during our period of interest, (Extended Data Fig. 3b); 14 received 1 dose of COVID-19 mRNA vaccination within 100 days, and 29 received 2 doses (Extended Data Fig. 3c). After accounting for covariables, including histology, steroid use, performance status, mutation status, comorbidities and treatment year with Cox proportional hazards regression, we found that receipt of a COVID-19 mRNA vaccine within 100 days of initiating ICI was associated with substantially improved OS (median OS, 26.67 months versus unmet; 36-month OS, 44.1% versus 67.6%; HRadj = 0.37, 95% CI = 0.18–0.74, P = 0.0048) (Fig. 1d and Supplementary Tables 8 and 9) and progression-free survival (PFS) (median PFS, 4.0 months versus 10.3 months; 36-month PFS, 23.7% versus 39.5%; HRadj = 0.63, 95% CI = 0.40–0.98, P = 0.0383) (Fig. 1e and Supplementary Tables 10 and 11). These effects were again similar for both vaccine manufacturers (Extended Data Fig. 3a), prime and boost vaccines (Extended Data Fig. 3b), patients receiving single or multiple vaccine doses (Extended Data Fig. 3c), when limiting the analysis to only patients who received their vaccine prior to initiating ICI (Extended Data Fig. 3d), when limiting the analysis to only those treated during the pandemic (Extended Data Fig. 3e,f) and when expanding the analysis to include patients on second- or third-line therapy and those without distant metastases (Extended Data Fig. 3g–i). Moreover, the survival advantage was magnified with PSM (OS: HR = 0.44, 95% CI = 0.18–0.77, P = 0.0063; PFS: HR = 0.46, 95% CI = 0.27–0.77, P = 0.0022) (Extended Data Fig. 3j,k).

Type I IFN mediates RNA vaccine immunity

To demonstrate whether effects observed in humans could be modelled in animals, we recreated commercial preparations of COVID mRNA vaccines for administration to tumour-bearing animals in conjunction with ICIs. As SARS-CoV-2 spike protein has been fully sequenced, we synthesized the published mRNA construct used for the Pfizer/BioNTech vaccine (BNT162b2) and validated the fidelity of synthesis based on mRNA size (Extended Data Fig. 4a,b) and the ability to elicit neutralizing antibodies after in vivo administration to animals (Extended Data Fig. 4c). mRNA was encapsulated in lipid nanoparticles (LNPs) and met the specification range delineated for clinical preparations of BNT162b2 based on encapsulation efficiency, size distribution, polydispersity and charge[18] (Extended Data Fig. 4d–g), the latter of which we found could be disproportionately affected by buffer conditions leading to a net positive charge (Extended Data Fig. 4h). We chose mouse B16F0 melanoma and Lewis lung carcinoma (LLC) as models to test immunogenicity and efficacy of spike RNA-LNP vaccines due to the effects of the vaccine in these clinical settings and because both tumour models are poorly responsive to ICIs. We first treated mice with two vaccine doses in conjunction with ICI treatment. We found this regimen to be superior to either monotherapy alone in mice with established B16F0 tumours with tumour volumes of about 80 mm^3 (Fig. 2a and Extended Data Fig. 5a), in mice with subcutaneous (s.c.) LLC (Fig. 2b and Extended Data Fig. 5b) and in mice with established s.c. LLC with tumour volumes around 100 mm^3 (Fig. 2c and Extended Data Fig. 5c). In mice with established LLC, we identified metastatic lesions in the lungs of untreated mice that were similar for monotherapy but significantly reduced with the combination treatment (Extended Data Fig. 5d,e). We also found that starting RNA-LNPs before ICI produced similar effects relative to concomitant treatment (Extended Data Fig. 5f). We next tested whether RNA-LNPs could reduce the growth of orthotopic intrapulmonary tumours. We implanted LLC cells orthotopically and administered treatment starting on day 3. In this model, the combination of RNA-LNPs and ICI resulted in superior inhibition of tumour growth as measured by lung weights (Extended Data Fig. 5g).

Fig. 2: Spike RNA-LNPs prime anti-cancer immunity in an IFN-I dependent manner.

a, The experimental design and tumour volume of mice inoculated with B16F0 cells. Groups included untreated (UT; n = 7), anti-PD-1 (n = 8), RNA-LNPs (n = 8) and RNA-LNPs + anti-PD-1 (n = 8). mAb, monoclonal antibody. b, The experimental design and tumour volume for mice inoculated with LLC cells. Groups included untreated (n = 8), anti-PD-1 (n = 9), RNA-LNP (n = 9) and RNA-LNPs + anti-PD-1 (n = 9). c, The experimental design and tumour volume measurements for mice inoculated with LLC cells. Groups included untreated (n = 9), anti-PD-1 (n = 10), RNA-LNP (n = 7) and RNA-LNPs + PD-1 (n = 8). d, The experimental design and tumour volume for mice inoculated with B16F0 cells. n = 12 per group. e, IFNα plasma enzyme linked immunosorbent assay (ELISA) from B16F0-tumour-bearing mice (n = 8 per group) 24 h after one RNA-LNP vaccine (day 3). f–i, Cellular phenotyping within 24 h of vaccine 3 (days 3, 6 and 20) of cells from spleens of mice bearing B16F0 tumours (n = 5 per group), including the percentage of activated (CD80+CD86+) DCs (f) and macrophages (g). h,i, PD-L1 median fluorescence intensity (MFI) on activated mature DCs (h) and macrophages (i). j–o, Characterization of antigen presentation among myeloid cells in tumour draining lymph nodes (tdLNs) and spleens of mice bearing B16F10-ova tumours 24 h after vaccine 2 (days 10 and 13). The overall percentage of CD45+ cells that express MHC-II+ (j), the percentage of MHC-II+ cells presenting SIINFEKL (k), the percentage of SIINFEKL-presenting MHCII+ cells that express the activation marker CD86 (l), the percentage of CD45+ cells that are MHC-II+Ly6C+ (m), SIINFEKL+MHC-II+Ly6C+ cells as a percentage of all CD45+ cells (n) and Ly6C+MHCII+ cells as a percentage of total SIINFEKL-presenting cells (o) are shown. n = 4 biologically independent mice per group. Significance was determined using two-way analysis of variance (ANOVA)/mixed-effect analysis with Geisser–Greenhouse correction (a–d) and two-tailed unpaired t-tests (e–o). n indicates the number of biologically independent samples. For the box plots, the whiskers extend to the highest and lowest values, the box limits show the first and third quartiles and the centre line shows the median value. For a–d, data are mean ± s.e.m.

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We next sought to understand the mechanism by which RNA-LNPs targeting the spike protein mediate these antitumour effects. To do this, we used an early treatment model of B16F0 with or without cytokine-blocking antibodies. In this model, RNA-LNPs and PD-L1 blockade each provided numerical but statistically insignificant survival benefits. However, combination therapy with RNA-LNPs and PD-L1 blockade strongly inhibited tumour growth (Fig. 2d and Extended Data Fig. 6a,b). Although we previously demonstrated a role for IFNα in the response to mRNA vaccines, recent evidence suggests a dominant role for IL-1 signalling in responses to the specific RNA-LNPs targeting the COVID-19 spike protein[15]. We therefore evaluated the importance of each pathway with antibodies blocking the IL-1 and IFNα receptors. While blockade of IL-1R had no effect on tumour growth, anti-tumour responses were completely abrogated when blocking type I interferon signalling with IFNAR1 monoclonal antibodies (Fig. 2d). Moreover, direct administration of supraphysiologic doses of type I IFN recapitulated the antitumour effects (Extended Data Fig. 6c). However, stimulation of type I IFN signalling with low molecular mass (LMW) poly(I:C) did not elicit similar immunity (Extended Data Fig. 6d). These results highlight the importance of IFN in driving innate and adaptive immunity, and the ability to manipulate and reset the immune setpoint away from tolerance toward effector immune responses in the presence of ICIs using a commercially available mRNA preparation.

Next, we sought to assess whether other mRNA species would elicit similar antitumour effects. We first modified the protein encoded by the mRNA by replacing the spike mRNA with mRNA encoding the cytomegalovirus antigen pp65, which is overexpressed in human glioma but not in B16F0. We found no significant difference in antitumour activity between these two groups, suggesting that innate immune sensing of the mRNA itself is the primary driver of antitumour activity from mRNA vaccines (Extended Data Fig. 6e,f).

As N^1-methyl-pseudouridine is used in place of uridine in COVID-19 mRNA vaccines to reduce innate immune activation, we hypothesized that replacing N^1-methyl-pseudouridine with uridine to activate pattern recognition receptors would provide even more robust innate immune activation and antitumour activity. As expected, replacing N^1-methyl-pseudouridine with uridine in pp65 mRNA resulted in further synergy with ICI (Extended Data Fig. 6e,f). However, replacing N^1-methyl-pseudouridine with uridine in spike mRNA provided only numerical improvement in antitumour response (Extended Data Fig. 6e,f), suggesting that innate immune sensing is a multifactorial process that may be influenced by specific mRNA constructs.

We then completed additional studies to identify how RNA-LNPs elicit antitumour immunity. Previous work has established that RNA-LNPs stimulate type I IFN production by stimulating the intracellular double-stranded RNA (dsRNA) sensor MDA5[19]. However, the mechanism by which RNA-LNPs activate this sensor is unclear. To understand this further, we first ruled out a role for dsRNA in our vaccines by measuring the level of dsRNA contamination in our manufactured ssRNA product. We found the dsRNA/ssRNA ratio in our mRNA to be 0.011% (Extended Data Fig. 7a). We then repeated our tumour growth curve incorporating an additional dsRNA-removal protocol[20]. With this step, we successfully removed all detectable dsRNA contamination (Extended Data Fig. 7a). Importantly, we found no change in antitumour efficacy with the complete elimination of dsRNA using this method (Extended Data Fig. 7b). Finally, we found no abrogation of type I IFN after RNA-LNP administration in mice lacking RIG-I, a critical sensor of dsRNA and activator of type I IFN response (Extended Data Fig. 7c). Together, these data suggest that dsRNA is not a major cause of the antitumour effects in our preclinical models.

We next tested whether the specific LNP construct impacted antitumour immunity with the same mRNA input. We found that anionic lipoplexes formulated as lipid particle aggregates (LPA) do not elicit similar antitumour effects (Extended Data Fig. 7b). Given that previous literature described that higher-order RNA structures may also stimulate MDA5 activation[21], we hypothesized that RNA-LNPs might create higher order structures. Consistent with this hypothesis, we found that RNA extracted from our RNA-LNPs contained high-molecular-mass secondary structures similar in mass to dsRNA (Extended Data Fig. 7d and Supplementary Fig. 1). Combined with previous reports of MDA5 activation with RNA-LNPs but not with ssRNA alone, these data may suggest a mechanism by which encapsulation with LNPs forms high-molecular-mass secondary structures enabling activation of double-stranded sensing machinery (that is, MDA5) for induction of type I interferons.

RNA vaccines stimulate innate immunity

We next sought to better understand the impact of RNA-LNPs on innate immune cells. IFNα was significantly elevated in both RNA-LNP and combination groups in conjunction with other T helper 1 chemokines (Fig. 2e and Extended Data Fig. 8). This surge in cytokine/chemokine response correlated with an increase in myeloid cell activation in the lymphoid organs of mice treated with RNA-LNP alone or in combination with ICI. After treatment with spike-encoding RNA-LNP, there was precipitous activation of APCs, including DCs, macrophages and Ly6C+ cells expressing MHC class II (MHC-II) in lymphoreticular organs, that was abrogated by IFNAR1 blockade (Fig. 2f–i, Extended Data Fig. 9a,b, Supplementary Table 12 and Supplementary Fig. 2). Although this response was dependent on interferon signalling, the magnitude of the response could not be achieved by simply administering systemic IFNα in combination with ICI (Fig. 2f–i and Extended Data Fig. 9a,b).

Myeloid activation extended to the tumour, where receipt of RNA-LNPs was associated with IFNAR1-dependent increases in activated Ly6C+ myeloid cells (Extended Data Fig. 9c and Supplementary Fig. 3). To determine whether increased myeloid activation corresponded to enhanced presentation of tumour antigens, we treated mice with B16F10 tumours expressing chicken ovalbumin (B16F10-OVA) at days 10 and 13 and evaluated presentation of ovalbumin on MHC-I using flow cytometry. We found that spike-encoding RNA-LNPs stimulate APCs to present tumour antigens in lymphoid organs in the presence of costimulatory molecules (Fig. 2j–l and Supplementary Fig. 4). Notably, presentation of tumour antigens in the presence of costimulatory molecules was particularly enriched in Ly6C+MHC-II+ cells (Fig. 2m–o).

RNA vaccines reprogram adaptive immunity

In addition to the precipitous activation of APCs, spike-encoding RNA-LNPs in combination with ICIs also elicited expansion of CD8+ T lymphocytes with a marked increase in activation within the effector and effector memory compartments (Fig. 3a,b, Extended Data Fig. 9d, Supplementary Table 13 and Supplementary Fig. 5). Concomitant with these findings, PD-1 expression was increased in T cells and effector/effector memory CD8+ cell subsets, underscoring the potent ability of combination therapy to rapidly prime T cells (Extended Data Fig. 9e,f and Supplementary Fig. 6). Overall, these results illustrate a role for systemic immunomodulation in the recruitment of myeloid cells to lymphoid organs for tumor antigen presentation to cognate T cells.

Fig. 3: Spike RNA-LNPs generate tumour-reactive T cells and increase PD-L1 expression on tumour cells.

a,b, The percentage of activated effector (a) and effector memory (b) T cells in the spleens of tumour-bearing mice on day 21 (vaccine days 3, 6, 17) (n = 5 per group). c, The percentage of tetramer+ cells of splenic CD8+ T cells collected from mice bearing B16F0 tumours on day 21 (vaccination days 14 and 17). Groups include untreated (n = 6), anti-PD-1 (n = 6), RNA-LNPs (n = 5) and RNA-LNPs + anti-PD-1 (n = 7). d, The normalized percentage of AIM+ T cells (n = 5 mice per peptide; exceptions are shown in the Supplementary Information) after splenocyte co-culture with overlapping peptide pools. e,f, Representative images (e) and blinded manual counting (n = 4 tumours per group with 4 counts per tumour) (f) of PD-1+CD3+ cells by immunofluorescence 24 h after vaccine 3 (days 3, 6 and 20) from s.c. tumors of B16F0-tumour-bearing mice treated with or without anti-PD-L1. For e, scale bars, 100 μm. AF647, Alexa Fluor 647. g, The percentage PD-1+CD8+ cells of CD3+ T cells in tumours of B16F0-bearing mice vaccinated with RNA-LNPs (days 14 and 17). Groups included untreated (n = 7), anti-PD-L1 (n = 8), RNA-LNPs (n = 9) and RNA-LNPs + anti-PD-L1 (n = 7). h, Pooled tetramer positivity (%) among CD8+ T cells in B16F0 tumours. Groups included untreated (n = 6), anti-PD-1 (n = 6), RNA-LNPs (n = 4) and RNA-LNPs + anti-PD-1 (n = 7) (RNA-LNPs days 14 and 17). i, PD-L1 expression on B16F0 tumour cells (CD45−FSC-Ahigh) isolated from mice 24 h after vaccine 3 (days 3, 6 and 17) as determined using flow cytometry. Groups included untreated (n = 4) and RNA-LNPs (n = 5). j,k, Blinded manual counting (n = 6 tumours per group with 4 counts per tumour) (j) and representative images (k) of PD-L1+ tumour cells (SOX10) by immunofluorescence 24 h after vaccine 3 (days 3, 6, 21) from B16F0-tumour-bearing mice. For k, scale bars, 50 μm. For j, the circle symbols indicate PBS treatment and the square symbols represent anti-PD-L1 treatment. For f and j, the colours represent individual tumours. Significance was determined using two-tailed unpaired t-tests (a–c and g–j), two-tailed Welch’s t-test (f), and two-tailed Brown–Forsythe and Welch ANOVA, followed by Dunnett’s T3 multiple-comparison test (d). For the box plots, the whiskers extend to the highest and lowest values, the box limits show the first and third quartiles and the centre line shows the median value. n values indicate biologically independent samples unless indicated otherwise.

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To confirm that spike RNA-LNPs were mediating the expansion of tumour reactive T cells, we isolated CD8+ cells from spleens of treated mice and stained them with tetramers targeting peptides with high predicted binding affinity from six melanoma-associated antigens: GP-100 EGSRNQDWL, GP-100 KVPRNQDWL, claudin 6 (CLDN6), survivin, WT1 and Trp2. We found that combination therapy with RNA-LNPs and ICIs stimulated expansion of tetramer reactive T cells targeting each of these antigens (Fig. 3c and Supplementary Fig. 7). To confirm that these T cells were truly tumour reactive, we used an activation-inducible marker (AIM) assay22,23. In this assay, antigen-reactive T cells cultured ex vivo with overlapping peptide pools from the same tumour antigens are identified through tandem expression of the AIMs CD69 and 4-1BB (Supplementary Fig. 8 and Supplementary Table 14). Using this approach, we detected a substantial increase in peptide-reactive (AIM+) CD8+ T cells from mice treated with the combination of RNA-LNPs and ICI, further supporting tumour reactivity (Fig. 3d). These data confirm that spike RNA-LNPs prime the immune response for activation, presentation and recognition of tumour-associated antigens in a manner that can be significantly expanded through concomitant treatment with ICI.

RNA vaccines induce T cell infiltration

We next evaluated the T cell compartment in treated tumours. In mice with B16F0 tumours, we identified substantial infiltration of PD-1+CD8+ T cells using both immunofluorescence (Fig. 3e,f, Supplementary Fig. 6 and Supplementary Table 15) and flow cytometry (Fig. 3g). Importantly, we found that treatment with RNA-LNPs and ICIs increased PD-1 expression on total tumour-infiltrating CD8+ cells by greater than twentyfold (2.39% versus 51.363%, P < 0.0001) (Fig. 3f), and that PD-1+CD8+ T cells dominated the total CD3+ T cell compartment in treated mice while representing only a small minority of total CD3+ T cells in untreated mice (5.69% versus 60.6%, P < 0.001) (Fig. 3g). We next evaluated the antigen specificity of these cells. As there were many fewer CD8+ T cells in tumours compared with in spleens after enriching CD8+ cells, we pooled the six tetramers described above for a single pan-tetramer stain containing all six targets. Using this approach, we found that CD8+ tumour-infiltrating lymphocytes from mice treated with RNA-LNPs and ICIs were twice as likely to be tetramer reactive compared with non-RNA-LNP controls (3.10% versus 7.98%, P = 0.0229) (Fig. 3h and Supplementary Fig. 7).

Commensurate with the increase in tumour-reactive T lymphocytes in the TME, we found that spike-encoding RNA-LNPs significantly increased PD-L1 expression on tumour cells (Fig. 3i–k, Extended Data Fig. 9g,h and Supplementary Figs. 9 and 10). Blockade of IFNα signalling abrogated PD-L1 expression, confirming its importance in initiating the immunotherapy response (Extended Data Fig. 9g,h). Together, these data suggest that spike RNA-LNPs stimulate the production and infiltration of activated, tumour-reactive CD8+ T cells that overcome compensatory expression of PD-1 and PD-L1 in the presence of ICIs.

COVID-19 vaccines shape immunity in humans

As there are species-specific differences in how humans and mice respond to mRNA, we next sought to confirm that the pathways identified in mouse models are relevant in humans. To do this, we collected blood and plasma samples from five healthy volunteers at the baseline and at 6 h, 24 h, 7 days and 14  days after receipt of mRNA-1273 Spikevax Monovalent XBB.1.5 (COVID-19 mRNA vaccine, 2023–2024 formulation, 50 μg mRNA) (Fig. 4a). We first evaluated plasma from these volunteers using the NULISA-Seq Inflammation Panel (Alamar Biosciences)—a multiplex assay designed to sensitively detect over 250 immune-related cytokines. As IFNα was found to be essential for antitumour activity in mice, we were particularly interested in the kinetics of this protein at 24 h. Notably, not only was IFNα upregulated, but it was the most upregulated cytokine at any timepoint, increasing by an average of around 280-fold relative to the baseline to a final plasma concentration between 1 and 10 pg ml−1 (Fig. 4b,c). More broadly, IL-6 and IFNγ were the only cytokines that were significantly elevated at 6 h after immunization (Fig. 4d). By contrast, a multitude of inflammatory cytokines surged at 24 h after immunization, including IFNα, IFNγ, IFNω and IFNγ-inducible protein-10 (CXCL10) (Fig. 4d and Supplementary Fig. 11). Although IL-1 was not found to be significantly increased at any timepoint, IL-1R antagonist (IL1RN) was significantly elevated at 24 h, suggesting a compensatory response to a surge in IL-1 (Fig. 4d). As expected, cytokine responses to COVID-19 mRNA vaccination were short-lived in healthy individuals, with all cytokines returning to the baseline levels by 7 days (Fig. 4d).

Fig. 4: COVID-19 mRNA vaccines generate a surge in IFNα, innate immune activation and adaptive immunity in humans.

a, Schematic of the experimental design in which blood was drawn from five healthy individuals at baseline and 6 h, 24 h, 7 days and 14 days after Spikevax (mRNA-1273) COVID-19 mRNA immunization. b,c, Individual datapoints highlighting changes in expression of IFNα from baseline to 24 h for each of five healthy volunteers. Data are expressed as the fold change measured using the NULISAseq Inflammation Panel (b). The concentration was also measured separately with NULISAseq absolute quantification (AQ) (c). d, Dynamic expression of the cytokines that are significantly elevated at 24 h at 6 h, 24 h, 7 days and 14 days after COVID-19 mRNA vaccination. Significant variables were defined as those with P < 0.05 and a log_2-transformed fold change with an absolute value of greater than 0.5 after linear modelling with fixed effects. Adjusted P values were calculated using moderated two-tailed t-tests with false-discovery rate (FDR) correction for multiple testing. e,f, PD-L1 expression on circulating myeloid cells (CD3−CD19−CD56−CD11b+) (n = 5) (e) and DCs (CD3−CD19−CD56−CD11c+MHC-II+) (n = 5) (f) at 6 h, 24 h and 7 days after immunization. g,h, Activation of natural killer cells (CD56+; n = 5) (g), and T cells expressed as numbers of CD69+ cells of CD8+CD3+ cells (n = 5) (h) at 6 h, 24 h, 7 days and 14 days after immunization. Data are mean ± s.e.m. P values were calculated using two-tailed paired t-tests.

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To evaluate the impact of COVID-19 mRNA-induced antiviral cytokines on immune cells, we next evaluated the phenotypes of circulating myeloid cells using flow cytometry. Consistent with our findings in mouse models, we found that mRNA immunization drove innate immune activation exemplified by increased expression of PD-L1 on circulating CD11b+ myeloid cells and CD11c+ DCs (Fig. 4e,f, Supplementary Table 16 and Supplementary Fig. 12). Vaccination was also associated with activation of natural killer cells exemplified by a doubling of expression of IL-2Rα (also known as CD25) on CD56high cells (Fig. 4g) and circulating T cells exhibited by a doubling of expression of CD69 (Fig. 4h, Supplementary Table 17 and Supplementary Fig. 13). Phenotypes for all cells normalized by 7 days, which may be a result of the lack of target in these healthy participants or lymphoreticular localization of activated T cells for polarization into memory cells.

We next repeated this study in 11 healthy volunteers who received the Comirnaty vaccine (BNT162b2, COVID-19 mRNA vaccine, 2024–2025 formula, with 30 μg mRNA) (Extended Data Fig. 10a–h). Although we found similar changes to the cytokine profile overall, the magnitude of the increase in type I IFN and innate immune activation by flow cytometry was significantly reduced with BNT162b2, which contains less mRNA relative to mRNA-1273 (Extended Data Fig. 10b–i).

RNA vaccines amplify PD-L1 in NSCLC

As infiltration of antigen-specific T cells in tumours is known to be associated with increases in PD-L1 expression on tumour cells and was correlated with antitumour response in our preclinical models, we hypothesized that patients who received a COVID-19 mRNA vaccine would exhibit higher PD-L1 expression on their tumours. To test this hypothesis, we assembled two cohorts. We first evaluated 2,315 pathology reports from patients with NSCLC with biopsies reporting tumour proportion score (TPS), separating patients into three groups based on the timing between their biopsy and their most recent mRNA vaccine (Fig. 5a). We found that patients who had received a COVID-19 mRNA vaccine less than 100 days before biopsy exhibited a 24% increase in mean TPS of PD-L1 compared with patients who had not received any COVID-19 mRNA vaccines before biopsy (31% versus 25%, P = 0.0450) and a 41% increase in mean TPS relative to patients who received an mRNA vaccine 100 or more days before biopsy (31% versus 22%, P = 0.0099) (Fig. 5b and Extended Data Fig. 10j). As a TPS of 50% is a clinically important threshold to determine whether patients with NSCLC are eligible for single-agent immunotherapy instead of chemoimmunotherapy, we next evaluated these data as a binary outcome around this threshold. Notably, we found that patients who received a COVID-19 mRNA vaccine were 29% more likely to meet or exceed the 50% TPS threshold over unvaccinated patients (36% versus 28%, P = 0.0295) (Fig. 5c), suggesting that mRNA vaccines have a sufficient impact on TPS to modify treatment decisions. By contrast, pre-biopsy influenza and pneumonia vaccines were not associated with TPS changes (Fig. 5d).

Fig. 5: COVID-19 mRNA vaccines are associated with increased PD-L1 expression on tumours and improved clinical outcomes across a broad set of tumour histologies.

a, Schematic of patients with NSCLC biopsies documenting PD-L1 TPS. b, TPS stratified by COVID-19 mRNA vaccination timing. c, The distribution of samples with TPS ≥ 50%. d, TPS stratified by influenza (left) or pneumonia (right) vaccination timing. e, Schematic of biopsies documenting TPS or combined positive score (CPS) of PD-L1 at our institution (January 2020 to October 2023). f, Primary tumour locations from this diverse cohort. g, TPS in the tissue-agnostic cohort stratified by COVID-19 mRNA immunization timing. h, TPS stratified by timing of influenza vaccination. P values were calculated using two-tailed unpaired t-tests (b, d and h), two-tailed unpaired t-tests with Welch’s correction for unequal variance (g) and two-sided Fisher’s exact test evaluating the likelihood of TPS greater than 50% (c). The violin plots show the distribution of data with individual datapoints included. i, Survival of patients in the tissue-agnostic cohort treated with ICI who received any COVID-19 vaccine within 100 days of initiating ICI or did not receive any COVID-19 vaccine. j, Survival of patients in i stratified by receipt of COVID-19 vaccine before ICI. k, Survival for patients in i who started ICI in the pandemic era (since 2 September 2020, 100 days before mRNA vaccine approval). Survival analyses in the tissue-agnostic cohort were not limited to only those patients with a clear TPS value. l–o, The OS for patients with metastatic stage IV NSCLC treated with ICI who received a COVID-19 mRNA vaccine within 100 days of initiating ICI or did not receive a COVID-19 vaccine who had baseline PD-L1 expression at baseline biopsy TPS < 1% (l), 1–49.9% (m) or ≥50% (n). To evaluate the impact of vaccination in each clinical setting, patients were excluded if they received a COVID-19 mRNA vaccine before their biopsy. o, OS of unvaccinated patients with stage IV NSCLC stratified by era of ICI start who had baseline TPS < 1% at biopsy. P values and HRs were calculated using log-rank (Mantel–Cox, two-sided) tests (i–o).

Source Data

RNA vaccines amplify tumour PD-L1 broadly

To expand our findings beyond NSCLC and melanoma, we assembled a separate cohort including all patients at our quaternary referral centre with pathology reports including the term ‘PD-L1’ in a four-year period including the pandemic era (Fig. 5e). Together, we identified 5,317 unique pathology reports from January 2020 to October 2023 with the term ‘PD-L1’ including 2,831 reporting TPS from a diverse array of patients representing a variety of primary sites and histologies (Fig. 5f). In this cohort, receipt of a COVID-19 mRNA vaccine within 100 days before biopsy was associated with a 37% increase in TPS (13.3% versus 9.7%, P = 0.0364) (Fig. 5g and Extended Data Fig. 10k), similar to the effect seen in patients with NSCLC. As in the NSCLC cohort, influenza vaccines were not associated with similar increases in TPS (Fig. 5h). We next repeated the survival analysis in this broad patient cohort, including patients at our institution with a biopsy for PD-L1 during our dates of interest who received ICI (n = 888). In this cohort, patients who received any COVID-19 vaccine within 100 days of initiating ICI experienced significantly improved survival relative to their unvaccinated peers (P = 0.0038, HR = 0.73, 95% CI = 0.60–0.90) (Fig. 5i). This effect was consistent when limited to patients who received their vaccine within 100 days before the start of ICI (P = 0.0311, HR = 0.76, 95% CI = 0.60–0.96) (Fig. 5j) and patients who started their ICI during the pandemic era (P = 0.0056, HR = 0.74, 95% CI = 0.60–0.91) (Fig. 5k).

RNA vaccines boost ICIs in cold tumours

We next sought to evaluate whether vaccination could restore immune sensitivity in patients with immunologically ‘cold’ tumors. We used pre-vaccine TPS as a surrogate for immune sensitivity, as NSCLC patients with TPS <1% have reduced benefit from ICIs compared to patients with baseline TPS >1%. Among patients with stage IV NSCLC and baseline TPS <1%, those who received a COVID-19 mRNA vaccine within 100 days of initiating ICI exhibited OS similar to that of patients with baseline TPS >1%, suggesting restored sensitivity to ICIs (Fig. 5l). In addition, the association between COVID-19 mRNA vaccination and OS was similar for patients with TPS <1% relative to those patients with a TPS at biopsy of 1–49.9% or ≥50% (Fig. 5l–n). This effect was not explained by changes in patient management during the pandemic period, as patients with stage IV NSCLC with TPS < 1% at biopsy who did not receive a vaccine had similar outcomes before and during the pandemic era (Fig. 5o).

Together, these data suggest a model in which mRNA vaccines targeting non-tumour-related antigens stimulate robust antitumour immune responses that sensitize tumors to ICIs (Extended Data Fig. 11). mRNA vaccines first stimulate a surge in antiviral cytokines, including IFNα, that drive systemic innate immune activation. Tumour-resident innate immune cells activated by this cytokine surge prime T cells, which become activated and infiltrate tumours. Although tumour cells evade attack by upregulating PD-L1 expression, combination with ICI enables COVID-19 mRNA vaccines to overcome this compensatory response, eliciting tumour regression and improved survival.

Discussion

Immunotherapy promises to deliver systemic anti-cancer therapy with long-term memory preventing recurrence. However, immunotherapy with ICIs relies on pre-existing anti-cancer immunity, which is absent in most patients. mRNA vaccines have recently emerged as a promising strategy to generate anti-cancer immunity to magnify the effects of ICIs24,25,26,27. Here we used mRNA vaccines targeting the COVID-19 spike protein to demonstrate a mechanism by which mRNA vaccines improve survival in combination with ICI, even when the mRNA does not encode tumour antigens. Spike RNA-LNPs elicit body-wide APC activation, leading to the expansion of highly activated tumour specific T cells. When combined with ICIs, these T cells mediate tumour regression. As personalized neoantigen vaccines require considerable manufacturing time, off-the-shelf RNA-LNPs targeting tumour-associated or even infectious disease antigens may represent widely available, low-cost alternatives for patients waiting for personalized neoantigen vaccines or in settings in which personalized neoantigen vaccines are not available.

Although the local injection of RNA-LNPs for infectious disease or personalized cancer vaccines does not aim to directly modify the tumour immune microenvironment, we show here that even local injection of RNA-LNPs containing N^1-methyl-pseudouridine to minimize innate immune activation produces sufficient stimulus to reprogram TMEs and sensitize tumours to ICI. Moreover, we find that augmenting innate immune activation by replacing N^1-methyl-pseudouridine-modified mRNA with unmodified mRNA may further enhance the antitumour effects of this approach. The surprising effectiveness of this approach may explain the relative success of mRNA vaccines compared with other methodologies that also generate T cells reactive against targeted neoepitopes10,11,12,13,28,29.

We show that COVID-19 mRNA vaccines increase PD-L1 expression on tumours, rendering immunologically cold tumours sensitive to ICIs. These results reveal that the timing of a routine immunization might influence treatment trajectories, and provide an over-the-counter means to overcome intrinsic ICI resistance. More broadly, they establish systemic innate immune modulation as a strategy to sensitize tumours to ICIs, and position off-the-shelf RNA therapeutics targeting infectious disease antigens as universal modulators of antitumour immunity.

These findings define a role for widely available vaccines for enhancing the efficacy of cancer immunotherapy. Although we focus on a single therapeutic due to its wide availability, these data could pave the way for other universal mRNA therapeutics specifically designed to reset patient immune systems for enhanced response to immunotherapy.

Methods

Retrospective studies

We completed a non-interventional, retrospective review of patient data using the MDACC electronic health record system, which contains a record of the patients who are treated at the primary campus of MDACC, a large quaternary cancer hospital in Houston, Texas. The chart review for this study involved three groups of patients: (1) patients with tumour biopsies confirming stage III or stage IV NSCLC between January 2017 and September 2022; (2) patients with melanoma of any stage who received single- or multi-agent immune checkpoint blockade between January 2019 and December 2022; and (3) a tissue-agnostic cohort, which included all patients with pathology results for PD-L1 from January 2020 to October 2023 at our institution across a wide range of histologies. This study was approved by the MD Anderson Cancer Center institutional review board. Informed consent was waived due to the retrospective and de-identified nature of the data. The data-collection cut-off was 1 September 2024; data analysis was performed from 1 September 2024 to 29 July 2025.

In the NSCLC dataset, patient information was collected regarding patient demographics (such as age at immunotherapy start, sex, ethnicity), primary tumour histology, clinical stage, known tumour mutations (such as those in EGFR, KRAS, HER2, ALK, MET, p53 and RET), metastatic burden at immunotherapy start (brain, liver), Eastern Cooperative Oncology Group (ECOG) performance status (PS; range, 0–5) near the initiation of immunotherapy, radiation therapy in the time around immunotherapy start, chemotherapy history, immunodeficiency, comorbidities (heart disease, kidney disease, liver disease, respiratory disease), history of other primary tumours, steroid use around immunotherapy initiation, date of last follow-up, date of death, date of first recurrence or progression, ICI agent names and start dates, COVID-19, influenza and pneumococcal vaccination dates, and TPS. Recorded PD-L1 expression as reported below was required for inclusion in the survival analysis for NSCLC. For patients with multiple biopsies, the PD-L1 expression recorded from the closest biopsy to ICI start was used for analysis.

In the melanoma dataset, patient information was collected regarding patient demographics (including age at immunotherapy start, sex, ethnicity), primary tumour histology, clinical stage, known tumour mutations (such as those in EGFR, KRAS, HER2, ALK, MET, p53 and RET), metastatic burden at immunotherapy start (for example, brain, liver), PS (range, 0–5) at the initiation of immunotherapy, chemotherapy history, immunodeficiency, comorbidities (for example, heart disease, kidney disease, liver disease, respiratory disease), other primary tumour data, steroid use around COVID-19 vaccination and immunotherapy initiation, date of last follow-up, date of death, date of first recurrence/progression, ICI agent names and start dates, and COVID-19 vaccination dates. Although our dataset did not include specific vaccine formulations administered to each patient, vaccine formulations administered during the study period included the original monovalent mRNA-1273 vaccine from Moderna (100 µg mRNA prime, 50 µg mRNA booster) released on 18 December 2020; the bivalent Moderna vaccine targeting the original strain and Omicron BA.4/BA.5 (50 µg mRNA) released on 1 September 2022; the original monovalent vaccine from Pfizer/BioNTech (30 µg mRNA prime and booster) released on 11 December 2020; and the Pfizer/BioNTech bivalent formulation (30 µg mRNA) released on 31 August 2022.

Patients were separated into two groups: (1) patients who received a COVID-19 mRNA vaccination within 100 days of ICI start; and (2) patients who did not receive a COVID-19 vaccination. Survival analysis was performed using these groups, with subanalysis involving staging of the tumour, brand of mRNA vaccine, number of doses of the COVID-19 vaccine, location of metastases and cycle of immunotherapy.

For patients who received COVID-19 mRNA vaccination in both the NSCLC and melanoma datasets, OS was calculated as the time between the date of immunotherapy start closest to the mRNA vaccination date, and the last follow-up date or date of death. For patients who did not receive COVID-19 mRNA vaccination, OS was calculated as the time between the initiation of their first ICI start and the date of death or last follow-up. For patients who received COVID-19 mRNA vaccination, PFS was calculated as the time between the initiation of ICI closest to mRNA vaccination and the first incidence of either pathology-confirmed recurrence or imaging-confirmed progression, whichever occurred earlier, that was declared progression in their primary medical oncologist’s clinical notes. For patients who did not receive COVID-19 mRNA vaccination, PFS was calculated as the time between their first ICI start date and clinician-confirmed progression as described above. Patients who progressed before the receipt of mRNA vaccination were included in the vaccination group for this analysis. Kaplan–Meier curves were generated using GraphPad Prism.

Cox proportional hazards regression

For Cox proportional hazards regression, time-dependent variables were defined as described in the figure captions. Continuous/numbered variables (such as age, BMI, PD-L1 expression, ECOG and treatment year) were retained as numeric. Binary and categorical variables (for example, stage, gender, mutational status, comorbidities) were converted into factors. For each variable, we constructed individual Cox proportional hazards models. HRs, 95% CIs (Wald) and P values were extracted from model summaries. Variables with P < 0.05 in univariable analysis were considered for multivariable modelling. Multivariable Cox proportional hazard regressions were similarly generated with significant variables from univariable analysis. For multivariable Cox proportional hazards regressions, patients with missing values were excluded from analyses.

Categories with fewer than five cases are reported for completeness but were considered not statistically meaningful due to convergence and insufficient data for reliable inference. Variables with significant relationships with survival were included in multivariable analyses (MVAs), and those significant after MVA (P < 0.05) were included in PSM. Certain variables (for example, steroid use within 1 month of vaccine) represented subgroups of the treatment cohort by definition. These were evaluated descriptively and in univariable models but were not included as covariables in MVAs, as they do not represent baseline confounders and were not significant on univariable analysis.

We assessed the proportional hazards assumption in multivariate models by evaluating Schoenfeld residuals and found no major violations.

Imputation and PSM

A small number of patients in the NSCLC dataset did not have documented ECOG scores, which were an important predictor of outcomes in Cox proportional hazards regression. BMI had missingness in initial univariable analyses but, because it was not required for PSM, imputation was not performed to address the missingness. Multivariable logistic regression and ridge regression were used to evaluate associations between ECOG missingness and clinically relevant pretreatment covariables. Ridge regression was selected due to multicollinearity between important variables that could not be excluded from the model. Variables not included in models predicting ECOG missingness were vaccination status, steroid usage within a timeframe of vaccination or ICI, PD-L1, concurrent chemotherapy, immunotherapy agent and BMI (as mentioned above). Subsequent ridge regression analysis suggested that ECOG scores in this analysis had a high probability of exhibiting missingness at random.

Given the small sample size in the stage III NSCLC dataset treatment group, multiple imputation was performed to estimate missing ECOG values using chained equations and the R package MICE, where predictive mean matching (PMM) (with set.seed(2025)) was used to create five imputed datasets. Variables used for imputation were chosen based on large absolute ridge regression coefficients (|β| > 0.3) and clinical relevance to ensure that prediction of ECOG missingness occurred with accuracy. After selection using regression coefficients, age and previous cycles of systemic therapy were included due to clinical importance. To facilitate PSM while maintaining power in patients with stage III NSCLC, the mode of the five imputed ECOG values was used as a single value for each patient (n = 5). This approach was necessary to minimize variance inflation and avoid diluting treatment effects in a very small sample. As PMM is random, small deviations in variable definition, factor levels and row ordering may result in slightly different imputed values. However, sensitivity analyses with slightly different mode selection in the case of ties, different seeds and an increased number of datasets (n = 20 or n = 10 rather than n = 5) revealed similar outcomes (P < 0.05) after subsequent matching. The plausibility of imputed values was assessed using density plots, which demonstrated appropriate range and overlap between observed and imputed ECOG values.

After imputation, PSM was performed using the R MatchIt package. A logistic regression model was used to estimate propensity scores, predicting treatment assignment of ‘vaccine within 100 days’ or ‘no vaccine’ with covariables based on significant factors associated with survival from MVA, including original or imputed ECOG scores. Across all propensity score analyses, nearest-neighbour matching was performed on the previously estimated propensity scores to identify a balanced cohort. In the case of stage III unresectable NSCLC PSM, nearest-neighbour PSM was performed with a caliper of 0.1 and a 2:1 ratio of control patients to treated patients in stage III UR NSCLC to improve covariable balance and maximize statistical power. Sensitivity analysis with removal of ECOG from the PSM model revealed similar trends.

In stage IV NSCLC, patients with missing ECOG were excluded due to sufficient remaining sample size. PSM was once again performed with nearest-neighbour matching, with a caliper of 0.1 and a 1:1 ratio of control patients to treated patients. Sensitivity analyses with removal of ECOG from PSM rather than imputation and with the same methods as reported in stage III NSCLC (with the same caliper of 0.1 but with a ratio of 1:1 rather than 2:1) also each revealed statistically significant results.

No imputation was performed in the melanoma cohort due to completeness of variables. The melanoma PSM analyses were run with nearest-neighbour matching with a caliper of 0.1 and a 1:1 ratio of control patients to treated patients.

Across all matched cohorts, absolute standardized mean differences (|SMD|) of pscores were consistently less than 0.05 after matching, indicating excellent balance between groups. Balance diagnostics were confirmed visually using jitter plots and histograms of propensity scores.

Survival analysis

The Kaplan–Meier method was used to estimate survival distributions for the matched groups. Log-rank tests were conducted to assess the differences between treatment groups. GraphPad Prism was used to visualize Kaplan–Meier curves, 95% CIs and at-risk numbers at 0, 10, 20, 30 and 40 months, including after extraction of propensity score-matched data from R. Comparisons between groups in primary datasets (Fig. 1) were reported as adjusted HRs calculated with Cox proportional hazard regression. Comparisons between groups in subgroup analyses were reported as unadjusted HRs calculated with log-rank tests to preserve robustness. P values are reported for primary survival analyses.

In settings in which Kaplan–Meier curves crossed, the restricted mean survival time was calculated using area under the curve analysis of the absolute differences between arms at 12 and 24 months.

All statistical analyses were conducted in R v.4.4.2 (2024-10-31) and GraphPad Prism.

Quantification of PD-L1 expression in biopsy samples

For patients in both the tissue-agnostic cohort and the NSCLC dataset, biopsy date, pathology reports, histological information and diagnosis codes were collected for patients who had received a biopsy with pathology evaluating PD-L1 at MD Anderson (from January 2020 to October 2023 for the tissue-agnostic cohort and August 2016 to August 2022 for the NSCLC cohort). PD-L1 on tumour cells (TPS) and PD-L1 on tumour cells and immune cells together (CPS) were obtained from pathology reports. Data were curated according to the following principles: (1) simple quantitative values were replicated exactly; (2) TPS values of “<1%” were reported as 0%; (3) for patients with a CPS of 0% or CPS <1% with no TPS value, TPS was reported as 0%, as CPS of 0 or <1% implies a lack of expression of PD-L1 on both tumour cells and myeloid cells; (4) TPS values between 1.1 and 49.9 reported as “<x%” or “>x%” were excluded due to lack of interpretability; (5) TPS values of >50% were recorded as 50% and TPS values >60% were recorded as 60%, and so on, as these values indicate high PD-L1 expression. For ranges of PD-L1 expression (for example, 0–5%), the median of the numbers in the range was taken as an approximate estimation of PD-L1 on tumours. Replicate patient biopsies (repeat biopsies performed on the same patient within 2 months of a previous biopsy) were excluded, and the earliest biopsy was included.

Separately, survival was analysed in the tumour-agnostic dataset with any COVID-19 mRNA vaccine within 100 days of ICI compared with patients who had no recorded history of a COVID-19 vaccine. Survival analysis was performed as listed in the NSCLC dataset for all patients in this dataset who were treated with ICI in this cohort. TPS values were not required for inclusion.

For group-wise analysis, patients in these cohorts were grouped into (1) those who received their closest COVID-19 mRNA vaccination less than 100 days before tissue biopsy; (2) patients who received their closest COVID-19 mRNA vaccine more than or exactly 100 days before tissue biopsy; and (3) patients who did not receive a COVID-19 mRNA vaccination before biopsy. Patients who received non-mRNA-based COVID-19 vaccines within 100 days before biopsy were included in the no-vaccine group.

Healthy human participants

Studies in healthy volunteers were approved by the MD Anderson Institutional Review Board. Informed consent was obtained from each healthy participant before plasma and peripheral blood mononuclear cells were collected and stored at baseline, 6 h, 24 h and 48 h, or baseline, 6 h, 24 h, 7 days and 14 days after COVID-19 mRNA vaccination, depending on the formulation used. BNT162b2 (COVID-19 mRNA vaccine, 2024–2025 formula, with 30 μg mRNA) and mRNA-1273 (Spikevax Monovalent XBB.1.5, COVID-19 mRNA vaccine, 2023–2024 formulation, with 50 μg mRNA) were used for the first and second timeframes, respectively. Frozen plasma was thawed and analysed using the NULISAseq Inflammation Panel 250 by Alamar Biosciences. Absolute quantification of IFNα concentrations was performed using the NULISAseq Inflammation Panel AQ. Statistical analysis was performed using R, in which we evaluated significance using a linear model with fixed effects based on the time from vaccine and controlling for patient ID (paired), including accommodation of biological variance between the baselines using the limma package’s duplicateCorrelation function. P values were calculated using moderated t-tests with FDR correction for multiple testing. For comparisons between Pfizer and Moderna vaccines, we used a linear modelling approach (limma) to assess the log_2-transformed fold change in cytokine concentrations per-patient from the baseline to 6 h and 24 h. Moderated t-tests with empirical Bayes shrinkage were applied, and P values were corrected using the Benjamini–Hochberg FDR method. Contrasts were constructed to directly compare Moderna versus Pfizer for each timepoint. Cytokines were visualized if they demonstrated significant differences between Moderna and Pfizer from the baseline to 6 h or 24 h (absolute value of log_2-transformed fold change greater than 0.5 and FDR-corrected P < 0.05). Mean expression values per condition were normalized to the baseline and visualized. Volcano plots were generated using R. Heat maps and graphs of cytokines with significant changes in relative expression in the plasma at 24 h were generated in GraphPad Prism.

Human flow cytometry

Peripheral blood mononuclear cells were thawed in a 37 °C water bath and immediately transferred into a 50 ml tube containing 9 ml of complete RPMI medium (RPMI + 10% FBS) at a ratio of 1 part cells to 9 parts medium. Cells were centrifuged at 400g for 10 min, and the supernatant was decanted. The cell pellet was resuspended in 1 ml of PBS, and incubated with live/dead marker (1 µl per 1,000 µl of cell suspension, containing 1–10 million cells per ml) for 30 min at 4 °C. After incubation, 3 ml of PBS + 2% FBS was added, and the cells were centrifuged again at 400g for 8 min and the supernatant was decanted. Cells were then pre-incubated with 5 µl of Human TruStain FcX (BioLegend, 422302) per 100 µl of cell suspension for 5 min at room temperature. The cells were then washed with PBS and centrifuged at 400g for 6 min. After decanting the supernatant, 10 µl of Brilliant buffer was added to each tube, and the mixture was allowed to sit for 5 min before addition of antibodies targeting extracellular markers and the cells were incubated for 20 min at room temperature in the dark. After incubation, cells were washed and fixed in the dark for 45 min at 4 °C with 500 µl of fixation solution (BD). Cells were then permeabilized with 2 ml permeabilization buffer. Intracellular stains were added in permeabilization buffer, and cells were incubated for approximately 40 min at room temperature. After incubation, cells were washed with permeabilization buffer and resuspended in 200 µl of PBS + 2% FBS. Analysis was then conducted using Cytek Aurora Spectral Flow Cytometer.

Preclinical experiments

All mouse experiments and procedures were approved by the University of Florida or the University of Texas MD Anderson Institutional Animal Care and Use Committee (IACUC). Mice (aged 4–10 weeks) were maintained at 21 ± 1 °C and 35% humidity under a 14 h–10 h light–dark cycle. Mice were randomized prior to treatment. Tumour measurements were performed blinded to the treatment group. Euthanasia was performed by CO_2 inhalation followed by cervical dislocation in accordance with approved protocols. Humane end points included tumour ulceration.

C57BL/6 and Rigi-knockout mice (C57BL/6NJ-Rigiem1(IMPC)J/Mmjax) were purchased from The Jackson Laboratory (046070-JAX) and bred in house. Tumour-bearing mice were implanted s.c. with 50,000 B16F0 melanoma cells, 1 million B16F10-OVA melanoma cells or 200,000 LLC cells in the right flank. Orthotopic LLC models were implemented by implanting 100,000 LLC cells into the left lung of C57BL6 mice through direct injection below the 9th rib. A mix of male and female mice was used for experiments with B16F0, and male mice were used for experiments with LLC. All mice were vaccinated intramuscularly with 25 μg per dose of RNA-LNP or RNA-LPA (mRNA fraction)[12]. We administered anti-mouse IFNα receptor (aIFNAR1, Bio X Cell, BE0241) and anti-mouse IL-1R (Bio X Cell, BE0256) antibodies intraperitoneally at an initial dose of 500 μg per mouse for the first dose, followed by a maintenance dose of 250 μg per mouse twice a week for the remaining treatment period. Anti-PD-L1 (Bio X Cell, BE0101) checkpoint inhibitor was administered at 400 μg per mouse for the initial dose, followed by a maintenance dose of 200 μg per mouse twice a week for the remaining treatment period. Anti-PD-1 (Bio X Cell, BE0146) checkpoint inhibitor was administered at an initial dose of 400 μg per mouse, followed by a maintenance dose of 200 μg per mouse. LMW poly(I:C) (InvivoGen, tlrl-picw) was administered intramuscularly with 25 μg per mice for two doses. Tumours were measured at a frequency of three times a week starting on day 8 until more than 20% of mice reached the end point. Mice were euthanized after reaching the humane end point.

mRNA

SARS-CoV-2 spike coding sequence with K986P and V987P mutations was inserted into a pGEM-4Z backbone downstream of the T7 promoter with previously published UTRs and poly(A) signal analogous to the mRNA in BNT162b230,31. The 5′ SpeI restriction site was removed from the sequence to allow for restriction of the poly(A) tail. The T7 promoter was changed to have an AGG initiator sequence by site-directed mutagenesis (NEB, E0554) according to the company’s recommended protocol. Plasmids were grown in NEB5a competent Escherichia coli and purified using the RNeasy Maxi kit (Qiagen, 75162) and sequenced using whole-plasmid sequencing by Genewiz. Plasmids were restricted by using 2 U μg−1 SpeI HF (NEB, R3133L) for 2 h at 37 °C followed by DNA precipitation. mRNA was synthesized using the mMESSAGE mMACHINE T7 mRNA Kit with CleanCap Reagent AG (Thermo Fisher Scientific, A57620). Spike mRNA was made using CleanCap reagent AG (3′ OMe) (Trilink, N-7413-5) and N^1-methylpseudouridine-5′-triphosphate (Trilink, N-1081-10). PP65 mRNA was capped using ARCA (NEB, S1411L) followed by treatment with mRNA cap 2′-O-methyltransferase (NEB, M0366L). The in vitro transcription reaction was performed at 20 °C for 10 h. DNA was removed according to the kit instructions and RNA was purified using the RNeasy maxi kit (Qiagen, 75162). RNA was eluted in purified RNase-free water and stored at −80°C until use. mRNA was checked for quality using an Agilent TapeStation 4150. After thawing, RNA was diluted to 100 µg ml−1 in RNase-free water followed by incubation at room temperature or 72 °C for 3 min. Then 100 ng of RNA or 1 µl of ladder was added to 5 µl of RNA ScreenTape sample buffer (Agilent, 5067-5577) and mixed at 2,000 rpm for 1 min. The samples were run using an RNA ScreenTape (Agilent, 5067-5576). For dsRNA removal, the method described previously was followed[20]. In brief, mRNA was precipitated and reconstituted in chromatography buffer. Cellulose fibres (Sigma-Aldrich, C6288-100G) suspended in chromatography buffer were added to Nucleospin filter units (Macherey-Nagel, 740606) followed by washing. Up to 500 μg of RNA per filter unit was added to cellulose and incubated by rapid mixing for 30 min. RNA was recovered by centrifugation, and incubation was repeated using a second column containing cellulose fibres. RNA was then filtered through a 0.45 µm syringe filter (Pall, 4604) to remove any cellulose particulate before being precipitated and reconstituted in RNase-free water[20].

Fabrication of LNPs

Before complexation, 0.5 M of RNase-free citrate buffer (pH 3.75) (Teknova, Custom order) was added to the RNA for a final concentration of 0.1 M. ALC-0159 (Avanti, 880155 P), ALC-0315 (Avanti, 890900 O), cholesterol (Sigma-Aldrich, C8667) and DSPC (Avanti, 850365P) were reconstituted in 100% ethanol at a ratio of 1.7:47.5:40.8:10. Lipids and RNA were mixed at a 3:1 FRR on a Nanonsemblr Ignite (Precision NanoSystems, now part of Cytiva) for an N/P ratio of 6. RNA-LNPs were dialysed overnight at 4 °C with two buffer exchanges at the 3 h and 6 h timepoints using a 3.5 kDA dialysis cassette (Thermo Fisher Scientific, A52967). After dialysis, LNPs were filtered through the 0.2 µM Supor EX ECV filter (Pall, KS2ECV2S). LNPs were concentrated using 30 kDA Amicon filters (Millipore, UFC903024) by centrifugation at 2,000g. The final LNP formulation had sucrose dissolved in PBS added for a final concentration of 12%. Anionic RNA-LPAs were made as previously described[12]. In brief, DOTAP liposomes 2.5 mg ml−1 were mixed with mRNA at a 1:1 mass to mass ratio and complexed for 15 min at room temperature before administration.

RNA concentration was determined in LNPs using the RiboGreen assay (Invitrogen, R11490) using the BioTek Cytation 3 plate reader. The encapsulation efficiency was determined by comparing readings to LNPs in TE buffer versus LNPs in 1% Triton X-100. LNPs were diluted up to 500 μg ml−1 with PBS before injection. Empty LNPs were used at a volume consistent with the dose of RNA-LNPs fabricated using the same steps and same amounts of lipids. Fluorescence values were plotted in a standard curve with an extrapolation factor set to 1.1. The values from wells with Triton X-100 (total mRNA) and TE buffer (free mRNA) was entered into the following equation: EE% = [1 – (Free RNA/Total RNA)] × 100%. Total mRNA RNA-LNPs were also loaded onto a 1% agarose gel made in 1× TAE buffer (Quality Biological, 351-008-131) with GelRed (Biotium, 41003-T). Then 500 ng of RNA was loaded into LNPs and free RNA was mixed with 6× SDS-free (NEB, B7025S) loading dye and run at 90 V for 45 min. Particle size was acquired using a Malvern Zetasizer Ultra and NanoSight’s NS300. Before particle-size, PDI, concentration and surface ζ potential analysis, LNPs were diluted 100-fold with HyClone HyPure water (Cytiva, SH30538.02). They were and run in triplicate at 25 °C on the Zetasizer. Orthogonal particle-size distribution and concentration measurements were carried out using Malvern’s NanoSight NS300 NTA equipment. Each LNP sample was diluted 500-fold with PBS and pumped through the equipment at constant speed for five captures with optimized camera settings. An optimized detection threshold was set for analysis. mRNA was extracted from LNPs by dissolving 750 μg ml−1 with 5 volumes of ethanol or 2 volumes of isopropanol followed by 2 washes with cold 70% ethanol. RNA pellets were dissolved in RNase-free H_2O before analysis using the TapeStation.

Plasma collection

Mice were bled through the retroorbital route using heparinized capillaries (Thermo Fisher Scientific, 22-362566) collecting a maximum of 200 µl into EDTA coated tubes (Thermo Fisher Scientific, NC9414041). Whole blood was centrifuged at 1,200g for 15 min. Plasma was collected and stored at −80 °C until use.

Processing tumours for flow cytometry analysis

Tumours were dissected from euthanized mice with the external fibrous sac left intact. Tumours were bisected using a scalpel and half of each sample was placed into a GentleMacs column GentleMACS C Tube (130-096-334) with an enzyme mix containing 10 mg ml−1 of collagenase (Sigma-Aldrich, C5138-1G), 1 mg ml−1 of hyaluronidase (StemCell Technologies, 07461) and 200 mg ml−1 of DNase (Sigma-Aldrich, D5025-150KU) or using the tumour dissociation kit (Miltenyi, 130-096-730) followed by debris removal (Miltenyi, 130-109-398). Samples were run on a m_TDK_1 cycle and centrifuged at 300g for 5 min after completion. The pellets were resuspended in cold PBS and filtered through a 70-µm cell strainer. The samples were washed twice using cold PBS and then manually counted using a haemocytometer.

Isolation of splenocytes

Whole spleens were collected from euthanized mice and placed into cold RPMI medium. The spleens were then teased through a 70-µm filter and lysed for 5 min at 37 °C using 1× BD Pharmalyse buffer (BD, 555899). Lysis buffer was quenched with medium and centrifuged at 500g for 5 min. Splenocytes were resuspended in cold PBS and filtered through a 70-µm cell strainer and washed once with PBS before being counted using the Beckman Coulter Vi-cell XR or haemocytometer.

Mouse flow cytometry analysis

In total, 1 × 10^6 cells from either tumours and spleens were placed into 96-well V-bottom plates. Unless stated otherwise, the washing steps were performed by centrifugation at 500g for 5 min at 4 °C followed by resuspension of cells with 200 µl of buffer, with mixing done by pipetting. Cells were washed with cold PBS and stained with 100 µl of live/dead stain (Thermo Fisher Scientific, L10119) for 30 min at 4 °C. Live/dead dye was quenched with 100 µl of cold PBS and the cells were washed once with cold FACS buffer (PBS with 2% FBS). Cells were centrifuged and resuspended with 10 µl True stain FCX buffer (BioLegend, 422302) diluted to 100 µg ml−1 with FACS buffer for 10 min. Then 90 µl of antibodies (Supplementary Tables 12 and 13) and Brilliant Stain buffer (BD, 563794) were added and incubated for 30 min at 4 °C. Next, 100 µl of FACS buffer was added to each well and the cells were washed twice with cold FACS buffer. Cells were fixed for 15 min with 100 µl of Cytofix buffer (BD, 554655) at 4 °C. Next, 100 µl of PBS was added to each sample and cells were washed twice and stored in FACS buffer at 4 °C in the dark until analysis. Initial compensation was acquired using Ultracomp eBeads (Thermo Fisher Scientific, 01-2222-42) and ArC amine reactive compensation beads (Thermo Fisher Scientific, A10346). Results were acquired using a BD Symphony A3 and analysed using FlowJo v.10.8.1 and v.10.10.0.

Tetramer production

Peptides selected to be high-affinity binders for Survivin (ATFKNWPFL), GP100A (EGSRNQDWL), GP100B (KVPRNQDWL), WT1 (RMFPNAPYL), CLDN6 or claudin 6 (KVYDSLLAL) and TRP2 (SVYDFFVWL) were purchased from GenScript. H2-Db and H2-Kb monomers were purchased as easYmers from ImmunAware (5001-01 and 5004-01, respectively), and tetramers were prepared according to the manufacturer’s instructions. In brief, peptides were reconstituted to 1 mg ml−1 in deionized water, further diluted to 75 μM and incubated with easYmer (Eagle Biosciences, 5004-01, 5001-01) at 18 °C for 48 h. The resulting complexes were tetramerized by gradually mixing with streptavidin-APC (BD Biosciences, 554067). After incubation, a final stock concentration of 500 nM for each tetramer was achieved.

Tetramer staining

According to the manufacturer recommendations, 1–2 × 10^6 cells were stained with tetramer diluted to 20 nM in FACS buffer. Cells were left to stain in the dark at room temperature for 1 h, washed once with FACS buffer, co-stained with surface antibodies (CD3, CD4) at room temperature for 20 min, washed twice with FACS buffer and analysed on a Cytek Aurora flow cytometer.

AIM assay

The AIM assay was performed as previously described[12]. In this iteration, whole splenocytes were used rather than isolated T cells. In brief, 24 h after the last RNA-LNP vaccine, splenocytes were collected for antigen recall assay. Spleens were collected and processed as described above. 1 million splenocytes were cultured in a 96-well round-bottom plate in T cell medium containing RPMI 1640 (Gibco, 11-875-119), 10% FBS (Thermo Fisher Scientific, 35-011-CV), 1% penicillin–streptomycin (Gibco, 30-002-CI), 1% MEM non-essential amino acids (NEAA, Gibco, 11140050), 1% sodium pyruvate (Gibco, 11360070), 0.1% β-mercaptoethanol (BME, Gibco, 21985-023) and 0.2 µg tumour-associated peptides without cytokines. Peptides were chosen based on their suggested upregulation in melanoma tumours[12]. Selected peptides were purchased from JPT Peptide Technologies and reconstituted according to the manufacturer’s guidelines. Co-culture was maintained for 48 h in an incubator at 37 °C under 5% CO_2. Cells were next collected and stained for the AIM assay for evaluating activation of antigen-specific T cells. Cells were centrifuged and resuspended in FACS buffer containing antibodies mix (Supplementary Table 14). Cells were washed three times and fixed using fixation medium (Thermo Fisher Scientific, GAS001S100) for 15 min at room temperature. Cells were washed once with PBS and were stored in FACS buffer at 4 °C in the dark until analysis. Results were acquired using a BD Fortessa III. Values were normalized by subtracting the percentage of AIM+ cells of tbe DMSO-only controls from each treatment condition. Statistical analysis was performed using Brown–Forsythe and Welch ANOVA, followed by Dunnett’s T3 multiple-comparison test.

ELISA and multiplex analysis

Plasma was analysed for cytokine concentration using ELISAs and multiplex cytokine arrays. A portion of the plasma samples were assessed for IFNα through ELISA (Invitrogen, BMS6027). Plasma was also sent for analysis by Eve Technologies for multiplex cytokine array (MD44). For quantification of spike IgG, mice that received three vaccines/vehicle doses during tumour studies were bled once the humane end point was reached. Spike-specific IgG was determined using an Anti-mouse SARS-CoV-2 IgG titre assay (AcroBiosystems, RAS-T023). Plasma was diluted 100,000-fold and run according to the company’s recommended parameters for semiquantitative analysis.

Immunofluorescence

Half of bisected tumours were fixed with 4% paraformaldehyde at 4 °C overnight. The samples were washed three times using PBS and then immersed successively into 10%, 20% and 30% sucrose for cryopreservation. Tissue was then embedded in O.C.T. (Tissue-Tek, 4583) and stored at −80 °C. Blocks of tissue were moved to −20 °C for 24 h and were sectioned at a thickness of 30 µm using the Leica cryostat, and the sections were placed onto microscope slides. Before staining, the slides were brought up to room temperature for 15 min and washed three times with PBS. Tissue was blocked at room temperature for 1 h using a blocking buffer containing 2% goat or donkey serum, 1% BSA and 0.1% Triton X-100 in PBS. The sections were then stained with primary antibodies (Supplementary Table 15) in blocking buffer overnight at 4 °C. After incubation, the sections were washed three times with PBS and stained with secondary antibodies (Supplementary Table 15) diluted in blocking buffer for 1 h at room temperature. After three 5 min PBS washes, the sections were incubated with DAPI (1:1,000 in PBS) for 10 min at room temperature. After a triple PBS wash, the sections were mounted with Prolong Glass Antifade Mountant (Thermo Fisher Scientific, P36984) and covered with a cover glass. Images were acquired using a Leica Stellaris 8 WLL Spectral Confocal Microscope. Image processing was performed using Fiji ImageJ software (NIH) and Imaris (Oxford Instruments).

Reporting summary

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

Data availability

Data generated in this study are available in the Article and its Supplementary Information. All clinical data are de-identified to protect patient privacy. Source data are provided with this paper.

Change history

- 05 November 2025

In the version of the article initially published, the text “ B.R.S. is a CPRIT Scholar in Cancer Research, RR250092” was missing from the Acknowledgements section and has now been added to the HTML and PDF versions of the article.

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Acknowledgements

Research reported in this publication was supported by the National Cancer (NCI) Institute under the University of Texas MD Anderson Cancer Center SPORE in Melanoma P50CA221703, the University of Texas MD Anderson Cancer Center Support Grant P30CA016672, T32-CA196561-08 (A.J.G.), R37CA251978 (E.J.S.) and R01CA266857 (E.J.S.). This research was also supported by an FDA orphan disease award R01FD007268 (E.J.S.), foundation grants from the American Brain Tumour Association, the Radiological Society of North America, Conquer Cancer Foundation, the Cullen M. Taniguchi Immuno-Radiotherapy Symposium Seed Award, ASTRO-LUNGevity, Live Like Bella Childhood Cancer Foundation/Florida Department of Health, CureSearch (Catapult Award), Stop Children’s Cancer and the Bonnie R. Freeman Professorship, Danny’s Dream, Ian’s Friend’s Foundation, Alex’s Lemonade Stand (R Accelerated Award), the Rally Foundation for Childhood Cancer Research, the Medulloblastoma Initiative (MBI) and Cure Group 4 medulloblastoma consortium, The National Pediatric Cancer Foundation and philanthropic contributions to The University of Texas MD Anderson Lung Moon Shot Program. B.R.S. is a CPRIT Scholar in Cancer Research, RR250092. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding sources including the National Cancer Institute. We thank D. Montgomery, L. Saldarriaga, X. Ma, C. von Roemeling and D. Zhang for their help with animals experiments; D. Li for his help with statistical analysis; and the staff at the Melanoma Informatics, Tissue Resource and Translational Pathology Core (MelCore) and the Flow Cytometry and Cellular Imaging Core Facility at The University of Texas MD Anderson Cancer Center and the ICBR Cytometry Core (RRID: SCR_019119) and animal care facilities at the University of Florida. Images provided in this Article were created using BioRender.

Author information

Author notes

1. These authors contributed equally: Adam J. Grippin, Christiano Marconi, Sage Copling, Nan Li

2. These authors jointly supervised this work: Elias J. Sayour, Steven H. Lin

Authors and Affiliations

1. Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Adam J. Grippin, Nan Li, Chen Braun, Cole Woody, Priti Gupta, Min Wang, Annette Wu, Michael K. Rooney, Benjamin R. Schrank, Yifan Wang, Yifan Ma, Enoch Chang, Ramez Kouzy, Ada Argueta, Diana Amaya, Joe Chen, Gabriel Alatrash, William Dennis, Richard Liang, Wen Jiang & Steven H. Lin

2. Lillian S. Wells Department of Neurosurgery, Preston A. Wells, Jr. Center for Brain Tumor Therapy, McKnight Brain Institute, University of Florida, Gainesville, FL, USA

Christiano Marconi, Dhruvkumar Soni, Frances Weidert, Chao Xie, Eden Goldenberg, Chong Zhao, Anna DeVries, Paul Castillo, Nagheme Thomas, Andria Doty, Hector R. Mendez-Gomez & Elias J. Sayour

3. McGovern Medical School, Houston, TX, USA

Sage Copling, Andrew Kim, Jordan Jafarnia & Nina Nariman

4. Department of Enterprise Data Engineering and Analytics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Elliana Young

5. Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Seong Dong Jeong & Betty Y. S. Kim

6. Brain Tumor Center, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Seong Dong Jeong & Betty Y. S. Kim

7. Department of Pediatrics, Division of Hematology-Oncology, University of Florida, Gainesville, FL, USA

Paul Castillo & Elias J. Sayour

8. Department of Dermatology, Baylor College of Medicine, Houston, TX, USA

Rishabh Lohray

9. Department of Pathology, Stanford University, Stanford, CA, USA

Kyle Dyson

10. Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Gregory Gladish

11. Scripps Health, San Diego, CA, USA

Jacob New

12. University of North Carolina, Chapel Hill, NC, USA

Nikhil Copling

13. Department of Surgical Oncology, Division Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Julie Simon, Alicia Bea Davies & Jennifer Wargo

14. Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Jeff Lewis, Xiong Wei, Waree Rinsurongkawong, Hai Tran, Jianjun Zhang, Don L. Gibbons & John V. Heymach

15. Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Ara A. Vaporciyan

16. Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Andrew Johns & Padmanee Sharma

17. Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Jack Lee & Ryan Sun

18. Department of Biostatistics, The University of Florida, Gainesville, FL, USA

Ji-Hyun Lee

19. Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Padmanee Sharma

20. James P. Allison Institute, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Padmanee Sharma

21. The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Ashley Aaroe, Sanu Abraham, Lee Andrews II, Kiran K. Badami, Janna A. Baganz, Pratibha Bajwa, Gregory R. Barbosa, Hannah C. Beird, Kristy Brock, Elizabeth M. Burton, Juan Cata, Caroline Chung, Catherine Claussen, John Crommett, Michael Cutherell, Bouthaina Dabaja, Hiba Dagher, Kevin M. Daniels, Mary Domask, Giulio Draetta, Paul Edelkamp Jr, Sarah Fisher, Katy Elizabeth French, Andrew Futreal, Maria Gaeta, Myrna Godoy, Drew Goldstein, Jillian Gunther, Kate Hutcheson, David Jaffray, Jeff Jin, Teny Matthew John, Trey Kell, Mark Knafl, Rayson C. Kwan, J. Jack Lee, Jennifer Litton, Kevin W. McEnery, Mary McGuire, Benjamin Mescher, Tejo Musunuru, Mayoora Muthu, Joseph Nates, Craig S. Owen, Priyadharshini Padmakumar, Nicholas Palaskas, Jay J. Patel, Sabitha Prabhakaran, Lucas Ramsey, Vinod Ravi, Cristhiam Rojas Hernandez, Bilja Sajith, Paul A. Scheet, Stephanie Schmidt, Kenna R. Shaw, Sanjay Shete, Daniel P. Shoenthal, Lessley J. Stoltenberg, Hussein Tawbi, Anastasia Turin, Samir Unni, Benju Vicknamparampil, Max C. Weber, John Weinstein, Scott Eric Woodman, Mark C. Wozny, Carol Wu, Jia Wu, James C. Yao, Chingyi Young, Emily Yu & Steven Zatorski

22. Ascension, St Louis, MO, USA

Thomas A. Aloia

23. University Health, San Antonio, TX, USA

John Cuenca Trujillo

24. Oracle Health, Kansas City, MO, USA

Christopher Gibbons

25. Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA

Anai Kothari

26. Cancer Care Equity and Professional Wellness, Sarah Cannon Research Institute (SCRI), Nashville, TN, USA

Ishwaria Subbiah

27. Sir Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

Phillip Thompson

28. Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia

Phillip Thompson

Consortia

D3CODE Team

- Ashley Aaroe

- , Sanu Abraham

- , Lee Andrews II

- , Kiran K. Badami

- , Janna A. Baganz

- , Pratibha Bajwa

- , Gregory R. Barbosa

- , Hannah C. Beird

- , Kristy Brock

- , Elizabeth M. Burton

- , Juan Cata

- , Caroline Chung

- , Catherine Claussen

- , John Crommett

- , Michael Cutherell

- , Bouthaina Dabaja

- , Hiba Dagher

- , Kevin M. Daniels

- , Mary Domask

- , Giulio Draetta

- , Paul Edelkamp Jr

- , Sarah Fisher

- , Katy Elizabeth French

- , Andrew Futreal

- , Maria Gaeta

- , Myrna Godoy

- , Drew Goldstein

- , Jillian Gunther

- , Kate Hutcheson

- , David Jaffray

- , Jeff Jin

- , Teny Matthew John

- , Trey Kell

- , Mark Knafl

- , Rayson C. Kwan

- , J. Jack Lee

- , Jennifer Litton

- , Kevin W. McEnery

- , Mary McGuire

- , Benjamin Mescher

- , Tejo Musunuru

- , Mayoora Muthu

- , Joseph Nates

- , Craig S. Owen

- , Priyadharshini Padmakumar

- , Nicholas Palaskas

- , Jay J. Patel

- , Sabitha Prabhakaran

- , Lucas Ramsey

- , Vinod Ravi

- , Cristhiam Rojas Hernandez

- , Bilja Sajith

- , Paul A. Scheet

- , Stephanie Schmidt

- , Kenna R. Shaw

- , Sanjay Shete

- , Daniel P. Shoenthal

- , Lessley J. Stoltenberg

- , Hussein Tawbi

- , Anastasia Turin

- , Samir Unni

- , Benju Vicknamparampil

- , Max C. Weber

- , John Weinstein

- , Scott Eric Woodman

- , Mark C. Wozny

- , Carol Wu

- , Jia Wu

- , James C. Yao

- , Chingyi Young

- , Emily Yu

- , Steven Zatorski

- , Thomas A. Aloia

- , John Cuenca Trujillo

- , Christopher Gibbons

- , Anai Kothari

- , Ishwaria Subbiah

- & Phillip Thompson

Contributions

A.J.G., S.H.L. and E.J.S. conceived the study, developed the study methodology, directed and supervised all analyses, analysed data, provided resources, acquired funding for the study and wrote the manuscript. C.M. and N.L. conducted, directed, supervised and analysed data for preclinical studies. S.C. conducted, directed, supervised and analysed data for clinical studies. J.-H.L. directed and supervised data analysis. R.S. directed and supervised some data analysis. M.K.R. contributed to overall statistical strategy and data analysis. A.K., E.Y., R. Lohray, E.C., R.K., W.D., R. Liang, J.J., G.G. and N.N. conducted chart review and analysed data for retrospective clinical studies. C.W., M.W., C.B., A.W., S.D.J., D.S., F.W., C.X., E.G., C.Z., A. DeVries, A. Doty, P.C., B.R.S., Y.W., Y.M., J.C., N.C., B.R.S., N.T., G.A. and H.R.M.-G. designed and conducted preclinical studies. A.A., D.A., P.G., K.D. and M.W. assisted in design and execution of translational studies. J.S., A.B.D., X.W., W.R., J. Lewis, J. Lee, A.A.V., D.L.G., H.T., J.Z., J.W., J.V.H and all of the members of the Data-Driven Determinants for Covid-19 Oncology Discovery Effort (D3CODE) Team analysed, aggregated and maintained clinical databases required for retrospective clinical studies. A.J., J.W. and J.V.H. provided guidance regarding retrospective clinical analysis. J.N., B.Y.S.K., P.S. and W.J. interpreted results and developed study methodology. All of the authors contributed to writing, reviewing and editing the final manuscript.

Corresponding authors

Correspondence to Adam J. Grippin, Elias J. Sayour or Steven H. Lin.

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Competing interests

A.J.G. has received travel funding from Alamar Biosciences and is a scientific advisor for Sift Biosciences and BeyondSpring Pharmaceuticals. J.Z. reports grants from Merck, grants and personal fees from Johnson & Johnson and Novartis, personal fees from Bristol Myers Squibb, AstraZeneca, GenePlus, Innovent and Hengrui outside the submitted work. D.L.G. is a scientific advisor for AstraZeneca, Eli Lilly, Menarini Richerche, 4D Pharma, Onconova Therapeutics and Sanofi. P.S. is on the scientific advisory committee for Achelois, Affini-T, Akoya Biosciences, Apricity, Asher Bio, BioAtla LLC, Candel Therapeutics, Catalio, C-Reveal Therapeutics, Dragonfly Therapeutics, Earli Inc, Enable Medicine, Henlius/Hengenix, Hummingbird, ImaginAb, InterVenn Biosciences, LAVA Therapeutics, Lytix Biopharma, Marker Therapeutics, Matrisome, Oncolytics, Osteologic, PBM Capital, Phenomic AI, Polaris Pharma, Soley Therapeutics, Spotlight, Trained Therapeutix Discovery, Two Bear Capital and Xilis, Inc., and reports private investments in Adaptive Biotechnologies, BioNTech, JSL Health, Sporos and Time Bioventures. S.H.L. receives grant funding from Beyond Spring Pharmaceuticals and Nektar Therapeutics, serves on the scientific advisory boards for Beyond Spring Pharmaceuticals, AstraZeneca and Creatv Microtech, and is co-founder of and holds stock options in Seek Diagnostics. J.V.H. reports being on advisory committees for BioNTech, Genentech, Mirati Therapeutics, Eli Lilly, Janssen, Boehringer Ingelheim, Regeneron, Takeda, BerGenBio, Jazz, Curio Science, Novartis, AstraZeneca, BioAlta, Sanofi, Spectrum, GlaxoSmithKline, EMD Serono, BluePrint Medicine and Chugai; support from AstraZeneca, Boehringer Ingelheim, Spectrum, Mirati, Bristol Myers Squibb and Takeda; and licensing or royalties from Spectrum. E.J.S. is a paid consultant for Siren Biotechnology, an external advisory board member at Nature’s Toolbox (NTX) with stock options, and a scientific advisor for iOncologi, Inc. The Article discusses patented technologies related to RNA therapeutics from A.J.G., C.M., S.H.L, D.S., H.R.M.-G. and E.J.S. Some of these technologies are licensed or under option to license by iOncologi, Inc. H.R.M.-G. and E.J.S. receive royalty payments from patents licensed to iOncologi. The other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 COVID mRNA Vaccines are associated with improved survival in NSCLC patients who receive ICI.

a-c, Overall survival for NSCLC patients who received immune therapy and obtained a COVID mRNA vaccine differentiated by vaccine manufacturer (a), whether the patient received their first vaccine during this period (“Prime only”), a booster (“Boost only”), or both a priming vaccine and a booster vaccine within the 100-day period (“Prime and Boost”) (b), and number of vaccines received within 100 days of ICI initiation (c). One patient who received 3 vaccines within 100 days is not represented. d, Overall survival among patients with NSCLC receiving their first round of ICI, stratified by receipt of COVID mRNA vaccine in the 100 days prior to ICI initiation. e, Overall survival for NSCLC patients who received immune therapy and obtained a COVID mRNA vaccine within 50 days of initiating immunotherapy. f, Overall survival for NSCLC patients receiving ICI starting on or after 9/2/2020, stratified by receipt of COVID mRNA vaccination within 100 days surrounding ICI initiation. g, Overall survival for NSCLC patients stratified by receipt of COVID mRNA vaccines with all events occurring in the first 100 days after initiating ICI removed to correct for immortal time bias. h-i, Propensity score matching for overall survival in patients with Stage III Unresectable NSCLC (h) and metastatic NSCLC (i) treated with ICI who received a COVID mRNA vaccine within 100 days of initiating ICI or did not receive a COVID mRNA vaccine. Hazard ratios and p values were calculated by log-rank (Mantel-Cox, two-sided) tests.

Source Data

Extended Data Fig. 2 COVID mRNA vaccines are uniquely associated with improved survival in patients with NSCLC treated with ICI.

a, Overall survival for NSCLC patients who did not receive immune checkpoint inhibition and received a COVID vaccine within 100 days of initiating chemotherapy or did not receive a COVID vaccine. b-e, Overall survival for NSCLC patients stratified by receipt of influenza vaccines (b-c), or pneumonia vaccines (d-e) with all events included (b, d) or, to correct for immortal time bias, including only events that occured greater than 100 days after initiating ICI (c, e). Patients who also received COVID vaccination were excluded from the influenza and pneumonia vaccine analyses. f-g, Overall survival for (f) all patients with Stage III NSCLC and (g) patients with Resectable Stage III NSCLC treated with ICI who received a COVID mRNA vaccine within 100 days of initiating ICI or did not receive a COVID mRNA vaccine. Hazard ratios and p values were calculated by log-rank (Mantel-Cox, two-sided) tests.

Source Data

Extended Data Fig. 3 COVID mRNA Vaccines are uniquely associated with improved survival in melanoma patients who are receiving their first round of ICI.

a-c, Overall survival for Stage IV melanoma patients who received their first round of immune therapy and obtained a COVID mRNA vaccine differentiated by vaccine manufacturer (a), whether the patient received their first vaccine during this period (“Prime only”), a booster (“Boost only”), or both a priming vaccine and a booster vaccine within the 100-day period (“Prime and Boost”) (b), and the number of vaccines received within 100 days of ICI initiation (c). d, Overall survival among patients with Stage IV melanoma who are receiving their first round of ICI stratified by receipt of COVID mRNA vaccine in the 100 days prior to ICI initiation. Hazard ratios are reported using log-rank tests. e-f, Overall survival for Stage IV Melanoma receiving ICI starting on or after 9/2/2020, stratified by receipt of COVID mRNA vaccination within 100 days surrounding ICI initiation. For crossing survival curves as in e, RMST was calculated rather than logrank (Mantel-Cox) testing (see Methods). f, Restricted Mean Survival Time (RMST) at 12 and 24 months. Absolute differences between arms are compared with a two-tailed non-parametric area under the curve (AUC) analysis. g-i, Overall survival for patients in the Melanoma dataset treated with ICI. g, Survival for patients in the Melanoma cohort treated with ICI who received a COVID mRNA vaccine within 100 days of initiating any line of ICI or did not receive a COVID mRNA vaccine. h, Survival for Stage III patients in the Melanoma cohort treated with ICI who received a COVID mRNA vaccine within 100 days of initiating any line of ICI or did not receive a COVID mRNA vaccine. i, Survival for all Stage IV patients in the Melanoma cohort treated with ICI who received a COVID mRNA vaccine within 100 days of initiating any line of ICI or did not receive a COVID mRNA vaccine. j-k, Propensity score matching for overall survival (j) and progression-free survival (k) in patients with metastatic melanoma treated with ICI who received a COVID mRNA vaccine within 100 days of initiating ICI or did not receive a COVID mRNA vaccine. Matching was performed using all variables significantly associated with survival on multivariable analysis. Hazard ratios and p values were calculated by log-rank (Mantel-Cox, two-sided) tests unless otherwise specified. Numbers underneath the graph indicate the number of patients at each timepoint.

Source Data

Extended Data Fig. 4 Synthesis and characterization of RNA-LNPs approximating BNT162b2.

a, Sequence map of mRNA. b, Quality of mRNA assessed on a BioAnalyzer. c, Total anti-Spike IgG generated by C57Bl/6 mice after 3 doses of vaccine (n = 3 biological replicates). Data are displayed as mean with SEM. d, Visualization of mRNA loading in LNPs via gel electrophoresis. e, Size distribution of LNPs assessed by DLS with 3 technical replicates. Data are displayed as mean with SD. f, Size distribution determined via nanoparticle tracking analysis. g, Table of LNP properties. h, pH and zeta potential at biologically relevant levels of sodium bicarbonate.

Source Data

Extended Data Fig. 5 Spike RNA-LNPs prime anti-cancer immunity in preclinical models.

a, Graphical experimental design and individual tumour growth curves of C57Bl6 mice inoculated with B16F0 (50,000 cells) and vaccinated with RNA-LNP i.m. (Day 14,17) with and without anti-PD1. In this experiment, groups included untreated (n = 7), PD1 mAb alone (n = 8), RNA-LNP alone (n = 8), and RNA-LNP and anti-PD1 mAb (n = 8). b, Graphical experimental design and individual tumour growth curves for C57Bl/6 mice inoculated with LLC (200,000 cells) and vaccinated with spike RNA-LNP (Day 3,6) with and without anti-PD1 mAbs. In this experiment, groups included untreated (n = 8), anti-PD1 mAb alone (n = 9), RNA-LNP alone (n = 9), and RNA-LNP and anti-PD1 mAb (n = 9). c, Graphical experimental design of individual tumour growth curves (c), boxplots of tumours weights (d) and counts of metastatic tumours in lungs on Day 17 (e) in C57Bl/6 mice inoculated with LLC (200,000 cells) and vaccinated with spike RNA-LNP i.m. (Day 9, 12) with and without anti-PD1. In this experiment, groups included untreated (n = 9), anti-PD1 mAb alone (n = 10), RNA-LNP alone (n = 7), and RNA-LNP and anti-PD1 mAb (n = 7). f, Tumour growth for C57Bl6 mice inoculated with B16F0 (50,000 cells) and vaccinated with RNA-LNP i.m. (Day 3, 6) with and without anti-PD1 (Day 6, 10, 13, 17) (n = 9). g, Lung weight in orthotopic LLC tumours (100,000 cells) treated with RNA-LNPs (Days 3 and 6) with or without anti-PD1. In this experiment, groups included untreated (n = 9), anti-PD1 mAb alone (n = 10), RNA-LNP alone (n = 9), and RNA-LNP and anti-PD1 mAb (n = 10). Significance was determined by two-tailed Mann-Whitney U test (d, e, g) and two-way ANOVA/mixed-effect analysis with Geisser-greenhouse correction (f). All p values are two-tailed. For d-e and g, whiskers extend to highest and lowest values from a box drawn between 1st and 3rd quartiles with a line centred at the median. f, data are represented as mean +/- SEM.

Source Data

Extended Data Fig. 6 Antitumor effects of RNA-LNPs are mediated by Type I IFN.

a, b, Graphical experimental design, individual tumour growth curves (a) and tumour volume (b) for C57Bl6 mice inoculated with B16F0 (50,000 cells) and vaccinated with RNA-LNPs (Day 3, 6, 20) with and without anti-PD-L1 and anti-IFNAR1 or anti-IL-1R mAbs (n = 12/group). c, Tumour growth for C57Bl/6 mice with subcutaneous B16F0 tumours (50,000 cells) treated with anti-PD-L1 and either RNA-LNPs or exogenous IFN-α (Days 3, 6, and 20) (n = 8/group). Early differences in tumour growth volumes were lost by day 20 without continued treatment. d, C57Bl/6 mice with s.c. B16F0 tumours (50,000 cells) are treated with anti-PD1 starting on Day 14/17/20 with or without RNA-LNPs or Poly I:C (Days 14,17) (n = 8/group). e, Tumour growth for C57Bl/6 mice with B16F0 tumours (50,000 cells) treated with anti-PD-L1(Days 3/6/10/13/17/20) with or without RNA-LNPs (Days 3,6,20) containing mRNA coding for the Spike or the CMV antigen pp65 incorporating N1-methyl pseudouridine (“modified”) or wild-type uridine (“unmodified”) and (f) boxplots of day 17 and day 20 tumour volumes (n = 8/group). Tumour measurements from mice that met humane end points prior to each measurement day are excluded (Day 17: n = 1 (Untreated), Day 20: n = 2 (Untreated (n = 1) and Modified Spike RNA-LNP + PD-L1 (n = 1)) (see data file). Significance was determined by two-tailed Mann-Whitney U test (b,f) and two-way ANOVA/mixed-effect analysis with Geisser-Greenhouse correction (c,d,e). Data are displayed as means with standard error. Boxplots in b and f display whiskers extending to the highest and lowest values from a box drawn between the 1st and 3rd quartiles with a line centred at the median. For c-e, data are represented as mean +/- SEM.

Source Data

Extended Data Fig. 7 ssRNA RNA extracted from RNA-LNPs contains high molecular weight secondary structures.

a, QC analysis of dsRNA contamination before and after purification (analysis performed by Genscript’s dsRNA residue assay). b, Tumour growth for mice with B16F0 tumours (50,000 cells) treated with anti-PDL1 mAbs with or without RNA-LNPs versus anionic (LPA) versus ssRNA (Days 3,6, 17) (n = 12). In this experiment, anionic LPA was synthesized by mixing DOTAP liposomes with mRNA at a 1:1 mass to mass ratio to formulate lipid particle aggregates (see methods). c, ELISA for IFN-α in serum collected from wildtype C57Bl/6 mice (n = 5) 24 h after treatment with PBS (WT PBS) or RNA-LNP (WT RNA-LNP), or RIG-I null mice (n = 3) treated with RNA-LNP (RIG-I -/- RNA-LNP). d, Non-complexed RNA (A1, B1) and LNP extracted RNA (A2,B2) analysed on a tape station with or without heating. Data are displayed as means with standard error. Significance was determined by two-way ANOVA/mixed-effect analysis with Geisser-greenhouse correction (b), and two-tailed unpaired t tests (c). For b, data are represented as mean +/- SEM. For c, the height of the bars represents mean and error bars represent +SEM.

Source Data

Extended Data Fig. 8 RNA-LNPs elicit a dramatic shift in systemic cytokines/chemokines.

a-l, Cytokine/chemokine multiplex panel. Values are represented as pg/mL (a-j) for samples within the standard curve, and as fluorescent intensity values for cytokines above the standard curve (k-l). Plasma from subcutaneous B16F0 (50,000 cells) bearing C57Bl/6 animals (n = 5/group) 24 h after one RNA-LNP vaccine i.m. Whiskers extend to highest and lowest values, with a box shown between 1st and 3rd quartiles with a line centred at median. Significance was determined by one-way ANOVA analysis followed by two-tailed Šidák correction.

Source Data

Extended Data Fig. 9 Spike RNA-LNP prime anti-cancer immunity in an IFN-I dependent manner.

a-b, Box-Plots of cellular phenotyping within 24 h of 3rd RNA-LNP vaccine i.m. (Days 3, 6, 20) from spleens of C57Bl/6 animals bearing subcutaneous B16F0 (50,000 cells, n = 5/group). a, Percentage of activated (MHCII + CD86 + ) Ly6C+ cells. b, MHCII and PD-L1 positive of Ly6C+ cells (%). c, Prevalence of CD86 + Ly6C+ cells of CD45+ cells in tumours. d-f, Box-plots of cellular phenotyping within 24 h of 3rd RNA-LNP vaccine i.m. (Days 3, 6, 20) from spleens of subcutaneous B16F0 (50,000 cells) bearing C57Bl/6 animals (n = 5/group) for (d) percentage of CD44 + T cells in the CD8+ compartment, (e) percentage of CD44+PD1+ among CD3+ cells, and (f) median fluorescence intensity (MFI) of PD1 on effector CD8 T cells. g,h. Upregulation of PDL1 on tumour cells is dependent on Type I IFN. Wild type and IFN-gamma KO mice with s.c. B16F0 tumours (50,000 cells) were treated with three doses of mRNA vaccines (Days 3, 6, and 17) with or without twice weekly antibodies blocking the IFN-a receptor (IFNAR1) (n = 4 untreated, n = 5 for all other groups). PDL1 expression on tumour cells was evaluated on Day 18 with flow cytometry. Whiskers extend to highest and lowest values from a box drawn between 1st and 3rd quartiles with a line centred at median. Significance was determined using two-tailed unpaired t tests.

Source Data

Extended Data Fig. 10 COVID mRNA vaccines generate a surge in IFN-α, innate immune activation and adaptive immunity in humans.

a, Schematic depicting the experimental design wherein blood was drawn from eleven healthy subjects at baseline and 6 h, 24 h, and 48 h after BNT162b COVID mRNA immunization. b, Heat map displaying dynamic expression of the cytokines that are significantly elevated at 24 h at the following time points: 6 h, 24 h, and 48 h after COVID mRNA vaccination. Significant variables were defined as those with p < .05 and log2-Fold-Change with absolute value greater than 0.5 following linear modelling with fixed effect. Adjusted p values were calculated using moderated two-tailed t-tests with FDR correction for multiple testing. c-d, Individual data points highlighting changes in expression of IFN-α from baseline to 24 h for healthy volunteers (n = 11) expressed as fold change from baseline (c) and concentration (d). e-h, PD-L1 expression on circulating myeloid cells (e) and dendritic cells (f), activation of NK cells (g), and activation of T cells expressed at percentage of CD69 + CD8+ cells (h) at baseline, 6 h (6 h), 24 h (24 h), and 48 h (48 h) after immunization (n = 7). Data are presented as means with standard error. p values in e-h are results of two-tailed paired t tests. i, Heatmap displaying differentially expressed cytokines for patients receiving Spikevax (2023-2024 formulation, 50 µg mRNA) relative to the Comirnaty COVID mRNA vaccine (2024-2025 formulation, 30 µg mRNA). Moderated t tests were performed on per-patient log_2 fold change differences between cytokines at baseline vs 6 h or 24 h, with direct comparison of fold change from baseline in volunteers treated with either Moderna or Pfizer at each timepoint. Relative fold change for Moderna compared to Pfizer was displayed for differences that were significant with |log_2FC | > 0.5 and p < 0.05 at either 6 h or 24 h after multiple comparisons testing. j,k. Cumulative moving average of PDL1 expression for patients in the NSCLC (j) and Tissue Agnostic (k) cohorts stratified by the time from each patient’s most recent COVID mRNA vaccine. Data indicates the average of all TPS measurements from patients who received biopsy within each period from COVID mRNA immunization. Blue lines indicate unvaccinated patient average TPS.

Source Data

Extended Data Fig. 11 Schematic describing how mRNA vaccines sensitize immunologically “cold” tumours.

RNA-LNPs stimulate production of IFN-α, leading APCs to prime T cells in lymphoid organs. These primed, tumour-reactive T cells then infiltrate tumours and begin killing tumour cells. Tumour cells respond by expressing PD-L1. Combination therapy with RNA-LNPs and ICIs overcomes this resistance mechanism, leading to tumour rejection. Image created in BioRender (Grippin, A. (2025) https://BioRender.com/zcaaisj).

Supplementary information

Supplementary Information

Supplementary Tables 1–17 and Supplementary Figs. 1–13.

Reporting Summary

Source data

Source Data Fig. 1–5 and Source Data Extended Data Fig. 1–10

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Grippin, A.J., Marconi, C., Copling, S. et al. SARS-CoV-2 mRNA vaccines sensitize tumours to immune checkpoint blockade. Nature 647, 488–497 (2025). https://doi.org/10.1038/s41586-025-09655-y

- Received: 06 November 2024

- Accepted: 19 September 2025

- Published: 22 October 2025

- Version of record: 22 October 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09655-y

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Loss-of-function mutations in PLD4 lead to systemic lupus erythematosus

Qintao Wang,

Honghao Zhu,

Xiangwei Sun,

Changming Zhang,

Shuangyue Ma,

Ying Jin,

Jinjian Fu,

Chenlu Liu,

Jiahui Peng,

Ruoran Wang,

Lin Liu,

Yi Zeng,

Cheng Gong,

Qing Zhou,

Xiaomin Yu &

…

Zhihong Liu

Nature volume 647, pages 498–505 (2025)

An Author Correction to this article was published on 10 October 2025

This article has been updated

Abstract

Monogenic lupus offers valuable insights into the underlying mechanisms and therapeutic approaches for systemic lupus erythematosus (SLE)1,2,3. Here we report on five patients with SLE carrying recessive mutations in phospholipase D family member 4 (PLD4). Deleterious variants in PLD4 resulted in impaired single-stranded nucleic acid exonuclease activity in in vitro and ex vivo assays. PLD4 loss-of-function mutations led to excessive activation of Toll-like receptor 7 (TLR7) and TLR9. Downstream inflammatory signalling pathways, especially type I interferon signalling, were hyperactivated in patient dendritic cells. Pld4-deficient mice presented with autoimmunity and cell-intrinsic expansion of plasmacytoid dendritic cells and plasma cells. Pld4-deficient mice responded to the JAK inhibitor baricitinib, suggesting that targeting type I interferon may be a potential therapy for patients with PLD4 deficiency.

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Main

SLE is a complex multiorgan condition of variable severity4,5. Monogenic lupus represents a subset of autoimmune disorders caused by mutations in single genes and encompasses a spectrum of diseases with lupus-like phenotypes[1]. To date, more than 30 disease-causing genes leading to lupus have been reported[1]. Identifying more disease-causing genes of lupus could improve diagnosis, deepen pathological understanding and develop targeted therapeutics for this complex disease.

Intracellular nucleic-acid-sensing pathways have a crucial role in defending against external pathogens, tissue damage and repair6,7. TLR7 and TLR9 located in the endosome are pivotal for sensing RNA and DNA and are crucial for the development of SLE2,8,9,10. They initiate downstream inflammatory signalling pathways such as type I interferon (IFN), nuclear factor kappa B (NF-κB) and mitogen-activated protein kinase (MAPK) by recognizing endogenous or exogenous nucleic acid11,12. In plasmacytoid dendritic cells (pDCs), activation of the TLR7 and TLR9 pathways leads to the release of large amounts of IFNs, promoting the presentation of autoantigens and the occurrence of inflammatory responses[13]. In B cells, activation of both pathways leads to the production of substantial autoantibodies against nucleic acids, contributing to SLE[14].

PLD415,16,17,18 is highly expressed in DCs, monocytes and B cells. It is a 5′ exonuclease that localizes in the endolysosomes and can cleave single-stranded RNA (ssRNA) and single-stranded DNA (ssDNA), thereby restricting the overactivation of TLR7 and TLR918,19,20,21,22,23. Pld4-knockout (KO) mice demonstrate a range of autoimmune phenotypes, including reduced body weight, enlarged spleen size, increased autoantibodies and immune complex deposition[24]. Furthermore, mice lacking both Pld4 and its family member Pld3 die early in life[19].

Although autoimmune phenotypes have been established in Pld4-deficient mice, PLD4 deficiency has not yet been implicated in human diseases. Here we report PLD4 recessive mutations in five patients with SLE.

PLD4 variants identified in patients with SLE

The five patients were diagnosed with SLE. All of the patients presented with a renal phenotype. Kidney biopsy showed proliferative lupus nephritis (Fig. 1a and Supplementary table 1). Haematological involvement was observed in all of the patients, including leukopaenia, anaemia and thrombocytopenia. Skin rash, such as urticaria, malar rash or patchy rash, was identified in four patients. Arthralgia/arthritis and serositis were each noted in three patients (Supplementary table 1). Moreover, all of the patients were positive for antinuclear antibodies (ANA) and hypocomplementaemia. Using whole-exome sequencing (WES), we found that all of the patients carry biallelic mutations in the PLD4 gene (Fig. 1b and Extended Data Fig. 1a). All of the mutations were localized in the catalytic domain of PLD4 and were predicted to be deleterious (Fig. 1c and Supplementary table 2). Although different mutations of PLD4 are not spatially clustered within a single structural domain, the structure predicts that they may affect exonuclease activity through different mechanisms, such as affecting the formation of hydrogen bonds or affecting binding with substrates (Extended Data Fig. 1b–e).

Fig. 1: Identification of biallelic PLD4 variants in five patients with SLE.

a, PASM staining and immunofluorescence staining of glomeruli in kidney biopsies of five patients (P1–P5). b, Pedigree of five patients carrying PLD4 mutations. The open circles and boxes indicate no clinical phenotype, and the solid black circles and boxes indicate the presence of a clinical phenotype. WT, wild type. c, Schematic of the location of mutations on the PLD4 protein structure. TM, transmembrane domain.

Enhanced type I IFN pathway in DCs

To comprehensively assess inflammatory levels in the patients, we performed RNA-sequencing (RNA-seq) analysis of peripheral blood mononuclear cells (PBMCs) from patient P1, patient P2 and healthy control individuals. Gene set enrichment analysis (GSEA) revealed that IFNα response, IFNγ response and TNF signalling through NF-κB were the most significantly enriched pathways in patients P1 and P2 (Fig. 2a and Extended Data Fig. 2a). The heat map showed genes associated with inflammatory signalling pathways, particularly type I IFN pathway, were significantly upregulated in PBMCs from patients P1 and P2 (Fig. 2b and Extended Data Fig. 2b). Flow cytometry results showed an upregulation in the phosphorylation of STAT1, further demonstrating activation of type I IFN pathway in PBMCs of patients P1 and P2 (Fig. 2c). Moreover, intracellular staining results showed that IFNα, IL-1β, IL-6 and IL-8 were significantly increased in the PBMCs of patient P1 (Extended Data Fig. 2c,d). Furthermore, the proportion of CD14+ monocytes increased, whereas the proportion of CD19+ B cells decreased in PBMCs of patients P1 and P2 (Extended Data Fig. 2e). These findings indicate that the type I IFN signalling pathway is significantly activated in the cells of patients P1 and P2.

Fig. 2: Aberrant activation of TLR signalling and type I IFN pathway in patient DCs.

a,b, RNA-seq analysis of PBMCs from patients P1 and P2 and healthy controls. Enriched hallmark gene sets are shown. a, The top 10 enriched pathways in GSEA of PBMCs from patients P1 and P2. NES, normalized enrichment score. b, The type I IFN pathway involved genes and type I IFN scores in control (C) and patient (P) samples. Data are mean ± s.e.m. c, Flow cytometry analysis of the phosphorylation levels of STAT1 in patients P1 and P2 and healthy control (C1–C5) PBMCs. d–f, scRNA-seq analysis of PBMCs from patients P1 and P2 and healthy controls. NK, natural killer cells; NKT, natural killer T cells; Tmem, memory T cells. d, Uniform manifold approximation and projection (UMAP) plot showing the differences in various cell types between patients P1 and P2 and healthy controls. e, UMAP plot of NF-κB and type I IFN signalling pathways genes. The red circle indicates PLD4-expressing cells (DCs and monocytes (mono)) and inflammation-induced low-density granulocytes (LDGs). f, The upregulated expression of key genes in type I IFN and TLR7/9 signalling pathways in major cell populations. TLR7/9, TLR7/9 signalling pathways. g, CyTOF analysis of PBMCs from patient P2 and healthy controls. The average expression of inflammatory cytokines in major cell populations is shown. The results in c are representative of two independent experiments. FC, fold change.

Source data

We performed single-cell RNA-seq (scRNA-seq) analysis of the PBMCs from patient P1, patient P2 and healthy control individuals to identify differences in the expression profiles among different cell types (Fig. 2d). Patient cells with high expression of NF-κB-pathway-related genes were distributed across most cell types. Notably, cells with high expression of genes related to the type I IFN pathway in patients P1 and P2, such as IFIT2, OAS2, IFI44 and IFI44L, were primarily found in PLD4-expressing cells, such as DCs and monocytes (Fig. 2e and 2f (top) and Extended Data Figs. 3 and 4a). Genes encoding TLR7/9 and TLR signalling pathway-related molecules, including TLR7, TLR9, TRAF6, IRAK1 and IRAK4, were also significantly elevated in DCs of patients P1 and P2 compared with in the healthy controls (Fig. 2f (bottom) and Extended Data Fig. 4b). The flow cytometry results demonstrate that TLR9 is upregulated in pDCs of patient P1, consistent with that in patients with SLE without PLD4 mutations (Extended Data Fig. 4c,d).

Furthermore, genes involved in the type I IFN pathway were also abnormally activated in the B cells of patients P1 and P2 (Extended Data Fig. 4e). Flow cytometry analysis of PBMCs from P1 and P4 showed normal IgG levels and B cell subset distributions during remission, aligning with healthy controls, whereas the flare phase of P4 mirrored those of patients with SLE without PLD4 mutations (Extended Data Fig. 5a–f).

Cytometry by time of flight (CyTOF) results showed that the levels of IFNα, IFNγ, TNF, IL-1β, CXCL2, CCL4, IL-23 and GM-CSF in PBMCs of patient P2, especially DCs, were higher than in the healthy controls (Fig. 2g and Extended Data Fig. 5g). Through measurements of inflammatory gene expression levels and cytokine levels in patients P1 and P2, we found that the TLR7/9 and downstream type I IFN pathway were most significantly activated in DCs, suggesting that PLD4 deficiency in DCs triggers systemic inflammation and autoimmunity in patients.

Mutations impair PLD4 function

To validate the effects of these mutations on PLD4 function, we used purified wild-type and mutant PLD4 proteins to assess the single-stranded nucleic acid exonuclease activity of PLD4. Our results demonstrated that these missense mutations (Pro181Leu, Asp189Glu, Arg201Gln, Tyr248Cys, Ala323Val and Gly457Asp) impaired the exonuclease activity of PLD4 (Fig. 3a and Extended Data Fig. 6a). To further elucidate the impact of these mutations on PLD4 exonuclease activity, we used cell lysates from HEK293T cells overexpressing these mutations to conduct exonuclease activity assays. The results consistently showed that the ssDNA in all mutant-PLD4 groups remained uncleaved (Fig. 3b and Extended Data Fig. 6b). Moreover, we evaluated the exonuclease activity of endogenous PLD4 in PBMCs from patient P1 and healthy controls. The results showed that, after 1, 2 and 4 h of the exonuclease activity assay, patient P1 exhibited more residual substrate over time compared with the healthy controls, suggesting that the exonuclease activity of patient P1’s endogenous PLD4 is markedly impaired (Fig. 3c).

Fig. 3: Mutations impair PLD4 exonuclease activity and result in aberrant type I IFN signalling activation.

a–c, The impact of the mutations on PLD4 exonuclease activity. Ctrl, control; nt, nucleotides. a, Single-stranded nucleic acid exonuclease activity of purified wild-type and mutant PLD4 at different times. b, ssDNA exonuclease activity of HEK293T PLD3-KO cells (eliminating the endogenous PLD3 interference) reconstituted with wild-type or mutant PLD4. EV, empty vector. c, ssDNA exonuclease activity of PLD4 in patient P1 and healthy control PBMCs. d, RNA-seq analysis of type I IFN pathway genes in patients P1 and P2 and healthy controls at the basal level and after CpG-DNA stimulation. UNS, unstimulated. e, NF-κB and type I IFN pathway gene expression in PBMCs of patients P1 and P2, healthy controls and patients with SLE. n = 6 healthy controls (HC) and n = 6 patients with SLE without PLD4 mutations (SLE); the dots in the patient group represent samples taken at a different time for a patient. f, NF-κB and type I IFN pathway gene expression at the basal level and after CpG-DNA treatment in PLD4-KO THP-1 cells. n = 3 (WT) and n = 3 (PLD4 KO). g,h, The effects of STING inhibitors H-151/C-176 on inflammatory pathways in THP-1 PLD4-KO monoclonal cells. g, Western blot analysis of the signalling pathways changed after H-151 treatment. h, NF-κB and type I IFN pathway gene expression after H-151 and C-176 treatment. n = 4 (WT) and n = 6 (KO, KO + H-151, KO + C-176). Data are mean ± s.e.m. The results are representative of at least three independent experiments (a, b and f–h), two independent experiments (c) and a summary of two independent experiments (e). Statistical analysis was performed using one-way analysis of variance (ANOVA) with Tukey’s post hoc analysis (h) and unpaired t-tests followed by false-discovery rate (FDR) correction (f); NS, not significant; *P < 0.05, **P < 0.01, ***P < 0.001.

Source data

Activated TLR7/9 pathways in patients

PLD4 enzymatically cleaves ssRNA and ssDNA to prevent excessive activation of TLR7 and TLR918,19,20. We demonstrated the upregulation of both full-length and activated forms of TLR7 in the PBMCs of patient P1 compared with in the healthy controls, with concomitant activation of the downstream inflammatory signalling, such as the type I IFN and MAPK pathways (Extended Data Fig. 6c).

Flow cytometry results showed an upregulation in the phosphorylation of STAT2, p65 and ERK, indicating activation of type I IFN, NF-κB and MAPK pathways in PBMCs of patients P1 and P2 at the basal level and after stimulation with the TLR9 agonist unmethylated cytosine-phosphate-guanine DNA (CpG-DNA; Extended Data Fig. 6d,e). Moreover, RNA-seq analysis of PBMCs from patients P1 and P2 revealed significantly elevated type I IFN pathway gene expression in patients compared with in the healthy controls, both at basal level and after CpG-DNA stimulation (Fig. 3d). Quantitative PCR (qPCR) analysis confirmed that the transcriptional levels of key inflammatory genes, such as TNF, IFNA2, IFIT1, ISG15 and RSAD2, were also upregulated in PBMCs from patients P1 and P2 compared with in the healthy controls at the basal level and after CpG-DNA stimulation, whereas CpG-DNA-stimulated PBMCs from patients with SLE without PLD4 mutations exhibited minimal responses (Fig. 3e). This is consistent with previous studies suggesting that the high IFNα levels and immune microenvironment in typical patients with SLE may lead to increased tolerance to TLR stimulation. Notably, after CpG-DNA stimulation of isolated monocytes from patient P1, several genes elevated fold upregulation relative to the healthy controls (Extended Data Fig. 6f,g).

Moreover, we generated a THP-1 PLD4-KO cell line, which exhibited upregulation of phosphorylated STAT1 (p-STAT1), p-p65 and p-ERK, indicating that TLR7/9 downstream pathways such as type I IFN, NF-κB and MAPK were activated (Extended Data Fig. 6h). Furthermore, transcription levels of inflammatory cytokines, chemokines and IFN-stimulated genes (ISGs) in KO cells were upregulated at the basal level. After CpG-DNA stimulation, IFIT1, IFI44L and CXCL3 exhibited a more pronounced increase in expression in KO cells compared with in the wild-type cells (Fig. 3f). In KO cells, the levels of pro-inflammatory cytokines IL-1β and IL-6 were also significantly higher than in the wild-type cells (Extended Data Fig. 6i).

Activated STING in PLD4-KO cell

Previous investigations have established that STING-dependent signalling, particularly type I IFN responses, activated in PLD3/PLD4-deficient mice models[19]. Besides, PLD3 ablation induces lysosomal accumulation of mitochondrial DNA followed by cytoplasmic leakage, thereby triggering cGAS–STING pathway activation and subsequent autophagy[25]. To dissect STING’s involvement in PLD4-deficiency-driven inflammation, we examined the activation of STING in THP-1 PLD4-KO monoclonal cells (Extended Data Fig. 6j). Immunoblotting demonstrated the phosphorylation of STING and inflammatory pathway enhanced in THP-1 PLD4-KO monoclonal cell lines (Fig. 3g).

Treatment with specific inhibitors of STING H-151[26] effectively attenuated the type I IFN signalling activation induced by PLD4 deficiency (Fig. 3g). Notably, downstream inflammatory genes, especially type I IFN involved genes such as IFIT1, IFI27, IFI44, OAS1 and ISG15, were significantly downregulated when treated with H-151 and C-176[26] (Fig. 3h). Furthermore, PLD4 and STING1 double-KO cell lines corroborated these findings. Both qPCR and western blot analysis demonstrated the rescue of type I IFN pathway activation after STING ablation, whereas NF-κB signalling showed only partial restoration (Extended Data Fig. 6k,l). These findings align with previous studies[19] and collectively establish an important role of the cGAS–STING signalling axis in mediating the immune dysregulation resulting from PLD4 deficiency.

Pld4 −/− mice manifest autoimmunity

Pld4 homozygous KO (Pld4−/−) mice exhibited autoimmune phenotypes, such as slower body weight gain, significantly elevated levels of anti-double-stranded-DNA (dsDNA) antibodies, anti-dsRNA antibodies and IgG in the plasma and splenomegaly compared with in the wild-type and heterozygous KO (Pld4+/−) mice (Fig. 4a,b and Extended Data Fig. 7a,b). Moreover, Pld4−/− mice displayed severe nephritic phenotypes, including thickening of the glomerular basement membrane (Extended Data Fig. 7c) and increased deposition of IgG and C3 (Extended Data Fig. 7d,e). Among various organs accumulating autoimmune damage, inflammatory genes (Tnf, Cxcl10, Mx2 and Ifng) were most prominently elevated in the kidneys of Pld4−/− mice compared with in the wild-type and Pld4+/− mice, consistent with the nephritis manifestation in patients (Extended Data Fig. 7f).

Fig. 4: Pld4 deficiency in mice results in autoimmunity and cell-intrinsic expansion of pDCs and plasma cells.

a,b, The autoimmune phenotypes in Pld4-deficient mice. a, Body-weight change between Pld4−/− and wild-type mice. n = 11 (WT), n = 11 (Pld4+/−) and n = 11 (Pld4−/−) mice. The asterisks represent statistical comparisons between Pld4−/− and wild-type mice. b, Plasma anti-dsDNA and anti-dsRNA antibodies levels of Pld4−/− and wild-type mice. Female: n = 10 (WT), n = 10 (Pld4+/−), n = 17 (Pld4−/−); male: n = 8 (WT), n = 12 (Pld4+/−) and n = 10 (Pld4−/−) mice. OD_450, optical density at 450 nm. c, UMAP visualization of type I IFN pathway genes in the kidney scRNA-seq data of Pld4−/− and wild-type mice. d–g, The kidney flow cytometry results of Pld4−/− and wild-type mice: immune cells (CD45+) (d), pDCs (CD45+CD3−CD19−CD11b−B220+CD11c+Siglec-H+BST2+) (e), plasma cells (PCs) (CD45+CD3−CD11b−B220low/−CD138+CD98+) (f) and CD4 effector T cells (CD45+CD3+CD4+CD44+CD62L−) (g). n = 12 (WT), n = 12 (Pld4+/−) and n = 12 (Pld4−/−) mice. h,i, The results of mixed bone marrow chimeric mice reconstituted with 1:1 ratio of CD45.1-WT–CD45.2-WT (+/+:+/+) or CD45.1-WT–CD45.2-Pld4−/− (+/+:−/−) bone marrow. h, The plasma anti-dsDNA and anti-dsRNA antibodies levels of chimeric mice. n = 20 (+/+:+/+) and n = 26 (+/+:−/−). i, Kidney immune cell phenotyping of chimeric mice. n = 15 (+/+:+/+) and n = 15 (+/+:−/−). Data are mean ± s.e.m. The results are representative of at least three independent experiments (b and d–g), two independent experiments (a) and a summary of two independent experiments (h and i). Statistical analysis was performed using two-way ANOVA (a and i), one-way ANOVA with Tukey’s post hoc analysis (b and d–g) and unpaired t-tests (h).

Source data

scRNA-seq analysis of the kidneys showed a twofold to threefold increase in the overall and various proportions of infiltrating immune cells, including macrophages, DCs, T and B cells, in Pld4−/− mice compared with in wild-type mice (Extended Data Fig. 7g–i). Moreover, genes involved in the type I IFN pathway, such as Ifi27, Isg15 and Ddx58, were significantly upregulated in both immune cells and renal tissue cells (podocytes, endothelial cells, principal cells and proximal tubule cells) in Pld4−/− mice compared with in wild-type mice (Fig. 4c and Extended Data Fig. 7j).

Flow cytometry analysis of mouse kidneys corroborated scRNA-seq data: immune cell populations exhibited marked expansion in Pld4−/− mice compared with in wild-type mice (Fig. 4d). Key cell populations implicated in the pathogenesis of SLE, including pDCs and plasma cells, exhibited significant elevations (Fig. 4e,f). Furthermore, CD4+ effector T cells and CD8+ effector T cells were markedly increased, whereas age-associated B cells and myeloid DCs (mDCs) remained unchanged (Fig. 4g and Extended Data Fig. 7k–m). Within the spleen, only pDCs and plasma cells demonstrated pronounced increases in Pld4−/− mice compared with in the wild-type mice (Extended Data Fig. 8a–h). These observations suggest distinct tissue-specific consequences arising from PLD4 deficiency, with particularly pronounced renal tissue damage.

To determine which immune cell expansion was cell intrinsic, mixed bone marrow chimeras were generated by transplants 1:1 mixes of bone marrow from WT-CD45.1–WT-CD45.2 or WT-CD45.1–Pld4−/−-CD45.2 into lethally irradiated WT-CD45.1 mice. The autoantibody analysis revealed that chimeric mice reconstituted with Pld4−/− bone marrow exhibited elevated levels of anti-dsDNA and anti-dsRNA antibodies (Fig. 4h). Flow cytometry results in the kidneys revealed that the expansion of pDCs and plasma cells was cell intrinsic, whereas the expansion of T cells was cell extrinsic (Fig. 4i and Extended Data Fig. 9a–f). Similarly, splenic analyses showed equivalent results, with pDCs and plasma cells also exhibiting a cell-intrinsic effect (Extended Data Fig. 9g–m).

Together, the manifestations observed in mice support the pivotal roles of PLD4 in the development of SLE and effect of inflammatory responses in nephritis pathogenesis.

JAKi rescues Pld4 −/− mouse phenotypes

Given that type I IFN pathway was significantly upregulated in Pld4−/− mice and patients, we hypothesized that treatment with the JAK inhibitor (JAKi) baricitinib might act as an effective therapy. After 8 weeks of simulated oral administration through gavage (Extended Data Fig. 10a), the baricitinib-treated mice showed significant improvements in body-weight gain, plasma levels of anti-dsDNA and anti-dsRNA antibodies, and spleen size compared with the untreated mice (Fig. 5a–c). Moreover, renal tissue inflammatory genes expression, such as Il1b, Il6, Tnf, Ifng, Mx2 and Ifit1 (Fig. 5d), and glomerular immune complex deposition like IgG and C3 in the baricitinib-treated mice were significantly reduced compared with in the untreated mice (Fig. 5e and Extended Data Fig. 10b).

Fig. 5: Baricitinib rescues phenotypes in Pld4-deficient mice.

a–e, The changes in autoimmune phenotypes in Pld4−/− mice after baricitinib (bari) treatment. a, Body-weight growth curves of different groups of mice. n = 6 (WT), n = 15 (Pld4+/−), n = 4 (Pld4−/−, no baricitinib) and n = 4 (Pld4−/−, +baricitinib). The asterisks represent the statistical comparison between Pld4−/− mice with baricitinib treatment and Pld4−/− mice without baricitinib treatment. b, Plasma anti-dsDNA and anti-dsRNA antibodies levels in Pld4−/− mice after 8 weeks of baricitinib treatment. Female: n = 7 (WT), n = 8 (Pld4+/−), n = 5 (Pld4−/−, no baricitinib), n = 6 (Pld4−/−, +baricitinib); male: n = 6 (WT), n = 8 (Pld4+/−), n = 5 (Pld4−/−, no baricitinib), n = 5 (Pld4−/−, +baricitinib). c, The spleen size and weight changes in Pld4−/− mice after 8 weeks of baricitinib treatment. n = 11 (WT), n = 20 (Pld4+/−), n = 6 (Pld4−/−, no baricitinib) and n = 6 (Pld4−/−, +baricitinib). d, qPCR analysis of renal tissue inflammation changes in Pld4−/− mice after 8 weeks of baricitinib treatment. n = 8 (WT), n = 8 (Pld4+/−), n = 5 (Pld4−/−, no baricitinib) and n = 5 (Pld4−/−, +baricitinib). e, IgG staining of kidney glomeruli in Pld4−/− mice after 8 weeks of baricitinib treatment. Scale bars, 10 μm. f, qPCR analysis of type I IFN and NF-κB signalling pathway genes expression of patients P1 and P2 and healthy control PBMCs after 16 h baricitinib treatment. Data are mean ± s.e.m. For a–f, the results are representative of two independent experiments. Statistical analysis was performed using two-way ANOVA (a) and one-way ANOVA with Tukey’s post hoc analysis (b–d).

Source data

Based on the positive responses in the mice, we treated patient PBMCs with baricitinib. The results showed that treatment with baricitinib markedly inhibited the elevated type I IFN pathway in patient PBMCs and partially inhibited the NF-κB pathway (Fig. 5f). In summary, the favourable effects of baricitinib suggest that the type I IFN is a critical pathway in the autoimmune and inflammatory phenotype after PLD4 deficiency (Extended Data Fig. 10c).

Discussion

SLE is a complex autoimmune disease, characterized by a range of clinical manifestations and substantial heterogeneity in treatment response and prognosis[27]. The study of monogenic lupus provides insights into the pathogenesis and targeted treatment of SLE2,28,29. Among these findings, TLR7, TLR9 and proteins in their pathways have been shown to be crucial for the development of SLE2,8,20,29. PLD4 acts as a limiting factor upstream of TLR7/9 and modulates the activation of these pathways18,19,20. Here we identified biallelic loss-of-function mutations in PLD4 in five patients with SLE, highlighting the pivotal role of endosomal nucleic acid homeostasis dysregulation in monogenic SLE. The identification of PLD4 as a disease-causing gene in SLE offers a deeper understanding of the molecular underpinnings of the SLE.

The role of TLR7 in lupus is well recognized2,8,30,31,32, but the role of TLR9 remains controversial9,33,34,35. A recent study identified MYD88-independent protective roles and MYD88-dependent proinflammatory role of TLR9, which offers a molecular explanation for understanding its context-dependent complexity[36]. In mouse models of PLD4 deficiency, TLR9 exhibits context-dependent roles across genetic backgrounds. Within the C57BL/6 strain, TLR9-mediated autoinflammation cooperates with TLR7 and cGAS–STING signalling pathways to drive disease pathogenesis in PLD3/PLD4 deficiency mice. Besides, in BALB/c mice, TLR9-driven autoimmunity after PLD4 deficiency is the cause of disease in this background[37]. These findings, combined with our CpG-DNA stimulation experiments in PLD4-deficient cell lines and patient-derived cells, suggest that substrate accumulation caused by PLD4 deficiency shifts the balance between TLR9 protective effects and TLR9 proinflammatory activity toward the latter.

pDCs are a unique cell population central to antiviral responses through nucleic acid sensing and robust type I IFN production13,38. PLD4 exhibits evolutionarily conserved high expression in pDCs[39] (versus PLD3), implicating its non-redundant role in pDC nucleic acid homeostasis. Patients with PLD4 deficiency uniformly develop lupus nephritis, with scRNA-seq and CyTOF analyses suggesting that pDCs are the predominant cellular drivers of upregulated type I IFN and TLR signalling in the patient PBMCs. Notably, plasma cells, a well-established pathogenic cell population in SLE, critically drive disease progression through the sustained production of autoantibodies. Flow cytometry further reveals expanded plasma cells in patient PBMCs. Mirroring human pathology, PLD4-deficient mice show preferential renal involvement, whereas mixed bone marrow chimeras demonstrate cell-intrinsic expansion of pDCs and plasma cells. This cross-species convergence underscores the central regulatory role of pDCs and plasma cells in PLD4-deficiency-mediated immune dysregulation.

Our study illustrates that although the PLD4–TLR7/9 axis contributes significantly to SLE, different patients have different severity. Besides, monogenic lupus driven by the PLD4–TLR7/9 axis shares similarities with interferonopathies in autoinflammatory diseases1,2,20,40 and genome-wide association studies have also shown that, in addition to SLE, PLD4 is associated with several other diseases, such as systemic sclerosis[41] and rheumatoid arthritis[42]. Given the important role of PLD4 in the immune system, it is possible that other phenotypes may be observed in individuals with PLD4 deficiency. The genetic and mechanistic insights from our study may inform the understanding and treatment of a wider spectrum of immune disorders.

Conventional treatments for SLE often lack specificity[43]. According to the significant activation of the type I IFN signalling pathway caused by PLD4 deficiency, we chose to inhibit inflammation by targeting this pathway. Our study demonstrates that baricitinib significantly ameliorates the immune phenotype in PLD4-deficient mice and patient cells. As the initial symptoms of the three patients were arthritis/arthralgia and skin rashes, targeted therapy with baricitinib could be a potential effective intervention to prevent irreversible sequelae during the progression from mild manifestations to SLE outcome. Conserved type I IFN signalling in PLD4-deficient models supports the translational applicability of JAK inhibitor, although interspecies discrepancies necessitate clinical validation in patients with PLD4 deficiency. Overall, PLD4 genetic screening could become a standard measure for patients with undiagnosed SLE to identify those who may benefit from PLD4-targeted therapies.

In summary, our study identifies a link between PLD4 deficiency and SLE in humans and we denoted the disorder PLD4 deficiency disorder, or PLDD. Our study expands the genetic landscape of SLE and provides compelling evidence for the role of PLD4 in disease pathogenesis. The promising results with baricitinib in PLD4-deficient models highlight the potential of targeted therapy in patients with SLE with PLD4 deficiency, paving the way for more personalized and effective treatment strategies.

Methods

Case reports

The first patient (P1) was male, with disease-onset at age 12 years. The patient presented with periorbital oedema after tonsillitis, accompanied by reduced urine output and recurrent fevers. Laboratory investigations showed proteinuria, haematuria, hypoalbuminaemia, leukopaenia, mild anaemia and hypocomplementaemia. ANA and anti-dsDNA autoantibodies were positive. Kidney biopsy showed diffuse proliferative lupus nephritis with crescent formation.

The second patient (P2) was female, with disease-onset at age 16 years. The patient had urticaria, alopecia, arthralgia, oedema and recurrent infection. Laboratory investigations showed significant proteinuria, haematuria, acute kidney injury, leukopaenia, thrombocytopenia, autoimmune haemolytic anaemia and hypocomplementaemia. ANA and anti-dsDNA autoantibodies were positive. Kidney biopsy revealed diffuse proliferative lupus nephritis with crescent formation and acute tubular injury.

The third patient (P3) was male, with disease-onset at age 19 years. The patient presented with arthritis, morning stiffness, malar rash, hair loss, photosensitivity, periorbital oedema, gross haematuria and abdominal distension. Laboratory investigations showed hypertension, elevated serum creatinine, massive proteinuria, haematuria, pancytopenia and hypocomplementaemia. ANA and anti-dsDNA autoantibodies, and rheumatoid factor were positive. Kidney biopsy revealed diffuse proliferative lupus nephritis with crescent formation and fibrinoid necrosis. Maintenance haemodialysis began at the age of 40 years.

The fourth patient (P4) was female, with disease-onset at age 49 years. The patient presented with generalized arthralgia, patchy facial erythema with itching and dry mouth. Laboratory evaluations revealed massive proteinuria, haematuria, pancytopenia and hypocomplementaemia. ANA, anti-nRNP/Smith (Sm), anti-Sm and anticardiolipin antibodies were positive. Kidney biopsy revealed membranoproliferative lupus nephritis.

The fifth patient (P5) was female, with disease-onset at age 37 years. The patient presented with patchy rashes with itching, muscle soreness and haemoptysis. Laboratory analysis indicated haematuria, proteinuria, elevated serum creatinine, anaemia and thrombocytopenia, and hypocomplementaemia. ANA and myeloperoxidase–antineutrophil cytoplasmic antibody (MPO–ANCA) were positive. Kidney biopsy showed membranoproliferative lupus nephritis with crescent formation. She began maintenance haemodialysis at the age of 43 years.

Detailed case presentations are provided in the Supplementary information.

Patients

All of the patients who met the diagnostic criteria for SLE were evaluated at Jinling Hospital. All patients enrolled in the study were evaluated under a protocol approved by the Institutional Review Boards evaluated at Jinling Hospital (2022DZKY-061-01). All patients and family members signed written informed consent.

PLD4 exonuclease activity

In HEK293T cells, plasmids encoding wild-type and various mutant PLD4 were transfected, followed by collection of protein lysates and purification through Flag-tag magnetic beads (Sigma-Aldrich, M8823). The purified protein or total cell lysates were then subjected to PLD4 enzymatic activity assay18,19 (50 mM MES pH 5.5, 150 mM NaCl, 2.5 μM substrates, 10 nM or 20 nM purified PLD4), incubated at 37 °C for different time and subsequently analysed by TBE–PAGE, with nucleic acid staining performed for 15 min before imaging.

Mice and mice treatment

Pld4-KO mice (NM-KO-200682), on the C57BL/6 background, were purchased from the Shanghai Model Organisms Center. CD45.1 mice (T054816) were purchased from GemPharmatech. All of the mice were maintained under a specific-pathogen-free environment in the Laboratory Animal Center of Zhejiang University and experimentation was approved by the Institutional Animal Care and Use Committee of Zhejiang University (ZJU20250573). No statistical method was used to calculate sample size. Sample sizes with mice were determined by the availability of animals with the correct genotypes or based on numbers used in previous publications[44] where comparable sample sizes produced statistically significant results. Age- and sex-matched mice were used in each experiment (littermates). Experimenters were blinded to genotypes in the kidney histology pathology analysis. Other data collection and analyses were not performed in a blinded manner to the conditions of the experiment.

Pld4-KO mice, aged 6 weeks and with similar body weights, underwent tail vein blood sampling to measure anti-DNA and anti-RNA autoantibodies. On the basis of autoantibody levels and body weights, mice were evenly divided into two groups. One group received daily oral gavage of baricitinib (Selleck, S2851) at a dose of 30 mg per kg per day, while the other group received a solvent gavage to exclude solvent effects. The baricitinib working solution was prepared with 5% baricitinib, 50% PEG 300, 5% Tween-80 and 40% double distilled H_2O.

WES and Sanger sequencing

DNA was extracted from peripheral blood using the Maxwell RSC Whole Blood DNA Extraction Kit (Promega, AS1520), with 1 μg of DNA used for WES. Data alignment was performed using the BWA, and variants were annotated using ANNOVAR (https://annovar.openbioinformatics.org/en/latest/). Variants were filtered using online databases, including gnomAD (https://gnomad.broadinstitute.org/), dbSNP (https://www.ncbi.nlm.nih.gov/snp/) and Kaviar (https://db.Systemsbiology.net/kaviar/). Subsequently, potential pathogenic variants were selected based on inheritance patterns and the biological functions of the genes. Finally, candidate mutations were confirmed by Sanger sequencing.

RNA-seq and scRNA-seq

Total RNA was extracted from PBMCs designated for RNA-seq using the QIAGEN RNeasy kit (74104). The RNA quality and purity were assessed using an Agilent Bioanalyzer RNA chip with 1 μg of total RNA. After assessment, the RNA was purified and fragmented, followed by the construction of an mRNA library using the Illumina TruSeq RNA Sample Preparation Kit V2. The RNA was then reverse-transcribed into cDNA, with an average fragment size of approximately 200 bp. Subsequent steps included end-repair, adding an A base to the 3′ ends, adaptor ligation and PCR amplification. RNA-seq was conducted on the Illumina NovaSeq platform. Sequencing data were aligned using HISAT2 in human reference genome (GRCh38), with reads counting performed by featureCounts. Differential gene expression analysis was conducted using DESeq2, and downstream heat-map visualization was performed using the R package pheatmap.

scRNA-seq used in this study was performed with samples from two sources: human PBMCs and mouse kidney cells. Human PBMCs were counted directly for library construction after separation. The mice kidney tissue was digested with collagenase IV for 120 min. After single-cell counting, the procedure was as follows: cells were uniformly mixed with gel beads using the 10x Genomics single-cell sequencer to prepare oil droplets encapsulating the cells, causing cell lysis, RNA release and reverse transcription. Adaptors were added to the cDNA from each cell, followed by PCR amplification and single-cell libraries were prepared for sequencing. The scRNA-seq analysis workflow was as follows: raw data were processed using Cell Ranger to count reads and generate expression matrices for different transcripts in each cell. The expression matrices were then quality-controlled, dimensionally reduced, annotated and visualized using the Seurat package[45] in R.

CyTOF analysis

The PBMCs from both patients and healthy controls were pretreated with brefeldin A (BFA) for 4 h to block cytokine secretion and stained with 250 nM cisplatin for 5 min to label dead cells. Subsequently, cells were incubated with an Fc receptor blocking agent and a mixture of surface antibodies for 30 min at room temperature, followed by fixation and permeabilization, and further stained with intracellular detection antibodies on ice for 30 min before acquisition.

CyTOF data were initially processed using FlowJo to remove dead cells, duplicate cells and background noise. Cells were subdivided into distinct subgroups based on the expression levels of marker genes. High-dimensional data were transformed into two-dimensional representations using t-SNE dimensional reduction analysis. The data were then annotated based on marker gene expression and compared for the expression levels of target proteins.

Cell preparation, culture and stimulation

HEK293T and THP-1 cell lines, obtained from the American Type Culture Collection, were cultured and stimulated as follows: HEK293T cells were maintained in Dulbecco’s modified Eagle medium (Thermo Fisher Scientific, C11995500CP) supplemented with 10% FBS (Noverse, NFBS-2500A) and 1% penicillin–streptomycin (Thermo Fisher Scientific, 15140163). THP-1 cells and PBMCs were maintained in RPMI-1640 (Thermo Fisher Scientific, C11875500CP) supplemented with 10% FBS and 1% penicillin–streptomycin. PBMCs were isolated from whole blood using lymphocyte-separation medium (LSM, MPbio, 0850494) through density-gradient centrifugation according to the manufacturer’s instructions. The THP-1 PLD4-KO cell line was generated using the CRISPR–Cas9 system by infecting THP-1 wild-type cells with sgPLD4 virus packaged in HEK293T cells, followed by selection with puromycin to establish stable KO cell lines.

In the phosphorylation flow cytometry experiments on PBMCs, cells were stimulated with 5 μM CpG-DNA (ODN 2216, InvivoGen, tlrl-2216) for 20 min. For qPCR and RNA-seq experiments in PBMCs, stimulation was performed with 4 μM CpG-DNA for 24 h. For intracellular cytokine staining experiments, PBMCs were stimulated with 2 μM CpG-DNA for 24 h, with BFA added 6 h before sampling to block cytokine secretion. In the CyTOF experiments on PBMCs, cells were treated with BFA for 4 h. In the qPCR experiments on PBMCs treated with baricitinib, the baricitinib concentration was 0.5 μM for 16 h treatment. In qPCR experiments on the THP-1 PLD4 polyclonal KO cell line, cells were stimulated with 5 μM CpG-DNA for 6 h. For STING inhibition experiment, THP-1 PLD4 monoclonal KO cells were treated with 5 μM H-151 or C-176 for 48 h, then subjected to western blotting or qPCR.

Plasmids and antibodies

PCR amplification of PLD4 cDNA from healthy control PBMCs was used to construct the human PLD4 plasmid. Site-directed mutagenesis was used to generate mutant plasmids. Western blotting, flow cytometry and immunofluorescence were performed using a variety of antibodies: NF-κB p65 (Cell Signaling Technology, 8242), p-NF-κB p65 (Cell Signaling Technology, 3033), p44/42 MAPK (Cell Signaling Technology, 4696), p-p44/42 MAPK (Cell Signaling Technology, 4370), STAT1 (Cell Signaling Technology, 14994), p-STAT1 (Cell Signaling Technology, 9167), p-STAT2 (Tyr690) (Cell Signaling Technology, 88410), STAT2 (Cell Signaling Technology, 72604), β-actin (Cell Signaling Technology, 4970), GAPDH (Cell Signaling Technology, 2118), TLR7 (Cell Signaling Technology, 5632), p-STING (Ser366) (Cell Signaling Technology, 50907), STING (Cell Signaling Technology, 13647), HRP-conjugated anti-DYKDDDDK tag (Flag) (Huabio, 0912-3), CD3-APC-H7 (BD Biosciences, 560176), CD4-FITC (BD Biosciences, 555346), CD8-APC (BD Biosciences, 561952), CD14-PE-CY7 (BD Biosciences, 557742), CD19-BB700 (BD Biosciences, 566396), PE mouse anti-human IFNα (BD Biosciences, 560097), BD Pharmingen p38 MAPK (pT180/pY182) PE (BD Biosciences, 612565), BD Phosflow BV421 anti-human NF-κB p65 (pS529) (BD Biosciences, 565446), Alexa Fluor 647 anti-p-ERK1/2 (BioLegend, 369504), PLD4 (Thermo Fisher Scientific, PA5-98680), anti-C3 (Abcam, ab11862), FITC anti-human CD21 (BioLegend, 354910), FITC anti-human CD24 (BioLegend, 311104), PerCP/Cyanine5.5 anti-mouse/rat/human CD27 (BioLegend, 124214), Brilliant Violet 605 anti-human CD38 (BioLegend, 303532), Brilliant Violet 421 anti-human IgG Fc (BioLegend, 410704), APC anti-human HLA-DR (BioLegend, 307610), Brilliant Violet 570 anti-mouse CD11c (BioLegend, 117331), V500 mouse anti-mouse CD45.2(104) (BD Biosciences, 562129), Brilliant Violet 650 anti-mouse CD45.1 (BioLegend,110736), BV421 hamster anti-mouse CD3e (BD Biosciences, 562600), FITC rat anti-mouse CD4 (BD Biosciences, 553046), APC-Cy7 rat anti-mouse CD8a (BD Biosciences, 557654), APC rat anti-mouse CD19 (BD Biosciences, 550992), PE rat anti-mouse CD138 (BD Biosciences, 553714), BV605 CD317 (BD Biosciences, 747606), PE-Cy7 rat anti-mouse CD45R/B220 (BD Biosciences, 552772), BUV496 CD11b (BD Biosciences, 749864), BV750 F4/80 (BD Biosciences, 747295), RB780 Ly-6C (BD Biosciences, 755871), BUV395 I-A, I-E (BD Biosciences, 569244), BUV805 CD44 (BD Biosciences, 741921), BUV563 CD62L (BD Biosciences, 741230), BV786 rat anti-mouse CD25 (BD Biosciences, 564023), UV737 rat anti-mouse CD21/CD35 (BD Biosciences, 612810), RB545 CD23 (BD Biosciences, 756344), BV480 CD95 (BD Biosciences, 746755), BUV615 CD49b (BD Biosciences, 751052), BV711 CD279 (BD Biosciences, 744547), PE-CF594 rat anti-mouse CD185 (CXCR5) (BD Biosciences, 562856), R718 Ly-6G (BD Biosciences, 567039), PERCPEF710 BCL-6 (Invitrogen, 46-5453-82), RB744 rat anti-mouse Siglec-H (BD Biosciences, 757466) and BUV661 rat anti-mouse CD98 (BD Biosciences, 752893).

Flow cytometry analysis

In experiments assessing the changes in phosphorylation levels of key proteins in inflammatory signalling pathways and intracellular inflammatory cytokines in PBMCs, cells were plated at a density of 1.5 × 10^6 cells per ml and stimulated with CpG-DNA for either 20 min or 24 h. After the removal of the stimulus, surface antibodies were added, and the cells were stained at room temperature for 30 min. After PFA fixation and permeabilization, intracellular antibodies were added, and the cells were stained at room temperature for 1 h before proceeding to flow cytometry analysis.

Single-cell suspensions from mouse spleens were prepared by gently triturating the tissue with the plunger end of a syringe and filtering the resultant mixture through 40 µm filters with 2% FBS in PBS (2% FBS–PBS). The filtrate was then centrifuged at 350g for 5 min. The cell pellet was resuspended in 2 ml of RBC lysis buffer and incubated at 25 °C for 3 min. The suspension was then diluted with 10 ml 2% FBS–PBS. After a second centrifugation at 350g for 5 min, the spleen cells were resuspended in PBS.

For mouse kidney single-cell suspension preparation, half of the kidney was minced into 1–2 mm^3 pieces using surgical curved scissors in a dish and transferred to a 15 ml centrifuge tube. To this, 2 ml of digestion solution (1 mg ml−1 collagen IV, 200 μg ml−1 DNase I) was added, and the tissue was incubated at 37 °C for 30 min. Digestion was then halted by the addition of 10% FBS–PBS and the resultant mixture was filtered through 40 µm filters. The mixture was subsequently centrifuged and resuspended in 2 ml of RBC lysis buffer according to the spleen single-cell suspension protocol.

Immunofluorescence

For immunofluorescence staining of mouse kidney tissue cryosections, the sections were first fixed and permeabilized, followed by blocking with freshly prepared 10% normal goat serum (NGS, Beyotime, C0265)/PBS for 1 h. Mouse antibodies diluted in 10% NGS/PBS (IgG 1:200; C3 1:200) were applied to the sections and incubated overnight at 4 °C. Secondary antibodies diluted at 1:500 in 10% NGS/PBS were then incubated with the sections at room temperature for 1 h. Finally, the sections were imaged using a Zeiss 710 inverted microscope.

Western blotting and immunoprecipitation

For western blotting, cells were lysed on ice for 20 min in NP-40 lysis buffer containing protease and phosphatase inhibitors (Thermo Fisher Scientific, 78442), followed by centrifugation at 12,000g for 10 min at 4 °C. The supernatant was mixed with SDS sample buffer, heated at 95 °C for 5 min, then subjected to separation by SDS–PAGE.

For immunoprecipitation of endogenous PLD4 in PBMCs, homemade PLD4 antibody whole immune serum was used at a 1:50 ratio to immunoprecipitate 500 µg of PBMC protein. The immunocomplexes were then eluted with 0.2 M glycine at pH 2.5 and subsequently neutralized using 1.0 M Tris-HCl at pH 8.0.

RT–qPCR

qPCR with reverse transcription (RT–qPCR) was performed using ABclonal’s 2× Universal SYBR Green Fast qPCR Mix (RK21203), Vazyme HiScript IV All-in-One Ultra RT SuperMix for qPCR(R433), ChamQ Blue Universal SYBR qPCR Master Mix (Q312-02) and Roche’s LightCycler480 qPCR system to measure mRNA expression levels in various cell or tissue samples. Fluorescence signal intensities from the collected samples and primers were used to obtain Ct values. Relative expression levels were normalized to the reference gene GAPDH/Gapdh and calculated using the ΔΔCt method.

ELISA

This study primarily conducted two types of enzyme-linked immunosorbent assay (ELISA): one for the detection of cytokines in cell lines and another for coating DNA/RNA to detect autoantibodies in mouse plasma, with similar steps for both. Initially, an appropriate amount of coating buffer was used to dilute coating antibodies or antigens (1 μg DNA or RNA), which were then added to the wells of an ELISA plate and incubated overnight at 4 °C. Subsequently, the plate was blocked with diluent buffer to reduce non-specific binding and incubated at room temperature for 1 h. Samples and standards were then diluted in diluent buffer and incubated at room temperature for 2 h. Detection antibodies were diluted in diluent buffer and further incubated for 1 h at room temperature. This was followed by dilution of HRP conjugate (Beyotime, A0216) in diluent buffer and a further 0.5 h incubation at room temperature. TMB substrate (Beyotime, P0209) was added and incubated for 30 min. Finally, stop solution (Beyotime, P0215) was added before measuring the absorbance at 450 nm using a microplate reader.

Bone marrow chimeras

B6-CD45.1(Ptprc-p.K302E) were taken lethally irradiated (7 Gly), then reconstituted with mixed bone marrow (5 × 10^6 cells, 1:1 ratio of B6-CD45.1 and either wild-type or Pld4-dificient (B6-CD45.2)). After 8 weeks of reconstitution, mice were euthanized, and plasma was collected for ELISA analysis, while kidneys and spleens were collected for flow cytometry analysis.

Statistical analysis

Data analysis and graphing were performed using GraphPad Prism 8, R v.3.5.2 and PyMOL (v.3.1)[46]. Data for statistical tests were derived from three or more independent experiments. Mouse, cell and human-derived data are presented as mean ± s.e.m. For comparisons between two groups, unpaired t-tests were used for significance analysis; for comparisons involving more than two groups, ANOVA was used; P < 0.05 was considered to be statistically significant.

Reporting summary

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

Data availability

The raw RNA-seq data reported in this paper have been deposited in the Genome Sequence Archive[47] in National Genomics Data Center[48], China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA012523). Gel source data are shown in Supplementary Fig. 1. All data supporting the findings of this study are available in the Article and its Supplementary Information, or from the corresponding authors on reasonable request. Source data are provided with this paper.

Change history

- 10 October 2025

A Correction to this paper has been published: https://doi.org/10.1038/s41586-025-09692-7

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (32141004, 82225022, 32321002, 82394424, 82170739, 82471844, 82394420); the Hundred-Talent Program of Zhejiang University, Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang (grant no. 2021R01012); the National Key Research and Development Program of China (2024YFC2511002); the Basic Research Program of Jiangsu Province (BK20243061); and the Jiangsu Provincial Science and Technology Resources Coordination Service Platform (BM2015004-1).

Author information

Author notes

1. These authors contributed equally: Qintao Wang, Honghao Zhu, Xiangwei Sun, Changming Zhang, Shuangyue Ma

Authors and Affiliations

1. Liangzhu Laboratory, Zhejiang University, Hangzhou, China

Qintao Wang, Honghao Zhu, Xiangwei Sun, Shuangyue Ma, Jiahui Peng, Ruoran Wang, Yi Zeng, Cheng Gong, Qing Zhou, Xiaomin Yu & Zhihong Liu

2. National Clinical Research Center of Kidney Diseases, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China

Changming Zhang, Ying Jin & Zhihong Liu

3. Life Sciences Institute, Zhejiang University, Hangzhou, China

Jinjian Fu, Chenlu Liu & Qing Zhou

4. Urology and Nephrology Center, Department of Nephrology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, China

Lin Liu

5. Department of Rheumatology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China

Qing Zhou

Contributions

Z.L., X.Y. and Q.Z. directed and supervised the research. Q.W., H.Z., X.S., C.Z. and S.M. contributed equally. Q.W., H.Z., X.S., J.P., L.L., Y.Z. and C.G. performed experiments. H.Z. and Q.Z. identified PLD4 mutations in patients with SLE. Q.W. and X.S. performed the exonuclease activity of PLD4 assays, animal experiments with baricitinib treatment and the mixed bone marrow chimera experiments. R.W. provided suggestions on confocal imaging. Q.W., H.Z., S.M., J.F. and C.L. analysed the data. Z.L., C.Z., and Y.J. enrolled the patients, and collected and interpreted clinical information. Q.W., Q.Z., X.S. and C.Z. wrote the manuscript, with input from the other authors. All of the authors contributed to the review and approval of the manuscript.

Corresponding authors

Correspondence to Qing Zhou, Xiaomin Yu or Zhihong Liu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Alexandre Belot, George Tsokos and Carola Vinuesa 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 Sanger sequencing verification and structure prediction of PLD4 mutations.

a, Sanger sequencing verification of PLD4 mutations in five families. b, Schematic diagram of the position of the mutations site in the PLD4 protein structure (PDB: 8V08). c-e, Potential effects of spatial structural simulation mutations on PLD4 protein function, including the disruption of hydrogen bonding with neighbouring amino acid residues (c, e) and altered DNA-binding capacity (d).

Extended Data Fig. 2 Inflammatory pathway upregulated in patients’ PBMCs.

a, b, Representative the RNA-Seq results of PBMCs from patients and healthy controls. a, The most significantly enriched inflammatory signalling pathways in GSEA of patients’ PBMCs. b, Heatmap of NF-κB pathway involved genes in the RNA-Seq data of the PBMCs from patients P1, P2 and healthy controls. c-e, Representative Flow cytometry analysis of the differences in inflammatory cytokine levels and major cell population proportions in PBMCs from patients and healthy controls. c, Flow cytometry of the increased expression levels of IFN-α, IL-6, IL-1β and IL-8 in the PBMCs from patient P1 and healthy controls. C: n = 4, in IFN-α and IL-1β staining, C: n = 3 in IL-6 and IL-8 staining. d, Flow cytometry analysis of upregulated IFN-α in CD14 positive cells from patients P1, P2 and healthy controls. C: n = 4. Data are mean ± s.e.m. e, Flow cytometry analysis of CD3, CD4, CD8, CD14 and CD19 staining of the PBMCs from patients and healthy controls. The cells selected in the black frame are positive cells, and the number is the proportion of positive cells in the parent group of cells. These results show a representative of three independent experiments in (c, d, e).

Source data

Extended Data Fig. 3 The expression of PLD4, NF-κB and type I interferon pathways genes in the PBMCs from patients and healthy controls.

a, Violin plot of the expression pattern of PLD4 in the PBMCs from patients P1, P2 and healthy controls. pDC: plasmacytoid dendritic cell; mDC: myeloid dendritic cell. b, UMAP plot of gene expression levels in NF-κB and type I interferon signalling pathways of the PBMCs from patients and healthy controls. The red circle indicates PLD4 specifically expressing cells (DCs and monocytes) and inflammation-induced LDGs.

Extended Data Fig. 4 scRNA-Seq shows significant activation of TLRs and downstream inflammatory pathways in patients’ DCs.

a,b,e Representative the scRNA-Seq analysis of type I interferon and TLR signalling pathway across different cell populations in PBMCs from patients P1, P2 and healthy controls. a, Violin plot of the expression of genes in NF-κB and type I interferon signalling pathways in major cell populations of the PBMCs from patients P1, P2 and healthy controls. pDC: plasmacytoid dendritic cell; mDC: myeloid dendritic cell. b, Violin plot of the upregulated expression of genes in TLR7/9 signalling pathway in major cell populations of the PBMCs from patients P1, P2 and healthy controls. TLR7/9: TLR7/9 signalling pathway related genes. c, Representative pDCs gating strategies from human PBMCs. d, Flow cytometry plot and quantification of TLR9 expression in pDCs from patient P1, healthy controls and SLE patients controls. PC: SLE patients control without PLD4 mutations. HC: n = 6, PC: n = 6, dots in patient group represent different time samples for a patient. Data are mean ± s.e.m. e, Heatmap of upregulated type I interferon pathway involved genes in patients and healthy controls’ B cells in scRNA-Seq. IFN: interferon. The results show a summary of two independent experiments in (d). ***P < 0.001. One-way ANOVA with Tukey’s post hoc analysis was used in (d).

Source data

Extended Data Fig. 5 B-cell aberrations in patients with PLD4 deficiency.

a, IgG levels of four patients at different stages. The green box represents the normal level of IgG. W/O: without treatment. T: treatment with steroids or multiple immunosuppressants. b-f, Representative the flow cytometry plot and quantification of IgG level (CD19+IgG+, b), ABCs (CD19+CD11c+, c), PCs (CD19+CD38+CD138+, d), Naïve B cells (CD19+CD27−IgD+, e), Memory B cells (CD19+CD27+IgD−, e), Double negative B cell (CD19+CD27−IgD−, e), non-class-switched B cells (CD19+CD27+IgD+, e) and CD21low B cells (CD19+CD21low, f) in patients P1, P4, healthy controls and SLE patients controls. HC: Healthy controls; P1 R: patient P1 remission phase; P4 R/F: patient P4 remission phase /flare phase; PC: SLE patients control without PLD4 mutations. Blue circles in patients P1 or P4: remission phase in patients P1 or P4; Red circles in patient P4: flare phase in patient P4. HC: n = 6, PC: n = 6, dots in patient group represent different time samples for a patient. Data are mean ± s.e.m. g, t-SNE plot of CyTOF depicting differences of various cell types between patient P2 and healthy controls. These results show a representative of two independent experiments in (b,c,d,e,f). *P < 0.05; **P < 0.01; ***P < 0.001. unpaired t-test was used in (b,c,d,e,f).

Source data

Extended Data Fig. 6 PLD4 deficiency leads to aberrant activation of downstream inflammatory signalling pathways.

a, Coomassie staining of purified WT and mutant PLD4 from HEK293T cell. b, Western blotting of HEK293T PLD3 KO cells overexpressing WT and mutant PLD4. c, Western blotting of TLR7 and downstream signalling pathways in the PBMCs from patient P1 and healthy controls. Fl.TLR7: full-length TLR7; Cl.TLR7: cleavage TLR7. d,e, Flow cytometry analysis in the phosphorylation of key proteins in the inflammatory pathway in PBMCs of patients P1 (e), P2 (d) compared to healthy controls. UNS: unstimulated; CpG: CpG-DNA stimulation. f, g, NF-κB and type I interferon pathways genes expression in the monocytes from patient P1 and healthy controls under untreated (f, upper) and CpG-DNA stimulation (f, lower). g, Fold induction of genes in (f) following CpG-DNA stimulation. h, Western blotting of inflammatory pathways activation in THP-1 PLD4 KO cells. i, The inflammatory cytokines levels in supernatant from THP-1 PLD4 KO cells cultured for 24 h. WT and PLD4 KO: n = 3. j, Sanger sequencing verification of THP-1 PLD4 monoclonal KO cells. k,l, Representative the change of signalling pathways and downstream inflammatory genes in THP-1 PLD4 monoclonal KO cell after STING KO. k, Western blotting of signalling pathways change after STING KO. l, NF-κB and type I interferon pathway genes expression after STING KO. WT, PLD4 KO and PLD4 KO + sgSTING1, n = 3. Data are mean ± s.e.m. These results show a representative of three independent experiments in (a,b,h,i,k,l), two independent experiments in (d,e). Experiment in (c,f,g) was performed once. n.s., no significant difference; *P < 0.05; **P < 0.01; ***P < 0.001. One-way ANOVA with Tukey’s post hoc analysis was used in (l), unpaired t-test was used in (i).

Source data

Extended Data Fig. 7 Pld4-deficient mice manifests autoimmunity and nephritic phenotypes.

a, Plasma IgG levels of WT and Pld4−/− mice. WT: Pld4+/−: Pld4−/− = 8: 9: 8 in female and 9: 12: 8 in male. b, The differences in spleen length and weight between WT and Pld4−/− mice. WT: Pld4+/−: Pld4−/− = 7: 9: 5. c-e, Representative the PAS (c), IgG (d) and C3 (e) staining of renal glomeruli in WT and Pld4−/− mice. Scale bar: 20 μm in (c-d), 10 μm in (e). f, Inflammatory genes expression in kidney, spleen and liver of WT and Pld4−/− mice. WT: Pld4+/−: Pld4−/− = 4: 4: 4. g-j, Representative the scRNA-Seq results in the kidney of WT and Pld4−/− mice. g, UMAP plots show the cell type differences. h,i, The proportion of total or various types of renal immune cells. j, Violin plot of the type I interferon pathway genes in major renal cell populations. Podo: podocytes; EC: endothelial cells; PT: proximal convoluted tubule; DTL/CTAL: descending limb of loop of Henle/thick ascending cortical limb; PC: principal cells; DCT: distal convoluted tubule. k-m, Representative the kidney flow cytometry plot and quantification of WT and Pld4−/− mice: CD8 Effector T cells (CD45+CD3+CD8+CD62L−CD44+) in (k), ABCs (CD45+CD3−CD21−CD23−B220+CD19highCD11c+) in (l), mDCs (CD45+CD3−B220−CD11b+CD11c+MHC-II+) in (m). WT: Pld4+/−: Pld4−/− = 12: 12: 12. Data are mean ± s.e.m. These results show a representative of at least three independent experiments in (c, d, e, f, k, l, m), two independent experiments in (b). Experiment in (a) was performed once with n > 8. n.s., no significant difference; *P < 0.05; **P < 0.01; ***P < 0.001. One-way ANOVA with Tukey’s post hoc analysis was used in (a, b, f, k, l, m).

Source data

Extended Data Fig. 8 Flow cytometric phenotyping of splenic cells from WT and Pld4-deficient mice.

a-h, Representative the spleen flow cytometry plots and quantification of WT and Pld4-deficient mice: pDCs (CD45+CD3−CD19−CD11b−B220+CD11c+Siglec-H+BST2+) in (a), PCs (CD45+CD3−CD11b−B220low/−CD138+ CD98+) in (b), ABCs (CD45+CD3−CD21−CD23−B220+CD19highCD11c+) in (c), mDCs (CD45+CD3−B220−CD11b+CD11c+MHC-II+) in (d), MZ B cells (CD45+CD3−CD19+B220+CD23−CD21+) in (e), GC B cells(CD45+CD3−CD19+B220+CD95+BCL-6+) in (f), CD4 Effector T cells (CD45+CD3+CD4+CD62L−CD44+) in (g), and TFH (CD45+CD3+CD4+PD-1+CXCR5+) in (h). WT, n = 12; Pld4+/−, n = 12; Pld4−/−, n = 12. Data are mean ± s.e.m. These results show a representative of three independent experiments in (a-h). n.s., no significant difference; *P < 0.05; **P < 0.01; ***P < 0.001. One-way ANOVA with Tukey’s post hoc analysis was used in (a-h).

Source data

Extended Data Fig. 9 Flow cytometric phenotyping of immune cells in mixed bone marrow chimeric mice.

a-f, Representative the kidney flow cytometry plot and quantification of pDCs (CD45+CD3−CD19−CD11b−B220+CD11c+Siglec-H+BST2+, a), PCs (CD45+CD3−CD11b−B220low/−CD138+CD98+, b), ABCs (CD45+CD3−CD21−CD23−B220+CD19highCD11c+, c), mDCs (CD45+CD3−B220−CD11b+CD11c+MHC-II+, d), CD4 Effector T cells (CD45+CD3+CD4+CD62L−CD44+, e), CD8 Effector T cells (CD45+CD3+CD8+CD62L−CD44+, f), in mixed bone marrow chimeric mice reconstituted with 1:1 ratio of CD45.1-WT: CD45.2-WT (+/+:+/+) or CD45.1-WT: CD45.2-Pld4−/− (+/+:−/−) bone marrow. g, Flow cytometry detected the immune cell phenotyping in the spleen of chimeric mice after 8 weeks of transplantation. h-m, Representative the spleen flow cytometry plot and quantification of pDCs (CD45+CD3−CD19−CD11b−B220+CD11c+Siglec-H+BST2+, h), PCs (CD45+CD3−CD11b−B220low/−CD138+CD98+, i), ABCs (CD45+CD3−CD21−CD23−B220+CD19highCD11c+, j), mDCs (CD45+CD3−B220−CD11b+CD11c+MHC-II+, k), CD4 Effector T cells (CD45+CD3+CD4+CD62L−CD44+, l), MZ B cells (CD45+CD3−CD19+B220+CD23−CD21+, m), in mixed bone marrow chimeric mice reconstituted with 1:1 ratio of CD45.1-WT: CD45.2-WT (+/+:+/+) or CD45.1-WT: CD45.2-Pld4−/− (+/+:−/−) bone marrow. +/+:+/+, n = 15; +/+:−/−, n = 15. These results show a summary of two independent experiments in (g) and a representative of two independent experiments in (a-f, h-m).

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Extended Data Fig. 10 Baricitinib treatment strategy and schematic model of PLD4 deficiency mutations causing SLE.

a, Baricitinib treatment strategy and sampling time of Pld4-deficient mice. b, C3 staining of kidney glomeruli in Pld4-deficient mice after Baricitinib treatment. Scale bar: 10 μm. c, Plasmacytoid dendritic cells (pDCs) sense exogenous or endogenous nucleic acids, moderately activating TLR7/9 and downstream signalling pathways dominated by type I interferon. This results in the production of IFN-α and other pro-inflammatory cytokines like IL-1β, IL-6 and TNF, which in turn activate monocytes and B cells to produce pro-inflammatory cytokines and autoantibodies, facilitating the clearance of apoptotic cells or external pathogens to maintain homeostasis. In the absence of PLD4, pDCs stimulated by exogenous or endogenous nucleic acids fail to degrade these nucleic acids, leading to persistent activation of TLR7/9 and downstream inflammatory signalling pathways. Moreover, the absence of PLD4 in monocytes and B cells results in sustained activation of downstream inflammatory signalling pathways, including type I interferon, exacerbating the release of pro-inflammatory cytokines and the production and deposition of autoantibodies. Given the significant upregulation of the type I interferon signalling pathway following PLD4 deficiency, the JAK inhibitor Baricitinib can inhibit the inflammatory pathway activation caused by PLD4 deficiency, thereby rescuing the autoimmune phenotypes in mice. These results show a representative of two independent experiments in (b).

Supplementary information

Supplementary Information

Supplementary Fig. 1: gel source data; detailed case presentations for five patients with PLD4 deficiency; and Supplementary Tables 1 and 2: detailed clinical characteristics of patients and PLD4 mutation information.

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Source Data Figs. 2–5 and Source Data Extended Data Figs. 2 and 4–9.

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Wang, Q., Zhu, H., Sun, X. et al. Loss-of-function mutations in PLD4 lead to systemic lupus erythematosus. Nature 647, 498–505 (2025). https://doi.org/10.1038/s41586-025-09513-x

- Received: 17 September 2024

- Accepted: 11 August 2025

- Published: 10 September 2025

- Version of record: 10 September 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09513-x

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Mechanical confinement governs phenotypic plasticity in melanoma

Miranda V. Hunter,

Eshita Joshi,

Sydney Bowker,

Emily Montal,

Yilun Ma,

Young Hun Kim,

Zhifan Yang,

Laura Tuffery,

Zhuoning Li,

Eric Rosiek,

Alexander Browning,

Reuben Moncada,

Itai Yanai,

Helen Byrne,

Mara Monetti,

Elisa de Stanchina,

Pierre-Jacques Hamard,

Richard P. Koche &

…

Richard M. White

Nature volume 647, pages 517–527 (2025)

Abstract

Phenotype switching is a form of cellular plasticity in which cancer cells reversibly move between two opposite extremes: proliferative versus invasive states1,2. Although it has long been hypothesized that such switching is triggered by external cues, the identity of these cues remains unclear. Here we demonstrate that mechanical confinement mediates phenotype switching through chromatin remodelling. Using a zebrafish model of melanoma coupled with human samples, we profiled tumour cells at the interface between the tumour and surrounding microenvironment. Morphological analysis of interface cells showed elliptical nuclei, suggestive of mechanical confinement by the adjacent tissue. Spatial and single-cell transcriptomics demonstrated that interface cells adopted a gene program of neuronal invasion, including the acquisition of an acetylated tubulin cage that protects the nucleus during migration. We identified the DNA-bending protein HMGB2 as a confinement-induced mediator of the neuronal state. HMGB2 is upregulated in confined cells, and quantitative modelling revealed that confinement prolongs the contact time between HMGB2 and chromatin, leading to changes in chromatin configuration that favour the neuronal phenotype. Genetic disruption of HMGB2 showed that it regulates the trade-off between proliferative and invasive states, in which confined HMGB2high tumour cells are less proliferative but more drug-resistant. Our results implicate the mechanical microenvironment as a mechanism that drives phenotype switching in melanoma.

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Main

The ability of cancer cells to adopt new phenotypes without further DNA mutations is now well understood to substantially influence tumour behaviour. Such plasticity has long been observed in melanoma, where early studies identified transcriptomic and phenotypic states not linked to specific genetic lesions[1]. More recent evidence indicates that most tumours encompass a heterogeneous yet reproducible number of transcriptional states3,4. The extent to which tumour cells transition between states is an open area of investigation and has been hypothesized to be regulated by cues from the tumour microenvironment (TME). The identification of such cues has clinical relevance because they may enable the conversion of a superficial melanoma into an invasive and drug-resistant one[5]. Here using a combination of zebrafish transgenics and human samples, we show that mechanical confinement by the adjacent microenvironment induces stable changes in chromatin architecture that cause melanoma cells to transition from a proliferative to an invasive state.

Tumour gene expression altered by TME

To study the influence of the local microenvironment on tumour invasion, we applied spatially resolved transcriptomics and single-cell RNA sequencing (scRNA-seq) to a transgenic zebrafish model of BRAFV600E-driven melanoma (Fig. 1a). Tumours from this model frequently invade into adjacent tissues, including the underlying dermis and muscle. Across fish, we found a conserved ‘interface’ transcriptional cell state that occurs where tumour cells invade into the microenvironment[6] (12.1% of zebrafish tumour cells; Fig. 1a).

Fig. 1: Confinement induces an undifferentiated neuronal gene program.

a, Schematic detailing the workflow of spatial transcriptomics and scRNA-seq experiments performed on zebrafish melanomas. b, Uniform manifold approximation and projection (UMAP) of human melanoma scRNA-seq dataset from Jerby-Arnon et al.[7]. Cluster annotations from the original paper are labelled. Tumour cell clusters are outlined. c, Gene module scoring for interface genes extracted from zebrafish spatial transcriptomics and scRNA-seq data, projected onto tumour cells outlined in b. The red arrow denotes the subpopulation with the highest expression of interface genes. d, Cell state classification for melanoma differentiation states identified by Tsoi et al.[2]. Cells were classified on the basis of the highest expression of the gene modules indicated. e, Module scores for melanoma cell state genes from Tsoi et al.[2] in interface cells. f, Normalized expression per cell in UMAP space for the indicated genes. The red arrow indicates the interface cluster identified in b. g, Top 20 most highly upregulated genes in the human interface cluster. Neuronal genes are labelled in purple. h,i, GSEA barcode plot for the Gene Ontology (GO) pathways ‘cell fate specification’ (h) and ‘regulation of neuron differentiation’ (i). Normalized enrichment score (NES) and false discovery rate (FDR) are labelled. j,k, Immunofluorescence of adult zebrafish tissue sections highlighting the centre of the tumour (j) and tumour–TME interface (k). Individual nuclei are pseudocoloured and displayed without image overlay at right. l, Schematic of in vitro confinement workflow using a polydimethylsiloxane (PDMS) piston. m, Principal component analysis plot for each RNA sequencing (RNA-seq) replicate. Percentage variance for each principal component (PC) is labelled. n = 3 biological replicates for each condition. n, Top 10 most highly upregulated pathways from GSEA of confined cells relative to unconfined cells. NES and FDR are indicated. Scale bars, 100 μm (a), 10 µm (j,k).

To investigate whether these interface cells occur in patients, we analysed a recently published human melanoma scRNA-seq dataset of 7,186 tumour and stromal cells from 31 patients with untreated or immunotherapy-resistant melanoma[7] (Fig. 1b and Extended Data Fig. 1a). We scored tumour cells for the relative expression of interface genes from our zebrafish transcriptomic datasets. Similar to our previous observations[6], a subpopulation of human tumour cells highly upregulated interface markers (12.3% of tumour cells; Fig. 1c and Extended Data Fig. 1b), mostly from immunotherapy-resistant patients (Extended Data Fig. 1d–f). To better understand the nature of these cells, we compared our interface population to human melanoma cell states. A previous study defined at least four cell states that encompass the melanoma differentiation trajectory (melanocytic, transitory, neural crest-like and undifferentiated)[2]. We scored tumour and interface cells for the relative expression of gene modules encompassing the entire melanoma cell differentiation trajectory and classified cells into each of the four states on the basis of their expression of gene modules annotated for that state[2]. Although tumour cells were relatively evenly distributed between the four cell states, interface cells showed clear upregulation of genes characteristic of the undifferentiated state (Fig. 1d,e and Extended Data Fig. 1c).

Melanoma behaviour is regulated by phenotype switching, in which cells transition between differentiated/proliferative and undifferentiated/invasive states. The transition between the proliferative and invasive states is regulated by the transcription factors MITF, SOX9 and SOX10, among others[8]. MITF and SOX10 regulate melanocyte differentiation and proliferation9,10, whereas SOX9 is associated with the undifferentiated invasive state[11]. Interface cells downregulated MITF and SOX10 and upregulated SOX9 (Fig. 1f and Extended Data Fig. 1g). Classical melanocyte pigmentation genes were minimally expressed by interface cells relative to the tumour bulk, including MITF (log_2 fold change (FC) = −5.19), TYRP1 (log_2FC = −7.75) and PMEL (log_2FC = −8.78) (Fig. 1f, Extended Data Fig. 1g and Supplementary Table 1). Unexpectedly, interface cells also upregulated genes involved in neuronal development, including SOX11 (log_2FC = 5.15), NNAT (log_2FC = 8.61), NEUROD1 (log_2FC = 6.22) and NEFM (log_2FC = 6.19) (Fig. 1g and Supplementary Table 1). Gene set enrichment analysis (GSEA) revealed that transcriptional programs linked to cell fate specification and neuronal development were highly enriched in interface cells (Fig. 1h,i). These data indicate that interface cells adopt an invasive state with markers of neuronal development.

Confinement induces a neuronal state

To examine factors within the local TME that may drive the interface state, we performed histology on tissue sections from our transgenic zebrafish melanoma model, focusing on the invasive front. Tumour cells invading into the TME showed elongated nuclei compared with the bulk tumour mass (Fig. 1j,k). A previous study showed that tumour nuclei become highly elongated when squeezing through mechanically restrictive environments[12]. Although numerous factors probably influence tumour invasion, we hypothesized that mechanical forces exerted on the cell/nucleus may cause stable changes in gene expression and tumour cell behaviour.

To test this hypothesis, we adapted a system to confine human melanoma cells (A375 cell line) in vitro at predefined heights (3 µm) using a polydimethylsiloxane piston and micropatterned coverslips[13] (Fig. 1l). Confinement in vitro was not cytotoxic because confined cells did not upregulate apoptosis markers cleaved caspase-3, annexin V or cleaved PARP (Extended Data Fig. 2a–f). Post-confinement, cells recovered typical morphology within 24 h without widespread cell death (Extended Data Fig. 2g). To profile confinement-induced changes in gene expression, we confined A375 cells for approximately 18 h and performed bulk RNA-seq. Principal component analysis revealed considerable transcriptional alterations induced by confinement (Fig. 1m and Supplementary Table 2). Similar to human interface cells, confined cells upregulated several neuronal pathways (Fig. 1n and Supplementary Table 2). We calculated the overlap between the human interface gene signature and the confined melanoma gene signature (Extended Data Fig. 2h and Supplementary Table 3). Pathway analysis showed that many of the co-regulated genes were related to neuronal development (Extended Data Fig. 2i and Supplementary Table 3). These data indicate that confinement causes interface cells to adopt a neuronal identity.

Confinement remodels the cytoskeleton

Neuronal development relies on microtubule (MT) architecture, influencing almost every aspect of neuronal structure and function[14]. The cytoskeleton often transmits force within and between cells and remodels in response to mechanical stimuli[15]. Recent reports indicate that the MT cytoskeleton is stabilized by force to protect confined cells from damage[16]. We hypothesized that melanoma cells hijack neuronal mechanisms to allow them to invade into the mechanically confined microenvironment. We used our in vitro confinement system to characterize how force remodels the MT cytoskeleton by imaging A375 cells labelled with the MT vital dye SiR-tubulin. Confinement extensively remodelled the MT cytoskeleton; within 2–4 h, curved MTs began encircling the cell and nuclear periphery (Fig. 2a,b). This was reminiscent of a previous study that showed that MTs bend to prevent buckling or rupture in response to force[16]. We also observed loss of a central MT organizing centre with radial MTs, reminiscent of neurons with centrosome-independent MT organization14,17. These results indicate that confined melanoma cells rapidly undergo structural changes in the MT cytoskeleton resembling neurons.

Fig. 2: Perinuclear acetylated tubulin cage assembles in response to confinement.

a, Representative stills from confocal imaging of A375 cells stained with SiR-tubulin. MTOC, microtubule-organizing centre. b, Line intensity profile of perinuclear tubulin intensity over time from the images shown in a. MTOC is highlighted (a,b). a.u., arbitrary units. c, Schematic detailing immunofluorescence staining of sections from adult zebrafish melanomas. d,e, Immunofluorescence images of acetylated tubulin staining at the invasive front (d) compared with the centre of the tumour (e). f, A375 cells stained with antibodies labelling acetylated tubulin (green) and total tubulin (purple). g, Inset of regions labelled in f. h, Quantification of whole-cell acetylated tubulin intensity. Each point represents one cell. Unconfined, n = 27 cells from three images; confined, n = 80 cells from nine images. Horizontal lines, mean; box, s.e.m.; vertical lines, s.d. P value is indicated (two-sample t-test; two-sided). i,j, A375 cells treated with 1-µm nocodazole (NZ) for approximately 18 h and stained for acetylated tubulin (yellow, top and bottom), total tubulin (purple) and Hoechst (blue) at ×63 (i) and ×20 (j) magnification. Scale bars, 50 µm (d,e,j), 20 µm (a,f), 10 µm (g,i). Illustrations in c were created using BioRender (https://biorender.com).

Source Data

Acetylated tubulin mediates neuronal architecture and function and marks axonal MTs[18]. Tubulin acetylation stabilizes MTs and thus the cell against mechanical pressure[19]. Curved MTs, such as those we observed in confined cells (Fig. 2a,b), are also indicative of long-lived, stabilized MTs[19]. We performed immunofluorescence on sections from adult fish with transgenic BRAFV600E-driven melanomas and observed enrichment of acetylated tubulin at the tumour border[6] (Fig. 2c–e). Confining A375 cells in vitro also resulted in significant upregulation of acetylated tubulin (P = 6.86 × 10−12; Fig. 2f–h). Acetylated tubulin filaments in unconfined cells were typically short and linear, whereas in confined cells, acetylated tubulin filaments were longer and more curved—again indicative of stabilization (Fig. 2f,g).

We noticed that in confined cells, the hyperacetylated tubulin network was often perinuclear (Fig. 2f,g) and hypothesized that this network may provide structural support to the nucleus. As the stiffest and largest organelle, the nucleus is vulnerable to confinement-induced stress, with confined migration often causing nuclear envelope rupture and DNA damage20,21. During migration through confined spaces, neurons assemble a perinuclear network of acetylated tubulin to protect the nucleus22,23. We confirmed the stability of the perinuclear MT network by treating confined cells with nocodazole, which induces MT disassembly through an acetylation-independent mechanism[24]. Acetylated MTs are resistant to nocodazole[19]. Nocodazole (1 µM) induced the disassembly of almost all non-modified tubulin filaments in both unconfined and confined cells (Fig. 2i,j). Although unconfined nocodazole-treated cells exhibited minimal acetylated tubulin, many confined nocodazole-treated cells contained a perinuclear acetylated tubulin cage (Fig. 2i,j). We generated a stable A375 cell line, in which the main eukaryotic tubulin acetyltransferase (ATAT1) was inactivated using CRISPR (Extended Data Fig. 3a,b), and found that this almost completely abolished acetylated tubulin, including the perinuclear network (Extended Data Fig. 3c–e). This indicates that ATAT1 is the acetyltransferase that responds to mechanical stress in melanoma cells by stabilizing the tubulin cytoskeleton.

In addition to acetylation, MTs exhibit a variety of post-translational modifications (PTMs), including tyrosination, glutamylation, glycylation and methylation, which influence MT stability[25]. To clarify the composition of the perinuclear tubulin network, we used immunofluorescence to characterize MT PTMs in confined melanoma cells. Detyrosination has been linked to stabilized MTs[26]. However, most MTs in confined and unconfined A375 cells were highly tyrosinated (Extended Data Fig. 4a–d), probably owing to the high concentrations of tyrosine and tyrosinase in melanoma[27]. Although confined perinuclear MTs were occasionally tyrosinated and rarely detyrosinated (Extended Data Fig. 4a–d), there was no specific enrichment of these PTMs in the perinuclear network, as observed for acetylated MTs. Similar results were observed for polyglutamylated MTs (Extended Data Fig. 4e,f). No polyglycylated MTs were found in either condition (Extended Data Fig. 4g,h). This indicates that acetylation is the primary PTM mediating the assembly and/or stability of the perinuclear tubulin network. These data indicate that confined melanoma cells assemble a stable perinuclear tubulin network to reinforce the nucleus against mechanical stress, similar to neurons.

Confined invasive cells upregulate HMGB2

Our results indicate that confinement induces a neuronal identity in interface melanoma cells, characterized by changes in cytoskeletal architecture and gene expression. We examined our zebrafish and human transcriptomic datasets to identify potential confinement-induced mediators of this state. Interface cells consistently upregulated high mobility group (HMG)-family proteins (Fig. 3a), which regulate chromatin architecture by binding and bending DNA to relieve mechanical strain[28]. Although HMG-enriched transcriptional programs are upregulated across many tumour types, including melanoma[29], their contribution to tumour progression remains poorly understood. We focused on HMGB2 (zebrafish hmgb2a and hmgb2b) because it was the most upregulated HMG family member in interface cells from zebrafish (Fig. 3a,b), and because our previous study identified HMGB2 as a signalling factor enriched in interface cells[6]. HMGB2 was also highly upregulated by human interface cells (P = 3.24 × 10−37; Fig. 3c,d and Extended Data Fig. 1h).

Fig. 3: HMGB2 is a confinement-induced marker of invasion.

a, HMG family expression in interface cells from zebrafish melanoma scRNA-seq. b, Normalized hmgb2a/hmgb2b expression. P values are noted (Wilcoxon rank-sum test; two-sided). c, HMGB2 expression per cell in human melanoma scRNA-seq data from Jerby-Arnon et al.[7]. The arrow indicates interface cluster. d, Mean HMGB2 expression per cluster. P value calculated using Wilcoxon rank-sum test with Bonferroni’s correction; two-sided. e, Zebrafish melanoma stained for HMGB2 and Hoechst. f, Inset of region indicated in d. Elongated HMGB2-high cells are labelled. g, Correlation between nuclear circularity and HMGB2 intensity. Red dashed line, line of best fit by linear regression. h, Immunofluorescence targeting HMGB2 in confined A375 cells. i–k, HMGB2 intensity (i), Hoechst intensity (j) and HMGB2 intensity normalized to Hoechst (k) per cell. Unconfined, n = 49 cells from three images; confined, n = 97 cells from nine images. Horizontal lines, mean; box, s.e.m.; vertical lines, s.d. P value is indicated (two-sample t-test; two-sided). l, TurboID workflow. m, Nesprin 2 protein abundance; n = 3 replicates per condition. Horizontal line, median; hinges, first and third quartiles; whiskers, range. NLS, nuclear localization signal. n, HMGB2 expression in confined A375 cells. siNT, non-targeting siRNA; siSYNE2, SYNE2-targeting siRNA. o, Quantification of HMGB2 intensity. siNT unconfined, n = 72 cells from eight images; siNT confined, n = 94 cells from eight images; siSYNE2 unconfined, n = 48 cells from eight images; siSYNE2 confined, n = 64 cells from eight images. P value is indicated (analysis of variance with Tukey post hoc test; two-sided). p, Images showing acetylated tubulin (yellow) and Hoechst (magenta) in confined A375 cells. q, Quantification of acetylated tubulin intensity in confined cells. siNT, n = 66 cells from eight images; siSYNE2, n = 104 cells from eight images. P value is indicated (two-sample t-test; two-sided). Horizontal line, median; edges, upper and lower quartiles; whiskers, non-outlier minima and maxima (o,q). Scale bars, 50 µm (e), 10 µm (f), 25 µm (h,n,p). Illustrations in l were created using BioRender (https://biorender.com).

Source Data

To validate our transcriptomic results indicating that HMGB2 is upregulated at the invasive front, we examined HMGB2 expression in zebrafish melanoma tissue sections. In invading tumour cells, HMGB2 was only upregulated in elongated or misshaped tumour cells that appeared to be under mechanical pressure from adjacent tissues (Fig. 3e–g), indicating that confinement induces HMGB2 upregulation. HMGB2 concentrations were inversely correlated to nuclear circularity (R = −0.474; P = 1.19 × 10−12; Fig. 3g). Similarly, the in vitro confinement of A375 human melanoma cells caused nuclear HMGB2 intensity to approximately double relative to unconfined cells (P = 1.42 × 10−7; Fig. 3h,i). This increase was not solely attributable to changes in nuclear density; although Hoechst intensity also slightly increased upon confinement (probably owing to compaction of chromatin/the nucleus; P = 0.0017; Fig. 3j), normalizing HMGB2 to Hoechst concentrations still showed a significant increase (P = 3.01 × 10−6; Fig. 3k). Using time-lapse confocal microscopy of A375 cells stably expressing HMGB2–GFP, we found nuclear HMGB2–GFP concentrations increased linearly over approximately 16 h (0.269 ± 0.020-fold per hour) to a final concentration of 1.76 ± 0.072-fold relative to initial concentrations (Extended Data Fig. 5a,b). We also examined the concentrations of HMG family members HMGB1 and HMGA1, which were transcriptionally upregulated in interface cells to a lesser extent than HMGB2, and quantified no change in their expression upon confinement (Extended Data Fig. 5c–f), indicating that confinement-induced upregulation of HMGB2 is not a general property of all HMG family members.

We examined a human melanoma tissue microarray to look for evidence of interface cells with elongated nuclei, high HMGB2 concentrations and perinuclear acetylated tubulin. As in the zebrafish, the invasive front in human samples often contained elongated nuclei with high HMGB2 and acetylated tubulin expression, although with expected interpatient variability (Extended Data Fig. 6a–e). Of the 40 patient samples analysed, 20 (50%) contained putative interface cells (elongated nuclei, HMGB2+ and AcTub+), nine (22.5%) contained cells exhibiting perinuclear acetylated tubulin enrichment only (AcTub+ and HMGB2−), three (7.5%) contained cells with elongated HMGB2+ nuclei but no acetylated tubulin (AcTub− and HMGB2+) and the remaining eight (20%) showed no enrichment of HMGB2 or acetylated tubulin (Extended Data Fig. 6d,e). This validates our scRNA-seq analyses, indicating that interface cells are present in human samples (Fig. 1c).

We then investigated whether the upregulation of HMGB2 and acetylated tubulin by confined cells is melanoma-specific or exhibited across other cancer types. We focused on two cancer types probably influenced by mechanical stress in vivo: pancreatic ductal adenocarcinoma and bladder cancer. In both, confinement induced significant upregulation of HMGB2 and acetylated tubulin (Extended Data Fig. 6f–q).

Mechanical stress can be associated with nuclear translocation of transcription factors, such as YAP, as well as epithelial–mesenchymal transition inducers, including Twist, Snail and SMAD3 (ref. [30]). To rule out a potential pro-invasive role for these transcription factors upon mechanical stress in melanoma, we quantified their expression in confined A375 cells. None of the factors examined (YAP, Twist, Snail and SMAD3) exhibited nuclear translocation upon confinement, and the nuclear expression of YAP, Twist and SMAD3 decreased (Extended Data Fig. 7).

HMGB2 upregulation requires nesprin 2

To investigate how mechanical confinement induces HMGB2 upregulation, we focused on the MT cytoskeleton, hypothesizing that the perinuclear acetylated tubulin network (Fig. 2f,g) may propagate confinement-induced force to the nucleus. To confirm that the perinuclear network is upstream of HMGB2 enrichment in the confinement response, we generated stable A375 cell lines in which HMGB2 was inactivated using CRISPR (Extended Data Fig. 8a–c). Upon confining two different A375–HMGB2KO cell lines, we confirmed that the perinuclear acetylated tubulin network was unaffected (Extended Data Fig. 8d,e).

We modulated tubulin dynamics and acetylation state in confined cells to determine whether the acetylated tubulin network influences HMGB2 upregulation. Tubulin acetylation is mediated by the acetyltransferase ATAT1 (ref. [31]) and deacetylation by HDAC6 (ref. [32]). We first treated A375 cells with the HDAC6 inhibitor tubacin[33], which increased tubulin acetylation without affecting histone acetylation (Extended Data Fig. 9a–f). In confined cells, tubacin treatment significantly increased both total nuclear HMGB2 accumulation (P = 1.1646 × 10−6; Extended Data Fig. 9g,h) and the rate of accumulation, which approximately doubled versus controls (0.353 ± 0.0364-fold per hour versus 0.145 ± 0.0171-fold per hour; P = 1.1589 × 10−6; Extended Data Fig. 9i). To stabilize MTs independent of acetylation, we treated confined cells with paclitaxel (Taxol), which binds β-tubulin and prevents tubulin monomer incorporation into MT filaments and thus should not influence the acetylation state of HMGB2 or other proteins[34]. Taxol (100 nM) significantly increased HMGB2 accumulation in confined cells (P = 0.00230; Extended Data Fig. 9g,h), with an accumulation rate remarkably similar to tubacin-treated cells (0.338 ± 0.0425-fold per hour versus 0.353 ± 0.0364-fold per hour; P = 0.961; Extended Data Fig. 9i). Although we cannot rule out off-target effects of tubacin on HMGB2, similar results in Taxol-treated and tubacin-treated cells indicate that confinement-induced HMGB2 enrichment is linked to MT stability, rather than acetylation state or other HDAC6 functions.

To confirm the role of perinuclear acetylated tubulin in mediating HMGB2 accumulation, we treated A375[HMGB2–GFP] cells with nocodazole before applying confinement. Nocodazole abolishes most MT networks, with the exception of stabilized acetylated tubulin filaments[24]. Because the perinuclear acetylated tubulin cage was resistant to nocodazole (Fig. 2i,j), nocodazole treatment allowed us to specifically interrogate the contribution of acetylated MTs to HMGB2 upregulation in confined cells. We observed near total loss of visible SiR-tubulin signal in confined nocodazole-treated cells (Extended Data Fig. 9j), whereas the perinuclear acetylated tubulin network remained intact (Fig. 2i,j) and HMGB2–GFP accumulation was unaffected (P = 0.714; Extended Data Fig. 9k,l). We quantified HMGB2 accumulation in confined A375–ATAT1KO cells lacking acetylated tubulin (Extended Data Fig. 3c–e). Unexpectedly, HMGB2 accumulation was not impaired in ATAT1KO cells (Extended Data Fig. 3f), suggesting that acetylated tubulin is sufficient but not necessary for the enrichment of HMGB2 in response to confinement.

Thus, we investigated other factors that may cooperate with the MT cytoskeleton to promote upregulation of HMGB2 in confined cells using TurboID[35] to perform proximity labelling proteomics targeting HMGB2 interactors (Fig. 3l and Supplementary Table 4). One highly enriched protein was nesprin 2 (gene name: SYNE2), a component of the linker of nucleoskeleton and cytoskeleton (LINC) complex that connects the cytoskeleton, nuclear lamina and chromatin[36] (Fig. 3m and Supplementary Table 4). We hypothesized that nesprin 2 may be required for enrichment of HMGB2 upon confinement. Accordingly, confined A375 cells upregulated nesprin 2 (Extended Data Fig. 10a,b), and targeting SYNE2 with short interfering RNA (siRNA) (Extended Data Fig. 10c) abolished the confinement-mediated accumulation of HMGB2 (Fig. 3n,o) and perinuclear tubulin network (Fig. 3p,q). This indicates that nesprin 2 and the tubulin cytoskeleton interact to upregulate HMGB2.

The LINC complex cooperates with the nuclear lamina to tune nuclear stiffness[36]. Lamin A/C increased by approximately 3-fold in confined cells (P = 1.28 × 10−162; Extended Data Fig. 10d,e), suggesting that the nuclear lamina was remodelled in response to confinement, which we validated by means of atomic force microscopy (AFM) showing increased nuclear stiffness in confined cells (Extended Data Fig. 10f,g). Together, our results demonstrate that confined melanoma cells remodel cytoskeletal and nuclear structures to reinforce the cell against mechanical force, resulting in LINC complex-mediated HMGB2 upregulation and nuclear stiffening.

Force affects HMGB2–chromatin dynamics

Our results indicate that the LINC complex and tubulin cytoskeleton cooperate to stabilize the nucleus against mechanical stress and upregulate HMGB2. HMGB2 typically binds chromatin without sequence specificity and bends DNA to relieve mechanical strain[37]. Confinement-induced nuclear shape changes could thus increase the strain on chromatin and create a densely packed nuclear environment that affects HMGB2 dynamics. We used fluorescence recovery after photobleaching (FRAP) to characterize nuclear HMGB2 dynamics upon confinement. As previously reported[38], HMGB2–GFP was highly dynamic even in unconfined cells (Fig. 4a–d), with only approximately 25% stably bound within the nucleus (mobile fraction = 75.21 ± 0.91%). In both conditions, FRAP recovery curves fitted well to a two-component exponential equation (average coefficient of determination (R^2) = 0.986 ± 0.00054; Fig. 4d,e and Methods), indicating two pools of nuclear HMGB2: fast-diffusing (stochastic interactions with chromatin) and slower-diffusing (more specific, stable interactions)[39]. Although more than 90% of HMGB2 remained fast-diffusing in both conditions, confinement significantly increased the proportion of slower-diffusing HMGB2 (6.76 ± 0.39% versus 4.21 ± 0.39%; P = 1.134 × 10−5; Fig. 4f), suggesting more specific, stable HMGB2–chromatin interactions in confined cells.

Fig. 4: Confinement-mediated stabilization of HMGB2 increases chromatin accessibility at neuronal loci.

a, Representative stills from time-lapse imaging of A375 cells expressing HMGB2–GFP and subjected to FRAP. The yellow dashed region indicates the photobleached area. Time is relative to photobleaching. Scale bars, 10 µm. b,c, FRAP recovery curves for HMGB2–GFP in unconfined (b) and confined (c) cells. Each curve represents fluorescence recovery within the area photobleached on a single cell. d,e, Representative plots showing a two-component exponential equation fit to HMGB2–GFP fluorescence recovery curves in unconfined (d) and confined (e) cells. f, Relative proportion of slow-diffusing HMGB2–GFP. Horizontal lines, mean; box, s.e.m.; vertical lines, s.d. P value is indicated (two-sample t-test; two-sided). Unconfined, n = 45 cells; confined, n = 54 cells (b–f). g, Volcano plot of differentially expressed peaks upon HMGB2OE. P-value cut-off, 0.05; fold change cut-off, log_2(1.25) and log_2(−1.25). h, Top 10 enriched GO: Biological Process (BP) pathways from genes mapped to open chromatin loci upon HMGB2OE. P values are indicated (two-sided hypergeometric test with Benjamini–Hochberg correction) (g,h). i,j, Tornado plots showing chromatin accessibility at loci linked to genes from the ‘Lee neural crest stem cell up’ (i) and ‘regulation of nervous system development’ (j) pathways. k, Homer de novo motif analysis of transcription factor motifs in open chromatin regions in promoter regions of genes upon HMGB2OE. P values are indicated (binomial test).

Source Data

HMGB2 targets plasticity-associated loci

Our FRAP analysis indicates that confinement upregulates HMGB2 and stabilizes its interactions with chromatin; therefore, we propose that this could affect chromatin accessibility. Assay for transposase-accessible chromatin (ATAC) sequencing (ATAC-seq) of A375 cells overexpressing HMGB2 showed broadly increased chromatin accessibility (Fig. 4g). Peak-gene mapping and pathway analysis revealed that open chromatin regions were highly enriched for neuronal genes, with increased accessibility at loci linked to the neural crest and neuronal development (Fig. 4h–j and Supplementary Table 5). Homer de novo motif analysis identified conserved transcription factor binding motifs enriched within the promoter of open chromatin regions upon HMGB2 overexpression (HMGB2OE). The top-ranked motif was an AP-1 motif (Fig. 4k; P = 1 × 10−49), implicated in melanoma and melanocyte plasticity40,41. Other highly ranked motifs included PRDM4, which functions in neural development[42] (Fig. 4k; P = 1 × 10−12), and SOX9, a critical regulator of the pro-invasive phenotype switch in melanoma[43] (Fig. 4k; P = 1 × 10−11). These data indicate that HMGB2 upregulation increases chromatin accessibility at loci associated with plasticity and a neuronal phenotype, promoting a confinement-induced dedifferentiation program.

To clarify potential HMGB2 targets, we performed chromatin immunoprecipitation (ChIP) sequencing (ChIP–seq) targeting HMGB2 in A375 cells using a double-crosslinking approach[44] (Extended Data Fig. 11a). We generated a peak atlas by comparing peaks present in cells expressing baseline concentrations of HMGB2 to those not present in HMGB2KO cells, yielding 843 peaks, consistent with previous studies44,45,46 (Supplementary Table 6). After manual filtering to remove intergenic, low-quality or non-specific peaks, we identified a final high-confidence set of 96 targets (Extended Data Fig. 11b and Supplementary Table 6). We observed HMGB2 binding at promoter regions of several AP-1 signalling genes, including FOSL1, JUNB and JUND (Extended Data Fig. 11c and Supplementary Table 6), validating our ATAC-seq results and supporting the pro-invasive role of HMGB2. Several neuronal genes were also identified as HMGB2 targets, including NOTCH2, NOTCH2NLC, TBX6, GBA1 and ZNF335 (Extended Data Fig. 11d and Supplementary Table 6), as well as pro-tumorigenic genes such as KMT2A (Extended Data Fig. 11e and Supplementary Table 6).

To examine the transcriptional effects, we performed bulk RNA-seq on A375 cells stably overexpressing HMGB2 (Extended Data Fig. 11f). HMGB2OE cells adopted a mesenchymal-like and invasive morphology relative to empty vector controls (Extended Data Fig. 11g). The most highly upregulated gene was UNC5D, a netrin receptor promoting neuronal survival and migration[47] (Extended Data Fig. 11h and Supplementary Table 7). Other upregulated neuronal genes included DCC, SH3GL2 and NPX2 (Extended Data Fig. 11h,i and Supplementary Table 7). GSEA revealed an overrepresentation of neuronal genes within enriched pathways (Extended Data Fig. 11j,k and Supplementary Table 7). Supporting a pro-invasive role for HMGB2, several invasive genes were also upregulated (MAGEA1, SPP1, CTAG2 and GDF6; Extended Data Fig. 11i and Supplementary Table 7). These data indicate that HMGB2 alters chromatin architecture to promote an invasive neuronal state.

HMGB2 drives invasion through Notch and BRN2

Phenotypic plasticity in melanoma is controlled by a regulatory axis of melanocytic transcription factors (MITF) driving proliferation versus invasive factors (BRN2, SOX9 and AP-1) that also slow proliferation. We investigated whether HMGB2 drives invasion through these factors. Using the SKMEL5 melanocytic cell line, we created a stable line expressing V5-tagged HMGB2 and performed ChIP–seq targeting V5 and HMGB2 (Extended Data Fig. 12a). We generated a peak atlas containing 1,361 peaks corresponding to 1,286 unique genes (Extended Data Fig. 12b and Supplementary Table 8). We identified robust HMGB2 binding to the MITF promoter (Extended Data Fig. 12c). However, loss of HMGB2 did not meaningfully alter MITF expression (log_2FC = 0.157; Extended Data Fig. 12e–g and Supplementary Table 9), indicating that HMGB2 may regulate pro-invasive factors instead. HMGB2 robustly bound the promoter of Notch signalling genes in both A375 (Extended Data Fig. 11d and Supplementary Table 6) and SKMEL5 (Extended Data Fig. 12d and Supplementary Table 8) cells. Notch family genes were also upregulated by human interface cells (DLL1, DLL3 and DLK2; Supplementary Table 1) and confined human melanoma cells (NOTCH2NLA, DLK2 and DLL4; Supplementary Table 2). Notch signalling promotes melanoma invasion through brain-2 (BRN2, also known as POU3F2) (ref. [48]), a well-characterized pro-invasive transcription factor in melanoma[49] that also functions in neuronal development[50]. This indicates that invasive and neuronal phenotypes in confined interface cells are induced by HMGB2-mediated upregulation of Notch/BRN2 signalling. Supporting this model, a BRN2/POU3F2 motif was enriched in the promoter region of HMGB2 target genes (Extended Data Fig. 12h). Although MITF and BRN2 expressions are often inversely correlated[51], previous reports52,53 and our data indicate that these factors can also be co-expressed. Characterizing the mechanisms that control the expression of these factors and the regulation of the proliferative/invasive trade-off will be an important area for future investigation.

HMGB2 mediates phenotype switching

One prediction of the melanoma phenotype switching model is the trade-off between opposing phenotypes; highly proliferative cells are less invasive, whereas highly invasive cells are less proliferative (Fig. 5a). Our data indicate that confinement-induced upregulation of HMGB2 could mediate this phenotypic plasticity. Confined A375 cells downregulated many proliferation-related pathways (Fig. 5b). To visualize the influence of confinement on this trade-off, we generated an A375 cell line stably expressing the FastFUCCI cell cycle sensor[54]. Upon confinement, all initially mitotic cells (mAG+ and S/G2–M phase) rapidly lost mAG fluorescence, indicating cell cycle exit (Fig. 5c,d).

Fig. 5: Confinement promotes drug tolerance by downregulating proliferation.

a, Schematic detailing the influence of confinement on melanoma phenotype switching. b, Enrichment scores for GO:BP pathways related to cell division from RNA-seq of confined cells relative to unconfined cells. FDR is indicated. c, Stills from confocal imaging of A375 cells stably expressing the FastFUCCI reporter and stained with SiR-tubulin. d, Quantification of FUCCI signal over time in confined cells; n = 37 cells from four videos. Error bars, s.e.m. e, Schematic of in vitro invasion assay workflow. f,h, Western blot for HMGB2 (top) and tubulin (f; loading control; bottom) or β-actin (h; loading control; bottom) for A375 cells transfected with the indicated siRNAs (f) or plasmids (h). siHMGB2, HMGB2-targeting siRNA. g,i, Quantification of in vitro invasion assay results for A375 transfected with the indicated siRNAs (g) or plasmids (i); n = 3 biological replicates. P value is indicated (two-sample t-test; two-sided). EV, empty vector; OE, overexpression (of HMGB2). j, Representative images of adult zebrafish with melanomas generated by means of Transgene Electroporation in Adult Zebrafish (TEAZ), 12 weeks after electroporation. NT, non-targeting; sgRNA, single-guide RNA. k, Tumour surface area over time for melanomas induced in zebrafish using TEAZ. Error bars, s.e.m. l, Tumour surface area at 12 weeks after TEAZ. P value is indicated (two-sample t-test; two-sided). Horizontal line, median; box edges, 25th and 75th percentiles; whiskers, data range. NT sgRNA, n = 10 fish; sgRNA targeting HMGB2 (sgHMGB2), n = 8 fish (j–l). m, Representative haematoxylin and eosin (H&E) images of tumour invasion in the indicated conditions at 12 weeks after TEAZ. n, Schematic of the drug treatment experiment workflow. o, Tumour volume over time for the indicated conditions; n = 11 mice per condition from two biological replicates. Error bars, s.e.m. *P < 0.05. P value calculated using likelihood ratio tests with a fitted exponential; two-sided (P = 0.043). Scale bars, 25 µm (c), 2 mm (j), 250 µm (m). Illustrations in e were created using BioRender (https://biorender.com). Mouse cartoons in n were adapted from Wikimedia, under a Creative Commons licence CC BY-SA 3.0.

Source Data

To directly test the role of HMGB2 in melanoma invasion, we used in vitro invasion assays (Fig. 5e) and found that targeting HMGB2 with siRNA significantly impaired invasion (P = 0.0133; Fig. 5f,g), whereas overexpression of HMGB2 increased invasion (P = 0.0180; Fig. 5h,i). Previous studies identified a fast amoeboid mode of migration induced by confinement and low adhesion13,55,56,57. We quantified minimal migration of confined A375 cells (mean velocity = 0.0838 ± 0.0025 µm min−1; Extended Data Fig. 13a–c), which indicates that fast amoeboid migration is not a major contributor to confinement-induced phenotypic plasticity in our system. To test the role of HMGB2 in phenotype switching in vivo, we generated BRAFV600E melanomas in zebrafish, in which zebrafish hmgb2a and hmgb2b were inactivated by CRISPR. Loss of hmgb2 and hmgb2b markedly increased melanoma growth, with tumours growing almost 2-fold larger by 10 weeks (P = 0.0193; Fig. 5j–l) while also appearing less invasive than non-targeting controls (Fig. 5m). These data indicate that HMGB2 is required for the invasive state, and that its loss pushes melanoma cells towards a hyperproliferative state.

HMGB family proteins contain three functional domains: A-box and B-box DNA-binding domains and an acidic tail region[58] (Extended Data Fig. 14a). We investigated the functional domains that control HMGB2 localization and activity in melanoma. We assembled constructs with each functional domain removed (Extended Data Fig. 14b) and generated stable A375 cell lines expressing GFP-tagged versions of each (Extended Data Fig. 14c). We were unable to express a truncated construct lacking both DNA-binding domains, probably because of its small size (24 amino acids). In in vitro proliferation and invasion assays, cells expressing either ΔA-box or ΔB-box constructs were significantly less proliferative (Extended Data Fig. 14d) but more invasive (Extended Data Fig. 14e), even compared to full-length HMGB2; this was probably because of higher expression of these smaller constructs (Extended Data Fig. 14f). Because we were unable to generate constructs lacking both DNA-binding domains, when we transplanted cells expressing each deletion construct into mice (Extended Data Fig. 14g), as expected, there was no significant difference in tumour growth rates (Extended Data Fig. 14h,i) or acetylated tubulin at the invasive front (Extended Data Fig. 14j), demonstrating that either HMGB2 DNA-binding domain is sufficient to maintain protein function.

HMGB2 is associated with drug tolerance

Phenotype switching, particularly the undifferentiated/invasive state, has been linked to drug resistance[2]. This has important clinical relevance because drug-tolerant persister cells are major contributors to relapse[59]. We examined how the confined invasive state affects therapeutic response, hypothesizing that the HMGB2-high neuronal state induced by confinement may promote drug tolerance. To test this, we treated confined cells with Taxol to stabilize MTs. Unconfined cells rapidly displayed fragmented nuclear morphology and underwent apoptosis, whereas confined cells were almost completely resistant to Taxol-induced cell death (Extended Data Fig. 13d). Taxol induces cancer cell death through cell cycle arrest[60], indicating that confinement may cause melanoma cells to exit the cell cycle, in accordance with a pro-invasive phenotype switch associated with downregulation of proliferation[8].

We then tested the role of HMGB2 in drug tolerance in vivo by transplanting human melanoma cells overexpressing HMGB2 into athymic mice and treating the resulting tumours with dabrafenib and trametinib, widely used targeted therapies in melanoma[61] (Fig. 5n). HMGB2 overexpression significantly impaired melanoma response to dabrafenib/trametinib in vivo (P = 0.043; Fig. 5o, Extended Data Fig. 13e and Supplementary Table 10). Together, our results support a role for confinement-mediated upregulation of HMGB2 inducing a pro-invasive and drug-tolerant state in melanoma (Extended Data Fig. 15).

Discussion

Single-cell profiling has uncovered reproducible transcriptional states that correspond to distinct tumour phenotypes[62]. Melanoma cells exist along a phenotypic axis spanning proliferative and invasive states, with a trade-off in which the invasive and proliferative states are mutually exclusive1,2. This is reminiscent of the ‘go or grow’ hypothesis, in which a cell is optimized for one phenotype over another[63]. The reproducibility of these states in the absence of genetic lesions indicates they may be epigenetically encoded. We found that the transition from proliferation to invasion is in part mediated by the chromatin-associated protein HMGB2. This family of proteins bends DNA to facilitate the action of transcription factors and other chromatin-associated proteins, making them ideal candidates for enforcing changes in chromatin configuration in response to external cues. Our data indicate that HMGB2 responds to mechanical forces exerted on the cell, in which upregulation of HMGB2 increases chromatin accessibility at invasive loci. This may explain the persistence of invasive behaviours once a melanoma cell is exposed to microenvironmental forces. Although our study primarily focused on DNA-centric roles for HMGB2, HMGB family proteins also bind RNA to regulate transcriptional and protein–protein interactions, among other functions[64]. Although we cannot rule out a role for HMGB2–RNA binding in melanoma on the basis of our current data, characterizing this interaction will be an important area for future study.

The role of the mechanical microenvironment on tumour cell phenotypes is still emerging. A previous study showed that increased pressure from a stiff collagen matrix could increase cancer invasion[65]. Cancer-associated fibroblasts can also alter the mechanical microenvironment by secreting matrix metalloproteinases that remodel collagen matrix stiffness[66]. Our study augments these findings by showing that force can also epigenetically remodel tumour cells through reorganization of cytoskeletal, nuclear and chromatin architectures.

Our results indicate that confined melanoma cells enact an invasion program reminiscent of developing neurons. During development, neurons assemble a perinuclear tubulin network to protect the nucleus from high levels of force during confined migration22,23, similar to what we observed in invasive melanoma cells. It is unclear whether melanoma cells hijack other neuronal behaviours to promote invasion. A previous study from our laboratory indicated that melanoma cells exploit neuronal-like mechanisms during tumour initiation[67], and future studies should aim to explore this phenomenon in later stages of melanoma progression.

Methods

Zebrafish husbandry

Stable transgenic zebrafish lines were kept at 28.5 °C in a dedicated aquatics facility with a 14 h on/10 h off light cycle. Casper fish with the following genotype were used for all experiments: mitfa-BRAFV600E;p53−/−;mitfa−/−. Fish were anaesthetized using Tricaine (MS-222; stock concentration of 4 g l−1), diluted until the fish were immobilized. All animal procedures were approved by the Memorial Sloan Kettering Cancer Center Institutional Animal Care and Use Committee (protocol no. 12-05-008).

Cloning of zebrafish CRISPR constructs

To generate hmgb2a and hmgb2b CRISPR guide RNA (gRNA) plasmids for use in vivo, three gRNAs for each gene were subcloned into Gateway entry vectors containing zebrafish-optimized U6 gRNA promoters. The resulting 3× gRNA plasmid was assembled through Gateway LR cloning. Validation of gRNA/Cas9 activity in vivo was performed using the Alt-R CRISPR-Cas9 system (Integrated DNA Technologies (IDT)) by injecting single-guide RNAs (sgRNAs) and purified Cas9 protein into one-cell-stage zebrafish embryos. Genomic DNA was isolated from five to ten embryos 24 h later, and mutation detection was performed using the Alt-R Genome Editing Detection Kit (IDT).

Zebrafish gRNA sequences:

hmgb2a sgRNA1: 5′-GAAAAGTTCACCGAGGTCCC-3′

hmgb2a sgRNA2: 5′-AAGGTGAAGGGCGACAACCC-3′

hmgb2a sgRNA3: 5′-GACAACCCGGGCATCTCTAT-3′

hmgb2b sgRNA1: 5′-CAAACCCAAGGGGAAGACGT-3′

hmgb2b sgRNA2: 5′-CTCAAACTTGACCTTGTCGG-3′

hmgb2b sgRNA3: 5′-AGAGAAGTTGACGGGCACGT-3′

NT sgRNA: 5′-AACCTACGGGCTACGATACG-3′

Zebrafish in vivo electroporation

Tumours were generated by means of TEAZ[68]. To generate hmgb2a/hmgb2b knockout melanomas, adult 3-month-old to 6-month-old fish were randomly assigned to groups and injected with the following plasmids: miniCoopR–GFP, mitfa:Cas9, Tol2, U6–sgptena, U6–sgptenb and either 394-zU6–3XsgRNA[hmgb2a] and 394-zU6–3XsgRNA[hmgb2b] or 394-zU6–3XsgRNA[NT]. Adult fish were anaesthetized using tricaine and injected with 1 µl of plasmid mixture below the dorsal fin, immediately electroporated and moved to fresh water to recover. Tumour growth was imaged every 1–2 weeks using a ZEISS Axio Zoom V16 fluorescence microscope. Male and female animals were used in equal proportions. No sample size calculation or blinding was performed.

Cell culture

The following cell lines were obtained from the American Type Culture Collection: A375 (CRL-1619), SKMEL5 (HTB-70), MIA-PaCa-2 (CRM-CRL-1420), Panc-1 (CRL-1469), HTB-4 (T24), HTB-9 (5637) and HEK293T. The cells were maintained in a 37 °C and 5% CO_2 humidified incubator. The cell lines were authenticated by the American Type Culture Collection and routinely checked to be free from Mycoplasma. The cells were cultured in DMEM (Gibco; 11965) supplemented with 10% fetal bovine serum (GeminiBio; 100-500).

Transfection of siRNAs

SiRNAs targeting the following genes were obtained from Horizon Discovery: HMGB2 (L-011689-00-0005), SYNE2 (L-019259-01-0005) and non-targeting control (D-001810-10-05). DharmaFECT 1 Transfection Reagent (Horizon Discovery; T-2001) was used to transfect 250,000 A375 cells per condition. The medium was changed after 24 h, and experiments were performed 72 h after changing the medium. HMGB2 knockdown was validated by western blot with an antibody targeting HMGB2 (MilliporeSigma; HPA053314). For gel source data, see Supplementary Fig. 1. Downregulation of SYNE2 was validated by means of quantitative polymerase chain reaction (qPCR) with the following primers:

SYNE2 F: 5′-CAAAGCACAGGAAACTGAGGCAG-3′

SYNE2 R: 5′-AGACAGTGGCAACGAGGACATG-3′

β-Actin F: 5′-CACCAACTGGGACGACAT-3′

β-Actin R: 5′-ACAGCCTGGATAGCAACG-3′

Cloning of human CRISPR constructs

The lentiCRISPRv2 system[69] was used to generate stable human knockout cell lines, and gRNAs targeting HMGB2 or ATAT1 were selected from the GeCKO2 library. Oligonucleotides containing each gRNA were obtained from IDT and cloned into the lentiCRISPRv2 backbone through restriction digest with BsmB1 and ligation with Quick Ligase (New England Biolabs). Ligated plasmids were transformed into Stbl3 bacteria (New England Biolabs) and sequenced to verify gRNA insertion. The final plasmids were used to create stable A375 and SK-MEL-5 lines using lentiviral transduction, as described below.

Human gRNA sequences:

HMGB2 sgRNA1: 5′-CTGCACGAAGAAGGCGTACG-3′

HMGB2 sgRNA2: 5′-AAGATCAAAAGTGAACACCC-3′

ATAT1 sgRNA1: 5′-CCAGAAGAACATCTACAGTG-3′

ATAT1 sgRNA2: 5′-CCTCACTGTAGATGTTCTTC-3′

NT sgRNA: 5′-AACCTACGGGCTACGATACG-3′

Cloning of HMGB2 overexpression and deletion constructs

To generate the HMGB2–GFP plasmid, the human HMGB2 coding sequence in a pENTR backbone (Horizon Discovery; OHS5898-202621565) was combined with a C terminus EGFP tag using In-Fusion Cloning. The HMGB2–GFP insert was then transferred into a lentiviral expression vector containing the cytomegalovirus promoter (pLX304; Addgene 25890) by means of Gateway cloning using LR Clonase II Plus (Thermo Fisher Scientific). Deletion constructs were generated through In-Fusion Cloning (Takeda Bioscience) using the HMGB2 open reading frame in the pENTR backbone as a template and were subsequently cloned into pLX304 by means of Gateway cloning, as described above. The primers were:

HMGB2-ΔA-box F: 5′-CCAACAAGCCTCCCAAAGGTGATAAGAAGGG-3′

HMGB2-ΔA-box R: 5′-TGGGAGGCTTGTTGGGGTCTCCTTTACC-3′

HMGB2-ΔB-box F: 5′-CCAATGCTGCCAAGGGCAAAAGTGAAGC-3′

HMGB2-ΔB-box R: 5′-CCTTGGCAGCATTGGGGTCCTTTTTCTTCCC-3′

HMGB2-Δacidic tail F: 5′-CATATCGTGACCCAGCTTTCTTGTACAAAG-3′

HMGB2-Δacidic tail R: 5′-CTGGGTCACGATATGCAGCAATATCCTTTTC-3′

Generation of stable cell lines

HMGB2OE, HMGB2–GFP, HMGB2KO, HMGB2del, ATAT1KO and FastFUCCI stable cell lines were generated by means of lentiviral transduction. The FastFUCCI reporter plasmid was obtained from Addgene (86849). The HMGB2–GFP reporter plasmid, HMGB2 gRNA+Cas9 plasmids, ATAT1 gRNA+Cas9 plasmids and non-targeting gRNA+Cas9 plasmids were assembled, as described above. The HMGB2OE plasmid was obtained from Horizon Discovery (OHS5897-202616132). Eight million HEK293T cells per condition were transfected with 1,200-ng lentiviral vector, 600-ng PAX2 plasmid and 300-ng MD2 plasmid using Effectene Transfection Reagent (QIAGEN). Virus was collected starting 24 h after transfection. Viral supernatant was filtered (0.45-µm filter) before adding to A375 cells at a 1:1 ratio with medium and 10 µg ml−1 of polybrene. Cells were infected for 72 h, allowed to recover for 24 h and then selected using blasticidin (5 µg ml−1; 7–10 days) or puromycin (1 µg ml−1; 3 days). For cell lines expressing a fluorescent reporter, cells were sorted using FACSAria III or FACSymphony S6 cell sorters (BD Biosciences). For HMGB2 overexpression and CRISPR lines, successful transduction was validated through western blot with an antibody targeting HMGB2 (MilliporeSigma; HPA053314). For ATAT1KO lines, knockdown was validated through qPCR. For gel source data, see Supplementary Fig. 1.

QPCR primer sequences:

ATAT1 F: 5′-CACAGTCCCACAGGTGAACA-3′

ATAT1 R: 5′-CTCCCTGCTTGGAGTCTTGG-3′

β-Actin F: 5′-CACCAACTGGGACGACAT-3′

β-Actin R: 5′-ACAGCCTGGATAGCAACG-3′

In vitro confinement and imaging

A375, HTB-4 and HTB-9 cells were subjected to overnight (approximately 16 h) confinement at a height of 3 µm using a static cell confiner (4Dcell). Pancreatic ductal adenocarcinoma cell lines (MIA-PaCa-2 and Panc-1) were confined at a 5-µm height owing to their larger size. The cells were plated 6 h before imaging in fibronectin-coated glass-bottom 35-mm dishes (FluoroDish) or glass-bottom six-well plates (MatTek). The cells were allowed to attach before confinement was applied. Confined cells were incubated at 37 °C and 5% CO_2 overnight. For live imaging, dyes plus 10 µM verapamil were added to the plated cells 2–3 h before imaging. The dyes used for live imaging were SiR-tubulin (Spirochrome; 100 nM) and SiR-DNA (Spirochrome; 250 nM). Pharmacological inhibitors were added immediately before applying confinement. The inhibitors used were Taxol (Tocris; 1097), tubacin (Selleck Chemicals; S2239), nocodazole (Tocris; 1228) and trichostatin A (MilliporeSigma; T8552). Live imaging was performed on an LSM 880 (ZEISS) confocal microscope at 37 °C and 5% CO_2, at ×63 magnification and 5–10 min of temporal resolution, using ZEN Black v.2.3 SP1 software (ZEISS). For immunofluorescence, cells were fixed with 4% paraformaldehyde for 15 min at room temperature before proceeding with staining and imaging, as described below.

In vitro proliferation and invasion assays

The CyQUANT Direct Red Cell Proliferation Assay (Thermo Fisher Scientific; C35013) was used to assay cell proliferation. Cells were plated at a density of 500 cells per well in 96-well plates and allowed to grow for 72 h. The cell number was quantified using the CyQUANT Direct Red Nuclei Acid Stain and Background Suppressor added at a 1:1 ratio to the cell culture medium, and the intensity was read out at 622 nm on a plate reader. For invasion assays, VitroGel Cell Invasion Assay Kit (TheWell Bioscience; IA-VHM01-1P) and Cultrex Collagen I Cell Invasion Assay kit (Bio-Techne; 3457-096-K) were used. The cells were serum-starved overnight in DMEM, plated in the upper chamber of the invasion assay insert and allowed to migrate for 18 h. The cell number was quantified using crystal violet or calcein staining.

Immunofluorescence staining and imaging

Cells were plated on glass CC2-coated chamber slides (Thermo Fisher Scientific) or fibronectin-coated glass-bottom dishes (FluoroDish) and allowed to attach for approximately 24 h. The cells were fixed with 4% paraformaldehyde for 15 min, permeabilized with 0.1% Triton in PBS and blocked in 10% goat serum (Thermo Fisher Scientific) for 1 h at room temperature. The primary antibodies used were rabbit anti-HMGB2 (Abcam; ab124670), rabbit anti-HMGB1 (Abcam; ab18256), rabbit anti-HMGA1 (Abcam; ab129153), mouse anti-α-tubulin (MilliporeSigma; CP06), chick anti-β-tubulin (Novus Biologicals; NB100-1612), mouse anti-acetylated tubulin (MilliporeSigma; 6793), rabbit anti-acetylated tubulin (Cell Signaling Technology (CST); 5335), rat anti-tyrosinated tubulin (MilliporeSigma; MAB1864-I), mouse anti-polyglutamylated tubulin (MilliporeSigma; T9822), mouse anti-GFP (Abcam; ab1218), rabbit anti-H3Ac (MilliporeSigma; 06-599), mouse anti-Annexin V (Santa Cruz Biotechnology; sc-74438), rabbit anti-cleaved caspase-3 (CST; 9661), rabbit anti-cleaved PARP (CST; 5625), rabbit anti-YAP (CST; 14074), mouse anti-Twist (Abcam; ab50887), rabbit anti-Snail (CST; 3879), rabbit anti-SMAD3 (Abcam; ab40854) and rabbit anti-SYNE2 (Abcam; ab204308). All primary antibodies were used at 1:200. The cells were incubated with primary antibodies overnight at 4 °C, washed in PBS and incubated with the appropriate fluorescently labelled secondary antibody (1:250). Alexa Fluor 488 conjugated phalloidin (CST; 8878S), when used, was added at 1:50, and Hoechst was added at 1:1,000. The cells were mounted in VECTASHIELD (Vector Laboratories) and allowed to cure overnight. Stained cells were imaged on a ZEISS LSM 880 confocal at ×40 or ×63 resolution using ZEN Black v.2.3 SP1 software (ZEISS). A Gaussian blur with a radius of 0.5–0.75 pixels was occasionally applied to images to reduce noise for visualization purposes only.

Staining of human tumour samples

Human melanoma tissue microarrays were obtained from TissueArray.Com (Me481f). Slides were baked at 60 °C for 20 min and deparaffinized in consecutive xylene and ethanol washes. Antigen retrieval was performed using 1X IHC Antigen Retrieval Solution (Thermo Fisher Scientific; 00-4955-58) heated at 95 °C for 20 min in a pressure cooker. After washing in PBS, the samples were blocked in 10% goat serum (Thermo Fisher Scientific) for 1 h at room temperature before incubation overnight at 4 °C in the following primary antibodies, all diluted in blocking buffer at 1:200: rabbit anti-HMGB2 (Abcam; ab124670) and mouse anti-acetylated tubulin (MilliporeSigma; 6793). After washing in PBS, the slides were incubated with the appropriate fluorescently labelled secondary antibody (1:250) and Hoechst (1:1,000). After washing in PBS, a final incubation was performed with a fluorescently conjugated rabbit anti-S100a6 antibody (Abcam; ab204028; 1:250) to label tumour cells before mounting the slides in VECTASHIELD. The slides were imaged on a Pannoramic slide scanner (3DHISTECH) using a ×20/0.8 numerical aperture objective, with higher-resolution images acquired on an LSM 880 confocal (ZEISS), as described above.

Image analysis

Images were analysed using CellProfiler[70], TrackMate[71] and MATLAB v.R2021b and R2023b (MathWorks). For images of fixed cells, the cells were segmented in CellProfiler using Hoechst staining to generate a nuclei mask and phalloidin or other cytoskeletal staining to generate a whole-cell mask. The mean intensity per cell/nucleus was quantified, and expression of nuclear-localized proteins was normalized to Hoechst intensity per nucleus. For quantification of live imaging data, HMGB2–GFP intensity per cell over time was quantified using TrackMate. The resulting intensity data were analysed in MATLAB by fitting a line to each curve and automatically removing curves in which more than four data points differed from the line of best fit by more than 0.2 a.u. In all cases, plotting and statistics were done in MATLAB. The images were assembled for figure preparation using Fiji (v.2.14).

Fluorescence recovery after photobleaching

A375 cells expressing HMGB2–GFP were confined for approximately 18 h before FRAP measurements. FRAP was done on an LSM 880 confocal at 37 °C with 5% CO_2 using a ×63 oil immersion lens and ZEN Black v.2.3 SP1 software (ZEISS). A 5-µm circular diameter region of interest was defined within the nucleus of each cell before photobleaching at 405 nm and 488 nm wavelengths for ten pulses. One time point was acquired before photobleaching. Fluorescence recovery was imaged at 0.2-s intervals for a total of 20 s. All analyses were performed in MATLAB. For analysis, fluorescence within the region of interest was normalized to the fluorescence at the initial time point (before photobleaching). Samples in which the fluorescence within the region of interest was not bleached to at least 25% of the pre-bleaching value were automatically removed from the analysis. Each recovery curve was fitted with a two-component exponential using the function ‘fit’ with the ‘exp2’ parameter: F(t) = y0 + A_1(1 − e−t/τ1) + A_2(1 − e−t/τ2), where y0 represents the fluorescence immediately after photobleaching, A_1 represents the amplitude of the fast-diffusing population, A_2 represents the amplitude of the slow-diffusing population, t is time and τ1 and τ2 correspond to the time constants for the fast-diffusing and slow-diffusing populations, respectively.

Atomic force microscopy

Cells were plated on glass-bottom Petri dishes (FluoroDish FD35) and confined for 18 h, as described above. Immediately after removing the dish from the confiner, cell stiffness was measured using a NanoWizard V microscope (JPK Bruker) in QI Advanced mode. The samples were maintained at 37 °C during imaging using the PetriDishHeater (Bruker). For cell stiffness mapping, 1-μm-diameter spherical AFM probe (silicon nitride cantilever; nominal spring constant k = 0.2 N m−1; SAA-SPH-1UM; Bruker) was used. Each spring constant of the AFM probe was measured using the thermal noise method in liquid at 37 °C. For the stiffness mapping, a 2 nN set point was used (60 μm × 60 μm image size with 32 × 32 pixels of resolution) to ensure up to 10–20% sample indentation to avoid glass surface influence. The data were processed with JPK Data Processing software using the Hertz model with 0.5 Poisson ratio as a fit parameter. To calculate nuclear stiffness, force maps were segmented on the basis of the corresponding cell height measurements to extract the nuclear region.

TurboID experiments

Generation of TurboID constructs

Cloning of TurboID constructs and validation was performed, as described in a previous study[35]. The cyto-TurboID plasmid was obtained from Addgene. For TurboID–HMGB2, the TurboID cassette was amplified by polymerase chain reaction and cloned into pENTR–HMGB2 at the N terminus of HMGB2 using In-Fusion Cloning. For nuclear localization signal–TurboID, an entry vector was assembled using In-Fusion Cloning, containing the TurboID cassette, followed by three consecutive nuclear localization signal sequences. The pENTR–TurboID–HMGB2 and pENTR–TurboID–3XNLS constructs were subcloned into the pLX304 backbone by Gateway cloning using LR Clonase II Plus (Thermo Fisher Scientific). Stable A375 cell lines were generated, as described above, and expression and localization of the TurboID fusion protein were confirmed by immunofluorescence targeting haemagglutinin (found at the N terminus of the TurboID cassette). The TurboID activity was validated by pulsing the cells with 10 mM biotin (MilliporeSigma; 1071508), followed by both western blotting and immunofluorescence using fluorescently labelled streptavidin. IRDye 800CW Streptavidin (LI-COR; 926-32230) was used for western blotting, and streptavidin conjugated to Alexa Fluor 488 (Thermo Fisher Scientific; S11223) or Alexa Fluor 555 (Thermo Fisher Scientific; S21381) was used for immunofluorescence.

Sample preparation

For mass spectrometry experiments, ten million cells per condition and replicate were plated in 15-cm dishes. The medium was removed from the dishes and replaced with 10 mM biotin for 1 h. The labelling reaction was stopped by placing the dishes on ice and washing the cells five times with ice-cold PBS. The cells were then detached by scraping in ice-cold PBS and then pelleted and resuspended in radio-immunoprecipitation assay buffer + protease inhibitors. The cells were lysed by means of sonication (10% amplitude; 2 s per cycle; six cycles), and Bradford assay was used to measure the protein concentration. For each sample, 1-mg protein was incubated with streptavidin magnetic beads (Thermo Fisher Scientific; 88817) in radio-immunoprecipitation assay buffer overnight with rotation at 4 °C. The next day, the beads were pelleted using a magnetic rack, the supernatant was removed and the beads were washed once in 50 mM Tris–HCl (pH 7.5) and twice in 2 M urea in 50 mM Tris–HCl (pH 7.5).

Protein digestion

The beads were resuspended in 80 ml of 2 M urea and 50 mM EPPS (pH 8.5) and treated with dl-dithiothreitol (1 mM final concentration) for 30 min at 37 °C with shaking (1,100 rpm) on a Thermomixer (Thermo Fisher Scientific). Free cysteine residues were alkylated with 2-iodoacetamide (3.67 mM final concentration) for 45 min at 25 °C at 1,100 rpm in the dark. The reaction was quenched using 3.67 mM dithiothreitol, and LysC (750 ng) was added, followed by incubation for 1 h at 37 °C at 1,150 rpm. Finally, trypsin (750 ng) was added, followed by incubation for 16 h at 37 °C at 1,150 rpm. After incubation, the digest was acidified to pH less than 3 with the addition of 50% of trifluoroacetic acid (TFA), and the peptides were desalted on Sep-Pak C18 cartridges (Waters). Briefly, the cartridges were conditioned by sequential addition of (1) 100% methanol; (2) 70% acetonitrile (ACN)/0.1% TFA; and (3) 5% ACN/0.1% TFA twice. After conditioning, the acidified peptide digest was loaded onto the cartridge. The stationary phase was washed with 5% ACN/0.1% formic acid twice. Finally, peptides were eluted using 70% ACN/0.1% formic acid twice. Eluted peptides were dried under vacuum in a SpeedVac centrifuge followed by reconstitution in 12 μl of 0.1% formic acid, sonication and transfer to an autosampler vial. Peptide yield was quantified using NanoDrop (Thermo Fisher Scientific).

Mass spectrometry

Peptides were separated on a 25-cm column with a 75-mm diameter and 1.7-mm particle size composed of C18 stationary phase (IonOpticks; Aurora 3 1801220) using a gradient from 2% to 35% B over 90 min and then to 95% B for 7 min (buffer A, 0.1% formic acid in high-performance liquid chromatography-grade water; buffer B, 99.9% ACN and 0.1% formic acid) with a flow rate of 300 nl min−1 using a nanoElute 2 system (Bruker). Mass spectrometry data were acquired on a timsTOF HT (Bruker) with a CaptiveSpray source (Bruker) using a data-independent acquisition parallel accumulation–serial fragmentation (PASEF) method (dia-PASEF). The mass range was set from 100 to 1700 m/z, and the ion mobility range was set from 0.60 V s cm−2 (collision energy of 20 eV) to 1.6 V s cm−2 (collision energy of 59 eV) with a ramp time of 100 ms and an accumulation time of 100 ms. The dia-PASEF settings included a mass range of 400.0–1,201.0 Da, mobility range of 0.60–1.60 and a cycle time estimate of 1.80 s. The dia-PASEF windows were set with a mass width of 26.00 Da, mass overlap of 1.00 Da and 32 mass steps per cycle.

Data analysis

Raw data files were processed using Spectronaut v.18.5 (Biognosys) and searched with the Pulsar search engine with a human UniProt protein database downloaded on 15 August 2023 (226,261 entries). Cysteine carbamidomethylation was specified as a fixed modification, whereas methionine oxidation, acetylation of the protein N terminus and deamidation (NQ) were set as variable modifications. A maximum of two trypsin missed cleavages were permitted. Searches used a reversed sequence decoy strategy to control peptide FDR, and 1% FDR was set as the threshold for identification. Unpaired t-test was used to calculate P value in differential analysis, and volcano plot was generated on the basis of log_2FC and q value (multiple testing corrected P value using Benjamini–Hochberg method). A q value ≤ 0.05 was considered the statistically significant cut-off.

Mouse experiments

Mouse in vivo studies were performed in accordance with the guidelines approved by the Memorial Sloan Kettering Cancer Center Institutional Animal Care and Use Committee and Research Animal Resource Center. The mice were housed under pathogen-free conditions, in an environment with controlled temperature (21.5 °C ± 1.5 °C) and humidity (55% ± 10%) and under 12 h light/dark cycles. For the drug efficacy studies, 6-week-old to 8-week-old athymic female mice (The Jackson Laboratory) were injected subcutaneously with five million A375 cells in a 50:50 mix with Matrigel (Corning). Once tumours reached an average volume of 100 mm^3, the mice were randomized into two treatment groups (n = 4–6 mice per group) to receive either a vehicle control or trametinib (1 mg kg−1) in combination with dabrafenib (30 mg kg−1). Both drugs were delivered through oral gavage daily five times for 3 weeks. The mice were observed daily throughout the treatment period for signs of morbidity/mortality. Tumours were measured twice weekly using calipers, and volume was calculated using the following formula: length × width^2 × 0.52. Body weight was also assessed twice weekly. For the HMGB2 deletion construct growth curve studies, tumour cells were implanted, as described above, and tumour volume was measured twice weekly. The animals were monitored until their tumour size reached 1,500 mm^3, at which point tumours were collected, fixed in 10% formalin for 24 h, transferred to 70% ethanol and processed for histology. In accordance with limits established by the Memorial Sloan Kettering Cancer Center (MSKCC) Institutional Animal Care and Use Committee, the mice were euthanized when tumour burden exceeded 1,500 mm^3. These limits were not exceeded in any of the experiments. Histology was performed by HistoWiz using the following antibodies for immunohistochemistry: mouse anti-BRAFV600E (Abcam; ab228461) and rabbit anti-acetylated tubulin (Abcam; ab179484). Two biological replicates consisting of four to six mice per condition (for a total of 10–11 mice per group) were performed for both experiments. No sample size calculation or blinding was performed.

Bulk RNA-seq and analysis

For bulk RNA-seq of A375 and SKMEL5 cells overexpressing HMGB2, three replicates of approximately one million cells each were pelleted and resuspended in TRIzol before snap freezing. For bulk RNA-seq of confined A375 cells, 200,000 cells were plated in each well of a six-well plate. Three wells were confined for approximately 18 h using a six-well static confiner (4Dcell) at 3-µm height, whereas the remaining three wells were left unconfined. The cells were then collected in TRIzol, pooling the three wells for each condition to generate samples of approximately 600,000 cells each. This process was repeated for a total of three independent biological replicates per condition. Library preparation and sequencing were done by Azenta Life Sciences. Raw sequencing reads were processed using FastQC (Babraham Bioinformatics) and Trimmomatic[72] before alignment to the human genome hg38. All downstream analyses were performed in R (v.4.3.1). Differential gene expression was analysed using DESeq2 (ref. [73]) with the default parameters. GSEA was performed using the fgsea[74] R package (v.1.26) with Gene Ontology biological process pathway sets from MSigDB[75].

Bulk ATAC-seq and analysis

Samples containing approximately 100,000 cells each were centrifuged at 700g for 5 min at 4 °C before being resuspended in 500-µl growth medium supplemented with 10% DMSO. The cells were frozen at −80 °C overnight before library preparation and sequencing were performed by Azenta Life Sciences. Sequencing reads were trimmed and filtered for quality control using TrimGalore (v.0.6.7) with a quality setting of 15, Cutadapt[76] (v.4.0) and FastQC v.0.12.1. Reads were aligned to the human genome assembly hg38 using Bowtie 2 (ref. [77]) (v.2.3.5.1) and were deduplicated using MarkDuplicates from Picard (Broad Institute; v.2.16). Peaks were identified using MACS2 (ref. [78]) with a P-value setting of 0.001 using a publicly available melanocyte dataset (GSM3191792) as control. To generate a global peak atlas, blacklisted regions were removed before merging all peaks within a 500-bp region and quantifying reads using featureCounts. Differentially enriched peaks were identified using DESeq2 (ref. [73]). Peak gene mapping was done by assigning all intergenic peaks to that gene and, in other cases, by genomic distance to the transcription start site. Pathway was analysed using clusterProfiler[79]. Tornado plots were generated with deepTools[80] (v.3.5.1) functions (computeMatrix and plotHeatmap), with genes annotated from the indicated pathway sets. Motif enrichment was analysed using Homer[81] (v.4.11.1) functions (findMotifsGenome and annotatePeaks).

ChIP sequencing

Sample preparation and sequencing

For profiling of HMGB2 binding in A375 and SKMEL5 cells, freshly collected cells (approximately 20 million cells/replicate/condition) were crosslinked first with 1.5 mM of EGS (Thermo Fisher Scientific; 21565) for 20 min at room temperature and subsequently with 1% formaldehyde (Thermo Fisher Scientific; 28906) for 40 min at 4 °C. The reaction was quenched by the addition of glycine to the final concentration of 0.125 M. Fixed cells were washed twice with PBS and resuspended in SDS buffer (100 mM NaCl, 50 mM Tris–HCl (pH 8.0), 5 mM EDTA, 0.5% SDS and 1× protease inhibitor cocktail; Roche). The resulting nuclei were spun down, resuspended in the immunoprecipitation buffer (100 mM NaCl, 100 mM Tris–HCl (pH 8.0), 5 mM EDTA and 5% Triton X-100) at 1 ml per 0.5 million cells mixed in 2:1 ratio, with the addition of 1× protease inhibitor cocktail (MilliporeSigma; 11836170001). The nuclei were processed on a Covaris E220 Focused-ultrasonicator to achieve an average fragment length of 200–300 bp with the following parameters: peak incident power = 140, duty factor = 5, cycles per burst/burst per second = 200 and time = 20 min (for A375 cells) or 45 min (for SKMEL5 cells). Chromatin concentrations were estimated using the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific; 23227) according to the manufacturer’s instructions. The immunoprecipitation reactions were set up in 500 µl of the immunoprecipitation buffer in Protein LoBind Tubes (Eppendorf; 22431081) and pre-cleared with 50 µl of Dynabeads Protein G (Thermo Fisher Scientific; 10004D) for 2 h at 4 °C. After pre-clearing, the samples were transferred into new Protein LoBind Tubes and incubated overnight at 4 °C with 5 µg of HMGB2 (Abcam; ab67282), V5 (Abcam; ab9116; used for SKMEL5 ChIP only) and H3K4me3 (Epicypher; 13-0041) antibodies. For normalization purposes, 5 µl of Drosophila spike-in chromatin (Active Motif; 53083) and 2 µl of spike-in antibody (Active Motif; 61686) were added to each reaction. The next day, 50 µl of BSA-blocked Dynabeads Protein G was added to each reaction and incubated for 2 h at 4 °C. The beads were then washed twice with low-salt washing buffer (150 mM NaCl, 1% Triton X-100, 0.1% SDS, 2 mM EDTA and 20 mM Tris–HCl (pH 8.0)), twice with high-salt washing buffer (500 mM NaCl, 1% Triton X-100, 0.1% SDS, 2 mM EDTA and 20 mM Tris–HCl (pH 8.0)), twice with LiCL wash buffer (250 mM LiCl, 10 mM Tris–HCl (pH 8.0), 1 mM EDTA, 1% Na deoxycholate and 1% IGEPAL CA-630) and once with TE buffer (10 mM Tris–HCl (pH 8.0) and 1 mM EDTA). The samples were then reverse-crosslinked overnight in the elution buffer (1% SDS and 0.1 M NaHCO_3) and purified using the ChIP DNA Clean & Concentrator kit (Zymo Research; D5205) following the manufacturer’s instructions. After quantification of the recovered DNA fragments, libraries were prepared using the ThruPLEX DNA-Seq Kit (Takara Bio; R400676) following the manufacturer’s instructions, purified with SPRIselect magnetic beads (Beckman Coulter; B23318) and quantified using a Qubit Flex fluorometer (Thermo Fisher Scientific) and profiled using TapeStation (Agilent). The libraries were sent to MSKCC Integrated Genomics Operation core facility for sequencing on an Illumina NovaSeq 6000 (approximately 30–40 million 100-bp paired-end reads per library).

Data analysis

ChIP–seq reads were trimmed and filtered for quality and library adaptors using TrimGalore (v.0.4.5) with a quality setting of 15 and running cutadapt[76] (v.1.15) and FastQC (v.0.11.5). Reads were aligned to human assembly hg38 using Bowtie 2 (v.2.3.4.1) (ref. [77]) and were deduplicated using MarkDuplicates in Picard Tools (v.2.16.0). To ascertain enriched regions, MACS2 (ref. [78]) was used with a P-value setting of 0.001 and run against a matched control for each condition. A peak atlas was created by combining the superset of all peaks using the ‘merge’ function in the BEDTools suite (v.2.29.2). Read density profiles were created using deepTools ‘bamCoverage’ (v.3.3.0), normalized to ten million uniquely mapped reads and with read pile-ups extended to 200 bp. The tool featureCounts (v.1.6.1) was used to build a raw count matrix, and DESeq2 was used to calculate the differential enrichment for all pairwise contrasts for experiments with replicates. For single-sample data, MACS2 was run by swapping bams of different conditions to find differential regions. Peak gene associations were created by assigning all intragenic peaks to that gene, whereas intergenic peaks were assigned using the linear genomic distance to the transcription start site. GSEA[82] was performed with the pre-ranked option and default parameters, in which each gene was assigned the single peak with the largest (in magnitude) log_2FC associated with it. Composite and tornado plots were created using deepTools[80] (v.3.3.0) by running computeMatrix and plotHeatmap on normalized bigwigs with average signal sampled in 25-bp windows and flanking region defined by the surrounding 2 kb. Motif signatures were obtained using Homer[81] (v.4.5).

Reanalysis of human melanoma scRNA-seq data

Human melanoma scRNA-seq data from a previous study[7] were downloaded from Gene Expression Omnibus (GEO) (GSE115978). All analyses were performed in R using Seurat[83] (v.4.4.0 and v.5.0.1). The count matrix was normalized using sctransform. Clustering was done using Seurat functions (FindNeighbors and FindClusters) with a resolution of 0.8. Cell types and treatment status were annotated using metadata from the original publication[7]. Cell types were classified using gene lists from a previous study[2] and the Seurat function AddModuleScore with default parameters. Module scores were scaled between 0 and 1. Cells were classified by differentiation state on the basis of the highest expression score for the given gene modules. Differentially expressed genes were calculated using the Seurat function FindMarkers with default parameters. GSEA was performed using fgsea, as described above.

Statistics and reproducibility

All statistical analyses and plotting were performed in either R (for RNA-seq and ATAC-seq data; v.4.3.1) or MATLAB (for imaging data; v.R2021b). For scRNA-seq data, P values were calculated using the Wilcoxon rank-sum test with Bonferroni’s correction for multiple groups (R function pairwise.wilcox.test). Pearson correlation coefficients and corresponding P values were calculated using the R function cor.test. For differential expression analyses of bulk RNA-seq and bulk ATAC-seq data, P values were calculated in DESeq2 using the Wald test. For image analysis, P values were calculated using MATLAB functions (anova1 and multcompare) using the Tukey post hoc test. To calculate the cell migration velocity, the Euclidean distance travelled by individual cells across time points was measured using the MATLAB function pdist, and the velocity was calculated by dividing by the time step. For mouse experiments, we performed a series of likelihood ratio tests to investigate growth rate differences. A biexponential model was fit to the growth curve using maximum likelihood estimation to obtain estimates for the early-time and late-time growth rates, with a single exponential fit to vehicle data. For all representative images shown, the images represent at least three independent replicates.

Reporting summary

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

Data availability

Raw and processed RNA-seq, ATAC-seq and ChIP–seq data generated in this study were deposited in GEO (accession no. GSE253803). Human melanoma scRNA-seq data were obtained from GEO (accession no. GSE115978). The TurboID proteomics data were deposited in the ProteomeXchange Consortium through the Proteomics Identifications Database partner repository with the dataset identifier PXD060265. All other relevant data supporting the key findings of this study are available within the Article and its Supplementary Information or from the corresponding authors upon request. Source data are provided with this paper.

Code availability

All codes used for analysis and plotting are available at GitHub (github.org/mvhunter1/Hunter_2024).

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Acknowledgements

We thank M. Sherman and the members of White and Sherman laboratories for their useful discussions. We thank the MSKCC Molecular Cytology Core Facility for assistance with imaging, the MSKCC Aquatics Core Facility for zebrafish care and maintenance and the MSKCC Flow Cytometry Core Facility for assistance with FACS. M.V.H. was funded by the K99/R00 Pathway to Independence Award from the National Cancer Institute (1K99CA266931), Scholarship for the Next Generation of Scientists from the Cancer Research Society and a postdoctoral fellowship from the Canadian Institutes of Health Research. Y.M. was supported by a Medical Scientist Training Program grant from the NIH under award number T32GM007739 to the Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program and Kirschstein National Research Service Award predoctoral fellowship under award number F30CA265124. I.Y. and R.M.W. were funded by NIH Research Program Grant 1U01CA260432. R.M.W. was funded by the Melanoma Research Alliance, Debra and Leon Black Family Foundation, NIH Research Program Grants R01CA229215 and R01CA238317, NIH Director’s New Innovator Award DP2CA186572, Pershing Square Sohn Foundation, The Mark Foundation for Cancer Research, The Alan and Sandra Gerry Metastasis Research Initiative at MSKCC, The Harry J. Lloyd Foundation, Consano, Starr Cancer Consortium and American Cancer Society RSG-19-024-01-DDC. This study was supported by the NCI Cancer Center Support Grant PO CA008748 to Memorial Sloan Kettering Cancer Center.

Author information

Authors and Affiliations

1. Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Miranda V. Hunter, Emily Montal, Yilun Ma & Richard M. White

2. Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Eshita Joshi, Zhifan Yang, Pierre-Jacques Hamard & Richard P. Koche

3. Antitumor Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Sydney Bowker & Elisa de Stanchina

4. Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Yilun Ma

5. Cell and Developmental Biology Program, Weill Cornell Graduate School of Medical Sciences, New York, NY, USA

Yilun Ma

6. Molecular Cytology Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Young Hun Kim & Eric Rosiek

7. Proteomics Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Laura Tuffery, Zhuoning Li & Mara Monetti

8. Mathematical Institute, University of Oxford, Oxford, UK

Alexander Browning & Helen Byrne

9. Institute for Systems Genetics, NYU Langone Health, New York, NY, USA

Reuben Moncada & Itai Yanai

10. Nuffield Department of Medicine, Ludwig Institute for Cancer Research, University of Oxford, Oxford, UK

Richard M. White

Contributions

M.V.H. and R.M.W. conceived the study. M.V.H. performed all experiments and analyses, unless otherwise noted. S.B. and E.d.S. performed mouse experiments. E.J., Z.Y. and P.-J.H. performed ChIP–seq experiments. L.T., Z.L. and M.M. performed TurboID experiments and analysis. Y.H.K. performed AFM. A.B. and H.B. performed statistical analyses. E.R. imaged the human tumour samples. E.M. and Y.M. performed TEAZ experiments. R.M. and I.Y. assisted with the transcriptomic experiments. R.P.K. performed ChIP–seq analysis and assisted with ATAC-seq analysis. M.V.H. and R.M.W. wrote the paper. All authors provided feedback before submission.

Corresponding authors

Correspondence to Miranda V. Hunter or Richard M. White.

Ethics declarations

Competing interests

R.M.W. is a paid consultant to N-of-One Therapeutics, a subsidiary of QIAGEN. R.M.W. is on the scientific advisory board of Consano but receives no income for this. R.M.W. receives royalty payments for the use of the Casper zebrafish line from Carolina Biological Supply. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Argyris Papantonis 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 Interface cells are found in human patient samples.

a. tSNE of human melanoma scRNA-seq dataset from Jerby-Arnon et al. Cluster annotations from the original manuscript are labeled. Tumor cell clusters are outlined. b. Gene module scoring for interface genes extracted from zebrafish spatial transcriptomics and scRNA-seq data, projected onto tumor cells outlined in a. Red arrow denotes the subpopulation with highest expression of interface genes. c. Cell state classification for melanoma differentiation states identified by Tsoi et al. Cells were classified based on highest expression of the gene modules indicated. d-f. Tumor and interface cells classified by treatment status. g-h. Normalized expression per cell for the indicated genes. Red arrow indicates the interface cluster identified in b.

Extended Data Fig. 2 Confinement does not cause apoptosis and induces a neuronal gene program.

a,c,e. IF of confined A375 cells labelled with antibodies against the apoptosis markers Annexin V (a), cleaved caspase-3 (c), and cleaved PARP (e). b,d,f. Intensity per cell for the indicated markers. b. Unconfined: n = 250 cells from 6 images. Confined: n = 225 cells from 6 images. d. Unconfined: n = 261 cells from 6 images. Confined: n = 185 cells from 6 images. f. Unconfined: n = 245 cells from 6 images. Confined: n = 174 cells from 6 images. g. IF of A375 cells confined for ~18 h and then left to recover for 24 h (right) or unconfined cells (right). a,c,e,g. Scale bars, 25 µm. h. Euler diagram depicting co-expression of differentially expressed (DE) genes with an adjusted P-value < 0.05. i. Results from pathway analysis on the co-upregulated genes between both datasets using the GO:BP pathway set. Pathway analysis performed with Enrichr. Neuronal pathways are highlighted.

Source Data

Extended Data Fig. 3 ATAT1 inactivation abolishes acetylated tubulin.

a. Schematic detailing generation of ATAT1 knockdown cell lines. b. Expression of ATAT1 mRNA in the noted cell lines. Expression is normalized to β-actin and the non-targeting control condition. For each cell line, n = 3 biological replicates for a total of n = 12 technical replicates. c-d. IF images showing expression of acetylated tubulin (yellow, top; bottom), HMGB2 (cyan, top) and actin (magenta, top) in unconfined (c) and confined (d) cells. Scale bars, 20 µm. e-f. Quantification of acetylated tubulin (e) and HMGB2 (f) intensity in the noted conditions. sgNT unconfined: n = 55 cells. sgNT confined: n = 60 cells. sgATAT1_1 unconfined: n = 75 cells. sgATAT1_1 confined: n = 73 cells. sgATAT1_2 unconfined: n = 41 cells. sgATAT1_2 confined: n = 94 cells. n = 8 images were used for quantification for each condition. ***, P < 0.001; *, P < 0.05; n.s., P > 0.05. Illustrations in a were created using BioRender (https://biorender.com).

Source Data

Extended Data Fig. 4 Localization of tubulin post-translational modifications in confined cells.

a-h. IF of A375 cells labeled with antibodies targeting acetylated tubulin, in addition to tyrosinated tubulin (a-b), detyrosinated tubulin (c-d), polyglutamylated tubulin (e-f), and polyglycylated tubulin (g-h). a,c,e,g. Scale bars, 25 µm. b,d,f,h. Scale bars, 10 µm.

Extended Data Fig. 5 Confinement specifically upregulates HMGB2.

a. Stills from confocal imaging of A375 cells expressing HMGB2-GFP. Cells are pseudocolored by HMGB2 intensity. b. HMGB2-GFP intensity per cell over time, normalized to intensity at the first time point acquired. n = 37 cells from 6 movies. c,e. Immunofluorescence images of A375 cells stained with antibodies targeting HMGB1 (c) and HMGA1 (e). Scale bars, 25 µm. d,f. Quantification of intensity in confined/unconfined cells for the indicated markers. Each point represents 1 cell. Horizontal lines, mean; box, SEM; vertical lines, SD. d. Unconfined: n = 105 cells from 9 images. Confined: n = 76 cells from 9 images. f. Unconfined: n = 125 cells from 9 images. Confined: n = 80 cells from 9 images.

Source Data

Extended Data Fig. 6 Interface cells are present in human samples and other cancers.

a. Immunofluorescence performed on a human melanoma tissue sample showing enrichment of HMGB2 and acetylated in elongated nuclei at the tumor border. b. Visualization of nuclear shape relative to distance from the tumor border. c. Inset of a. d. Schematic of a human melanoma tissue microarray colored by the presence of HMGB2+ and/or acetylated tubulin+ cells in each sample. e. Quantification of d. f,i,l,o. Immunofluorescence images of confined MIA-PaCa-1 (f), Panc-1 (i), HTB-4 (l), and HTB-9 (o) cells showing HMGB2 (red/orange) and acetylated tubulin (white). Scale bars, 25 µm. g-h,j-k,m-n,p-q. Quantification of HMGB2 (g,j,m,p) and acetylated tubulin (h,k,n,q) intensity. U = unconfined, C = confined. P-values are indicated. For quantification of nuclear HMGB2 intensity: MIA-PaCa-2 unconfined: n = 74 cells from 8 images. MIA-PaCa-2 confined: n = 128 cells from 8 images. Panc-1 unconfined: n = 159 cells from 14 images. Panc-1 confined: n = 119 cells from 9 images. HTB-4 unconfined: n = 63 cells from 8 images. HTB-4 confined: n = 78 cells from 9 images. HTB-9 unconfined: n = 121 cells from 8 images. HTB-9 confined: n = 103 cells from 8 images. For quantification of whole-cell acetylated tubulin intensity: MIA-PaCa-2 unconfined: n = 41 cells from 8 images. MIA-PaCa-2 confined: n = 84 cells from 8 images. Panc-1 unconfined: n = 82 cells from 14 images. Panc-1 confined: n = 77 cells from 9 images. HTB-4 unconfined: n = 26 cells from 8 images. HTB-4 confined: n = 33 cells from 9 images. HTB-9 unconfined: n = 75 cells from 8 images. HTB-9 confined: n = 88 cells from 8 images.

Source Data

Extended Data Fig. 7 Expression of commonly mechanosensitive transcription factors in confined melanoma cells.

a-b, d-e, g-h, j-k. IF images of A375 cells stained with antibodies against YAP (a-b), Twist (d-e), Snail (g-h) or SMAD3 (j-k). Scale bars, 20 µm. c,f,i,l. Quantification of intensity in confined/unconfined cells for the indicated markers. Each point represents one cell. Horizontal line, median; box edges, lower and upper quartiles; whiskers, upper and lower limits of data without outliers. U = unconfined; C = confined. c. Unconfined: n = 27 cells from 6 images. Confined: n = 111 cells from 6 images. f. Unconfined: n = 33 cells from 6 images. Confined: n = 52 cells from 6 images. i. Unconfined: n = 56 cells from 6 images. Confined: n = 54 cells from 6 images. l. Unconfined: n = 19 cells from 4 images. Confined: n = 25 cells from 6 images.

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Extended Data Fig. 8 Loss of HMGB2 does not affect perinuclear acetylated tubulin assembly.

a. Schematic showing generation of HMGB2KO A375 cell lines using the LentiCRISPR approach. b. Western blot showing expression of HMGB2 in A375 cells stably expressing each of the 6 HMGB2 gRNAs. Red boxes indicate KO cell lines selected for further analyses. c. Quantification of HMGB2 expression in b relative to tubulin (loading control). d. Immunofluorescence of acetylated tubulin (white) and HMGB2 (red/orange/yellow) intensity in cells expressing the indicated sgRNAs. Scale bars, 25 µm. e. Quantification of acetylated tubulin intensity. sgNT unconfined: n = 58 cells from 8 images. sgNT confined: n = 69 cells from 8 images. sgHMGB2_1 unconfined: n = 71 cells from 8 images. sgHMGB2_1 confined: n = 104 cells from 8 images. sgHMGB2_2 unconfined: n = 82 cells from 8 images. sgHMGB2_2 confined: n = 77 cells from 8 images.

Source Data

Extended Data Fig. 9 Tubulin stabilization upregulates nuclear HMGB2.

a-d. A375 cells treated with the indicated tubacin concentrations (b-d) or DMSO as a vehicle control (a) and stained with antibodies labelling acetylated histone H3 and acetylated tubulin. Scale bars, 25 µm. e-f. Quantification of whole-cell acetylated tubulin intensity (e) and nuclear histone H3 acetylation (g). ***, P < 0.001. n.s., not significant. g. HMGB2-GFP accumulation in A375 cells treated with DMSO, tubacin or Taxol. Scale bars, 25 µm. Time after applying confinement is indicated. h. HMGB2-GFP intensity in confined cells over time. i. HMGB2-GFP intensity per cell at the final time point imaged (~16 h). h-i. DMSO: n = 38 cells from 7 movies. Tubacin: n = 31 cells from 7 movies. Taxol: n = 10 cells from 3 movies. j. HMGB2-GFP and SiR-tubulin intensity over time for confined cells treated with DMSO or 1 µM nocodazole. Scale bars, 25 µm. k. HMGB2-GFP intensity per cell at the final time point imaged (~ 16 h). NZ = nocodazole. Horizontal lines, mean; box, SEM; vertical lines, SD. l. HMGB2-GFP intensity over time. Error bars, SEM. k-l. DMSO: n = 19 cells from 3 movies. Nocodazole: n = 30 cells from 3 movies.

Source Data

Extended Data Fig. 10 The LINC complex is required for HMGB2 enrichment upon confinement.

a. Immunofluorescence images showing expression of nesprin-2 in A375 cells. Scale bars, 25 µm. b. Quantification of nesprin-2 intensity in A375 cells. Unconfined: n = 93 cells from 8 images. Confined: n = 85 cells from 8 images. c. Expression of SYNE2 mRNA in the noted conditions. Expression is normalized to β-actin and the non-targeting control condition. For each cell line, n = 3 biological replicates for a total of n = 12 technical replicates. d. Immunofluorescence images showing expression of lamin A/C (blue/green) and phalloidin in A375 cells. Scale bars, 25 µm. e. Quantification of lamin A/C intensity. Unconfined: n = 180 cells from 8 images. Confined: n = 135 cells from 8 images. f. Representative AFM force maps of the nuclear region of A375 cells. Scale bars, 10 µm. g. Quantification of nuclear stiffness. Unconfined: n = 70 cells from 3 biological replicates. Confined: n = 71 cells from 3 biological replicates.

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Extended Data Fig. 11 ChIP-sequencing and RNA-sequencing identify HMGB2 targets in A375 cells.

a. Schematic detailing ChIP-seq experiment workflow. b. Composite plots showing read density at target gene TSS in HMGB2WT and KO cells. c-e. Integrated genome browser tracks representing HMGB2 binding and H3K4me3 signal near the TSS of FOSL1 (c), NOTCH2 (d), and KMT2A (e). One representative replicate per condition is shown (of two replicates per condition performed). A full list of HMGB2 targets can be found in Supplementary Table 6. f. Western blot for HMGB2 (top) and tubulin (loading control, bottom) in stable A375 lines infected with lentivirus encoding HMGB2 or an empty vector control. g. Representative images of cells from cell lines indicated in f. h. Volcano plot of differentially expressed genes upon HMGB2OE. i. Heatmap of Z-scored expression of selected neuronal and invasive genes across replicates. j. Double waterfall plot of top GO biological processes pathways by normalized enrichment score (NES). Neuronal pathways are labeled in purple. k. Top 10 GO biological processes pathways by NES upregulated upon HMGB2OE. Schematic in a was created using BioRender (https://biorender.com).

Extended Data Fig. 12 Characterization of HMGB2 targets in SKMEL5 cells.

a. Western blot showing expression of V5-tagged HMGB2 in SKMEL5 cells, and ChIP-seq experimental workflow. b. Composite plots of read density at target gene TSS in HMGB2EV and OE cells. c-d. Integrated genome browser tracks representing HMGB2-V5 (purple) and HMGB2 (blue) binding and H3K4me3 (green) and input (gray) signal around the TSS of MITF (c), and NOTCH2 (d). One representative replicate per condition is shown (of two replicates per condition performed). A full list of HMGB2 targets in SKMEL5 cells can be found in Supplementary Table S8. e. Western blot showing expression of HMGB2 in SKMEL5KO cells and RNA-seq experiment workflow. f. Principal component analysis of SKMEL5 RNA-seq samples. g. Expression of selected genes in SKMEL5-HMGB2KO cells relative to SKMEL5-NT cells. h. HOMER de novo motif analysis showing enrichment of a BRN2/POU3F2 motif in the promoter region of HMGB2 targets identified from ChIP-seq.

Extended Data Fig. 13 Confined cells do not display fast amoeboid migration and are drug tolerant.

a. Stills from time-lapse imaging of confined A375 cells expressing HMGB2-GFP. Nuclei are pseudocolored. Scale bars, 20 µm. b. X-Y velocity over time per cell. c. Histogram showing mean velocity per cell. Dotted line indicates mean velocity across all cells. b-c. n = 38 cells from 7 movies. d. Hoechst (DNA) staining of cells treated with Taxol and confined (right) or unconfined control (left). Scale bars, 50 µm. e. Spaghetti plot showing tumor volume over time in A375-HMGB2EV (blue) and A375-HMGB2OE (red) mouse xenografts treated with dabrafenib/trametinib. Raw data can be found in Supplementary Table S10.

Source Data

Extended Data Fig. 14 Structure-function analysis of HMGB2.

a. HMGB2 protein sequence (Uniprot). Functional domains are labeled. b. Schematics illustrating each deletion construct. c. Immunofluorescence images showing expression of GFP-tagged constructs outlined in a. Scale bars, 25 µm. d-e. Quantification of in vitro proliferation (d) and invasion (e) assay results for A375 cells expressing the indicated constructs (n = 3 biological replicates). f. Quantification of nuclear HMGB2-GFP intensity for the noted constructs. FL: n = 94 cells from 3 images. ΔAT: n = 105 cells from 3 images. ΔA-box: n = 111 cells from 3 images. ΔB-box: n = 82 cells from 3 images. g. Schematic detailing in vivo tumor growth assay. h-i. Tumor volume over time (h) and at endpoint (i; 22–24 days post transplant). n = 10 mice per condition from 2 biological replicates. j. Immunohistochemistry targeting acetylated tubulin in cells at the tumor-microenvironment interface. Mouse cartoons in g adapted from Wikimedia, under a Creative Commons licence CC BY-SA 3.0.

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Extended Data Fig. 15 Confinement governs phenotypic plasticity in melanoma.

Model for the role of confinement in melanoma phenotype switching: nuclear compression induces HMGB2 contact with chromatin, increasing chromatin accessibility and gene expression at neuronal loci.

Supplementary information

Supplementary Fig. 1

Full scans of western blots. Uncropped gels for Fig. 5f,h and Extended Data Figs. 8b, 11f and 12a,e.

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Tables 1–10.

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Hunter, M.V., Joshi, E., Bowker, S. et al. Mechanical confinement governs phenotypic plasticity in melanoma. Nature 647, 517–527 (2025). https://doi.org/10.1038/s41586-025-09445-6

- Received: 30 January 2024

- Accepted: 23 July 2025

- Published: 27 August 2025

- Version of record: 27 August 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09445-6

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Design of facilitated dissociation enables timing of cytokine signalling

Adam J. Broerman,

Christoph Pollmann,

Yang Zhao,

Mauriz A. Lichtenstein,

Mark D. Jackson,

Maxx H. Tessmer,

Won Hee Ryu,

Masato Ogishi,

Mohamad H. Abedi,

Danny D. Sahtoe,

Aza Allen,

Alex Kang,

Joshmyn De La Cruz,

Evans Brackenbrough,

Banumathi Sankaran,

Asim K. Bera,

Daniel M. Zuckerman,

Stefan Stoll,

K. Christopher Garcia,

Florian Praetorius,

Jacob Piehler &

…

David Baker

Nature volume 647, pages 528–535 (2025)

Abstract

Protein design has focused on the design of ground states, ensuring that they are sufficiently low energy to be highly populated[1]. Designing the kinetics and dynamics of a system requires, in addition, the design of excited states that are traversed in transitions from one low-lying state to another2,3. This is a challenging task because such states must be sufficiently strained to be poorly populated, but not so strained that they are not populated at all, and because protein design methods have focused on generating near-ideal structures4,5,6,7. Here we describe a general approach for designing systems that use an induced-fit power stroke[8] to generate a structurally frustrated[9] and strained excited state, allosterically driving protein complex dissociation. X-ray crystallography, double electron–electron resonance spectroscopy and kinetic binding measurements show that incorporating excited states enables the design of effector-induced increases in dissociation rates as high as 5,700-fold. We highlight the power of this approach by designing rapid biosensors, kinetically controlled circuits and cytokine mimics that can be dissociated from their receptors within seconds, enabling dissection of the temporal dynamics of interleukin-2 signalling.

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Main

Protein–protein interactions orchestrate much of biological function. High-affinity interactions enable protein circuits to respond to low concentrations of stimuli and to act potently on targets; fast exchange enables them to respond quickly to changes in stimuli. These two properties cannot usually be achieved simultaneously in binary interactions because they depend on the interaction off-rate in opposite ways: high affinity usually requires slow dissociation (low off-rate), whereas rapid exchange requires fast dissociation (high off-rate) (Supplementary Fig. 1). Several natural systems exhibit ‘facilitated dissociation’10,11,12,13,14,15,16,17,18,19,20, in which an effector (E) can bind to a target–host (TH) complex to form an excited ternary complex (THE)20,21,22,23,24,25,26 from which the target dissociates quickly (Fig. 1a–c). In such a system, the target can bind tightly to the host, yet can also be rapidly released by adding the effector[27]. In engineered DNA systems, the kinetic control afforded by an analogous phenomenon (toehold-mediated strand displacement) has enabled the construction of many complex functions28,29, but DNA systems have limited utility for directly interfacing with biology. Protein binding and unbinding can be readily coupled to biological processes, but there has been no general approach to design kinetic control over protein interactions.

Fig. 1: Strategy for designing proteins that reconfigure through facilitated dissociation.

a–c, A high-affinity interaction can rapidly exchange through facilitated dissociation (bottom pathways), but not through mutually exclusive competition (top pathways). a, Reaction diagram. b, Energy diagram. c, Schematic of induced-fit facilitated dissociation (bottom) compared with slow mutually exclusive competition (top). The host protein (H, subscripted by conformational state X or Y) is shown in blue, the target (T) in pink and the effector (E) in orange. d, Structural models of starting components (effector-responsive switch and arbitrary binder–target pair) combined to construct facilitated dissociation systems. e, Structural models of example proteins designed to undergo a facilitated dissociation process starting from a tightly interacting state X (left) through a structurally frustrated ternary intermediate in state Y (right, solid). State X (transparent) is included to show the conformational change. Thinner arrows indicate structural features and thicker arrows indicate state changes.

We set out to design protein systems that undergo facilitated dissociation. Given an interacting protein binder–target pair, we reasoned that we could construct host proteins with controllable dissociation kinetics by fusing an effector-responsive conformational switch to the binder such that when the effector is not bound, the target can bind normally, but in the alternate effector-bound conformation, the switch clashes with the target, leading to strain in the target–host–effector ternary complex that resolves when the target dissociates[30] (Fig. 1d,e; nomenclature detailed in Supplementary Note 1). This would allosterically couple the effector and target, and facilitated dissociation could proceed through the strained ternary complex intermediate faster than spontaneous dissociation of the target in mutually exclusive competition (Fig. 1c). Crucially, the energy of this ternary intermediate must be neither too high (otherwise the facilitated dissociation pathway would not be faster) nor too low (otherwise the target would not dissociate) (Fig. 1b). To set the ternary intermediate energy within this optimal range (Supplementary Fig. 2), we reasoned that we could control the level of strain in the ternary complex by varying the geometry of the switch–binder fusion. To access this strained state effectively, we reasoned that binding the effector should rapidly drive the conformational change against the resisting force associated with generating strain. Such a ‘driven’ motion would be akin to the power strokes of motor proteins: a large conformational change that is both thermodynamically and kinetically favoured (a low-barrier descent down a steep energy gradient)8,31. Compared with the mechanism of direct steric overlap between target and effector, as in many existing facilitated dissociation systems17,18,19,20,26,32, this allosteric mechanism should be quite modular: such a force-generating switch could be fused to almost any binder to enable facilitated dissociation of its target.

We began by using a previously designed effector-responsive conformational switch (hinge protein cs221; ref. [33]) to test the concept of allosteric coupling by switching steric clashes between the host and the target. This switch can undergo a rigid-body hinge motion to transition from a closed state (X) to an open state (Y), and in the open state Y can bind an effector peptide quite tightly (koff = 5 × 10−6 s−1). As a model binder–target interaction, we chose a designed heterodimer pair (LHD101 (ref. [34]) modified as described in Supplementary Fig. 3) with dissociation slow enough (koff = 9 × 10−5 s−1) to allow us to easily measure substantial effector-induced acceleration of target dissociation, but not slower than the effector dissociation so that the target would be more likely than the effector to dissociate from the ternary complex. To allosterically couple binding of the effector to dissociation of the target, we designed structured fusions of the hinge switch and binder such that when the switch is in state X, the target can bind, but when in the effector-bound state Y, it will clash strongly (Fig. 1d,e, Supplementary Fig. 4a,b and Methods). We obtained synthetic genes encoding 12 designs, expressed and purified the proteins from Escherichia coli and found that the best (allosteric switch 0; AS0) showed slow and reduced effector association in the presence of the target (Supplementary Fig. 5d), indicating the desired allosteric coupling but also the need for designs with a faster, driven pathway for effector binding against the target.

In these first designs, because the effector-binding cleft is closed in state X, the hinge switch must first transition to the open state Y before the effector can bind (a conformational selection mechanism). Because state Y clashes with the target, this conformational change is slow when the target is bound, limiting the rate of effector association and the overall rate of facilitated dissociation (Supplementary Fig. 4c). This slow step could be bypassed if the effector could instead first bind to state X and accelerate the transition to state Y. An induced-fit mechanism could provide the driving force: an intrinsically disordered effector could weakly engage with state X, and fold and make more extensive interactions in the strained ternary complex in state Y[35], driving the transition in a power-stroke-like motion36,37.

Such a mechanism would require new switches that, in state X and throughout the conformational transition, retain an open effector-binding cleft where a flexible effector could associate (Fig. 1e and Supplementary Fig. 4d). Starting from AS0, we constructed a new state X by shifting the two domains from their state Y positions relative to each other (by one heptad along the helix of domain 1 that contacts domain 2), building a new loop between the domains, and optimizing single sequences that could adopt both this new state X and the original effector-bound state Y (Supplementary Fig. 4e and Methods). This approach maintains the open cleft in state X by introducing minimal rotation from state Y and simplifies the multi-state sequence-design challenge by minimizing the local structural differences between the two states. Transition between conformational states occurs by a register shift throughout which the cleft could remain open and bind the effector (Supplementary Fig. 4f). Because these new hosts retain the state Y backbone validated to clash with the target in AS0, we should observe allosteric coupling if the switch works as designed (Fig. 1e).

We obtained synthetic genes encoding ten such designs, and found that four tightly bound the effector (dissociation constant (Kd) < 1 nM; Supplementary Fig. 6d). To measure facilitated dissociation kinetics, we used surface plasmon resonance (SPR): with the target affixed to the SPR surface, we incubated with the host, then measured target–host dissociation under flow of various concentrations of effector. For these four designs, the target dissociates slowly in the absence of the effector; adding the effector increases the rate of dissociation markedly (Fig. 2a and Supplementary Fig. 7), but minimally affects the target off-rate from a control static binder fusion that lacks an effector-binding site (Supplementary Fig. 7).

Fig. 2: Kinetic characterization of facilitated dissociation in AS1.

a, Slow dissociation of the target from the host in the absence of effector (solid line) and fast dissociation in the presence of 2 μM effector (dashed line) assessed by SPR. Slow dissociation data (solid grey) fitted with a double exponential (pink). b, Kinetic model describing pathways of competition. Top, mutually exclusive competition; middle, facilitated dissociation with effector binding rate-limited by conformational selection; bottom, facilitated dissociation with induced-fit effector binding. The k labels are rate constants. c, Cartoon representations of the peptide (left) and 3hb (right) effectors; interface residues are shown in grey. d, Circular dichroism spectra of the peptide and 3hb effectors. e,f, Kinetic characterization of the formation and breakage of the ternary complex intermediate with the peptide (e) and 3hb (f) effectors. Top left, fast dissociation of the target from the ternary complex; data (grey) fitted with double exponentials (orange and green). Bottom left, effector association to form the ternary complex and extremely slow subsequent dissociation; data (grey) fitted with single exponentials (colours) in the association phase. Right, apparent effector on-rate constant plotted against effector concentration (circles) and a linear (e) or hyperbolic (f) fit. kswitch is the saturating value of the hyperbolic fit. g,h, Kinetic characterization of the full facilitated dissociation pathway with the peptide (g) and 3hb (h) effectors. Left, effector-concentration-dependent dissociation of the target; data (grey) fitted (colours) as described in the Methods. Right, rate constant of facilitated target dissociation plotted against effector concentration (circles) and fitted with a hyperbolic equation (black line). In a and in the left plots of e–h, schematics show the arrangement of proteins relative to the SPR chip (grey). In the right plots of e–h, schematics show the mechanism that can be inferred from the data.

To investigate the mechanism of facilitated dissociation (Fig. 2b), we focused on AS1, the design with the tightest effector binding (Kd,H:E ≈ 10 pM). We directly measured the rate constant of target dissociation from the strained ternary complex (koff,T:HE) by flowing pre-incubated host–effector complex at a high concentration to form the strained ternary complex with the target on the SPR surface, then tracking target–host dissociation under continued flow of the effector (Fig. 2e, Supplementary Fig. 7 and Methods). In the full facilitated dissociation process, as the concentration of added effector increases, the rate constant of facilitated target dissociation approaches koff,T:HE (Fig. 2g and Supplementary Fig. 7), strongly suggesting that the ternary complex is an intermediate in the facilitated dissociation process (Fig. 2b, lower pathways).

To further analyse the mechanism of forming the strained ternary complex, we characterized facilitated dissociation with two different effectors: the peptide and a three-helix bundle ‘3hb’ (3hb21; ref. [33]) (Fig. 2c). The two effectors make nearly identical interactions with AS1, but when unbound, the peptide is disordered whereas the 3hb is structured (Fig. 2d). With AS1 affixed to the SPR surface, we measured the rate of effector association to form the strained ternary complex by first saturating AS1 with target, then flowing varying concentrations of effector mixed with constant excess target (to ensure that the target remains bound after effector association). The apparent on-rate for binding to the target–AS1 complex increases linearly with concentration for the peptide (Fig. 2e) but hyperbolically for the 3hb, saturating at the rate of a concentration-independent step (Fig. 2f). Notably, with the 3hb, the rate of facilitated target dissociation saturates at this same value (Fig. 2h). The simplest explanation of these results is that the rigid 3hb can only bind to the fully open state Y of AS1 and that the THX → THY conformational change is slow (owing to partial blocking by the bound target), so it becomes rate limiting for both the association with the target–AS1 complex and the overall facilitated dissociation process (Fig. 2b, middle pathway). In contrast to the 3hb, peptide effector binding (Fig. 2e) and resulting target destabilization (Fig. 2g) can both occur more rapidly than the THX → THY conformational change, suggesting that the more flexible peptide effector can bind to AS1 in state X to accelerate this conformational change through an induced-fit mechanism12,35,38 (Fig. 2b, bottom pathway).

We used X-ray crystallography to structurally characterize multiple states of our designed systems. For both the AS1 and the AS5 systems, the crystal structures of the hosts alone (Fig. 3a and Extended Data Fig. 1a) and of the host–effector complexes (Fig. 3b and Extended Data Fig. 1b) closely match the design models (maximum 1.3 Å Cα root mean square deviation (RMSD)). The unbound structures show an open hydrophobic cleft in the new designed state X poised to bind the effector, and the effector-bound structures show that binding the effector causes the two switch domains to register-shift into the designed state Y (Supplementary Note 2). Structures of the target–AS1 complex show that the target binds as designed (Fig. 3c, top), with some strain suggested by small variations among the structures (Fig. 3c, bottom, Extended Data Fig. 1c,d and Supplementary Note 3).

Fig. 3: Structural characterization of AS1.

a, Crystal structure of AS1 alone (grey) overlaid with the design model of AS1 in state X (blue). Inset, detailed view of side chains in the partially open effector-binding cleft. b, Cocrystal structure of AS1 and peptide effector (grey) overlaid with the design model of the AS1–effector complex in state Y (blue and orange). Inset shows the same view as in a. c, Top, cocrystal structure of AS1 (with intact cleft) and target (grey) overlaid with the design model of the target–AS1 complex in state X (blue and pink). Bottom, cocrystal structure of AS1 (with collapsed cleft) and target (grey) overlaid with the design model of the target–AS0 complex (blue and pink) whose state X resembles this collapsed state. d, Cocrystal structure of AS1 (with hydrophobic surface mutations), target and peptide effector (grey) aligned at the switch region with the design models of the target (pink) and AS1–effector complex in state Y (blue and orange) showing the designed clash. e, Top, detailed view of the target interface side chains in the ternary complex (grey) and the target–AS1 complex (pink) interacting with AS1 (blue). Bottom, detailed view of the backbone hydrogen bonding in the interfacial strand pairing. The target–AS1 complex (pink and blue) hydrogen bonds (green) are less strained than the ternary complex (grey) hydrogen bonds (red).

We next sought to determine how the target–host–effector ternary complex deforms to resolve the designed clash. At the concentrations of the components used in the above experiments, the AS1 ternary complex is an only transiently populated excited state, but at high concentrations it becomes the dominant state (Kd,T:HE = 200 nM; Supplementary Fig. 9). This enabled us to solve structures of AS1 in the ternary complex intermediate with both target and effector. In structures from two different crystals, the switch region closely matches the state Y design model, and the rest of the structure strains to resolve the structural frustration from simultaneously binding the target and occupying state Y (Fig. 3d). This strain distributes across multiple locations in the structure: the binder fusion bends, the portion of the target that directly clashes with the switch becomes disordered and the target twists at its interface, disrupting the interfacial hydrophobic packing (Fig. 3e, top) and shearing the interfacial strand pairing (Fig. 3e, bottom). Double electron–electron resonance (DEER) spectroscopy (Extended Data Fig. 2) and molecular dynamics (MD) simulations (Extended Data Fig. 3) suggest that the ternary complex is dynamic, with varying amounts of strain at each location (Supplementary Note 3).

Modulation of dissociation acceleration

We next investigated the factors that contribute to the dissociation kinetics, seeking to maximize the dissociation rate enhancement by (1) reducing the base rate of target dissociation and (2) increasing the rate of facilitated dissociation. For (1), the target dissociates 20-fold faster from AS1 than from an unhindered binder fusion (Supplementary Fig. 7), probably because of minor strain in the target–AS1 complex (Extended Data Fig. 1c). For (2), with AS1, effector association can occur at least five times faster than target dissociation from the ternary complex (Fig. 2e); thus, moderately increasing the energy of the ternary complex could accelerate the target dissociation step without making the effector association rate limiting, thereby increasing the overall rate of facilitated dissociation. Assuming a simple spring model of the ternary complex, this could be achieved by deforming with a greater magnitude or in a direction of higher stiffness (Supplementary Fig. 10a).

We sought to maximize strain energy in the ternary complex by modulating the magnitude and direction of the deformation required to resolve the designed clash (Fig. 4a). Avoiding any clash in state X while maintaining a strong clash in state Y, we sampled a variety of target + binder positions relative to the switch from AS1; rebuilt fusions between the switch and the newly located binder; and selected variants that were predicted by AlphaFold2 (AF2) to have substantial deformations spanning a variety of directions (AF2 predictions of the strained AS1 ternary complex were within 1.0 Å Cα RMSD of the target–AS1–effector crystal structure). These changes to the fusion region, although distant from both binding sites, caused considerable variation in the kinetics of target dissociation and their modulation by the effector (Supplementary Fig. 11). Most of these variants showed reduced target off-rates (compared with AS1) in the absence of effector that increased in the presence of effector (Supplementary Fig. 11 and Supplementary Table 1), and for the fastest variant (AS117), adding effector accelerated target dissociation by 2,400-fold, reaching a rate exceeding that of the original AS1 (Fig. 4b). By comparing the predicted deformations with the facilitated dissociation rates in the forward and reverse directions (Supplementary Note 4), we found that the global strain energy of the ternary complex depends on both the magnitude and the direction of the deformation (Supplementary Fig. 10) and that strain can distribute non-uniformly throughout the structure, leading to kinetic asymmetry[24] (Supplementary Fig. 12 and Extended Data Fig. 4).

Fig. 4: Modulation and applications of facilitated dissociation.

a,b, Comparison of three representative designs with different facilitated dissociation kinetics. a, For each design: left, model of host in state X (blue) aligned to the target (pink) to show any clash influencing the target off-rate in the absence of effector; right, model of host–effector complex in state Y (blue and orange) aligned to the target (pink) to show the designed clash, and (grey) AF2 prediction of the target position relative to the switch in the ternary complex to show how the clash resolves through global strain. b, Forward and reverse facilitated dissociation rates: target off-rate constants versus effector concentration (orange circles) and effector off-rate constants versus target concentration (pink circles) fitted with hyperbolics (black lines). Cartoons illustrate the forward and reverse facilitated dissociation pathways. Dashed lines mark the base and accelerated off-rate constants for forward (orange) and reverse (pink) facilitated dissociation. c, Chain reactions. FRET time courses showing slow transfer of a kinetically trapped effector (blue, top schematic) and accelerated transfer through facilitated dissociation (orange, bottom schematic). d, Breaking split enzymes. Luminescence time courses showing breakage of a reversible split luciferase through slow direct competition (blue, top schematic) and faster facilitated dissociation (orange, bottom schematic). e, Rapid sensing. Luminescence time course (orange) showing rapid sensing of SARS-CoV-2 through facilitated dissociation (schematic). c–e, Data fitted with single exponentials (black lines).

Applications of facilitated dissociation

We next set out to use facilitated dissociation to construct protein systems with kinetic behaviours that were previously inaccessible to design. First, inspired by toehold-mediated strand displacement in DNA[28], we sought to create a kinetically trapped system which, on stimulation, quickly reconfigures through a chain reaction. To investigate this, we fused the effector peptide (E2) for a second reporter hinge (H2) to the target such that E2 is occluded when this fusion (E2–target) is bound to a host (Extended Data Fig. 5). As intended, the release of E2–target from AS114 and the subsequent switching of H2 is slow, but accelerates markedly upon addition of the original effector, which accelerates E2–target dissociation (we include excess original target to prevent E2–target rebinding[39]; Fig. 4c and Extended Data Fig. 5). In principle, multiple orthogonal hosts could be constructed and chained together through such target–effector fusions.

Second, we reasoned that our designs could complement split protein systems with high affinity and enable them to be switched off rapidly. To test this, we tagged AS1 and the target with NanoBiT split luciferase fragments, LgBiT and SmBiT (ref. [40]). When combined, these components exhibit high luciferase activity that disappears much more rapidly upon addition of effector and excess untagged target than upon addition of excess untagged target alone (Fig. 4d).

Third, in previously designed thermodynamically controlled biosensors that operate through a conformational selection mechanism, there is a trade-off between dynamic range and response time (reducing background by lowering the energy of the ‘off’ state generally slows interconversion with the ‘on’ state)41,42. We reasoned that sensors based on facilitated dissociation could be limited by the rate of target association rather than by internal conformational switching, and found that by caging the SmBiT peptide within the effector bound to a host, facilitated dissociation (in the reverse direction compared with previously) upon binding the target uncaged the SmBiT rapidly to enable luciferase reconstitution (Methods). Using this strategy together with a designed SARS-CoV-2 receptor-binding domain (RBD) binder (LCB1; ref. [43]), we generated 16 ‘AScov’ sensor designs in which the RBD clashes with the switch in state Y but not in state X. After addition of SARS-CoV-2 RBD, the best sensor shows a 30-fold increase in luciferase activity, with a half-time of 30 s (Fig. 4e and Extended Data Fig. 6)—70 times faster than a previously designed LOCKR-based SARS-CoV-2 sensor that relies on conformational selection[41]. Thus, using this platform, a binder to almost any target can be turned into a single-component sensor that is sufficiently fast that, in most practical applications, its response time will be limited by target association rather than by a slow conformational change.

Rapid modulation of IL-2 signalling

Finally, we investigated whether facilitated dissociation could be used to control cellular processes with high temporal resolution. In cellular signalling, the residence times of ligands on their cognate receptors is thought to modulate the signalling and cellular responses44,45,46. The central immune cytokine interleukin-2 (IL-2) activates the IL-2 receptor (IL-2Rβγc) by inducing heterodimerization of chains β and γc (ref. [47]). The resulting complexes dissociate or degrade on timescales of hours48,49, so controlling the temporal dynamics of IL-2 signalling is difficult: there is no off-switch (Fig. 5a).

Fig. 5: Characterization of a rapidly switchable IL-2 mimic.

a, Natural pathways for terminating IL-2 signalling are slow. b, Through facilitated dissociation, signalling could be rapidly terminated. c, Model of ASNeo2 binding IL-2Rβγc to activate signalling (left), which quickly terminates after adding effector (right). d, Left, accelerated dissociation of γc; data (grey) fitted (colours) as described in the Methods. Right, γc dissociation rate constant versus effector concentration (circles) fitted with a hyperbolic (black line). e, Relative IL-2Rβ/γc dimerization on the cell surface at first (grey; n = 37), after adding ASNeo2 (blue; n = 32) and after subsequently adding effector (orange; n = 33). f, Time courses of IL-2Rβ/γc dimerization after pre-stimulation with ASNeo2 then adding nothing (blue) or effector (orange), fitted with an exponential (black) yielding the rate constant kapp. g, Dose–response of STAT5 phosphorylation from stimulation with ASNeo2 alone (blue) or with effector (orange) (n = 1). h, Time courses of STAT5 phosphorylation after stimulation with ASNeo2 for 5 min then adding nothing (blue), effector (orange) or ruxolitinib (green) (n = 3). i–n, Human T cells were stimulated with ASNeo2 or left untreated as a control (grey). Signalling was sustained (blue) or terminated with effector (orange) after the indicated duration. i,j, Cell division (i, by carboxyfluorescein succinimidyl ester (CFSE) staining) and survival (j) three days after stimulation (n = 4); statistics from ANOVA with two-sided Tukey’s post-test. NS, not significant. k, Time courses of BCL2 expression (by quantitative PCR (qPCR); n = 3). l–n, RNA-seq analysis six hours after stimulation (n = 3). l, Principal component (PC) analysis. m, Changes in gene expression after transient stimulation. Points denote differentially expressed genes; the most significant are labelled. n, Heat map of differentially expressed genes from hallmark gene sets with high gene correlation. In e,h–k, lines and bars represent medians (e) or means (h–k); error bars and shaded areas represent 95% confidence intervals. n refers to biologically independent samples.

We set out to construct a switchable IL-2 mimic that enables control over the lifetime of the IL-2Rβγc complex with a time resolution of seconds (Fig. 5b). Neo2, a previously designed IL-2 mimic, tightly binds to IL-2Rβγc to elicit downstream signalling[50]. Sampling a variety of γc positions relative to the switch from AS1, we rigidly fused Neo2 to the switch such that in state X, γc can bind, but in the effector-bound state Y, it would strongly clash (Fig. 5c). We identified several designs for which binding the effector markedly accelerates the dissociation of γc from the active signalling complex (Supplementary Fig. 13). For one of these, ASNeo2, binding the effector induces a 1,500-fold increase in the γc off-rate (Fig. 5d). We designed variants of ASNeo2 with different dissociation kinetics and topological safeguards against degradation (Supplementary Note 5); for one, the effector accelerated γc dissociation by 5,700-fold, the highest fold change of all our designed systems (Extended Data Fig. 8).

To investigate the switching capability of ASNeo2 in a physiological context, we quantified the dimerization of labelled IL-2Rβ and γc in the plasma membrane of live cells by single-molecule fluorescence microscopy. Dual-colour co-tracking and mobility analyses confirmed that ASNeo2 efficiently dimerizes IL-2Rβ and γc and that adding the effector reverses this association rapidly and completely, even at a high excess of γc (Fig. 5e,f, Extended Data Fig. 7c–i and Supplementary Video 1). By double labelling ASNeo2 on opposite sides, we could observe the effector-induced intramolecular conformational change at the plasma membrane using single-molecule Förster resonance energy transfer (smFRET) (Extended Data Fig. 7j,k). ASNeo2 activates signalling in human natural killer (NK) cells (YT-1 cell line), and its activity is greatly reduced in the presence of the effector (Fig. 5g). After the effector is added, STAT5 phosphorylation immediately stops accumulating and gradually decreases to a low level (Fig. 5h). The effector blocks ASNeo2 activity nearly as effectively as does ruxolitinib, a JAK1 inhibitor.

To examine how the duration of IL-2 signalling affects the downstream cellular response, we stimulated primary human T cells with ASNeo2 and induced dissociation at various time points. Whereas sustained stimulation was required for proliferation[51] (Fig. 5i), protection from apoptosis was evident after a short transient stimulation: cells treated with ASNeo2 and then with effector 5 min afterwards survived three days later at double the rate of an unstimulated control (Fig. 5j), despite the suppression of most downstream IL-2 target proteins (Extended Data Fig. 9e). This likely reflects reduced caspase-3 activity and increased BCL2 expression after transient stimulation (Fig. 5k and Extended Data Fig. 9g).

To further investigate the dependence of IL-2 signalling on the lifetime of the signalling complexes, we compared RNA sequencing (RNA-seq) data from T cells that were treated with ASNeo2 transiently, continuously or not at all (Fig. 5l–n and Supplementary Fig. 14). Transient stimulation (through incubation with ASNeo2 followed by effector-induced dissociation) upregulated genes that are involved in suppressing apoptosis (BCL2, NIBAN2 and RNF157), cell-cycle regulation (CDKN2B, CDK6, CCND2 and PIM1), suppressing cytokine signalling (SOCS2 and CISH) and immune activation (TNFSF8, HAVCR2 and PTGER2) (Fig. 5m). On activation, T cells typically shift their energy production from oxidative phosphorylation to glycolysis[52], and we observed that transient stimulation downregulates oxidative-phosphorylation genes (Fig. 5n and Supplementary Fig. 15; glycolysis genes are not yet upregulated). Sustained stimulation activated genes associated with mTORC-driven metabolic changes and MYC- and E2F-driven cell-cycle progression (for example, CDK4 and POLD2), but transient stimulation did not (Fig. 5n, Extended Data Fig. 9i and Supplementary Fig. 15), suggesting that IL-2 signalling must be sustained to pass the G1/S checkpoint. Transient stimulation did activate genes associated with formation of the mitotic spindle (for example, DOCK2 and KIF1B) (Fig. 5n, Extended Data Fig. 9i and Supplementary Fig. 15), suggesting that preparations for mitosis are made immediately after T cell activation, before cell-cycle checkpoints. These results show how designed facilitated dissociation can be used to tackle unanswered questions in cell biology.

Conclusions

We show here that by explicitly considering excited intermediate states when designing coupled protein systems, a broad range of facilitated dissociation systems can be designed. We use a switchable target binder (host) and a flexible effector that can rapidly bind to and switch the target–host complex to induce a large steric clash with the target, forming a strained excited ternary complex. By modulating the strain energy of the ternary complex, we can tune the resulting acceleration of target dissociation. Crystal structures throughout the facilitated dissociation process confirm our ability to design excited states and large register-shift conformational changes. Our designed dynamic systems show both a high dynamic range and a rapid stimulus response, demonstrating the kinetic advantage of facilitated dissociation over mutually exclusive competition.

Power-stroke mechanisms8,31 can generate force more efficiently than can ratchet mechanisms (in which a single large step is rectified at the end of the conformational change)[37]. Induced folding underlies the kinesin power stroke[53], and our flexible effector likely also folds upon binding, accelerating the conformational transition by lowering the energy barrier: the energetic costs of uphill steps along the transition coordinate can be compensated by the formation of additional interactions with the folding peptide35,36,54. In contrast to this induced-fit mechanism of the flexible effector, we find that a rigid effector provides reduced rate acceleration even though it binds more tightly[38]. In kinesin and other biological systems, it is difficult to directly assess the role of flexibility and folding upon binding in overall function; by contrast, our designed model systems allow the direct comparison of flexible and rigid effectors and show that the former yield faster conformational transitions against loads.

Most known examples of facilitated dissociation couple the target and the effector through a combination of direct steric overlap17,18,19,20,26,32 and intricate allostery14,15,22,23,24,25. By contrast, because our allosteric mechanism of switching steric clashes places no specific requirements on the binder or target, our approach can be used to dynamically regulate protein–protein interactions quite generally: by fusing to our switch, almost any binding interaction can be made to switch off rapidly in the presence of an effector. Our approach transferred immediately to rapidly switching active IL-2-like signalling complexes: we obtained several working designs on the first attempt (in which 24 designs were tested). Controlling the duration of IL-2 signalling with such switchable cytokines could enable investigation of early events in signalling or tuning of the cell response through timing-sensitive regulatory mechanisms further downstream[51] (for example, disrupting signalling complexes at the cell surface or later in the endosome[49] could be used to distinguish the cellular responses induced by signalling from each compartment[55]). Therapeutically, systemic administration of the effector and local administration of the switchable cytokine could elicit strong immune activation at only the site of administration (because any cytokine that escapes into circulation would be deactivated by the effector). More generally, our work provides a route to designing the rates and pathways of protein motion and change, which should ultimately enable the construction of complex, lifelike protein machinery.

Methods

Design of structured switch–binder fusions (hosts) allosterically coupling the target and the effector

In PyMOL, we manually positioned the switch relative to the binder–target complex subject to several constraints: there is no steric overlap between the target and switch state X; there is large steric overlap between the target and switch state Y; the smallest deformation that could be undergone by the switch and the target to resolve this clash is in the desired direction; and the switch C terminus and binder N terminus are relatively oriented such that sensible additional structure could be built between the switch and the binder to rigidly fuse them as positioned. To aid in visualizing this additional structure, we also included placeholder helices while manually positioning the switch and binder, effectively ‘sketching’ the fusion (Supplementary Fig. 4b).

We then refined these sketches into plausible backbones. For the initial fusions including AS0, we extracted the centre four residues of the placeholder helix, then used inpainting with RosettaFold[56] to scaffold that fragment between the switch and the binder. For later fusions, including the AS1 variants and ASNeo2 designs, we first used Rosetta FastDesign[57] to sample around the starting sketch for designable positions of the switch, binder and placeholder helices while keeping the region of the state X switch that clashes in state Y fixed relative to the target, then used RFDiffusion[7] (conditioned on the secondary structure and block adjacency of the sketches) to build structure between the sampled switch and binder positions. During both structure-generation approaches, we masked noncritical residues on the switch and binder interfacing with the fusion structure. After generation of the fusion backbone structure, we used ProteinMPNN[58] to optimize sequences for the fusion structure and the masked residues on the switch and binder. To filter designs, we used AF2[59] (with initial guess (AF2-IG)[60] for complex predictions) to predict the structure of the fusions alone, with the target, and with the effector, selecting designs for which each structural state is correctly and confidently predicted by a majority of the five model weight sets. Finally, for the AS1 variants and ASNeo2 designs, we used AF2-IG to predict the structure of the strained ternary complexes, selecting sets with a diversity of deformation directions.

Design of induced-fit register-shift switches

Designs AS1, AS2, AS5 and AS7 were generated starting from design AS0, which contains the hinge switch cs221. When cs221 was designed, its state Y was generated by copying the N-terminal domain (helices 1–4) of its parent scaffold (DHR20; ref. [61]), aligning helix 4 of the copy to the corresponding helix of DHR20 offset by three residues, and combining the transformed N-terminal domain and original C-terminal domain into a single protein[33]. Thus, relative to the C-terminal domain, the N-terminal domain is both rotated around and translated along the axis of helix 4, exposing a cleft between the domains for binding a helical peptide. Now, to generate a new state X for this switch that retains an open cleft, we repeated this procedure but aligned the N-terminal domain with a residue offset of −4 instead of 3, so that overall this position of the N-terminal domain is shifted from state Y along helix 4 by one heptad (Supplementary Fig. 4e). This introduces minimal rotation and thus maintains the open cleft. We combined this new position of the N-terminal domain with the entire C-terminal domain of AS0 (including the fusion to LHD101B), then used Rosetta FastDesign to further sample designable positions of the N-terminal domain around this starting point. In half of the design trajectories, we included a placeholder helix in the cleft to help ensure it remains open. We then used inpainting with RosettaFold to generate loops connecting the domains, then paired these complete state X backbones with the original effector-bound state Y of AS0 (also generating new loops between domains in state Y as necessary to match the loop lengths of each new state X).

To generate single sequences that support both states, we first performed multi-state design with Rosetta FastDesign (enforcing sequence symmetry between states) to further refine the paired backbones so that they are more mutually compatible, then used ProteinMPNN with residue probabilities tied at corresponding positions between states to optimize sequences simultaneously for both conformations[33]. During these sequence-design steps, the sequence of the effector and of the binder fusion was kept fixed. To filter designs, we used AF2-IG to predict the structure of the switches both with and without the effector, selecting designs for which the new state X (in the absence of effector) and state Y (in the presence of effector) are correctly and confidently predicted by a majority of the five model weight sets. To further ensure that these designs favour state X in the absence of effector and state Y in the presence of effector, we only selected designs that scored more favourably in Rosetta in state X than in state Y, but also scored more favourably in state Y with the effector bound than the sum of the scores of state X and the unbound effector.

Design of rapid sensors

We first sought to cage the SmBiT peptide within the effector so that, through reverse facilitated dissociation, binding of the target would rapidly uncage the SmBiT to enable luciferase reconstitution. We grafted SmBiT onto the effector peptide at a range of positions, then screened these ‘SmBiTgraft’ variants for binding to AS0 and rapid dissociation after binding of the target. We used AS0 as the base design, reasoning that its closed state X would slow undesired peptide reassociation. For each working SmBiTgraft, we then generated a flexibly linked LgBiT–SmBiTgraft–AS0 construct. The best construct shows low luciferase activity that increases rapidly after addition of target, but only slowly after addition of effector peptide (which must compete directly with SmBiTgraft) (Extended Data Fig. 6). To demonstrate the modularity of this platform, using the methods described above for designing hosts, we rigidly fused a SARS-CoV-2 RBD binder (LCB1; ref. [43]) to the switch in place of the target binder such that the RBD clashes with the switch in state Y but not in state X, and tested 16 of these AScov fusion designs.

Recombinant expression and purification

Synthetic DNA fragments encoding each design were obtained from IDT as eBlocks and cloned into custom vectors using Golden Gate assembly[62]. Designs usually contained a C-terminal sequence-specific nickel-assisted cleavage (SNAC) tag[63] and a 6×His tag (MSG-Protein-GSGSHHWGSTHHHHHH). Proteins to be captured on the chip for SPR experiments contained an N-terminal AviTag and a C-terminal 6×His tag (MSGLNDIFEAQKIEWHESSG-Protein-GSGHHHHHH). For the size-exclusion chromatography (SEC) binding experiments shown in Supplementary Fig. 6 and for screening SmBiTgraft effector peptide variants with SPR, the effector was fused to superfolder GFP in a sfGFP-GSSG-Effector-GSHHHHHH construct. To rapidly break split luciferases, AS1 was fused to LgBiT in a MSG-AS1-linker-LgBiT-GSHHHHHH construct and the target was fused to SmBiT in a MSG-Target-linker-SmBiT-GSHHHHHH construct. In the rapid sensors, AS0 and AScov were fused to LgBiT and a SmBiT-containing effector in a MSGHHHHHHGS-LgBiT-linker-SmBiTgraft-linker-(AS0 or AScov)-GS construct. Sequences for all designs are available in Supplementary Table 4.

All proteins were expressed from NEB BL21(DE3) E. coli cells using TBII (MpBio) autoinduction medium with 0.5% (w/v) glycerol, 0.05% (w/v) glucose, 0.2% (w/v) lactose, 20 mM MgSO_4, trace metal mix and 50 μg ml−1 kanamycin. Expression cultures (50 ml) were grown either at 37 °C for 16–20 h or at 37 °C for 6–8 h, then at 18 °C for 16–20 h. Cells were collected by centrifugation, resuspended in 5 ml lysis buffer (100 mM Tris HCl pH 8.0, 200 mM NaCl, 50 mM imidazole, 1 mM PMSF, and 1 Pierce Protease Inhibitor Mini Tablets, EDTA-free per 50 ml) and lysed by sonication. The lysate was clarified by centrifugation at 14,000g for 30 min. Protein in the soluble lysate was bound to 1 ml Ni-NTA resin (QIAGEN), washed with 5 ml low-salt wash buffer (20 mM Tris HCl pH 8.0, 200 mM NaCl and 50 mM imidazole), 5 ml high-salt wash buffer (20 mM Tris HCl pH 8.0, 1 M NaCl, 50 mM imidazole) and 5 ml low-salt wash buffer, and eluted in 1.2 ml elution buffer (20 mM Tris HCl pH 8.0, 200 mM NaCl and 500 mM imidazole) after a 0.4 ml pre-elution. The proteins were further purified by SEC on a fast protein liquid chromatography (FPLC) system with a Superdex 200 Increase 10/300 GL column in Tris-buffered saline (TBS; 20 mM Tris pH 8.0 and 100 mM NaCl) with 1-ml fractions. Where possible, fractions that probably corresponded to protein monomers were selected. Final protein concentrations were estimated using molar extinction coefficients predicted from the protein sequence and integrating the absorbance at 280 nm over the selected fractions. Correct protein molecular weights were confirmed using liquid chromatography–mass spectrometry (LC–MS).

Peptide synthesis

The effector peptide cs221B was chemically synthesized by GenScript. The TAMRA-labelled effector used in fluorescence polarization (FP) experiments was synthesized as previously described[33].

SEC binding assay

Individual host proteins, sfGFP–effector and 1:1 host:sfGFP–effector mixtures were prepared at 20 μM in TBS (20 mM Tris pH 8.0 and 100 mM NaCl). A 0.5-ml quantity of each solution was injected onto a Superdex 200 Increase 10/300 GL column in TBS and the absorbance at 230 nm was monitored for changes in the retention volumes of the mixture compared with the individual proteins.

Fluorescence polarization

FP binding experiments with TAMRA-labelled effector were performed at 25 °C in TBS (20 mM Tris pH 8.0 and 100 mM NaCl) with 0.05% v/v TWEEN20 in 96-well plates (Corning 3686). Parallel and perpendicular fluorescence intensity was measured using a Synergy Neo2 plate reader with an FP 530/590 filter cube. Fluorescence polarization P (in units of mP) was calculated by the following expression:

$$P=\frac{\{{\mathsf{parallel}}\;{\mathsf{fluorescence}}\;{\mathsf{intensity}}\}-\{{\mathsf{perpendicular}}\;{\mathsf{fluorescence}}\;{\mathsf{intensity}}\}}{\{{\mathsf{parallel}}\;{\mathsf{fluorescence}}\;{\mathsf{intensity}}\}+\{{\mathsf{perpendicular}}\;{\mathsf{fluorescence}}\;{\mathsf{intensity}}\}}\times 1,000.$$

For affinity measurements, host proteins were titrated by twofold serial dilution across TAMRA–effector (at a constant concentration between 0.1 and 1 nM) through 24 wells, with a final volume of 80 μl in each well. Plates were incubated for at least 12 h at room temperature to fully equilibrate before measurement. To determine affinities, the following binding isotherm function was fitted to the measured polarization values using nonlinear least-squares minimization:

$$P={P}_{0}+{P}_{1}\,{f}_{{\rm{bound}}}$$

$${f}_{{\rm{bound}}}=\frac{1}{2[{\rm{E}}]}([{\rm{H}}]+[{\rm{E}}]+{K}_{{\rm{d}}}-\sqrt{{([{\rm{H}}]+[{\rm{E}}]+{K}_{{\rm{d}}})}^{2}-4[{\rm{H}}][{\rm{E}}]}),$$

where P is the modelled polarization, P_0 is the polarization of free effector, P_1 is the change in polarization after binding the host, fbound is the fraction of effector bound to the host, [H] and [E] are the total concentrations of host and effector, respectively, and Kd is the affinity between the host and the effector. When the fit Kd is lower than [E], affinities are too strong to be accurately measured with this method, so ‘Kd < [E]’ is reported.

For kinetic competition measurements, the target LHD101An1 was titrated by twofold serial dilution across the host protein (at a constant concentration of 22 nM) through five wells (a sixth well with just the host was also included) with a final volume of 72 μl in each well. Plates were incubated for one hour to allow the host and the target to equilibrate. To each well, 8 μl of 200 nM TAMRA–effector was added and rapidly mixed using a multichannel pipette, and the measurement was started immediately afterwards. This resulted in a 20 nM final concentration of both host and effector in a final volume of 80 μl per well. The following single exponential decay function was fitted to the measured polarization time courses using nonlinear least-squares minimization:

$$P={P}_{0}+{P}_{1}(1-{{\rm{e}}}^{-{k}_{{\rm{app}}}t}),$$

where P is the modelled polarization, P_0 is the polarization of free effector, P_1 is the amplitude of the change in polarization, kapp is the apparent rate constant and t is the time after the start of the measurement.

To measure facilitated dissociation in the reverse direction, host and TAMRA–effector were incubated at a 2:1 ratio to fully saturate the effector with host, diluted to 4 nM host and 2 nM effector, and distributed across 11 wells (40 μl in each well). In nine separate wells, the target was titrated by a fourfold serial dilution with constant excess unlabelled effector (40 μM). Just excess unlabelled effector at 40 μM was prepared in the 10th separate well (to measure the base rate of effector dissociation), and buffer was prepared in the 11th (to confirm the baseline remains stable). Forty microlitres of these target + effector solutions were added to the 11 host + TAMRA–effector wells and rapidly mixed using a multichannel pipette, and the measurement was started immediately afterwards. This resulted in final concentrations of 2 nM host, 1 nM TAMRA–effector, 20 μM unlabelled effector (to make the dissociation of TAMRA–effector irreversible) and varying concentrations of target in a final volume of 80 μl per well. The following single exponential decay function was fitted to the measured polarization time courses using nonlinear least-squares minimization:

$$P={P}_{0}+{P}_{1}{{\rm{e}}}^{-{k}_{{\rm{app}}}t}.$$

The following hyperbolic function was fitted to the apparent rate constants kapp:

$${k}_{{\rm{app}}}=\frac{{k}_{{\rm{off}},{\rm{TH}}:{\rm{E}}}[{\rm{T}}]}{{K}_{1/2}+[{\rm{T}}]}+{k}_{{\rm{off}},{\rm{H}}:{\rm{E}}},$$

where koff,H:E is the base effector off-rate constant, koff,TH:E is the off-rate constant for accelerated effector dissociation from the ternary complex, K1/2 is the concentration at which half the rate increase from koff,H:E to koff,TH:E is reached and [T] is the concentration of target present.

SPR

Proteins to be captured on the SPR chip were expressed with an N-terminal AviTag and purified as described above, except that the proteins were biotinylated after elution from the Ni-NTA resin: to the elutions, 5 μg ml−1 BirA (Avidity), 10 mM ATP, 10 mM Mg(OAc)2 and 100 μM d-biotin were added and allowed to incubate at room temperature for at least 4 h before further purification by SEC. Successful biotinylation was confirmed using LC–MS. SPR measurements were performed at 25 °C in HBS-EP+ buffer (Cytiva) on a Biacore 8K instrument. Biotinylated proteins were immobilized on the chip using the Biotin CAPture system (Cytiva).

For measurements of target off-rate constants, biotinylated target protein was immobilized on the chip. To measure base off-rate constants koff,T:H, host proteins at 50 nM were flowed over the chip for 60 s, then dissociation was measured for 2 h. To measure accelerated off-rate constants koff,T:HE, pre-incubated host–effector complexes (host at 1 μM and effector at 5 μM to ensure that the host is saturated with effector) were flowed for 60 s to form the ternary complex on the chip, then dissociation was measured for 4–20 min under a constant flow of 5 μM effector. Most dissociation data were fitted with the following double exponential decay function to account for populations of host protein with different dissociation kinetics:

$$S={S}_{0}+{S}_{1}{{\rm{e}}}^{-{k}_{{\rm{app}},1}(t-{t}_{0})}+{S}_{2}{{\rm{e}}}^{-{k}_{{\rm{app}},2}(t-{t}_{0})},$$

where S is the modelled SPR response, S_0 is the baseline, S_1 and S_2 are amplitudes relating to the sizes of each host population, kapp,1 and kapp,2 are apparent rate constants corresponding to each host population, t is the time and t_0 is the time at which dissociation initiates. The reported rate constant typically corresponds to the faster and higher amplitude exponential in the fit; instances in which other criteria are used to determine which rate constant corresponds to the change of interest are noted. When clearly only one host population is present (often indicated by kapp,1 ≈ kapp,2 or a large difference between S_1 and S_2 when fitting a double exponential), the following single exponential decay function was fitted instead, in which the parameters are the same as above (instances in which single exponentials were used are noted):

$$S={S}_{0}+{S}_{1}{{\rm{e}}}^{-{k}_{{\rm{app}}}(t-{t}_{0})}.$$

To measure the rate constant of effector association to form the ternary complex, biotinylated host protein was immobilized on the chip. The host protein was saturated with target by flowing 5 μM target over the chip for 4 min, then a varying concentration of effector and 5 μM target was flowed over the chip for 2–4 min to associate the effector, and finally 5 μM target was flowed over the chip for 4 min to monitor effector dissociation. Target was included at 5 μM (higher than its affinity to the ternary complex) throughout the experiment to ensure that the host remained saturated with target, preventing changes in target binding from convoluting the response from effector binding. Association data were fitted with the following single exponential decay function:

$$S={S}_{0}+{S}_{1}(1-{{\rm{e}}}^{-{k}_{{\rm{app}}}(t-{t}_{0})}),$$

where S is the modelled SPR response, S_0 is the baseline, S_1 is the amplitude, kapp is the apparent rate constant, t is the time and t_0 is the time at which dissociation initiates.

With the peptide effector, the following linear function was fitted to the apparent rate constants:

$${k}_{{\rm{app}}}={k}_{{\rm{on}},{\rm{TH}}:{\rm{E}}}[{\rm{E}}]+{k}_{{\rm{off}},{\rm{TH}}:{\rm{E}}},$$

where kon,TH:E and koff,TH:E are on-rate and off-rate constants and [E] is the concentration of effector flowed over the chip.

With the 3hb effector, the following hyperbolic function was fitted to the apparent rate constants:

$${k}_{{\rm{app}}}=\frac{1}{2}({k}_{{\rm{switch}}}+{k}_{{\rm{unswitch}}}+{k}_{{\rm{on}},{\rm{TH}}:{\rm{E}}}[{\rm{E}}]+{k}_{{\rm{off}},{\rm{TH}}:{\rm{E}}})$$

$$-\frac{1}{2}\sqrt{{({k}_{{\rm{switch}}}+{k}_{{\rm{unswitch}}}-{k}_{{\rm{on}},{\rm{TH}}:{\rm{E}}}[{\rm{E}}]-{k}_{{\rm{off}},{\rm{TH}}:{\rm{E}}})}^{2}+4{k}_{{\rm{unswitch}}}{k}_{{\rm{on}},{\rm{TH}}:{\rm{E}}}[{\rm{E}}]},$$

where kswitch is the rate constant for the THX → THY conformational change, kunswitch is the rate constant for the THY → THX conformational change, kon,TH:E and koff,TH:E are on-rate and off-rate constants and [E] is the concentration of effector flowed over the chip. This function describes the slow relaxation rate constant of binding by conformational selection[64]. The data can be fitted with only the slow relaxation because, as the conformational pre-equilibrium favours state X (that is, kswitch « kunswitch), the effect of the fast relaxation on kapp is minimal. During the fit, koff,TH:E was constrained to a low value (<1 × 10−4 s−1), as observed.

To measure the effector-concentration-dependent rate constant of the full facilitated dissociation process, either biotinylated target protein or biotinylated common gamma γc ectodomain (Acro Biosystems ILG-H85E8) was immobilized on the chip. For experiments with ASNeo2 designs, the ASNeo2 hosts were pre-incubated with IL-2Rβ ectodomain (Acro Biosystems CD2-H5221) before association with γc. Each experiment involved multiple cycles of host association and induced dissociation under the flow of various concentrations of effector obtained by twofold serial dilution (Supplementary Fig. 8a). Throughout these cycles, a small population of host that is unresponsive to the effector (‘Hn’, owing to partial degradation or misfolding induced by the strain in the ternary complex) could accumulate on the chip (Supplementary Fig. 8a,b). The following system of differential equations describing the expected behaviour of the proteins on the chip (accounting for this accumulation) can be fitted to the dissociation curve of cycle n (Supplementary Fig. 8c):

$$\frac{{\rm{d}}[{\rm{T}}{\rm{H}}{\rm{E}}]}{{\rm{d}}t}=-{k}_{{\rm{o}}{\rm{f}}{\rm{f}},{\rm{T}}:{\rm{H}}{\rm{E}}}[{\rm{T}}{\rm{H}}{\rm{E}}]+{k}_{{\rm{o}}{\rm{n}},{\rm{T}}{\rm{H}}:{\rm{E}}}[{\rm{T}}{\rm{H}}][{\rm{E}}]-{k}_{{\rm{o}}{\rm{f}}{\rm{f}},{\rm{T}}{\rm{H}}:{\rm{E}}}[{\rm{T}}{\rm{H}}{\rm{E}}]$$

$$\frac{{\rm{d}}[{\rm{T}}{\rm{H}}]}{{\rm{d}}t}=-{k}_{{\rm{o}}{\rm{f}}{\rm{f}},{\rm{T}}:{\rm{H}}}[{\rm{T}}{\rm{H}}]-{k}_{{\rm{o}}{\rm{n}},{\rm{T}}{\rm{H}}:{\rm{E}}}[{\rm{T}}{\rm{H}}][{\rm{E}}]+{k}_{{\rm{o}}{\rm{f}}{\rm{f}},{\rm{T}}{\rm{H}}:{\rm{E}}}[{\rm{T}}{\rm{H}}{\rm{E}}]$$

$$\frac{{\rm{d}}[{\rm{T}}{\rm{H}}{\rm{n}}]}{{\rm{d}}t}=-{k}_{{\rm{o}}{\rm{f}}{\rm{f}},{\rm{T}}:{\rm{H}}{\rm{n}}}[{\rm{T}}{\rm{H}}{\rm{n}}],$$

with initial values computed for each cycle by

$${[{\rm{TH}}]}_{{\rm{final}},0}=0$$

$${[{\rm{THn}}]}_{{\rm{final}},0}=0$$

$${[T]}_{{\rm{final}},n-1}={1-[{\rm{TH}}]}_{{\mathsf{final}},n-1}{-[{\rm{THn}}]}_{{\rm{final}},n-1}$$

$${[{\rm{THE}}]}_{{\rm{initial}},n}=0$$

$${[{\rm{T}}{\rm{H}}]}_{{\rm{i}}{\rm{n}}{\rm{i}}{\rm{t}}{\rm{i}}{\rm{a}}{\rm{l}},n}={[{\rm{T}}{\rm{H}}]}_{{\rm{f}}{\rm{i}}{\rm{n}}{\rm{a}}{\rm{l}},n-1}+{f}_{{\rm{r}}{\rm{e}}{\rm{s}}{\rm{p}}{\rm{o}}{\rm{n}}{\rm{s}}{\rm{i}}{\rm{v}}{\rm{e}}}{[{\rm{T}}]}_{{\rm{f}}{\rm{i}}{\rm{n}}{\rm{a}}{\rm{l}},n-1}$$

$${[{\rm{T}}{\rm{H}}{\rm{n}}]}_{{\rm{i}}{\rm{n}}{\rm{i}}{\rm{t}}{\rm{i}}{\rm{a}}{\rm{l}},n}={[{\rm{T}}{\rm{H}}{\rm{n}}]}_{{\rm{f}}{\rm{i}}{\rm{n}}{\rm{a}}{\rm{l}},n-1}+({1-f}_{{\rm{r}}{\rm{e}}{\rm{s}}{\rm{p}}{\rm{o}}{\rm{n}}{\rm{s}}{\rm{i}}{\rm{v}}{\rm{e}}}){[{\rm{T}}]}_{{\rm{f}}{\rm{i}}{\rm{n}}{\rm{a}}{\rm{l}},n-1}$$

and the modelled concentrations of each complex state on the chip is related to the SPR response by

$$S={f}_{n}({a}_{{\rm{TH}}}[{\rm{TH}}]+{a}_{{\rm{THE}}}[{\rm{THE}}]+{a}_{{\rm{THn}}}[{\rm{THn}}]),$$

where S is the modelled SPR response, [THE], [TH] and [THn] are concentrations of complex states on the chip, t is the time after the start of the dissociation cycle, [E] is the concentration of effector flowed over the chip, koff,T:H, koff,T:HE, kon,TH:E, koff,TH:E and koff,T:Hn are on-rate and off-rate constants, aTH, aTHE and aTHn are amplitudes relating the concentration of each state on the chip to an SPR response, fn is an amplitude fudge factor for cycle n accounting for small differences in amplitude across cycles and fresponsive is the fraction of host that is responsive to the effector. Varying all of these parameters, this model is then globally fitted to the dissociation curves of all cycles using nonlinear least-squares minimization. Note that with a sufficiently high value of kon,TH:E, this model’s dissociation kinetics are determined mainly by the target dissociation parameters koff,T:H and koff,T:HE; meanwhile, the effector binding parameters kon,TH:E and koff,TH:E tend to tightly covary and cannot be accurately determined from these fits. Also note that for designs with low values for koff,T:HE, this model may be less accurate because the assumption that [THE] = 0 at the beginning of each cycle may no longer be valid. To compute the effective rate constant of the full facilitated dissociation process for intact host, we used the following simplified system of differential equations, which no longer accounts for a small population of unresponsive host:

$$\frac{{\rm{d}}[{\rm{T}}{\rm{H}}{\rm{E}}]}{{\rm{d}}t}=-{k}_{{\rm{o}}{\rm{f}}{\rm{f}},{\rm{T}}:{\rm{H}}{\rm{E}}}[{\rm{T}}{\rm{H}}{\rm{E}}]+{k}_{{\rm{o}}{\rm{n}},{\rm{T}}{\rm{H}}:{\rm{E}}}[{\rm{T}}{\rm{H}}][{\rm{E}}]-{k}_{{\rm{o}}{\rm{f}}{\rm{f}},{\rm{T}}{\rm{H}}:{\rm{E}}}[{\rm{T}}{\rm{H}}{\rm{E}}]$$

$$\frac{{\rm{d}}[{\rm{T}}{\rm{H}}]}{{\rm{d}}t}=-{k}_{{\rm{o}}{\rm{f}}{\rm{f}},{\rm{T}}:{\rm{H}}}[{\rm{T}}{\rm{H}}]-{k}_{{\rm{o}}{\rm{n}},{\rm{T}}{\rm{H}}:{\rm{E}}}[{\rm{T}}{\rm{H}}][{\rm{E}}]+{k}_{{\rm{o}}{\rm{f}}{\rm{f}},{\rm{T}}{\rm{H}}:{\rm{E}}}[{\rm{T}}{\rm{H}}{\rm{E}}]$$

$${[{\rm{THE}}]}_{{\rm{initial}}}=0$$

$${[{\rm{TH}}]}_{{\rm{initial}}}=1$$

For each effector concentration [E], this system was solved for the half-time of the target:host interaction t1/2 using the rate parameters determined from the original model fitted to the data, and the effective rate constant of the full dissociation process keff was computed from each half-time as follows:

$$[{\rm{THE}}]({t}_{1/2})+[{\rm{TH}}]({t}_{1/2})=0.5$$

$${k}_{{\rm{eff}}}=\frac{{\rm{ln}}(2)}{{t}_{1/2}}$$

The following hyperbolic function was fitted to the effective rate constants (constrained to the values of koff,T:HE and koff,T:H obtained from the global fit):

$${k}_{{\rm{e}}{\rm{f}}{\rm{f}}}=\frac{{k}_{{\rm{o}}{\rm{f}}{\rm{f}},{\rm{T}}:{\rm{H}}{\rm{E}}}[{\rm{E}}]}{{K}_{1/2}+[{\rm{E}}]}+{k}_{{\rm{o}}{\rm{f}}{\rm{f}},{\rm{T}}:{\rm{H}}},$$

where koff,T:H is the base target off-rate constant, koff,T:HE is the off-rate constant for accelerated target dissociation from the ternary complex, K1/2 is the concentration at which half the rate increase from koff,T:H to koff,T:HE is reached and [E] is the concentration of effector flowed over the chip.

The discrepancy in the 3hb EC_50 between the 3hb association experiment in Fig. 2f (right) and the target facilitated dissociation experiment in Fig. 2h (right) could result from the fusion tag that was used to fix AS1 to the surface competing with the 3hb for binding the cleft, reducing the apparent 3hb on-rate in the 3hb association experiment but not in the target facilitated dissociation experiment.

All SPR measurements in the main figures were repeated at least once, with similar results.

Circular dichroism spectroscopy

Circular dichroism spectra were measured at 25 °C on protein samples at 0.2 mg ml−1 in TBS (20 mM Tris pH 8.0, 100 mM NaCl) using a Jasco J-1500 spectrophotometer.

X-ray crystallography

The AS1 TH and THE complexes required increased hydrophobicity to crystallize. This was accomplished by lysine methylation (for crystals AS1_TH 1, AS1_TH 2 and AS1_THE 1) or with the hydrophobic surface mutations K46L, E50W, K172W and E173Y (for crystal AS1_THE 2).

Protein was expressed from NEB BL21(DE3) E. coli cells using TBII autoinduction medium as above but at a larger scale: either 8 × 50 ml or 1–2 × 500 ml cultures. Cells were collected by centrifugation, resuspended in lysis buffer and lysed by sonication. The lysate was clarified by centrifugation at 14,000g for 30 min. Protein in the soluble lysate was bound to 8 ml Ni-NTA resin (QIAGEN), washed with 10 ml low-salt wash buffer, 30 ml high-salt wash buffer and 10 ml SNAC cleavage buffer (100 mM 2-(N-cyclohexylamino)ethanesulfonic acid (CHES), 100 mM acetone oxime, 100 mM NaCl and 500 mM guanidinium chloride, pH 8.6)[63] and incubated in 40 ml SNAC cleavage buffer + 2 nM NiCl_2 for 12 h at room temperature to cleave. Afterwards, the flowthrough was collected and the beads were washed with 40 ml lysis buffer (minus the protease inhibitors). The amount of cleaved protein in the flowthrough and wash was assessed with SDS–PAGE, and fractions with enough cleaved protein were concentrated and further purified using SEC on an FPLC system with either a Superdex 75 Increase 10/300 GL column or a HiLoad 20/600 Superdex 75 pg column in either TBS (20 mM Tris pH 8.0 and 100 mM NaCl) or lysine methylation buffer (50 mM HEPES pH 7.5 and 250 mM NaCl) if lysine residues in the protein were to be methylated. For protein complex cocrystallization, the purified proteins and/or chemically synthesized effector were mixed at equimolar ratios. For some samples (resulting in crystals AS1_TH 1, AS1_TH 2 and AS1_THE 1), lysine residues were methylated as previously described[65], and the reaction was quenched using SEC on an FPLC system to buffer-exchange into TBS. Finally, the samples were concentrated to crystallization levels.

Crystallization experiments were done using the sitting drop vapour diffusion method. Initial crystallization trials were set up in 200-nl drops using the 96-well plate format at 20 °C. Crystallization plates were set up using a Mosquito LCP from SPT Labtech, then imaged using UVEX microscopes and UVEX PS-256 from JAN Scientific. Diffraction-quality crystals formed in 0.2 M magnesium chloride hexahydrate, 0.1 M sodium cacodylate pH 6.5 and 50% v/v PEG 200 for AS1_H; in 0.2 M sodium chloride, 0.1 M Na/K phosphate pH 6.2 and 50% v/v PEG 200 for CS221B; in 0.1 M sodium acetate pH 5.0, 5% w/v γ-PGA (Na+ form, LM) and 30% v/v PEG 400 for LHD101An1; in 0.2 M magnesium chloride hexahydrate, 0.1 M Tris pH 8.5 and 25% w/v polyethylene glycol 3,350 for AS5_HE; in 0.1 M sodium acetate pH 5.0 and 20% (v/v) MPD for AS5_H; in 0.2 M 1,6-hexanediol, 0.2 M 1-butanol, 0.2 M 1,2-propanediol, 0.2 M 2-propanol, 0.2 M 1,4-butanediol, 0.2 M 1,3-propanediol, sodium HEPES, MOPS (acid) pH 7.5, 40% v/v PEG 500 MME and 20% w/v PEG 20000 for AS1_HE; in 0.1 M citric acid pH 3.5 and 2.0 M ammonium sulfate for AS1_THE 1; in 1.0 M lithium chloride, 0.1 M citrate pH 4.0 and 20% w/v PEG 6000 for AS1_THE 2; in 2.4 M sodium malonate pH 7.0 for AS1_TH 1 (P 6_1 2 2); and in 1.8 M ammonium citrate tribasic pH 7.0 for AS1_TH 2 (P 2_1 2_1 2_1).

Diffraction data were collected at the National Synchrotron Light Source II beamline 17-ID-1 (FMX/AMX), the Advanced Light Source beamline 821/822 or the Advanced Photon Source NECAT 24ID-C. X-ray intensities and data reduction were evaluated and integrated using XDS[66] and merged and scaled using Pointless or Aimless in the CCP4 program suite[67]. Structure determination and refinement starting phases were obtained by molecular replacement with Phaser[68], using the designed model for the structures. After molecular replacement, the models were improved using phenix.autobuild with rebuild-in-place to false and using simulated annealing. Structures were refined in PHENIX[69]. Model building was performed using Coot[70]. The final model was evaluated using MolProbity[71]. Data collection and refinement statistics are provided in Extended Data Tables 1–3.

DEER spectroscopy

Spin-label modelling and distance distribution predictions were performed as previously described[33] using chiLife[72] with the off-rotamer sampling method[73]. Site pair selections were performed as previously described[33]. Host protein variants containing cysteine residues at the selected sites were purified as described above, except that 0.5 mM TCEP was included in the lysis buffer and the first two Ni-NTA resin washes, and the proteins were labelled immediately after elution from the Ni-NTA resin: to the elutions, 50 μl of 200 mM of the nitroxide spin label 1-oxyl-2,2,5,5-tetramethyl-3-pyrroline-3-methyl)methanethiosulfonate (MTSL) in dimethyl sulfoxide (DMSO) was added and allowed to incubate for at least 2 h at room temperature before further purification by SEC. Successful labelling was confirmed using LC–MS. DEER samples were prepared at 20 µM labelled host protein in deuterated solvent buffered by 20 mM Tris at pH 8.0 with 100 mM NaCl and 20% d_8-glycerol (Cambridge Isotope Laboratories) as a glassing agent. When appropriate, target and effector were added to a concentration of 100 µM each. Then, 15–30 µl of each sample was loaded into a quartz capillary (Sutter Instrument, 1.1 mm inner diameter, 1.5 mm outer diameter) and flash-frozen with liquid nitrogen. Samples were stored at −80 °C until the DEER experiments were performed.

DEER experiments were performed as previously described[33]. An ELEXSYS E580 spectrometer (Bruker) at Q-band (around 34 GHz) with an EN5107D2 resonator (Bruker) was used for all experiments. The temperature was maintained at 50 K using a cryogen-free cooling system (ColdEdge). The four-pulse DEER sequence was used, using 60-ns Gaussian observer pulses with a full width at half maximum (FWHM) of 30 ns and a frequency near the centre of the field-swept spectrum and 150-ns sech/tanh probe pulses with centre 80 MHz above the observer frequency, 80 MHz bandwidth and a truncation parameter of 10. All shaped pulses were generated using the SpinJet arbitrary waveform generator (Bruker). Pulse shapes were calculated using PulseShape (https://github.com/stolllab/PulseShape), using both resonator compensation and transmitter nonlinearity compensation. All data were collected using a 2 ms shot repetition time, 8-step phase cycling and τ_1 averaging from 400 ns to 528 ns in 16-ns steps. All other experimental parameters (including pump pulse time step, τ_2 delays, number of scans and others) were chosen on a per-sample basis and are reported in Supplementary Table 3.

All DEER data were analysed using the DeerExp module of the eprTools Python package (https://github.com/mtessmer/eprTools). All data were fitted using separable nonlinear least squares[74]. The foreground signal was modelled using Tikhonov regularization with the second derivative operator. The regularization parameter was selected using generalized cross-validation. The background was modelled using a 3D-homogeneous spin distribution. An additional penalty restraining the modulation depth to be low was used to prevent the fitting of long-distance artefacts in the foreground, as was done previously[75]. Confidence intervals were estimated using bootstrap sampling with 100 samples using a fixed regularization parameter. Fit parameters such as the regularization parameter, the modulation depth and the signal-to-noise ratio are listed in Supplementary Table 3.

MD simulations

Input files for the MD simulation were prepared with CHARMM-GUI76,77, with AF2 structure as the initial structure. A rectangular water box of edge length 10 Å was placed around the protein. The water box contained potassium and chlorine ions of concentration 0.15 M that neutralized the protein’s net charge. The ions were placed in the water box using the Monte Carlo method. To model the system, the CHARMM36m force field[78] was used. After the explicit solvent system was made, the system was minimized and equilibrated before the production MD run. All three steps were performed with the GROMACS 2020.2 MD engine79,80.

The steepest descent method was used for energy minimization, for 5,000 steps with an energy tolerance of 1000 kJ mol−1 nm−1. The neighbour list was updated every ten steps with a cut-off distance of 1.2 nm. Cut-off was used to calculate Van der Waals interactions with a switch distance of 1.0 nm and a cut-off distance of 1.2 nm. The force was smoothly switched off between the switch distance and the cut-off distance. The fast smooth particle-mesh Ewald method was used to calculate electrostatics with a cut-off distance of 1.0 nm. All bonds with hydrogen atoms were treated as rigid using the linear constraint solver (LINCS) algorithm[81].

The equilibration step was performed with a leap-frog algorithm using a time step of 1 fs and a total simulation time of 125 ps. The system was propagated in the NVT ensemble. The temperature was maintained at 303.15 K using the velocity rescaling method. The solute and solvent were coupled with a time constant for coupling of 1 ps. The centre-of-mass translational velocity was removed every 100 steps to prevent the system from drifting. The cut-off schemes for Van der Waals and electrostatic interactions were the same as those used in the minimization step, except that the neighbour list was updated every 20 steps. The velocities were generated from a Maxwell distribution with a temperature of 303.15 K. The same LINCS method was used to constrain the hydrogen atoms during the equilibration step.

The production step was performed with a leap-frog algorithm using a time step of 2 fs and a total simulation time of 1 μs. The trajectories with the same initial equilibrated structure were obtained in triplicate. The system was propagated in the NPT ensemble. The temperature was maintained at 303.15 K using the velocity rescaling method. The solute and solvent were coupled with a time constant of 1 ps. Exponential relaxation pressure coupling and isotropic coupling with a time constant of 5.0 ps was used to maintain the pressure at 1.0 bar. The cut-off schemes for Van der Waals and electrostatic interactions were the same as those used in the minimization step, except that the neighbour list was updated every 20 steps and the Coulomb cut-off distance was set as 1.2 nm. The centre-of-mass velocity removal was the same as that used in the equilibration step. The same LINCS method was used to constrain the hydrogen atoms during the production step.

Analysis of the trajectories, such as RMSD and root mean square fluctuation (RMSF) calculations, was performed with MDAnalysis[82]. To simulate DEER distance distributions from these trajectories, structures along each trajectory were clustered using the Gromos clustering algorithm[83] in GROMACS, distance distributions were predicted (using chiLife[72] with the off-rotamer sampling method[73]) for the centre structure of each cluster and the resulting distributions were averaged weighted by the occupancies of their corresponding clusters.

Chain reactions with FRET readout

Hinge protein cs201F_E249L (H2) was purified as described above, except that 0.5 mM TCEP was included in the lysis, wash and elution buffers, and the SEC buffer was phosphate-buffered saline (PBS; 20 mM sodium phosphate pH 7.0, 100 mM NaCl and 0.5 mM TCEP). To double label with dyes, 50 μM H2 was incubated with 250 μM Alexa Fluor 555 C_2 maleimide (donor; Thermo Fisher Scientific) and 250 μM Alexa Fluor 647 C_2 maleimide (acceptor; Thermo Fisher Scientific), shaking at room temperature for at least 2 h. The reaction was quenched by adding DTT to 10 mM, and the proteins were separated from excess dye by SEC in TBS (20 mM Tris pH 8.0, 100 mM NaCl).

FRET binding experiments were performed at 25 °C in TBS (20 mM Tris pH 8.0 and 100 mM NaCl) with 0.05% v/v TWEEN20 in 96-well plates (Corning 3686). Fluorescence intensity was measured using a Synergy Neo2 plate reader, exciting the donor at a 520-nm wavelength and reading acceptor emission at a 665-nm wavelength.

To measure the E2–target on-rate constant, 40 μl H2 at 10 nM was prepared in six wells. To each well, 40 μl E2–target at various concentrations was added and rapidly mixed using a multichannel pipette, and the measurement was started immediately afterwards. The following single exponential decay function was fitted to the measured FRET time courses using nonlinear least-squares minimization,

$$S={S}_{0}+{S}_{1}{{\rm{e}}}^{-{k}_{{\rm{app}}}t},$$

where S is the modelled fluorescence signal, S_0 is the fluorescence at equilibrium, S_1 is the amplitude of the change in fluorescence, kapp is the apparent rate constant and t is the time after the start of the measurement. The following linear function was fitted to the apparent rate constants:

$${k}_{{\rm{app}}}={k}_{{\rm{on}}}[{\rm{E2}}]+{k}_{{\rm{off}}},$$

where kon and koff are on-rate and off-rate constants and [E2] is the total concentration of E2–target.

To measure the accelerated transfer of E2 from AS114 to H2, 1,067 nM AS114 and 533 nM E2–target were incubated for 15 min to fully cage E2–target in AS114. A control solution of just 1,067 nM AS114 was also prepared. A 37.5-μl quantity of 42.7 nM H2 was prepared in eight wells; 37.5 μl of AS114 + E2–target was added to four wells and 37.5 μl of AS114 was added to the other four wells using a multichannel pipette, and the measurement was started immediately afterwards. Five minutes later, four solutions (5 μl buffer, 100 μM target, 16 μM effector or 100 μM target + 16 μM effector) were added to each set of four wells (each solution to a different well of each set) using a multichannel pipette, and the measurement was immediately continued for 1 h. Mixing components at these concentrations resulted in final concentrations of 500 nM AS114, 250 nM E2–target, 20 nM H2, 1 μM effector and 6 μM target. A baseline drift function of the following form was fitted to the AS114 + buffer data and subtracted from the other time courses:

$$S=\frac{{S}_{1}}{1+{{\rm{e}}}^{-k(t-{t}_{1/2})}}+{S}_{0},$$

where S is the modelled fluorescence signal, S_0 is the fluorescence at equilibrium, S_1 is the amplitude of the change in fluorescence, k is a rate constant, t is the time after the start of the measurement and t1/2 is the time at which the fluorescence has changed by half the full amplitude. AS114 was used because, in state X, it does not clash with E2 extending past the target in E2–target.

Rapid sensors and split enzymes with luminescence readout

Luminescence experiments were performed at 25 °C in TBS (20 mM Tris pH 8.0 and 100 mM NaCl) with 0.05% v/v TWEEN20 in 96-well plates (Corning 3686). Luminescence was measured using a Synergy Neo2 plate reader with a LUM filter cube.

To measure rapid breakage of a split luciferase, 111 pM AS1–LgBiT and 22 nM target SmBiT were incubated for one hour to load AS1 with the target and reconstitute the split luciferase. Next, 72 μl of this mixture was added to two wells, then 8 μl of 1/10-diluted Nano-Glo substrate (Promega N1130) and either 10 μM effector and 200 μM target or just 200 μM target was added to these wells using a multichannel pipette, and the measurement was started immediately afterwards. Excess target was included to fully outcompete target–SmBiT to enable measurement of the dissociation rate. Mixing components at these concentrations resulted in final concentrations of 100 pM AS1–LgBiT, 20 nM target–SmBiT, 1 μM effector, 20 μM target and 1/100-diluted Nano-Glo substrate.

To measure rapid analyte sensing, 64 μl of 12.5-pM sensor was added to eight wells, 8 μl of 1/10-diluted Nano-Glo substrate was added to these wells using a multichannel pipette and the measurement was started immediately afterwards. Five minutes later, 8 μl of various concentrations of analyte obtained by tenfold serial dilution was added using a multichannel pipette, and the measurement was immediately continued for 30–60 min. Mixing components at these concentrations resulted in final concentrations of 10 pM sensor and 1/100-diluted Nano-Glo substrate. The analytes were target, effector or SARS-CoV-2 RBD (Acro Biosystems SPD-C52H3). Figure 4e shows a time course with 800 nM SARS-CoV-2 RBD.

Live-cell single-molecule imaging

For cell-surface labelling, receptors were N-terminally fused to suitable tags using a pSems vector including the signal sequence of Igκ (pSems leader). Common gamma chain (γc) was fused to the ALFA-tag[84] and IL-2Rβ was fused to nonfluorescent monomeric GFP (mXFP)[85]. HeLa cells (ACC 57, DSMZ) were cultured as previously described[86]. For transient transfection, cells were incubated for 4–6 h with a mixture of 150 mM NaCl, 10 µl of 1 mg ml−1 polyethylenimine (PEI MAX, Polysciences 24765), 200 ng of DNA of pSems leader ALFA-tag-γc and 2,800 ng of pSems leader mXFPe1-IL-2Rβ[87]. Labelling, washing and subsequent imaging were performed after mounting the coverslips into custom-made incubation chambers with a volume of 1 ml. Cells were equilibrated in medium with fetal bovine serum (FBS) but lacking phenol red, supplemented with an oxygen scavenger and a redox-active photoprotectant (0.5 mg ml−1 glucose oxidase (Sigma-Aldrich), 0.04 mg ml−1 catalase (Roche), 5% w/v glucose, 1 μM ascorbic acid and 1 μM methylviologene) to minimize photobleaching[88].

Selective cell-surface receptor labelling was achieved by using anti-GFP and anti-ALFA-tag nanobodies (NBs), which were site-specifically labelled by maleimide chemistry via a single cysteine residue at their C termini[88]. Anti-ALFA NB labelled with Cy3B (degree of labelling (DOL): 1.06) and anti-GFP NB labelled with ATTO 643 (DOL: 1.0) were added at concentrations of 3 nM each, at least 10 min before imaging. Coverslips were precoated with poly-l-lysine-graft-poly(ethylene glycol) to minimize unspecific binding of NBs and were functionalized with RGD peptide for efficient cell adhesion[89].

During the imaging experiments, ASNeo2 was used at 100 nM, and 10 μM effector was used to induce receptor dissociation.

Dual-colour imaging was performed by total internal reflection fluorescence (TIRF) microscopy using an inverted microscope (IX83, Olympus) equipped with a spectral image splitter (QuadView, Photometrics) and an EMCCD camera (iXon Ultra, Andor) as described in detail elsewhere[90]. Fluorophores were excited by sequential illumination with a 561-nm laser (2RU-VFL-P-2000-560-B1R, 2,000 mW, MPB Communications) and a 642-nm laser (2RU-VFL-P-2000-642-B1R, 2,000 mW, MPB Communications). Alternating laser excitation was achieved with a simple micro-controller (Arduino Uno) and open-source acquisition software[91] synchronizing laser shuttering with an acousto-optic tunable filter (AOTF; AA.AOTFnC-400.650-TN, AA Opto Electronic) and camera triggering. For long-term tracking experiments, 1,500 frames per channel were acquired at 40 fps. The resulting image stacks were divided into five-frame stacks and dimerization was determined for each stack. For Fig. 5f, three time courses, each normalized to its average initial relative dimerization, are overlaid for each condition. For all other tracking experiments, 200 frames per channel were acquired at 40 fps and dimerization was determined over the whole image stack.

Dual-colour single-molecule co-tracking time-lapse images were evaluated using an in-house-developed MATLAB software (SLIMfast4C, https://zenodo.org/record/5712332)[88]. After channel registration based on calibration with fiducial markers, molecules were localized using the multi-target tracking algorithm[92]. Immobile emitters were filtered out by spatio-temporal cluster analysis[93]. Frame-by-frame co-localization within a cut-off radius of 150 nm was applied followed by tracking of co-localized emitters using the utrack algorithm[94]. Molecules co-diffusing for ten frames or more were then identified as co-localized. Relative levels of co-localization were determined on the basis of the fraction of co-localized particles relative to all localizations in the ALFA-γc channel (561 nm). Diffusion properties were determined from the pooled single trajectory using mean squared displacement analysis for all trajectories with a lifetime greater than ten frames. Diffusion constants were determined from the mean squared displacement by linear regression. Relative dimerization was estimated by:

$${\rm{R}}{\rm{e}}{\rm{l}}{\rm{a}}{\rm{t}}{\rm{i}}{\rm{v}}{\rm{e}}\,{\rm{d}}{\rm{i}}{\rm{m}}{\rm{e}}{\rm{r}}{\rm{i}}{\rm{z}}{\rm{a}}{\rm{t}}{\rm{i}}{\rm{o}}{\rm{n}}=\frac{\{\text{Co-localizations}\}}{\{\text{IL-2R}{\rm{\beta }}\,\text{localizations}\}}$$

FRET efficiencies were evaluated using an in-house-developed MATLAB software (provided and described in detail elsewhere as supplementary software[90]). In brief, alternating laser excitation FRET experiments provide three separated emission channels: directly excited donor \({D}_{{D}_{{\rm{ex}}}}^{{D}_{{\rm{em}}}}\) and acceptor \({A}_{{A}_{{\rm{ex}}}}^{{A}_{{\rm{em}}}}\) channels, as well as a sensitized FRET \({F}_{{D}_{{\rm{ex}}}}^{{A}_{{\rm{em}}}}\) channel. First, channels were aligned by calibration with fiducial markers. Then, after applying a single-molecule localization algorithm[95], single-molecule intensities were determined from background subtracted images (\({I}_{{D}_{{\rm{ex}}}}^{{D}_{{\rm{em}}}}\), \({I}_{{A}_{{\rm{ex}}}}^{{A}_{{\rm{em}}}}\), \({I}_{{D}_{{\rm{ex}}}}^{{A}_{{\rm{em}}}}\)). To evaluate FRET efficiencies, donor–acceptor pairs were co-localized with an optimized search radius. For these pairs, the apparent FRET efficiency was calculated by:

$${E}_{{\rm{raw}}}=\frac{{I}_{{D}_{{\rm{ex}}}}^{{A}_{{\rm{em}}}}}{{I}_{{D}_{{\rm{ex}}}}^{{D}_{{\rm{em}}}}+{I}_{{D}_{{\rm{ex}}}}^{{A}_{{\rm{em}}}}}.$$

To achieve accurate FRET efficiencies, standard further corrections were applied. These include the donor leakage coefficient, cross-talk-corrected proximity ratio and the correction factor γ (refs. 90,96).

pSTAT5 signalling assay with human NK (YT-1) cells

Human NK (YT-1) cells were cultured in RPMI 1640 complete medium, supplemented with 10% FBS, 2 mM l-glutamine, minimum essential non-essential amino acids, sodium pyruvate, 25 mM HEPES and penicillin–streptomycin (Gibco). For the flow-cytometry-based pSTAT5 detection assay, 2–5 × 10^5 IL-2Rα-positive YT-1 cells were seeded in 350 µl of medium per well in a 96-well plate. The cells were stimulated with 1 nM ASNeo2 or Neo2 for 5 min at 37 °C. As a control, 50 µl of untreated YT-1 cells were set aside at the start of each experiment and evaluated after 45 min alongside the treated cells. After stimulation, all cells were transferred to three separate wells containing a control (no treatment), 10 μM effector or 40 μM ruxolitinib. One-seventh of the cells (50 µl) were resuspended in 17 µl of 16% paraformaldehyde (PFA) for immediate fixation. This process was repeated at 5-, 10-, 15-, 30- and 45-min intervals. After all of the time points were fixed, the cells were refixed in 4% PFA for 15 min at room temperature. After fixation, the cells were washed once with PBS containing 0.5% bovine serum albumin (BSA) (PBSA) and permeabilized with 100% methanol for 45 min at 4 °C. After permeabilization, the cells were washed twice with PBSA and stained for one hour at room temperature using Alexa Fluor 647-conjugated phospho-STAT5 (Tyr694) rabbit monoclonal antibody (Cell Signaling Technology, 9365, clone C71E5) diluted 1:100. After three washing steps, the cells were analysed using a CytoFlex S flow cytometer (Beckman Coulter). Data were analysed with CytExpert software, and cells were gated on SSC-A versus FSC-A. Each experiment was done in triplicate, and the results were analysed accordingly.

For Fig. 5g, pSTAT5 signal is normalized to the background level. For Fig. 5h, n = 3 time courses were normalized to the signal at time 0 (just before effector addition) and a baseline signal was determined by the average of this normalized pSTAT5 of the untreated cells, then each time course was renormalized to this baseline signal. Some batches of cells were unresponsive to stimulation; data from these batches were excluded from analysis.

Cell-line sources

HeLa cells for single-molecule imaging were sourced from the DSMZ (ACC 57); YT-1 (CD25+) cells were sourced from ATCC. Cell lines were authenticated by short tandem repeat profiling. HeLa cells tested negative for mycoplasma contamination by PCR.

Activation and stimulation of human T cells

Primary human peripheral blood mononuclear cells (PBMCs) isolated from a healthy donor by leukapheresis were thawed and resuspended at a cell density of 2 × 10^6 cells per ml in T cell medium, which contained RPMI 1640 (Gibco), FBS (10% v/v, Gibco, Thermo Fisher Scientific), HEPES (25 mM, Gibco, Thermo Fisher Scientific), penicillin–streptomycin (1% v/v, Gibco, Thermo Fisher Scientific), sodium pyruvate (1% v/v, Gibco, Thermo Fisher Scientific), MEM non-essential amino acids solution (1% v/v, Gibco, Thermo Fisher Scientific) and 2-mercaptoethanol (0.1% v/v, Gibco, Thermo Fisher Scientific), and supplemented with human IL-2 (100 U ml−1). For human T cell activation, cells were activated with plate-bound anti-human CD3ε (1 μg ml−1, clone OKT-3, BioXCell) and soluble anti-human CD28 (5 μg ml−1, clone 9.3, BioXCell) for 48 h. Cells were then expanded in T cell medium with human IL-2 (100 U ml−1) for eight days. On day 9, IL-2 was withdrawn for 36 h, after which cells were resuspended at 1 × 10^6 cells per ml and stimulated with ASNeo2 (1 nM or 5 nM) for either 5 or 25 min, as indicated. Effector peptide (10 μM) was then added to terminate signalling. After 48 or 72 h, cells were collected for counting and phenotypic analysis by flow cytometry.

For Fig. 5i,j, cells were stimulated with 1 nM ASNeo2. For Fig. 5k–n, cells were stimulated with 5 nM ASNeo2.

Flow-cytometry analyses of human T cells

For surface-marker staining, cells were collected into U-bottom 96-well plates (Thermo Fisher Scientific), blocked with Human TruStain FcX (BioLegend) and incubated with the indicated antibodies at 4 °C for 20 min, followed by live–dead staining by 4’,6-diamidino-2-phenylindole (DAPI; Thermo Fisher Scientific). Cells were then washed and resuspended with FACS buffer (PBS containing 0.2 % BSA; Sigma-Aldrich) for flow-cytometry analyses. For phospho-STAT staining, primary human T cells were rested in T cell medium lacking IL-2 for 24 h before signalling assays. Cells were plated in a 96-well round-bottom plate in 50 μl T cell medium. Cells were stimulated with 50 µl ASNeo2 for 5 min or 25 min, followed by the addition of effector peptide and incubation for another 20 min at 37 °C, and the reaction was terminated by fixation with 1.5% PFA for 15 min at room temperature with agitation. Cells were washed and permeabilized with ice-cold 100% methanol for 60 min on ice. Afterward, cells were washed with FACS buffer before staining with pSTAT5 antibodies for one hour at 4 °C in the dark. Cells were washed and resuspended in FACS buffer for flow-cytometry analyses. For caspase-3 staining, cells were first stained for surface markers and Zombie Violet Fixable Dye (BioLegend), followed by staining with a FITC Active Caspase-3 Apoptosis Kit (BD Biosciences) according to the manufacturer’s protocol. For transcription factor staining, cells were first stained for surface markers and Zombie Violet Fixable Dye, then fixed and permeabilized using a Foxp3/Transcription Factor Staining Buffer Set (eBioscience) as per the manufacturer’s instructions. Cells were subsequently incubated with the indicated antibodies for intracellular staining. Detection was performed using a CytoFlex (Beckman Coulter), and data were analysed with FlowJo (v.10.10.0).

Antibodies and reagents for flow cytometry of human T cells

The following antibodies or staining reagents were purchased from BioLegend: human CD3 (OKT3, 317324), human GATA3 (W19195B, 386906), human CD69 (FN50, 310932), human CD25 (BC96, 302611), human BCL2 (100, 658708), human Ki-67 (Ki-67, 350526), Human TruStain FcX (422302) and Zombie Violet Fixable Viability Kit (423114). The following antibodies or staining reagents were purchased from BD Biosciences: pSTAT5 (47/Stat5, 612599), and BD Pharmingen FITC Active Caspase-3 Apoptosis Kit (571606). DAPI was purchased from Thermo Fisher Scientific.

Antibodies were diluted 1:200 for surface markers (CD3, CD28, CD69 and CD25), 1:100 for intracellular proteins (GATA3, Ki-67 and BCL2) and 1:50 for pSTAT and caspase-3 staining.

Antibodies were validated by the manufacturers using flow cytometry.

qPCR

Human T cells on day 9 after activation were subjected to IL-2 starvation for 36 h. The unstimulated control cells were maintained in culture without IL-2. For transient IL-2 signalling, cells were stimulated with ASNeo2 (5 nM) for 25 min, followed by the addition of effector peptide (10 μM) to terminate signalling. For sustained signalling, ASNeo2 (5 nM) was continuously maintained in the culture medium. Total RNA was extracted using the Quick-RNA Miniprep Kit (Zymo Research) following the manufacturer’s instructions. cDNA was synthesized using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific) with random primers. qPCR was performed using TaqMan master mix (Thermo Fisher Scientific) on a StepOnePlus Real-Time PCR System. BCL2 expression was measured using the TaqMan BCL2 assay, and gene expression was normalized to GAPDH (Thermo Fisher Scientific). Relative expression levels were calculated using the ΔΔCt method.

RNA-seq sample preparation and data analysis

Human T cells on day 8 after activation were subjected to IL-2 starvation for 36 h. The unstimulated control cells were maintained in culture without IL-2. For transient IL-2 signalling, cells were stimulated with ASNeo2 (5 nM) for 25 min, followed by the addition of effector peptide (10 μM) to terminate signalling. For sustained signalling, ASNeo2 (5 nM) was continuously maintained in the culture medium. After six hours, cells were collected, and total RNA was extracted using the Quick-RNA Miniprep Kit (Zymo Research). RNA libraries were prepared using a poly(A) enrichment-based mRNA library preparation kit following the manufacturer’s instructions. Libraries were pooled and sequenced on the NovaSeq X Plus Series (PE150). Reads were aligned to the reference genome (GRCh38) using Rsubread, and gene expression was quantified with featureCounts. For analysing pathway enrichment, gene set co-regulation analysis (GESECA) was performed with hallmark gene sets from the Human Molecular Signatures Database (MSigDB). To reduce redundancy, we performed hierarchical clustering of hallmark gene sets with Jaccard’s distance to yield eight gene-set clusters with minimal gene overlaps. Only one gene set with the lowest false discovery rate (FDR)-adjusted P value per cluster is shown in Fig. 5. Differential expression analysis was performed using DESeq2, with FDR-adjusted P < 0.05 as a threshold for differential expression. Heat maps were created using normalized counts of genes identified as differentially upregulated or downregulated in both sustained versus unstimulated and transient versus unstimulated comparisons.

Statistics and reproducibility

The main-text SPR, circular dichroism, FP, FRET and luminescence experiments, and also a subset of the DEER and SEC experiments, were performed twice (SPR in Figs. 2d,h and 4b; FRET in Fig. 4c; and SEC in Supplementary Fig. 9a) or three times (SPR in Fig. 2a,e–g; FP in Fig. 4b and Supplementary Figs. 5c and 12; luminescence in Fig. 4d,e; SEC in Supplementary Fig. 6b; and DEER with AS1 in Extended Data Fig. 2) to ensure reproducibility and low variance, and one representative experiment was reported owing to low variance among replicates. Having established low variance for these experiments, some similar measurements reported in the Supplementary Information were performed once. For all microscopy, cell staining, qPCR and RNA-seq experiments, three or four independent biological replicates were performed unless otherwise noted in the figure legend. All attempted replications of all experiments were successful, with the exception that some batches of YT-1 cells were unresponsive to stimulation; the findings in Fig. 5h were not observed with those batches. Allocation was random. No sample size calculations were performed; rather the sample sizes were chosen based on experience and were sufficient for the important observed differences between groups to be strongly statistically significant.

Reporting summary

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

Data availability

All data generated during this study are included either in the main text or as Supplementary Information. Sequences for all designs are available in Supplementary Table 4. Atomic coordinates and structure factors for all crystal structures reported in this paper have been deposited in the Protein Data Bank (PDB; https://www.rcsb.org) with accession codes 9DCX, 9DCY, 9DCZ, 9DD0, 9DD1, 9DD2, 9DD3, 9DD4, 9DD5 and 9OLQ. PDB models and sequences for all designs and source data with analysis scripts have been deposited at Zenodo under https://doi.org/10.5281/zenodo.16749448 (ref. [97]). Single-molecule tracking data have been deposited at Zenodo: calibration beads, unstimulated samples, long-term measurements and labelled ligand experiments under https://doi.org/10.5281/zenodo.13957447 (ref. [98]), Neo2 and Neo2 + effector under https://doi.org/10.5281/zenodo.13957498 (ref. [99]) and ASNeo2 and ASNeo2 + effector, as well as smFRET data, under https://doi.org/10.5281/zenodo.13957540 (ref. [100]). The raw RNA-seq data have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession code PRJNA1302552. The SKEMPI database can be accessed at https://life.bsc.es/pid/skempi2/database/index. The reference genome GRCh38 (accession code GCF_000001405.26) can be accessed at https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/. Hallmark gene sets can be accessed from MSigDB at https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp.

Code availability

Code used to generate designs and analysis scripts used to generate all figures has been deposited at Zenodo (design code: https://doi.org/10.5281/zenodo.16749263 (ref. [101]); data analysis: https://doi.org/10.5281/zenodo.16749448 (ref. [97])).

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Acknowledgements

We thank P. J. Y. Leung, K. L. Shelley, A. Pillai, C. Demakis, M. Exposit, K. Thompson, C. Savvides, R. J. Ragotte, G. Ahn and M. Glögl for discussions and technical support; K. VanWormer and L. Goldschmidt for technical support; S. R. Gerben and A. Murray for protein production support; and X. Li, M. Lamb, Z. Taylor and V. Adebomi for LC–MS support. This work was supported by the Audacious Project at the Institute for Protein Design (A.J.B., A.K., J.D.L.C., E.B. and A.K.B.); by a gift from Microsoft (A.J.B.); by the Nordstrom Barrier Institute for Protein Design Directors Fund (M.H.A. and F.P.); by Bill and Melinda Gates Foundation OPP1156262 (A.K. and J.D.L.C.); by the Open Philanthropy Project Improving Protein Design Fund (E.B. and A.K.B.); by the National Institutes of Health (NIH) National Institute of Allergy and Infectious Disease grant R0AI160052 (A.K.B.); by CRI Irvington Postdoctoral Fellowship 315511 (Y.Z.); by National Cancer Institute K00 award 4K00CA274708 (M.O.); by National Science Foundation grant MCB 2119837 and NIH grant GM115805 (W.H.R. and D.M.Z.); by NIH grant GM151956 (S.S.); by NIH AI-51321 (K.C.G.); by the DFG grants PI 405/15 and SFB 1557 (C.P. and J.P.); and by the Howard Hughes Medical Institute (A.K.B., K.C.G. and D.B.). The EPR spectrometer used for the DEER experiments was in part supported by NIH grant S10OD021557. This research used resources (FMX/AMX) of the National Synchrotron Light Source II, a US Department of Energy (DoE) Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under contract DE-SC0012704. The Center for BioMolecular Structure (CBMS) is supported mainly by the NIH National Institute of General Medical Sciences (NIGMS) through a Center Core P30 Grant (P30GM133893), and by the DoE Office of Biological and Environmental Research (KP1607011). This work is based on research performed at the Northeastern Collaborative Access Team beamlines, which are funded by the NIGMS (P30 GM124165). The research used resources of the Advanced Photon Source, a US DoE Office of Science User Facility operated for the DoE Office of Science by Argonne National Laboratory under contract DE-AC02-06CH11357. The Berkeley Center for Structural Biology is supported by the NIH, NIGMS and the Howard Hughes Medical Institute. The Advanced Light Source is supported by the Director, Office of Science, Office of Basic Energy Sciences and US DoE (DE-AC02-05CH11231).

Author information

Author notes

1. Florian Praetorius

Present address: Institute of Science and Technology Austria, Klosterneuburg, Austria

Authors and Affiliations

1. Institute for Protein Design, University of Washington, Seattle, WA, USA

Adam J. Broerman, Mauriz A. Lichtenstein, Mohamad H. Abedi, Danny D. Sahtoe, Aza Allen, Alex Kang, Joshmyn De La Cruz, Evans Brackenbrough, Asim K. Bera, Florian Praetorius & David Baker

2. Department of Biochemistry, University of Washington, Seattle, WA, USA

Adam J. Broerman, Mohamad H. Abedi, Danny D. Sahtoe, Aza Allen, Alex Kang, Joshmyn De La Cruz, Evans Brackenbrough, Asim K. Bera, Florian Praetorius & David Baker

3. Department of Chemical Engineering, University of Washington, Seattle, WA, USA

Adam J. Broerman

4. Department of Biology/Chemistry, Osnabrück University, Osnabrück, Germany

Christoph Pollmann & Jacob Piehler

5. Center for Cellular Nanoanalytics, Osnabrück University, Osnabrück, Germany

Christoph Pollmann & Jacob Piehler

6. Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA

Yang Zhao, Masato Ogishi & K. Christopher Garcia

7. Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA

Yang Zhao, Masato Ogishi & K. Christopher Garcia

8. Institute for Chemistry and Biochemistry, Freie Universität Berlin, Berlin, Germany

Mauriz A. Lichtenstein

9. Department of Chemistry, University of Washington, Seattle, WA, USA

Mark D. Jackson, Maxx H. Tessmer & Stefan Stoll

10. Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA

Won Hee Ryu & Daniel M. Zuckerman

11. Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Banumathi Sankaran

12. Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

K. Christopher Garcia

13. Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA

David Baker

Contributions

A.J.B. developed the facilitated dissociation design concept. A.J.B. developed the computational design pipeline, designed the allosteric switches and characterized their binding kinetics with SPR, FP, FRET, luminescence and SEC experiments, with mentorship from F.P. C.P. performed and analysed single-molecule localization microscopy and NK cell signalling activity experiments. Y.Z. performed and analysed transient stimulation experiments on primary T cells. M.A.L. and A.J.B. designed and characterized the rapid sensors. M.D.J. and M.H.T. performed and analysed DEER experiments. W.H.R. and A.J.B. performed and analysed MD simulations. M.O. and A.J.B. analysed RNA-seq data. A.J.B. and M.H.A. performed initial signalling activity experiments. D.D.S. and A.A. performed site-saturation mutagenesis on LHD101. A.K., A.J.B., J.D.L.C., E.B., B.S. and A.K.B. determined and analysed crystal structures. D.B., J.P., F.P., K.C.G., S.S. and D.M.Z. supervised research. A.J.B. and D.B. wrote the manuscript. F.P., C.P., M.A.L., Y.Z., M.H.T. and W.H.R. contributed to the manuscript. F.P. and M.A.L. helped edit the manuscript. All authors read and commented on the manuscript.

Corresponding authors

Correspondence to Adam J. Broerman, Florian Praetorius or David Baker.

Ethics declarations

Competing interests

A.J.B., F.P., A.K.B. and D.B. are in the process of filing a provisional patent application that incorporates discoveries described in this article. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature thanks Hideki Aihara, Doug Barrick 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 Structural characterization of AS5 and structural frustration of AS1 state TH[X].

a, Crystal structure of AS5 alone (grey) overlaid with the design model of AS5 in state X (blue). Inset shows a detailed view of side chains in the partially open effector-binding cleft. b, Cocrystal structure of AS5 and peptide effector (grey) overlaid with the design model of the AS5–effector complex in state Y (AS5 in blue, effector in orange). Inset shows the same view of the side chains in the effector-binding cleft as in a. c, Design model of AS1 in state X (blue) aligned to the target (pink), showing a minor clash. d, Three cocrystal structures of AS1 (with intact cleft) and target with methylated lysines (grey) overlaid with the AF2 model of the target–AS1 complex in state X (AS1 in blue, target in pink), showing fluctuation in the target binding conformation.

Extended Data Fig. 2 DEER characterization of AS1 and AS114.

a, Raw DEER traces (black), foreground fits (colours), and background fits (grey) for AS1 and AS114 with all combinations of target and effector. Experiments on complexes included the target, effector, or both in excess over the host at concentrations higher than required to fully form the complex (Supplementary Fig. 9), and spin labels were placed far from the target and effector binding sites. Thus, changes in the DEER distance distributions with different combinations of target and effector should only reflect changes in host conformation. b, Distance distributions from experiment (colours) and simulated from the structural state represented by the cartoons (black). For AS1, the simulated and experimental distance distributions agree well, further validating that each state adopts its designed conformation. For AS114, the simulations consistently overestimate the experimental distribution by ~5 Å, but the shift in the distance distributions with the effector compared to those without validates the designed conformational change. c, Experimental distance distributions of all states, coloured corresponding to b. For both AS1 and AS114, the ternary complex distribution (green) aligns with the host–effector complex distribution (orange) and not with the host alone (blue) or target–host complex (pink) distributions, confirming that the ternary complex is primarily in state Y. b,c, Lines represent the distance distribution which best fits the time domain data; shaded regions represent 95% confidence intervals from bootstrapping (Methods).

Extended Data Fig. 3 MD analysis of the AS1 ternary complex.

a, The Cα RMSD of the MD trajectories from the crystal structure (grey) is lower than from the aligned clashing design models of target and host–effector complex (black), showing that the MD simulations strain away from the clashing state in a manner similar to the crystal structure. b, Cα RMSD of the switch (blue) and target (pink) from their position in the crystal structure when the entire structures are aligned. Compared to trajectories 1 and 2, trajectory 3 shows reduced target deformation and increased switch deformation, showing that these trajectories differ in where they localize strain to resolve the clash. c, Per-residue Cα RMSF of the host (blue), target (pink), and effector (orange) in the ternary complex computed from each trajectory. The clashing region of the target (highlighted in grey) shows considerable flexibility, according with this region being disordered in the crystal structure. d, Comparison of MD simulations to experimental data. Left, crystal conformation of the ternary complex (grey) aligned to representative conformations from each MD trajectory (red, yellow, and light blue). DEER spin-label positions are shown in green. In the crystal structure, the clashing region on the target is disordered. In the MD simulations, although flexible, this region remains mostly ordered, causing additional deformation compared to the crystal structure. Illustrating the differences in strain localization among trajectories shown in b, in the first two trajectories (red and yellow), the switch conformation aligns with the crystal structure and the target deforms more; in the third trajectory (light blue), the target conformation aligns with the crystal structure and the switch deforms instead. Right, experimentally measured DEER distance distribution of the ternary complex (green line representing the best fit to the time domain data and shaded region representing 95% confidence interval from bootstrapping (Methods)) and distance distributions simulated from the crystal structure (grey line) or MD trajectories (dashed lines, colours correspond to the conformations shown at left). The distance distribution simulated from the crystal structure aligns with the left peak in the experimental distance distribution, whereas the distance distributions simulated from the MD trajectories span the experimental distance distribution, suggesting that these trajectories more fully sample the space of ternary complex dynamics.

Extended Data Fig. 4 Nonuniform distribution of strain in the ternary complex.

a, Ratios of fold accelerations in the forward direction to those in the reverse direction, given by the equation: acceleration ratio = (koff,T:HE/koff,T:H) ÷ (koff,TH:E/koff,H:E). Acceleration ratios corresponding to designs with asymmetric distributions of strain are coloured orange, and to symmetric blue. b, Plot of forward vs reverse acceleration ratios, with linear fits for the symmetric and asymmetric groups. Note that on a log-log plot, the slope of a straight line passing through the origin becomes the y-intercept. c, For each design, design model of host–effector complex in state Y (blue and orange) aligned to the target (pink) to show the designed allosteric clash, and (grey) AF2 prediction of the target position relative to the switch in the ternary complex to show how the clash resolves through global strain. The target deforms downward in designs with an asymmetric distribution of strain, shearing the beta sheet, whereas it deforms outward in designs with a more symmetric distribution of strain, bending the beta sheet. d, Comparison of the AS1 binder–target interface (left) and switch–effector interface (right) (aligned at the host side) in the binary (colours) and ternary (grey) complex conformations, all from crystal structures, showing the binder–target interface deforms substantially more than the switch–effector interface in the ternary complex.

Extended Data Fig. 5 Construction and characterization of the chain reaction.

a, Design model of E2–target, comprising the target LHD101A (with mutations R43V and V69Q) fused to the effector peptide “E2” (cs201B) for hinge cs201. E2 is coloured green and LHD101A is coloured pink. b, Design models of E2–target (green/pink) bound to AS114 (blue) in state X showing no clash (left) and in state Y with the effector peptide (orange) showing a strong clash (right). c, Design models of the reporter hinge “H2” (cs201F with mutation E249L (sticks) which increases E2 on-rate and labelled with Alexa Fluors 555 and 647 at positions indicated by stars) in state X (left) and in state Y with E2–target (right). AS114 and H2 would overlap considerably if simultaneously bound to E2–target, so their binding should be mutually exclusive: AS114 should cage E2 until its release by the effector. d, Kinetics of E2–target and H2 association, measured by a change in FRET efficiency due to the conformational change in H2 upon binding. (Left) FRET time courses (normalized to the initial signal) with varying concentrations of E2–target and 5 nM H2; data (circles) fit with single exponentials (lines). (Right) apparent on-rate constants plotted against E2–target concentration (circles) and a linear fit. The value of the association rate constant (5e + 4 M−1s−1) is higher than the reported value (4.5e + 3 M−1s−1) for the original hinge cs201F with effector cs201B, suggesting that mutation E249L on H2 biases its conformational pre-equilibrium toward state Y to increase the apparent association rate. e, Additional data for the kinetically governed chain reaction shown in Fig. 4c. In the grey control time course, 500 nM AS114 was added to 20 nM H2, then 1 μM effector and 6 μM target was added at time 0, showing that none of these components bind to H2 to cause a change in FRET signal. In the other time courses, pre-incubated 500 nM AS114 and 250 nM E2–target was added to 20 nM H2, then buffer (blue), 1 μM effector (green), 6 μM target (pink), or both (orange) were added at time 0. A baseline drift (obtained from 500 nM AS114 after adding 20 nM H2 then at time 0 adding buffer) was subtracted from each time course, and time courses were normalized to the initial signal. The chain reaction proceeds faster when just excess target is added, probably due to blocking rebinding of E2–target to AS114 after transient dissociation[39], but this effect is insufficient to achieve full acceleration. The chain reaction also proceeds faster when just effector is added, but probably due to transient rebinding of E2–target to re-form the strained ternary complex, this also does not achieve full acceleration. Adding both effector (to accelerate E2–target dissociation from AS114) and excess target (to prevent E2–target rebinding to AS114) is required to fully accelerate the chain reaction. Note that if a single-chain effector is desired to fully accelerate the chain reaction, the effector and target could be flexibly fused into a single construct. Such a multivalent effector would be reminiscent of CITED2, whose multivalency enables rapid and unidirectional competition against HIF-1α (ref. [102]).

Extended Data Fig. 6 Construction and characterization of rapid sensors.

a, Structural model of the best SARS-CoV-2 sensor construct, comprising AScov (blue), the SmBiTgraft peptide with the effector (orange) and grafted SmBiT (green), LgBiT (grey or cyan), and flexible linkers (black). In this design, SmBiT is caged in a helical conformation when SmBiTgraft is bound to the switch and is free to reconstitute the luciferase when SmBiTgraft is released. To form SmBiTgraft, SmBiT was grafted onto the effector peptide such that most of its hydrophobic residues are buried within the switch–SmBiTgraft interface when in the bound helical conformation. Because the original effector peptide binds so strongly to the switch, it could accommodate replacing some of its interface residues with residues from SmBiT without reducing its affinity so much that it no longer effectively cages the SmBiT. b, SPR data showing sfGFP-SmBiTgraft binding to AS0 (blue and orange, association phase), slow subsequent dissociation in the absence of target (blue), and rapid subsequent dissociation upon addition of 10 μM target (orange) caused by rapid target binding to form a transient ternary complex, causing the spike at the beginning of the dissociation phase. c–f, Rapidly sensing the target through a facilitated dissociation mechanism (top), slowly sensing the effector limited by the slow base exchange rate of SmBiTgraft between binding AS0 and LgBiT (middle), and rapidly sensing the SARS-CoV-2 RBD with facilitated dissociation (bottom). c, Schematics showing the mechanism of sensing. d, Luminescence time courses (normalized to the initial signal) of 10 pM sensor construct then at time 0 adding varying concentrations of analyte (target, effector, or SARS-CoV-2 RBD); data (colours) fit (black) with single exponentials up to the maximum signal for time courses which showed appreciable signal increase. In some time courses, signal slowly decreases due to depletion of luciferase substrate. e, Luminescence signal fold change plotted against analyte concentration. f, Sensor response rate constant plotted against analyte concentration for time courses that showed appreciable signal increase.

Extended Data Fig. 7 Detailed functional characterization of ASNeo2.

a, Schematic depiction of the labelling strategy for single-molecule tracking experiments. b, Single-molecule trajectories of IL-2Rβ (red), γc (blue) and ASNeo2-induced heterodimers (magenta). c, Data from Fig. 5e as box plots to display datapoint variation including Neo2 (+/- effector) (green and orange, left side) and single-molecule tracking experiments with labelled ASNeo2 and γc (right side). Sample sizes and independent repeats are: unstimulated: 37 and 3; ASNeo2: 32 and 3; ASNeo2 + Effector: 33 and 3; Neo2: 44 and 4; Neo2 + Effector: 26 and 2; labelled ASNeo2: 21 and 2; labelled ASNeo2 + Effector: 18 and 2. d, Relative dimerization in relation to receptor cell surface density ratio indicates that high dimerization data variance is caused by differing IL-2Rβ to γc ratios at the plasma membrane. Even at high γc excess, effector-bound ASNeo2 shows no residual affinity for γc. e, Diffusion properties of IL-2Rβ and γc are reverted to the ground state after addition of effector. f, Immobile particles are increased upon stimulation, but not decreased after effector addition, potentially indicating receptors internalizing in membrane proximal endosomes. For e,f, the left box always corresponds to IL-2Rβ and the right one to γc. Sample sizes for d–f are as in c. g, Normalized localization density over time confirms minimal single-molecule bleaching in long-term single-molecule tracking experiments. Sample sizes and independent repeats are: without Effector: 5 and 5; with Effector: 3 and 3. h,i, Dissociation of ASNeo2-induced IL-2Rβ/γc dimers at the cell surface upon addition of 10 µM effector as detected by time-lapse single-molecule co-tracking (h) with colour-coded corresponding co-trajectories (i). j,k, Conformational change of ASNeo2 bound to the cell-surface receptor probed by smFRET. j, FRET efficiency histograms for ASNeo2 E4C/K211C labelled with Cy3B and ATTO643 bound to cells expressing IL-2Rβ and γc in the absence (blue) and presence (yellow) of 10 µM effector. Sample sizes are: without Effector: 7; with Effector: 5. k, smFRET co-localizations of one individual cell before the effector was added (left) and after it was added (right) colour-coded for FRET efficiency, highlighting the observation of individual molecules. Statistics for c,e,f were performed using two-sided two-sample Kolmogorov–Smirnov tests (ns, not significant, P values noted). Box plots show the distribution of the dataset, highlighting the median, quartiles, and outliers, with whiskers extending to the range limits. Scale bars in b,i: 5 µm.

Extended Data Fig. 8 Characterization of cyclic permutations of ASNeo2.

a, Design models of ASNeo2 and selected cyclic permutations in state X, rainbow-coloured from N terminus (blue) to C terminus (red) to illustrate the protein topology. In ASNeo2, the switch is at the N terminus and Neo2 is at the C terminus. In the cyclic permutations, although the relative position of the switch and Neo2 changes minimally, the switch is in the middle of the protein, part of Neo2 is at the N terminus, and the other part is at the C terminus. This way, the regulatory switch cannot degrade without also breaking Neo2. b, SEC purifications performed on a Superdex 200 Increase 10/300 GL column. The cyclic permutations are prone to aggregation during expression, but distinct monomer peaks can be picked out. c, Fast effector-concentration-dependent dissociation of γc from the ASNeo2–IL-2Rβ–γc complex upon addition of peptide effector. Data (grey) fit (colours) as described in methods (neglecting the accumulation modelling because accumulation on the SPR surface was negligible with these proteins). d, Rate constants of facilitated γc dissociation computed from the model fit by ln(2)÷{half-time of γc–host interaction} plotted against effector concentration (circles) and fit with hyperbolic equations (black lines).

Extended Data Fig. 9 Additional characterization of differential effects of transient ASNeo2 stimulation on T cell behaviour.

Human T cells were stimulated with 1 nM (a–g) or 5 nM (i) ASNeo2 for 5 min (a–h), 30 min (h), or 25 min (i) or left untreated as a control. Signalling was either sustained by continued ASNeo2 treatment or terminated by the addition of 10 μM effector. Cells were collected for counting and phenotypic analysis by flow cytometry after 72 h (b,c; n = 4) or 48 h (a,e–g; n = 4), for pSTAT signalling analysis by flow cytometry after 20 min (h, n = 3), or for RNA-seq analysis after 6 h (i, n = 3). a, Changes in viable T cell counts from 0 h to 48 h across each group. b,c, Representative flow-cytometry histogram of CFSE (b) and quantitative analysis of divided cells (c). d, Frequencies of live T cells. e, Mean fluorescence intensity (MFI) of Ki-67, CD25, GATA3, CD69, and BCL2. f,g, Representative flow-cytometry plots (f) and quantitative analysis of caspase-3+ cells (g; n = 4). h, Dose-dependent pSTAT5 curves. i, Transcripts per million (TPM) of CDK4 and POLD2 in the MYC_TARGETS_V1 gene set (left); TPM of DOCK2 and KIF1B in the MITOTIC_SPINDLE gene set (right). a,c–e,g, Statistics were obtained from ANOVA with two-sided Tukey’s post-test (ns, not significant (P > 0.05), *P = 0.04, **P = 0.001, ***P = 0.0002, ****P < 0.0001). Lines or bars represent means; error bars represent s.e.m. (a,h,i) or SD (c–e,g). n refers to biologically independent samples.

Extended Data Table 1 Crystallographic data collection and refinement statistics for structures AS1_H, AS1_HE and AS1_TH 1

Extended Data Table 2 Crystallographic data collection and refinement statistics for structures AS1_TH 2, AS1_THE 1 and AS1_THE 2

Extended Data Table 3 Crystallographic data collection and refinement statistics for structures AS5_H, AS5_HE, LHD101An1 and CS221B

Supplementary information

Supplementary Information

Supplementary Notes 1–5, providing additional description and analysis of the designs; Supplementary Figs. 1–17, depicting design and experimental strategies and additional data; and Supplementary Tables 1–4, listing fit parameters for kinetics and DEER data and providing sequences for all designs.

Reporting Summary

Peer Review File

Supplementary Video 1

Example of IL-2Rβγc facilitated dissociation on the cell surface. Microscopy recording showing single-molecule trajectories of fluorescently labelled IL-2Rβ (red) and γc (blue) on the plasma membrane of live cells. The cells had been incubated with 100 nM ASNeo2, causing co-localization of one IL-2Rβγc pair (circled in white) within this frame. Seven seconds into the recording, 10 μM effector was added, leading to γc dissociation four seconds later. Scale bar, 100 nm.

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Broerman, A.J., Pollmann, C., Zhao, Y. et al. Design of facilitated dissociation enables timing of cytokine signalling. Nature 647, 528–535 (2025). https://doi.org/10.1038/s41586-025-09549-z

- Received: 24 October 2024

- Accepted: 19 August 2025

- Published: 24 September 2025

- Version of record: 24 September 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09549-z

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Structural basis for mTORC1 activation on the lysosomal membrane

Zhicheng Cui,

Alessandra Esposito,

Gennaro Napolitano,

Andrea Ballabio &

…

James H. Hurley

Nature volume 647, pages 536–543 (2025)

Abstract

The mechanistic target of rapamycin complex 1 (mTORC1) integrates growth factor (GF) and nutrient signals to stimulate anabolic processes connected to cell growth and inhibit catabolic processes such as autophagy1,2. GF signalling through the tuberous sclerosis complex regulates the lysosomally localized small GTPase RAS homologue enriched in brain (RHEB)[3]. Direct binding of RHEB–GTP to the mTOR kinase subunit of mTORC1 allosterically activates the kinase by inducing a large-scale conformational change[4]. Here we reconstituted mTORC1 activation on membranes by RHEB, RAGs and Ragulator. Cryo-electron microscopy showed that RAPTOR and mTOR interact directly with the membrane. Full engagement of the membrane anchors is required for optimal alignment of the catalytic residues in the mTOR kinase active site. Converging signals from GFs and nutrients drive mTORC1 recruitment to and activation on lysosomal membrane in a four-step process, consisting of (1) RAG–Ragulator-driven recruitment to within ~100 Å of the lysosomal membrane; (2) RHEB-driven recruitment to within ~40 Å; (3) RAPTOR–membrane engagement and intermediate enzyme activation; and (4) mTOR–membrane engagement and full enzyme activation. RHEB and membrane engagement combined leads to full catalytic activation and structurally explains GF and nutrient signal integration at the lysosome.

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Main

RHEB is required for phosphorylation of the canonical substrates of mTORC1 (ref. [5]), including the ribosomal protein S6 kinase (S6K) and eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1) that mediate mTORC1 stimulation of protein synthesis[1], whereas it is dispensable for the phosphorylation of noncanonical substrates, such as transcription factor EB (TFEB)[6]. RHEB is present in cells at an estimated concentration[7] of 650 nM, yet >100 μM soluble RHEB–guanosine triphosphate (GTP) is needed for half-maximal mTORC1 activation in solution[4]. Thus, the discrepancy between physiological concentrations and the in vitro biochemistry of RHEB, the most fundamental activator of mTORC1, is greater than two orders of magnitude. RHEB is farnesylated8,9,10, which is essential for mTORC1 activation and is responsible for targeting RHEB to lysosomes. Despite the fact that the receptor–PI3K–AKT signalling pathway originates at the plasma membrane, RHEB signalling to mTORC1 occurs exclusively on the cytosolic face of lysosomes[3]. mTORC1 is recruited to lysosomes by the RAS-related GTP-binding (RAG) GTPases RAGA–RAGD under amino acid-replete conditions11,12,13. The RAGs function as heterodimers, with RAGA or RAGB paired with RAGC or RAGD. Active RAG dimers consisting of RAGA–GTP or RAGB–GTP and RAGC–guanosine diphosphate (GDP) or RAGD–GDP recruit mTORC1 to the lysosome by binding to its RAPTOR subunit14,15. The RAGs, in turn, are recruited to the lysosomal membrane by the pentameric Ragulator complex, specifically, by its myristoylated and palmitoylated LAMTOR1 subunit[16]. Amino acid-dependent mTORC1 recruitment to lysosomes by the RAGs and Ragulator brings mTORC1 in proximity to the lysosome-bound pool of RHEB–GTP. The dual dependence on lysosomal localization and RHEB engagement serves as a logical ‘AND’ gate for a GF signal and an ample pool of biosynthetic precursors before protein synthesis and cell growth. How this physiologically critical AND gate might be organized and implemented at the structural level is unknown.

We proposed that the lysosomal membrane itself might be the missing link that orchestrates the AND gate and compensates for the more-than-two orders of magnitude in concentration needed for RHEB–GTP activation of mTORC1. The feasibility of atomistic single-particle cryo-electron microscopy (cryo-EM) reconstructions of liposome-bound peripheral protein assemblies has recently been demonstrated17,18. This prompted us to reconstitute the concerted activation of mTORC1 on liposomes by RHEB and RAGA–RAGC–Ragulator and elucidate its structural basis.

mTORC1 activation on membranes

We used large unilamellar vesicles (LUVs), with a lipid composition of 72.8% DOPC, 7% POPS, 10% cholesterol, 5% DGS-nitrilotriacetic acid (NTA), 5% PE MCC and 0.2% DiR, as the membrane platform to investigate the role of RAG–Ragulator and RHEB in mTORC1 activation. The active RAGAGTP–RAGCGDP–Ragulator complex was recruited to LUVs containing the lipid DGS-NTA(Ni) by means of a 6×His tag fused to the N terminus of the LAMTOR1 subunit of Ragulator in place of the physiological myristoyl and palmitoyl modifications (Fig. 1a). RHEB was tethered to the LUVs by means of a thiol–maleimide reaction between the functionalized lipid PE MCC and its only cysteine residue, C181, which is the farnesylation site of endogenous RHEB (Fig. 1a). RHEB and RAGA–RAGC–Ragulator were used at essentially physiological concentrations of 250 and 300 nM, respectively. We monitored mTORC1 kinase activity as a function of LUVs, RHEB and RAG–Ragulator by detecting Thr37 and Thr46 phosphorylation of full-length 4E-BP1. mTORC1 kinase activity increased more than 35-fold in the presence of LUVs, RAG–Ragulator and RHEB–GTP (Fig. 1b) but not with RHEB–GDP. As expected, no activity was observed in the presence of the mTOR inhibitor Torin1 or in the absence of mTORC1. LUVs and RHEB–GTP increased mTORC1 activity by about threefold, whereas other combinations did not affect its activity. No increase in mTORC1 activity was observed when 250 nM RHEB–GTP was present but LUVs were absent, consistent with a previous report that >100 μM soluble RHEB–GTP is required for activation[4]. Therefore, the combination of liposomes and liposome-tethered RHEB and RAGA–RAGC–Ragulator synergistically and potently activates mTORC1 phosphorylation of 4E-BP1.

Fig. 1: In vitro reconstitution and mTORC1 kinase activity with liposomes.

a, Cartoon showing the in vitro membrane-containing reconstitution of mTORC1 kinase activity. N-terminal 6×His LAMTOR1 is tethered with DGS-NTA(Ni), and Cys181 of RHEB is lipidated by reaction with PE MCC. b, Determinants of mTORC1 activation. Western blot is shown for anti-phospho (p)-4E-BP1 (Thr37/Thr46). Activation fold change is computed relative to the activity of mTORC1 alone. Quantifications are indicated with bars from three experiments and are mean ± s.d. All samples were derived from the same experiment, and gels and blots were processed in parallel.

Source Data

mTORC1 structure on a membrane

We reconstituted mTORC1–RHEB–RAG–Ragulator with the full-length substrate 4E-BP1 complex on liposomes and acquired cryo-EM images (Fig. 2a). Two-dimensional (2D) class averages suggested that the complex was rigidly oriented with respect to the phospholipid bilayer (Fig. 2b). We determined the cryo-EM structure of the entire assembly to an overall resolution of 3.23 Å (Extended Data Fig. 1 and Table 1). The overall structure of mTORC1 and its interactions with RHEB and RAG–Ragulator are consistent with previous structures determined in the absence of membranes4,14,15,19,20, including the large conformational change induced by RHEB binding[4]. However, when superimposing the membrane-bound mTORC1–RHEB structure with the soluble mTORC1–RHEB structure on the basis of the mTOR subunit, we observed a conformational change in the RAPTOR subunit, with a rotation of about 7° towards the HEAT domain of mTOR (Extended Data Fig. 2a). This rotational movement of RAPTOR in membrane-bound mTORC1–RHEB aligns with the transition from apo-mTORC1 to soluble mTORC1–RHEB, suggesting that the membrane plays a part in further constricting mTORC1 when bound to RHEB. Local refinement of the mTOR–RHEB–MLST8 and RAPTOR–RAG–Ragulator subcomplexes yielded cryo-EM density maps at resolutions of 3.12 Å and 2.98 Å, respectively, allowing us to build atomically detailed models of the entire assembly (Fig. 2c,d). The high-resolution cryo-EM density of nucleotides confirmed the active states of RAG GTPases (Extended Data Fig. 2b,c). The density of inositol hexakisphosphate was observed, surrounded by the lysine–arginine cluster in the FAT domain of mTOR as previously reported in mTORC2 structures[21] and the TFEB-containing megacomplex of mTORC1 (ref. [20]) (Fig. 2e). Despite the inclusion of full-length 4E-BP1, only the TOR signalling motif was visualized (Fig. 2f), bound to the same location on the RAPTOR subunit as previously identified4,22.

Fig. 2: Cryo-EM structure of mTORC1–RHEB–RAG–Ragulator–4E-BP1 on the membrane.

a, Representative example chosen from 29,301 cryo-EM images showing protein-decorated liposomes. b, Representative 2D averages showing side and top views of the protein–membrane complex. c, A composite cryo-EM density map of mTORC1–RHEB–RAG–Ragulator–4E-BP1 on a membrane, assembled from two focused refinement maps (mTOR–RHEB–MLST8 and RAPTOR–RAG–Ragulator), overlaid with the unsharpened cryo-EM map (contour level, 0.033) from the overall refinement with C2 symmetry. The active sites of mTOR are labelled with arrows. The twofold axis is labelled as a black oval symbol in the top view. Different contour levels were used for optimal visualization using UCSF ChimeraX. d, Atomic model of mTORC1–RHEB–RAG–Ragulator–4E-BP1 is overlaid with the unsharpened cryo-EM map from the overall refinement with C2 symmetry. e, Close-up view of the density for inositol hexakisphosphate (IP6) (contour level, 0.156) and the surrounding lysine–arginine cluster. f, Close-up view of the density for the 4E-BP1 TOR signalling (TOS) motif (contour level, 0.097). Scale bars (a,b), 20 nm.

Table 1 Cryo-EM data collection, refinement and validation statistics

Fig. 3: Membrane-interacting sites of mTOR and RAPTOR subunits.

a, Cartoon representation of the mTORC1–RHEB–RAG–Ragulator–4E-BP1 complex on a membrane shown from the side view. The distance between two RAPTOR membrane-interacting sites is shown. Coloured stars indicate the membrane contact sites in the asymmetric unit. The lipidation sites of LAMTOR1 and RHEB are indicated at the end of arbitrary linkers that anchor them to membranes. b, Close-up view of RHEB density (contour level, 0.168) above the membrane (left) and overall RHEB position relative to the membrane (right). The last visible residue is shown, and the potential membrane-tethering loop is indicated with a dashed line. Close-up view of membrane-interacting sites of the RAPTOR (c) and mTOR (d) subunits. For better visualization, the unsharpened cryo-EM map from the overall refinement with C2 symmetry is used (contour level, 0.033). Residues of RAPTOR and mTOR are indicated with dots. e, Geometry of the residues involved in membrane interaction. f, In vitro mTORC1 kinase activity with different liposome sizes; quantifications were calculated with three repeats and are mean ± s.d. g, In vitro kinase activity of mTORC1 mutants; quantifications were calculated with three (M1 and M4) or four (wild type (WT), M2 and M3) repeats and are mean ± s.d. mTORC1 M1, RAPTORF1296E/M1297E; mTORC1 M2, mTOR468–476GS5 + RAPTORF1296E/M1297E; mTORC1 M3, mTORK471D/R472D/K474D + RAPTORF1296E/M1297E; mTORC1 M4, mTORK471D/R472D/K474D. h, Inducible RAPTOR knockout MEFs, treated with 0.5 μM 4-hydroxytamoxifen (4-OHT) for 48 h or left untreated, were transfected with an empty vector control or constructs expressing RAPTORWT, RAPTORF1296E or RAPTORF1296E/M1297E. After transfection (24 h), cells were starved of amino acids (a.a.) for 60 min or starved and restimulated with amino acids for 30 min and analysed by immunoblotting with the indicated antibodies. Quantifications of p-S6K/S6K and p-4E-BP1/GAPDH are shown with mean ± s.e.m. of n = 3 experiments (****P < 0.0001, two-way ANOVA, Dunnett’s multiple-comparison test). All samples derive from the same experiment, and gels and blots were processed in parallel.

Source Data

We noticed two three-dimensional (3D) classes with extra RAG–Ragulator densities bound to the MLST8 subunit of mTORC1. These were refined to overall resolutions of 3.47 Å and 3.81 Å for classes with the one and two extra copies of RAG–Ragulator, respectively (Extended Data Fig. 3). We pooled these classes together and carried out a focused refinement of the MLST8–RAG–Ragulator subcomplex, yielding a cryo-EM map with a resolution of 4 Å. The structure showed direct interactions between MLST8 and RAGA–GTP. Residues His49 and Arg51 of the interswitch region of RAGA maintained electrostatic interactions with an acidic patch on MLST8, involving residues Asp181, Asp213 and Glu300 (Extended Data Fig. 4a,b). The ordered interswitch region of RAGA in the GTP-bound state ensures that only RAGA–GTP can interact with MLST8 (Extended Data Fig. 4c). This previously unknown RAG–Ragulator site partially overlaps the mSIN subunit of mTORC2, consistent with the absence of interaction between RAG–Ragulator and mTORC2 (Extended Data Fig. 5a). This site is near the binding site of the mTORC1 inhibitory protein PRAS40 (ref. [4]) (Extended Data Fig. 5b).

mTOR–membrane and RAPTOR–membrane interactions

The cryo-EM reconstruction showed that mTOR and RAPTOR subunits directly interact with the membrane, in addition to the expected membrane attachment by lipidated LAMTOR1 and RHEB (Fig. 3a). In line with previous publications14,15,23,24,25, the N-terminal membrane-anchoring segment of LAMTOR1 was not visualized. The last ordered residue, Ala173 of RHEB, is in the middle of the hypervariable domain, indicating a partially folded hypervariable domain and a flexible eight-residue loop attached to the membrane through Cys181, which places RHEB about 13 Å above the membrane (Fig. 3b). The hydrophobic side chains Phe1296 and Met1297 in the WD40 domain of RAPTOR, which we denote as the FM finger, manifested membrane interactions in the cryo-EM density map (Fig. 3c). Specific residues in the N-HEAT domain of mTOR, namely Lys471, Arg472 and Lys474, form a basic loop that is also in close contact with the membrane (Fig. 3d). The entire active mTORC1 assembly showed a large membrane-covering footprint (Fig. 3e), owing to the membrane contact sites of the mTOR and RAPTOR subunits and the small membrane–RHEB gap (Fig. 3b).

The two RAPTOR–membrane contact sites in the dimeric structure are separated by 230 Å (Fig. 3e). To accommodate both contact sites simultaneously, the liposome diameter must be large enough to provide a relatively flat platform within the potential flexibility range of the complex. Accordingly, we reasoned that liposomes with smaller diameters would result in reduced mTORC1 activation. Liposomes with mean diameters of 67–81 nm showed an approximate 65% reduction in mTORC1 kinase activity compared with those with a mean diameter of 355 nm (Fig. 3f), bearing out the structural prediction. After reading a report that phosphatidylinositol 3-phosphate and phosphatidylinositol 4-phosphate have opposing effects on mTORC1 activity in cells[26], we tested these lipids in the in vitro kinase assay (Extended Data Fig. 6a) and found no difference in activity between these two phosphoinositides and another anionic lipid, phosphatidylserine. These data show that membrane curvature, but not phosphoinositide identity, directly modulates mTORC1 activity on the membrane surface.

To validate the biochemical role of the membrane-interacting residues of RAPTOR and mTOR, we mutated RAPTOR residues Phe1296 and Met1297 and mTOR residues Lys471, Arg472 and Lys474 to acidic residues to maximally disrupt membrane anchoring. We then purified mTORC1 with either the RAPTOR mutations, the mTOR mutations or a combination of both and tested them using an in vitro kinase assay. Mutations in mTORC1 containing RAPTORF1296E/M1297E alone, mTORK471D/R472D/K474D alone or the combination of both reduced mTORC1 kinase activity (Fig. 3g), consistent with the membrane-docked structure. To validate the functional role of the RAPTOR–membrane interaction, we tested the RAPTORF1296E and RAPTORF1296E/M1297E constructs in inducible RAPTOR knockout mouse embryonic fibroblasts (MEFs) (Fig. 3h). After treatment with 4-hydroxytamoxifen, we confirmed RAPTOR knockout. We observed a significant reduction in 4E-BP1 and S6K phosphorylation in both RAPTOR mutants compared with the wild type upon amino acid replenishment. As expected, given that these RAPTOR mutants are competent to bind RAGA, lysosomal mTOR localization was not affected by the membrane-anchoring mutations (Extended Data Fig. 6b). These results collectively show that the full activation, but not lysosomal localization, of mTORC1 requires interactions between the membrane and the mTOR and RAPTOR subunits.

RHEB activation of mTORC1 on the membrane

Using symmetry expansion and particle subtraction to remove RAPTOR–RAG–Ragulator, we performed 3D classification on the subpopulation that does not contain extra RAG–Ragulator copies and identified two distinct conformations of the mTOR–RHEB–MLST8 subcomplex. We designate these states as intermediate and fully active for reasons described below. The final resolutions of the intermediate and fully active states were 3.61 Å and 3.16 Å, respectively (Extended Data Fig. 1).

By aligning these states on the basis of the RHEB structure, we showed conformational changes between the intermediate and fully active states. The M-HEAT domain of the active state moved closer to the N-HEAT domain than it did with the intermediate state, with an average distance of about 5 Å (Fig. 4a and Extended Data Fig. 7a). The FAT and kinase domains of the fully active state moved about 5–7 Å towards RHEB compared with those in the intermediate state. As a result, the MLST8 subunit more than towards RHEB in the fully active state (Extended Data Fig. 7a). The soluble mTORC1–RHEB structure is in between the conformational pathway of the intermediate state and the fully active state when superimposed on the RHEB subunit (Extended Data Fig. 7b).

Fig. 4: Conformational flexibility of mTOR on the membrane.

a, The intermediate and fully active states of mTOR are superimposed by overlaying their bound RHEB molecules. Movement between intermediate and active states is indicated with arrows. b, Close-up view of the interaction between residues 1,255 and 1,260 of mTOR and switch I of RHEB in the intermediate and active states. The cryo-EM density corresponding to switch I is shown (contour levels of 0.139 and 0.137 for the intermediate state and the fully active state, respectively). c, Close-up view of interactions between the FAT and C-lobe domains of mTOR, with insets indicating residues in the intermediate and fully active states. The FAT (residues 1,255–1,453 are omitted) and kinase domains of the intermediate and active states are superimposed on the basis of the C-lobe (residues 2,200–2,400). d, Different states of mTOR structures are superimposed on the basis of the C-lobe (residues 2,200–2,400). The distance of the γ-phosphate of ATP between soluble mTORC1–RHEB and the fully active state is shown in red.

The mTOR–RHEB interaction is largely maintained in both intermediate and fully active states; however, the residues Met1255 and Lys1256 of the FAT domain of mTOR are disordered in the intermediate state but engaged with RHEB in the active state (Fig. 4b). In the mTOR intermediate state, Tyr35 in the switch I region of RHEB flips to the opposite side and is disengaged from the nucleotide, whereas, in the mTOR fully active state, the switch I region of RHEB adopts the canonical conformation of the RHEB–GTP-bound state. In this light, it is interesting that the mutation RHEBY35N is linked to cancer[27] and expression of RHEBY35N hyperactivates mTORC1 (refs. 28,29). Moreover, we observed an ordered loop in the fully active state, located in the opposite site of the adenosine triphosphate (ATP)-binding pocket, whereas the loop is disordered in the intermediate state and in the soluble mTORC1–RHEB structure (Extended Data Fig. 7c).

To reveal local conformational differences in the kinase domain and in the ATP-binding pocket, we superimposed the intermediate and fully active states of mTOR on the basis of the C-lobe of the kinase domain. The FAT domain of mTOR is more engaged with the C-lobe in the fully active state than in the intermediate state (Fig. 4c and Extended Data Fig. 8a). The established electrostatic interactions between residues Glu1581 and Gln1355 in the FAT domain and between residues Arg1585 of the FAT domain and Glu2311 of the C-lobe in the fully active state indicate direct communication between the FAT domain and the kinase domain to promote the active state (Fig. 4c). In the ATP-binding pocket, the intermediate state had a conformation similar to that of the apo state, despite the drastic movement of the M-HEAT domain upon RHEB binding (Fig. 4d). In the fully active state, the ATP molecule was positioned 1.5 Å closer to the catalytic residues in the C-lobe than in the soluble mTORC1–RHEB structure, aligning more closely with other atypical serine–threonine kinases in the phosphatidylinositol-3-kinase-related kinase (PIKK) family in their active states (Extended Data Fig. 8b).

To characterize the relationships between different mTORC1 states and membrane engagement, we performed 3D variability analysis (3DVA) using the same symmetry-expanded and RAPTOR–RAG–Ragulator-subtracted particles that were used for 3D classification (Extended Data Fig. 9a). We identified one component that did not involve protein stretching into the membrane. We then extracted particles that were at the two ends of this variability component for further local refinement (Extended Data Fig. 9b). The two cryo-EM maps resembled the intermediate and fully active states identified in the 3D classification, as the absence or presence of the corresponding loop (residues 904–920) was confirmed, respectively (Extended Data Fig. 9c). The fully active state from 3DVA showed clear density for the basic loop of Lys471, Arg472 and Lys474 and an extended helix, indicating stronger membrane engagement, whereas the basic loop and the helix were not observed in the intermediate state (Fig. 5). The RAPTOR–membrane interaction was present in both intermediate and fully active states (Extended Data Fig. 9d). This implies that direct RAPTOR–membrane interaction is a general requirement for mTORC1 membrane engagement, whereas mTOR–membrane interaction promotes full activation of the mTOR kinase.

Fig. 5: A model of mTORC1 recruitment and activation on the lysosomal membrane.

a, Average distances between protein and membrane are indicated with black arrows. Grey arrows indicate the most possible extended positions relative to the membrane surface. Linkers that anchor LAMTOR1 and RHEB to membranes are arbitrary. Numbers indicate each step in the process of mTORC1 activation on the lysosomal membrane. Double-headed arrows indicate movement between RAPTOR and mTOR upon binding with RHEB. Curves and stars on the membrane indicate RAPTOR and mTOR engagement with the membrane, respectively. b,c, Top, close-up view of the cryo-EM density (from 3DVA, contour levels of 0.049 and 0.042 for the intermediate state and the fully active state, respectively) of the membrane-interacting site of mTOR in the intermediate (b) and fully active (c) states. Bottom, cryo-EM density (from 3D classification, contour levels of 0.13 and 0.11 for the intermediate state and the fully active state, respectively) of the ATP-binding pocket of the intermediate (b) and active (c) states.

Discussion

Among the questions we set out to address was why RHEB–GTP, which is critical for GF-dependent activation of mTORC1 in cells3,5, is such a low-affinity activator in solution[4]. We found that physiological concentrations of lipidated RHEB–GTP, in the presence of liposomes, membrane-tethered Ragulator and an active RAG dimer, could potently activate mTORC1 in a biochemical reconstitution. This could be explained, at least in part, simply by the increased local concentration of RHEB–GTP in the vicinity of mTORC1. Reduction of dimensionality, which limits the search space accessible by diffusion to two dimensions[30], could be a contributing factor, but the limitations of this mechanism in biology have been noted[31]. We probed the mechanism more deeply using cryo-EM structure determination of the reconstituted system on the membrane. Although the addition of the membrane context greatly increases the realism of the system compared with previous membrane-free structural analyses, it is of course possible that yet further changes will be seen once it becomes feasible to reach atomistic resolution in situ and determine the structure as found on lysosomes in cells. We found that precise structural responses to the membrane context are also important.

By carrying out an atomically detailed analysis of intermediate and fully active conformations in the membrane context, we mapped an activation pathway mediated by the membrane itself. We found that both the mTOR and RAPTOR subunits of mTORC1 directly engage with the membrane. Membrane contacts by RAPTOR and mTOR residues separated by more than 230 Å promote large-scale conformational rearrangements of the N-HEAT, M-HEAT, FAT and kinase domains of mTOR. These large-scale changes in turn reorient the N-lobe and the C-lobe of the kinase domain to fine-tune the kinase active site in a catalytically optimal geometry. The contributions of membrane anchoring and membrane shape to catalytic activation were verified by reconstitution of membrane-binding site mutants of RAPTOR and mTOR and analysing the shape dependence of liposome activation. Lysosomes undergo tubulation and swelling in the course of their normal function and under stress32,33, which raises the possibility that membrane shape changes could influence mTORC1 activation.

mTOR is a member of the PIKK superfamily. Although the role of membrane anchors with large spatial separations in kinase activation is unique, similar local rearrangements of the N-lobes and C-lobes of other PIKK family members, ATM, MEC1ATR and DNA-PK, have been reported in response to reactive oxygen species-dependent activation[34], constitutive activating mutation (F2244L)[35] and DNA activation[36], respectively. We observed that the ATP molecule in the fully active state of membrane-bound mTORC1 aligns well with that of other activated PIKK family members, consistent with its identification as part of a more active conformation of mTORC1.

The observation in the membrane-bound structure of a second, previously unknown RAG–Ragulator binding site on MLST8 was unexpected. The MLST8 subunit is common to both mTORC1 and mTORC2, but the additional presence of SIN1 in mTORC2 blocks the previously unknown RAG–Ragulator site, consistent with the known lack of interaction of mTORC2 with RAGs. The previously unknown RAG–Ragulator site overlaps[4] with the binding site for a proline-rich AKT substrate of 40 kDa (PRAS40) on MLST8 in mTORC1. PRAS40 antagonizes GF-dependent mTORC1 activation37,38,39,40. MLST8 is dispensable for mTORC1 activity[41]. Therefore, it remains to be determined whether the previously unknown RAG–Ragulator site has a physiological activating role, whether by antagonizing PRAS40 inhibition or simply by augmenting lysosomal recruitment. Another open question concerns the role of membrane contacts in mTORC1 phosphorylation of TFEB and other noncanonical substrates, which requires RAG–Ragulator but not RHEB6,20,42.

mTORC1 is activated on lysosomes in response to GF signalling, despite the fact that only a small fraction of the key mediator of GF signalling, RHEB, is transiently localized on lysosomes43,44. Physiologically, this serves to AND gate the signal to proliferate with the availability of amino acids needed as building blocks for growth. Yet it has been unclear how signal integration is executed at the structural levels. The RAG GTPase dimer is tethered to the lysosome by the Ragulator complex. Ragulator is anchored by lipidation of its LAMTOR1 subunit, and the lipid anchor is connected to the folded core of Ragulator by a 45-residue N-terminal disordered region23,24,45,46,47, with an estimated end-to-end length of ~47 Å on average and an extended length[48] of ~100 Å (Extended Data Fig. 10a). The distance is potentially longer because of volume exclusion effects owing to its tethering to the membrane surface[49]. Augmented to the dimensions of the RAG–Ragulator complex leads us to estimate that the membrane-docking site of mTOR would be tethered within ~40 Å of the membrane surface upon RAG–Ragulator engagement. This is close enough to markedly increase the probability of encountering membrane-tethered RHEB, which itself is tethered at a mean distance of ~15 Å and an extended distance of ~40 Å from the membrane (Extended Data Fig. 10b). Even RHEB is still not close enough to drive full membrane engagement and activation on its own. Thus, lysosomal membrane engagement and enzyme activation is a four-step process, in which (1) initial localization to within an approximate vicinity of 10 nm of the membrane is driven by RAG–Ragulator. This allows (2) capture by RHEB at a distance of ~1.5–4 nm from the membrane. At this stage, the large conformational change characteristic of the RHEB-bound state[4] occurs. Close docking to the membrane is driven in the first instance (3) by the RAPTOR FM finger, which contacts the membrane in both the intermediate and fully active states. Finally, the mTOR–membrane-interacting site is only seen to dock in the fully active state (4), suggesting that this interaction is involved in finally reaching the highest level of activation (Fig. 5). One remaining challenge is relating these structural transitions in a precise way to changes in catalytic rates. Further kinetic investigation of this model using single-molecule Förster resonance energy transfer probes might further advance understanding of the relationship between enzyme kinetics on the one hand and movements at various size scales on the other hand. This model provides a satisfying structural explanation for how mTORC1 integrates GF and nutrient signals in the context of the lysosomal membrane.

Methods

Protein expression and purification

The full-length, codon-optimized genes encoding human RAGC with the S75N substitution and human RAGA with the Q66L substitution were synthesized (Twist Bioscience) and individually cloned into a pCAG vector. Mutations were introduced in the genes encoding RAGA (Q66L) and RAGC (S75N) to lock the active state of RAG GTPases (RAGAGTP–RAGCGDP). The RAGC (S75N) construct included sequence for a TEV-cleavable GST tag at the N terminus, whereas the RAGA (Q66L) construct was tagless. For expression and purification of RAG GTPases, HEK293F GnTI- cells were transfected with a total of 1 mg plasmid DNA (550 μg for RAGA and 450 μg for RAGC) and 4 mg polyethylenimine (PEI) (Sigma-Aldrich) per l at a density of 1.8 × 10^6 cells per ml. Cells were collected after 72 h and lysed by gentle nutation in wash buffer (50 mM HEPES, 150 mM NaCl, 2.5 mM MgCl_2, 1 mM TCEP, pH 7.4) supplemented with 0.4% CHAPS and Protease Inhibitor (Roche) for 1 h. The lysate was cleared by centrifugation at 35,000g for 35 min. The supernatant was incubated with glutathione Sepharose 4B (GE Healthcare) resin for 2 h. The resin was then washed first with a modified wash buffer (200 mM NaCl and 0.3% CHAPS) and then with wash buffer. The complex was eluted by on-column TEV cleavage overnight without nutation. Eluted complexes were concentrated and further purified by size exclusion chromatography (SEC) using a Superose 200 10/300 GL (GE Healthcare) column equilibrated with wash buffer.

The full-length, codon-optimized genes encoding the human Ragulator complex (LAMTOR1–LAMTOR5) were synthesized (Twist Bioscience) and individually cloned into a pCAG vector. The Lamtor1 construct includes sequence for a TEV-cleavable GST tag at the N terminus, followed by sequence for a His_6 tag, which replaces its first seven residues. The Lamtor2 construct features sequence for a TEV-cleavable tandem 2× Strep II-1× Flag tag (2SF-TEV) at the N terminus. A total of 1 mg plasmid DNA (200 μg each of the five constructs) and 4 mg PEI (Sigma-Aldrich) were used to transfect HEK293F GnTI- cells at a density of 1.8 × 10^6 cells per ml per l. The cells were pelleted after 72 h of transfection and lysed in wash buffer containing 1% Triton X-100 and protease inhibitor. The cleared supernatant after centrifugation was applied to glutathione Sepharose 4B (GE Healthcare) resin and incubated for 2 h. The complex was eluted by on-column TEV cleavage overnight without nutation and supplemented with a final concentration of 0.5 mM EDTA. Further purification was performed by SEC using a Superdex 200 10/300 GL column equilibrated with wash buffer. The fractions containing all five subunits were pooled and concentrated.

To assemble the RAG–Ragulator complex, an excess amount of RAG was incubated with Ragulator and Ni-NTA resin (Thermo Scientific) at 4 °C for 1 h. Excess RAG was removed by washing the resin with wash buffer containing 40 mM imidazole. The assembled RAG–Ragulator complex was eluted using wash buffer with 250 mM imidazole and further purified by SEC with a Superdex 200 10/300 GL column.

The human mTORC1 complex was purified in a manner similar to that of previous methods[20]. The full-length, codon-optimized genes encoding human mTOR, RAPTOR and MLST8 were cloned into pCAG vectors (mTOR with TEV-cleavable 2SF, RAPTOR with uncleavable 2SF and MLST8 with uncleavable 2SF). A total of 1.35 mg plasmid DNA (900 μg for mTOR, 250 μg for RAPTOR and 200 μg for MLST8) and 4 mg PEI (Sigma-Aldrich) were used to transfect HEK293F GnTI- cells at a density of 1.8 × 10^6 cells per ml per l. Cells were collected 72 h after transfection and lysed in the same buffer used for RAGA–RAGC purification. The complex was purified using Strep-Tactin resin (IBA Lifesciences) and eluted with wash buffer containing 10 mM d-desthiobiotin. The eluate was then diluted in an equal volume of salt-free buffer (50 mM HEPES, 1 mM TCEP, pH 7.4) and applied to a 1-ml HiTrap Q column (GE Healthcare). The mTORC1 complex and free RAPTOR were separated with a 20-ml salt gradient to a final concentration of 0.5 M NaCl, using salt-free buffer and high-salt buffer (50 mM HEPES, 1 M NaCl, 1 mM TCEP, pH 7.4). The flow rate was 0.2 ml min−1, and the fraction size was 0.2 ml. The fractions containing the mTORC1 complex and free RAPTOR were collected and concentrated with Amicon Ultra-4 concentrators. Mutations in the genes encoding mTOR and Raptor were generated using NEBuilder HiFi DNA Assembly. The mTORC1 complex mutants were produced by altering the combination of wild-type and mutated genes during transfection and purified as described above.

Plasmids containing full-length genes encoding human 4E-BP1 and RHEB were gifts from the Zoncu laboratory (University of California, Berkeley). These genes were individually cloned into a 2GT vector from QB3 MacroLab (https://qb3.berkeley.edu/facility/qb3-macrolab/), which features sequence for a TEV-cleavable tandem GST–His_6 tag at the N terminus. Genes encoding 4E-BP1 and RHEB were overexpressed in Escherichia coli Rosetta 2(DE3) strains and purified using the same method. E. coli cells were grown in LB medium at 37 °C until an OD of 0.6 was reached and then induced by adding 0.2 mM IPTG at 18 °C overnight. Cells were collected, resuspended in Ni buffer (50 mM Tris-Cl, pH 7.5, 300 mM NaCl, 20 mM imidazole, 5 mM 2-mercaptoethanol, 1 mM PMSF) and lysed by sonication. Protein was purified using HisPur Ni-NTA Resin (Thermo Scientific), washed with Ni buffer containing 40 mM imidazole and eluted with 250 mM imidazole. The GST–His_6 tag was cleaved by incubating with TEV enzyme overnight. Further purification was performed by SEC using a Superdex 75 10/300 GL column equilibrated with buffer (50 mM HEPES, pH 7.5, 150 mM NaCl, 0.5 mM TCEP). Fractions containing the desired proteins were passed through the Ni column to remove residual GST–His_6 and then concentrated.

To charge RHEB with GTPγS (Abcam) or GDP (Sigma-Aldrich), RHEB was first diluted in buffer (50 mM HEPES, pH 7.5, 150 mM NaCl, 1 mM TCEP, 5 mM EDTA) and then supplemented with GDP or GTPγS at a 30-fold molar excess. The mixture was incubated at 30 °C for 1 h, followed by adding 20 mM MgCl_2. Further purification was carried out by SEC using a Superose 75 10/300 GL (GE Healthcare) column to remove excess nucleotides.

All steps of protein purification were performed at 4 °C, and aliquoted proteins were flash frozen in liquid nitrogen and stored at −80 °C.

LUV preparation

A lipid mixture was prepared in a glass vial using the lipid composition shown in Supplementary Table 1. To form a thin film on the glass wall, the glass vial was slowly shaken on a vortex while drying under nitrogen gas. The glass vial was then placed in a vacuum oven overnight at room temperature to evaporate any remaining solvent. Lipids were hydrated in a lipid buffer (25 mM HEPES, pH 7.2, 100 mM NaCl) to a final concentration of 1.8 mM for 1 h, with intermittent vortexing during hydration. The solution was transferred to a 15-ml Eppendorf tube and subjected to nine freeze–thaw cycles using liquid nitrogen and a 40 °C water bath. The lipid mixture was either stored at −80 °C or immediately extruded using an Avanti Polar Lipids Mini Extruder (610023) at least 41 times through a 200-nm filter (Whatman Nuclepore Track-Etched Membranes, diameter of 19 mm) for mTORC1 kinase activity assays and cryo-EM studies. Filters of different diameters were also used to generate liposomes of various sizes to assess the effect of liposome size on mTORC1 kinase activity. The mean diameter of extruded liposomes was measured using dynamic light scattering (Zetasizer Ultra). The average sizes of liposomes for filters of 400 nm, 200 nm, 100 nm, 50 nm and 30 nm were about 355 nm, 148 nm, 110 nm, 81 nm and 67 nm, respectively. The lipid solution after extrusion was stored at 4 °C for up to 2 weeks.

mTORC1 kinase activity with LUVs

The kinase assay was conducted in a buffer containing 25 mM HEPES (pH 7.2), 100 mM NaCl, 10 mM imidazole, 10 mM MgCl_2 and 2 mM DTT at 30 °C for 10 min in a final volume of 50 μl. First, RHEB was incubated with liposomes at concentrations of 0.25 μM and 0.18 mM, respectively, at 4 °C overnight in 40 μl lipid buffer. In parallel, RHEB alone and liposomes alone were diluted to the same concentrations in lipid buffer and incubated at 4 °C overnight. After overnight incubation, the reactions were stopped with 2 mM DTT at room temperature for 30 min, followed by adding 10 mM MgCl_2 and 10 mM imidazole. The His_6-tagged RAG–Ragulator complex was then added to the reactions and incubated on ice for 30 min. Subsequently, 4E-BP1 and mTORC1 were added to the reactions at final concentrations of 10 μM and 5 nM, respectively. The reactions were initiated by adding ATP at a final concentration of 1 mM and incubated in a thermocycler (Bio-Rad, T100) at 30 °C for 10 min. The reactions were stopped by diluting tenfold into a urea denaturing buffer (50 mM Tris-Cl, pH 7.5, 150 mM NaCl, 8 M urea). All reactions were then diluted into 4× NuPAGE LDS sample buffer and boiled for 2 min. Proteins were resolved on a 4–12% NuPAGE Bis-Tris gel and transferred to PVDF membranes using the Trans-Blot Turbo Transfer System. Western blotting was performed using the anti-p-4E-BP1 antibody (Cell Signaling Technology, 2855, 1:10,000 dilution). For dot blot analysis, 2 μl of the denatured sample in urea buffer was directly applied to nitrocellulose membranes, and protein was detected with the same 4E-BP1 antibody. Unprocessed images are included in Supplementary Fig. 1.

Cryo-EM sample preparation and imaging

The mTORC1–RHEB–RAG–Ragulator–4E-BP1 complex on liposomes was reconstituted with the following steps. First, a mixture of RHEB and liposomes (200 nm) was incubated at 4 °C overnight in lipid buffer at concentrations of 8 μM and 1.8 mM, respectively. The RHEB–liposome mixture was supplemented with 2 mM DTT and 10 mM MgCl_2 and incubated at room temperature for 30 min. In parallel, mTORC1 was incubated with His_6–RAG–Ragulator in lipid buffer containing 5 mM TCEP at concentrations of 1 μM and 4 μM, respectively. Equal volumes of the RHEB–liposome mixture and the mTORC1–RAG–Ragulator mixture were then combined and incubated on ice for 2 h. Finally, 4 μM of 4E-BP1 and 1 mM of AMPPNP were added to the mixture for 10 min before application to cryo-EM grids for vitrification.

Cryo-EM samples were prepared by applying 3 μl of the aforementioned complex to a glow-discharged (PELCO easiGlow, 45 s in air at 15 mA and 0.37 mbar) holey carbon grid (C-flat, 2/1-3C-T) and vitrified using the FEI Vitrobot Mark IV System (Thermo Fisher Scientific). The samples were incubated on grids for 1 min and blotted for 3 s with a blot force of 15, using two Whatman 595 papers on the sample side and one Whatman 595 paper on the backside, at 6 °C with 95% relative humidity.

Cryo-EM images of the mTORC1–RHEB–RAG–Ragulator–4E-BP1 complex on liposomes were recorded using a Titan Krios G3 microscope (Thermo Fisher Scientific) equipped with a Gatan Quantum energy filter (slit width of 20 eV) and operated at 300 kV. Automated data acquisition was performed using SerialEM[50] on a K3 Summit direct detection camera (Gatan) in superresolution correlated double-sampling mode with a pixel size of 0.52 Å and a defocus range of −0.9 to −2.2 μm. A total of 36 exposures per stage shift were enabled by large beam shift. Beam intensity was adjusted to a dose rate of around 1 e− Å−2 per frame for a 30-frame movie stack with a total exposure time of 5.4 s. A total of 58,092 movies were recorded.

Cryo-EM data processing

The data-processing scheme for the mTORC1–RHEB–RAG–Ragulator–4E-BP1 complex on liposomes using cryoSPARC (v.4)[51] is shown in Extended Data Fig. 1, and statistics are summarized in Table 1. Owing to the uneven distribution of liposomes in grid squares, 29,301 micrographs containing liposomes were manually selected for processing. Owing to the size of the dataset, micrographs were split and processed following the same protocol and then combined at the homogeneous refinement stage. Initially, the Blob Picker was used to maximize the number of particles. Two-dimensional classification was only used to remove obvious junk particles (for example, ice and chaperonin contaminants). An initial model of mTORC1 from an ab initio reconstruction in a previous dataset and three bad initial models from ab initio reconstruction in this dataset were used for heterogeneous refinement. Iterative heterogeneous refinement was performed to select good particles, retaining rare views that may not have been identified in 2D classification. To minimize the possibility of membrane density biasing the alignment in the first iterations of refinement, the original initial models were used instead of reconstructions from each heterogeneous refinement. After extensive cleaning using heterogeneous refinement, particles were merged, and duplicates were removed with a cutoff distance of 50 Å. Additional heterogeneous refinement was used to further sort out particles. Homogeneous refinement was then performed for the full dataset. After identifying a good particle set, it was used to train Topaz particle picking[52]. Particles from Topaz picking underwent the same sorting procedure and were merged with the blob-picked particles, with duplicates removed using a cutoff distance of 50 Å.

In total, 337,347 particles containing protein complexes and membranes were selected, and reference-based motion correction was used to produce polished particles[53]. Symmetry expansion, particle subtraction and local refinement were used to produce an overall cryo-EM map of an asymmetric unit. Focused 3D classification was then used to separate particles without extra RAG–Ragulator copies and with either one or two extra copies of RAG–Ragulator. Further particle subtraction and local refinement were used to focus on either mTOR–RHEB–MLST8 or RAPTOR–RAG–Ragulator subcomplexes. Focused 3D classification was used to separate the intermediate and fully active states of mTOR. Populations containing extra copies of RAG–Ragulator were pooled together, and further particle subtraction and local refinement were used to obtain the cryo-EM map of MLST8–RAG–Ragulator.

In summary, 179,506 particles were refined to 3.23 Å with C2 symmetry for the mTORC1–RHEB–RAG–Ragulator–4E-BP1 complex without extra RAG–Ragulator copies on the membrane. Local refinement of mTOR–RHEB–MLST8 (359,012 particles after symmetry expansion) and RAPTOR–RAG–Ragulator (189,975 particles after symmetry expansion) yielded maps of 3.12 Å and 2.98 Å, respectively. Further classification of mTOR–RHEB–MLST8 identified the fully active and intermediate conformations. Final refinement of mTOR–RHEB–MLST8 in fully active (133,193 particles after symmetry expansion) and intermediate (109,105 particles after symmetry expansion) conformations resulted in resolutions of 3.16 Å and 3.61 Å, respectively. Final resolutions for the mTORC1–RHEB–RAG–Ragulator–4E-BP1 complex containing either one (128,189 particles) or two (29,652 particles) extra copies of RAG–Ragulator were 3.47 Å (C1 symmetry) and 3.81 Å (C2 symmetry), respectively. Three-dimensional variability analysis for populations without extra RAG–Ragulator copies was accomplished with cryoSPARC v.4 (Extended Data Fig. 9).

The overall resolution of all these reconstructed maps was assessed using the gold-standard criterion of Fourier shell correlation[54] at a cutoff of 0.143 (ref. [55]). cryoSPARC v.4 was used to estimate local resolution.

Atomic model building and refinement

To build the atomic model for the mTORC1–RHEB–RAG–Ragulator–4E-BP1 complex on the membrane, we first fit our previous models into the cryo-EM map as a rigid body using UCSF ChimeraX[56], with substituted models of mTOR and RAPTOR from AlphaFold2 prediction[57]. A composite map combining the local refinement maps was assembled in UCSF ChimeraX. Model refinement against local maps was accomplished using PHENIX for real-space refinement[58]. Manual model building was conducted with Coot[59] and ISOLDE[60] to iteratively inspect and improve local fitting. All figures were created using UCSF ChimeraX.

Cell culture

Inducible RAPTOR knockout MEFs were kindly provided by M.N. Hall (University of Basel). MEFs were cultured in DMEM High Glucose medium (ECM0728L, Euroclone) supplemented with 10% inactivated FBS (ECS0180L, Euroclone), 2 mM glutamine (ECB3000D, Euroclone), penicillin (100 IU ml−1) and streptomycin (100 μg ml−1) (ECB3001D, Euroclone) and maintained at 37 °C with 5% CO_2. All RAPTOR mutants used in these cellular assays were generated using the QuikChange II-E Site-Directed Mutagenesis Kit (200555, Agilent Technologies). Cells were transfected in 10-cm dishes using Lipofectamine 2000 Transfection Reagent (Invitrogen).

Cell treatment

For experiments involving amino acid starvation, cells were rinsed twice with PBS and incubated for 60 min in amino acid-free DMEM (MBS6120661) supplemented with 10% dialysed FBS. Serum was dialysed against 1× PBS through dialysis tubing (molecular weight cutoff of 3,500 Da) to ensure the absence of contaminating amino acids. For amino acid refeeding, cells were restimulated for 30 min with a 1× water-solubilized mix of essential (11130036, Thermo Fisher Scientific) and non-essential (11140035, Thermo Fisher Scientific) amino acids resuspended in amino-acid-free DMEM supplemented with 10% dialysed FBS plus glutamine.

Western blotting

Antibodies used in cellular studies include anti-p-p70 S6K (Thr389) (1A5) (mouse mAb, 9206, 1:1,000 for western blotting), anti-p70 S6K (rabbit, 9202, 1:1,000 for western blotting), anti-4E-BP1 (rabbit, 9644, 1:1,000 for western blotting), anti-p-4E-BP1 (Ser65) (rabbit, 9456, 1:1,000 for western blotting) and anti-RAPTOR (24C12) (rabbit, 2280, 1:1,000 for western blotting) from Cell Signaling Technology; anti-GAPDH (6C5) (rabbit, sc-32233, 1:15,000 for western blotting) from Santa Cruz; and anti-Flag M2 (mouse, F1804, 1:1,000 for western blotting) from Sigma-Aldrich. Cells were rinsed once with PBS and lysed in ice-cold lysis buffer (250 mM NaCl, 1% Triton, 25 mM HEPES, pH 7.4) supplemented with protease and phosphatase inhibitors. Total lysates were passed ten times through a 25-gauge needle with a syringe, kept at 4 °C for 10 min and then cleared by centrifugation in a microcentrifuge (14,000 rpm at 4 °C for 10 min). Protein concentration was measured with the Bradford assay. We performed densitometry analysis to calculate the intensity of phosphorylated and total protein using ImageJ software. The ratios between the values of phosphorylated and total protein were normalized to those of a control condition. Values in quantitative graphs are mean ± s.e.m. of at least three independent experiments. For statistical analysis, two-way ANOVA and Dunnett’s post hoc test were used to compare differences between groups that had been split into two factors. Unprocessed gel images are shown in Supplementary Fig. 1.

Reporting summary

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

Data availability

Structural coordinates were deposited in the PDB with accession codes PDB 9ED4 (mTORC1–RAG–Ragulator–4E-BP1), PDB 9ED6 (MLST8–RAG–Ragulator), PDB 9ED7 (fully active state of mTOR–RHEB) and PDB 9ED8 (intermediate state of mTOR–RHEB). Cryo-EM density maps were deposited in the Electron Microscopy Data Bank with accession numbers EMD-47932 (a composite map of mTORC1–RAG–Ragulator–4E-BP1 on the membrane), EMD-47933 (focused refinement of the MLST8–RAG–Ragulator subcomplex), EMD-47934 (mTORC1–RAG–Ragulator–4E-BP1 on the membrane with two extra RAG–Ragulator copies), EMD-47935 (mTORC1–RAG–Ragulator–4E-BP1 on the membrane with one extra RAG–Ragulator copy), EMD-47936 (mTORC1–RAG–Ragulator–4E-BP1 complex on the membrane with C2 symmetry), EMD-47937 (focused refinement of mTORC1–RAG–Ragulator–4E-BP1 on the membrane with mTOR–MLST8–RHEB mask), EMD-47938 (focused refinement of mTORC1–RAG–Ragulator–4E-BP1 on the membrane with Raptor–RAG–Ragulator mask), EMD-47939 (fully active state of mTOR on the membrane) and EMD-47940 (intermediate state of mTOR on the membrane). All other data are provided in the Article and its Supplementary Information. Materials are available by request from the corresponding author with no restrictions beyond those of the Uniform Biological Material Transfer Agreement. Source data are provided with this paper.

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Acknowledgements

We thank D. Toso and R. Thakkar for cryo-EM facility support and members of the Hurley laboratory for insightful discussions. This work was supported by Genentech as part of the Alliance for Therapies in Neuroscience and the National Cancer Institute (NIH, R01 CA285366) (J.H.H.); the Italian Telethon Foundation (to G.N. and A.B.); the Fondazione AIRC per la Ricerca sul Cancro (MFAG‐23538 to G.N. and IG‐29230 to A.B.); MIUR (PRIN 2022CRFNCP to G.N., PRIN 202032AZT3 to A.B. and PRIN P2022T4PKT to G.N. and A.B.); Wereld Kanker Onderzoek Fonds, as part of the World Cancer Research Fund International grant programme (IIG_FULL_2022_009 to G.N.); and the European Research Council (INCANTAR, grant 101097752 to A.B.).

Author information

Authors and Affiliations

1. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA

Zhicheng Cui & James H. Hurley

2. California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA, USA

Zhicheng Cui & James H. Hurley

3. Telethon Institute of Genetics and Medicine, Naples, Italy

Alessandra Esposito, Gennaro Napolitano & Andrea Ballabio

4. Genomics and Experimental Medicine program, Scuola Superiore Meridionale, School for Advanced Studies, Naples, Italy

Alessandra Esposito, Gennaro Napolitano & Andrea Ballabio

5. Medical Genetics Unit, Department of Medical and Translational Science, Federico II University, Naples, Italy

Gennaro Napolitano & Andrea Ballabio

6. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

Andrea Ballabio

7. Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, USA

Andrea Ballabio

8. Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA

James H. Hurley

Contributions

Z.C. and J.H.H. conceived and designed research, Z.C. and A.E. carried out research, G.N., A.B. and J.H.H. supervised research, Z.C. and J.H.H. wrote the first draft, and all authors edited the manuscript.

Corresponding author

Correspondence to James H. Hurley.

Ethics declarations

Competing interests

J.H.H. is a cofounder and shareholder of Casma Therapeutics and receives research funding from Hoffmann-La Roche. A.B. is a cofounder and shareholder of Casma Therapeutics and an advisory board member of Avilar Therapeutics and Amplify Therapeutics. G.N. is an advisory board member of Amplify Therapeutics. The other authors declare no competing interests.

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Nature thanks Roger Williams 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 Cryo-EM workflow of mTORC1-Rheb-Rag-Ragulator-4EBP1 on membrane.

Intermediate cryo-EM density maps of iterative heterogenous refinement are shown, particles belonging to the boxed maps are selected for the next round of heterogeneous refinement. The percentage of the particles in the good class in each iteration is indicated.

Extended Data Fig. 2 Comparison between mTORC1-Rheb on membrane and in solution and Representative cryo-EM density of the active sites of Rag GTPases.

a, Asymmetric units of apo mTORC1, mTORC1-Rheb in solution, and mTORC1-Rheb on membrane are aligned based on the mTOR subunit. The rotational direction of Raptor subunit between soluble and membrane-bound mTORC1-Rheb is indicated with an arrow. Cryo-EM density of RagA (b) and RagC (c) at contour level 0.25 and level 0.2, respectively.

Extended Data Fig. 3 Cryo-EM maps of mTORC1-Rheb-Rag-Ragulator-4EBP1 with extra copy of Rag-Ragulator.

a, Cryo-EM density map of mTORC1-Rheb-Rag-Ragulator-4EBP1 on membrane with one extra copy of Rag-Ragulator, overlaid with the unsharpened map from the overall refinement with C1 symmetry. b, Cryo-EM density map of mTORC1-Rheb-Rag-Ragulator-4EBP1 on membrane with one extra copy of Rag-Ragulator, overlaid with the unsharpened map from the overall refinement with C2 symmetry.

Extended Data Fig. 4 The interaction between mLST8 and RagA.

a, Cryo-EM density map (contour level 0.149) and model of mLST8-Rag-Ragulator subcomplex. b, Electrostatic surface potentials of mLST8 and RagA are shown. The interaction surfaces are outlined. c, Close-up view of the interaction between mLST8 and RagA. d, Overlay between GTP-bound and GDP-bound RagA (PDB: 6NZD), the missing interswitch region is indicated with arrow. The GDP-bound RagA has a disordered interswitch region, which is responsible for the interaction with mLST8. In addition, the α8 helix in the RagA-GDP has potential clash with mLST8.

Extended Data Fig. 5 Relative position of mSIN and PRAS40 to mLST8-bound RagA.

Structures are superimposed based on mLST8. a, A potential clash is observed between mSIN and RagA, indicating this interaction is not compatible with mTORC2. b, The mLST8-interacting fragment of PRAS40 is localized close to the mLST8-bound RagA.

Extended Data Fig. 6 Effects of PI3P and PI4P on mTORC1 activity and lysosomal localization of mTOR with Raptor membrane-interaction mutants.

a, In vitro kinase assay showing mTORC1 kinase activity with different composition of negatively charged lipids. The quantification is shown for 3 repeats. b, iRaptor KO MEFs, untreated or treated with 0.5 μM 4-hydroxy-tamoxifen (4-OHT) for 48 h, were transfected with either WT, F1296E, or F1296/M1297E Raptor. 24 h after transfection, cells were starved of amino acids for 60 min, then re-stimulated with amino acids for 30 min. The cells were analyzed by immunofluorescence and quantified for mTOR-LAMP1 colocalization using Manders’ colocalization coefficient. Results are mean ± s.e.m.; n = 3 (One-way ANOVA, Dunnett’s multiple comparisons test; ns: not significant). Scale bar, 10 μm.

Source Data

Extended Data Fig. 7 Structural comparison between the intermediate and fully active states of mTOR.

Structures are superimposed based on Rheb. a, A carton representation of the mTOR-Rheb-mLST8 subcomplex. The motion between intermediate and fully active states are indicated with lines that connect the backbone of both structures. The distance of the motion is labeled with a rainbow color. b, The soluble structure of mTORC1-Rheb is in between the conformational change of the intermediate and fully active states of mTORC1-Rheb on membrane. c, Only the fully active state contains the ordered loop in the backside of the ATP pocket. The ordered loop (904–920) is colored red.

Extended Data Fig. 8 Comparison of the FAT-Kinase domain of mTORC1 in different states and the ATP position relative to other PIKKs.

a, The FAT (residues 1255–1453 are omitted) and kinase domains of the apo, soluble, intermediate, and fully active states of mTOR are shown side by side. The constriction between the FAT and kinase domain is indicated by double arrow curved lines. b, A close-up view of the ATP binding pocket of mTOR, superimposed by ATM and Mec1 based on the C-lobe (residues 2200–2400). The substrate of ATM, p53, is indicated with a red arrow.

Extended Data Fig. 9 3D variability analysis.

a, 3DVA processing workflow. b, Particles corresponding to the frames at the two ends of the continuous motion were used for local refinement. Final refinement was done with FSC filtering at 6 Å. c, Close-up view of the ordered loop region in the opposite of ATP binding pocket. The ordered loop is colored red. d, Close-up view of the intermediate and fully active states with Raptor-Rag-Ragulator subcomplex. The membrane-interacting site of Raptor is indicated by red circle.

Extended Data Fig. 10 Predicted end-to-end distance for Lamtor1 and Rheb.

The predictions are made by the ALBATROSS-Colab (https://colab.research.google.com/github/holehouse-lab/ALBATROSS-colab/blob/main/example_notebooks/polymer_property_predictors.ipynb). The residues from Lamtor1 (a, C3-A46) and Rheb (b, A174-S180) are used as input. The mean distance from ALBATROSS is indicated. The mean distance and distribution by ν-SAW model are shown with dashed blue line and pink columns, respectively.

Supplementary information

Supplementary Information

Supplementary Table 1 shows the lipid composition for liposomes used in this study. Supplementary Fig. 1 shows the uncropped images. The cropped regions shown in Figs. 1b and 3f–h and Extended Data Fig. 6a are indicated with dashed lines. For the in vitro kinase assay, the same amount of substrates is used for the reaction.

Reporting Summary

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Source data

Source Data Fig. 1

Source Data Fig. 3

Source Data Extended Data Fig. 6

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Cui, Z., Esposito, A., Napolitano, G. et al. Structural basis for mTORC1 activation on the lysosomal membrane. Nature 647, 536–543 (2025). https://doi.org/10.1038/s41586-025-09545-3

- Received: 24 October 2024

- Accepted: 18 August 2025

- Published: 17 September 2025

- Version of record: 17 September 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09545-3

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Amendments & Corrections

- Retraction Note: sFRP2 in the aged microenvironment drives melanoma metastasis and therapy resistance

Retraction Note Published: 29 October 2025

Retraction Note: sFRP2 in the aged microenvironment drives melanoma metastasis and therapy resistance

Amanpreet Kaur,

Marie R. Webster,

Katie Marchbank,

Reeti Behera,

Abibatou Ndoye,

Curtis H. Kugel III,

Vanessa M. Dang,

Jessica Appleton,

Michael P. O’Connell,

Phil Cheng,

Alexander A. Valiga,

Rachel Morissette,

Nazli B. McDonnell,

Luigi Ferrucci,

Andrew V. Kossenkov,

Katrina Meeth,

Hsin-Yao Tang,

Xiangfan Yin,

William H. Wood III,

Elin Lehrmann,

Kevin G. Becker,

Keith T. Flaherty,

Dennie T. Frederick,

Jennifer A. Wargo,

Zachary A. Cooper,

Michael T. Tetzlaff,

Courtney Hudgens,

Katherine M. Aird,

Rugang Zhang,

Xiaowei Xu,

Qin Liu,

Edmund Bartlett,

Giorgos Karakousis,

Zeynep Eroglu,

Roger S. Lo,

Matthew Chan,

Alexander M. Menzies,

Georgina V. Long,

Douglas B. Johnson,

Jeffrey Sosman,

Bastian Schilling,

Dirk Schadendorf,

David W. Speicher,

Marcus Bosenberg,

Antoni Ribas &

…

Ashani T. Weeraratna

Nature volume 647, page 544 (2025)

The Original Article was published on 04 April 2016

Retraction to: Nature https://doi.org/10.1038/nature17392 Published online 4 April 2016

The Editors are retracting this article. After being alerted of several issues with the data presented, an editorial investigation revealed that some results in Extended Data Fig. 9e are no longer statistically significant, which affects the conclusions about therapy resistance as presented in the published study.

In addition, several errors in image and source data consistency as well as stated sample numbers were identified in Figs. 1, 3a, 5, Extended Data Figs. 4b, 5c and 9c.

Luigi Ferrucci, Hsin-Yao Tang, Rugang Zhang and Roger S. Lo, agree with this retraction. Amanpreet Kaur, Marie R. Webster, Katie Marchbank, Reeti Behera, Abibatou Ndoye, Curtis H. Kugel III, Vanessa M. Dang, Michael P. O’Connell, Phil Cheng, Rachel Morissette, Andrew V. Kossenkov, William H. Wood III, Elin Lehrmann, Kevin G. Becker, Keith T. Flaherty, Jennifer A. Wargo, Katherine M. Aird, Xiaowei Xu, Qin Liu, Giorgos Karakousis, Zeynep Eroglu, Alexander M. Menzies, Georgina V. Long, Douglas B. Johnson, Dirk Schadendorf, Marcus Bosenberg, Antoni Ribas and Ashani T. Weeraratna do not agree with this retraction. Nature was not able to get a response from Jessica Appleton, Alexander A. Valiga, Nazli B. McDonnell, Xiangfan Yin, Dennie T. Frederick, Zachary A. Cooper, Michael T. Tetzlaff, Courtney Hudgens, Edmund Bartlett, Jeffrey Sosman, Bastian Schilling, Katrina Meeth, Matthew Chan and David W. Speicher.

Author information

Authors and Affiliations

1. The Wistar Institute, Philadelphia, Pennsylvania, USA

Amanpreet Kaur, Marie R. Webster, Katie Marchbank, Reeti Behera, Abibatou Ndoye, Curtis H. Kugel III, Vanessa M. Dang, Jessica Appleton, Michael P. O’Connell, Alexander A. Valiga, Andrew V. Kossenkov, Hsin-Yao Tang, Xiangfan Yin, Katherine M. Aird, Rugang Zhang, Qin Liu & David W. Speicher

2. University of the Sciences, Philadelphia, Pennsylvania, USA

Amanpreet Kaur

3. Department of Dermatology, University of Zurich, Zurich, Switzerland

Phil Cheng

4. The National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA

Rachel Morissette, Nazli B. McDonnell, Luigi Ferrucci, William H. Wood III, Elin Lehrmann & Kevin G. Becker

5. Department of Dermatology and Pathology, Yale University, New Haven, Connecticut, USA

Katrina Meeth & Marcus Bosenberg

6. Massachusetts General Hospital Cancer Center, Developmental Therapeutics, Boston, Massachusetts, USA

Keith T. Flaherty & Dennie T. Frederick

7. Department of Surgical Oncology, MD Anderson Cancer Center, Houston, Texas, USA

Jennifer A. Wargo, Zachary A. Cooper, Michael T. Tetzlaff & Courtney Hudgens

8. Departments of Surgery and Pathology, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA

Xiaowei Xu, Edmund Bartlett & Giorgos Karakousis

9. Department of Medical Oncology, City of Hope Medical Center, Duarte, California, USA

Zeynep Eroglu

10. Department of Medicine, Division of Hematology-Oncology, University of California Los Angeles, Los Angeles, California, USA

Roger S. Lo & Antoni Ribas

11. Crown Princess Mary Cancer Centre, Westmead Hospital, Westmead, Australia

Matthew Chan

12. Melanoma Institute Australia and The University of Sydney, Sydney, Australia

Alexander M. Menzies & Georgina V. Long

13. Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA

Douglas B. Johnson & Jeffrey Sosman

14. Department of Dermatology, University Hospital, West German Cancer Center, University Duesburg-Essen, Essen, Germany

Bastian Schilling & Dirk Schadendorf

15. German Cancer Consortium (DKTK), Heidelberg, Germany

Bastian Schilling & Dirk Schadendorf

16. Department of Biochemistry and Molecular Biology, Johns Hopkins School of Medicine, Baltimore, MD, USA

Ashani T. Weeraratna

Corresponding author

Correspondence to Ashani T. Weeraratna.

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Kaur, A., Webster, M.R., Marchbank, K. et al. Retraction Note: sFRP2 in the aged microenvironment drives melanoma metastasis and therapy resistance. Nature 647, 544 (2025). https://doi.org/10.1038/s41586-025-09719-z

- Published: 29 October 2025

- Version of record: 29 October 2025

- Issue date: 13 November 2025

- DOI: https://doi.org/10.1038/s41586-025-09719-z

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