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This Week

- African countries must take control of health policy

- Support people and their livelihoods rather than fossil-fuel industries

- How to rescue the aid industry: focus on conflict prevention, not just relief

EDITORIAL 11 February 2026

African countries must take control of health policy

Massive cuts to global health-care funding have had a huge impact on the continent, but a more resilient system can be built from within.

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Universal health care must be a priority for African nations.Credit: Hajarah Nalwadda/Getty

One Plan, One Budget, One Report.

There is little doubt that this is what African countries need if they are serious about universal health coverage — ensuring that every member of their populations has access to this fundamental human right. But such an approach has never been implemented in Africa. Some of the reasons for this are outlined in a report on health financing by the Africa Centres for Disease Control and Prevention (Africa CDC), the continent’s public-health agency based in Addis Ababa, published last week (see go.nature.com/3o9wxfc).

But if ever there was a time to put the idea into practice, this is it. Africa faces a seismic challenge: finding a way to protect public health when financial assistance for health care from Europe and the United States has halved. In 2021, it amounted to US$26 billion; last year, the figure was $13 billion. What might happen going forwards remains unclear. Initial estimates suggest that the cuts will increase the death toll from preventable diseases such as malaria, HIV/AIDS and tuberculosis by millions (D. M. Cavalcanti et al. Lancet 406, 283–294; 2025).

The one plan, one budget, one report (OPBR) approach is not new. In this instance, it encapsulates the idea that African countries must be able to control their nations’ health-care policies. It means forging a single plan, to be owned and funded by the nations themselves. It means building capacity from the ground up, with more emphasis on nations’ needs, less on the (often competing) priorities of donors, and with centralized accountability for delivery. Considering the burden of preventable infectious diseases in many parts of Africa, a policy for immunization is a top priority (see go.nature.com/4rmcirr).

The call must now be heeded, by the continent’s leaders and by all those in and outside Africa who work in or support health and science on the continent. They face a daunting task. It will require a step-change in public spending on health care, and ways must be found to fund the sector that do not involve burdening households with greater taxation. A wholly new approach is also needed for relationships with donors, replacing what Africa CDC describes as “asymmetric power”.

The scale of the challenge is evident in the numbers: just 35% of African health-care expenditure is funded by the governments themselves. Almost one-quarter of funding comes from donor countries elsewhere in the world. Most of the remainder is described as “out-of-pocket spending”. This is health-policy jargon to describe personal spending on health, which often means individuals having to sell assets, go without essentials or take on debt just to be able to see a doctor or access treatment.

Last August, African leaders met in Accra and pledged to increase health-care spending. In April, they will meet on the sidelines of the World Health Summit in Nairobi to discuss how to achieve this in concrete terms.

African countries are increasing national health-care budgets, but the rate of change so far will do little to compensate for what has been, and is being, lost. A notable concern is that the cost of covering the required increase should not fall disproportionately on the poorest people. As the Africa CDC report shows, health-care spending can be increased in innovative ways, without unfairly burdening vulnerable groups or requiring an overhaul of the machinery of government.

The World Health Organization (WHO), for example, last year launched a campaign to raise $1 trillion over ten years, which it says can come from raising taxes on alcohol, tobacco and sugary drinks — known as sin taxes. Such taxes “cut the consumption of harmful products and create revenue governments can reinvest in health care, education, and social protection”, Jeremy Farrar, an assistant director-general for the WHO, said at the launch.

Africa CDC is also right to highlight the necessity of developing a new kind of partnership with international donors. African countries struggle to assert ownership over public-health policy because of the need to satisfy the individual requirements of the many donors involved in health care on the continent, each demanding compliance with the wishes of a government or philanthropist. It is also common for a substantial amount of aid funding to be spent in the donor’s own country. According to the US Agency for International Development, in 2024 just 12% of its funding was given directly to organizations in recipient countries (see go.nature.com/4a9viw3). And only around 35% of activities were classified as being fully “locally led”.

The US government also insists that aid spending aligns with the priorities of its administration, regardless of the impact on countries receiving that aid. In Kenya, for example, the United States has offered aid in exchange for access to the country’s health data, an arrangement that Kenya’s courts suspended in December. And in Guinea-Bissau, it is funding a controversial clinical trial into a hepatitis B vaccine that has been suspended pending an ethics review.

If the OPBR is to succeed, African countries and their supporters must continue to push back when donor policies risk causing harm. Global solidarity will also be essential. Almost a quarter of a century ago, African countries established a development aid organization in partnership with high-income countries. This was called NEPAD, an acronym for New Partnership for Africa’s Development. It was supposed to be a model for a new way of working together, development that would be “African owned”. A genuine partnership of equals did not materialize, but it is not too late to forge one, or for Africa to build a system to provide accessible health care for all.

Nature 650, 273 (2026)

doi: https://doi.org/10.1038/d41586-026-00381-7

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EDITORIAL 11 February 2026

Support people and their livelihoods rather than fossil-fuel industries

Geopolitical trends might be heading in the wrong direction, but economic forces are aligning around a future economy centred on clean electricity.

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People who earn a living in the fossil-fuel industry (such as on this pipe-laying vessel in China) will need support during the energy transition.Credit: Zhang Wujun/VCG/Getty

In the mid-1990s, when world leaders began to develop the first plans to reduce emissions of greenhouse gases, the path forwards was hardly clear. A lot of energy had gone into negotiating the Kyoto Protocol at the third climate conference (COP3) in Japan, resulting in the world’s first legally binding agreement to cut climate-warming gases. Much less time was given to discussing precisely how high-income countries could achieve the agreed emissions reductions by 2012 — requiring an average cut of 5.2% from nations’ 1990 levels.

You could argue it is the opposite today. Almost three decades later, it seems that countries lack the will or the foresight of previous generations to agree on the cuts necessary to avoid dangerous climate change. And yet, despite the political headwinds, the long-hoped-for energy transition is now well under way, as we report in a News feature (see go.nature.com/4quit62). Prices of renewable technologies are plummeting and technological breakthroughs in battery storage have ushered in a clean-energy revolution.

That said, the current speed at which countries are changing from fossil fuels to renewables is not yet sufficient to rein in dangerous climate change. Average temperatures are still projected to rise to nearly 3 °C above pre-industrial levels by 2100. Such warming will result in further harmful heat waves, extreme rainfall and droughts, melting of ice sheets, sea ice and glaciers, heating of the ocean and rising sea levels, says the World Meteorological Organization (see go.nature.com/45yqkzh). Such effects might be tempered if governments could take just one action: divert fossil-fuel subsidies to more-deserving causes.

Governments worldwide are paying at least US$1 trillion annually in subsidies for fossil fuels, according to an article in Our World in Data (see go.nature.com/4a43xys). However, the International Monetary Fund thinks the figure is closer to $7 trillion. Such funds are explicitly designed to keep fossil-fuel prices low. By contrast, the G20 group of the world’s richest economies paid out only $168 billion in subsidies for renewable power, finds a 2024 estimate by the International Institute for Sustainable Development (see go.nature.com/46tip6k).

To give some indication of the scale of the transition already achieved: last year, the total amount of wind and solar power generated exceeded that produced by coal, which was, for roughly a century, the world’s main source of electricity. Moreover, whereas production of coal-derived power is projected to remain constant at around 11,000 terawatt-hours annually, the amount of solar and wind power produced will keep increasing and is estimated to reach 12,000 terawatt-hours this year.

The costs of these renewable technologies have fallen to the point that constructing and running conventional fossil-fuel power plants is uncompetitive by comparison. The United States is one of several countries where it can be cheaper to install clean-energy technologies than it is to continue purchasing coal to keep existing power plants running. And yet, even there, subsidies for fossil fuels (nearly $40 billion last year) are showing no signs of abating.

These subsidies might be one reason why solar and wind sources still make up a relatively small fraction of the total energy consumed by households and industries (see ‘Sobering statistics’). Energy consumed is a measure distinct from just power production. Some 80% of all energy consumed comes from coal, oil and natural gas and their consumption continues to increase. It is this dependence on fossil-fuel-derived energy that is boosting greenhouse-gas emissions — at a time when such emissions need to be coming down.

Source: IEA World Energy Outlook 2024

As we have argued previously, the climate transition must be a just one (see Nature 629, 8; 2024). It has to take into account the needs of less-industrialized economies and those of people everywhere who depend on fossil-fuel industries to support their families. Subsidies cannot be phased out instantly, but they can be used instead to protect the people whose lives and livelihoods will be affected by the clean-energy transition. That is a much better application of such investments.

Renewables’ falling costs mean that there are fewer reasons why countries cannot make faster progress on limiting climate change by rolling out existing clean-energy technologies more quickly. And diverting the subsidies creates an opportunity: the money that currently goes to coal, gas and oil producers can instead be used to support all those who stand to be negatively affected by the energy transition.

Nature 650, 274 (2026)

doi: https://doi.org/10.1038/d41586-026-00382-6

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WORLD VIEW 10 February 2026

How to rescue the aid industry: focus on conflict prevention, not just relief

In an era of escalating conflict, a development-only approach to aid will yield fleeting results.

By

Rabah Arezki

Displaced Palestinians gather to receive food at a charity kitchen in Gaza in December 2025.Credit: Eyad Baba/AFP via Getty

The collision of two trends should concern us all more: the world has been experiencing both a record number of active conflicts — 61 in 36 countries in 2024 — since the Second World War, and a decline in international aid.

In 2025, the administration of US President Donald Trump ordered the US Agency for International Development to be closed; this year, it withdrew the country from 66 international organizations. Other Western nations that are plagued with high levels of debt and pressure to prioritize domestic challenges have slashed their foreign aid, too. According to projections, official development assistance dropped by 9–17% in 2025, amounting to some US$55 billion, after a 9% decrease in 2024. Multilateral institutions, such as the United Nations and World Bank, have also embarked on drastic restructurings with lay-offs.

Meanwhile, conflicts are intensifying amid rising geopolitical tensions, in places as diverse as Gaza, Iran, Haiti, Myanmar, Sudan and Ukraine. The UN reports that 305 million people in conflict zones need urgent humanitarian aid; more than 123 million have been forcibly displaced, facing malnutrition and lack of shelter. The cost of rebuilding cities in Gaza and the West Bank has been put at $53 billion; in Syria, that number is at least $250 billion.

The unravelling of the aid industry must force a reset of the nexus between peace and economic development. The international-development model has changed little in eight decades. In 1949, at his inaugural address as US president, Harry Truman introduced a linear concept of development — in which countries progress from ‘under-developed’ to ‘developed’ — and recognized that poverty was a “threat” to both less- and more-prosperous areas. Since then, the proportion of the world’s population in extreme poverty has plummeted, from 50–60% to about 10%. Yet, conflicts have surged. Clearly the relationship between economic development and conflict is a complicated one, which is being explored in empirical research.

My own studies point to an asymmetry: it takes at least a decade for a society to rebuild after a conflict, whereas a burst of economic development (including that through aid) barely affects conflict intensity. Quantitatively, the half-life — or how long it takes the cumulative effect of a shock to decay by half — of the adverse effects of conflicts on development goals is around eight years. By contrast, shocks to development performance — be they improvements or deteriorations — exhibit only transient effects on conflict, with a half-life of around two years.

This finding challenges the premise that peace is a byproduct of economic development and carries sobering implications for the global aid industry.

In short, the aid industry should shift to a ‘peace first’ approach, with conflict prevention being its main concern. Considering the huge costs of war and long set backs to development, preventing conflicts from breaking out holds immense value. Peace-building requires investment in scientific approaches for assessing the risk of conflicts before they happen. Diplomacy, mediation and preventive security have higher long-term returns than previously recognized.

From that perspective, it is paradoxical that the UN, whose mission is to maintain peace and security, is under attack. The aid industry and other multilateral and development institutions should speak with one voice about the devastating effects of conflicts and show solidarity and commitment to peace building efforts worldwide.

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Nature 650, 275 (2026)

doi: https://doi.org/10.1038/d41586-026-00384-4

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Competing Interests

The author declares no competing interests.

News in Focus

- Is UK science in jeopardy? Huge funding reforms spark chaos and anxiety

- NIH rolls back red tape on some experiments — spurring excitement and concern

- Guinea-Bissau suspends US-funded vaccine trial as African scientists question its motives

- First ‘practical PhDs’ awarded in China — for products rather than papers

- More than one-third of cancer cases are preventable, massive study finds

- The ‘bible for psychiatry’ is getting a rewrite: your guide to the next DSM

- Longevity is in the genes: half of lifespan is heritable

- 48 hours without lungs: artificial organ kept man alive until transplant

- What drugs are safe during pregnancy? There’s a shocking lack of data

NEWS 05 February 2026

Is UK science in jeopardy? Huge funding reforms spark chaos and anxiety

Major reforms to the United Kingdom’s national research funding agency are aimed at boosting the nation’s economy.

By

David Adam

Funding reforms could affect UK physicists’ collaborations at CERN, Europe’s particle-physics laboratory.Credit: CERN

The United Kingdom’s researchers are an under-used asset “that we need to sweat” to boost economic growth, according to the head of the country’s largest funding agency.

Speaking at a parliamentary committee hearing in London on Tuesday, Ian Chapman, the physicist who leads UK Research and Innovation (UKRI), said that the high quality of the United Kingdom’s research and innovation base is its only competitive advantage over rival nations.

UKRI is the United Kingdom’s national funding agency for research and innovation, and has distributed about £9 billion (US$12 billion) of public money in the current financial year. Chapman said that UKRI needs major reform to convert expertise into ideas and companies that could create jobs and money for the UK economy. “It is latent at the moment. I think it’s under-exploited.”

However, media reports suggest that the reforms could drain university science of funds and put the nation’s participation in major international science projects in jeopardy, and have provoked concern and anxiety among researchers.

“This will hurt the UK research community very badly,” says Lucien Heurtier, a postdoctoral researcher in physics at King’s College London. He is organizing an open letter from postdocs and postgraduate students to make that point to Chapman, which he expects to publish later this week.

The reforms could leave large-scale physics infrastructure projects facing funding uncertainty. Those projects include an experiment called the LHCb, run using the Large Hadron Collider (LHC) at CERN, Europe’s particle-physics laboratory near Geneva in Switzerland.

“If the UK pulls out of these massive projects, it’s not clear that they will go ahead. It will have an international impact,” agrees Maggie Lieu, an astrophysicist at the University of Nottingham, UK.

Funding crisis

Three actions taken by UKRI have caused particular concern. First, of the nine research councils and other bodies that make up UKRI, three — the major funders of work in the medical, biological and physical sciences — have announced that they are temporarily blocking some grant applications. Second, another of the nine, the Science and Technology Facilities Council (STFC), has said it will cease or reduce investments in existing physics and astronomy projects. And third, researchers expecting money directly from the UKRI infrastructure fund have been told they have been de-prioritized.

Chapman said that the blocks on applications to the Medical Research Council (MRC) and the Biotechnology and Biological Sciences Research Council (BBSRC) are short-term measures. The BBSRC will reopen grant applications in a few weeks, and the pause at the MRC will last only until the summer, as it reassesses priorities.

The situation at the STFC, which supports both university-led research and elements of the United Kingdom’s involvement in major international collaborations, is the result of a funding crunch. The UKRI leadership has asked the STFC to find about £60 million in savings in what Chapman says are not budget cuts but “cost reduction against forecast costs”.

Although UKRI says no decisions have yet been made, physicists and astronomers are worried.

“Cuts of this scale are a devastating blow for the foundations of UK physics,” says Paul Howarth, president-elect of the UK Institute of Physics. “The government has promised boosts for areas like quantum, green tech and AI, and all of this is welcome, but the reality on the ground is hundreds of scientists being told their research is being slashed, jobs under threat, and no sign of the funding we’re told will replace it.”

Lieu fears her department will lose its remaining information-technology support worker. “How do you do science without these resources? It’s massively concerning,” she says. “Astronomy degrees are what turn out the data scientists, or the machine-learning engineers.”

The STFC cost reductions could also threaten the United Kingdom’s continued participation in international projects such as the Vera C. Rubin Observatory in Chile — which, its team says, has the largest digital camera ever built.

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Nature 650, 277-278 (2026)

doi: https://doi.org/10.1038/d41586-026-00346-w

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NEWS 04 February 2026 Update 04 February 2026

NIH rolls back red tape on some experiments — spurring excitement and concern

Some human research will no longer be classified as a clinical trial, easing the paperwork burden, but not everyone agrees with the approach.

By

Heidi Ledford

Researchers conducting some classes of research with human participants will have less paperwork under a new federal guideline.Credit: Getty

Many researchers are surprised and relieved over an unusual step taken by the US National Institutes of Health (NIH): the agency is rolling back the red tape on a host of basic-science experiments that involved human participants and had been classified as clinical trials.

The decision, which was announced on 29 January and is part of a broader NIH effort to reduce the administrative burden, should free such research from the heavy bureaucratic requirements that are designed for clinical trials but are sometimes ill-suited to other fields, such as basic psychology and behavioural studies. “It was definitely a nuisance,” says Jeremy Wolfe, a cognitive psychologist at Harvard Medical School in Boston, Massachusetts.

Others, however, say that the agency should refine implementation of the requirements, rather than dropping them altogether. “This is not the right solution,” says Holly Fernandez Lynch, a lawyer and ethicist at the University of Pennsylvania in Philadelphia. “We should go back to why we thought this was an important requirement to impose in the first place and figure out how we can achieve the underlying goal.”

An NIH spokesperson said that removing the requirements would make it easier for scientists to conduct research involving human participants that does not require the same level of scrutiny as a clinical trial. “However, it does not remove the ability for researchers to register with ClinicalTrials.gov or deposit their results, if they so choose,” the spokesperson said.

Broad definition

In 2014, the NIH expanded its definition of ‘clinical trial’ to include some studies of foundational biology to increase the transparency of research on human participants, says Deborah Zarin, a former director of the NIH database ClinicalTrials.gov. The clinical-trial designation usually comes with an obligation to preregister experiments and publish results on ClinicalTrials.gov.

This was intended, in part, to address the fact that many studies on human participants are never published in academic journals: negative results, in particular, often remained locked away in laboratory notebooks. That made it difficult for researchers to learn from each other’s failed experiments and replicate results, and for participants and ethics committees to fully evaluate a proposed study.

In addition, preregistration of experiments, which is common for clinical trials, can improve the quality of the work by pushing researchers to adhere to their predetermined study designs, making it more difficult to ‘cherry-pick’ results by reporting only positive findings.

Reporting results and ensuring they are analysed properly are particularly important when it comes to research involving human participants, who often offer their time and, in some cases, take on personal risk because they are told that their efforts will advance science, says Zarin.

Inconsistent advice

Many scientists agree with those concerns, says Wolfe: “It seems everybody is basically on the same page on the ethics and the open-science aspects of all of this.” The trouble, he says, was in the implementation of the NIH’s requirements.

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Nature 650, 278-279 (2026)

doi: https://doi.org/10.1038/d41586-026-00337-x

Updates & Corrections

- Update 04 February 2026: This story has been updated to note that the NIH did not respond to a request for comment.

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NEWS 23 January 2026

Guinea-Bissau suspends US-funded vaccine trial as African scientists question its motives

Officials at the US Department of Health and Human Services, which awarded funding for the controversial study, say it will proceed as planned.

By

Abdullahi Tsanni

Guinea-Bissau will implement a universal birth-dose policy for the Hepatitis B vaccine in 2027.Credit: Enrique Lopez-Tapia/Nature Picture Library/Alamy

Lire en français

Public-health authorities in Guinea-Bissau say that they have suspended a controversial US-funded hepatitis B vaccine study that has raised questions about who has authority over clinical research conducted in Africa conducted by research teams from other countries. At a press conference held on 22 January, officials from Guinea-Bissau’s ministry of health said that the study was being suspended pending a technical and ethical review by the nation’s public-health institute. The announcement followed multiple conflicting statements over whether or not the trial would proceed.

“There has been no sufficient coordination in order to take a final decision regarding the study,” said Quinhin Nantote, the minister of public health for Guinea-Bissau. “Faced with this situation, we decided to suspend it.”

Mixed messages

The meeting, convened by the Africa Centres for Disease Control and Prevention (Africa CDC), comes after an official at the organization signalled last week that the trial would not proceed. In an e-mail to Nature the day of the press conference, an official at the US Department of Health and Human Services (HHS), a prominent funder of the research, said that the study was still on track, leaving the trial’s status uncertain.

“The study is proceeding as planned and we continue to work with our partners to finalize the study’s protocols,” wrote Emily Hilliard, a spokesperson for the HHS.

The continuing row highlights long-standing tensions over clinical research trials in Africa that are proposed and run by researchers in other countries. African scientists say that the Guinea-Bissau study shows how political pressure, funding interests and fragmented oversight can push local health priorities aside.

The hepatitis B vaccine trial was designed by researchers at the Bandim Health Project, which is based at the University of Southern Denmark in Copenhagen, and funded, in part, by a US$1.6-million research grant from the US Centers for Disease Control and Prevention, part of the HHS. The study aims to randomize 14,000 newborns in Guinea-Bissau, starting in early 2026, to receive their first dose of the hepatitis B vaccine either at birth or at the age of six weeks, when it is currently given, before the country’s planned universal birth-dose vaccine policy begins in 2027.

The Bandim researchers have said that the study offers a chance to investigate whether the vaccine has broader effects on infant health outcomes.

But vaccine scientists, public-health researchers and bioethicists around the world have argued that by randomizing some newborns to not receive the vaccine at birth, the trial would deny a safe and life-saving intervention to infants in Guinea-Bissau, where the prevalence of hepatitis B is about 19%. The immune systems of newborns are immature, and about 90% of those infected at birth go on to develop chronic, lifelong infections that can lead to liver disease and early death.

Researchers who oppose the study also argue that it is structured in a way that increases the likelihood of detecting harm from the birth dose, even if that harm is not real or clinically meaningful. “They’re trying to use African children to prove a case for reducing vaccines in the US,” says Seye Abimbola, a health-systems specialist at the University of Sydney in Australia who researches decolonizing global health. “That’s problematic.” The United States cut its own recommendations for a birth dose of the hepatitis B vaccine in December.

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Nature 650, 279-280 (2026)

doi: https://doi.org/10.1038/d41586-026-00220-9

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NEWS 05 February 2026

First ‘practical PhDs’ awarded in China — for products rather than papers

The programme is designed to train more elite engineers who can help to boost the country’s innovation.

By

Xiaoying You

Civil engineer Zheng Hehui invented Lego-like blocks, used in the Changtai Yangtze River Bridge pylons, as part of his practical PhD.Credit: Cynthia Lee/Alamy

Last month, Zheng Hehui gave an oral defence of his PhD in civil engineering at Southeast University in Nanjing, China. But Zheng had not written a thesis. Instead, he talked about a product he had developed: a set of Lego-like blocks, made with reinforced steel, that fit together to form a bridge pylon.

Zheng is among the first cohort of Chinese doctoral students to be assessed on the basis of practical achievements that lead to new products, techniques, projects and installations. His invention is being used in a huge cable-stayed rail and road bridge built across the Yangtze River.

Since September, at least 11 such ‘practical PhD’ students, all engineers, have obtained their doctoral degrees through this route. Their work includes the development and application of a welding technique and its equipment, and the creation of a fire-fighting system for a large seaplane. Universities in other countries offer ‘industrial PhDs’, for which students work closely with a company, but many of these degrees still require a written thesis.

Practical PhDs are part of the Chinese government’s broader education reforms, which started in 2010, to cultivate ‘elite engineers’ that can help to boost innovation in the country. In 2022, the government instructed top-tier universities to team up with major companies to set up graduate colleges for engineers.

A law passed in 2024 allows universities to let master’s and PhD students graduate on the basis of practical achievements. At present, only students in engineering-related subjects are eligible for this no-thesis arrangement.

Real-world problems

This alternative degree-granting model is important and urgently needed, says Guo Tong, a civil engineer at Southeast University. “[It] can guide students to carry out real research that can solve real-life problems in those industries that carry strategic importance or have technological choke points in China.”

For Li Jiang, an information scientist at Nanjing University, the new evaluation model addresses a problem that has dogged the education of engineers: “There is a big gap between the theoretical knowledge they learn from books and the hands-on ability our society needs from them.”

Candidates for the practical PhDs have to make prototypes and prove that their inventions can be used in real-life and at scale, according to Sun Yutao, who researches innovation policy at Dalian University of Technology in China.

Future talent

The programme is part of China’s effort to build a talent pool for key and emerging industries, such as artificial intelligence and semiconductors, to drive innovation, Zhu Xiumei, a deputy director at the Chinese Ministry of Industry and Information Technology, said at a press conference in December.

Over the past three years, 50 graduate colleges for engineers have been established in China to bring the policy into practice. Students at these colleges work with two supervisors simultaneously: one with an academic background, the other with solid practical experience. During oral examinations, they are evaluated by a panel comprising both scholars and practising engineers.

“Many engineering professors in Chinese universities have always been academics and never worked in the industry. That is why it is important to pair them up with experts from the industry to teach those PhDs,” Li says.

Tsinghua University in Beijing, for example, has partnered with 56 companies in 14 key sectors over the past three years, its vice-president Wu Huaqiang said at the press conference. Under the programme, the university has recruited 1,430 graduate students, who have solved many industrial problems and received more than 100 patents, Wu said.

Some of the universities offering the programme have courses with strong links to national defence. One, Northwestern Polytechnical University in Xi’an, is working with 16 major Chinese groups, including China North Industries Group, which makes weapons and military equipment.

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Nature 650, 280-281 (2026)

doi: https://doi.org/10.1038/d41586-026-00356-8

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NEWS 03 February 2026

More than one-third of cancer cases are preventable, massive study finds

Many cancers are linked to two modifiable habits: tobacco smoking and alcohol consumption.

By

Gemma Conroy

Tobacco smoking is a leading cause of preventable cancer cases.Credit: Jorge Sanz/SOPA Images/LightRocket/Getty

Nearly 40% of new cancer cases worldwide are potentially preventable, according to one of the first investigations[1] of its kind, which analysed dozens of cancer types in almost 200 countries.

The study found that in 2022, roughly seven million cancer diagnoses were linked to modifiable risk factors — those that can be changed, controlled or managed to reduce the likelihood of developing the disease. Overall, tobacco smoking was the leading contributor to worldwide cancer cases, followed by infections and drinking alcohol. The findings suggest that avoiding such risk factors is “one of the most powerful ways that we can potentially reduce the future cancer burden”, says study co-author Hanna Fink, a cancer epidemiologist at the World Health Organization’s International Agency for Research on Cancer in Lyon, France.

The study was published today in Nature Medicine.

Troubling trend

Cancer continues to be a leading cause of illness and death worldwide, with cases expected to rise over the next decades if current trends continue. Previous studies[2] have estimated that around 44% of global cancer deaths can be attributed to avoidable or controllable causes. Estimates of preventability have focused mainly on the number of deaths rather than cases and have mostly investigated a single risk factor, says Fink.

To address this gap, Fink and her colleagues examined global case data from 2022 for 36 different cancers across 185 countries. The study included 30 modifiable risk factors that are well-established causes of cancer — such as tobacco smoking, alcohol consumption and infections.

The researchers combined this information with data from 2012 that captured people’s exposure to each risk factor. Fink and her colleagues then estimated the proportion of cases that were directly linked to each risk factor.

Drinking and smoking

In 2022, there were a total of 18.7 million new cancer cases worldwide. Roughly 38% — or 7.1 million — of these cases could be attributed to avoidable causes. Globally, tobacco smoking was the leading contributor, accounting for around 15% of preventable cases. This was followed by infections (10%) and drinking alcohol (3%). Lung, stomach and cervical cancers made up nearly half of all preventable cancer cases.

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Nature 650, 281-282 (2026)

doi: https://doi.org/10.1038/d41586-026-00333-1

References

1. Fink, H. et al. Nature Med. https://doi.org/10.1038/s41591-026-04219-7 (2026).

2. GBD 2019 Cancer Risk Factors Collaborators. Lancet 400, 563–591 (2022).

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NEWS 03 February 2026 Correction 03 February 2026

The ‘bible for psychiatry’ is getting a rewrite: your guide to the next DSM

Could the next version of the DSM be a ‘living’ document that has more focus on the causes of mental illness?

By

David Adam

DSM-5, released in 2013, will be superseded by a new guide for diagnosing, classifying and treating mental-health conditions.Credit: Olga Pankova/Getty

Mental illness affects one in four adults, which should make The Diagnostic and Statistical Manual of Mental Disorders (DSM) one of the most well-thumbed medical texts in the world. The handbook, produced by the American Psychiatric Association (APA), lists symptoms for all known conditions and aims to steer psychiatrists, doctors and others towards a correct diagnosis.

But in a field that struggles to connect people’s inner experiences to measurable changes in their brains and bodies, the DSM is a lightning rod for criticism. It does not delve into the possible causes of mental illness, for example, or acknowledge that sociocultural and environmental factors could be important.

Last week, the APA responded to that criticism by publishing a series of articles in The American Journal of Psychiatry describing the strategy for the future of the DSM. It remains unclear when a new version will supersede the current DSM-5, released in 2013.

“We want to know how to continue to raise the bar for diagnoses for mental health and substance-use disorder, and, of course, we do that really staying very grounded to the science,” Marketa Wills, chief executive and medical director of the APA, based in Washington DC, told a press conference.

“You probably are aware that there are many critiques out there,” added Maria Oquendo, head of the APA’s Future DSM Strategic Committee and a psychiatrist at the University of Pennsylvania in Philadelphia. “The ultimate goal, however, is to make sure that we have a clinically pragmatic, yet scientifically rigorous, manual that has inclusivity and is adaptable.”

Science not statistics

One focus of the APA’s roadmap is dimensionality: the idea that the diagnosis of psychiatric conditions should not be fixed in discrete categories, but instead operate along scales of shared symptoms. The concept was played down in the DSM-5 but is highlighted now as a “possible new direction” for its successor.

Other ideas include a greater focus on the possible causes of mental illness — from cultural and environmental to biological — and the research that can identify them. The new version could also emphasize how a patient feels their quality of life is affected.

One fix does seem to have been agreed. The APA is changing the name to the Diagnostic and Scientific Manual. That reflects a shift in emphasis. Those working on the new version say it’s intended to reach an audience beyond mental-health professionals; they want it to educate people, and to serve as a resource in lobbying for attention and funds.

Future unknown

The work is at an early stage, and it is not known exactly what the next DSM will say. It will probably be years before anything is finalized and, even then, much of the content is likely to point out what can’t be done.

Take biomarkers. Infamously, no mental condition can currently be objectively diagnosed on the basis of brain scans, blood samples, genetic sequences or any other reliable test grounded in a person’s biology. And that’s unlikely to change by the time the next DSM is published.

“It will actually probably not include any biomarkers initially,” admits Anissa Abi-Dargham, a member of the DSM subcommittee on biomarkers and biological factors and a psychiatrist at Stony Brook Medicine in New York. “But we want to just initiate the process and have a roadmap for how to include biomarkers when they will become available.”

It could also encourage future research, she adds. “It’s almost to kind of shine the light on their importance.”

There are already promising avenues of research towards usable biomarkers. One is using scans from functional magnetic resonance imaging to find increased connectivity between the striatum and other brain regions in people with schizophrenia[1]. Another is genetic signatures for autism[2]. “And in depression, there’s a lot of excitement about inflammatory markers,” Abi-Dargham says.

Health and environment

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Nature 650, 282-283 (2026)

doi: https://doi.org/10.1038/d41586-026-00283-8

Updates & Corrections

- Correction 03 February 2026: An earlier version of the article misspelled the name of Maria Oquendo.

References

1. Karcher, N. R., Rogers, B. P. & Woodward, N. D. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 4, 956–965 (2019).

2. Litman, A. et al. Nature Genet. 57, 1611–1619 (2025).

3. Williams, D. Y. et al. Front. Psychiatry 14, 1205581 (2023).

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NEWS 29 January 2026

Longevity is in the genes: half of lifespan is heritable

Understanding the genetic controls of ageing could lead to more therapies that forestall it.

By

Max Kozlov

Studies on twins can help to elucidate how much a person’s genes contribute to specific traits such as lifespan.Credit: Pierre Andrieu/AFP via Getty

Genetics has a much larger role in how long a person lives than previously thought, finds a new analysis that challenges decades of scientific consensus.

About 55% of the human lifespan is heritable, meaning that more than half of the observed variation in longevity across a population is attributable to genetics. That is a much greater proportion than the previous estimates[1],[2] of 10–25%, according to the research, which was published today in Science[3].

The findings should aid in the quest to find specific genes involved in ageing and to develop treatments for ageing and age-related diseases, says study co-author Ben Shenhar, a biophysicist at the Weizmann Institute of Science in Rehovot, Israel.

“There is much to be learnt from the genetics of ageing, if we can understand what genes are responsible for healthy ageing,” he says.

Twin efforts

Shenhar and his colleagues say that previous estimates were much too low because they did not effectively separate deaths caused by extrinsic factors, such as infectious diseases or accidents, from intrinsic ones inside the body, such as the gradual decline of organ function stemming from DNA damage over time.

To tease out these factors, the researchers re-examined data going back to the 1800s from twin studies in Denmark and Sweden, as well as studies on siblings of centenarians in the United States. Such studies can identify genetic components of traits, Shenhar says, because identical twins share 100% of their DNA, whereas fraternal twins and other sibling pairs share about half, on average.

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Nature 650, 283-284 (2026)

doi: https://doi.org/10.1038/d41586-026-00300-w

References

1. Herskind, A. M. et al. Hum. Genet. 97, 319–323 (1996).

2. McGue, M. et al. J. Gerontol. 48, B237–B244 (1993).

3. Shenhar, B. et al. Science 391, 504–510 (2025).

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NEWS 29 January 2026

48 hours without lungs: artificial organ kept man alive until transplant

The external, artificial-lung system could be used to treat other people who are critically unwell and awaiting transplants.

By

Rachel Fieldhouse

X-rays show a 33-year-old patient’s new lungs (left) and old lungs (right).Credit: Northwestern Medicine

A 33-year-old man survived for 48 hours without his lungs, after a medical team replaced the organs with an external artificial-lung system[1] that it developed to keep him alive until he could receive a double lung transplant.

There have been cases in which people have had their lungs removed and been connected to an external device to maintain oxygen levels. But, the devices used in these cases don’t count as artificial lungs because they do not maintain blood flow across the heart, meaning it cannot function normally, says Ankit Bharat, a thoracic surgeon at Northwestern University Feinberg School of Medicine in Chicago, Illinois, who helped to develop the artificial system.

Bharat says his team’s design is unique because it maintains a balanced and continuous flow of blood to the heart, reducing the risk of blood clots that could trigger a heart attack. The findings were published today in the journal Med.

The engineering behind the artificial-lung system is remarkable, says Natasha Rogers, a transplant clinician at Westmead Hospital in Sydney, Australia. It is difficult to maintain normal heart function in the absence of lungs, she says. “They were really very brave.”

The team’s artificial-lung system could be used in other critically unwell people while they become healthy enough to receive lung transplants, she adds.

Life-threatening condition

Before being placed on the artificial-lung system, the man had developed acute respiratory distress syndrome — an often-life-threatening condition in which the lungs cannot absorb enough oxygen — triggered by the influenza virus. He was then placed on a ventilator but developed a drug-resistant Pseudomonas aeruginosa infection. The infection caused parts of his lungs to fill with pus, and he went into septic shock, at which point his heart and kidneys began to fail.

“He was so sick, he had a cardiac arrest and he was actively dying,” says Bharat. Because the man was too unwell to receive a lung transplant, the team decided to remove his lungs — the source of the infection.

Surgeons removed a patient’s infected lungs. An artificial-lung system kept him alive.Credit: Northwestern Medicine

Surprisingly, the man began to improve quickly. “Within 48 hours, he was off all the medication to support his blood pressure, his kidney function was completely restored and his heart was working normally,” says Bharat. At that point, the man received a double lung transplant and has showed no signs of organ rejection or impaired lung function years later. “We are now approaching almost three years since we did this, and the patient is doing really great,” says Bharat.

Pandemic invention

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Nature 650, 284 (2026)

doi: https://doi.org/10.1038/d41586-026-00239-y

References

1. Yan, Y. et al. Med https://doi.org/10.1016/j.medj.2025.100985 (2026).

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NEWS FEATURE 11 February 2026

What drugs are safe during pregnancy? There’s a shocking lack of data

Drug trials have typically excluded pregnant people for safety reasons. But that’s now starting to change.

By

Giorgia Guglielmi

Pregnant people and their health-care providers often rely on limited or indirect data to weigh the risks of taking medications. Credit: Salvador Melendez/AP/Alamy

In 2021, amid the COVID‑19 pandemic, Kristin Wall became pregnant with her second child. Her physician told her that little was known about the COVID-19 vaccine’s safety and effectiveness in pregnant people. Observational data — collected from those vaccinated before they knew that they were pregnant — suggested that the vaccine was safe, so she could have it. Still, she’d have to weigh up the risks and benefits herself.

Wall is an epidemiologist at Emory University in Atlanta, Georgia, and is therefore unusually qualified to assess health risks. But her experience is a common one — and far from unique to COVID-19. When it comes to pregnancy, people must often make important medical decisions with limited or imperfect information, says Wall. Pregnancy changes the body, including how it metabolizes and distributes medications, so a drug with a predictable effect in a non-pregnant person might work very differently in someone who is pregnant.

There is now solid evidence from the huge numbers of people vaccinated, and subsequent clinical trials, that the COVID-19 vaccines that use messenger RNA (mRNA) — as well as influenza vaccines and others that do not use live viruses — are generally safe and effective during pregnancy. Safety is also well established for certain other medications: some anti-allergy drugs, some antidepressants and some antibiotics are considered safe for use in pregnancy. Paracetamol (acetaminophen) — the main ingredient in Tylenol — is also considered safe, despite claims to the contrary by US President Donald Trump. Other drugs, however, are known to carry risks. An example is the anti-epilepsy drug sodium valproate, which can cause severe developmental disorders when taken during pregnancy.

Most people take medications during pregnancy — more than 90% in the United States according to some tallies[1]. Despite this, the safety and effectiveness of many drugs — including some anti-nausea treatments, some anti-inflammatories and the vast majority of medicines approved over the past decade — remain uncertain because they have never been tested in pregnant people. Fears over adverse consequences and associated policy restrictions mean that pregnant people have been largely excluded from clinical trials. That leaves public-health officials and health-care providers basing recommendations on indirect evidence, including animal studies, trials in non-pregnant people and observational studies of pregnant people taking a drug after it is already on the market.

“The gap is huge,” says Wall. She and others have designed trials that have successfully tested treatments for Ebola, HIV and some pregnancy-specific conditions in pregnant people — collecting reliable data while protecting the health of study participants and their babies. Based on that experience, they have developed tools and guidance to help other researchers to include pregnant people in studies, too. But despite these advances, logistical, regulatory and ethical barriers remain.

There’s no evidence yet that Trump-era policies, such as the ‘Make America Healthy Again’ movement, have reduced the inclusion of pregnant people in trials, Wall says. But she adds that the broader increase in anti-pharmaceutical rhetoric might reinforce long-standing hesitancy to study medications in pregnancy — slowing efforts to close evidence gaps.

For Denise Astill, who took sodium valproate during pregnancy before clear guidance advised against its use, real progress “is about equity”. Her twin daughters were diagnosed at the age of four with a wide range of health conditions, including cognitive difficulties, hearing problems, kidney impairment, autism and attention deficit hyperactivity disorder.

Her experience led her to found the charity Foetal Anti-Convulsant Syndrome New Zealand in Auckland, which offers guidance on taking anti-seizure medications in pregnancy. Pregnant people should have the same access to evidence-based care as everyone else, she says. “Can we truly make an informed choice when our health system and research are based on men?”

Thalidomide’s enduring impact

Much of the caution around research in pregnancy stems from a mid-twentieth-century medical disaster. In the late 1950s, a drug called thalidomide was marketed in 46 countries as a morning-sickness treatment, although it had not been tested in pregnant people. Physicians soon saw a surge in the number of babies with missing or shortened limbs born to mothers who had taken the drug during pregnancy.

In the early 1960s, after studies confirmed that it was linked to congenital disorders in more than 10,000 children, thalidomide was withdrawn. The disaster reshaped drug regulation, leading to stricter testing standards that ended up excluding pregnant people.

In 1964, the Declaration of Helsinki — a document on ethical principles for medical research involving humans — classified pregnant people as a ‘vulnerable population’, grouping them with people who are unable to give informed consent, such as children or those with severe cognitive impairments.

In 1977, the US drug regulator barred not only pregnant people but also ‘women of childbearing potential’ from participating in clinical trials. Although this policy began to shift in the late 1980s and early 1990s — when the US National Institutes of Health required women to be included in research — pregnant people continued to be sidelined because they were considered to be a vulnerable group.

Over the past decade, some of these restrictions have eased. The World Health Organization (WHO) now says that pregnant people should be considered ‘complex’ rather than ‘vulnerable’ and be included in trials when possible. But progress remains slow: an analysis of US drug trials between 2008 and 2023 found that fewer than 1% specified that they included pregnant participants[2] (see ‘Enrolment gap’), and fewer than 0.4% of trials submitted in the European Union include pregnant people.

Source: Ref. 2

Once a therapy is approved, it can take a long time for evidence to accumulate. In the case of COVID-19 vaccines, so many people were having the shot that evidence built up quickly. By the end of the pandemic, most countries recommended vaccination for pregnant people. But a review of medicines approved by the US Food and Drug Administration (FDA) found that when a drug’s safety in pregnancy is unknown at the time of approval, it takes, on average, 27 years before there’s enough evidence to confidently rate its safety[3].

This evidence gap leaves physicians and pregnant people with a difficult choice: treat a condition with drugs for which the risks are uncertain, or leave it untreated, says Alyssa Bilinski, a statistician at Brown University in Providence, Rhode Island. “Sometimes ‘ask your doctor’ is really good advice, because your doctor has a lot of information,” she says. “But ‘ask your doctor’ cannot substitute for underinvestment in research over decades.”

Bilinski’s work suggests that excluding pregnant people from randomized controlled trials can do much more harm than good. In a study last year, she and her colleagues looked at two kinds of risk: testing a drug in pregnant people if the drug turns out to cause harm, and not testing it in pregnant people at all. Their model estimated that if thalidomide had been tested in a trial with 200 pregnant participants, about 33 children would have been harmed[1]. But such a trial could have prevented about 8,000 congenital disorders, according to the model, or 99.6% of all thalidomide-related cases between 1956 and 1962.

The same model also suggested that lives would have been saved if pregnant people hadn’t been excluded from early COVID-19 vaccine trials. When the US drug regulator first authorized the vaccines in December 2020, no pregnancy-specific data were available. Although access wasn’t restricted, the US Centers for Disease Control and Prevention did not strongly recommend the vaccine in pregnancy until August 2021 — by which point uptake by pregnant people already lagged behind uptake in the general population.

If pregnancy-specific data from clinical trials had been available, the resulting vaccine uptake and reduction in infections would have prevented about 20% of COVID-related maternal deaths and stillbirths in the United States between March and November 2021, totalling more than 200 deaths, the study concluded.

Many people assume that risk comes only from taking medications, but doing nothing can be even more dangerous, leaving both the pregnant individual and their baby exposed to uncontrolled disease or serious infections, Bilinski says. “What happens absent a trial is quite possibly the worst of both worlds.”

Breaking down barriers

Some of the barriers to including pregnant people in clinical trials are real and others are perceived, says Anna David, an obstetrician and maternal–fetal medicine specialist at University College London. A common myth is that pregnant people won’t volunteer, David says. Her experience shows the opposite: when they are given as much information as possible about a trial, they are able to give informed consent, and many are willing to participate.

Another perceived barrier, says David, is the idea that fetal health can’t be assessed during a trial, which would be essential to identify adverse effects. “That’s completely wrong, because as a fetal medicine specialist, I do that all the time,” she says.

To make it easier to track and compare risks in trials including pregnant people, David and her colleagues developed standardized definitions and grading scales for problems that can affect the person and the fetus, including issues detectable with fetal imaging, such as growth problems and fluid build-up[4]. Published in 2021, these measures have been incorporated into drug trials in pregnant people, cited in dozens of papers and are being validated by regulatory agencies, David says.

Despite these resources, she adds, pharmaceutical companies are often hesitant to test drugs in pregnant people. That’s partly owing to fear of reputational damage from adverse outcomes. “Imagine headlines saying, ‘new drug has bad outcome’ — you wouldn’t want that,” she says.

Another barrier is the difficulty that researchers face in securing insurance, David says. When deciding whether to cover a trial, insurers often rely on data from past clinical trials to assess risk, including complications such as congenital disorders. But because so few trials are conducted in pregnant populations, there is little information to guide these assessments. That uncertainty leads insurers to decline coverage or to charge high premiums.

Evidence shows that COVID-19 mRNA vaccines are generally safe during pregnancy, but for a lot of medications, such data are scant.Credit: Lauren DeCicca/Getty

Even when insurance is in place, clinical trials in pregnant people are typically more expensive than those in other people, requiring specialized expertise and infrastructure. One estimate by the non-profit organization the Concept Foundation in Geneva, Switzerland (see go.nature.com/4tdnvtm) suggests that developing a medicine specifically for use during pregnancy could cost an extra US$5.7 million compared with drugs for use in other therapeutic areas, with most of the added cost due to safety and efficacy studies.

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Nature 650, 286-288 (2026)

doi: https://doi.org/10.1038/d41586-026-00331-3

References

1. Bilinski, A., Emanuel, N. & Ciaranello, A. Ann. Intern. Med. 178, 868–877 (2025).

2. Bilinski, A. & Emanuel, N. Am. J. Obstet. Gynecol. 232, e136–e139 (2025).

3. Adam, M. P., Polifka, J. E. & Friedman, J. M. Am. J. Med. Genet. C Semin. Med. Genet. 157, 175–182 (2011).

4. Spencer, R. N. et al. Prenat. Diagn. 42, 15–26 (2022).

5. Nyombayire, J. et al. Nature Med. 31, 3899–3906 (2025).

6. Cluver, C. A. et al. BMJ 374, n2103 (2021).

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Books & Arts

- The dark side of green technology: what do electric vehicles really cost?

BOOK REVIEW 09 February 2026

The dark side of green technology: what do electric vehicles really cost?

A powerful book reveals the corrupt deals and human exploitation behind the global scramble for strategic metals.

By

Chris Stokel-Walker

As global demand for battery components rises, many resource-rich nations such as Indonesia are intensifying mining efforts for key minerals. Credit: Ulet Ifansasti/Getty

The Elements of Power: A Story of War, Technology, and the Dirtiest Supply Chain on Earth Nicolas Niarchos Penguin & William Collins (2026)

You probably don’t think about the Democratic Republic of the Congo (DRC) when scrolling on your phone. Or about the millions of people worldwide whose job it is to dig up and sell vast quantities of metals such as cobalt, copper or tungsten. But you ought to. Electronic devices have turned the metals used in batteries into strategic resources; green technologies such as electric vehicles have accelerated the scramble for them. Metal-rich nations, from Chile to Indonesia, have been pulled into a contest between governments, multinational corporations and armed groups.

In The Elements of Power, journalist Nicolas Niarchos refuses to let the realities of the critical-mineral supply chain be overlooked. He weaves together many seemingly disparate threads, from the DRC’s colonial history to how the mineral-extraction industry has grown in several nations to battery development in leading laboratories around the world. He lays out clearly the emergence of resource nationalism and superpower competition to secure dependable supplies. Rather than a dull account of business deals, Niarchos shares a vivid story of how the greed of a handful of high-ranking individuals has hurt millions of people.

The humans behind the tech

Niarchos, whose work has been cited in hearings held in Washington DC on the effects of mining for battery metals in the DRC, has seen the damage that the race to dig is inflicting up close. He describes children scrabbling to prise out minerals in the province of Lualaba, for example, despite the risks of developing respiratory illnesses from breathing contaminated dust.

Minerals such as lithium are needed to manufacture rechargeable batteries.Credit: Krisztian Bocsi/Bloomberg/Getty

The blame lies, he writes, in part with the behaviour of wealthy nations and their insatiable desire for faster, quicker and sleeker technologies. But Niarchos’ reporting is even-handed. He points out the questionable morality of energy company representatives jetting into the DRC to sign deals with new leaders after military coups. He questions those leaders of resource-rich nations who signed mineral-exploitation contracts — and reneged on these agreements when a higher bidder came through or when an unhappy counterparty needed to be bought off. The only people who Niarchos can’t bring himself to find responsible are the general populations in mineral-rich nations, those whose lives are turned upside down by resource extraction.

Reality check

Niarchos tells this story through his personal experiences — the lives of the people he speaks to and the sights, sounds and smells he encounters. In one memorable moment, a truck driver ploughs into Niarchos’ hire car on the ‘cobalt highway’ in the DRC, along which 70% of the world’s cobalt travels on its way to be exported. As Niarchos comes to his senses after the accident, the truck driver stumbles out of his cab. “Slurring his words, he asked if I wanted to go and smoke a joint,” Niarchos recalls.

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Nature 650, 290-291 (2026)

doi: https://doi.org/10.1038/d41586-026-00385-3

Competing Interests

The author declares no competing interests.

Opinion

- How to deal with the survey-taking AI agents that threaten to upend social science

- China’s biotech boom: why the nation must collaborate to stay ahead

COMMENT 09 February 2026

How to deal with the survey-taking AI agents that threaten to upend social science

Researchers need new bot-detection strategies that exploit the limits of human reasoning rather than AI weaknesses.

By

Folco Panizza,

Yara Kyrychenko &

Jon Roozenbeek

Illustration: Bratislav Milenković

Surveys are a cornerstone of social-science research. Over the past two decades, online recruitment platforms — such as Amazon Mechanical Turk, Prolific, Cloud Research’s Prime Panels and Cint’s Lucid — have become essential tools for helping researchers to reach large numbers of survey participants quickly and cheaply.

There have long been concerns, however, about inauthentic participation[1]. Some survey takers rush the task simply to make money. Because they are often paid a fixed amount based on the estimated time taken to complete the survey (typically US$6–12 per hour), the faster they complete the task, the more money they can make.

Studies suggest that between 30% and 90% of responses to social-science surveys can be inauthentic or fraudulent[2],[3]. This problem is exacerbated in studies targeting specialized populations or marginalized communities because the intended participants are harder to reach and are often recruited online, raising the risk of fraud and interference by automated programs called bots[4],[5]. Those percentages are much higher than the amount of fraudulent responses most studies can cope with, if they are to produce results that are statistically valid: even 3–7% of polluted data can distort results, rendering interpretations inaccurate[6]. And the problem is getting worse.

A parallel industry has emerged offering scripts, bots and tutorials that (legitimately) make it easy to partially or fully automate form filling (see, for example, go.nature.com/4q8kftd). The use of artificial intelligence for crafting responses is on the rise, too. For instance, answers mediated by large language models (LLMs) accounted for up to 45% of submissions in one 2025 study[7]. The advent of AI agents that can autonomously interact with websites is set to escalate the problem, because such agents make the production of authentic-looking survey responses trivially easy, even for people without coding experience.

Researchers and survey providers have long developed tools to prevent, deter or detect inauthentic survey responses. CAPTCHA[8], for example, tests whether a user is human by requiring them to identify distorted text, sounds or images. Such methods could confuse unsophisticated bots (and inattentive humans), but not AI agents.

A few detection measures can distinguish agent-generated responses from genuine ones by exploiting the way LLMs rely on training data to produce responses and their lack of ability to reason contextually[9]. For example, LLMs might label an image of a distorted grid or colour-contrast pattern as an optical illusion even after the illusion-inducing elements have been digitally removed, relying on learned associations rather than perception[7],[10]. Humans, by contrast, respond to what they actually see, creating a detectable difference between human and AI interpretations. However, these distinctions are likely to fade as AI advances, rendering such tests unreliable in the near future.

AI-agent detection has been described as a continual game of cat and mouse, in which “the mouse never sleeps”[11]. Here, we lay out four steps to minimize the risk of survey pollution by AI agents. Using a combination of these detection strategies will probably be necessary to enable researchers to continue to separate out authentic responses from AI bots (see ‘Outwitting AI agents’).

Outwitting AI agents

As AI capabilities improve, online survey designers need new detection methods that exploit human–AI differences that are likely to endure, to protect research integrity. See https://osf.io/zudt4 for more details.

Detector

Mitigation type

Core ideas

Key limitations

Humans as upper bound

Human fingerprinting

Track a survey taker’s navigation and input patterns (e.g. keystrokes).

AI agent can mimic human patterns.

Humans as upper bound

Factors humans ignore

Embed hidden instructions in surveys that humans are likely to overlook, but AI won’t.

Can be ignored by AI agent if suspicious.

Humans as upper bound

Tasks that humans do well but AI agents do poorly

Require complex outputs (such as drag-and-drop)or modify standard tests (correct optical illusions).

Agent can warn a human user-in-the-loop; tasks will quickly become outdated as AI capability improves.

Quality filters

Tasks that both humans and agents can do poorly

Flag rushed, repeated or low-effort responses.

Easily bypassed as AI abilities improve and human weaknesses persist.

Humans as lower bound

Tasks that humans do poorly but agents do well

Exploit human intuition limits using rapid estimation tasks (such as guessing the number of US shopping malls).

AI agent can be trained to make deliberate mistakes and imitate human patterns.

Look for response patterns

One approach is to assign probabilistic scores to survey submissions by comparing each response to known patterns in human and AI-generated answers[12]. LLMs tend to produce answers that have lower variability. For example, when asked to describe their political views by expressing their level of agreement on a series of statements, humans tend to use the extremes of the scale more often than bots do. When sufficient text is available, open-ended responses can also reveal linguistic patterns[13],[14].

Detection tools can exploit such distinctions. By comparing individuals’ responses with sample replies to the same questions answered by genuine humans and by AI, tools can flag responses in which patterns closely match the latter. The potential of such an ‘AI or human’ filter is buttressed by recent findings that LLMs continue to struggle to accurately simulate human psychology and behaviour[15].

Although promising, this method has one crucial limitation: it cannot reliably identify individual responses as bot-generated (because the detection method is probabilistic, not deterministic), making it extremely difficult to flag a single survey entry with certainty.

Track paradata

Paradata refers to the information that describes how survey responses were generated, such as the number of keystrokes a respondent used, the use of copy–paste functionality or the time spent answering a given question. It is relatively straightforward to embed basic keystroke tracking into a survey (although appropriate ethical considerations such as consent and justified use should be taken into account).

Tracking paradata can help to identify likely inauthentic responses by highlighting inconsistencies. For instance, if leaving a 100-word open-text response took 5 seconds, it is likely that the response was at least low effort or potentially AI-generated. Furthermore, if that response appeared in the survey window all at once (instead of gradually, stroke-by-stroke), this provides evidence that the response was generated outside of the survey environment — say, in another browser window[16]. Some survey companies have developed their own tools to flag open-text responses that are suspected to be inauthentic in such ways.

However, this approach is not always appropriate, because some survey tasks might require access to external information. Another problem is that this might not work as well for survey questions that don’t require text input, although suspicious click and mouse-movement patterns can still help to identify low-quality data. Importantly, newer LLMs and some AI browser agents are already capable of creating realistic paradata[6],[17].

Find vetted survey populations

Researchers can rely on recruitment platforms that draw participants from census-based, probability-sampled pools. For example, panels in the Netherlands and France recruit households using official population registers maintained by national statistics agencies. Although this does not prevent participants from using AI to complete surveys, it ensures that responses come from real individuals, and typically only one per household.

Social-science research relies on gathering online survey data for experiments.Credit: Milky Way/Getty

Collaborating with such panels can enhance data integrity. However, these panels are generally more expensive to use than typical online platforms because enrolment involves strict verification of identities and/or census-based selection, and the participant pool is regularly vetted. Furthermore, data collection occurs only a few times per year, often combining multiple surveys, which requires careful timing and limits the number of questions that can be included in any single study.

For survey platforms, there are several potential pathways forwards as they try to adapt to AI. Platforms might consider capping the number of allowed submissions per day from a user; combined with identity verification, this could prove to be an effective deterrent for inauthentic submissions. Platforms could also enforce a reputation or sanctions system to incentivize user authenticity.

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Nature 650, 293-295 (2026)

doi: https://doi.org/10.1038/d41586-026-00386-2

References

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17. Venugopalan, H. et al. in Proc. 2025 ACM Internet Meas. Conf. 134–149 (ACM, 2025).

18. rlebäck, J. B. & Albarracín, L. ZDM Math. Educ. 51, 979–990 (2019).

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Competing Interests

The authors declare no competing interests.

COMMENT 10 February 2026

China’s biotech boom: why the nation must collaborate to stay ahead

Increased geopolitical tensions are prompting some in China to argue that the country should go it alone in biotechnology. That would be a backwards step — for China and the rest of the world.

By

Lizzi C. Lee &

Jing Qian

China is now responsible for most of the global supply chain for many essential pharmaceutical products. Credit: VCG via Getty

In biomedical science, China still lags behind the United States and Europe when it comes to fundamental research and conducting clinical trials involving investigators and participants from several countries[1]. But the nation is now a global leader when it comes to drug development and manufacturing. And it is becoming increasingly important in frontier science.

Industry analysts estimate that China now accounts for 70–95% of the global supply chain for many essential pharmaceutical products, including ibuprofen and paracetamol. In 2024, Chinese biotechnology firms developed more than 1,250 new drugs, surpassing the European Union and approaching the US total of roughly 1,440 (see ‘A growing force in biomedical innovation’). In 2018, China conducted only 9% of the clinical trials conducted by companies around the world. Now, it is responsible for about one-fifth of such trials[1] (see ‘Top contributors to commercial clinical trials’). And in the past few years, it has achieved several therapeutic milestones.

As China’s biotech industry gathers pace, however, so does geopolitical scrutiny.

Last December, the US Biosecure Act was signed into law in response to concerns about national security. This act prevents US pharmaceutical companies that receive federal funding from working with certain Chinese biotech companies. Such increased outside scrutiny, stemming from ongoing concerns about how genetic and clinical information is handled in China, has been prompting Chinese government officials, state-affiliated think tanks and industry stakeholders to advocate for building a closed ‘secure’ biotech ecosystem in China.

Source: https://doi.org/QQDD

Although a desire to sever ties is understandable, closing off China’s biotech and pharma industries and its preclinical and clinical research from the rest of the world would be scientifically and economically counterproductive. It would blunt China’s momentum, restrict people’s access to life-saving medications in nations around the world and stall innovation globally.

Biomedical progress depends on shared knowledge, diverse patient cohorts and the development of regulations that aligns with global standards. In our view, China — and the rest of the world — should be striving to become more collaborative, not less.

China’s biotech rise

Several domestic shifts have been driving China’s biotech boom.

The country’s advance to become the world’s leading provider of many ‘active pharmaceutical ingredients’ (the biologically active components that produce a medication’s intended therapeutic effect), is thanks to decades of investment in chemical manufacturing, as well as in the transport, storage and export of compounds.

China’s contract-development and manufacturing organizations and genomics-service providers are continuing to underpin global pipelines for drug development — even amid increased US legislative scrutiny. Thanks to China’s manufacturing efficiency, lower regulatory hurdles and ability to recruit large numbers of participants in clinical trials, these organizations can provide services more quickly and cheaply than equivalent ones elsewhere can. (China’s large, centralized hospital networks, for instance, make it easier for researchers to recruit participants and coordinate trials across several sites.) This is the case for preclinical studies; for procedures to assess the physical and chemical characteristics of a drug and ensure its quality; and for assistance with obtaining approval from regulators.

In addition, to better align the country’s biotech and pharma sectors with global standards, the Chinese government launched a slew of regulatory reforms for medical products in 2015. These reforms have helped to make China an attractive option for pharma companies wanting to conduct early-stage clinical trials — particularly for drug development in oncology and immunology, and for trials needing participants from only one country.

Source: Ref. 1

Although it is not without problems, China’s National Reimbursement Drug List is another factor that has helped to keep the costs of the drugs developed and produced in China relatively low. To ensure that medical products are affordable, the Chinese government negotiates notable price cuts with companies that want to get their products listed in China and therefore made available to more people. Listed drugs are covered by state-sponsored insurance schemes, with 2025 updates to the scheme particularly benefiting people with rare diseases and those with chronic illnesses such as diabetes and autoimmune disorders.

Finally, over the past decade or so, there has been a rise in the number of people trained in drug discovery and development in China. Under the China Initiative, implemented by the US government in 2018, thousands of researchers and academics affiliated with China, but working in the United States, faced new restrictions and scrutiny — intended to safeguard US laboratories and businesses from espionage. Anyone receiving funds from China or involved in partnerships with institutions from China, for example, had to declare this to the US government.

The climate of anxiety that this created, combined with recruitment programmes by the Chinese government — which have, since 2008, offered research funds and other benefits to try to entice researchers back to China — seem to have catalysed the return of many US-trained Chinese life scientists[2]. Many of these people have seeded Chinese biotech start-ups.

So can China go it alone?

Some Chinese government officials and industry stakeholders are arguing that, given all these developments, China could compensate for any tools, materials and knowledge lost as a result of the country cutting biotech and pharma ties with the United States or other countries.

A State Council directive (a high-level administrative order) issued by the Chinese government in September 2025, for example, instructs government procurement offices, which manage the buying of goods and other services for government organizations, to prioritize domestic products. And some policy advisers are already discussing how a ‘closed loop’ of home-grown agencies, such as contract-research organizations, contract-development and manufacturing organizations, regulators and health-insurance companies, could be established in China.

The electronics sector faced a similar challenge in 2022, when US export controls reduced China’s access to materials, such as lithography tools, needed to make cutting-edge semiconductor chips. And China’s response was to pour billions of US dollars into a workaround — chiplet technology. Chiplets are less advanced than conventional chips and easier to manufacture, but can be connected to make a functional system. The Chinese government also supported developers of artificial intelligence in finding ways to work around computing limits, leading to home-grown successes such as the AI start-up Zhipu in Beijing.

But ultimately, even China’s advances in semiconductors and AI have depended on importing information, materials, software and manufacturing equipment. And probably more so than in semiconductors and electronics, advances in biology depend on the circulation of ideas, enabled by researchers publishing their work in high-impact journals and attending international conferences.

Although China’s advances in biotech and pharma are impressive, the country is still far from being a self-sufficient biotech superpower.

Several Chinese companies chasing the same leads amid a hypercompetitive corporate culture continues to result in inefficiencies and diminishing returns. Also, the country’s biotech industry continues to drive the incremental optimization of pre-existing treatments rather than groundbreaking discoveries. A 2024 review showed, for example, that nearly 40% of registered clinical trials for cell therapies (which involve transferring cells into a person to treat or prevent disease) conducted in China between 2021 and 2023 focused on known molecular targets[3].

China’s biotech filings under the Patent Cooperation Treaty — an international agreement that allows inventors to seek patent protection in multiple countries — surged from 119 in 2010 to more than 1,900 in 2023. By comparison, the EU filed 1,369 and the United States 3,721 applications in 2023. But, in part because of their long history of commanding science, the United States and the EU still lead when it comes to representation in high-impact journals and the discovery of genuinely new mechanisms.

Multinational pharmaceutical firms are investing in drug research and production in China.Credit: AFP via Getty

Besides all these challenges, a lack of international trust continues to be a problem, and tensions are sustained in part by events in China. In 2024, for instance, US intelligence officials reported that Chinese biotech firms had transferred intellectual property of US clients to Chinese authorities without the clients’ consent (see go.nature.com/4twedie).

Lastly, most Chinese biotech and pharma companies lack the capital needed to weather failures in high-risk, early-stage research, or to complete the full innovation cycle from discovery to commercialization, to generating enough returns to sustain innovation.

China’s capital markets are still immature. And although the National Reimbursement Drug List has expanded people’s access to drugs, including in low- and middle-income countries, the list makes it harder for China’s biotech and pharma industries to become profitable. In 2024, the average negotiated discount was roughly 63% — the highest so far.

Collaboration works

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Nature 650, 296-298 (2026)

doi: https://doi.org/10.1038/d41586-026-00387-1

References

1. IQVIA & European Federation of Pharmaceutical Industries and Associations. Assessing the Clinical Trial Ecosystem in Europe (EFPIA, 2024).

2. Vogel, K. M. & Ouagrham-Gormley, S. B. Politics Life Sci. 42, 32–64 (2023).

3. Du, X. et al. J. Hematol. Oncol. 17, 96 (2024).

4. Cao, C., Baas, J., Wagner, C. S. & Jonkers, K. Sci. Public Policy 47, 172–183 (2020).

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Competing Interests

The authors declare no competing interests.Financial competing interests: The Asia Society receives funding from AstraZeneca. AstraZeneca was not involved in any stage of the study design, data collection, analysis, manuscript drafting or the decision to publish.Non-financial competing interests: Jing Qian provides strategic advisory services to AstraZeneca’s leadership. AstraZeneca was not involved in any stage of the study design, data collection, analysis, manuscript drafting or the decision to publish. This relationship is disclosed in the interest of transparency.

Work

- My mission to make life more user friendly for the disability community

- Lab morale got you down? Try a handbook

CAREER FEATURE 09 February 2026 Correction 11 February 2026

My mission to make life more user friendly for the disability community

Inventor Josh Miele says that accelerating change requires uprooting social attitudes about blindness and other disabilities.

By

Laurie Udesky

Find a new job

Josh Miele explains the nuances of a tactile map of a Bay Area Rapid Transit station.Credit: Laurie Udesky

Working scientist profiles

This article is part of an occasional series in which Nature profiles scientists with unusual career histories or outside interests.

Fifty-seven-year-old Josh Miele is a blind scientist, an inventor of adaptive technology and a 2021 MacArthur Foundation ‘genius’ fellow. In the 1990s, as an undergraduate and graduate student at the University of California, Berkeley (UCB) — before GPS use was commonplace — Miele could be seen around town climbing up street signs and feeling the embossed letters to work out which street he was on when travelling in unfamiliar areas, all to the surprise of bystanders. “Some accessibility is just about getting things done, and some accessibility is about teaching others about how much of a pain in the neck it is to get things done,” says Miele.

Miele was nurtured by his mother from a young age to buck the system. In his 2025 memoir, Connecting Dots, he recounts a visit to an art museum, during which his mother urged him to get up close to a sculpture and “feel it with his hands”.

As he did so, he was mortified to hear his mother berating the museum staff for trying to deprive him of the hands-on experience. It was one of many instances of his mother making him “practise breaking the rules, thinking about when they needed to be broken and practising being visible, all of which are essential for me now”, says Miele, a polymath whose pursuits have included physics and space-science studies, working on a Mars probe and doctoral work on the psychology of sound perception. All of which would set him up for a career in designing accessible technology. Miele met a reporter from Nature’s careers team at his neat, compact woodworking studio in Berkeley, where he goes “to get out of his head”, carving chopstick holders and other things.

Outspoken start

Miele wasn’t born blind — a neighbour attacked him with acid when he was four years old, blinding and badly burning him. He reflects in his memoir that his young age probably protected his outlook: “I had a life to enjoy, and I couldn’t let being blind and burned prevent it.”

Instead, the incident forced him to begin engineering the world around him in his neighbourhood of Brooklyn, New York City, to make it work for him. He felt around his home to map it out, built a map of his neighbourhood in his mind and took apart radios and household appliances to understand how they worked. Miele used the echo of the sound his roller skates made to help him steer clear of objects that he might crash into while zooming down the pavement in front of his family’s house. At age 12, with a friend’s mother dictating instructions, he coded his first computer program, commanding an early home computer to count on screen from one to ten. In secondary school, inspired by the 1983 film WarGames “about a computer hacker guy with a talking computer” and with the help of his Braille teacher, Miele set up a speech synthesizer as a rudimentary screen reader on his home computer.

Several years later, during his physics undergraduate degree, he helped to update the features of outSPOKEN, a software for people with low vision or who are blind that reads aloud what is displayed on a computer’s graphical user interface. The Mac version was originally released in 1989 by Berkeley Systems, a small, local software company.

Miele got the job through his connection with Marc Sutton, whom he describes as “a laid-back blind hippie”. They met in The Cave, the basement of a UCB library in which blind students congregated to use Braille machines and other accessible equipment, talk about disability rights and learn from each other. Miele says that students worked and played hard at all hours in The Cave.

His interactions with The Cave cohort and the wider disability-rights community marked a turning point for Miele. Before UCB, he says, “I didn’t think of myself as being part of a disability community. Why would I?” There weren’t any positive portrayals of blind and disabled people, in general, he says. With his new-found peers, he quickly realized that “we all deal with other people telling us that we can’t do stuff, building things that we can’t use and marginalizing us intentionally or unintentionally”.

A long-term friend and colleague of Miele’s who is also blind, UCB English professor Georgina Kleege, says that outSPOKEN was the first screen reader she ever used — although she didn’t know Miele at the time. “It changed my life. It made my life possible, because it meant I could use a computer,” she says.

In 1993, Miele pursued an internship at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, indulging his childhood interest in rockets and outer space. There, he helped to develop software to calibrate optical sensors aboard the Mars Observer probe that launched in 1992. But the spacecraft disappeared before it entered Mars’ orbit.

Deeply disheartened, Miele felt like physics was no longer the right place for him. Once back in Berkeley, he realized that building accessible technologies “was probably the most value I could add”, he says. “I knew that it would be fun. I knew it would be interesting. I knew that there was plenty of work to do.” So, he put his undergraduate studies on hold to work full time at Berkeley Systems.

Quick-fire Q&A

Josh Miele enjoys playing music.Credit: 2016 Barbara Butkus

Why did it take 10 years for you to finish your undergraduate degree?

Because I find classwork really unsatisfying. I am motivated by doing things that make a difference in people’s lives. I’m always thinking about problems that people with disabilities have and I learn about new technologies as they come along. Sometimes things come to me in the shower.

Who are your greatest influences and mentors?

My stepfather, a geophysicist, was one. He was incredible at explaining things to me. For example, when I was young, he laid out two ropes on a table for me to feel to explain the difference between the wave height and wave density of AM and FM radio frequencies. Science-fiction writer Ursula Le Guin is another, because she wrote and thought about people in unusual ways. And she’s a master of the pencil sketch with words.

What drew you to playing guitar and being in a band when you were younger?

I love music and enjoy performing. And I wanted to be cool. As young kids, aged 13–14 years, we formed a band called Child Labor. It was a play on ‘Men at Work’, a group that was popular at the time.

A fresh direction

Eventually, Miele realized that he needed to return to UCB to finish his bachelor’s degree and did so in 1997. He’d already completed his physics requirements and spent his last semester taking courses in music appreciation, disability studies and psychology.

In 1998, he began his PhD in psychoacoustics and cognitive psychology at UCB. “I wanted to study ways of using non-speech sounds, including 3D immersive audio, to present information for screen-reader users, to speed up those interactions,” he says. For instance, he explains, imagine a screen reader reading out information in the top-left cell of a spreadsheet or table and it sounding like the voice was coming from the left and above the user. “It’s more efficient and intuitive than having the speech say ‘top-left cell’,” he says.

That same year, he started an internship at the Smith-Kettlewell Eye Research Institute in San Francisco, California. There, he worked on the data-analysis software MATLAB, to make it accessible to blind people. His Smith-Kettlewell stint evolved into a 19-year relationship, both during and after his PhD.

The institute’s environment “was incredibly flexible and accommodating”, says Miele. “It was also one of the few research institutions in the world that valued the kind of accessibility technology research programme that I wanted to build.” His proudest work included developing a way for blind and partially sighted people to print embossed, or raised, tactile street maps for any US location — to get a full picture of a neighbourhood or address through their fingers. Miele started the project, called tactile map automated production (TMAP), in 2003 as a postdoc at Smith-Kettlewell.

For it, he repurposed coding that he had developed for his graduate work, which enabled him to print research charts and graphs in Braille. He applied the program to data from geographical information systems (GIS) freely available through the US Census Bureau. Other people had used GIS to create tactile maps, Miele explains, but these required visual interfaces. “They were sighted people making maps for blind people.”

Josh Miele (standing) teaches a participant at a Blind Arduino Project workshop; he launched this series of maker gatherings in 2015.Credit: Jean Miele 2026

TMAP enabled blind people to create a Braille printable map at the scale and location of their choice and on their own. “This revolutionized the availability of tactile street maps for blind travelers,” noted a Smith-Kettlewell announcement in September 2021 (see go.nature.com/3zabzja).

Steve Landau is a close friend and frequent collaborator of Miele’s and the founder and president of Touch Graphics, an accessibility technology company in Newark, Delaware. The firm specializes in products that convey spatial information through touch. He has wrestled with Miele over the usability of the firm’s assistive technologies. Landau says that Miele can be blunt and has frequently “burst his bubble”, explaining why something he proposed won’t work.

Landau worked with Miele on creating a digital version of TMAP known as the Talking Tactile Tablet (TTT). One disagreement centred on how people would use screen touching on the tablet. As a sighted person, Landau explains, he was overlooking the complex ways in which blind people swipe and rotate their fingers on surfaces. “I needed to be schooled to make the device as intuitive as possible.”

The TTT enabled users to print an embossed map to overlay the tablet’s screen. Then, by tapping a map area, the tablet would spit out spoken information, such as street names, the number of traffic lanes, the direction of traffic and the location of pedestrian crossings. The handheld tablet worked well even for people who don’t know Braille, Landau explains.

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Nature 650, 509-511 (2026)

doi: https://doi.org/10.1038/d41586-026-00389-z

Updates & Corrections

- Correction 11 February 2026: This Career feature misstated the timing of GPS invention and misnamed Josh Miele’s 2025 memoir.

TECHNOLOGY FEATURE 10 February 2026

Lab morale got you down? Try a handbook

Documents that lay out a research group’s ethos and practical guidelines are becoming increasingly popular in the academic community.

By

Amanda Heidt

Sharing experiences and expectations can help scientists to create a lab handbook. Credit: skynesher/Getty

As a neuroscience postdoc at the MRC Laboratory of Molecular Biology in Cambridge, UK, Letizia Mariotti was part of a core group of friends navigating the early steps of their careers. Even after the four friends scattered to academic posts across Europe they continued to meet virtually to talk through the challenges of launching a laboratory.

It quickly became apparent that their duties as principal investigators far exceeded the bench skills that they’d learnt as postdocs. Mariotti, for example, struggled with the paperwork needed to hire her first international postdoc at the Institute of Neuroscience of the National Research Council in Padova, Italy. And one of her friends discovered that researchers in France embrace formal academic titles in ways that they hadn’t experienced in the United Kingdom. “You could never predict when something would happen that you’d never had to think about before,” Mariotti says.

The group collectively recognized that labs need a document that, rather than simply laying out methods and bench protocols, focused on culture and ethos: everything from where to access on-campus resources to intangibles such as a group’s shared mission and codes of conduct.

In 2024, Mariotti and her friends held the first of two workshops dedicated to creating such a manual — an effort that ultimately kick-started the Starting Aware Fair & Equitable (SAFE) Labs initiative. The resulting SAFE Labs handbook, now freely available online, is one of a growing number of resources dedicated to producing lab handbooks and making academia a more-supportive space. As researchers, institutions and funding agencies take notice of them, these documents are encouraging a collective shift away from unrealistic, and sometimes unhealthy, expectations in academia towards those that prioritize cohesion and support.

“These handbooks are for anyone who wants to create a positive and transparent lab space,” Mariotti says. “The interest and overall positive feedback we’ve gotten really attests to how much this kind of support is needed.”

The core tenets

Exactly how many labs, worldwide, have adopted a lab handbook is unclear, but anecdotally, sources say, interest is growing — partly thanks to the broader recognition that academia is due a change.

Scientists across the career spectrum are burnt out, and people are leaving careers in science, technology, engineering and mathematics in startling numbers, particularly those from under-represented groups.

In 2025, the UK Research Excellence Framework announced its intention to focus more heavily on initiatives that “enable positive research culture” — a move also shared by funders such as the biomedical charity Wellcome in London. Institutions have taken notice, too. The University of Liverpool and the University of York, both in the United Kingdom, have held workshops on lab handbooks. And University College London supported Mariotti’s workshops, allowing her group to survey researchers from across Europe to learn more about the challenges that researchers are facing while running labs in different countries and disciplines (E. Doná et al. Preprint at bioRxiv https://doi.org/qqdp; 2025).

At its core, a lab handbook should lay out the research focus and expectations for other members of the lab. Typical elements include which platforms a group uses to communicate, codes of conduct and harassment policies, and what students should anticipate in terms of expectations, mentorship and career development.

Several scientists say that reflecting on their own experiences as students helped while creating their own handbooks. Leonardo Uieda, a geophysicist at the University of São Paulo in Brazil, recalls that his PhD lab lacked a sense of shared identity. “It was always each student for themselves,” he says. And although his experience was positive overall, he adds, “I do feel I missed out on the benefits of a cohesive group”.

For his lab handbook, Uieda worked with his team to set shared goals and to outline expectations. Students in his lab needn’t stick to conventional working hours, for instance, but they should attend weekly meetings and department seminars. He set different priorities for students and staff in the lab, and laid out a clear authorship policy for publications. “That’s one of the places I’ve seen the most conflict in my career, so I try to cover that really early on,” he says.

Letizia Mariotti and her fellow SAFE Labs founders: Pip Coen (back), Federico Rossi (front) and Stéphane Bugeon (left).Credit: Federico Rossi

Other academics embraced lab handbooks after experiencing a conflict that they wished they’d handled differently. Ben Marwick, an archaeologist at the University of Washington in Seattle, is committed to the concept of ‘open science’, for example, but acknowledges that following best practices to make science transparent and accessible to everyone can take time. At one point, he found himself on the PhD committee of a student who didn’t adhere to such practices. Bringing the student’s data into alignment with Marwick’s policies created a lot of extra work for them, Marwick says, including uploading their code into a new repository and annotating it so that it could be replicated.

“Ultimately, they weren’t sure if it was worth having me on their committee,” Marwick allows, noting that he could have avoided the issue if he had been clearer about his expectations or if he’d asked the student to complete a statement of expectation before Marwick joined his committee. Open science now forms a core part of his lab handbook, and Marwick says that sharing the document with potential students and collaborators has helped to rule out projects that don’t align with his values early on. “The longer I have it up, the more ways I’ve found it to be useful in navigating all kinds of professional relationships,” he says.

Making them your own

Beyond baseline expectations, lab handbooks might also contain sections that are specific to your research discipline. Some disciplines require fieldwork, for example, or rely heavily on particular types of data. Laying out clear guidelines about how lab members should conduct themselves, and how data should be handled and organized, can prevent future headaches.

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Nature 650, 512-513 (2026)

doi: https://doi.org/10.1038/d41586-026-00390-6

Research

- Multimodal learning with next-token prediction for large multimodal models

- Ferromagnet-like binary switching of a Stoner–Wohlfarth antiferromagnet

- New discoveries of Australopithecus and Homo from Ledi-Geraru, Ethiopia

- An ancient DNA perspective on the Russian conquest of Yakutia

- Causal modelling of gene effects from regulators to programs to traits

- Spatiotemporal cellular map of the developing human reproductive tract

- Human assembloids recapitulate periportal liver tissue in vitro

- Gut micro-organisms associated with health, nutrition and dietary interventions

- Potent neutralization of Marburg virus by a vaccine-elicited antibody

- Transient hepatic reconstitution of trophic factors enhances aged immunity

- Temporal tissue dynamics from a spatial snapshot

- GTP release-selective agonists prolong opioid analgesic efficacy

Article Open access Published: 28 January 2026

Multimodal learning with next-token prediction for large multimodal models

Xinlong Wang,

Yufeng Cui,

Jinsheng Wang,

Fan Zhang,

Yueze Wang,

Xiaosong Zhang,

Zhengxiong Luo,

Quan Sun,

Zhen Li,

Yuqi Wang,

Qiying Yu,

Yingli Zhao,

Yulong Ao,

Xuebin Min,

Chunlei Men,

Boya Wu,

Bo Zhao,

Bowen Zhang,

Liangdong Wang,

Guang Liu,

Zheqi He,

Xi Yang,

Jingjing Liu,

Yonghua Lin,

Zhongyuan Wang &

…

Tiejun Huang

Nature volume 650, pages 327–333 (2026)

Abstract

Developing a unified algorithm that can learn from and generate across modalities such as text, images and video has been a fundamental challenge in artificial intelligence. Although next-token prediction has driven major advances in large language models[1], its extension to multimodal domains has remained limited, and diffusion models for image and video synthesis2,3 and compositional frameworks that integrate vision encoders with language models[4] still dominate. Here we introduce Emu3, a family of multimodal models trained solely with next-token prediction. Emu3 equals the performance of well-established task-specific models across both perception and generation, matching flagship systems while removing the need for diffusion or compositional architectures. It further demonstrates coherent, high-fidelity video generation, interleaved vision–language generation and vision–language–action modelling for robotic manipulation. By reducing multimodal learning to unified token prediction, Emu3 establishes a robust foundation for large-scale multimodal modelling and offers a promising route towards unified multimodal intelligence.

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Main

Since AlexNet[5], deep learning has replaced heuristic hand-crafted features by unifying feature learning with deep neural networks. Later, Transformers[6] and GPT-3 (ref. [1]) further advanced sequence learning at scale, unifying structured tasks such as natural language processing. However, multimodal learning, spanning modalities such as images, video and text, has remained fragmented, relying on separate diffusion-based generation or compositional vision–language pipelines with many hand-crafted designs. This work demonstrates that simple next-token prediction alone can unify multimodal learning at scale, achieving competitive results with long-established task-specialized systems.

Next-token prediction has revolutionized the field of language models[1], enabling breakthroughs such as ChatGPT[7] and sparking discussions about the early signs of artificial general intelligence[8]. However, its potential in multimodal learning has remained uncertain, with little evidence that this simple objective can be scaled across modalities to deliver both strong perception and high-fidelity generation. In the realm of multimodal models, vision generation has been dominated by complex diffusion models[2], whereas vision–language perception has been led by compositional approaches[9] that combine CLIP[10] encoders with large language models (LLMs). Despite early attempts to unify generation and perception, such as Emu[11] and Chameleon[12], these efforts either resort to connecting LLMs with diffusion models or fail to match the performance of task-specific methods tailored for generation and perception. This leaves open a fundamental scientific question: can a single next-token prediction framework serve as a general-purpose foundation for multimodal learning?

In this work, we present Emu3, a new set of multimodal models based solely on next-token prediction, eliminating the need for diffusion or compositional approaches entirely. We tokenize images, text and videos into a discrete representation space and jointly train a single transformer from scratch on a mix of multimodal sequences. Emu3 demonstrates that a single next-token objective can support competitive generation and understanding capabilities, while being naturally extendable to robotic manipulation and multimodal interleaved generation within one unified architecture. We also present the results of extensive ablation studies and analyses that demonstrate the scaling law of multimodal learning, the efficiency of unified tokenization and the effectiveness of decoder-only architectures.

Emu3 achieves results comparable with those of well-established task-specific models across both generation and perception tasks, equals the performance of diffusion models in text-to-image (T2I) generation, and rivals compositional vision–language models that integrate CLIP with LLMs in vision–language understanding tasks. Furthermore, Emu3 is capable of generating videos. Unlike Sora[3], which synthesizes videos through a diffusion process starting from noise, Emu3 produces videos in a purely causal manner by autoregressively predicting the next token in a video sequence. The model can simulate some aspects of environments, people and animals in the physical world. Given a video in context, Emu3 extends the video and predicts what will happen next. On the basis of a user’s prompt, the model can generate high-fidelity videos following the text description. Emu3 stands out and competes with other video diffusion models for text-to-video (T2V) generation. In addition to standard generation, Emu3 supports interleaved vision–language generation and even vision–language–action modelling for robotic manipulation; this demonstrates the generality of the next-token framework.

We open-source key techniques and models to facilitate future research in this direction. Notably, we provide a robust vision tokenizer to enable transformation of videos and images into discrete tokens. We also investigate design choices through large-scale ablations, including tokenizer codebook size, initialization strategies, multimodal dropout, and loss weighting, providing comprehensive insights into the training dynamics of multimodal autoregressive models. We demonstrate the versatility of the next-token prediction framework, showing that direct preference optimization (DPO)[13] can be seamlessly applied to autoregressive vision generation and aligning the model with human preferences.

Our results provide strong evidence that next-token prediction can serve as a powerful paradigm for multimodal models, scaling beyond language models and delivering strong performance across multimodal tasks. By simplifying complex model designs and focusing solely on tokens, it unlocks significant potential for scaling during both training and inference. We believe this work establishes next-token prediction as a robust and general framework for unified multimodal learning, opening the door to native multimodal assistants, world models and embodied artificial intelligence.

Emu3 architecture and training

We present a unified, decoder-only framework that models language, images and video as a single sequence of discrete tokens and is trained end-to-end with a next-token prediction objective. Figure 1 illustrates the framework. Our method comprises five tightly integrated components: (1) a large, mixed multimodal training dataset (see section 3.1 of the Supplementary Information); (2) a unified tokenizer that converts images and video clips into compact discrete token streams (‘Vision tokenizer’); (3) a transformer-based decoder-only architecture that extends an LLM’s embedding space to accept vision tokens while otherwise following standard decoder-only design choices (‘Architecture’); (4) a two-stage optimization recipe including large-scale multimodal pretraining with balanced cross-entropy loss and high-quality post-training to align with task formats and human preferences (‘Pretraining’ and ‘Post-training’); and (5) an efficient inference back end supporting classifier-free guidance (CFG), low latency and high throughput for autoregressive multimodal generation (‘Inference’).

Fig. 1: Emu3 framework.

Emu3 first tokenizes multimodal data such as images, text, video and actions into discrete tokens and then sequences these tokens by order and performs unified next-token prediction at scale with a Transformer decoder. We have also seamlessly generalized the framework to robotic manipulation by treating vision, language and actions as unified token sequences.

Vision tokenizer

We trained a unified vision tokenizer that can encode a 4 × 512 × 512 video clip or a 512 × 512 image into 4,096 discrete tokens from a codebook of size 32,768. The tokenizer achieves 4× compression in the temporal dimension and 8 × 8 compression in the spatial dimension and is applicable to any temporal and spatial resolution. Building on the SBER-MoVQGAN architecture[14], we incorporated two temporal residual layers with three-dimensional convolution kernels into both the encoder and decoder modules to perform temporal downsampling and enhance video tokenization capabilities.

Architecture

The Emu3 model retains the architectural framework of established LLMs such as Llama-2 (ref. [15]), the primary modification being the expansion of the embedding layer to accommodate discrete vision tokens. A dropout rate of 0.1 was implemented to improve training stability. Methods section ‘Architecture design’ includes the architecture details and comparisons with architectural variants. We compared our approach with encoder-based vision–language architectures and diffusion baselines and found that a decoder-only token prediction architecture trained without any pretrained vision or language components could match traditional pipelines that rely on strong unimodal priors and thus offered a more unified, general-purpose design. This finding challenges the prevailing assumption that compositional or diffusion-based models are inherently superior for multimodal learning.

Pretraining

During pretraining, we first established a unified multimodal data format to allow Emu3 to process text, images and videos in a single autoregressive framework. In contrast to diffusion-based models that depend on at least one external text encoder, Emu3 accepts textual context into the model naturally and directly, enabling native joint modelling of multimodal data. All images and videos are resized with the aspect ratio preserved to a target scale. The visual contents are then converted into discrete vision tokens produced by our tokenizer. These tokens are combined with natural language captions and further metadata describing resolution, and, in the case of video, frame rate and duration. These components are interleaved using a small set of special tokens that delineate text segments, visual segments, and structural boundaries such as line and frame breaks. This yields a document-style sequence that standardizes heterogeneous multimodal inputs into a single token stream suitable for next-token prediction. We also included variants of the data in which captions appeared after the visual content rather than before it. This bidirectional arrangement encourages the model to learn both language-to-vision and vision-to-language mappings in a unified setting. As all information is fully tokenized, Emu3 can be trained end-to-end using a single next-token prediction objective with a standard cross-entropy loss. To maintain balanced learning across modalities, we slightly reduced the relative weight assigned to vision tokens so that a large number of visual tokens would not dominate optimization.

Emu3 uses an extensive context length during pretraining to handle video data. To facilitate training, we used a combination of tensor parallelism, context parallelism and data parallelism, simultaneously packing text–image data into the maximum context length to fully utilize computational resources while ensuring that complete images were not segmented during the packing process. Extended Data Table 1 details the training pipeline, including stage configurations, parallelism strategies, loss weights, optimization settings and training steps. The training computations are listed in Supplementary Table 7.

Post-training

Following the pretraining phase, we conducted post-training for vision generation tasks to enhance the quality of generated outputs. We applied quality fine-tuning (QFT) using high-quality data. The model continues training with the next-token prediction task using standard cross-entropy loss; however, supervision is applied exclusively to the vision tokens. During training, we increased the data resolution from 512 pixels to 720 pixels to improve generation quality. In addition, at the end of training, we used an annealing strategy to linearly decay the learning rate to zero. We adopted DPO[13] to enable better alignment of models with human preferences. Human preference data were leveraged to enhance model performance for autoregressive multimodal generation tasks. The DPO model minimizes the DPO loss and the next-token prediction cross-entropy loss.

For vision–language understanding, the pretrained model underwent a two-stage post-training process: (1) image-to-text (I2T) training; and (2) visual instruction tuning. During the first stage, our approach integrates image-understanding data with pure-text data, and losses associated with vision tokens are disregarded for text-only prediction. Each image is resized to a resolution of about 512 × 512 while preserving the original aspect ratio. In the second stage, a subset of visual question answering data is sampled to enhance vision-instruction-following ability. Images with resolution less than 512 × 512 or greater than 1,024 × 1,024 are resized to the lower or upper resolution limit while keeping the aspect ratio, whereas all other images are retained at their original size. Figure 2 presents qualitative visualizations across diverse multimodal tasks.

Fig. 2: Qualitative visualizations across diverse multimodal tasks.

Representative qualitative results for T2I generation, T2V generation, future prediction, vision–language understanding, interleaved image–text generation and embodied manipulation. First image of embodied manipulation reproduced from ref. [48], under a CC BY 4.0 licence.

Inference

Our multimodal inference framework inherits most of the key advantages of existing LLM infrastructures. It was built upon FlagScale[16], a multimodal serving system developed on top of vLLM[17]. FlagScale extends the inference back end to support CFG[18] for autoregressive multimodal generation. Specifically, we integrated CFG directly into the dynamic batching pipeline by jointly feeding conditional and negative prompts within each batch iteration. This CFG-aware extension introduces negligible overhead while maintaining the low-latency and high-throughput characteristics of vLLM.

Notably, we also present a vision for token-centric multimodal infrastructure in Fig. 3a; this is both efficient and extensible, demonstrating the practicality and scalability of our multimodal token prediction framework for large-scale real-world deployment. In this framework, data tokenization is performed directly on edge devices, and only the resulting discrete token IDs are transmitted to large-scale servers for unified multimodal training and inference. This approach greatly improves efficiency, as token IDs are substantially more compact than raw data such as images or videos.

Fig. 3: Token-centric multimodal infrastructure and architectural comparisons with diffusion models and the encoder + LLM compositional paradigm.

a, Multimodal data tokenization can be performed directly on edge devices, and only the resulting discrete token IDs are transmitted to large-scale servers for unified multimodal training and inference. b, GenEval overall scores as a function of training sample count for the image-generation task, comparing the latent diffusion and next-token prediction paradigms. c, Validation loss of text tokens as a function of training sample count for the image-understanding task, contrasting the decoder-only paradigm with the encoder + LLM compositional paradigm in the scenario in which the LLM is trained from scratch, with further comparisons according to whether CLIP initialization is applied. Init., initialization.

Evaluation

Main results

We identified consistent scaling laws as a core principle underlying unified multimodal learning at scale. Our analysis, which was inspired by the Chinchilla scaling law[19], demonstrated that diverse tasks including T2I, I2T and T2V followed a shared scaling behaviour when the model was trained jointly in a unified next-token prediction framework. We used a power-law formulation to model the validation loss L(N, D) as a function of model size N and training data size D:

$$L(N,D)=E+\frac{A}{{N}^{\alpha }}+\frac{B}{{D}^{\beta }}.$$

All tasks exhibited a consistent data scaling exponent β = 0.55. T2I and I2T shared a model scaling exponent α = 0.25, whereas T2V showed steeper scaling with α = 0.35. These results were supported by high-quality fits, with mean absolute percentage error below 3% and R^2 values exceeding 0.99. Figure 4 summarizes the scaling behaviour of Emu3 across model size, dataset scale and predictive accuracy for the three multimodal tasks (T2I, I2T and T2V). The validation loss surfaces revealed clear power-law relationships as functions of training tokens and model parameters, exhibiting consistent trends across modalities. The predicted versus observed curves for the 7B model further validated the reliability of these scaling laws: extrapolations based solely on smaller models closely matched the measured 7B losses (R^2 ≥ 0.95, mean absolute percentage error < 3%). Together, these results demonstrate that unified multimodal next-token training follows stable and predictable scaling dynamics, enabling accurate performance forecasting before full-scale training. These findings reinforce our central claim that a unified next-token prediction paradigm, when scaled appropriately, can serve as a simple yet powerful mechanism for multimodal learning, obviating the need for complex modality-specific fusion strategies.

The main results for image generation, vision–language understanding and video generation are summarized in Table 1, with well-established task-specific model series20,21,22 listed as references. We assessed the T2I generation capability of Emu3 through both human evaluation and automated metrics on several established benchmarks, including MSCOCO-30K[23], GenEval[24], T2I-CompBench[25] and DPG-Bench[26]. As shown in Extended Data Table 2, Emu3 attained performance on par with that of state-of-the-art diffusion models. Supplementary Fig. 14 shows images generated by Emu3 to demonstrate its capabilities. Emu3 supports flexible resolutions and aspect ratios and is capable of handling various styles.

Table 1 Evaluation across multimodal tasks

For video generation, Emu3 natively supports generation of 5-s videos at 24 fps and can be extended through an autoregressive approach. Supplementary Fig. 15 presents qualitative examples of video generation, with 6 frames extracted from the first 3 s. We quantitatively evaluated video generation performance with VBench toolkit[27]. As shown in Extended Data Table 3, Emu3 produced results highly competitive with those of other video diffusion models.

Emu3 can extend videos by predicting future frames. Figure 2 shows qualitative examples of video extension, with 2-s videos at 24 fps tokenized into discrete vision tokens as context. Emu3 predicts the subsequent 2 s of content in the same form of discrete vision tokens, which can be detokenized to generate future predicted videos. These examples demonstrate that use of only next-token prediction facilitates temporal extension of videos, including prediction of human and animal actions, interactions with the real world, and variations in three-dimensional animations. Furthermore, by extending the video duration in this manner, our approach is capable of iteratively generating videos that surpass its contextual length.

Fig. 4: Scaling laws of Emu3 across multimodal tasks.

a, Validation loss surfaces for three tasks: T2I, I2T and T2V, shown as functions of model size and number of training tokens. All three tasks demonstrated clear power-law behaviour with respect to scale. b, Predicted versus observed validation loss using the fitted scaling laws for the 7B Emu3 model on T2I, I2T and T2V tasks. The predictions were closely aligned with measured performance, which validated the extrapolation capability of the learned scaling relationships. MAE, mean absolute error; MAPE, mean absolute percentage error.

To evaluate the vision–language understanding capabilities of our approach, we tested it across various public vision–language benchmarks. The primary results, detailed in Extended Data Table 4, compare two categories of methods: (1) encoder-based approaches that use pretrained CLIP vision encoders; and (2) encoder-free methodologies that operate without pretrained encoders. Emu3 stands out as a pure encoder-free method, reaching the performance of its counterparts across several benchmarks. This was achieved without dependence on a specialized pretrained LLM and CLIP, underscoring the intrinsic capabilities and promising potential of Emu3 in multimodal understanding.

Ablations

To evaluate the effectiveness of our unified video tokenizer, we compared its video reconstruction performance on UCF-101 (ref. [28]) with that of its image tokenizer counterpart, for which we used the SBER-MoVQ model with 270M parameters. We randomly sampled 16 consecutive frames from each video in UCF-101. Under the same input resolution, our video tokenizer achieved comparable reconstruction Fréchet video distance (rFVD) (27.893 versus 26.675) and peak signal-to-noise ratio (PSNR) (27.546 versus 30.499) using four times fewer tokens. Moreover, when using the same number of latent tokens, the unified video tokenizer significantly outperformed the standalone image tokenizer, especially in terms of rFVD (27.893 versus 139.930), demonstrating both its efficiency and its effectiveness. A qualitative comparison is provided in Fig. 5. Although the video tokenizer used four times fewer latent tokens, it showed comparable reconstruction quality to that of the image tokenizer. It also preserved finer details than the image tokenizer when downsampling to match the number of latent tokens.

Fig. 5: Reconstruction samples of the tokenizer and comparison of unified video tokenizer and standalone image tokenizer.

a, Original and reconstructed videos and images. Videos are at 540 × 960 resolution, with a sampling of 8 frames at 30 fps, and images are of 512 × 512 resolution. b, The video tokenizer achieved comparable reconstruction with four times fewer latent tokens at the same resolution. When the image tokenizer was downsampled to match the total token count, its reconstruction quality degraded noticeably. Zoom in for details. Images from Pexels (https://www.pexels.com/).

We conducted architectural comparisons with diffusion models and the encoder + LLM compositional paradigm. To ensure fair comparison between next-token prediction and diffusion paradigms for visual generation, we trained both a 1.5B diffusion transformer (using the SDXL variational autoencoder) and a 1.5B decoder-only transformer (using the video tokenizer in Emu3) on the OpenImages dataset under identical settings. The next-token prediction model converged faster than the diffusion counterpart for equal training samples, demonstrating the potential of next-token prediction as a data-efficient framework for visual generation. We further compared three vision–language architectures of similar model scale and  training samples, including a discrete token decoder-only model (Emu3) and two late-fusion encoder-decoder variants resembling LLaVA with different vision encoders. All were trained without any pretrained LLM initialization. Notably, when models were trained from scratch, the presumed advantage of the encoder-based LLaVA-style compositional architecture largely diminished. The decoder-only next-token prediction model achieved comparable performance, challenging the prevailing belief that encoder + LLM architectures are inherently superior for multimodal understanding.

More ablation experiments on the training recipe are provided in section 3.2.3 of the Supplementary Information. Large-scale unified multimodal learning is highly sensitive owing to the diverse distributions of multimodal data. An improper recipe easily leads to training collapse; this represents a fundamental difficulty of stable optimization at scale. We found that a small dropout rate was essential for stable convergence, as training collapsed without it. Careful weighting of visual and text token losses prevented task bias and ensured generalizable performance. We did not use pretrained LLM initialization in primary experiments to avoid strong priors and to clearly evaluate the capability of next-token prediction from scratch in a multimodal setting. Pretrained LLM initialization accelerated early convergence but offered little long-term advantage. These results demonstrate that Emu3 scales effectively without relying on pretrained language priors, supporting its potential as a general-purpose, unified multimodal learner.

Extensive applications

We applied our framework to robotic manipulation by transferring Emu3 to a vision–language–action model. Our approach achieved competitive results compared with specialized approaches including RT-1 (ref. [29]) and RoboVLMs[30]. We represented language, visual observations and actions as interleaved discrete tokens within a unified autoregressive sequence. This formulation naturally aligns instruction-following, visual prediction and action prediction under a single next-token prediction objective. Actions were tokenized using the FAST tokenizer[31], enabling efficient compression of continuous control signals. Extended Data Table 5 presents experimental results obtained in simulation environments. Evaluated on the CALVIN benchmark, our method reached the performance of well-established models on long-horizon manipulation. In contrast to UniVLA[32], which explored post-training techniques, we performed direct discrete encoding of vision, language and actions without video post-training. These results highlight the versatility of next-token prediction as a general framework extending seamlessly from perception and generation to embodied decision-making.

We extended Emu3 to interleaved image–text generation, in which structured textual steps are accompanied by corresponding illustrative images in a single output sequence. Owing to the flexibility and generalizability of the framework, we could directly fine-tune the model to autoregressively generate such multimodal sequences in an end-to-end manner. Extended Data Fig. 1 shows the visualized results. Even with basic fine-tuning using limited interleaved image–text data, the model exhibited a promising ability to generate interleaved image–text sequences. This suggests that next-token prediction for unified multimodal generation is scalable and flexible and can be extended beyond single-modality text or image synthesis.

To demonstrate the flexibility of Emu3, we evaluated it across alternative token prediction orders, including diagonal, block-raster and spiral-in, in addition to the standard raster scan. These orders modify the spatial autoregressive dependencies, posing a more challenging generalization problem. Using the pretrained Emu3 model, we fine-tuned each variant on 50B tokens with the same training recipe and observed that the model with pretrained initialization significantly outperformed that with training from scratch (Extended Data Table 6). Notably, the spiral-in order aligned with region-completion tasks, enabling zero-shot image inpainting without task-specific tuning (Extended Data Fig. 2). These results indicate that the pretrained priors learned from large-scale raster training can be transferred effectively to new token orders, highlighting the robustness and general-purpose adaptability of the approach.

Related work

Recent advances in vision–language modelling have leveraged pretrained image encoders such as CLIP[10] to produce generalizable representations, which are then combined with LLMs to form powerful vision–language models. Approaches such as BLIP-2 (ref. [4]) and LLaVA[9] achieve strong performance by training on large-scale image–text pairs and instruction-following data. Further gains have been made through use of curated datasets and improved training strategies33,34. Although models such as EVE[35] directly feed image patches into language models, they still face challenges in competing with state-of-the-art vision–language models. Here we show that Emu3, a decoder-only model trained purely with next-token prediction, can reach the performance of these encoder-based systems.

Recent progress in image and video generation has been largely driven by diffusion models, which achieve high-resolution synthesis through iterative denoising. The open-source release of the Stable Diffusion series2,20 has led to widespread research and development in this direction. Autoregressive approaches36,37 predict images token by token, and extensions[38] apply similar ideas to video. However, these models either fail to reach the performance of diffusion models or rely on cascade and/or compositional approaches. In this work, Emu3 demonstrates powerful image and video generation capabilities with a single Transformer decoder. Notably, we open-source to support further research and development in this direction.

There have been early efforts to unify vision understanding and generation11,39,40, exploring various generative objectives on image and text data. Emu and Emu2 (refs. 11,41) introduce a unified autoregressive objective: predicting the next multimodal element by regressing visual embeddings or classifying textual tokens. Chameleon[12] trained token-based autoregressive models on mixed image and text data. Other efforts have also explored unified multimodal models42,43,44,45, but have these either focused on traditional vision tasks such as segmentation or achieved performance barely close to that of task-specific architectures across general multimodal tasks of video generation, image generation and vision–language understanding. Strong results have been reported for recent models including Bagel[46] and Nano Banana[47], yet the scopes and methodologies of these approaches differ substantially: Bagel is a hybrid architecture with diffusion model expert and does not handle videos; and Nano Banana remains proprietary without public implementation or details. Emu3, by contrast, demonstrates that next-token prediction across images, video, action and text can match the performance of well-established models, without relying on compositional methods. This work shows the scalability, effectiveness and generality of next-token prediction for unified multimodal learning across artificial-intelligence-generated content, multimodal understanding and robotic manipulation.

Conclusions, limitations and future work

Emu3 demonstrates that next-token prediction alone can unify multimodal learning at scale. By discretizing text, images and videos into a shared token space and training a single decoder-only Transformer, Emu3 equals the performance of well-established task-specific models across both perception and generation, matching flagship systems while removing the need for diffusion or compositional architectures. The resulting scaling laws demonstrate predictable efficiency across modalities, confirming that next-token prediction can serve as a general foundation for multimodal sequence modelling.

Despite the promising results, our approach has several notable limitations. First, the inference could be accelerated. The current inference process uses a naive decoding strategy, whereas more advanced parallel decoding strategies can be leveraged to speed up. Second, the current tokenizer design presents trade-offs in both compression ratio and reconstruction fidelity, which could be further optimized for efficiency and effectiveness in downstream tasks, for example, exploring new quantization approaches and increasing the codebook size. Third, the diversity and quality of multimodal datasets, particularly for long-horizon video-centric scenarios, remain insufficient to capture the full range of real-world complexity. Although we acknowledge these challenges, addressing them lies beyond the scope of this work. We also highlight several underexplored technical directions for future research, including the development of efficient architectures for ultralong multimodal contexts, enhancing tokenizer expressiveness, and constructing more robust and realistic benchmarks.

Unified next-token modelling offers a promising route towards world models that integrate perception, language and action. Such systems could ground linguistic reasoning in visual and embodied experience, enabling more general forms of understanding, creativity and control. We believe this framework represents a key step towards scalable and unified multimodal intelligence.

Methods

Tokenizer design

A unified tokenizer discretizes texts, images and videos into compact token sequences using shared codebooks. This enables text and vision information to reside in a common discrete space, facilitating autoregressive modelling. For text tokens and control tokens, we leveraged a byte pair encoding (BPE)-based text tokenizer for tokenization, whereas a vector quantization (VQ)-based visual tokenizer was used to discretize images and videos into compact token sequences.

Text tokenizer

For text tokenization, we adopted Qwen’s tokenizer[49], which uses byte-level byte-pair encoding with a vocabulary encompassing 151,643 regular text tokens. To reserve sufficient capacity for template control, we also incorporated 211 special tokens into the tokenizer’s vocabulary.

Vision tokenizer

We trained the vision tokenizer using SBER-MoVQGAN[14], which can encode a 4 × 512 × 512 video clip or a 512 × 512 image into 4,096 discrete tokens from a codebook of size 32,768. Our tokenizer achieved 4× compression in the temporal dimension and 8 × 8 compression in the spatial dimension and is applicable to any temporal and spatial resolution. Building on the MoVQGAN architecture[50], we incorporated two temporal residual layers with three-dimensional convolution kernels into both the encoder and decoder modules to perform temporal downsampling and enhance video tokenization capabilities. The tokenizer was trained end-to-end on the LAION high-resolution image dataset and the InternVid[51] video dataset using combined objective functions of Euclidean norm (L2) loss, learned perceptual image patch similarity (LPIPS) perceptual loss[52], generative adversarial network (GAN) loss and commitment loss. Further details on video compression metrics, the impact of codebook size, and comparisons between the unified and standalone image tokenizers are provided in section 1 of the Supplementary Information.

Architecture design

Emu3 uses a decoder-only Transformer with modality-shared embeddings. We used RMSNorm[53] for normalization and GQA[54] for attention mechanisms, as well as the SwiGLU[55] activation function and rotary positional embeddings[56]. Biases in the qkv and linear projection layers were removed. In addition, a dropout rate of 0.1 was implemented to improve training stability. Overall, the model contains 8.49 billion parameters, including 32 layers with a hidden size of 4,096, intermediate size of 14,336 and 32 attention heads (8 key-value heads). The shared multimodal vocabulary comprises 184,622 tokens, enabling consistent representation across language and vision domains.

Architectural comparisons with diffusion models

To fairly compare the next-token prediction paradigm with diffusion models for visual generation tasks, we used Flan-T5-XL[57] as the text encoder and trained both a 1.5B diffusion transformer58,59 and a 1.5B decoder-only transformer[60] on the OpenImages[61] dataset. The diffusion model leverages the variational autoencoder from SDXL[20], whereas the decoder-only transformer uses the video tokenizer in Emu3 to encode images into latent tokens. Both models were trained with identical configurations, including a linear warm-up of 2,235 steps, a constant learning rate of 1 × 10−4 and a global batch size of 1,024. As shown in Fig. 3c, the next-token prediction model consistently converged faster than its diffusion counterpart for equal training samples, challenging the prevailing belief that diffusion architectures are inherently superior for visual generation.

Architectural comparisons with encoder + LLM compositional paradigm

To fairly evaluate different vision–language architectures, we compared three model variants (trained without any pretrained LLM initialization) on the I2T validation set (an image-understanding task), as shown in Fig. 3b. All models were trained on the EVE-33M multimodal corpus[35], using a global batch size of 1,024, a base learning rate of 1 × 10−4 with cosine decay scheduling and 12,000 training steps, and evaluated on a held-out validation set of 1,024 samples with comparable parameters. The models compared were: (1) a decoder-only model that consumes discrete image tokens as input (Emu3 variant, 1.22B parameters); (2) a late-fusion architecture comprising a vision encoder and decoder (LLaVA-style variant, 1.22B = 1.05B decoder + 0.17B vision encoder); and (3) a late-fusion architecture initialized with a CLIP-based vision encoder (LLaVA-style variant, 1.35B = 1.05B + 0.30B). The late-fusion LLaVA-style model initialized with a pretrained CLIP vision encoder showed substantially lower validation loss. Notably, when that pretraining advantage was removed, the apparent superiority of the encoder-based compositional architecture was largely diminished. The decoder-only next-token prediction model showed comparable performance, challenging the prevailing belief that encoder + LLM architectures are inherently superior for multimodal understanding. When evaluated under equal scratch training conditions, without prior initialization from LLMs and CLIP, it matched compositional encoder + LLM paradigms in terms of learning efficiency. Further architectural analyses are provided in section 2.1 of the Supplementary Information.

Data collection

Emu3 was pretrained from scratch on a mix of language, image and video data. Details of data construction, including sources, filtering and preprocessing, are provided in Extended Data Table 7. Further information on dataset composition, collection pipelines and filtering details is provided in section 3.1 of the Supplementary Information.

Pretraining details

Data format

Images and videos were resized to areas near 512 × 512 while preserving the aspect ratio during pretraining. We inserted special tokens [SOV], [SOT] and [EOV] to delimit multimodal segments:

$$[\text{BOS}]\{\text{caption text}\}[\text{SOV}]\{\text{meta text}\}[\text{SOT}]\{\text{vision tokens}\}[\text{EOV}][\text{EOS}],$$

where [BOS] and [EOS] mark the start and end of the whole sample, [SOV] marks the start of the vision input, [SOT] marks the start of vision tokens, and [EOV] indicates the end of the vision input. In addition, [EOL] and [EOF] were inserted into the vision tokens to denote line breaks and frame breaks, respectively. The ‘meta text’ contains information about the resolution for images; for videos, it includes resolution, frame rate and duration, all presented in plain text format. We also moved the ‘caption text’ field in a portion of the dataset to follow the [EOV] token, thereby constructing data aimed at vision understanding tasks.

Training recipe

Pretraining followed a three-stage curriculum designed to balance training efficiency and optimization stability. Stage 1 used a learning rate of 1 × 10−4 with cosine decay, no dropout and a sequence length of 5,120. This configuration enabled rapid early convergence; however, the absence of dropout eventually led to optimization instability and model collapse in late training. Stage 2 therefore introduced a dropout rate of 0.1, which stabilized optimization while retaining the warm-start benefits established in stage 1. Stage 3 extended the context length to 65,536 tokens to accommodate video–text data. The sampling ratio gradually shifted from image–text pairs towards video–text pairs. This curriculum substantially improved overall efficiency: the first two stages focused on image data for stable and cost-effective initialization, whereas the third stage expanded the context window and incorporated video data for full multimodal training. Tensor and pipeline parallelism remained constant across stages, with context parallelism scaling from 1 to 4 only in stage 3 to support the extended sequence length. Further implementation details including multimodal dropout for stability, token-level loss weighting, LLM-based initialization and mixture-of-experts configuration are provided in section 3.2.3 of the Supplementary Information.

Post-training details

T2I generation

QFT. After pretraining, Emu3 underwent post-training to enhance visual generation quality. We applied QFT to high-quality image data while continuing next-token prediction with supervision restricted to vision tokens. Training data were filtered by the average of three preference scores: HPSv2.1 (ref. [62]), MPS[63] and the LAION-Aesthetics score[64], and the image resolution was increased from 512 to 720 pixels. We set the batch size to 240 with a context length of 9,216, with the learning rate cosine decaying from 1 × 10−5 to 1 × 10−6 over 15,000 training steps. Subsequently, a linear annealing strategy was used to gradually decay the learning rate to zero over the final 5,000 steps of QFT training.

DPO. We further aligned generation quality with human preference using DPO[13]. For each prompt, the model generated 8–10 candidate images that were evaluated by three annotators on visual appeal and alignment. The highest and lowest scoring samples formed preference triplets \(({p}_{i},{x}_{i}^{{\rm{chosen}}},{x}_{i}^{{\rm{rejected}}})\) for optimization. Tokenized data from this process were reused directly during training to avoid retokenization inconsistencies. Emu3-DPO jointly minimizes the DPO loss and the next-token prediction loss, with a weighting factor of 0.2 applied to the supervised fine-tuning loss for stable optimization. During DPO training, we use a dataset of 5,120 prompts and train for one epoch with a global batch size of 128. The learning rate follows a cosine decay schedule with a brief 5-step warm-up and then decays to a constant value of 7 × 10−7. A KL penalty of 0.5 is applied to the reference policy to balance alignment strength and generation diversity.

We present the performance of Emu3 through automated metric evaluation on popular T2I benchmarks: MSCOCO-30K[23], GenEval[24], T2I-CompBench[25], and DPG-Bench[26]. Evaluation details are provided in the Supplementary Information, section 4.1.2.

T2V generation

Emu3 was extended to T2V generation by applying QFT to high-quality video data (each sample was 5 s long, 24 fps), with strict resolution and motion filters to ensure visual fidelity. We set the batch size to 720 with a context length of 131,072, with the learning rate set to 5 × 10−5 over 5,000 training steps. We evaluated video generation using VBench[27], which assesses 16 dimensions including temporal consistency, appearance quality, semantic fidelity and subject–background coherence. Evaluation details are provided in section 4.2.2 of the Supplementary Information.

Vision–language understanding

Emu3 was further adapted to vision–language understanding through a two-stage post-training procedure. In the first stage, the model was trained on 10 million image–text pairs using a batch size of 512, mixing image-understanding data with pure language data while masking losses on vision tokens for text-only prediction. All images were resized to approximately 512 × 512 while preserving the aspect ratio. In the second stage, we performed instruction tuning on 3.5 million question–answer pairs sampled from ref. [65], also using a batch size of 512; images with shorter or longer resolution were clipped to the 512–1,024 pixel range. For both stages, we used a cosine learning rate schedule with a peak learning rate of 1 × 10−5. Evaluation details are provided in section 4.3 of the Supplementary Information.

Interleaved image–text generation

We further extended Emu3 to interleaved image–text generation, in which structured textual steps are accompanied by corresponding illustrative images within a single output sequence. The model was fine-tuned end-to-end to autoregressively generate such multimodal sequences, leveraging the flexibility of the unified framework. Training was performed for 10,000 steps with a global batch size of 128 and a maximum sequence length of 33,792 tokens. Each sequence included up to 8 images, each resized to a maximum area of 512^2 pixels while preserving the aspect ratio. We used the Adam optimizer with a cosine learning rate schedule and a base learning rate of 7 × 10−6 and applied a dropout rate of 0.1 with equal weighting between image and text losses. Further details on data formatting and visualization results are provided in section 4.4 of the Supplementary Information.

Vision–language–action models

We further extended Emu3 to vision–language–action tasks by fine-tuning it on the CALVIN[66] benchmark, a simulated environment designed for long-horizon, language-conditioned robotic manipulation.

The model was initialized from Emu3 pretrained weights, whereas the action encoder used the FAST tokenizer[31] with a 1,024-size vocabulary, replacing the last 1,024 token IDs of the language tokenizer. RGB observations from third-person (200 × 200) and wrist (80 × 80) views were discretized using the Emu3 vision tokenizer with a spatial compression factor of 8. Training used a time window of 20 and an action chunk size of 10, forming input sequences of two consecutive vision–action–vision–action frames. Loss weights were set to 0.5 for visual tokens and 1.0 for action tokens. The model was trained for 8,000 steps with a batch size of 192 and a cosine learning rate schedule starting at 8 × 10−5. During inference, it predicted actions online by means of a sliding two-frame window. Visualizations are shown in Extended Data Fig. 3. Although the CALVIN benchmark is simulation-based, Emu3’s vision–language–action formulation was designed with real-world deployment challenges in mind. The next-token prediction paradigm naturally conditions on arbitrary-length histories, allowing the model to integrate feedback over time and recover from partial or imperfect sensor inputs, thereby accommodating noisy sensors or delayed feedback. In practice, real-world robotic validation requires substantial data collection (for instance, time-consuming tele-operation or on-hardware rollouts) and system-level engineering efforts to ensure safety, latency guarantees and reliable actuation, which made large-scale evaluation on physical robots difficult within the scope of this work. Although large-scale physical-robot validation will be part of our future work, the simulation results show that Emu3 can model complex, interleaved perception–action sequences without task-specific components, indicating strong potential for transfer to real robotic systems.

Data availability

Details including collection pipelines, preprocessing, composition and other information are available in section 3.1 of the Supplementary Information. Details of the post-training data are presented in section 4 and the publicly available training and evaluation datasets in section 8 of the Supplementary Information. The publicly available training data include: FineWeb-Edu (https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu), LAION-5b (https://laion.ai/blog/laion-5b/), LAION-AESTHETICS (https://laion.ai/blog/laion-aesthetics/), Datacomp (https://github.com/mlfoundations/datacomp), COYO-700m (https://github.com/kakaobrain/coyo-dataset), OpenImages (https://storage.googleapis.com/openimages/web/index.html), SA-1B (https://segment-anything.com/dataset/index.html), YT-Temporal-1B (https://rowanzellers.com/merlotreserve/), JourneyDB (https://huggingface.co/datasets/JourneyDB/JourneyDB), DiffusionDB (https://huggingface.co/datasets/poloclub/diffusiondb), midjourney-prompts (https://huggingface.co/datasets/vivym/midjourney-prompts), and LLaVA-OneVision-Data (https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data).

Code availability

The custom computer code used and the models produced in this study are available via GitHub at https://github.com/baaivision/Emu3. The code was released under the Apache-2.0 license. The model weights are publicly available at https://huggingface.co/collections/BAAI/emu3, including the tokenizer, the pretrained model and two post-trained derivatives.

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Acknowledgements

This research is supported by BAAI, where this work was carried out. We thank W. Wang, X. Li, X. Liu, S. Nie, Q. Ma, H. Zhou, Y. Jiao, L. Zhang, M. Lu, Y. Shao, Y. Chen, D. Hao, M. Lv, T. Dai, J. Liu and Y. Si for their support of the Emu3 project.

Author information

Author notes

1. These authors contributed equally: Xinlong Wang, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Zhen Li

Authors and Affiliations

1. Beijing Academy of Artificial Intelligence (BAAI), Beijing, China

Xinlong Wang, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Zhen Li, Yuqi Wang, Qiying Yu, Yingli Zhao, Yulong Ao, Xuebin Min, Chunlei Men, Boya Wu, Bo Zhao, Bowen Zhang, Liangdong Wang, Guang Liu, Zheqi He, Xi Yang, Yonghua Lin, Zhongyuan Wang & Tiejun Huang

2. Tsinghua University, Beijing, China

Jingjing Liu

3. Peking University, Beijing, China

Tiejun Huang

Contributions

X.W. led the research. X.W., Y.C., J.W., F.Z., Yueze Wang, X.Z., Z. Luo, Q.S. and Z. Li are equally contributing authors and developed the architecture, training and core experiments. Yuqi Wang and Q.Y. contributed to additional experiments. Y.Z., Y.A., X.M. and C.M. developed the training and inference infrastructure. B.W., B. Zhao, B. Zhang, L.W. and G.L. contributed to data processing. Z.H. and X.Y. contributed to evaluation. X.W.,Y.C., J.W., F.Z., Yueze Wang, X.Z., Z. Luo, Q.S., Z. Li and Yuqi Wang contributed to the writing of the paper. J.L. advised Q.Y. Y.L., Z.W. and T.H. advised on the project.

Corresponding authors

Correspondence to Xinlong Wang, Zhongyuan Wang or Tiejun Huang.

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Extended data figures and tables

Extended Data Fig. 1 Visualization of interleaved image-text generation results.

Qualitative examples of generated interleaved image–text sequences, illustrating step-by-step multimodal generation for instructional tasks.

Extended Data Fig. 2 Zero-shot image inpainting with Emu3 using spiral-in token order.

Given the input images (left of each row) and corresponding prompts, Emu3 accurately fills the masked regions within the bounding boxes, generating semantically aligned content without task-specific fine-tuning.

Extended Data Fig. 3 Visualization of visual prediction on the Droid dataset.

Qualitative examples of visual sequences predicted under language instructions. The first frame (red box) is the observed input, followed by predicted frames.

Extended Data Table 1 Training recipe for Emu3 pretraining

Extended Data Table 2 Comparison on text-to-image benchmarks

Extended Data Table 3 Comparison on VBench benchmark

Extended Data Table 4 Comparison on vision-language benchmarks

Extended Data Table 5 Comparison on the CALVIN long-horizon robotic manipulation benchmark

Extended Data Table 6 Token-order adaptation study

Extended Data Table 7 Dataset construction and preprocessing

Supplementary information

Supplementary Information

Supplementary Figs 1–11, Tables 1–11 and Notes 1–7.

Peer Review File

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Wang, X., Cui, Y., Wang, J. et al. Multimodal learning with next-token prediction for large multimodal models. Nature 650, 327–333 (2026). https://doi.org/10.1038/s41586-025-10041-x

- Received: 11 November 2024

- Accepted: 11 December 2025

- Published: 28 January 2026

- Version of record: 28 January 2026

- Issue date: 12 February 2026

- DOI: https://doi.org/10.1038/s41586-025-10041-x

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Ferromagnet-like binary switching of a Stoner–Wohlfarth antiferromagnet

Zhanshan Wang,

Yining Xiang,

Ruohan Chen,

Zeyuan Sun,

Canyu Hong,

Xinyu Chen,

Jingjing Gao,

Shuang Wu,

Zhongxun Guo,

Yi Chen,

Qixi Mi,

Zhongkai Liu,

Shaohua Yan,

Hechang Lei,

Wei Ruan,

Yuanbo Zhang,

Weichao Yu,

Wei-Tao Liu,

Zhe Yuan &

…

Shiwei Wu

Nature volume 650, pages 340–345 (2026)

Abstract

The Stoner–Wohlfarth antiferromagnet (AFM), an extension of the classical Stoner–Wohlfarth model originally describing the magnetization reversal in ferromagnetic nanoparticles1,2, refers to a single-domain AFM whose Néel vector can be coherently switched by the magnetic field. These AFMs not only retain the inherent advantages of antiferromagnetism but also feature controllable Néel vector and a perfect switching ratio, thus emerging as promising building blocks for ultradense magnetic memories and high-throughput computing systems3,4. However, bulk AFMs are not the Stoner–Wohlfarth AFMs owing to the hard-to-switch Néel vector and inevitable multidomain structure3,5,6,7. Here we report that CrPS_4, a two-dimensional (2D) van der Waals (vdW) A-type AFM, exhibits ideal characteristics of the Stoner–Wohlfarth AFMs, because of the dominance of antiferromagnetic exchange over the magnetic anisotropy and high quality of vdW interfaces. The antiferromagnetic order undergoes a ferromagnet (FM)-like binary switching with the magnetic field rather than the layer-by-layer flipping observed in other 2D A-type AFMs. Moreover, we deduce the characteristic exchange length of several vdW A-type AFMs and propose a criterion for judging the Stoner–Wohlfarth AFMs. Our work therefore establishes a universal framework for understanding the magnetization reversal in layered AFMs and promotes the effective use of 2D AFMs in advanced spintronic devices.

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Main

The response of magnetization to external stimuli has drawn attention for more than a century, providing fundamental insights into the physical mechanisms behind magnetization dynamics. The Stoner–Wohlfarth model, proposed in 1948, describes the magnetization reversal in the form of coherent rotation of a single-domain ferromagnetic nanoparticle[1] (Fig. 1a). Beyond its fundamental importance as a type of hydrogen model for ferromagnetism[2], it provides guidance for the design of magnetic devices for data computing and information storage8,9. However, the simplicity of the model makes it arduous for explaining the magnetic behaviour of other ferromagnetic systems, which usually have multidomain structures associated with unavoidable defects. In this regard, vdW magnets exhibit a natural advantage, as their defect-free vdW interfaces allow them to be considered single-domain, at least in the vertical dimension. Such 2D FMs with strong interlayer coupling can be called the Stoner–Wohlfarth FMs, such as Fe_3GeTe_2, whose magnetic reversal behaviours are well described by the Stoner–Wohlfarth model below a certain thickness[10].

Fig. 1: Classification of various types of magnetism and evolution of the magnetic order.

a, Binary switching of magnetization controlled by the magnetic field in FMs. b, Magnetic-field-insensitive Néel vector in conventional 3D AFMs. c, Interlayer-free 2D A-type AFMs (2D A-AFMs). Left, the evolution of magnetization with the magnetic field (taking 4L as an example). The corresponding magnetization is marked on each plateau. Right, schematic of interlayer-free flipping from M = 0 to M = +2. The red or blue quadrilaterals represent a ferromagnetic layer with opposite magnetization, +1 or −1, respectively. d, SHG hysteresis loop on 4L CrSBr with the in-plane magnetic field between ±0.6 T sweeping along the easy axis of CrSBr. The assignment of magnetic states during transitions is discussed in ref. [12]. e, Interlayer-locked 2D A-AFMs. Left, FM-like binary switching of Néel vector controlled by the magnetic field. Right, schematic of interlayer-locked flipping between +L and −L. f, SHG hysteresis loop on 4L CrPS_4 with the out-of-plane magnetic field between ±2 T. a.u., arbitrary units.

As an extension, is there a Stoner–Wohlfarth AFM—a single-domain AFM whose antiferromagnetic order (Néel vector L) can be coherently switched 180° by the magnetic field? From an application perspective, seeking such AFMs is crucial for improving the data-storage density and information-processing efficiency attributed to their zero stray field and ultrafast dynamics3,4. For the conventional three-dimensional (3D) collinear AFM, however, a 180° switching of the Néel vector is unavailable owing to the vanishing Zeeman energy (Fig. 1b). Although recently a new type of collinear AFM known as altermagnets has been demonstrated to exhibit 180° switching of the Néel vector, they are not the Stoner–Wohlfarth AFMs, as the incomplete switching ratio revealed by the anomalous Hall effect suggests the existence of microscopic multidomain structures5,6,7. Of particular interest are the 2D vdW A-type AFMs, in which spins within each layer order ferromagnetically and adjacent layers couple antiferromagnetically. The weak interlayer magnetic coupling leads to their controllable antiferromagnetism. The magnetic evolution of some representative 2D A-type AFMs, such as CrSBr (refs. 11,12) and CrI_3 (refs. 13,14), has been extensively studied in the past, following a behaviour of layer-by-layer flipping with the magnetic field, as sketched in Fig. 1c and illustrated in Fig. 1d for 4L CrSBr and Extended Data Fig. 1 for 4L CrI_3. Such AFMs are also not the expected Stoner–Wohlfarth AFMs because the Néel vector is not coherently switched to its antiphase state at once (L to −L) but tends to a metastable state by an interlayer-free flipping (Fig. 1c).

Here we report a new type of 2D vdW A-type AFM, CrPS_4 as a representative, whose antiferromagnetic order undergoes an interlayer-locked antiferromagnetic switching (Fig. 1e). The magnetic evolution manifests as a FM-like binary switching (Fig. 1f) rather than the layer-by-layer flipping observed in interlayer-free AFMs. CrPS_4 is an air-stable vdW semiconductor and crystallizes into a non-centrosymmetric monoclinic structure with space group C_2 (refs. 15,16). As shown in Fig. 2a, the material forms an A-type antiferromagnetic order along the c-axis below the Néel temperature 38 K (refs. 16,17,18). Transport measurements in CrPS_4 indicate that its magnetic behaviour undergoes a spin-flop transition into a canted state at approximately 0.7 T, followed by a spin-flip transition into a ferromagnetic state around 7 T under an out-of-plane magnetic field19,20,21 (schematically shown in Extended Data Fig. 2). Besides, recent reflective magnetic circular dichroism (RMCD) studies show a clear hysteresis loop below the spin-flop field in odd-layer CrPS_4, suggesting the existence of further magnetic transitions[16]. However, RMCD is only sensitive to the net magnetization, so it fails to uncover the layer-resolved magnetization reversal for this transition and, more importantly, it is incompetent to investigate the magnetic evolution of even-layer CrPS_4 whose net magnetization is zero.

Fig. 2: FM-like binary switching in odd-layer and even-layer AFM CrPS_4.

a, Crystallographic and magnetic structure of CrPS_4. The magnetic moments denoted by the red and blue arrows on Cr3+ alternate oppositely between adjacent layers. b, Optical microscopic image of a CrPS_4 flake (sample 1) with the labelled layer thicknesses. Scale bar, 5 μm. c–h, SHG and RMCD hysteresis loops on 2L (c,f), 3L (d,g) and 4L (e,h) CrPS_4 with the out-of-plane field between ±0.1 T. i, Symmetry transformation of the non-centrosymmetric antiferromagnetic state in even-layer CrPS_4 under the spatial-inversion operation i. The spatial-inversion operation converts one antiferromagnetic state to the other but not to itself. j, Symmetry transformation of the centrosymmetric antiferromagnetic state in odd-layer CrPS_4. a.u., arbitrary units.

To reveal the barely detectable antiferromagnetic order and its potential evolution, we use the second-harmonic generation (SHG) microscopic technique because the nonlinear optical signal is no longer restricted by the net magnetization but is sensitive to the symmetry changes12,14,22,23. The SHG process typically originates from the dominant electric-dipole mechanism and the prerequisite for this process is the broken spatial-inversion symmetry. The layered antiferromagnetic order such as even-layer CrPS_4 simultaneously breaks both the spatial-inversion and time-reversal symmetries, thus contributing time-noninvariant (c-type) electric-dipole SHG and permitting SHG to detect the antiferromagnetic order and symmetry-related phenomena.

Figure 1f shows a typical magnetic-field-dependent SHG loop on tetralayer CrPS_4 at 6.5 K, in which the field is perpendicular to the sample and sweeps forward and backward between ±2 T. At high magnetic fields beyond ±0.7 T, the loop exhibits a smooth trajectory, originating from the gradual canted states associated with spin-flop transition (Extended Data Fig. 2). Notably, a clear FM-like hysteresis loop appears at much lower fields (±0.01 T), indicating the existence of further magnetic transitions in CrPS_4.

We further focus on this FM-like loop and examine its layer-dependent evolution. Figure 2b shows the optical microscopic image of few-layer CrPS_4 with thicknesses of 2L–4L. As well as the tetralayer, this FM-like switching also exists in the bilayer but is absent in the trilayer (Fig. 2c–e). Without loss of generality, similar odd–even-layer contrast is shown for other thicknesses in Extended Data Fig. 3. Notably, when the polar RMCD is used for the same sample, the features are exactly opposite to that of SHG. Only the odd layers show the hysteresis loop, whereas all of the even layers do not (Fig. 2f–h). As the temperature increases, the hysteresis loops observed in both SHG and RMCD shrink and eventually vanish at the critical temperature of around 34 K, at which the antiferromagnetic order disappears (Supplementary Text 1 and Extended Data Fig. 4).

For even-layer CrPS_4, the single FM-like loop present in SHG but absent in RMCD suggests that the magnetic transition near 0 T originates from the antiferromagnetic binary switching. That means, when applying a magnetic field along the out-of-plane direction of CrPS_4, all layers are antiferromagnetically locked and simultaneously flipped to their time-reversal counterpart, that is, switching from the +L state to the −L state. Because the layered antiferromagnetic order of even-layer CrPS_4 breaks the spatial-inversion symmetry (Fig. 2i), c-type SHG χ(c) emerges and couples linearly to the Néel vector, which is expressed by χ(c)(−L) = −χ(c)(L). Besides, the crystallographic structure of CrPS_4 is non-centrosymmetric, resulting in the extra time-invariant (i-type) SHG χ(i). Therefore, when the switching between the time-reversal antiferromagnetic counterparts occurs, the self-interference |χ(i) ± χ(c)|2 leads to the intensity contrast in SHG loops24,25,26. The antiferromagnetic switching is further supported by the helicity-reversed SHG loops (Extended Data Fig. 5), in which the helicity of excitation is equivalent to exerting a time-reversal operation to the system (see details in Supplementary Text 2). However, the switching is invisible in RMCD signals as the magnetization of even layers is completely compensated.

For odd-layer CrPS_4, owing to the uncompensated magnetization, a hysteresis loop emerges in RMCD signals, verifying that the antiferromagnetic switching also exists in odd-layer samples. To understand the absence of SHG loop in odd layers, we noted that the layered antiferromagnetic order alone for odd layers is centrosymmetric, as illustrated in Fig. 2j. Yet, because of the broken spatial-inversion symmetry in crystallographic lattice, both χ(i) and χ(c) can be non-zero. The centrosymmetric antiferromagnetic order substantially alleviates the degree of symmetry breaking—leading to negligible χ(c). As a result, the SHG signals of odd-layer samples remain constant during the antiferromagnetic switching.

Very recently, a study by Ho et al.[26] also provided evidence for the antiferromagnetic switching in few-layer CrPS_4 using the SHG technique. By applying opposite saturating magnetic fields (±9 T) and then returning to 0 T, they observed weak distortions in polarization-resolved SHG patterns, consistent with antiferromagnetic switching. As discussed below, interlayer-free AFMs can also exhibit magnetic-field-driven switching but through a fundamentally different mechanism. However, the work by Ho et al.[26] did not track the switching hysteretically with magnetic fields, leaving this key mechanistic distinction unresolved.

To explain the distinct antiferromagnetic switching between the interlayer-locked and interlayer-free AFMs, magnetic-field-dependent SHG was also performed on an interlayer-free AFM CrSBr. CrSBr is an A-type AFM with in-plane antiferromagnetism along its easy b-axis direction11,23,27. Because SHG signals from two antiferromagnetic ground states nearly degenerate in intensity but reverse in phase, we use the interference between the SHG fields from the antiphase states and that from an external reference to distinguish them effectively[12] (see Methods and Supplementary Text 3 for details). Figure 3 shows the phase-resolved SHG hysteresis loops with an in-plane field along the easy axis under different sweeping ranges on the tetralayer CrSBr. The stepwise jump between adjacent signal plateaus is caused by the magnetization flipping of an individual layer. When an antiferromagnetic state from a unidirectional sweep is driven to a ferromagnetic state at ±0.6 T and then returns to 0 T (major loop in Fig. 3a), the non-overlapping signal plateau indicates that it can be switched to the other antiferromagnetic counterpart. By contrast, if the antiferromagnetic ground state is only driven to ±0.35 T, the switching to the other antiferromagnetic counterpart would not occur (minor loops in Fig. 3b,c). Thus, for the interlayer-free AFM, the antiphase states can only be obtained by a complete multistep layer-by-layer sequence, rather than by reversible binary switching as observed in the interlayer-locked AFM CrPS_4.

Fig. 3: Multistep antiferromagnetic switching in 4L CrSBr.

a, Major phase-resolved SHG (denoted ‘Phase-SHG’) loop on 4L CrSBr, in which the in-plane field is swept from −0.6 T to +0.6 T and then back to −0.6 T along the easy axis of CrSBr. The antiferromagnetic state at 0 T is switchable but requires undergoing a complete layer-by-layer switching. b,c, Minor phase-resolved SHG loops on 4L CrSBr, in which the in-plane field is swept from −0.6 T to +0.35 T and then back to −0.6 T in b and from +0.6 T to −0.35 T and then back to +0.6 T in c. The antiferromagnetic state is not switched when the magnetic field reverses in an intermediate state before the ferromagnetic state. a.u., arbitrary units.

To gain a better understanding of the switching behaviour, we apply the micromagnetic module in COMSOL Multiphysics28,29 to simulate magnetic switching of few-layer CrPS_4. Starting from the A-type antiferromagnetic order with out-of-plane intralayer magnetizations, we artificially reverse the bottom-layer magnetization and track the evolution of the others. Figure 4a shows the resulting phase diagram as a function of the interlayer exchange J⟂ and the effective anisotropy K (see Methods for details). Both the interlayer-free and interlayer-locked behaviours are obtained, with their boundaries shown by the circles for each thickness. When J⟂ is sufficiently strong, adjacent layers overcome the anisotropy barrier and all layers ultimately reverse, producing the interlayer-locked behaviour; otherwise, only the bottom layer switches, yielding the interlayer-free behaviour. In the few-layer limit, the phase boundary between them depends on the layer thickness. When the layer number reaches four or more, the phase boundary tends to converge. Experimental30,31 and computational[16] estimates of J⟂ and K for CrPS_4 fall well below this boundary, consistent with the interlayer-locked switching.

Fig. 4: Switching mechanism and phase diagrams.

a, Simulated phase diagram of magnetization switching in 2D A-type AFMs. The dots are the simulated boundaries between interlayer-free and interlayer-locked switching and the solid lines illustrate the linear fitting. The stars indicate the experimental and calculated magnetic parameters of CrPS_4, all of which lie below the phase boundary. b, Energy-barrier profile of magnetic switching for 3L CrPS_4. The high-energy plateau corresponds to an intermediate state containing a non-collinear domain wall between two collinear antiferromagnetic configurations with a lower energy. c, Calculated energy landscape of 3L CrPS_4 as a function of domain-wall position rDW and external field Hz. The dashed white line represents the iso-energy contour of the high-energy plateau with a domain wall in b. The coercive field Hils for interlayer-locked switching provides a Zeeman energy to overcome the energy barrier between high-energy and low-energy plateaus. d, Calculated coercive fields (indicated by red lines) for switching the Néel orders (grey arrows) of 4L CrPS_4, with the adjacent 3L contributing to the net magnetization. Yellow/blue regions denote +z/−z Néel orders, respectively.

Both experiment and simulation indicate that few-layer CrPS_4 is a representative Stoner–Wohlfarth AFM. To describe the FM-like binary switching in AFMs, we extend the Stoner–Wohlfarth model originally for the FMs[1] by explicitly including the interlayer antiferromagnetic exchange energy (see Methods). This extended Stoner–Wohlfarth model yields a characteristic exchange length, \({l}_{{\rm{ex}}}=\sqrt{{J}_{\perp }d/(2K)}\), with d being the interlayer distance, for determining the coherent Néel-vector switching. For a quantitative comparison, we estimate lex of the reported A-type AFMs, including CrI_3, CrSBr, CrPS_4 and MnBi_2Te_4, as listed in Extended Data Table 1. For CrPS_4 and MnBi_2Te_4, lex exceeds the interlayer distance, so switching one layer must influence its neighbours. Indeed, the RMCD loops measured on few-layer MnBi_2Te_4 show the similar interlayer-locked switching behaviour (Extended Data Fig. 6). For the sample thickness larger than lex, the switching can still coherently extend to the entire stack unless interrupted by disorder, such as stacking fault. Experimentally, interlayer-locked switching is seen in CrPS_4 up to 8L using SHG, whereas thicker flakes with opposite Néel vectors cannot be distinguished by SHG (Extended Data Fig. 7). By contrast, for CrI_3 and CrSBr, the exchange lengths are shorter than the interlayer distance, resulting in their interlayer-free switching, which excludes them from the Stoner–Wohlfarth AFMs.

Examining the hysteresis loops shown in Fig. 2 and Extended Data Fig. 3, the coercive fields of the few-layer CrPS_4 are mostly symmetric with respect to the positive and negative magnetic fields and only a few exhibit some noticeable lateral exchange bias effect that was recently observed in micron-sized samples by using scanning nitrogen-vacancy centre magnetometry32,33,34. Moreover, the coercive fields between odd-layer and even-layer CrPS_4 have comparable magnitude, on the order of 10–100 mT. The small difference suggests that the magnetization-switching dynamics in the lateral dimension occurs through the domain-wall propagation rather than uniform coherent rotation, as the former is energetically more favourable than the latter.

For odd-layer samples, the ground states are collinear antiferromagnetic with non-zero net magnetization pointing up or down, shown as the low-energy plateaus in Fig. 4b. Switching between them occurs by means of a propagating domain wall and the strong interlayer exchange of Stoner–Wohlfarth AFMs consistently ensures coherent rotational coupling along the vertical dimension. The introduction of a domain wall increases the energy of the system owing to its non-collinear magnetic components, resulting in an energy barrier. When one ground state acquires sufficient Zeeman energy under the magnetic field, it can overcome the barrier, with the coercive field Hils = ΔE/Mnet, in which Mnet is the net magnetization and ΔE denotes the energy barrier determined only by the intralayer exchange. Figure 4c shows the calculated energy landscape of a trilayer CrPS_4 as a function of domain-wall position rDW and external magnetic field Hz. The predicted switching field matches the experimental results in Fig. 2.

For even-layer samples with fully compensated magnetization, the absence of total Zeeman energy makes the switching much more challenging than in the odd-layer counterparts. Nevertheless, our samples are non-isolated. The laterally connected odd-layer regions can first undergo interlayer-locked switching under a magnetic field. The layer-sharing effect[12], originating from the strong intralayer exchange, enables subsequent antiferromagnetic switching in even layers by means of the domain-wall propagation (see animation in Supplementary Video 1). Taking a laterally connected trilayer and tetralayer as an example, we calculated the coercive field needed to switch the tetralayer Néel order, with the adjacent trilayer providing net magnetization. As shown in Fig. 4d, the even-layer coercive field is comparable with that of the adjacent odd layer and scales inversely with the uncompensated magnetization. Consistently, the measured coercive fields vary with sample configurations (Extended Data Fig. 8) and modifying the local environment of an even-layer flake by means of the in situ femtosecond laser microcutting technique[12] greatly alters its coercive field (see details in Supplementary Text 4 and Supplementary Fig. 1).

It is necessary to clarify that such domain-wall-mediated magnetization reversal is universally present in vdW A-type AFMs, as well as being observed in the interlayer-free AFMs, such as CrSBr (ref. [12]). As illustrated in Supplementary Video 1, however, the weak interlayer exchange interaction can only sustain domino-like behaviour within a monolayer. Such magnetization dynamics, restrained by the vertical exchange length, also distinguishes the interlayer-free AFMs from the Stoner–Wohlfarth AFMs.

In summary, we have demonstrated few-layer CrPS_4 as a representative of the Stoner–Wohlfarth AFM and revealed its interlayer-locked antiferromagnetic switching behaviour, which is distinct from the layer-by-layer flipping observed in the interlayer-free AFMs. Such Stoner–Wohlfarth AFMs shall be ubiquitous in A-type layered antiferromagnetic materials, for example, MnBi_2Te_4. Moreover, the properties of Stoner–Wohlfarth AFMs, such as switching field and remnant magnetization, may be manipulated by breaking the interlayer symmetry using a displacement field and electrostatic doping, as demonstrated in recent work on bilayer CrPS_4 (ref. [21]). Furthermore, the interconversion between the two types of layered AFM could be controlled by effectively tuning the ratio between interlayer exchange and magnetic anisotropy, for example, through adjustable nonmagnetic interlayer spacers[35] and manipulative interlayer stacking36,37,38 or strain[39]. Our study therefore consolidates the fundamental understanding of magnetic switching in layered AFMs and exhibits great potential for integrating 2D antiferromagnetic materials as active components into future spintronic applications.

Methods

Sample preparation

Atomically thin CrPS_4 flakes were mechanically exfoliated from bulk crystals (SixCarbon Technology) onto 285-nm-SiO_2/Si substrates. The exfoliation process was performed in a nitrogen-filled glovebox (<0.1 ppm of water and oxygen). Their thicknesses were determined by optical contrast and further confirmed by atomic force microscopy (Supplementary Fig. 2). To avoid exposure to air during the sample mounting to the optical cryostat, samples were held in a copper cave and sealed by a coverglass with high-vacuum grease. The preparation methods of CrSBr, CrI_3 and MnBi_2Te_4 samples were similar to that of CrPS_4. Also, the CrI_3 sample was encapsulated by two hexagonal boron nitride flakes (20–30 nm thick) using standard dry transfer methods inside the glovebox to avoid degradation.

SHG and RMCD measurements

The measurements were performed in a home-built variable-temperature magneto-optical cryostat under high vacuum (<1 × 10−8 torr). The sample temperature was 6–8 K unless otherwise mentioned. The optical cryostat was held inside a room-temperature bore superconducting magnet with an out-of-plane magnetic field up to 7 T. For the SHG measurements, a femtosecond Ti:sapphire laser (Spectra-Physics) tuned at 785 nm was focused onto the sample by a 50× objective (numerical aperture = 0.55). Unless otherwise stated, the excitation power was 1.2 mW. To avoid the extrinsic Faraday effect, the excitation light was circularly polarized rather than linearly polarized[40] and the polarization does not influence the magnitude of coercive fields (Supplementary Fig. 3). The reflected SHG signals were detected by a photomultiplier tube in photon-counting mode and examined by a spectrometer equipped with a liquid-nitrogen-cooled charge-coupled device (Supplementary Fig. 4). For the RMCD measurements, a linearly polarized He–Ne laser (632.8 nm) was modulated between left and right circular polarization by a photoelastic modulator (PEM) at 50 kHz and focused on the sample through the same 50× objective. The excitation power was 3.2 μW. The reflected light was collected by a silicon-based avalanche photodiode. The RMCD signal was processed using a lock-in amplifier by the ratio of the PEM-modulated signal to the chopper-modulated signal (197 Hz).

Phase-resolved SHG

The measurements were performed in another home-built variable-temperature magneto-optical cryostat with 7 T/2 T/2 T vector magnetic field. The method for discriminating the antiphase antiferromagnetic states in few-layer CrSBr is described in our previous publication[12]. A Y-cut quartz with a thickness of 0.2 mm was used to seal the copper cave instead of the coverglass. The angle between the crystal axis of Y-cut quartz and the b-axis of CrSBr was pre-aligned and the gap between them was tuned to approximately 70 μm with the aid of an optical microscope inside the glovebox. When a linearly polarized laser (Spectra-Physics) excited the aligned axes of quartz and CrSBr, mutually orthogonal SHG signals were generated. The excitation wavelength was 925 nm, with an average power of 0.3 mW. A Soleil–Babinet compensator (SBC) acted as a phase-shifting unit to adjust the relative phase, with its fast-axis and slow-axis aligned to the two orthogonal signals. Then the two signals were projected to a polarizer for interference before being detected by a photomultiplier tube. The phase-resolved SHG hysteresis loops were measured at ϕSBC = 430° (Supplementary Fig. 5), with the in-plane sweeping field along the b-axis of CrSBr at 6.5 K.

Micromagnetic simulations

We used the micromagnetic module28,29 based on COMSOL Multiphysics to simulate the switching behaviour of 2D vdW A-type AFM CrPS_4. An N-layer (N = 2–6) CrPS_4 was constructed with the lateral area of 1 μm × 1 μm and each layer was discretized onto an ultrafine finite-element grid. The magnetization dynamics was studied in the framework of the Landau–Lifshitz–Gilbert equation:

$$\frac{{\rm{d}}{\bf{m}}}{{\rm{d}}t}=-\gamma {\bf{m}}\times {{\bf{H}}}_{{\rm{eff}}}+\alpha {\bf{m}}\times \frac{{\rm{d}}{\bf{m}}}{{\rm{d}}t},$$

(1)

in which m = M/MS is the normalized vector of the magnetization, γ is the gyromagnetic ratio and α is the Gilbert damping. The effective field Heff consists of the intralayer exchange, interlayer exchange (between the nearest neighbours) and anisotropy.

A previous study[30] has demonstrated that CrPS_4 exhibits the monoclinic anisotropy energy, which is defined as \({\mu }_{0}{M}_{{\rm{S}}}\,\left[\frac{1}{2}{K}_{1}{m}_{x}^{2}\,+\right.\) \(\left.\frac{1}{2}{K}_{2}{m}_{y}^{2}+{K}_{m}{m}_{x}\,({m}_{x}\cos \theta +{m}_{z}\sin \theta )\right]\). Here K_1, K_2 and Km are the anisotropy constants associated with the three principal axes. θ = 91.9° is the angle between the a-axis and the c-axis. The demagnetizing field is not considered in the simulation.

We also used the material parameters reported in the literature[30]: the anisotropy constants K_1 = 12,485 J m−3 (μ_0HK1 = 0.167 T), K_2 = 8,897 J m−3 (μ_0HK2 = 0.119 T), Km = 1,271 J m−3 (μ_0HKm = 0.017 T), the saturation magnetization MS = 74,803 A m−1 (μ_0MS = 0.094 T) and the interlayer exchange interaction J⟂ = 1.58 × 10−4 J m−2 (μ_0HC = 3.45 T). The Gilbert damping coefficient α = 0.1 and the intralayer exchange interaction J∥ = 1.48 × 10−2 J m−2 were applied in our simulations. Starting from the A-type AFM order, we artificially switched and fixed the magnetization of the bottom layer. Then the magnetization dynamics in other layers were computed by solving equation (1). With the above material parameters, all other layers switched their magnetization following the bottom layer, resulting in a 180° Néel order switching of the whole system. The interlayer-locked behaviour was reproduced in agreement with experiments.

To verify the switching behaviour determined by the competition between the interlayer exchange and magnetic anisotropy, we repeated the micromagnetic simulation with only variable parameters of J⟂ and K1(2). Specifically, to ensure that the out-of-plane easy axis of CrPS_4 remains unchanged, we fixed Km and varied K_1 and K_2 proportionally with a fixed ratio of K_2/K_1. Then both the interlayer-locked and interlayer-free switching behaviours were obtained, as shown in Fig. 4a. The above conclusion still holds if the magnetic switching starts from an interior layer instead of a surface layer. Furthermore, the boundary in the phase diagram is examined to be independent of the intralayer exchange interaction J∥, as plotted in Supplementary Fig. 6.

Original and extended Stoner–Wohlfarth model

The original Stoner–Wohlfarth model describes the coherent rotation of atomic magnetic moments in ferromagnetic materials[1]. The Hamiltonian reads

$${H}_{{\rm{F}}{\rm{M}}}={E}_{{\rm{e}}{\rm{x}}{\rm{c}}}-K{(\hat{{\bf{M}}}\cdot \hat{z})}^{2}-{\mu }_{0}{M}_{{\rm{S}}}\hat{{\bf{M}}}\cdot {{\bf{H}}}_{{\rm{e}}{\rm{x}}{\rm{t}}}.$$

The unit vector \(\hat{{\bf{M}}}\) describes the direction of all magnetic moments in a system, which are coupled by sufficiently strong exchange interaction, resulting in rigid magnetization rather than magnetic domains. Therefore, the exchange energy Eexc in the original Stoner–Wohlfarth model is a constant and is usually omitted in the Hamiltonian. K is the uniaxial anisotropy energy along the easy axis \(\hat{z}\). The last term describes the Zeeman energy under the external magnetic field Hext. Recently, the original Stoner–Wohlfarth model has been applied in describing the magnetization dynamics of 2D vdW ferromagnetic material Fe_3GeTe_2 (ref. [10]).

Explicit inclusion of the exchange energy allows us to describe the generalized 2D vdW magnetic materials, including the A-type AFMs:

$$H=\sum _{i}\left[A{({{\rm{\nabla }}}_{\parallel }{\hat{{\bf{M}}}}_{i})}^{2}+\frac{{J}_{\perp }}{2}({\hat{{\bf{M}}}}_{i}\cdot {\hat{{\bf{M}}}}_{i-1}+{\hat{{\bf{M}}}}_{i}\cdot {\hat{{\bf{M}}}}_{i+1})-K{({\hat{{\bf{M}}}}_{i}\cdot \hat{z})}^{2}-{\mu }_{0}{M}_{{\rm{S}}}{\hat{{\bf{M}}}}_{i}\cdot {{\bf{H}}}_{{\rm{ext}}}\right],$$

in which A is the intralayer spin stiffness and J⟂ describes the interlayer exchange interaction. Positive and negative J⟂ correspond to antiferromagnetic and ferromagnetic interlayer coupling, respectively. The original Stoner–Wohlfarth model is then extended to describe 2D vdW A-type AFMs with J⟂ > 0 between neighbouring layers. As the consequence of a sufficiently strong interlayer exchange energy, the neighbouring layers always tend to align antiparallel to each other. This type of antiferromagnetic material is therefore referred as to the Stoner–Wohlfarth AFMs.

To estimate the interlayer exchange length, we assume a collinear magnetization in each atomic layer (\({{\rm{\nabla }}}_{\parallel }{\hat{{\bf{M}}}}_{i}=0\)) such that the first term in the Hamiltonian vanishes. Without the external field, the total energy only contains the interlayer exchange energy and magnetic anisotropy energy, that is,

$${E}_{{\rm{total}}}={E}_{{\rm{exc}}}+{E}_{{\rm{ani}}}=\sum _{i}\left[\frac{{J}_{\perp }}{2}({\hat{{\bf{M}}}}_{i}\cdot {\hat{{\bf{M}}}}_{i-1}+{\hat{{\bf{M}}}}_{i}\cdot {\hat{{\bf{M}}}}_{i+1})-K{({\hat{{\bf{M}}}}_{i}\cdot \hat{z})}^{2}\right].$$

Here we rewrite the energy in the continuous limit and the magnetization becomes a function of position along the vertical (z) direction. We define an angle θ to describe the deviation of magnetization away from the easy axis. Then the total energy can be rewritten as

$${E}_{\mathrm{total}}=\mathop{\mathop{\int }\limits^{+\infty }}\limits_{-\infty }{\varepsilon }_{\mathrm{total}}(z){\rm{d}}z=\mathop{\mathop{\int }\limits^{+\infty }}\limits_{-\infty }\left[{A}_{\perp }{\left(\frac{{\rm{d}}\theta }{{\rm{d}}z}\right)}^{2}+K{\sin }^{2}\theta (z)\right]{\rm{d}}z,$$

in which \({A}_{\perp }\equiv \frac{{|J}_{\perp }|d}{2}\) is the interlayer spin stiffness parameter with the interlayer distance d. The magnetic anisotropy energy is redefined as the increase by the spin texture compared with the collinear case. The magnetization variation of every layer θ(z) can be obtained by minimizing the total energy. Following the standard Euler–Lagrange variation method, we eventually arrive at \(\tan \frac{\theta (z)}{2}={{\rm{e}}}^{z/\sqrt{{A}_{\perp }/K}}\). The decay length \({l}_{{\rm{ex}}}=\sqrt{{A}_{\perp }/K}\) or \(\sqrt{{|J}_{\perp }|d/(2K)}\) describes the characteristic interlayer exchange length scale for varying the order parameter (the magnetization for J⟂ < 0 or the Néel order for J⟂ > 0). When lex is larger than the interlayer distance, the A-type AFMs become the Stoner–Wohlfarth AFMs, so that the neighbouring layers are switched together, leading to the reversal of the Néel order. The exchange length lex of the reported A-type AFMs, including CrI_3, CrSBr, CrPS_4 and MnBi_2Te_4, are listed in Extended Data Table 1. The layered FM Fe_3GeTe_2 also follows the above analysis. Its exchange length lex is estimated to be approximately 2.2 layers using the calculated magnetic parameters[41], suggesting its nature of the Stoner–Wohlfarth FM[10].

Energy landscape calculations

The total energy of a 3L CrPS_4 shown in Fig. 4c comprises the exchange energy Eexc, the uniaxial anisotropy energy Eani and the Zeeman energy Eext. For collinear spin configurations that align the magnetic moments along the easy axis, only Eext changes with the external field Hext, because the odd layer leaves a finite net magnetization. The Zeeman contribution is written as \({E}_{{\rm{ext}}}=-\,{\mu }_{0}{M}_{{\rm{S}}}{\sum }_{i}{\hat{{\bf{M}}}}_{i}\cdot {{\bf{H}}}_{{\rm{ext}}}\), in which MS is the saturation magnetization per layer and \({\hat{{\bf{M}}}}_{i}\) is the unit vector of the ith layer magnetization. When Hext exceeds a critical value, the system enters a transition state that hosts an internal magnetic domain wall. This transition occurs once the Zeeman energy equals the summation of the intralayer exchange and magnetic anisotropy costs of the wall (the energy barrier indicated in Fig. 4b), \({\sum }_{i}[A{({{\rm{\nabla }}}_{\parallel }{\hat{{\bf{M}}}}_{i})}^{2}-K{({\hat{{\bf{M}}}}_{i}\cdot \hat{z})}^{2}]\). Here we have used the intralayer spin stiffness A = 1.5 × 10−12 J m−1 and the uniaxial anisotropy constant K = 6,000 J m−3. Owing to the strong antiferromagnetic interlayer coupling, neighbouring layer moments remain strictly antiparallel, so the interlayer exchange energy is constant and, therefore, omitted. The energy landscape in Fig. 4c was obtained from \({E}_{{\rm{tot}}}={\sum }_{i}[A{({{\rm{\nabla }}}_{\parallel }{\hat{{\bf{M}}}}_{i})}^{2}-K{({\hat{{\bf{M}}}}_{i}\cdot \hat{z})}^{2}-{\mu }_{0}{M}_{{\rm{S}}}{\hat{{\bf{M}}}}_{i}\cdot {{\bf{H}}}_{{\rm{ext}}}]\) using the Walker domain-wall profile[42].

For the even-layer case, we treat a 4L flake that contains a laterally connected 3L region, as sketched in the inset of Fig. 4d. The Néel vector of the 4L CrPS_4 is then controlled by the uncompensated magnetization inside the laterally connected 3L, whereas the coercive field is determined by the balance of its Zeeman energy against the energy cost of a magnetic domain wall, \({\mu }_{0}{M}_{{\rm{net}}}{H}_{{\rm{ils}}}={\sum }_{i}[A{({{\rm{\nabla }}}_{\parallel }{\hat{{\bf{M}}}}_{i})}^{2}-K{({\hat{{\bf{M}}}}_{i}\cdot \hat{z})}^{2}]\). Note that the lateral exchange bias effect32,33,34 is ignored in the theoretical calculations, which would require us to consider the finite size of a domain wall and become prominent in the samples with the lateral dimension of only 1 micron or less. In our work, the size of samples for optical SHG and RMCD measurements is mostly larger than 5 microns and, therefore, the lateral exchange bias effect is relatively weak.

Data availability

The data supporting the findings of this study are available on figshare, https://doi.org/10.6084/m9.figshare.30746897 (ref. [43]).

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Acknowledgements

The work at Fudan University was supported by National Key Research and Development Program of China (grant nos. 2024YFA1409800, 2022YFA1403300 and 2024YFA1408500), National Natural Science Foundation of China (grant nos. 12034003, 12427807, 12574115, 12250002, 1235000130, 12350401, 12274087 and 12204115), Science and Technology Commission of Shanghai Municipality (grant nos. 23JC1400400, 2019SHZDZX01 and 23JC1400600), Shanghai Municipal Education Commission (grant no. 2021KJKC-03-61) and Innovation Program for Quantum Science and Technology (grant no. 2024ZD0300104). H.L. was supported by National Key Research and Development Program of China (grant nos. 2022YFA1403800 and 2023YFA1406500) and National Natural Science Foundation of China (grant no. 12274459). Z.L. acknowledges the support from National Natural Science Foundation of China (grant nos. 92365204 and 12274298) and National Key Research and Development Program of China (grant no. 2022YFA1604400/03).

Author information

Author notes

1. These authors contributed equally: Zhanshan Wang, Yining Xiang

Authors and Affiliations

1. State Key Laboratory of Surface Physics, Key Laboratory of Micro and Nano Photonic Structures (MOE) and Department of Physics, Fudan University, Shanghai, China

Zhanshan Wang, Ruohan Chen, Zeyuan Sun, Canyu Hong, Xinyu Chen, Jingjing Gao, Shuang Wu, Zhongxun Guo, Wei Ruan, Yuanbo Zhang, Wei-Tao Liu & Shiwei Wu

2. Institute for Nanoelectronic Devices and Quantum Computing and Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China

Yining Xiang, Zeyuan Sun, Yuanbo Zhang, Weichao Yu, Zhe Yuan & Shiwei Wu

3. Interdisciplinary Center for Theoretical Physics and Information Sciences, Fudan University, Shanghai, China

Yining Xiang & Zhe Yuan

4. School of Physical Science and Technology, ShanghaiTech University, Shanghai, China

Yi Chen, Qixi Mi & Zhongkai Liu

5. ShanghaiTech Laboratory for Topological Physics, ShanghaiTech University, Shanghai, China

Zhongkai Liu

6. School of Physics and Beijing Key Laboratory of Optoelectronic Functional Materials & MicroNano Devices, Renmin University of China, Beijing, China

Shaohua Yan & Hechang Lei

7. Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education), Renmin University of China, Beijing, China

Shaohua Yan & Hechang Lei

8. Shanghai Branch, Hefei National Laboratory, Shanghai, China

Wei Ruan & Yuanbo Zhang

9. New Cornerstone Science Laboratory, Shenzhen, China

Yuanbo Zhang

10. Shanghai Research Center for Quantum Sciences, Shanghai, China

Yuanbo Zhang & Shiwei Wu

Contributions

Shiwei Wu and Z.Y. conceived and supervised the project. Z.W. conducted the measurements, with assistance from R.C., Z.S., C.H. and X.C. Y.X. and Z.Y. performed the theoretical calculations, with support from W.Y., Y.C., Q.M. and Z.L. provided the CrSBr single crystals. S.Y. and H.L. provided the CrI_3 single crystals. J.G., Z.G., W.R. and Y.Z. provided few-layer MnBi_2Te_4 samples. X.C. and Shuang Wu performed RMCD measurements on MnBi_2Te_4. Z.W., Y.X., Z.Y. and Shiwei Wu analysed the data and wrote the paper, with input from W.-T.L.

Corresponding authors

Correspondence to Zhe Yuan or Shiwei Wu.

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Extended data figures and tables

Extended Data Fig. 1 RMCD hysteresis loops on 4L CrI[3] and 4L CrPS[4].

a,b, RMCD hysteresis loops on 4L CrI_3 at 15 K (a) and 4L CrPS_4 at 6.5 K (b) with the out-of-plane field between ±2 T.

Extended Data Fig. 2 Magnetic evolution of tetralayer CrPS[4] with the out-of-plane magnetic field.

This schematic illustrates the evolution of magnetic states under a positive magnetic field. Red and blue arrows indicate upward and downward spins, respectively. When applying an out-of-plane magnetic field, the A-type antiferromagnetic state first undergoes an interlayer-locked switching to its time-reversed state below 0.1 T, followed by a spin-flop transition at about 0.7 T and then through a smooth transition along the direction of the magnetic field until reaching the spin-flip field at about 7 T.

Extended Data Fig. 3 Odd–even-layered effect in SHG and RMCD hysteresis loops.

a,b, SHG (a) and RMCD (b) hysteresis loops on 2L–8L CrPS_4 with the out-of-plane field between ±0.1 T.

Extended Data Fig. 4 Temperature-dependent RMCD and SHG measurements on 3L and 4L CrPS[4].

a,b, RMCD hysteresis loops on 3L (a) and SHG hysteresis loops on 4L (b) CrPS_4 with the out-of-plane field between ±0.1 T at the given temperatures. c, RMCD intensity of the 3L CrPS_4 as a function of temperature. The intensity is extracted from a and defined as half of the signal change around 0 T. The dashed red line indicates the baseline at which the signal is zero. d, Temperature-dependent SHG intensity of the 4L CrPS_4 at a weak field of 0.1 T. The solid red curves in graphs c and d are guides to the eyes. cps, counts per second.

Extended Data Fig. 5 SHG hysteresis loops on 2L and 4L CrPS[4] with opposite circularly polarized excitations.

a,b, SHG hysteresis loops excited by σ+ polarization (a) and σ− polarization (b) on 2L and 4L CrPS_4 with the out-of-plane field between ±0.1 T. We define the SHG plateau swept from the negative field as the −L state and that swept from the positive field as the +L state.

Extended Data Fig. 6 Interlayer-locked switching in another Stoner–Wohlfarth AFM MnBi[2]Te[4].

a, Crystallographic and magnetic structures of MnBi_2Te_4. The septuple atomic layers are stacked through vdW interfaces. The spins of the Mn2+ ions ferromagnetically couple to each other within one septuple layer, whereas adjacent septuple layers are antiferromagnetically ordered. b, Layer-dependent RMCD loops with the out-of-plane magnetic field. The shaded area highlights the interlayer-locked switching in odd-layer MnBi_2Te_4. Each arrow indicates the out-of-plane magnetization of each septuple layer.

Extended Data Fig. 7 SHG measurements on CrPS[4] thick flakes.

a,b, SHG hysteresis loops on CrPS_4 thick flakes (15–20 nm) with the out-of-plane field between ±0.5 T. c, Symmetry transformation of the antiferromagnetic state in thick CrPS_4 under spatial inversion i and vertical translation τ. No step-like hysteresis loops were observed. This is because the vertical translational symmetry preserves the inversion symmetry of the system as thickness increases, leading to negligible c-type SHG and thus failing to distinguish two antiferromagnetic states with opposite Néel vectors.

Extended Data Fig. 8 SHG hysteresis loops on 4L CrPS[4] with different sample configurations.

Three tetralayer flakes are indicated by dashed white lines. The laser polarization was σ+ for each measurement. The out-of-plane magnetic field was between ±0.1 T for samples 1 and 4 and between ±0.2 T for sample 5.

Extended Data Table 1 Exchange lengths of typical A-type vdW AFMs

Supplementary information

Supplementary Information

Supplementary Texts 1–4, Supplementary Figs. 1–6 and Supplementary References

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Supplementary Video 1

Simulated magnetization reversal dynamics of vdW A-type AFMs. A specific configuration of laterally connected trilayer and tetralayer is considered. Hils and Hifs represent the coercive fields for interlayer-locked (a) and interlayer-free (b) switching, respectively. The lateral magnetization reversal in both processes is achieved through the domain-wall propagation. For Stoner–Wohlfarth AFMs, the strong interlayer exchange interaction ensures that all layers maintain coherent rotational coupling along the vertical dimension, resulting in interlayer-locked switching. By contrast, those A-type AFMs with weak interlayer coupling only sustain magnetization reversal within a monolayer, manifesting as interlayer-free switching

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Wang, Z., Xiang, Y., Chen, R. et al. Ferromagnet-like binary switching of a Stoner–Wohlfarth antiferromagnet. Nature 650, 340–345 (2026). https://doi.org/10.1038/s41586-025-10019-9

- Received: 17 April 2025

- Accepted: 05 December 2025

- Published: 28 January 2026

- Version of record: 28 January 2026

- Issue date: 12 February 2026

- DOI: https://doi.org/10.1038/s41586-025-10019-9

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New discoveries of Australopithecus and Homo from Ledi-Geraru, Ethiopia

Brian Villmoare,

Lucas K. Delezene,

Amy L. Rector,

Erin N. DiMaggio,

Christopher J. Campisano,

David A. Feary,

Baro’o Mohammed Ali,

Daniel Chupik,

Alan L. Deino,

Dominique I. Garello,

Mohammed Ahmeddin Hayidara,

Ellis M. Locke,

Omar Abdulla Omar,

Joshua R. Robinson,

Eric Scott,

Irene E. Smail,

Kebede Geleta Terefe,

Lars Werdelin,

William H. Kimbel,

J. Ramón Arrowsmith &

…

Kaye E. Reed

Nature volume 650, pages 374–380 (2026)

Abstract

The time interval between about three and two million years ago is a critical period in human evolution—this is when the genera Homo and Paranthropus first appear in the fossil record and a possible ancestor of these genera, Australopithecus afarensis, disappears. In eastern Africa, attempts to test hypotheses about the adaptive contexts that led to these events are limited by a paucity of fossiliferous exposures that capture this interval. Here we describe the age, geologic context and dental morphology of new hominin fossils recovered from the Ledi-Geraru Research Project area, Ethiopia, which includes sediments from this critically underrepresented period. We report the presence of Homo at 2.78 and 2.59 million years ago and Australopithecus at 2.63 million years ago. Although the Australopithecus specimens cannot yet be identified to species level, their morphology differs from A. afarensis and Australopithecus garhi. These specimens suggest that Australopithecus and early Homo co-existed as two non-robust lineages in the Afar Region before 2.5 million years ago, and that the hominin fossil record is more diverse than previously known. Accordingly, there were as many as four hominin lineages living in eastern Africa between 3.0 and 2.5 million years ago: early Homo[1], Paranthropus[2], A. garhi[3], and the newly discovered Ledi-Geraru Australopithecus.

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Main

Over the past few decades, the African hominin fossil record predating 3.0 million years ago (Ma) has expanded to include several potential ancestors for later taxa4,5,6,7. Currently, systematic analyses place A. afarensis as the most likely candidate for the middle Pliocene stem taxon from which multiple later hominin genera (that is, Homo and Paranthropus), and possibly other species of Australopithecus, descended8,9,10. A. afarensis was geographically and temporally widespread, and fossil sites with its remains are known from Tanzania to northern Ethiopia and, potentially, Chad. However, temporally, A. afarensis is not known after 2.95 Ma (ref. [11]).

Paranthropus and Homo are well-documented in the eastern African fossil record after 2.0 Ma, especially in the Omo-Turkana Basin and at northern Tanzanian localities12,13,14,15, but the hominin fossil record between the last appearance of A. afarensis (around 2.95 Ma) and 2.0 Ma is patchy. For example, no Paranthropus fossils have been recorded from the Afar Region to date, despite its presence in the Omo-Turkana Basin and at Nyayanga, Kenya[2] at approximately 2.7 Ma and in the Upper Ndolanya Beds at Laetoli[16] at about 2.66 Ma. Additionally, a Homo specimen at Ledi-Geraru, Ethiopia, extends the genus closer in time[17] (2.78 Ma) to the last known appearance of A. afarensis1,11. However a simple cladogenic model of A. afarensis, or any other stem taxon, splitting into these daughter genera is complicated by the presence of A. garhi in the Afar[3] at approximately 2.5 Ma.

The Ledi-Geraru Research Project (LGRP) area is located towards the northern extent of palaeoanthropological sites in the Afar Region, Ethiopia (Extended Data Fig. 1). New discoveries in Ledi-Geraru suggest that early Homo and Australopithecus were both present in the Afar Region before 2.5 Ma, just as early Homo and Paranthropus are sympatric in the Omo-Turkana Basin and sites to the south18,19 after about 2 Ma. Whether the apparent absence of Paranthropus from the Afar Region reflects the spotty nature of the fossil record or a biogeographical signal is yet to be determined. What is clear is that the Afar Region has currently yielded the only definitive evidence for Australopithecus in eastern Africa after 2.95 Ma—A. garhi and the newly discovered specimens from Ledi-Geraru.

Ledi-Geraru geologic context

The LGRP area contains fossiliferous sediments from the critical 3.0–2.0 Ma time period[17]. The fossil sites in the Lee Adoyta and Asboli regions of the LGRP area are located west of the Awash River in a region incised by the Mille and Geraru River drainages and their tributaries (Extended Data Fig. 1). The 3.0–2.5 Ma fossiliferous sediments are exposed in fault blocks bounded by post-depositional northeast–southwest and nothwest–southeast trending faults (Fig. 1); age control is provided by many dated and correlated tephra deposits and by magnetostratigraphy[17]. Detailed 40Ar/39Ar dating methods are described in Methods.

Fig. 1: Geologic context of the Lee Adoyta and Asboli regions.

a, Geologic map of the Lee Adoyta basin. Interbedded tuffs are shown as thin coloured lines. The black line indicates the position of the A–A′ cross-section shown in d. b, Stratigraphic sections at hominin localities showing the stratigraphic level of hominin fossil discoveries (yellow bones), fossiliferous horizons (white bones) and marker beds (coloured tuffs). Fossil and stratigraphic section locations are shown in a,c,d. c, Geologic map of the Asboli region superimposed on a hillshade image. Outcrops of multiple tephra deposits are shown as red lines, and these include the Giddi Sands and Lee Adoyta Tuffs that also occur in the Lee Adoyta region. d, South–north geologic cross-section of the Lee Adoyta basin showing the locations of hominin fossil sites. Vertical scale is doubled relative to the horizontal (2×VE).

The distribution and stratigraphy of fossiliferous units and tephra deposits provide context for the hominin fossils described here, which were discovered in sediments cropping out in the Lee Adoyta and Asboli regions (Fig. 1 and Extended Data Fig. 1). The Gurumaha sedimentary package is present in narrow fault-bounded exposures in the central Lee Adoyta basin and in drag-faulted blocks adjacent to basalt ridges bounding the basin to the east (Fig. 1a). The Gurumaha Tuff (GT), a light grey lapilli tuff dated to 2.782 ± 0.006 Ma (1σ; recalculated17,20), provides age control for the unit (Fig. 1b). Stratigraphically younger, the Lee Adoyta sedimentary package is widely exposed in the Lee Adoyta basin and is correlated to exposures in Asboli (Fig. 1). The Lee Adoyta Tuffs (LAT) comprise two thin, geochemically distinct tephras: a yellow altered basaltic ash which occurs approximately 10 cm above a white rhyolitic ash dated to 2.631 ± 0.011 Ma (1σ; recalculated17,20). These tuffs, and an underlying olive-green clay, provide an excellent marker sequence (Fig. 1b and Extended Data Fig. 2a). The Giddi Sands sedimentary package (Tgs; containing the Giddi Sands Tuff (GST)) and the Markaytoli sedimentary package (Tmk; containing the Markaytoli Tuff) crop out in the eastern Lee Adoyta basin (Fig. 1a). The base of the Giddi Sands sedimentary package is an erosional unconformity cutting into the Lee Adoyta sedimentary package in the Lee Adoyta Basin (Fig. 1b). The GST is a 3- to 8-cm-thick laminated, multicoloured (orange, yellow and white), crystal bentonite tuff. 40Ar/39Ar single-crystal incremental heating (SCIH) yielded a weighted-mean age of 2.593 ± 0.006 Ma (1σ) for the GST sampled in Asboli (Methods and Supplementary Figs. 1–3). In Asboli, the Giddi Sands sedimentary package containing the AS 100 fossil site unconformably overlies a sequence of mudstones containing the Asboli Tuffs, which in turn overlies the LAT exposed approximately 500 m to the south (Fig. 1c).

New Ledi-Geraru hominin specimens

Hominin dental specimens (Fig. 2) were recovered from the three sedimentary packages described above—the Gurumaha, Lee Adoyta, and Giddi Sands (Table 1). Comparative methods and sampling protocols are described in Methods.

Fig. 2: New hominin dentition from the LGRP.

Right, from top: LD 302-23 P_3, LD 750 P_4, AS 100 M^1 and AS 100 M^2. Left, images show the LD 760 assemblage (top, from left: maxillary molar, I^2, I^1, maxillary canine; bottom, mandibular molars).

Table 1 New Ledi-Geraru hominin dental specimens with geological contexts, ages and identifications

Gurumaha hominin specimen

A mandibular right third premolar, LD 302-23 (Fig. 2 and Extended Data Fig. 3), was recovered approximately 22 m to the southwest and 7 m stratigraphically below the LD 350 early Homo mandible locality[1], above the Gurumaha Tuff[17] (Fig. 1b). An enamel fragment is missing from the lingual corner, but the crown is otherwise well preserved. The crown is shorter mesiodistally (MD) than buccolingually (BL) (11.5 mm BL × 10.5 mm MD; Extended Data Fig. 3) and the major crown axis is oriented buccolingually. A relatively small metaconid sits well mesial to the protoconid, creating a tiny, centrally placed anterior fovea that is bounded by a low, but continuous, mesial marginal ridge. Occlusal wear makes it difficult to determine if the metaconid would have been completely distinct from the protoconid at the outer enamel surface. The talonid is mesiodistally short and occupies a relatively small portion of the crown area in occlusal view.

LD 302-23 departs strongly in shape and occlusal form from P_3s attributed to Australopithecus anamensis and Australopithecus deyiremeda, and unicuspid specimens of A. afarensis (for example, A.L. 128-23 and A.L. 288-1), in which the major axis of an asymmetric crown runs mesiobuccal to distolingual and in having a continuous mesial marginal ridge that creates a ‘closed’ anterior fovea7,21,22 (Extended Data Fig. 3a). Some A. afarensis specimens (such as A.L. 437-2) resemble the crown outline of LD 302-23 (Extended Data Fig. 3a); however, LD 302-23 also differs from those specimens in having a mesially shifted metaconid, which creates an acute angle between the transverse crest and mesial protoconid crest, and in having a highly reduced anterior fovea relative to crown size[23]. In crown shape index (MD/BL), LD 302-23 falls in the middle range of the A. afarensis distribution (Extended Data Fig. 3b,c). In area (BL × MD), LD 302-23 falls within the range of the A. afarensis size distribution, at the lower limit of the range of Paranthropus boisei, but within the range of values of Paranthropus robustus (Extended Data Fig. 3b,d). Unlike Paranthropus[12], the talonid of LD 302-23 is not expansive. We attribute LD 302-23 to Homo for a number of reasons. Although the sample of P_3s attributed to early Homo is morphologically diverse (for example, KNM-ER 5431, OMO 75-14, KNM-ER 1802 and OH 7), LD 302-23 is clearly derived relative to pre-3.0 Ma Australopithecus, lacks the suite of derived nonmetric features characteristic of Paranthropus, and is consistent with the size, crown morphology and mesiodistal compression of the LD 350-1 specimen (the LD 350-1 P_3 crown is damaged mesiolingually, with only around 65% of the crown remaining), which was found in the same sedimentary package and shares numerous dental and mandibular apomorphies with Homo.

Lee Adoyta hominin specimens

An isolated P_4, LD 750-115670 (Fig. 2, Extended Data Fig. 4 and Supplementary Fig. 4), was collected from locality LD 750 at the base of an approximately 8-m-thick fossiliferous exposure of mudstone and sandstone. The stratigraphic position of the locality lies between the 2.63 Ma LAT and the 2.59 Ma GST (Fig. 1b and Extended Data Fig. 2a). The LD 750 P_4 is unworn, with salient mesial cusps (Extended Data Fig. 4a). The roots are broken beneath the crown, but the mesial portion preserves a maximum root height of about 2.0 mm. The tooth may not have been fully erupted, as there are no interproximal contact facets and the occlusal surface is unworn. The protoconid and metaconid are shifted relatively mesially on the crown and tightly compressed BL, giving the tooth a slightly ‘puffy’ appearance. The anterior fovea is distinct and deep, but relatively small (reflecting the mesial placement of the cusps), centrally placed and bounded by a narrow mesial marginal ridge. The talonid is relatively large, the distal marginal ridge is low and rounded, and a distinct, but low, distolingual cusp is present, giving the tooth an asymmetric occlusal profile.

With dimensions of 12.4 mm BL × 11.4 mm MD, the tooth area is in the upper size range of A. afarensis, exceeded in area only by A.L. 996-1 (Extended Data Fig. 4b). Morphologically, however, the LD 750 P_4 departs from the typical Hadar A. afarensis appearance. For example, the lingual to mesial face transition is smooth and rounded, whereas in Hadar P_4s there is a more distinct corner. The occlusal layout differs from that of Hadar P_4s in that the metaconid and protoconid are placed more centrally in relation to buccolingual crown breadth and shifted mesially in relation to crown length. Thus, compared with Hadar A. afarensis teeth, the buccal face is more convex occlusocervically and the anterior fovea more confined. The index of protoconid–metaconid apex distance/BL breadth shows the central placement of the LD 750 mesial cusp apices in relation to crown breadth compared with A. afarensis (Extended Data Fig. 4c,d).

Not only is LD 750 large relative to most A. afarensis specimens, it also exceeds the area of all specimens attributed to A. anamensis and all but two specimens of Australopithecus africanus (StW 498 and StW 384). The LD 750 crown area falls within the size distribution of P. robustus and Paranthropus aethiopicus; however, it lacks any of the distinct ‘molarization’ seen in Paranthropus P_4s (for example, expanded talonid and distal cusps, thick marginal ridges, symmetrically rounded occlusal profile caused by the filling of the crown distobuccally, and surface complexity in unworn specimens[12]). We note that the known specimen of A. garhi lacks any mandibular remains[3], but LD 750 cannot be excluded from that species on the basis of size alone (see randomization analysis in Extended Data Fig. 4e). In occlusal form and crown outline, LD 750 does resemble OMO 75-14 (Extended Data Fig. 4a), which has been attributed to aff. Homo by Suwa et al.[12] and Hlusko et al.[24], and KNM-ER 5431, which is also attributed to Homo by Suwa et al.[12]. However, these attributions are provisional and based mostly on the nonmetric morphology of their associated molars (for example, presence of C7 on M_1 and M_2 of both specimens). In fact, P_4 morphology poorly distinguishes between early Homo and Australopithecus12,24. Thus, the phenetic resemblance between OMO 75-14, KNM-ER 5431 and LD 750, which are all large but lack features typical of Paranthropus, could be most parsimoniously interpreted as symplesiomorphic retentions. Since LD 750-115670 lacks any clear Homo synapomorphies associated with other specimens of early Homo from Koobi Fora (such as talonid reduction in KNM-ER 992[25]), we provisionally assign it to aff. Australopithecus.

An associated set of five mandibular molars, a maxillary molar fragment, and three maxillary anterior teeth (Fig. 2 and Table 1) representing a single individual were collected at locality LD 760, a relatively flat-lying area dominated by fine-grained sand and silts (Supplementary Video 1). The hominin teeth were all found in close association, in a relatively dense cluster among scattered faunal elements (Extended Data Fig. 2b). The mandibular molars were found eroding from the surface and the maxillary dentition was sieved from the surface silt (approximately 5–10 cm of loose and eroded surface soil) downslope on a gentle gradient over a 7 × 4 m area to the northwest. The LD 760 collection area lies approximately 10 m below the 2.631 ± 0.011 Ma LAT[17] mapped in hillslopes surrounding the site (Fig. 1a and Extended Data Fig. 2a,b). Mammalian fossils were also observed eroding from sand units around 3.5 m and 6.5 m above the LAT (Fig. 1b). However, on the basis of the slope and distance to these sand units, as well as the abundance and spread of other fossil specimens, the hominin teeth and other fossils on the LD 760 surface represent a distinct fossiliferous unit below the LAT.

The LD 760 mandibular molars are moderately worn, with dentine exposure visible at buccal cusp apices in the M_1 (Fig. 3). They are relatively wide BL compared to their MD length (Extended Data Figs. 5 and 6) and do not taper strongly distally, which gives the crowns, especially M_1 and M_2, a squarish profile (Fig. 4). The buccal grooves are indistinct, and the buccal profile is convex in occlusal view, in part because a protostylid partially fills in the buccal groove. Further, the buccal crown face slopes occlusocervically, reflecting an internal placement of cusp apices.

Fig. 3: LD 760 molars compared to A. afarensis.

Left molars from Ledi-Geraru specimen LD 760 (left) and Hadar specimen A.L. 400-1 (right). Measurements in mm of the LD 760 molars (BL × MD): LM_1: 13.3 × 13.4, LM_2: 14.5 × 14.6, LM_3: 14.0 (estimated) × 15.7, RM_1: 13.2 × 13.1, RM_2: 14.8 × 15.2 (Supplementary Data 1). Specimens are oriented with their buccal surfaces to the left and mesial surfaces up.

Fig. 4: Contour of LD 760 compared to A. afarensis.

Detail of distal taper (orange lines) and bilobate contour (blue lines) seen in A. afarensis M_1 specimen A.L. 400-1 (right) contrasted with the overall more equilateral occlusal profile of the LD 760 M_1 (left). The M_2s also show this pattern (Fig. 3). Specimens are oriented with their buccal surfaces to the left and mesial surfaces up.

There is no hint of M_3 reduction (as seen, for example, in some early Homo such as LD 350-1 and OMO-75-14); instead, the molars follow the plesiomorphic pattern of relative molar size, M_1 < M_2 < M_3. The M_1 and M_2 are subequally square (Extended Data Figs. 5 and 6), unlike in early Homo which tend to have rectangular crowns that are mesiodistally elongated. Further, the teeth are relatively simple, with few accessory cusps; the M_1s show no expression of a C7, unlike LD 350-1 and KNM-ER 1802, and the M_2s also lack a C7, which is seen in many specimens attributed to early Homo (for example, OMO-75-14, KNM-ER 5431, KNM-ER 60000 and OH 7). The mesiodistal dimensions of the mandibular molars comfortably fit within the metric variation seen in individual A. afarensis molars (Extended Data Fig. 5), and the buccolingual breadths fall in the upper third of the A. afarensis distribution. However, the sum of the molar areas for the LD 760 individual (614.3 mm^2) exceeds all but one (NFR-VP-1/29; Woranso-Mille, Afar Region, Ethiopia) of the 11 A. afarensis individuals that preserve all 3 molars; yet, among Paranthropus, only 1 individual (DNH 7) is smaller in M_1–M_3 area than LD 760 (Extended Data Fig. 5d). The LD 760 molars depart from the typical feature set of A. afarensis in three ways: (1) the M_1s are buccolingually broad relative to their mesiodistsal length (A. afarensis M_1s and M_2s are buccolingually widest across the mesial cusps, the LD 760 M_1 is equally wide at midcrown and the M_2 is only slightly narrower at midcrown) (Extended Data Figs. 5 and 6); (2) the M_1 and M_2 do not taper distally to the extent seen in A. afarensis (Figs. 3 and 4); and (3) the LD 760 molars lack the distinct bilobate buccal contour seen in A. afarensis[12] (Fig. 4).

A fragmentary maxillary molar, LD 760-115911, was recovered at the LD 760 locality (Extended Data Fig. 7a). Although the protocone is the only fully preserved cusp, the lingual groove is distinct occlusally and the mesial edge of the hypocone is present. On A. garhi upper molars, the lingual groove is indistinct, but the lingual margin of the crown begins to curve buccally distal to the lingual groove, reflecting the relatively small hypocone and buccolingually narrow distal crown (Extended Data Fig. 7a). This reduction in hypocone area produces a triangular form for the Bouri specimen’s protocone. The LD 760 upper molar fragment shows no similar curvature of the crown margin distal to the lingual groove. Instead, it appears to have had a fully expressed hypocone. However, in terms of absolute molar area (BL × MD), the ratio of LD 760 lower molars to the A. garhi upper molars does not falsify a single-species hypothesis using modern ape comparative data—see resampling analysis in Extended Data Fig. 6b.

A right maxillary canine, a complete left maxillary lateral incisor and a left fragmentary maxillary central incisor were also found at the LD 760 locality (Fig. 2 and Table 1). The canine (LD 760-115979) is well preserved, with all but the root apex present (Fig. 5). The crown has mesial and distal interproximal contact facets. A distal interproximal contact facet is also present on the LI^2, which indicates bilateral absence of a canine/I^2 diastema; a diastema is present on the BOU-VP-12/130 A. garhi maxilla and is variably present in A. afarensis. The canine crown is worn apically and along the mesial and distal crests. Apical wear makes it difficult to examine the relative placement of the crown shoulders, but the mesial shoulder appears to have been placed apically. Despite the wear, the canine is not small (Extended Data Fig. 8a–c), and the ratio of the canine to molar mesiodistal dimensions puts this specimen in the upper range of Australopithecus and well beyond the observed values for Paranthropus (Extended Data Fig. 8d).

Fig. 5: Comparative maxillary canine morphology.

a, Lingual (left) and labial (right) views of the Ledi-Geraru LD 760-115979 canine (left) with Hadar A. afarensis specimens A.L. 763-1 (middle) and A.L. 333x-3 (right). Note that the LD 760 canine is a right canine, whereas the A. afarensis canines are from the left and are mirrored in these images. b–d, LD 760-115979 (b; shown in lingual view) contrasted with Hadar A. afarensis specimen A.L. 199-1 (c; right canine shown; distal to the upper right) and Bouri A. garhi specimen BOU-VP-12/130 (d; left canine, mirrored; distal is to the right). Note the simple mesial–distal chisel-like wear pattern on the LD 760 canine (b) in contrast to the complex multi-faceted wear pattern of A. afarensis (c) and the broad curved basin on the distal side of the A. garhi upper canine (d; this is seen on both left and right canines). Images are oriented differently to emphasize the distinctive relevant morphology. Photos of BOU-VP-12/130 A. garhi holotype fossil by T. White; use courtesy of the Middle Awash research project. Images in b–d are not to scale.

The LD 760 canine departs subtly from the macrowear of A. afarensis (Fig. 5). For example, its wear pattern lacks the characteristic ‘j-pattern’ along the distal crest[26]; instead, apical wear dominates. In basal size (10.4 mm BL × 10.5 mm MD) the tooth resembles A. afarensis and is narrower than the labio-lingually broad canine of A. garhi (Extended Data Fig. 9). The LD 760 canine also departs morphologically from A. garhi. The A. garhi canine, uniquely among hominins, possesses a broad, shallow distal basin reminiscent of a talon (Fig. 5d); this smooth basin is contiguous with a wide wear furrow that characterizes the entire postcanine dental row (Extended Data Fig. 9a).

The LD 760 individual cannot be attributed to Paranthropus. It lacks the derived reduction of anterior tooth size, which is reflected both in the absolute sizes of the anterior teeth and their size relative to the postcanine dentition (Extended Data Fig. 8d). The mandibular molars do not express relatively large C6s[12] and they show a buccolingual wear gradient distinct from the planar pattern typical of Paranthropus[12]. The LD 760 teeth also lack any derived Homo traits (such as steeply walled molars, narrow M_1, C7 presence and M_3 reduction). We regard the morphology of LD 760 as inconsistent with known eastern African Australopithecus species because: (1) the lack of any of the derived A. garhi traits, notably the reduced hypocone of the maxillary molars and highly distinctive upper canine ‘talon’ basin; and (2) a morphology derived relative to A. afarensis (that is, lack of bilobate mandibular molar walls and distal taper, and canine wear pattern). Because of the plesiomorphic resemblance to A. afarensis, we make a generic-level attribution and assign the LD 760 assemblage to Australopithecus sp. indet.

Giddi Sands hominin specimens

A partial left M^1 crown with lingual cusps only partially preserved (AS 100-1a) and 2 fragments assembled into a complete left M^2 crown (AS 100-1b) were discovered eroded from the approximately 1-m-thick Giddi Sands unit in the Asboli region immediately below the GST (Fig. 2, Table 1 and Supplementary Fig. 5). Subsequent screening produced no additional hominin material.

Wear on these teeth is slight, with crown apices salient and no dentine exposed. Interstitial wear is present and the interproximal contact facets are congruent, consistent with identification of AS 100-1a as an M^1 and AS 100-1b as an M^2 (Fig. 2 and Extended Data Fig. 7). The AS 100-1b M^2 is rhomboidal in occlusal outline, with the cusps offset such that the buccal pair sits mesial to the lingual pair, the hypocone is relatively large and projects distolingually, and the lingual profile is straight. A. afarensis M^2s range in shape from squarish (for example, A.L. 199-1, A.L. 486-1) to more rhomboidal (for example, A.L. 444-2, A.L. 200-1)[27], but A. afarensis specimens invariably show more pronounced lingual occlusocervical sloping, indicating a more internally placed cuspal arrangement than is apparent for AS 100-1b. The lingual cusps of the AS 100 molars are relatively steep vertically and lack the ‘puffy’ contour seen in relatively unworn specimens of A. afarensis (for example, A.L. 200-1). The rhomboidal crown outline, relatively large and projecting hypocone, and cuspal placement of AS 100-1b are closely matched by A.L. 666-1, a Homo specimen from the Busidima Formation at Hadar[28] from 2.3 Ma, and MLP-1549, a Homo specimen from Mille-Logya[29], 2.5–2.4 Ma. This feature set strongly contrasts with the A. garhi M^2 in which the hypocone is relatively small and placed transverse to the metacone, the lingual profile is strongly curved, and the cusps are more internally placed (Extended Data Fig. 7a). In size, the AS 100-1b M^2 (14.2 mm BL × 13.9 mm MD) and Homo specimen A.L. 666-1 are nearly identical and both slightly exceed the Mille-Logya M^2 in BL breadth; AS 100-1b is smaller in area than the M^2 of A. garhi and eastern African Paranthropus (Extended Data Fig. 7b). We attribute AS 100-1 to Homo sp. indet. based on the presence of a derived M^2 crown outline (that is, rhomboidal with projecting relatively large hypocone[30]), a feature shared with other specimens of early Homo.

Taxonomic and phylogenetic implications

The presence of both early Homo and Australopithecus at Ledi-Geraru has implications for hominin taxonomy and diversity in this region in the 3.0–2.0 million year interval. Despite the relative paucity of fossils discovered in this time interval, evidence for multiple non-robust lineages in eastern and southern Africa indicates that taxonomic diversity had already evolved by 2.5 Ma. Here we examine taxonomic and phylogenetic hypotheses for the newly discovered hominin specimens from Ledi-Geraru.

First, although the Asboli sample contains only two molars (Table 1), and they predate Homo specimens A.L. 666-1 and MLP-1549 by more than 150,000 years, we regard the most parsimonious hypothesis to be that these are members of the same species of Homo[30]. The existence of Homo at Ledi-Geraru by around 2.78 Ma was previously established by the LD 350-1 mandible[1]; the new dental material from the Asboli area, as well as the LD 302-23 premolar described here from the Gurumaha sedimentary package, provide additional evidence of Homo prior to 2.5 Ma.

Second, LD 750 and LD 760 are separated by 24 m of strata and straddle the LAT. We provisionally regard all teeth recovered from the Lee Adoyta sedimentary package as representatives of a single species of Australopithecus, partially premised on our expectation of the low likelihood of two Australopithecus species existing in such close geographic and temporal proximity. The following are potential phylogenetic scenarios for the LD 750 premolar and the LD 760 teeth:

1. (1)
The Ledi-Geraru australopith teeth represent a late-surviving population of A. afarensis. The Lee Adoyta sample is approximately 350,000 years younger than the last appearance of A. afarensis, which is documented by specimens from the Kada Hadar 2 Submember at Hadar[11]. Contradicting this hypothesis, the mandibular M_1s and M_2s are not bilobate and are squarer in crown shape than the majority of A. afarensis; the P_4 and molar sizes fall just at the upper limit of size variation in that species; and the pattern of maxillary canine wear in the LD 760 canine is not seen in A. afarensis. Accordingly, if these teeth do represent A. afarensis, they may capture further evolutionary change within the lineage. The resemblances between the Lee Adoyta Australopithecus specimens and A. afarensis are symplesiomorphic. In addition to A. garhi, this is the only other record of the persistence of this genus in eastern Africa after 2.95 Ma, although it has approximate contemporaneity with A. africanus found in South Africa.

2. (2)
The Lee Adoyta Australopithecus teeth represent a population ancestral to later Paranthropus, which is currently undocumented in the Afar Region; this inference could be supported by the large size of the postcanine teeth. However, the appearance of Homo by 2.78 Ma implies a divergence between that clade and Paranthropus by at least that date, and Paranthropus is present as early as 2.66 Ma in the Upper Ndolanya Beds at Laetoli, Tanzania[16] and around 2.7 Ma at Nyayanga, Kenya[2]. Moreover, the Lee Adoyta teeth share no synapomorphies with Paranthropus (such as presence of C6 on the molars, reduced anterior tooth size or planar wear pattern) and megadonty is common in early hominin lineages, including A. garhi, A. africanus and early Homo[3]. Given these constraints, it is unlikely that the Lee Adoyta Australopithecus is ancestral to Paranthropus.

3. (3)
The Lee Adoyta Australopithecus teeth are early representatives of A. garhi. Accepting this hypothesis would require that A. garhi includes very different canine and maxillary molar forms and postcanine wear patterns on their upper and lower dentitions. This alternative is difficult to evaluate as there are only a few overlapping anatomical elements to compare between the current A. garhi fossil material and the Lee Adoyta Australopithecus sample. However, our assessment is that the lack of similar forms on the few shared elements suggests the Lee Adoyta australopith is not A. garhi.

4. (4)
The Lee Adoyta teeth are a previously unknown species of Australopithecus from the early Pleistocene. This hypothesis is the only alternative that offers no contradictions with the data presented in the previous three hypotheses.

The Ledi-Geraru fossils described here demonstrate that there were at least 3 lineages in the Afar Region between 3.0 and 2.5 Ma: Homo, A. garhi and the newly identified Australopithecus from Ledi-Geraru. The diversity of hominins in the interval around 2.5 Ma demonstrates the ways in which evolution was experimenting with the overall hominin pattern; in addition to these Afar region hominins, A. africanus is present in South Africa and Paranthropus is found in Kenya, Tanzania and southern Ethiopia by this time. The apparent absence of Australopithecus after approximately 2.0 Ma in eastern Africa means that only two hominin genera remained, Homo and Paranthropus, and these were well differentiated in terms of their dietary ecology[31]. Moreover, the discoveries of Australopithecus and Homo at Ledi-Geraru between 2.78 Ma and 2.59 Ma reveal that a drier, more open habitat was not uniquely associated with the appearance of Homo17,32, raising intriguing questions about niche breadth and landscape use among early hominins—including why Australopithecus was able to survive in the Afar Region alongside Homo until at least 2.5 Ma, and whether Paranthropus was competitively excluded from the Afar Region by late-surviving Australopithecus filling a similar niche.

Methods

40Ar/39Ar dating

Individual phenocrysts of K-feldspar (sanidine to anorthoclase, ~400–1,000 μm) from tuff sample LG-1048-1 of the Giddi Sands Tuff were analysed at the Berkeley Geochronology Center by the SCIH 40Ar/39Ar dating technique[17]. The feldspar concentrate was irradiated in the Cd-lined CLICIT position of the Oregon State University TRIGA reactor for four hours (BGC irradiation no. 461). Sanidine phenocrysts from the Alder Creek Rhyolite of California were used as the neutron-fluence monitor mineral, with an orbitally referenced age[20] of 1.1848 ± 0.0006 Ma. Reactor-induced isotopic production ratios for this irradiation was: (36Ar/37Ar)Ca = 2.65 ± 0.02 × 10−4, (38Ar/37Ar)Ca = 1.96 ± 0.08 × 10−5, (39Ar/37Ar)Ca = 6.95 ± 0.09 × 10−4, (37Ar/39Ar)K = 2.24 ± 0.16 × 10−4, (38Ar/39Ar)K = 1.220 ± 0.003 × 10−2, (40Ar/39Ar)K = 2.5 ± 0.9 × 10−4. Atmospheric 40Ar/36Ar = 298.56 ± 0.31 (ref. [33]) and decay constants follow ref. [34].

In total, 26 individual grains were analysed separately by the SCIH approach. One grain was rejected after the initial step as an old xenocryst (27361-08, ~100 Ma), while all others yielded geologically reasonable eruption ages and were carried to conclusion (Supplementary Table 1). All completed experiments demonstrated apparent-age plateaus, typically across the entirety of the 39Ar release spectrum (Supplementary Fig. 1), indicating an absence of complexity to the internal argon systematics, and that these feldspars show no evidence of alteration or inherited argon. A probability density plot of the plateau ages is shown in Supplementary Fig. 2, revealing that the age distribution is symmetrical and gaussian-like. The weighted-mean age of the accepted plateaus is 2.593 ± 0.006 Ma (1σ analytical error). Isochrons (36Ar/40Ar versus 39Ar/40Ar) calculated from the plateau steps yield an indistinguishable weighted-mean age of 2.597 ± 0.006 Ma (Supplementary Fig. 3) but, given the high proportion of radiogenic 40Ar of total 40Ar (typically >90%) in these crystals, we prefer the straightforward plateau weighted mean as the reference age for this sample (2.593 ± 0.006 Ma).

Dental analysis

Hominin specimens discovered in 2015 and 2018 from the LGRP area are described and evaluated taxonomically, using comparative fossil material from eastern and southern Africa including A. anamensis, A. afarensis, A. africanus, A. deyiremeda, A. garhi, Paranthropus aethiopicus, P. boisei, P. robustus, Homo habilis, Homo rudolfensis, and Homo sp. (see Extended Data Table 1 for sites, localities and references for these taxa). Resampling data from Pan troglodytes and Gorilla gorilla metrics are also used to evaluate the comparative relative variation within and between samples. Bootstrapping (resampling with replacement) was performed in R v. 4.4.1[35]. The analyses were conducted using code written by the authors that utilizes the sample function. For each analysis, distributions of values for extant taxa were derived from 10,000 bootstraps. The ratio of the Ledi-Geraru and A. garhi specimens were compared to the bootstrapped distributions for chimpanzees and gorillas; all statistical tests were two-tailed using α = 0.05. All photographed material (including comparative specimens) were examined directly by the authors. Quantitative data came from a variety of sources, including the authors’ and published measurements; both quantitative and qualitative comparative analyses were performed. See Extended Data Table 1 for references and Supplementary Data 1 for all hominin dental metrics.

Survey and collection for each locality

LD 302-23

This mandibular premolar was found 6 February 2015 by K.G.T. during palaeontological survey ~22 m to the southwest and ~7 m stratigraphically below the LD 350 early Homo mandible locality[1], above the 2.782 ± 0.006 Ma Gurumaha Tuff[17] (Fig. 1b). The fossil was recorded in the LGRP database with GPS coordinates. Numerous non-hominin fossils were also recovered in the area.

LD 750-115670

This mandibular premolar was found by O.A.O. during palaeontological survey on 14 February 2018 (Supplementary Fig. 4). It was located ~9 m stratigraphically above the 2.63 Ma Lee Adoyta Tuff, on the eastern side of the Lee Adoyta Basin (Extended Data Fig. 2a) The fossils were recorded in the database with GPS coordinates and an in situ photograph. The sequence includes fine sandy silts to very fine sand, tuffaceous silts and brown mudstones.

LD 760-115329, LD 760-115685, LD 760-115533, LD 760-115316, LD 760-115323, LD 760-115979, LD 760-115934, LD 760-115884 and LD 760-115911

The first molar, LD 760-115329, an M_1, was found by O.A.O. on 14 February 2018, followed by the discovery of four more lower molars by A.L.R. and O.A.O. within ~30 min. These teeth were located ~10 m stratigraphically below the 2.63 Ma Lee Adoyta Tuff on the eastern side of the Lee Adoyta Basin (Extended Data Fig. 2). All fossils surrounding the hominin teeth were flagged and collected during surveying for additional hominin material (Supplementary Video 1). The fossils were recorded in the database with GPS coordinates and in situ photographs. The area was subsequently (19–25 February 2018) screened with 2 mm screen mesh and the maxillary dentition was discovered in the surrounding 4 × 7 m in the upper 5–7 cm of loose surface material. Expanded sieving in 2020 did not produce additional fossils. Fossil survey, collection, and screening are shown in Supplementary Video 1. The sediments below the Lee Adoyta Tuff at the site include fine sands overlain by brown mudstones.

AS 100-1a and AS 100-1b

On 23 February 2015, one partial hominin molar, AS 100-1a (LM^1), was found by B.M.A. and a tooth fragment, AS 100-1b, was subsequently found by E.M.L. during palaeontological survey (Supplementary Fig. 5). Further exploration of the small area revealed a second fragment, found by B.M.A., which fit onto AS 100-1b (completing the LM^2). All teeth were found within 0.5 m^2. All fossils were recorded in the database with GPS coordinates. The area was subsequently screened with 2 mm screen mesh without recovering more fossils. The sequence includes ~1 m thick sand (Giddi Sands) and thin pebble conglomerate units immediately below the Giddi Sands Tuff (Fig. 1).

For all localities, geological data were collected using StraboSpot 2.12.18, Avenza 5.4 and GaiaGPS 2025.2 and integrated in ArcGIS Pro 3.2.2.

Ethics and inclusion

The Ledi-Geraru Research Project has long partnered with local Afar team members to successfully conduct palaeoanthropological research in the Afar Region, Ethiopia. Three Ledi-Geraru Afar team members whose contributions and discoveries were critical for this study are recognized as co-authors. The Ledi-Geraru Research Project is committed to formally acknowledging the contributions of all our collaborators and team members in this and future work. This acknowledgement is one step in our long-term commitment to building research capacity and access for the local Afar community.

Reporting summary

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

Data availability

All data are presented in Supplementary Table 1 (and online at https://tinyurl.com/LediGeraru40Ar39Ar) and Supplementary Data 1 (and online at https://tinyurl.com/LediGeraru).

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Acknowledgements

The authors thank the Ethiopian Heritage Authority, especially EHA representatives D. Zerihun in 2018 and G. Tekle in 2020; the Afar National Regional State Tourism Bureau; our 2015 Afar representative A. Mohammed; our 2020 representative A. Yusuf; T. White and the Middle Awash research project for access to and photographs of BOU-VP-12/130; and J. Moggi-Cecchi for A. africanus data. We acknowledge support of NSF Grant EAR-2020044. This research was funded by NSF Grant BCS-1853362, the Institute of Human Origins, and the School of Human Evolution and Social Change at ASU. We share decades of appreciation for Mesfin Mekonnen Bekele and the rest of our field crew. We dedicate this work to the late Charlie Lockwood, who was a founding director of this project and a talented and cherished colleague.

Author information

Author notes

1. Deceased: Mohammed Ahmeddin Hayidara, Omar Abdulla Omar, William H. Kimbel

Authors and Affiliations

1. Department of Anthropology, University of Nevada Las Vegas, Las Vegas, NV, USA

Brian Villmoare

2. Department of Anthropology, University of Arkansas, Fayetteville, AR, USA

Lucas K. Delezene

3. Anthropology, Virginia Commonwealth University, Richmond, VA, USA

Amy L. Rector

4. Department of Geosciences, Pennsylvania State University, University Park, PA, USA

Erin N. DiMaggio

5. Institute of Human Origins and School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA

Christopher J. Campisano, William H. Kimbel & Kaye E. Reed

6. School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA

David A. Feary & Daniel Chupik

7. Ledi-Geraru Research Project Field Team, Mille Woreda, Ethiopia

Baro’o Mohammed Ali & Omar Abdulla Omar

8. Berkeley Geochronology Center, Berkeley, CA, USA

Alan L. Deino

9. Department of Geology and Geophysics, Louisiana State University, Baton Rouge, LA, USA

Dominique I. Garello

10. Afar Tourism Bureau, Semera, Ethiopia

Mohammed Ahmeddin Hayidara

11. Department of Anatomy, Idaho College of Osteopathic Medicine, Meridian, ID, USA

Ellis M. Locke

12. Archaeology Program, Boston University, Boston, MA, USA

Joshua R. Robinson

13. Department of Biology, College of Natural Sciences, California State University, San Bernardino, San Bernardino, CA, USA

Eric Scott

14. Department of Biomedical Sciences, West Virginia School of Osteopathic Medicine, Lewisburg, WV, USA

Irene E. Smail

15. Techno Link College Burayu Campus, Shager City, Ethiopia

Kebede Geleta Terefe

16. Department of Palaeobiology, Swedish Museum of Natural History, Stockholm, Sweden

Lars Werdelin

17. School of Earth and Space Exploration and Institute of Human Origins, Arizona State University, Tempe, AZ, USA

J. Ramón Arrowsmith

Contributions

K.E.R. and J R.A. designed, initiated and managed the overall project. A.L.R., B.V. and L.K.D. outlined the topic. B.V., L.K.D. and W.H.K. analysed and interpreted hominin data. B.M.A., M.A.H., O.A.O., E.M.L., A.L.R., K.E.R., J.R.R., I.E.S., E.S., K.G.T., B.V. and L.W. conducted field surveys, fossil recovery and analysis. J.R.A., C.J.C., D.C., E.N.D., D.A.F. and D.I.G. carried out geological survey, mapping, 3D modelling and analyses. E.N.D. created geology and overview figures and A.L.D. conducted geochronological analyses. B.V., L.K.D., A.L.R., K.E.R., E.N.D., C.J.C., D.A.F. and J R.A. wrote the manuscript with contributions and discussion from all authors.

Corresponding author

Correspondence to Kaye E. Reed.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Zeresenay Alemseged, Jacopo Moggi-Cecchi, Elizabeth Niespolo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Map of the Ledi-Geraru Research Project area.

a, The Ledi-Geraru Research Project area (yellow star) is located towards the northern extent of palaeontological sites (red circles) in the Afar depression, Afar Region, Ethiopia. b, Within the Ledi-Geraru project area, the Lee Adoyta and Asboli fossil sites are located approximately 12.5 km apart.

Extended Data Fig. 2 Oblique and vertical views of the northern Lee Adoyta basin hominin sites.

a, Geologic mapping draped on orthophotograph created from unmanned aerial vehicle (UAV) images with cm-scale ground resolution, showing LD 750 and LD 760 fossil sites, including traces of the Lee Adoyta Tuffs (2.63 Ma; shown in green) and the Giddi Sands Tuff (2.59 Ma; shown in yellow), as well as NE-SW and NW-SE trending faults. Small offset faults (a few metres) in the Lee Adoyta sedimentary package (light green shading) are present near the fossil sites. b, Hominin specimens recovered from the LD 760 locality are represented by yellow stars and other faunal fossils by red circles. The hominin locality topography is relatively flat-lying, grading downslope gently to the northwest (Supplementary Video S1).

Extended Data Fig. 3 Comparative mandibular third premolar morphology and dimensions.

a, P_3 occlusal morphology of eastern African hominins. A.L. 128-23, A.L. 333w-1 (mirrored), KNM-ER 5431, A.L. 437-2, OMO 33-73-5496, and LD 302-23. The crown of LD 302-23 measures 10.5 mm MD x 11.5 mm BL (est.). Mesial is oriented up and lingual is oriented to the right. Scale bar in centimetres; images are to scale. b, Bivariate plot of P_3 crown dimensions. The LD 302-23 P_3 (magenta star) falls near the upper range of Homo and well within Australopithecus. Authors’ data supplemented by references in Extended Data Table 1. Although LD 302-23 is large for a Homo P_3, our interpretation of the occlusal morphology is more consistent with assignment to this genus. c, P_3 (MD/BL ratio) shape variation. LD 302-23 falls in the middle range of Australopithecus and lower in the range of Homo, although these patterns show overlap. d, P_3 variation in area (mm^2). LD 302-23 falls in the upper ranges of Australopithecus and Homo but is small compared to P. boisei.

Extended Data Fig. 4 Comparative fourth premolar morphology and dimensions.

a, Comparison of the Ledi-Geraru LD 750-115670 P_4 to other pre-2.0 Ma hominin specimens. All A.L. specimens attributed to A. afarensis. OMO 75-14 and LD 350-1 are attributed to Homo1,2, StW 404 and OMO L51-79 are attributed to Paranthropus[1]. LD 750-115670 measures 11.4 mm MD x 12.4 mm BL. Mesial is oriented up and lingual is oriented to the right. Scale bar in centimetres; images are to scale. b, Bivariate plot of P_4 crown dimensions. The LD 750-115670 premolar (magenta star) falls at the upper range of A. afarensis P_4 areas and just outside the range of eastern African early Homo. Authors’ data supplemented by references in Extended Data Table 1. c, Premolar cusp apices are marked in yellow with the outline of anterior fovea to aid identification of trait. d, Protoconid-metaconid cusp apex distance relative to overall BL breadth in unworn Hadar A. afarensis P_4 specimens (n = 8 including A.L. 966-1, in D) and LD 750-115670. This specimen falls outside the known range of variation for unworn P_4s from Hadar (unpublished data from W.H.K.). e, Ratio of Bouri (A. garhi) P^4 area to LD 750 P_4 area compared to bootstrapped ratios of upper to lower P4 areas from Pan troglodytes (n = 98P_4s/95P^4s) and Gorilla gorilla (n = 119P_4s/120P^4s). The ratio of the Bouri upper premolar to the lower LD 750 specimen could be resampled within the 95% confidence intervals of the randomly selected gorilla (two-way p-value = 0.486) and chimpanzee (two-way p-value = 0.402) samples; although there are no lower A. garhi premolars, the size of LD 750 does not preclude it from being within the appropriate size range for A. garhi based on gorilla and chimp models of upper premolar areas compared to lower premolar areas. The 95% confidence intervals were calculated from the bootstrapped distributions using the percentile method; no adjustments were made for multiple comparisons. Authors’ data supplemented by references in Extended Data Table 1.

Extended Data Fig. 5 Comparative mandibular molar dimensions.

a, Bivariate plot of M_1 crown dimensions (mm) for LD 760 and comparative taxa. The LD 760 M_1s fall just at the upper edge of Homo but well within Australopithecus. Note that LD 760 M_1 crown dimensions have no overlap with P. boisei. b, Bivariate plot of M_2 crown dimensions (mm) for LD 760 and comparative taxa. As with the M_1s, the LD 760 M_2s fall just at the upper edge of Homo but well within Australopithecus. Note the lack of overlap with Paranthropus. c, Bivariate plot of M_3 crown dimensions (mm) for LD 760 and comparative taxa. The LD 760 M_3 falls just at the upper edge of Homo but well within Australopithecus. Note the lack of overlap with eastern African Paranthropus. d, Area of the LD 760 M_1–M_3 molar row (mm^2 on the y-axis) compared to distributions of eastern and southern African Australopithecus, early Homo, and Paranthropus. The LD 760 molar row area is larger than all but one A. afarensis individual. Authors’ data supplemented by references in Extended Data Table 1.

Extended Data Fig. 6 Molar shape variation.

a, Ratio of MD/BL measurements (mm) on the y-axis as a representation of shape variation for the LD 760 molars compared to Hadar A. afarensis and eastern African early Homo and Paranthropus molars. Authors’ data supplemented by references in Extended Data Table 1. b, Ratio of Bouri (A. garhi) upper molar row area to LD 760 lower molar area compared to bootstrapped ratios of upper to lower molar row areas from Pan troglodytes (n = 74 mandibular molar row areas/71 maxillary molar row areas) and Gorilla gorilla (n = 77 mandibular molar row area/87 maxillary molar row areas). The ratio of the Bouri upper molar row area to the lower LD 760 molar row area could be resampled within the 95% confidence intervals of randomly selected upper and molar rows from these two African apes (Gorilla gorilla two-way p-value = 0.196; Pan troglodytes two-way p-value = 0.269); the LD 760 molar row area size does not preclude it from being within the appropriate size range for A. garhi based on gorilla and chimpanzee variation. The 95% confidence intervals were calculated from the bootstrapped distributions using the percentile method; no adjustments were made for multiple comparisons. Authors’ data supplemented by references in Extended Data Table 1.

Extended Data Fig. 7 Comparative maxillary first and second molar morphology and metrics.

a, Comparison of M^1 and M^2 Australopithecus and Homo pairs with AS 100-1a and 100-1b. Hadar A. afarensis specimens A.L. 444-2 (opposite side, mirrored – note missing enamel on lingual M1), and A.L. 199-1. Australopithecus garhi specimen BOU-VP-12/130 (mirrored), early Homo maxilla A.L. 666-1, and fragmentary M^1 specimen AS 100-1a and M^2 specimen AS 100-1b (M^2 MD 13.9 ×14.2 BL). Note the relatively small hypocone and expanded BL dimensions of the A. garhi specimen across the mesial cusps. The LD 760-115911 upper right molar (mirrored) preserves the protocone. The mesial edge of the hypocone is visible distal to the lingual groove. In A. garhi, the highly reduced hypocone results in a triangular form for the protocone as well as a generally expanded protocone-paracone axis in molars. The LD 760 upper molar specimen shows no sign of hypocone reduction. Photos of BOU-VP-12/130 A. garhi holotype fossil by T. White; use courtesy of the Middle Awash research project. Mesial is oriented up and buccal is oriented to the right. Scale bar in centimetres; images are to scale. b, Bivariate plot of M^2 crown dimensions. Note that AS 100-1b (magenta star) falls within the size distribution of early Homo.

Extended Data Fig. 8 Comparative canine dimensions.

a, Bivariate plot of canine crown dimensions for LD 760 and comparative taxa. The LD 760 specimen falls within the size ranges of early Homo and Australopithecus but not Paranthropus. MD – mesiodistal; LaL – labiolingual. b, Ratio of MD/LaL measurements (mm) on the y-axis as a representation of shape variation for the LD 760 canine and comparative taxa. Authors’ data supplemented by references in Extended Data Table 1. MD – mesiodistal; LaL – labiolingual. c, Maxillary canine area (mm^2) on the y-axis as a representation of size variation for the LD 760 canine and comparative taxa. Authors’ data supplemented by references in Extended Data Table 1. d, Upper canine MD length (mm) to lower molar MD length (mm) ratios for individuals preserving the maxillary canine and mandibular M1 and/or M2 for early Homo; Australopithecus (A. afarensis, A. africanus); and Paranthropus (P. boisei and P. robustus). The LD 760 canine/molar ratios do not fall within the Paranthropus range but are within the distribution for Australopithecus and Homo ratios. Authors’ data supplemented by references in Extended Data Table 1.

Extended Data Fig. 9 Comparative wear patterns.

a, The A. garhi BOU-VP-12/130 specimen has a very distinctive wear pattern with a continuous, broad longitudinal furrow from the maxillary canine to the molars, extending mesiodistally through the basin of each tooth, with differential wear on the lingual cusps (pers. obs., BV, LKD). This is different from the pattern on upper postcanine teeth in A. afarensis, A. africanus, P. robustus, and P. boisei, in which wear is more even across the breadth of the teeth (typically with a slight lingual-buccal wear gradient), and the cusps are ultimately reduced to the level of the central basin, producing a relatively flat occlusal profile. In A. garhi, the wear in the longitudinal furrow is sufficient to obliterate the central fissure, even in teeth with relatively unworn cusp apices (for example, contrast the P^3s in the two specimens). We see no evidence of any wear pattern on the LD 760 assemblage that would indicate similarity to BOU-VP-12/130 (notably on the preserved fragmentary upper molar). The LD 760 wear pattern is more consistent with A. afarensis. b, In A. afarensis (A.L. 199-1), the canine lacks the large concave talon; the postcanine wear pattern is flatter with more wear on the cusps, ultimately approaching the level of the tooth’s central basin, which creates a flat rather than concave surface (note the visibility of the fissures on the first upper molar of A.L. 199-1 despite considerable cusp wear). Photos of BOU-VP-12/130 A. garhi holotype fossil by T. White; use courtesy of the Middle Awash research project. Scale in b is in centimetres.

Extended Data Table 1 Hominin taxa used in comparative analyses and their corresponding sites and localities

Supplementary information

Supplementary Information

Supplementary Figs. 1–5

Reporting Summary

Supplementary Table 1

40Ar/39Ar analytical data. Data derived from individual grain analysis with the SCIH approach.

Supplementary Data 1

Hominin dental metrics including Ledi-Geraru and comparative specimens.

Peer Review file

Supplementary Video 1

Survey, collection, and screening at Lee Adoyta localities.

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Villmoare, B., Delezene, L.K., Rector, A.L. et al. New discoveries of Australopithecus and Homo from Ledi-Geraru, Ethiopia. Nature 650, 374–380 (2026). https://doi.org/10.1038/s41586-025-09390-4

- Received: 06 May 2024

- Accepted: 10 July 2025

- Published: 13 August 2025

- Version of record: 13 August 2025

- Issue date: 12 February 2026

- DOI: https://doi.org/10.1038/s41586-025-09390-4

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An ancient DNA perspective on the Russian conquest of Yakutia

Éric Crubézy,

Perle Guarino-Vignon,

Andaine Seguin-Orlando,

Clio Der Sarkissian,

Kristian Hanghøj,

Sylvie Duchesne,

Patrice Gérard,

Catherine Thèves,

Ameline Alcouffe,

Liubomira Romanova,

Daryia Nikolaeva,

Lilia Alekseeva,

Christiane Hochstrasser-Petit,

Vincent Zvénigorosky,

Christine Keyser,

Bertrand Ludes,

Michel Petit,

Henri Dabernat,

Annie Géraut,

Edouard Jyrkov,

Arkadiy Sharaborin,

Nikolai Kirianov,

Natalia Tsydenova,

Irina Dambueva,

Boris Bazarof,

Anne Boland,

Jean-François Deleuze,

Rosalia Bravina,

Anatoly Alexeev,

Étienne Patin,

Charles Stépanoff,

Lluis Quintana-Murci &

…

Ludovic Orlando

Nature volume 650, pages 389–398 (2026)

Abstract

Yakut communities from northeastern Siberia inhabit some of the coldest environments on Earth, preserving an extraordinary archaeological record. Their history was profoundly reshaped by the Russian conquest, which introduced cereals, pathogens and Christianity beginning in 1632 (refs. 1,2,3,4,5). However, the biological impact of these transformations remains unknown. Here we generated extensive ancient DNA data to elucidate contemporary changes in Yakut genomic diversity and oral microbiomes. We found Yakut origins tracing back to local populations that admixed with Trans-Baikal groups migrating as the Great Mongol Empire spread. Despite the Russian conquest, the Yakut gene pool and oral microbiomes appeared largely stable, although smallpox strains distinct from those documented in Europe by approximately 1650 circulated. Marital practices generally maintained low consanguinity, with the exception of one female bearing the latest markers of traditional shamanism, who was the daughter of second-degree relatives.

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Main

The Yakuts are the largest Indigenous group in Yakutia, northeastern Siberia, with a population of approximately half a million (Supplementary Information section 1.1). They inhabit one of the coldest regions on the planet, where annual thermal fluctuations exceed 100 °C, and winter temperatures can drop below −60 °C. Genetic and historical evidence suggest that the Yakuts descend from an ancestral population that migrated from the Lake Baikal area, possibly following the Great Mongol Empire expansion in the thirteenth and fourteenth centuries6,7. To survive in this extreme environment, they developed specific cultural adaptations, with subsistence strategies centred on horse and cattle breeding, which provided transportation, clothing, meat and milk[8]. This contrasts with the reindeer-based economies of neighbouring Indigenous groups[1] (for discussion of ethnonyms terminology, see Supplementary Information section 1.1.3). Furthermore, the Yakuts, along with other northeastern Siberian peoples, traditionally practiced shamanism, wherein practitioners intermediate with the spirit realms to guide and protect their communities[8]. The traditional lifeways of the Yakuts were dramatically disrupted beginning in 1632, when the Russian Empire initiated its conquest, primarily to expand its fur-hunting territories[9].

The Russian conquest, followed by the development of Chinese trade by the late seventeenth century, introduced tobacco, vodka and carbohydrate-rich cereals into the region10,11. New exchanges brought infectious diseases, including smallpox, tuberculosis and pertussis, exposing immunologically naive Indigenous populations to devastating outbreaks2,12,13. Beyond its impact on diet and health, the Russian conquest altered the power dynamics among patrilocal clans, with one of them gaining control over hunting grounds and becoming key in the lucrative fur trade, accumulating substantial economic wealth[3]. Alongside traders, Christian proselytes increasingly reached the region, ultimately converting predominantly animist and shamanic Indigenous populations to Christianity[3].

The upheavals of this period, combined with an archaeological record exceptionally well preserved within permafrost14,15,16, provide a unique opportunity to obtain fine-grained resolution into the biological, medical and societal consequences of a major lifestyle transition. Here we analyse an extensive collection of human skeletal remains and dental calculus from individuals living in Yakutia over the last 2,000 years, with a primary focus on approximately 1500–1922. Using extensive ancient DNA sequencing, we first reconstruct the historical origins of Yakut communities and assess the impact of the Russian conquest on their gene pool. We then examine whether associated dietary, lifestyle and social transformations influenced oral microbiota, pathogen exposure, marital practices and methylomes as potential markers of social and economic status.

Dataset and experimental design

Excavations by the Mission Archéologique Française en Sibérie Orientale (MAFSO) between 2002 and 2018 (ref. [5]) teaming together Russian and French archaeologists, uncovered an outstanding archaeological record across four key regions of Yakutia: Central Yakutia and the river basins of the western Vilyuy, northern Verkhoyansk (Yana) and eastern Indigirka (Fig. 1a,b, Supplementary Information sections 1.2 and 1.3 and Supplementary Fig. 1_2). The MAFSO expeditions aimed to investigate traditional Yakut societies and assess potential transformations following the Russian conquest[3]. To focus on Indigenous burial practices, excavations avoided Christian cemeteries, although funerary assemblages featuring Christian stellae, or yielding Christian crosses once unearthed, were included14,16 (Supplementary Information section 1.2). The Siberian climate preserved nearly intact human remains, textiles16,17 and a whole array of cultural artefacts, including ritual tripod or monopod wooden cups called chorons, which were owned by Yakut elites and used for drinking fermented horse milk (Fig. 1c,d).

The rich funerary assemblage provided exceptional insights into the social status, religious practices and presence of shamanism, among the buried individuals, spanning approximately 1500–1922 (Fig. 1c and Supplementary Table 1c). This period is divided into four archaeological stages, reflecting increasing Russian influence (Extended Data Fig. 1 and Supplementary Information section 1.2). Stage 1 (approximately 1500–1689) represents the traditional Yakut society before and during the early Russian conquest. Stage 2 (1689–1750) marks the Yakut ‘Golden Age’, when the Bozekov clan from AtDaban gained control over the prosperous fur trade, dealing in sables, martens and squirrels3,15. This period saw unprecedented economic, spatial and demographic expansion, with the Yakut language emerging as the lingua franca of northeastern Siberia. In stage 3 (1750–1800), demographic growth continued, whereas Russian influence over belief systems intensified, including legal measures against Indigenous spiritual traditions in favour of Orthodox Christian religion[3]. This process culminated in stage 4 (1800–1922), with widespread religious conversion campaigns.

Fig. 1: Study area and archaeological material.

a, Location of archaeological remains. Biological material sampled from 123 individuals spread across 113 archaeological sites. Those sites located in very close areas were grouped in pie charts to improve readability, with colours reflecting time stages. The origins of the three individuals positive for smallpox are indicated with a graphical representation of the virus. b, Map of Eurasia showing Yakutia and the burial location of one seventeenth century individual from Buryatia (black square). c, Cultural heat map summarizing the number of archaeological artefacts tracing wealth, leadership, shamanism and Christianity (Supplementary Information section 2.20) for stages 1–4. The M and F suffixes appended to individual names indicate genetic males and females, respectively. d, Typical Yakut artefacts reflecting wealth, leadership, shamanism and Christianity. α, Cauldrons, copper alloy with horse meat offering in a kytia (wooden pot) (individual Alyy, α1) or iron-made (Ebuguey2, α2); β, Signet ring (Celysse, β1 + β2; AC1, β3 + β4; Kerdugen, β5; Boulgouniakh1 and Boulgouniakh2, β6 + β7; Toutekh, β8; Atakh, β9 + β10); χ, Ring (Kureleekh); δ, Solar disk (Sordonokh); ε, Bronze torque necklace, twisted with counterweight composed of pearls and metal ornaments (Eletchei2, ε1) or flat with counterweight made of silver coins (Sordonokh, ε2); φ, Earrings (Lepsei2, φ1; Kyrdjakhastaala, φ2; Bere1, φ3); γ, Batilla, sword with a long wooden handle (AtDaban12, γ1; AtDaban11, γ2); η, Copper alloy bracelet (Kureleekh); ɩ, Choron, carved wooden monopod or tripod vase (Kerdugen, ɩ1; Bakhtakh3, containing an offering of dairy product, ɩ2); σ, Mammoth ivory comb (Eletchei1); κ, Pipes (wooden bowl κ1; Bere, with its copper alloy bowl and wooden stem, κ2; AC1, mammoth ivory bowl, κ3); λ, Carved wooden spoon (Tottouk1); μ, Iron knife (IeralaakhA); ν, Iron curb chain for the harness (Boulgounniakh3); ο, Wooden saddle covered with decorative metal plate (Eletchei1); π, Iron stirrup (Boulgounniakh1).

Source Data

To investigate Yakut history and assess the impact of the Russian conquest on population structure, marital practices, diet and health, we analysed a comprehensive collection of 122 individuals spanning stages 1–4. The dataset included 70 petrous bones, 17 long bone fragments, 54 teeth and 78 dental calculi, three lung biopsies and one muscle tissue sample (Supplementary Table 1a,b). Shotgun sequencing of 44.77 billion DNA templates from 612 libraries yielded genome-scale data suitable for downstream analyses in 59 males and 46 females, with a median depth of coverage of 2.13-fold (range of 0.017–69.85) and nuclear contamination estimates 0.5% or less for males (Supplementary Information sections 2.1–2.6 and Supplementary Figs. 2_1 and 2_2). Additionally, 74 oral microbiomes were reconstructed (Extended Data Fig. 2 and Supplementary Table 1a,b).

To further contextualize our findings, we characterized two more human genomes from a tooth of a seventeenth-century male from Buryatia (Fig. 1b) and a petrous bone of an Iron Age male from Central Yakutia (Mokp; Fig. 1a). The sequence data enabled the characterization of 16 methylomes and the imputation of 78.4 million genotypes in 90 individuals (Supplementary Information section 2.7 and Supplementary Table 1a). Population genetic analyses were conducted on pseudo-haploid data for 1.24 million single-nucleotide polymorphisms (SNPs) in the Allen Ancient DNA Resource v.5 (ref. [18]) and genotypes imputed from the 1000 Genomes Project catalogue[19].

Population history and Russian conquest

To contextualize Yakut genetic diversity within a broader human population framework, we performed a principal component analysis (PCA), projecting all ancient genomes (Supplementary Table 1d) onto the variation observed in modern Eurasian and American genomes (Fig. 2a, Extended Data Fig. 3b and Supplementary Table 1e). The first principal component separated European from Asian and American populations, with both ancient and modern Yakuts clustering with Asian populations. The second principal component further refined genetic affinities across Asia and the Americas, positioning modern and historical (stages 1–4) Yakuts near other Siberian groups, including the Evenk and Ulchi from the Russian Far East, as well as the Buryat from the Trans-Baikal region (Fig. 2a and Extended Data Fig. 3a).

Fig. 2: Population structure and history.

a, PCA conducted on 597,573 SNPs for 2,761 modern Eurasian and American individuals, with 892 ancient Eurasian and American individuals and 106 ancient Yakut individuals projected. For clarity, all the modern individuals have been removed from the enlarged panel, except the modern Yakuts (black dots). The percentage indicated on each principal component reflects the explained fraction of genetic variance. b, Unsupervised ADMIXTURE[20] genetic ancestry profiles (K = 10) calculated for 327,582 unlinked SNPs and 3,639 Eurasian modern and ancient individuals. Low-coverage individuals (less than 0.35-fold) are shown with transparency. In addition to the newly published Yakuts, two previously published[22] Neolithic and one Iron Age Yakuts are shown. IA, Iron Age; LN, Neolithic. c, Length of haplotypic blocks donated by modern individuals (Asia refers to East, North-East and South-East Asian populations) to imputed ancient Yakut individuals (coverage of 0.35-fold or higher; minor allele frequency (MAF) of 0.01 or higher). Genetic outliers discussed in the text are highlighted in red. Box plots represent the 25%, 50% and 75% quantiles, with upper and lower whiskers showing values within the 1.5 interquartile range. Horizontal segments indicate the mean value for each category. d, Time estimates for admixture and Yakut founder events (generation time of 29 years), with error bars representing the standard error. Ni and N refer to the number of imputed and non-imputed individuals used, respectively.

Source Data

The genetic makeup of historical Yakuts was remarkably homogeneous and closely resembled that of modern Yakuts. This was evident in their PCA placement (Fig. 2a, Extended Data Fig. 3b, Supplementary Information section 2.10 and Supplementary Figs. 2_5 and 2_6), ADMIXTURE[20] ancestry profiles (Fig. 2b) and fineSTRUCTURE[21] clustering (Fig. 2c and Extended Data Fig. 3c). By contrast, the Iron Age Mokp individual exhibited distinct genetic affinities, clustering with modern Nganasans and Neolithic-to-Iron-Age individuals from Yakutia[22]. The genetic ancestry component maximized in Mokp was also predominant in all these individuals but was diluted in most historical and modern Yakuts through admixture with other genetic ancestries, shared with a subset of historical populations from the Baikal region (Fig. 2b, Supplementary Information section 2.11 and Supplementary Figs. 2_7 and 2_8). These ADMIXTURE patterns align with the fineSTRUCTURE results, indicating a greater genetic contribution from eastern and northern Asia in Mokp than in all but one historical Yakut (Omouk1; Fig. 2c). Combined, these analyses suggest that Yakuts from approximately 1500 ce onwards were not genetically continuous with the populations inhabiting Yakutia until approximately 280 bce (the radiocarbon age of Mokp).

The stage 4 Omouk1 female was a clear genetic outlier relative to the historical and modern Yakuts in the PCA (Fig. 2a). She also exhibited increased Asian haplotype sharing (Fig. 2c) and an ADMIXTURE profile resembling that of the modern Trans-Baikal Evenk (Fig. 2b). Notably, the Yakut word ‘omuk’ means ‘Tungus’ or stranger, and the associated archaeological material reflects Tungus (Evenk) reindeer herding traditions[16]. These findings suggest that Omouk1, while buried in Yakutia, originated from a different genetic and cultural background than other historical and modern Yakuts.

The stage 4 Yakut Omouk3 female also displayed a slightly atypical genetic profile, including increased European haplotype sharing (Fig. 2c). A similar pattern was observed in another stage 4 female (Khoumakhtaakh), a stage 2 female (Haras) and a stage 1 male showing cranial deformation characteristic of Tungus groups (Byljasyk3)[16]. Using qpAdm[23], their genetic makeup was best modelled with a three-way admixture, incorporating an extra western Eurasian source, distinct from the two common to all other Yakuts post-1500 (Supplementary Table 1f,g). This extra ancestry could have represented European genetic input during Russian conquest. However, the estimated admixture date for Haras (718–1373; Supplementary Table 1h) pre-dates Russian expansion, suggesting earlier contact with an already admixed population, possibly from the Trans-Baikal region. The European-like admixture in Khoumakhtaakh and Omouk3 was dated to 1454–1790 (Supplementary Table 1h), aligning with both pre-conquest contacts and the Russian conquest.

Except for rare outliers (Supplementary Table 1f), the genetic makeup of all other stages 1–4 Yakuts could be modelled as a two-way admixture between populations inhabiting Yakutia in the Iron Age and the Baikal region from the thirteenth to fifteenth centuries (Baikal_his, 16–38% with Yakutia_IA (62–84%) and Baikal_sib, 40–79% with Yakutia_IA (21–60%); Supplementary Information section 2.12, Supplementary Fig. 2_9 and Supplementary Table 1f). Two-way admixture models involving a Western Russian source (Vologda Administrative Region) and a Baikal source (Baikal_his or Baikal_sib) were infeasible and/or rejected (P ≤ 0.01 and/or admixture proportion estimates ± 2 s.e. fell outside the (0, 1) interval; Supplementary Table 1f). Three-way models including Yakutia_IA, Baikal_his/Baikal_sib and European sources from 11 populations representing four language families, including from Western Russia, proved infeasible or returned negligeable admixture proportions and did not improve statistical fit relative to two-way nested models (Supplementary Table 1f,g).

To further explore the genetic impact of the Russian conquest, we calculated D-statistics[23] of the form D(Mbuti, European; Yakut_X, Yakut_Y), where Yakut_X and Yakut_Y represent various combinations of Yakut groups, excluding the previously identified genetic outliers (Extended Data Fig. 3d). Compared with stage 1, neither the groups forming each subsequent stage nor modern Yakuts showed a statistically significant excess of genetic sharedness with Europeans, including Western Russians (Extended Data Fig. 3d). Furthermore, no enrichment was detected in any given archaeological stage relative to earlier stages (Z scores < 3; Extended Data Fig. 3d). Only one of the 100 tested combinations showed a slightly higher Western Russian genetic contribution to modern Yakuts than to stage 2 individuals. Collectively, these analyses indicate a marginal genetic impact of the Russian conquest on the Yakut groups analysed.

The stable and homogeneous genetic composition of historical and modern Yakuts helped refine existing models of their origins. DATES[24] estimated the admixture time between the two primary ancestry sources between approximately 1100 and 1250 (using Yakutia_IA + Nganasan and Baikal_sib; see Supplementary Information section 2.12 for the rationale; Fig. 2d and Supplementary Table 1h). ASCEND analyses[25] (Fig. 2d, Supplementary Information section 2.13, Supplementary Fig. 2_10 and Supplementary Table 1i) further suggested that the Yakut population emerged shortly after this admixture (approximately 1210–1400), following a founder event of relatively mild intensity. The associated demographic bottleneck, quantified as the ratio between the bottleneck duration and twice the effective size, was estimated to 3.0–6.4%. Because HAPROH-based[26] estimates indicated an already limited effective size throughout stages 1–4 (Ne of approximately 532–721; Supplementary Table 1j), our analyses support a brief bottleneck underlying the foundation of the Yakut population. These findings align with oral tradition and historical sources that depict the origin of the Yakut people in the aftermaths of the Great Mongol Empire expansion from the early thirteenth century6,7.

Oral microbiomes and disease outbreaks

The arrival of Russian settlers introduced new food items, including barley, rye and various beverages10,27, into a diet traditionally dominated by meat and fish. To assess the potential impact of these dietary changes on the oral microbiome, we analysed the taxonomic and functional composition of 78 dental calculi and 55 teeth belonging to 85 Yakuts (Supplementary Table 1a,k). A subset of 74 individuals, spanning stages 1–4, exhibited typical oral microbial profiles with limited environmental contamination (Extended Data Fig. 4a). The most abundant bacterial species identified was Actinomyces dentalis, followed by other species, such as Desulfomicrobium orale, Desulfobulbus oralis and Olsenella sp. oral taxon 80, all known to be prevalent in past oral environments and to contribute to dental biofilms and plaque[28] (Supplementary Fig. 2_11).

Taxonomic abundance profiles, determined using MetaPhlAn4 (ref. [29]), as well as species-level alpha diversity, did not show significant shifts over the course of the Russian conquest (two-sided Wilcoxon test; P ≥ 0.66; Fig. 3a and Extended Data Fig. 4b). Similarly, the diversity of functional pathways, assessed through HUMAnN 3.0 (ref. [30]), UniRef90 (ref. [31]) and ChocoPhlAn[32], remained stable throughout the entire period (Fig. 3b). Both principal coordinate analyses (PCoA) and PCA, along with network-based clustering (Supplementary Information section 2.14 and Supplementary Fig. 2_14), revealed global overlap in taxonomic and functional diversity across archaeological stages. This stability was further supported by statistical tests, including analysis of similarities (ANOSIM) (Supplementary Table 1v) and multivariate analysis of variance (adonis2; Fig. 4b and Supplementary Table 1v), both rejecting greater similarity within archaeological stages than between them (0.55 ≤ P ≤ 0.89). Shifting focus to metabolic pathways to improve resolution, no statistically significant changes were observed in the abundance profiles of carbohydrate and amino acid metabolic pathways (Kruskal–Wallis test; P ≥ 0.067; Fig. 3c, Supplementary Information section 2.16 and Supplementary Fig. 2_18). Restricting all the above analyses on the subset of 66 dental calculi did not alter the conclusions (Supplementary Information sections 2.14 and 2.16, Supplementary Figs. 2_13 and 2_19 and Supplementary Table 1m). Combined, these results depict a striking stability in oral microbiomes between approximately 1500 and 1900, despite the dietary changes introduced by the Russian conquest[10].

Fig. 3: Composition and function of oral microbiomes and smallpox phylogeny.

a, PCoA of 74 oral samples carried out on Aitchison distances calculated on high-quality counts for 719 unique taxonomic species (abundance greater than 1%) from the MetaPhlAn4 database[29]. b, PCA of 65 oral samples performed on the abundance of 303 functional pathways after a centred log-ratio transformation. c, Relative abundance of the pathways underlying carbohydrate metabolism (for details on pathways, see Supplementary Fig. 2_18). d, Relative species abundance of five bacterial complexes and five oral pathogens (for details on each species from these complexes, see Supplementary Fig. 2_12). Similar analyses are provided in Extended Data Fig. 4c for three other pathogens. Box plots represent the 25%, 50% and 75% quantiles, with upper and lower whiskers showing values within the 1.5 interquartile range. The horizontal black segment indicates the mean value for each species at each stage. e, Maximum likelihood phylogenetic tree of the two Yakut smallpox strains (red labels). Node supports are on the basis of 1,000 ultrafast bootstraps. Scale bar, 0.01.

Source Data

Fig. 4: Marital practices, inbreeding and statistical associations between genetic and non-genetic factors.

a, Kinship network obtained from two analyses (cases in which both analyses disagree are shown reporting both corresponding relationships). Mitochondrial and Y-chromosome haplogroups are colour-coded and represented by symbols on the right and left sides of each individual. Family groups (with first-degree and second-degree relationships) are circled and named. b, Heat map of P values for tests of dissimilarity between DNA-based or material-based (rows) and non-genetic (columns) factors. Grey means that the test was not performed because cultural categories have been defined on the material culture. c, Proportion of long and short run of homozygosity (ROH) and inbreeding coefficient. d, Key elements of material culture excavated together with the individual UsSergue1. From top to bottom and left to right: wood comb; ushanka with a bronze solar disk worn by the deceased; choron, traditional pot for drinking fermented mare’s milk deposed between the coffin and the chest; pendants fixing together the short and virgin belt worn by the deceased; bronze stirrup; bridle, snaffle bit and bell representing burial offerings; saddle; picture of the skull of the deceased, adorned with her jewellery.

Source Data

A previous study reported a shift in carbohydrate consumption during the Russian conquest[33], from pine sapwood flour (dendrophagy) to barley and rye, alongside increased risks of dental infection linked to smoking, particularly in stage 4 (ref. [10]). We investigated whether these changes affected oral health by analysing the abundance of five bacterial complexes involved in biofilm formation and periodontal disease[34] and eight oral pathogens (Supplementary Information section 2.14). Our analyses revealed no significant increase in abundance levels from stages 1 to 4 (Kruskal–Wallis test; P ≥ 0.053; Fig. 3d, Extended Data Fig. 4c and Supplementary Fig. 2_12). Additionally, strain-level analysis of six oral pathogens or abundant oral species, which offer improved resolution into the oral health of ancient individuals relative to bacterial complexes[35], indicated different strains circulating in Yakutia and the rest of the world, as well as no shifts during the Russian conquest (Supplementary Information section 2.15 and Supplementary Figs. 2_15–2_17). These findings indicate that oral health remained largely unchanged during this period, consistent with the low prevalence of dental cavities observed archaeologically[27].

However, our data revealed the presence of smallpox in three stage 2 individuals from Central Yakutia (AC1S2, AC1S3 and Rassoloda; Fig. 1, Extended Data Fig. 2b, Supplementary Information section 2.17 and Supplementary Fig. 2_20), one of whom previously reported polymerase chain reaction (PCR)-positive for smallpox[12]. Sequence data were derived from permafrost-preserved lung and bone tissues, as well as teeth and dental calculi (Supplementary Table 1p), although only limited portions of the smallpox genome could be characterized (average depths of coverage of 0.12-fold and 0.15-fold for AC1S2 and Rassoloda, respectively). Maximum likelihood phylogenetic reconstruction indicated that the virus belonged to a strain distinct from those previously identified in Scandinavian and western Russian individuals from the seventh to tenth centuries[36] and the two main clusters responsible for the twentieth-century outbreaks worldwide (VARV-PI and VARV P-II; Fig. 3e and Supplementary Table 1q). Topological tests rejected clustering with the VD21 strain previously detected in a seventeenth-century female from Lithuania[37], supporting a deeper phylogenetic placement (Supplementary Information section 2.18 and Supplementary Figs. 2_21 and 2_22). This suggests that different smallpox strains existed in Eurasia during the seventeenth and eighteenth centuries.

Social life of past Yakut communities

Historical and ethnographic sources describe Yakut communities as structured around patrilineal and patrilocal clans, with strict exogamy[38]. Mitochondrial diversity was high across stages 1–4, whereas the number of Y-chromosomal haplotypes remained markedly limited (Fig. 4a). Furthermore, the proportion of long identity-by-descent (IBD) segments (greater than 12 cM) in pairs of same-sex adults buried in different regions was significantly greater among males than females (Wilcoxon test; P < 0.0001; Extended Data Fig. 5a). These findings are consistent with the expectations of patrilocal clans practicing exogamy, although we caution that the predominance of males in the sampled assemblages may enhance the detection of genetic relatedness among males.

Kinship analyses identified 61 individuals genetically related up to the third degree, including four cases of first-degree and second-degree relatives buried at the same site (AtDaban, Oktiom, Oulakh and Arbre Chamanique, which translates to ‘shamanic tree’ in English; Fig. 4a, Supplementary Information section 2.8 and Supplementary Table 1r–t). At Oulakh, three adult males, all first-degree relatives, were buried within a few metres of one another, and at AtDaban, a father and his adult son were buried together (Extended Data Fig. 5b), consistent with patrilocal practices. However, a grandmother was buried with her daughter and two grandchildren at Arbre Chamanique (Extended Data Fig. 5b), whereas at Oktiom, a juvenile male was buried alongside his sister’s children and their father (Extended Data Fig. 5b). Although these findings suggest non-strict patrilocal practices, the funerary context of the latter two sites is unique across Yakutia[16]. It shows individuals deposited simultaneously, which is indicative of a catastrophic event, such as the smallpox outbreak that decimated people buried at Arbre Chamanique (AC1S2 and AC1S3). Further research, incorporating genetic analyses of large-scale funerary sites that represent entire communities, alongside denser sampling across broader geographic regions, is needed to assess the prevalence of patrilocal residence patterns.

In the present dataset, exogamy was primarily restricted to individuals within the same geographic region because no first-degree or second-degree relatives were identified across different regions (Fig. 4a). Accordingly, IBD segment sharing was stronger within pairs of individuals buried in the same region compared with those buried in different regions (t-test; P < 0.0001; Extended Data Fig. 5c). The number of inter-regional third-degree relationships was also markedly lower than expected under panmixia (χ^2 test; P < 2.2 × 10−16). Furthermore, analysis of allele-sharing dissimilarity (ASD), microbiome composition and DNA methylation profiles, although from a limited subset of samples, revealed significant regional clustering (adonis2 non-parametric permutational multivariate analyses of variance; P = 0.0002, 0.0045 and 0.0037, respectively; Fig. 4b and Supplementary Table 1v). Combined, these results indicate that unions, interactions and microbial exchanges occurred primarily within regional boundaries. Although the current sample size and geographic range are limited relative to the vast expanse of Yakutia, this pattern contrasts with the prevailing archaeological interpretation, which proposed, on the basis of the same assemblages, that extensive migration between regions was responsible for the striking similarities in the material culture[15].

Changes in ASD, pairwise distances on the basis of microbial communities and functional pathways, and DNA methylation (Supplementary Information section 2.19 and Supplementary Figs. 2_23–2_27) showed no statistical association with archaeological stages, which aligns with the observed stability of the Yakut gene pool and microbiome profiles over time. Although material culture linked to males and females was clearly distinct (adonis2; P = 0.0001; Fig. 4b, Supplementary Information section 2.20, Supplementary Fig. 2_28 and Supplementary Table 1v), sex did not significantly influence the similarity measured between genomes, methylomes or microbiomes (adonis2; P ≥ 0.15; Fig. 4b and Supplementary Table 1v). The latter aligns with stable isotope analyses, which reflect no dietary differences between males and females[4]. Furthermore, no associations were found between economic wealth, leadership, Christianity or shamanism and biological distances, whether estimated by genetic proximity, inbreeding or microbiomes (adonis2; P ≥ 0.14; Fig. 4b). This suggests that individuals with similar social and spiritual statuses were not necessarily biologically closer than those with different statuses. The lack of association between methylomes and socio-economic factors, such as wealth and leadership, further suggests that current approaches for reconstructing ancient methylomes provide limited resolution into past socio-economic status. Finally, the significant association between genetic distances and regional affiliation, but not with indicators of leadership, suggests that political power was not concentrated in a single clan.

Archaeological evidence of shamanic practices was found within close kin groups (AC1S2 and ACS3; Fig. 4a). However, shamanism was not associated with pairs of individuals close genetically (Pearson’s χ^2 test; P = 0.33; Fig. 4b), indicating that the practice was not confined to a single genetic familial clan. Although historical inbreeding levels were low (Supplementary Information section 2.9), the most inbred Yakut (UsSergue1; Fig. 4c and Supplementary Fig. 2_4) also happened to be the last individual excavated with clear markers of traditional shamanism14,16 (Fig. 4d). She was genetically identified as the great-granddaughter of AtDaban6, who was married to Bozekov, the most prominent clan leader from the late seventeenth/early eighteenth centuries[39]. Her high inbreeding level suggests mating between second-degree relatives. However, it seems unlikely that UsSergue1 had access to shamanism because she was the offspring of exceptionally close kins, whereas the female buried with the most extensive collection of shamanic artefacts (KyysOunouoga)[16] showed no evidence of consanguinity (Fig. 4c).

Discussion

Our study clarifies the genetic origins of the Yakut people; the complex interplay between their marital, funerary and spiritual practices; and the extent to which these aspects were altered by the Russian conquest.

First, we found that the Yakut gene pool emerged through an admixture between a local population with roots in the Iron Age and another group probably from the Trans-Baikal region, which entered the area during the expansion of the Great Mongol Empire in the early thirteenth century6,7. This timeline, along with the broad connection with the Baikal region, concurs with oral traditions about their origins[3]. However, the persistence of a substantial local genetic component contrasts with cultural and linguistic evidence pointing to a predominant central Asian Turkic–Mongol influence[7]. We note, however, that the estimated contribution from the local population varies depending on the Baikal source considered, underscoring the need for denser sampling across the region and time period. Current models rely on population sources defined by a limited number of genetically characterized individuals, which probably do not capture the full extent of genetic diversity across the vast territory of Yakutia at the time of admixture. Despite these limitations, our results indicate that the Yakut history represents a fusion of a local population with a migrating group, both genetically and culturally.

Our analyses also improved previous studies on the basis of uniparental markers indicative of a founder event40,41 by showing that the demographic bottleneck preceding the Yakut expansion was rapid, occurring shortly after admixture, by no later than 1400. The earliest Yakut sequenced in our study (Atlasovka), buried by the late fifteenth/early sixteenth century, appeared to be a key ancestor in the reconstructed kinship networks, further supporting her foundational role[42]. Additionally, kinship analysis revealed that although first-degree and second-degree relatives could be buried at the same archaeological site, they were primarily found in geographically close locations, within a maximum of 159 km from one another (median distance of 45 m). This strong genetic proximity among burials from the same area remained evident even when close kins were excluded. Combined, these findings emphasize the deep-rooted connection between the Yakuts and the land of their fathers and grandfathers.

Furthermore, our genetic data revealed exceptions to patrilocality, and, despite the small effective population size, consanguinity remained limited. Although further research is required to assess how common such practices were, social norms allowed for close-kin unions, as shown by the UsSergue1 female, identified as the daughter of second-degree relatives. Crucially, she was buried with some funerary objects atypical of the late eighteenth century, reminiscent of the earliest Yakut material culture16,43. This female was the most recent bearer of traditional shamanic artefacts excavated. She was buried in one of the richest tombs identified and descended from the wealthiest clan documented[39]. Because she died right when Christian belief began to gain influence in Yakut society, she may be seen as an embodiment of a clan’s attempts to preserve its cultural and spiritual traditions.

Finally, the Russian conquest was found to have had marginal genetic impact on the Yakut population, at least within the time periods and regions investigated in this study. Because the Yakuts traditionally deposited their dead on open platforms rather than burying them, the extent of Russian admixture within the broader population not represented archaeologically remains unknown. However, we note that the individuals buried span a wide range of economic and social statuses, from small group leaders, shamans and suicides to major clan leaders, suggesting minimal sampling bias. Although the extent of Russian admixture in Christian cemeteries is also uncharacterized, the Yakut genetic pool appears stable throughout all archaeological stages and into the present day, indicating limited admixture during Russian conquest. Therefore, our study portrays Yakutia as a middle ground[44], where Yakuts and Russians engaged in an economic partnership rather than a site of military or demographic conquest. Historical sources highlight Yakut peoples as pivotal to the success of the then-expanding fur trade[11], which not only motivated Russian conquest1,9 but also provided prominent Yakut clans with opportunities for considerable wealth and power[39]. Additionally, the harsh environment, often lethal to European settlers and unsuitable for large-scale cereal agriculture, prevented the establishment of large Russian settlements (Supplementary Information section 1.1 and Supplementary Fig. 1_1). Consequently, the Russian expansion in Yakutia relied heavily on local populations, representing a form of colonialism distinct from the settler colonialism of the Americas, which proceeded through Indigenous displacement and eradication[45]. It also diverged from the indirect rule characteristic of colonial India, where colonial authority was maintained through local leaders without administrating the territory. By contrast, Russian authorities imposed their tax systems on Yakut communities and co-opted Yakut elites by granting them citizenship and incorporating them into the imperial structure, fundamentally altering traditional Yakut political and social organization while making them subjects of a foreign empire.

Despite the absence of significant genetic impact, Russian conquest profoundly affected Yakut livelihoods and demographics. Russian contact introduced numerous infectious agents, including the smallpox identified here and Mycobacterium tuberculosis[13], with devastating demographic effects on immunologically naive Indigenous populations. The severe outbreaks documented in historical sources were not detected in hapROH, indicating limited but stable and effective population sizes. This probably reflects the extremely rapid progression of these outbreaks or other factors limiting detection power. Although dietary shifts are well-documented10,27, our study failed to identify changes in the taxonomic and functional diversity of the Yakut oral microbiome. This contrasts with earlier studies that reported major changes in Neolithic Europe, alongside a dramatic increase in carbohydrate consumption[46]. Our findings also conflict with oral microbiome shifts documented in Great Britain (2200 bce–1853 ce), which have been linked to lifestyle, hygiene and dietary changes, including increased dairy and carbohydrate consumption[47]. The consistency of the Yakut oral microbiome over the short microevolutionary timescale investigated here (approximately 1500–1922) may reflect a shift in the carbohydrate types consumed (from wood-based flour to cereal flour)[33] rather than a drastically increased intake. Moreover, our findings are in agreement with other studies reporting stable oral microbiomes over deep phylogenetic scales, including between Neanderthals and anatomically modern humans[48], and during the Neolithic transition from hunting and foraging to farming[49]. Future research is needed to understand the drivers of oral microbiome composition and function; the true capacity of dental calculus to capture these dynamics; and the broader response of the digestive microbiome to lifestyle, diet and health.

The exceptional archaeological record preserved in the Yakut permafrost provided a unique opportunity to reconcile evidence from material culture with the full spectrum of ancient DNA analysis, shedding light on population origins, social practices, health and individual status. In doing so, it contributed to the growing body of ancient DNA research aimed at capturing the multigenerational life of ancient communities.

Methods

Radiocarbon dating

Radiocarbon dating of the Mokp individual (UCIAMS210906: 2205 ± 20 BP, 364–197 International Radiocarbon Calibration Curve (2020 version, IntCal20) calibrated years bce) was carried out at the Keck Laboratory, University of California, Irvine, following the methodology described by Librado et al.[50].

Sex and age-at-death estimations of the human remains

Age-at-death determination methods rely on a variety of skeletal indicators, including stages of auricular surface for adults51,52, stages of iliac crest or sternal end of the clavicle fusion, measurement of long bones for immature individuals53,54 and dental eruption sequences55,56. Biological sex is on the basis of genetic data, especially the so-called Ry ratio (Y to Y + X sequence coverage)[57] (Supplementary Table 1a).

DNA extraction

Samples were processed in the clean laboratory facilities at the Centre for Anthropobiology and Genomics of Toulouse (CAGT), University of Toulouse, or at the Centre for GeoGenetics (CGG), University of Copenhagen, following ancient DNA procedures (Supplementary Information section 2.2).

Bone and tooth samples

After gentle surface abrasion, a portion of the dense part of the bone samples was collected using a diamond wheel (PROXXON or ARGOFILE instruments). For tooth samples, the cementum was isolated as recommended by Damgaard et al.[58]. The samples were either crushed into smaller fragments using a manual mortar or cutting pliers, or pulverized using a Retsch MM200 instrument and then placed in 5-ml Eppendorf LoBind tubes. DNA was extracted following a silica-column-based method, as described by Librado et al.[59], without bleach pretreatment (Supplementary Information section 2.2).

Calculus samples

Calculus samples were isolated, as described by Sabin and Yates[60]. Samples labelled as ‘Name_C’ in Extended Data Fig. 4a (for example, Eletchei3_C_C_P4) were extracted for DNA following a protocol similar to that used for bones and cementum, except that no 1-h predigestion was performed and the digestion volume was limited to 1 ml. Samples labelled as ‘Name_CE’ (for example, Eletchei3_CE_C_P4) were subjected to an overnight digestion at 50 °C in 555 µl of a buffer consisting of 0.45 M EDTA, 1.8 mg ml−1 of proteinase K and 9 mM dithiothreitol. The supernatant was further purified on a QIAGEN MinElute column and eluted in 40-µl sterile water.

Soft tissue samples

Fragments of soft tissues (lung and muscle) were digested in 1.11 ml of a buffer containing 0.45 M EDTA, 1.8 mg ml−1 of proteinase K and 9 mM dithiothreitol, following an overnight incubation at 50 °C with agitation. After 12 min of centrifugation at 8,000 rpm, the supernatant was collected and purified on a silica column (MinElute; QIAGEN; 40-µl sterile water elution).

USER treatment, DNA library building and indexing

An aliquot of 22.8 µl of each DNA extract was incubated with 7-µl USER Enzyme mix (New England Biolabs) for 3 h at 37 °C to limit the impact of post-mortem cytosine deamination in downstream analyses by removing uracil residues. For a few samples, another DNA extract aliquot was also directly converted into a sequencing library.

Sequencing libraries were constructed from double-stranded DNA molecules by ligation of universal (method by Gamba et al.[61], adapted from Meyer and Kircher[62]) or indexed[63] blunt-end adaptors. To determine the optimal number of PCR cycles for amplifying DNA libraries and obtaining sufficient material for Illumina sequencing, quantitative real-time PCR was performed on 20X dilution aliquots of most of the libraries. The libraries were amplified for 5–15 cycles using AccuPrime Pfx DNA polymerase (Thermo Fisher Scientific), with 3.5–6.5 µl of unamplified DNA library and 0.2 mM of each PCR primer in a total reaction volume of 50 µl. One primer of each pair contained an external 6-bp index, read during the Illumina Indexing Read. To limit the proportion of PCR duplicates, up to six independent amplifications were carried out for most DNA libraries. The PCR products were subsequently purified using either MinElute columns (QIAGEN) or AMPure XP beads (Beckman Coulter), eluted in 20 µl or 25 µl of elution buffer (EB) supplemented with 0.05% Tween and quantified on TapeStation 2100/4200 or Bioanalyzer instruments (Agilent Technologies) and Qubit HS Assay (Invitrogen).

Sequencing

DNA library pools were sequenced at CAGT on the Illumina MiniSeq instrument; at CGG on Illumina NextSeq, HiSeq2000, HiSeq2500 and HiSeq4000 instruments; or at Centre National de Recherche en Génomique Humaine on the Illumina HiSeq X instrument. The vast majority of the sequencing data consisted of paired-end reads.

Reads preprocessing

The demultiplexed FASTQ paired reads were processed using PALEOMIX[64] bam_pipeline (v.1.2). Sequencing adaptors were trimmed (-mm 5) as well as poor-quality end, and paired-end reads were collapsed using AdapterRemoval 2 (v.2.3.1; ref. [65]). All the resulting reads and those remaining paired were mapped against the hs37d5 reference genome using Bowtie 2 (ref. [66]) with local sensitive mapping parameters. The binary alignment/map (BAM) alignment file was further filtered for alignment size superior or equal to 25 bp and mapping quality superior to 30. PCR duplicates were removed using Picard MarkDuplicates (http://picard.sourceforge.net), and realignment around indels was performed using GATK[67]. Sequencing statistics, as numbers of sequencing reads, endogenous DNA content and coverage are provided in Supplementary Table 1a,b.

All resulting alignments were merged into a single BAM file before pseudo-haploidization, with one read randomly sampled at positions characterized by one or more alignments. Pseudo-haploid genotypes were called using ANGSD (v.0.930; ref. [68]) (htslib: 1.9), skipping positions and/or reads showing base and/or mapping Phred quality scores strictly lower than 30 (--doHaploCall 1 -doCounts 1 -minMapQ 30 -minQ 30 -remove_bads 1 -uniqueOnly 1) and restricting calls for those 1,233,013 SNP positions forming the 1240K panel[18].

Post-mortem damage and error rates

DNA fragmentation and nucleotide misincorporation patterns were visualized using mapDamage2 (v.2.0.8; ref. [69]), with default parameters on a subset of 100,000 random reads. All damage profiles and base compositions were aligned with expected profiles, with or without USER treatment of DNA extracts[70].

Error rates were calculated using ANGSD[68] and the methodology used in a previous study[71] (Supplementary Information section 2.4). Overall, the global error rates of each individual genome characterized in this study ranged between 0.000262 and 0.002819 substitutions per base on average, mostly inflated through transition misincorporations (Supplementary Table 1b).

Uniparental markers, contamination estimates and ploidy check

A total of 46 women and 61 men were identified on the basis of Ry ratio (Supplementary Table 1a). Mitochondrial haplotypes were called using Haplogrep (v.2.266; ref. [72]) after aligning reads against the revised Cambridge Reference Sequence reference mitogenome (GenBank accession no. NC_120920.1) and discarding those shorter than 25 bp, with mapping and base qualities below 30 (Supplementary Information section 2.5). The resulting variant call format file was then processed through Haplogrep[72], calculating the best 100 hits. Contamination rates on the basis of mitochondrial data were estimated using schmutzi[73] and the same base quality threshold as above. Nuclear contamination rates were estimated for male individuals, following the methodology by Rasmussen et al.[74] and implemented in ANGSD[68]. Transition substitutions and sites covered once or more than 200 times were discarded.

Mitochondrial contamination estimates were assessed within a 0–5% confidence range for all individuals but three (Supplementary Information section 2.5 and Supplementary Table 1a), which were conservatively excluded from those analyses conditioned on archaeological stages. Nuclear contamination estimates were found to be limited (median of 0.24%) and inferior to 0.4% (Supplementary Table 1a). Y-chromosome haplotypes were called using the Yleaf statistical package[75] (Supplementary Information section 2.5). The ploidy levels of each individual were checked following the methodology described by Sehnert et al.[76] (Supplementary Information section 2.6 and Supplementary Fig. 2_3).

Imputation

We imputed a subset of genomes using GLIMPSE2 (ref. [77]) and the 1000G[19] panel as reference dataset, following the instructions provided by the developers on the software website. To test for the minimal coverage needed to obtain accurate imputation, we downsampled the data of four high-coverage individuals, imputed the resulting genotypes and then assessed imputation accuracy by measuring the squared Pearson correlation between original and imputed genotypes (Supplementary Information section 2.7). We found that a minimal coverage of 0.35-fold was necessary for imputing genotypes represented at MAF of 5% or higher. A total of 90 Yakut individuals (coverage of 0.35-fold or higher) were then imputed and filtered for MAF of 5% or higher and genotype probability of 0.99 or higher for all downstream analyses. The imputed individuals were combined with the phased 1000G dataset for all downstream analyses, except for those on the basis of fineSTRUCTURE[21], which required a liftover to the hg38 positions to include the matrix of phased genotypes released by Bergström et al.[78], which included 20 modern Yakut individuals.

Kinship analyses

Relatedness between historical Yakuts was assessed on the basis of the pseudo-haploid data using a combination of three complementary methodologies: READ2 (refs. 79,80), lcMLkin[81] and TKGWV2 (ref. [82]) (Supplementary Information section 2.8 and Supplementary Table 1r–t). For READ2 (refs. 80,81) and TKGWV2 (ref. [82]), the autosomal positions overlapping the 1240K dataset were used, restricting the former to MAF of 1% or higher. We disregarded first-degree and second-degree relationships if estimated from less than 1,000 and 2,000 SNPs, respectively, whereas the default filter of READ2 (refs. 79,80) was used for assessing third-degree relationships. Precise genealogies were reconstructed using the READ2 (refs. 79,80) results, age-at-death estimations, uniparental markers and estimated period of burial of each individual (Extended Data Fig. 5b and Supplementary Information section 2.8).

Identity-by-descent (IBD) contents were calculated using ancIBD[83] on the direct output of GLIMPSE2 without MAF and genotype probability filters. As recommended, the Yakut dataset was downsampled to 1,240,000 SNPs, for which ancIBD was optimized, and IBD sharing was screened for every pair of imputed individuals (coverage of 0.35-fold or higher), with default settings[83]. For population analyses, individuals with the least SNPs covered in each pair of first-degree or second-degree relatives were removed.

Inbreeding and diversity estimates

The effective population sizes for each stage and region were estimated on the 1240K SNP pseudo-haploid panel, restricted to individuals with at least 400,000 SNPs covered, using hapROH[26] with default parameters and 5,008 haplotypes from the 1000G project as a reference panel (Supplementary Table 1j). For each archaeological stage, PCA individual outliers were removed.

ROH were identified on the imputed dataset using plink[84] (--homozyg) on set of 1000G biallelic transversions with MAF higher than 5%, removing any positions not fully covered (--geno 0). Inbreeding scores were calculated with plink[84] (--het) using transversions only and MAF of 5% or higher (Supplementary Information section 2.9 and Supplementary Table 1a). To further confirm our results, we performed ROH detection using hapROH[26] on the pseudo-haploid data for individuals with at least 400,000 SNPs covered on the 1240K panel (Supplementary Information section 2.9 and Supplementary Fig. 2_4).

Principal component analysis

PCA was carried out using the Human Origins reference panel for 597,573 autosomal genotypes. Genotypes were downloaded from the Allen Ancient DNA Resource (v.5) website[18]. We also included the genotypes from those Central Asian individuals with relevant genetic ancestry profiles reported by Zhang et al.[85]. PCA was on the basis of pseudo-haploid genotype calls for all the individuals presented in this study and carried out using smartPCA from EIGENSOFT (v.7.2.170; ref. [86]), projecting 913 ancient Eurasian and American individuals and 106 ancient Yakut individuals (coverage of higher than 0.02-fold) onto the principal components obtained from in 2,761 Eurasian modern individuals (lsqproject, YES; shrinkmode, YES; Supplementary Information section 2.10 and Supplementary Fig. 2_5, where a non-projected PCA is shown). Projections on the first two principal components are provided in Fig. 2a, whereas PC2 and PC3 are provided in Extended Data Fig. 3b. A second PCA was carried out to validate our imputation pipeline by confirming similar projections for imputed genotype data and pseudo-haploid data (Supplementary Information section 2.10 and Supplementary Fig. 2_6).

ADMIXTURE

Unsupervised ADMIXTURE (v.1.3.0; ref. [20]) analyses were carried out to estimate the proportions of genetic ancestries present in Yakuts (coverage of 0.03-fold or higher; pseudo-haploid) using autosomal positions as part of the 1240K Human Origins panel and a total of 3,639 Eurasian and American individuals. Sites were thinned for linkage disequilibrium with plink[84] (--indep-pairwise 200 25 0.4), resulting in a total of 327,582 SNPs. Confidence intervals were estimated from 100 bootstrap pseudo-replicates. Analyses were repeated ten times using ten random seeds to assess convergence (Supplementary Information section 2.11 and Supplementary Fig. 2_7). Full ancestry profiles are provided in Supplementary Fig. 2_8 for the entire dataset.

FineSTRUCTURE

A fineSTRUCTURE (v.2; ref. [21]) analysis was performed on the imputed data to explore patterns of haplotype sharedness. Imputed transversion genotypes were converted to hg38 positions with the tool LiftoverVcf from the Picard Toolkit 2019 (https://github.com/broadinstitute/picard), and related individuals were removed before merging with the phased genotypes from Bergström et al.[78]. The genotype positions showing missingness in at least one individual were removed, and MAF of 1% or higher was required, resulting in 1,059,615 autosomal sites. The merged dataset was split by chromosome, rephased using SHAPEIT (v.2; ref. [87]) and transformed into ChromoPainter (v.2; ref. [21]) format using ‘impute2chromopainter.pl’ and a chromosome-based recombination map generated through the ‘makeuniformrecfile.pl’ script. ChromoPainter (v.2; ref. [21]) analyses were on the basis of 20 expectation–maximization iterations (-s1emits 20 -in -iM), with a starting switch rate of 250 (-n 250) and a global mutation rate of 0.0005 (-M 0.0005). The fineSTRUCTURE Markov chain Monte Carlo model was run on the ChromoPainter (v.2) output for 3,000,000 burn-in iterations and 2,000,0000 sampling iterations with no thinning (-s3iters 5000000 -s3iterssample 2000000 -s3itersburnin 3000000). The resulting co-ancestry matrix is shown in Extended Data Fig. 3c.

D-statistics

Different combinations of D-statistics were calculated using qpDstat in ADMIXTOOLS (v.5.056; ref. [23]) to detect gene flow by testing whether pairs of modern and ancient Yakuts from each archaeological stage were symmetrically related to modern Eurasian populations. Calculations were carried out on the pseudo-haploid 1240K dataset using Mbuti (N = 10; ref. [78]) as outgroup. The topologies investigated were in the form of (outgroup, Eurasian modern populations; StageX, StageY/modern Yakut). The results of the different D-statistics calculations, with Z scores corrected for multiple testing (Benjamini–Hochberg), are provided in Extended Data Fig. 3d, permuting StageX and StageY among the four archaeological stages and modern Yakuts. Positive values indicate closer genetic proximity between the modern Eurasian population and StageY (or modern Yakuts), relative to StageX.

Admixture modelling and dating

Admixture models for ancient Yakut individuals (coverage of 0.1-fold or higher) were assessed using the pseudo-haploid 1240K dataset and qpAdm from ADMIXTOOLS (v.5.056; ref. [23]), applying the feasibility criteria recommended by Flegontova et al.[88], that is, coefficient ± 2 s.e. within the [0, 1] interval (P ≥ 0.01). The qpAdm models were aimed at testing whether the Yakut genomic makeup was compatible with a two-way admixture from a local Siberian background (Yakutia_IA, N = 2, comprising Mokp and yak03041 because they showed similar genetic profiles and PCA placements) and another source, potentially from the Baikal region (Baikal_his (N = 4) or Baikal_sib (N = 11)) or Russia (Russian[78]) (Supplementary Table 1f). Baikal sources were defined as Baikal_sib (N = 11) and Baikal_his (N = 4). The former included Mongolia_Khuvsgul_LateMedieval[89] (N = 2), Mongolia_Dornod_LateMedieval[89] (N = 7) and Mongolia_Khentii_LateMedieval[89] individuals (N = 2), whereas the latter comprised Russia_AngaraRiver_Medieval.SG[22] (N = 1), Mongolia_Sukhbaatar_Xiongnu (N = 1) and Mongolia_Khuvsgul_MLBALateMedieval[89] individuals (N = 2). A full range of qpAdm admixture models were tested to identify the best sources for Baikal_his and the best western Russian source, including Yakutia_IA, Russia_AngaraRiver_Medieval.SG[22] (N = 1), Mongolia_Sukhbaatar_Xiongnu (N = 1), Mongolia_Khuvsgul_MLBALateMedieval[89] individuals (N = 2) and Buryat.SG90,91 (N = 4), and extending western sources to Polish, Bulgarian, Czech in addition to Russian (accounting for Slavic-speaking populations), Adygei, Abkhasian, Chechen, Lezgin and North Ossetian groups (accounting for the North Caucasus), Mansi (to represent Uralian-speaking populations) and Altaian (Turko–Mongolic-speaking populations) (Supplementary Information section 2.12 and Supplementary Table 1g). This resulted in the exclusion of Buryat.SG from the Baikal_his group because almost all of its models failed, whereas the other groups tested yielded consistent results. No other western sources outperformed the Russian group; therefore, we kept it as a proxy for the western source for the final models (Supplementary Information section 2.12 and Supplementary Table 1g). The Baikal_sib populations were selected because they exhibited the closest ADMIXTURE[20] ancestry profiles (Supplementary Information section 2.11). Each ancient and modern individual from Yakutia was tested for every combination of two or three populations, putting the non-used population in the right group[92] (Supplementary Table 1f).

We further applied DATES[24], using both the pseudo-haploid and imputed datasets, to two-way models to estimate the time of the admixture event between the local ancestry source (Yakutia_IA + Nganasan) and Baikal populations (Supplementary Information section 2.12 and Supplementary Table 1h). Because the confidence intervals using the Baikal_sib source were more restrained (Supplementary Table 1h) and Baikal_sib covered more individuals, analyses incorporating the Baikal_sib source were preferred (Fig. 3d). The time of admixture between a Russian source[78] and either a historical Yakut ancestry source (Yakut_his, comprising all newly sequenced Yakut individuals from the four stages, excluding related individuals and genetic outliers; N = 92) or the local ancestry source (Nganasan + Yakut_IA; N = 37) was also estimated for the imputed genomes of the PCA genetic outliers (Supplementary Table 1h). The corresponding weighted linkage disequilibrium decay curves are shown in Supplementary Fig. 2_9 and discussed in Supplementary Information section 2.12).

Bottleneck dating

We used ASCEND[25] to assess the intensity and estimate the time for the bottleneck underlying the foundation of the Yakut gene pool. These analyses were first run without specifying an outgroup and then repeated by choosing an outgroup (N = 15) randomly from the populations present in our dataset. Analyses were carried out by considering archaeological stages individually or the entire group of ancient Yakuts, both for the pseudo-haploid and imputed datasets, with the following parameters: binsize, 0.001; mindis, 0.001; maxdis, 0.3; maxpropsharingmissing, 1; minmaf, 0; usefft, YES; qbins, 100 (Supplementary Information section 2.13 and Supplementary Table 1i). The allele-sharing correlation decay curve together with the fitted exponential model from our outgroup tests are shown in Supplementary Fig. 2_10 and discussed in Supplementary Information section 2.13).

Microbial profiling

Microbial taxonomic profiles were determined for each individual DNA sample, restricting analyses to the fraction of collapsed reads. Reads aligned to the human genome (hg37) and the human mitochondrial genome were filtered out (Supplementary Information section 2.14). Microbial read counts were obtained using MetaPhlAn4 (ref. [29]) (Supplementary Table 1l), discarding unclassified and too short reads. We applied a minimal read length filter set to the most frequent read length value (visually checked) minus ten, with strict boundaries set at less than 30 bp and greater than 70 bp (Supplementary Information section 2.14). This procedure was repeated on a panel of known sources (Supplementary Information section 2.14 and Supplementary Table 1k (for details and references)) that were used to assess the proportion of oral microbes contributing to each ancient DNA library, using SourceTracker2 (ref. [93]), conditioning analyses on species level (Extended Data Fig. 4a). Samples showing more than 25% of oral sources were retained for further analyses because such proportions were observed in oral samples previously analysed and identified as authentic[48]. In cases where both tooth and calculus samples from the same individual passed filters, the profile maximizing oral microbial sources was kept, resulting in a final dataset of 74 individual oral microbiomes.

Bacterial taxa showing abundances lower than 1% were disregarded before carrying out composition visualization (Supplementary Fig. 2_11) and PCoA on the basis of Bray–Curtis distances (Fig. 3a). Species abundances of microbes belonging to different bacterial complexes (red, orange, yellow, green and purple), together with eight known oral pathogens, were measured and tested for potential shifts across archaeological stages (Kruskal–Wallis test; Fig. 3d, Supplementary Information section 2.14 and Supplementary Fig. 2_12). These analyses were repeated on a dataset restricted to calculus samples (Supplementary Information section 2.14 and Supplementary Fig. 2_13).

We also performed two complementary analyses to reveal subtle commonalities in the microbial compositions of the different samples that may have remained undetected in PCoA (Supplementary Information section 2.14). The first analysis followed Quagliariello et al.[46] and their network and clustering methodology. No association was found in the distribution of individuals among clusters and archaeological stages (Pearson’s χ^2 test; P = 0.92; Supplementary Information section 2.14 and Supplementary Fig. 2_14). The second analysis investigated strain-level variation in the oral pathogens detected using StrainPhlAn4 (refs. 29,94), considering the most abundant bacterial species of the red complex and eight pathogens. Metagenomic data from dental calculus of several individuals, including Neanderthal outgroups and Eurasian individuals who lived within the past 500 years (Supplementary Table 1n), were accessed through the AncientMetagenomeDir (v.24.09; ref. [95]) repository. These data were processed similarly to Yakut data before running StrainPhlAn4 with default parameters to extract species-specific MetaPhlAn markers. We prepared multi-FASTA alignments combining those markers together across all individuals and reconstructed maximum likelihood phylogenies in IQ-TREE (v.1.6.12; ref. [96]) to assess whether or not new strains arrived in Yakutia at a specific archaeological stage (Supplementary Information section 2.15 and Supplementary Figs. 2_15–2_17). The best substitution model was estimated using the Akaike information criterion (-m MFP), and node support was assessed from 1,000 ultrafast bootstrap[97] pseudo-replicates (UFBoot) (each bootstrap tree optimized using a hill-climbing nearest-neighbour interchange search; -bb 1000 -bnni). When the number of Neanderthal hits was found too limited to use them to root, the trees were rooted at midpoint.

The sequence data passing the SourceTracker2 filters described above were also subjected to functional analyses using the methodology implemented in HUMAnN 3.0 (ref. [30]), with default parameters (Supplementary Table 1o). This step generated per-individual functional profiles on the basis of the UniRef90 (ref. [31]) and ChocoPhlAn (January 2023; ref. [32]) databases, which were further joined by pathways, normalized by counts per millions and centred log-ratio transformed to deal with compositional values that may arise from specific normalization in sequencing data, before conducting PCA (Fig. 3b). Selected pathways associated with carbohydrate or amino acid metabolism were scrutinized for their relative abundances across individuals and compared by archaeological stages using a Kruskal–Wallis test (P ≥ 0.067; Fig. 3c, Supplementary Information section 2.16 and Supplementary Fig. 2_18). These analyses were repeated on a dataset restricted to calculus samples (Supplementary Information section 2.16 and Supplementary Fig. 2_19).

Pathogen screening

Reads aligned to the human genome (hg37) and the revised Cambridge Reference Sequence mitochondrial genome were filtered out (Supplementary Information section 2.17). The resulting filtered FASTQ files were used for mapping against a selection of reference genomes from candidate pathogens (N = 26; Supplementary Information section 2.17 and Supplementary Fig. 2_20a). This alignment step was carried out using PALEOMIX[64] bam_pipeline (v.1.2) and bwa-0.6 (ref. [98]) (backtrack; MinQuality, 30; no seed; -n, 0.1), which produced high-quality BAM alignments that were removed for PCR duplicates. The number of aligned reads against each reference genome was counted per sample, together with average read-to-reference edit distances. We considered a sample positive for the presence of any given pathogen as long as a minimal number of 100 high-quality alignments were identified, and the average edit distance was equal to or below 0.01. This conservative approach resulted in the identification of three individuals positive for Variola major, the aetiologic agent of smallpox (AC1S2, AC1S3 and Rassoloda; Supplementary Information section 2.17 and Supplementary Fig. 2_20b).

Smallpox genome analysis

All the sequence data generated for the three smallpox-positive individuals were realigned against the variola virus (VARV) smallpox reference genome (accession no. NC_001611.1), using the same procedure as above, except that the minimum alignment size was restricted to 30 bp instead of 25 bp to maximize potential sequence coverage. Although positive, AC1S3 did not provide a sufficient number of reads (N = 199) to proceed further with the rest of the analyses (Supplementary Table 1p). We next used mapDamage2 (v.2.0.8; ref. [69]) with default parameters, and genotypes were called using bcftools (v.1.17; ref. [99]) mpileup and call modules, requiring a maximum depth corresponding to the 99.5th percentile of the depth distribution, minimal base and mapping Phred qualities of 30 and considering the genome haploid. Low-quality genotypes (Phred quality score lower than 30), indels and polymorphisms within two base pairs of an indel were removed using the bcftools (v.1.17; ref. [99]) filter.

To place the smallpox strains identified in the smallpox phylogenetic tree, we applied the same procedure as above to the raw reads previously published for five ancient samples36,37. Additionally, the FASTA sequence data corresponding to 45 smallpox genomes from the twentieth century previously characterized were downloaded100,101 (Supplementary Table 1q). The multi-FASTA sequence data, corresponding to the 45 modern viral genome, including the reference genome, were further aligned using MAFFT[102] and manually corrected wherever appropriate. Gaps were added to the six ancient samples according to the gaps in the reference genome after the alignment procedure, and all FASTA were merged to form a multi-FASTA sequence of 52 viral genomes. Positions in which at least 50% of the sequences were covered were retained for maximum likelihood reconstruction in IQ-TREE (v.1.6.12; ref. [96]) (-m MFP). Node support was estimated from 1,000 ultrafast bootstrap[97] pseudo-replicates (-bb 1000 -bnni). A tree was also generated using the same procedure as described above, removing the manual correction of the modern genome alignment (Supplementary Information section 2.18 and Supplementary Fig. 2_21). The position of our sample in the tree obtained was then tested against seven alternative tree conformations by running an approximately unbiased topology test[103] (Supplementary Information section 2.18 and Supplementary Fig. 2_22).

Ancient DNA methylation values calibration

We used DamMet[104] to evaluate DNA methylation levels in the genomes of 21 individuals with coverage greater than 9-fold, as a previous study established that relatively high coverage thresholds were needed to obtain reliable estimates. Overall, we followed the procedure previously described by Liu et al.[105] to identify the best combination of parameters for DamMet[104] DNA methylation inference (Supplementary Information section 2.19). The average cellular methylation fraction (M) was found to have no impact on correlation levels (Supplementary Information section 2.19, Supplementary Fig. 2_23 and Supplementary Table 1u); hence, a value of 75% was retained. Maximal correlation levels (0.38–0.8) were otherwise obtained for a maximum window size of 1 kb, windows of 25 CpGs and a minimum depth of 400 reads per window. Four individuals presented low correlation scores (Spearman correlation; R^2 < 0.55) and were thus disregarded.

Despite encouraging correlation levels, two DNA methylation categories associated with scores of 0 and 1 were under-represented in the remaining samples (Supplementary Information section 2.19 and Supplementary Fig. 2_24a), in line with the work from Liu et al.[105]. We therefore followed the mitigation procedure developed by those authors to improve ancient DNA methylation inference using approximately 27.2 million CpGs in two modern bones published by Gokhman et al.[106] (Supplementary Information section 2.19 and Supplementary Fig. 2_24b).

The validity of the resulting DNA methylation inference was also assessed by checking for the presence of well-established patterns along the genome (CpG islands, exons and introns and CTCF binding site regions), following the method by Hanghøj et al.[107] (Supplementary Information section 2.19 and Supplementary Figs. 2_25–2_27). The DNA methylation profile observed for the Otchugoui individual did not align with expectations for CpG islands, exons and introns and CTCF binding regions, and it was therefore disregarded.

Statistical associations between cultural and non-cultural data

We generated a presence–absence matrix summarizing the characteristics of each burial (Supplementary Information section 2.20 and Supplementary Table 1a) and calculated pairwise Bray–Curtis between individuals (Supplementary Information section 2.20 and Supplementary Fig. 2_28). To test whether the distribution of distances calculated between pairs of individuals within categories (sex, region and archaeological stages) was significantly different from random permutations of individuals across categories, we used ANOSIM (anosim from the vegan package[108] in R[109]) and a permutational multivariate analysis of variance (adonis2 from the vegan package[108] in R[109]) (Fig. 4b and Supplementary Table 1v).

Moreover, we binned individuals into four extra categories defining wealth, leadership, Christianity and shamanism on the basis of the collection of cultural goods found in their burials (Supplementary Information section 2.20). To test whether the similarity of the oral microbiome between groups in these categories was lower than the similarity within each group, we used ANOSIM and permutational multivariate analysis of variance (Fig. 4b and Supplementary Table 1v). These analyses were repeated for taxonomic and functional distances, genetic distances (ASD) and DNA methylation distances (Bray–Curtis; Fig. 4b and Supplementary Table 1v).

Ethics and inclusion

This study builds upon more than 15 years of archaeological research conducted in Yakutia, Sakha Republic, an autonomous region of the Russian Federation located in northeastern Siberia (Supplementary Information section 1.3). The fieldwork was conducted under the MAFSO programme (French Archeological Mission in Eastern Siberia), a collaboration between French researchers and local Yakut experts, including scholars from North-Eastern Federal University in Yakutsk. The programme was approved in June 2012 by the Local Committee for Biomedical Ethics of the Federal State Budgetary Institution, known as the Yakut Scientific Center of Complex Medical Problems of the Siberian Branch of the Russian Academy of Medical Sciences. Throughout the programme, local experts were fully engaged as equal partners, contributing to research design, archaeological excavations, material selection for analysis, community outreach, permit acquisition and critical feedback on analyses and manuscripts. Their contributions are reflected in their co-authorships in this study and 21 scientific articles and reviews published between 2004 and 2021. The research team also implemented a wide array of activities to engage with local communities, including fieldwork and student training, and played an active role in public outreach through documentaries, press interviews, television programs and exhibitions. The programme was supported by several inter-university collaborative research agreements, notably between Université Paul Sabatier, Krasnoyarsk State Medical University and North-Eastern Federal University in Yakutsk. It also received endorsement from the Institute of Ecology and Evolution at CNRS through the International Associated Laboratory ‘Coevolution Human–Environment in Eastern Siberia’. The programme facilitated extensive community engagement, highlighted by the 2019 exhibition at the Historical Park Rossiya-Moya Istoriya in Yakutsk, which showcased the main archaeological discoveries made under MAFSO.

Reporting summary

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

Data availability

The raw sequencing data (FASTQ files) and aligned BAM files generated in this study have been deposited to European Genome-Phenome Archive (accession no. EGAS50000001329). Ethical authorization for the study was granted in June 2012 for conducting ancient DNA analyses from the Local Committee for Biomedical Ethics of the Federal State Budgetary Institution (‘Yakut Scientific Center of Comprehensive Medical Problems’ of the Siberian Branch of the Russian Academy of Medical Sciences). This authorization supports analyses aimed at collecting information on the evolution of bacteria or viruses identified for the period studied (fifteenth to nineteenth centuries), as well as studying the influence of socioecological factors on movement, adaptation and dynamics of ancient populations. Access to the sequence data of the ancient individuals analysed in this study will be granted for investigations filling these objectives. Decisions will be made upon request by the LifeChange Data Access Committee (EGAC50000000713), which overviews data access requests for European Genome-Phenome Archive study under accession no. EGAD50000001903. Any further information required to reanalyse the data reported in this paper is available from the lead contacts upon request. Source data are provided with this paper.

Code availability

No original code has been produced. All other software packages used in this study were previously published.

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Acknowledgements

We are grateful to S. Kolodeznikov, a Yakut archaeologist and historian who co-led many excavation campaigns. We extend our warm thanks to V. Popov of the Yakut State United Museum of History and Culture of the People of the North, who sees landscapes as an ancient Yakut; S. Buraeva (scientific museum) and P. Konovalov, Institute for Mongolian, Buddhist and Tibetan Studies of the Siberian Branch of the Russian Academy of Sciences; the technical staff at CGG; the two laboratory managers at CAGT for the maintenance of ancient DNA facilities; and A. Lacombe for hardware maintenance of computational clusters at CAGT. This study received funding from the North-Eastern Federal University in Yakutsk, the French Ministry of Foreign and European Affairs, the French Polar Institute Paul-Émile Victor, the CNRS/Université Paul Sabatier International Associated Laboratory (LIA-COSIE), the French National Institute for Preventive Archaeological Research, the French Embassy in Russia, the Institute of Forensic Medicine in Strasbourg (for excavation), ANR (LifeChange, MEET, GenIn and ArkeoAG), the Villum Fonden (miGENEPI), the Marie Curie Intra-European Fellowship (FP7 YAKUTS and Horizon 2020 ELITE), the CNRS AAP2020 (IndigenousHealth), the CNRS MITI and the Simone and Cino Del Duca Foundation (HealthTimeTravel) (for DNA and bioinformatic work). A.S.-O. and L.O. have received funding from the European Research Council (StG anthropYXX no. 101117101, CoG PEGASUS no. 681605 and SyG Horsepower no. 101071707).

Author information

Author notes

1. These authors contributed equally: Perle Guarino-Vignon, Andaine Seguin-Orlando

Authors and Affiliations

1. Centre d’Anthropobiologie et de Génomique de Toulouse (CNRS UMR 5288), Faculté de Santé, Université de Toulouse, Toulouse, France

Éric Crubézy, Perle Guarino-Vignon, Andaine Seguin-Orlando, Clio Der Sarkissian, Sylvie Duchesne, Patrice Gérard, Catherine Thèves, Ameline Alcouffe & Ludovic Orlando

2. Institut Universitaire de France, Faculté de Santé, Université de Toulouse, Toulouse, France

Éric Crubézy

3. Department of Biology, Section for Computational and RNA Biology, University of Copenhagen, Copenhagen, Denmark

Kristian Hanghøj

4. Institut National de Recherches Archéologiques Préventives (INRAP), Paris, France

Sylvie Duchesne

5. Mission Archéologique Française en Sibérie Orientale (MAFSO), Faculté de Médecine, Toulouse, France

Liubomira Romanova, Daryia Nikolaeva, Christiane Hochstrasser-Petit, Michel Petit & Henri Dabernat

6. North-Eastern Federal University, Yakutsk, Russian Federation

Lilia Alekseeva, Edouard Jyrkov & Arkadiy Sharaborin

7. SAGE Laboratory, CNRS UMR 7363, Strasbourg, France

Vincent Zvénigorosky

8. Institut de Médecine Légale, Strasbourg, France

Christine Keyser & Annie Géraut

9. BABEL Laboratory, CNRS UMR 8045, Paris, France

Bertrand Ludes

10. Museum of Arctic Archeology–S.A. Fedoseeva, Yakutsk, Russian Federation

Nikolai Kirianov

11. Institute for Mongolian, Buddhist and Tibetan Studies of the Siberian Branch of the Russian Academy of Sciences (IMBTS SB RAS), Ulan-Ude, Russian Federation

Natalia Tsydenova & Boris Bazarof

12. Institute of Biological Problems of the North, Far Eastern Branch, Russian Academy of Sciences (IBPN FEB RAS), Magadan, Russian Federation

Irina Dambueva

13. Centre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry, France

Anne Boland & Jean-François Deleuze

14. Institute for Humanities Research and Indigenous Studies of the North (IHRISN), Siberian Branch of the RAS, Yakutsk, Russian Federation

Rosalia Bravina & Anatoly Alexeev

15. Institut Pasteur, Université Paris Cité, CNRS UMR2000, Human Evolutionary Genetics Unit, Paris, France

Étienne Patin & Lluis Quintana-Murci

16. Laboratoire d’Anthropologie Sociale, Paris, France

Charles Stépanoff

17. Collège de France, Chair of Human Genomics and Evolution, Paris, France

Lluis Quintana-Murci

Contributions

Conceptualization: L.O. Validation: P.G.-V., A.S.-O., C.D.S. and L.O. Formal analysis: P.G.-V. and L.O., with preliminary analyses from C.D.S. and K.H. Investigation: E.C., A.S.-O., C.D.S., S.D., P.G., C.T. and L.O. Writing—original draft: L.O. Writing—review and editing: E.C., P.G.-V., A.S.-O., C.D.S., C.T., A. Alcouffe, E.P., C.S., L.Q.-M. and L.O., with approval from all co-authors. Visualization: P.G.-V., with input from P.G. and L.O. Supervision: E.C. and L.O. Project administration: L.O. Funding acquisition: E.C., A.S.-O., C.D.S., B.L., C.S., L.Q.-M. and L.O. Resources: E.C., S.D., P.G., L.R., D.N., L.A., C.H.-P., V.Z., C.K., M.P., H.D., A.G., A.B., J.-F.D., E.J., A.S., N.K., N.T., I.D., B.B., R.B., A. Alexeev and L.O.

Corresponding authors

Correspondence to Éric Crubézy or Ludovic Orlando.

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Nature thanks Dmitry Arzyutov, Pavel N. Flegontov 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 Main historical events punctuating the four main Archaeological Stages of Yakutia.

More extensive discussions about the chronology of the various archaeological contexts can be found in Crubézy and Nikolaeva (Crubézy & Nikolaeva 2017).

Extended Data Fig. 2 Geographic maps of the biological material sampled to characterize ancient genomes and oral microbiomes across Yakutia.

a) Location of the individuals for whom sufficient genomic data could be retrieved. b) Location of the individuals for whom sufficient oral microbiome data could be retrieved as well as genetic signatures of smallpox strains (identified with brown graphical representations of the virus in Central Yakutia). Adapted from ©Stadia Maps (https://stadiamaps.com/), ©OpenMapTiles (https://openmaptiles.org/), ©OpenStreetMap (https://www.openstreetmap.org/copyright) and ©Stamen Design (https://stamen.com/).

Extended Data Fig. 3 Genetic homogeneity of the Yakut individuals from ~1,500.

a-b) PCA conducted on 597,573 SNPs for 2,761 modern Eurasian and American individuals, with 892 ancient Eurasian and American individuals and 106 ancient Yakut individuals projected. The percentage indicated on each PC reflects the explained fraction of genetic variation. In panel a) PC1 and PC2 are zoomed in on the Yakut cluster, with modern Yakut individuals indicated with black filled dots. Panel b) shows the placement of ancient individuals, including Yakuts, on PC2 and PC3. c) Co-ancestry matrix of modern Eurasian individuals, modern Yakuts and imputed ancient Yakuts. The tree was defined from fineSTRUCTURE[21] clustering. The Yakut gene pool is not structured by time period. The three individuals in our dataset clustering within Asia correspond to, from top to bottom, the 17th century Buryat (Tungus), the Stage 4 Omouk1 individual, and the Mokp Iron Age individual. d) D-statistics testing for excess of genetic sharedness into Stage 1 to Stage 4 Yakut individuals, disregarding 5 genetic outliers (Byljasyk3, Haras, Khoumakhtaakh, Omouk1, Omouk3). Z-scores are adjusted (Zadj) for multiple testing using Benjamini-Hochberg correction. D-statistics are of the form D(Outgroup, Eurasian modern populations; StageX, StageY/modern Yakut), where positive values indicate closer genetic proximity of modern Eurasian population with StageY or modern Yakuts than with StageX individuals. Error bars reflect two times standard error.

Extended Data Fig. 4 Authentication and analysis of oral microbiome sequence data.

a) Authentication with Sourcetracker2[93]. The analysis included our data plus five published samples of known to be of high quality, and three others of low quality, that we used as controls (names in pink). Different microbiome sources were used (Supplementary Table 1k): bones as environmental controls (N = 10), modern dental calculus (N = 18), subgingival plaque (N = 20), skin (N = 10), and gut samples (N = 20). Most of the calculus samples and approximately half the tooth samples demonstrated a genuine oral profile (with the sum of Modern Calculus and Plaque source contribution above 25%). b) Diversity indexes for taxonomic diversity across the four archaeological stages. Left: Boxplot of both Shannon (top) and Simpson (bottom) diversity indexes for each Stage. Right: Per-individual diversity indexes. c) Relative species abundance of three oral pathogens. Boxplots represent the 25%, 50% and 75% quantiles, with upper and lower whiskers showing values within the 1.5 interquartile range. Horizontal black segments indicate the mean value for each species at each stage.

Source Data

Extended Data Fig. 5 Examples of genealogical reconstructions and IBD sharing.

a) Sum of IBD segments longer than 12 cM shared between pairs of adult individuals of the same sex from the same region, or from different regions. Y-axis is log-scaled. The p-value of the two-sided Wilcoxon test, with Benjamini-Hochberg correction for multiple testing, is shown. b) Genealogy of the individuals from AC (Arbre Chamanique), AtDaban and Oktiom burials. c) Sum of IBD segments longer than 12 cM shared between pairs of individuals from the same region (red), or from different regions (blue). Y-axis is log-scaled. The p-value of the two-sided t-test is shown if significant. Boxplots represent the 25%, 50% and 75% quantiles, with upper and lower whiskers showing values within the 1.5 interquartile range. Horizontal segments indicate the mean value for each category.

Supplementary information

Supplementary Information

Supplementary Information section 1. Further information about Yakut history, archaeological survey and ethics, including Figs. 1_1–2. Background information on Yakutia, the Yakuts and the Russian conquest. Archaeological surveys, stages and samples. Ethics and engagement with Indigenous communities. Supplementary Information section 2. Supplementary methods and further statistical analyses, including Supplementary Figs. 2_1–28.

Reporting Summary

Supplementary Table 1

Supplementary Tables 1a–v.

Peer Review File

Source data

Source Data Figs. 1–4 and Extended Data Fig. 4

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Crubézy, É., Guarino-Vignon, P., Seguin-Orlando, A. et al. An ancient DNA perspective on the Russian conquest of Yakutia. Nature 650, 389–398 (2026). https://doi.org/10.1038/s41586-025-09856-5

- Received: 02 August 2024

- Accepted: 04 November 2025

- Published: 07 January 2026

- Version of record: 07 January 2026

- Issue date: 12 February 2026

- DOI: https://doi.org/10.1038/s41586-025-09856-5

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Causal modelling of gene effects from regulators to programs to traits

Mineto Ota,

Jeffrey P. Spence,

Tony Zeng,

Emma Dann,

Nikhil Milind,

Alexander Marson &

…

Jonathan K. Pritchard

Nature volume 650, pages 399–408 (2026)

Abstract

Genetic association studies provide a unique tool for identifying candidate causal links from genes to human traits and diseases. However, it is challenging to determine the biological mechanisms underlying most associations, and we lack genome-scale approaches for inferring causal mechanistic pathways from genes to cellular functions to traits. Here we propose approaches to bridge this gap by combining quantitative estimates of gene–trait relationships from loss-of-function burden tests[1] with gene-regulatory connections inferred from Perturb-seq experiments[2] in relevant cell types. By combining these two forms of data, we aim to build causal graphs in which the directional associations of genes with a trait can be explained by their regulatory effects on biological programs or direct effects on the trait[3]. As a proof of concept, we constructed a causal graph of the gene-regulatory hierarchy that jointly controls three partially co-regulated blood traits. We propose that perturbation studies in trait-relevant cell types, coupled with gene-level effect sizes for traits, can bridge the gap between genetic association and biological mechanism.

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Main

Genome-wide association studies (GWAS) and rare variant burden tests have identified tens of thousands of reproducible associations for a wide range of human traits and diseases. These signals have identified many genes that can serve as therapeutic targets4,5,6; driven discoveries of new molecular mechanisms7,8, critical cell types[9] and physiological pathways of disease risks or traits10,11,12; and enabled genetic risk prediction for complex diseases[13].

But despite these successes, interpreting the vast majority of associations remains challenging. Aside from coarse-grained analyses such as identifying trait-relevant cell types and enriched gene sets, we lack genome-scale approaches for interpreting the molecular pathways and mechanisms through which hundreds, if not thousands, of genes affect a given phenotype.

One challenge for interpreting genetic associations is the observation that many hits act indirectly, via trans-regulation of other genes14,15,16,17,18,19. This observation is formalized in the omnigenic model3,20, which proposes that, for any given trait, only a subset of genes, referred to as core genes, are located within key molecular pathways that act directly on the trait of interest. Meanwhile, many more genes affect the trait indirectly, by regulating core genes through links in gene-regulatory networks. In this model, we can interpret the effect size of a variant in terms of all paths through the network by which it affects core genes.

The central role of trans-regulation underlying many GWAS hits implies that fully understanding the genetic basis of complex traits requires tools to measure how genetic effects flow through networks. However, until recently, we have had very limited information about gene-regulatory networks in any human cell type, with the main information coming from observational data such as trans-expression quantitative trait locus (trans-eQTL) and co-expression mapping14,16,21. However, both approaches have important limitations including low power20,22 and confounding effects of cell-type composition[14] in the case of trans-eQTLs, and ambiguous causality in co-expression analysis23,24.

Advances in genome editing and single-cell RNA sequencing, including Perturb-seq, now provide new opportunities to measure causal gene-regulatory connections at genome scale25,26,27,28. In Perturb-seq experiments, a pool of cells is transduced with a library of guide RNAs, each of which causes knockdown (or other perturbation) of a single gene. After allowing the cells time to equilibrate, single-cell sequencing is used to determine which genes were knocked down in each cell and measure the transcriptome of the cell. Critically, Perturb-seq enables measurement of the trans-regulatory effects of each gene in a controlled experimental setting at the genome-wide scale. Recent work has shown that such approaches are a promising tool for interpreting GWAS data, finding that GWAS hits are often enriched in specific transcriptional programs identified by CRISPR perturbations of a subset of genes29,30,31,32,33.

Major challenges remain as we aim to move beyond identifying enriched programs to inferring genome-scale causal cascades of biological information. In this paper, we developed a new systematic approach to this problem. We demonstrate how, by combining loss-of-function (LoF) burden results with Perturb-seq, we can infer an internally coherent graph linking genes to functional programs to traits, and derive biological insight into the key genes and pathways that control these traits (Fig. 1a). The resulting graph helps us to understand not only the trait-relevant pathways but also the functions of genes and programs within the graph, to explain why those genes are associated with the traits. On the basis of our results, we expect that forthcoming efforts to generate perturbation data in a wide variety of cell types will provide a critical interpretative framework for human genetics.

Fig. 1: Study overview and selection of model traits.

a, Overview of the study. The square nodes represent genes, the coloured arrows between genes represent regulatory effects and the arrows from genes to traits represent associations. sgRNA, single guide RNA. b, Heritability enrichment of UKB traits to open chromatin regions in K562. Traits are ordered based on the P value of enrichment, which was estimated using the Jackknife test in S-LDSC. The dashed line indicates the threshold for Bonferroni significance. ATAC-seq, assay for transposase-accessible chromatin using sequencing; Cou, count; HLSR, high light scatter reticulocyte count; MCV, mean corpuscular volume; MRV, mean reticulocyte volume; MSCV, mean sphered corpuscular volume; Per, percentage; RBC, red blood cell; Ret, reticulocyte. c, Schematic of the human haematopoietic tree. Traits of interest are annotated near their relevant cell types. CLP, common lymphoid progenitor; CMP, common myeloid progenitor; GMP, granulocyte–monocyte progenitor; HSC, haematopoietic stem cell; MPP, multipotent progenitor. d, Comparison of heritability enrichment to UKB traits, between MEP and K562 open chromatin regions. P values were estimated using the Jackknife test in S-LDSC. The dashed line indicates the threshold for Bonferroni significance.

Selection of model traits

To integrate genetic association data with Perturb-seq, our first step was to evaluate whether there are any traits with high-quality genetic data where the most relevant cell type (or types) can be well modelled by existing Perturb-seq data. At the time of writing, the only published genome-wide Perturb-seq dataset was collected in a leukemia cell line: K562 (ref. [2]). In that experiment, every expressed gene was knocked down using CRISPR interference, one gene per cell, before single-cell RNA sequencing.

To determine which traits could reasonably be modelled in terms of the gene-regulatory networks of K562 cells, we compiled published GWAS and LoF burden test data for a wide range of traits measured in the UK Biobank (UKB)1,34. Of these, we selected 234 quantitative traits with single-nucleotide polymorphism (SNP) heritability > 0.04 for further consideration (Supplementary Table 1) and performed stratified linkage disequilibrium score regression (S-LDSC)[9] across all 234 traits. We observed that open chromatin regions in K562 exhibited significant heritability enrichment exclusively for traits related to morphology or quantity of erythroid lineage cells (Fig. 1b).

This result is intuitive, as the K562 cell line was derived from erythroleukaemia cells, which are a neoplastic form of erythroid progenitors (Fig. 1c), and K562 cells retain multipotency and can differentiate into erythroid cells[35].

We also performed S-LDSC across the same set of traits for various primary cell types, and found a very similar enrichment for erythroid traits in megakaryocyte–erythroid progenitor cells (MEPs), which are the natural progenitor cells for erythrocytes (Fig. 1c,d and Extended Data Fig. 1a). The open chromatin regions in MEPs were also more similar to those in K562 cells than other cell types (Extended Data Fig. 1b). These results support the notion that K562 cells share similar chromatin features with primary progenitor cells and could serve as a cellular model for studying the gene-regulatory network associated with erythroid traits.

Among the enriched traits, we selected three traits that are relatively independent, with pairwise genetic correlations ranging from −0.39 to 0.15, for detailed analysis (Extended Data Fig. 1c). We focused primarily on mean corpuscular haemoglobin (MCH), which measures the mean amount of haemoglobin per erythrocyte; but, we also analysed red cell distribution width (RDW)—the standard deviation of the size of erythrocytes per individual—and the immature reticulocyte fraction (IRF). For these traits, a considerable amount of SNP heritability was explained by open chromatin regions in the K562 cell line (53%, 44% and 36% of the total SNP heritability, respectively), further supporting the use of K562 Perturb-seq to interpret their genetic associations (Supplementary Table 2).

Pathway enrichment for trait associations

Before attempting to build causal models for these traits, we first explored the genetic associations for MCH, RDW and IRF with standard approaches (Fig. 2 and Supplementary Fig. 1). GWAS of MCH in the UKB identified 634 independent genome-wide significant signals. Many of the lead hits fall into a few significantly enriched pathways, including haem metabolism, haematopoiesis and cell cycle (Fig. 2a,c). These enriched pathways are crucially involved in the maturation of erythrocytes. For example, tight control of cell cycle is important at several steps in erythropoiesis36,37,38,39.

Fig. 2: Pathway enrichments for blood trait associations.

a, Genetic associations identified from UKB GWAS for MCH. Variants located within a 100-kb window centred on the transcription start site of the genes in the gene set are coloured. ‘Macromolecule synthesis + reg’ refers to the positive regulation of the macromolecule biosynthetic process. b, Gene associations with MCH from UKB LoF burden tests. The colours indicate the same gene sets as panel a. Labelled genes have FDR < 0.01 and belong to the gene sets. c, Pathway enrichment of GWAS and LoF burden test top genes. For GWAS, the closest genes from the independent top variants were used. For the LoF burden test, genes were ranked by the absolute posterior effect size from GeneBayes, and the same number of genes as in GWAS was used. P values are from one-sided Fisher’s exact test. d, Comparison of LoF burden test effect sizes after GeneBayes between MCH and RDW. The solid line corresponds to the first principal component.

In addition to GWAS, UKB has also released whole-exome sequencing data for more than 450,000 participants[40]. Here we focused on the phenotypic effects of LoFs, which are variants such as frameshift and premature stop mutations that are predicted to cause complete LoF of a gene. To estimate the average effect of different LoF variants in the same gene on a phenotype, we compared the phenotypic values for carriers of LoF variants in a given gene versus non-carriers. This approach, known as a burden test, generates a score for each gene that estimates the effect of half loss of gene dosage on the phenotype.

Previously reported burden test statistics for LoF variants[1] identified 90 genes associated with MCH at a false discovery rate (FDR) = 0.1 (Fig. 2b). Although the rankings of top hits differ between GWAS and LoF burden tests (Extended Data Fig. 2a), the lead hits from GWAS and LoF are generally enriched in the same pathways (Fig. 2c). This is consistent with the expectation that common and rare variants associated with a trait act through similar biological pathways, but frequently prioritize different genes41,42.

As one might expect, LoF variants in the genes that encode components of adult human haemoglobin, HBB, HBA1 and HBA2, all show strong negative effects on MCH (Fig. 2b). Clinically, these mutations cause α-thalassaemia or β-thalassaemia, in which a decrease in MCH is characteristic. This highlights a key feature of burden tests: in addition to significance testing, they also provide a quantitative, directional estimate of LoF effects, referred to here as γ.

The directions of associations in the burden tests also help us to interpret the pleiotropic effects of genes. When looking at genes associated with MCH and RDW, which have a negative genetic correlation in GWAS (Extended Data Fig. 1c), the LoF effects for most genes were associated in opposite directions (r = −0.53; Fig. 2d). However, a handful of genes had strong same-direction effects on both traits (Fig. 2d). For instance, CAD encodes a multifunctional enzyme of which biallelic mutations cause megaloblastic anaemia[43], whereas heterozygous LoFs increase both MCH and RDW (Fig. 2d). One goal of building a causal mechanistic graph for these traits will be to explain these seemingly discordant associations.

For many genes, the LoF γs have large standard errors, due to the low frequency of LoF variants[41]. To improve estimation of the γs, we applied an empirical Bayes framework called GeneBayes that we developed previously[44]. Our approach incorporated previous information about gene expression, protein structure and gene constraint to share information across functionally similar genes (Methods). We found that the GeneBayes estimates of γ are far more reproducible than naive estimates in the independent All of Us cohort[45] (Extended Data Fig. 2b,c). Furthermore, we observed greater enrichment of genes associated with traits in functional pathways even though we did not directly use that information (Extended Data Fig. 2d,e). These improvements are important for making full use of the beneficial features of LoF burden tests while reducing unwanted noise. Therefore, we used the GeneBayes posterior mean effect sizes in Fig. 2c,d and for the remainder of the paper. For further discussion about the choice of prior information for GeneBayes, see the Supplementary Note.

Gene regulation shapes genetic signals

Next, we investigated whether Perturb-seq from K562 could allow us to interpret genetic associations in the context of the gene-regulatory network. Perturb-seq estimates the effect of knocking down a gene x on the expression of another gene y, which we denote as βx→y (Methods). βx→y represents the total effect of x on y, including both direct and indirect pathways through the gene-regulatory network. Previous studies using perturbations to interpret GWAS have identified enrichment of hits in co-regulated gene sets, sometimes referred to as ‘programs’29,30,31,32,33, but have had limited success at identifying GWAS enrichment among program regulators (Supplementary Note).

As an initial proof of concept, we focused on the genes encoding constituents of adult haemoglobin. We focused on the gene HBA1, which is the only one abundantly expressed in K562 cells, and which has one of the largest LoF effect sizes for MCH (γHBA1 = − 1.5). We reasoned that if K562 Perturb-seq is relevant for interpreting MCH, then genes that regulate HBA1 should also be associated with MCH. Moreover, we should be able to predict the direction of effect on MCH from the Perturb-seq data: positive regulators of HBA1 should, themselves, have promoting effects on MCH, and vice versa for negative regulators. (Note that we refer to genes with negative β or negative γ from knockdown or LoF, respectively, as promoting and coloured them red; positive β and γ are considered repressing and coloured blue).

As predicted, we found that across all 9,498 genes that were perturbed and also tested in the LoF burden test, the LoF effect of a gene x on MCH, denoted γx, is significantly positively correlated with the knockdown effects of that gene on HBA1 expression, βx→HBA1 (β-coefficient = 0.052, P = 3 × 10−7; Fig. 3a). Of note, among the perturbed genes, of the top ten genes ranked by LoF effects on MCH, seven had nominally significant Perturb-seq effects on HBA1, and for all seven, the sign of the Perturb-seq β matched what we predicted from γ.

Fig. 3: Regulatory effects in Perturb-seq explain genetic association signals.

a, Gene effects on MCH can be predicted by regulatory effects on HBA1. Genes perturbed in Perturb-seq experiment are ordered by their effect sizes on MCH from LoF burden test. Perturb-seq β refers to log fold change of HBA1 expression after knockdown of the genes. Significant (P < 0.05) regulatory associations in Perturb-seq are connected with arrows. The protein structure of haemoglobin is presented using UCSF ChimeraX[66] based on Protein Data Bank entry 1A3N. The P value is from the linear regression and is two-sided. KDreg, knockdown of a regulator. b, Enrichment analysis testing whether the top n HBA1 regulators (ranked by P values) are enriched at LoF or GWAS top hits. GWAS hits are the closest genes to the independently associated variants (Methods). Points indicate the odds ratio in the exact Fisher’s test. Enrichment was calculated with all the perturbed genes in Perturb-seq as a background. The error bars indicate 95% confidence intervals. The P values for the enrichment of the top 200 HBA1 regulators are 9.6 × 10−5 for the top 90 LoF hits, 0.65 for the top 90 GWAS hits and 0.01 for the top 543 GWAS hits. c, For every expressed gene in K562, regulator–burden correlation is plotted against their γ for MCH. The y axis shows the –log_10(P) of the regulator–burden correlation, multiplied by the sign of the correlation. The P values are from the linear regression. Quadrants with a yellow background correspond to ‘concordant’ association, in which the sign of regulator–burden correlation aligns with the sign expected from the γ of the gene. d, Genome-wide QQ-plots for regulator–burden correlations among representative traits. Each dot represents one gene. Traits without significant signals lie along the dotted line. For other traits, see Extended Data Fig. 3c. P values are from the linear regression.

We also attempted a similar analysis for GWAS hits, testing whether significant GWAS hits were enriched near HBA1 regulators (Fig. 3b). We observed that GWAS hits were enriched (OR = 2.1 for the top 200 regulators), but to a lesser extent than for significant LoF burden test hits (OR = 6.3 for the top 200 regulators). This cannot be solely explained by inaccurate gene linking, as the same set of GWAS hits showed high enrichment for some of the gene sets (Fig. 2c and Extended Data Fig. 3a). This suggests a benefit of LoF burden tests over GWAS for identifying the trait-relevant regulatory networks.

We were curious whether similar patterns of correlation between LoF effect and Perturb-seq regulatory effects might be found for other genes or other traits. Consistent with the central role of HBA1 in determining the MCH phenotype, we found that the correlation of γx with βx→y, which we call regulator–burden correlation, was the highest for y = HBA1 among all genes expressed in K562 cells (Fig. 3c). As a negative control, we also tested for correlations between regulatory effects on HBA1 with LoF effects on unrelated traits. As expected, we only detected HBA1 regulator signals for erythroid traits (Extended Data Fig. 3b). These HBA1 regulator signals for traits were also detected if we used raw burden effect estimates without applying GeneBayes, but with weaker significance (Extended Data Fig. 2f,g), supporting our approach.

Another key question for Perturb-seq studies is whether regulatory relationships learned in one cell type—K562 in this case—are useful for studying traits that are determined by less-related cell types. To examine this, we computed the regulator–burden correlation for all expressed genes, with LoF γs for various traits. For each trait, we visualized the distribution of regulator–burden correlations in a two-sided quantile–quantile (QQ)-plot (Fig. 3d).

Starting with our three main erythroid traits, MCH, RDW and IRF, we saw that all three traits show large excesses of both positive and negative correlations compared with the null (x = y line), indicating significant relationships between Perturb-seq and LoF burden tests for many genes. By contrast, there was minimal correlation between regulatory effects and γ for other blood traits, including lymphocyte and eosinophil counts (Fig. 3d and Extended Data Fig. 3c). This suggests that cell types that are not differentiated from MEPs cannot be modelled well using K562 cells (Fig. 1c). This observation implies the importance of obtaining Perturb-seq data in trait-relevant cell types.

However, we were surprised to see that some non-erythroid traits, including serum levels of IGF-1 and CRP, as well as body mass index, did show highly significant correlations of regulatory effects with γ (Extended Data Fig. 3c). The strongest correlations were seen for insulin-like growth factor 1 (IGF-1), which connects the release of growth hormone to cell growth, acting on many cell types[46]. Further examination revealed that these signals appear to be driven by the regulation of cellular growth markers, including MKI67. We hypothesize that essential programs for cellular growth may be broadly shared across cell types that regulate IGF-1 and other traits that share this signal (Extended Data Fig. 3d). Indeed, with further analysis using Perturb-seq in additional cell lines, we confirmed the broad sharing of regulatory effects on the essential programs associated with IGF-1 (Extended Data Fig. 10 and Supplementary Note).

Together, these results confirm the relevance of gene-regulatory relationships learned from Perturb-seq for interpreting complex traits. They highlight the role of both cell-type-specific pathways—for which the cell type used in Perturb-seq must be closely matched to the trait of interest—and broadly active pathways that may be detectable in many cell types.

Trait-associated program regulations

We next aimed to develop a more comprehensive framework to explain genotype–phenotype associations in terms of the regulatory hierarchy inferred from Perturb-seq data. In principle, one might imagine inferring a complete gene-regulatory network from Perturb-seq that contains all causal gene-to-gene edges. However, the inference of accurate genome-scale causal graphs is extremely challenging, if not infeasible, from current Perturb-seq data.

As a more robust alternative, we followed previous work by clustering genes into co-expressed groups, referred to here as programs[30]. To identify programs, we applied consensus non-negative matrix factorization (cNMF)[47] to the gene expression matrix from Perturb-seq (Fig. 4a). This allowed us to quantify the activity of each program in every cell. Similar to ref. [30], we then used the perturbation data to estimate the causal regulatory effects of knockdown of every gene x on the activity of each program P, denoted as βx→P.

Fig. 4: Association of program regulation with blood traits.

a,b, Overview of our pipeline for the analysis to find the trait-relevant programs. c–e, Program burden effects (x axis) and regulator–burden correlation (y axis) of 60 programs in three blood traits: MCH (c), RDW (d) and IRF (e). Programs with significant associations after Bonferroni correction (P < 0.05/60) are coloured. Pathway annotations of representative programs are labelled. For annotations of other programs, see Supplementary Table 3. The P values for program burden effects are from the permutation test and are two-sided. The P values for regulator–burden correlations are from the linear regression and are two-sided. f, Schematic for the concordant and discordant patterns between program burden effects and regulator–burden correlation. P, program; R, regulator; T, trait. g, Co-regulation patterns between programs. Each dot represents a gene that has significant regulatory effects on the G2/M phase program. The gene effect size on the program activity was calculated by comparing the program usage of cells between perturbed cells and control cells using linear regression (βx→P; Methods). The lines and their 95% confidence intervals are from locally estimated scatterplot smoothing. h, A summary of signs of regulatory effects on the programs. i, Average γ and its standard errors for 115 genes in RA and 154 genes in RB MCH. j, Program association with MCH in GWAS–trans-eQTL analysis (Methods) and LoF–Perturb-seq analysis.

On the basis of the preliminary analyses, we chose to model the data using 60 programs (see Methods; Extended Data Fig. 4a). We found that a large fraction of the 60 programs successfully captured biological pathways (Supplementary Table 3). Using external ENCODE data[48], we found evidence for coordinated transcriptional control of many programs: for 49 of the 60 programs at least one transcription factor showed significant binding site enrichment near program genes and knockdown of that transcription factor significantly changed program expression (Extended Data Fig. 4b and Supplementary Table 3).

We next quantified the average effects of programs, and their regulators, on traits (Fig. 4b). To measure program effects, we note that in NMF, the gene loadings on each program are non-negative by definition. Thus, a natural measure of the effect of a program on a trait is simply to compute the average LoF effects (γs) of highly loaded genes as a measure of the effect of that program on the trait. We refer to this as the program burden effect. A positive program burden effect is interpreted to mean that the program has a repressing function on the trait; a negative value implies that it is promoting. Significance was determined by permutations (Methods).

To measure the effects of regulators of program P on each trait, we needed to account for the fact that distinct regulators can have either positive or negative effects on P. Thus, for each program P, we computed the correlation across regulators, x, of βx→P with γx. We refer to this measure as the regulator–burden correlation; this measure is analogous to the measure of regulatory effects used for single genes above. A positive regulator–burden correlation is interpreted to mean that upregulation of program P promotes the trait; a negative value suggests that upregulation of P has a repressing effect on the trait.

The program effects on each trait are shown in Fig. 4c–e. For MCH, the haemoglobin synthesis program genes and their regulators were both significantly enriched, consistent with our single-gene analysis of HBA1. In addition, five programs associated with the cell cycle were all enriched in the program burden effect axis. This mirrors the enrichment of this pathway from the over-representation analysis of GWAS and LoF top hits (Fig. 2c), but here we can confirm the enrichment of both regulators and program genes for these programs (Fig. 4c).

For RDW, the program reflecting ATP-dependent activity was highlighted from both program and regulator axes (Fig. 4d). Iron is incorporated into haem in the mitochondria, and its dysregulation results in high RDW. In extreme cases, mitochondrial dysfunction leads to sideroblastic anaemia, characterized by high RDW[49]. The association of the ATP activity program with RDW is consistent with this biology. For IRF, the program representing the maintenance of the erythroid progenitor population was enriched for both program and regulator axes (Fig. 4e). This program showed the enrichment of binding sites for transcription factors that are important for the maintenance of stem cell and progenitor populations, including TAL1, NFIC, MAX and MNT50,51,52 (Extended Data Fig. 4b).

Overall, the Perturb-seq data efficiently captured biological pathways and their regulators, and comparison with gene associations enable us to identify the pathways relevant to each trait.

Complex interplay of programs

Although the significant programs in Fig. 4c–e provide insight into biological controls of these three traits, they also revealed puzzling inconsistencies. Some programs, including haemoglobin synthesis for MCH, show consistent directional effects for program genes and program regulators, but for other programs, the directions of effects initially appeared to be inconsistent (Fig. 4f). Examination of these programs revealed important principles about the regulatory architecture of programs, and design considerations for building regulatory models of complex traits.

The first principle is revealed by three programs with strong effects on MCH: the S and G2/M phase cell-cycle programs, and the autophagy program. For the G2/M phase, the program and regulator effects have directionally concordant effects on MCH, but for the S phase, the program genes and their regulators imply effects with opposite directions. In addition, for autophagy, only the regulators—but not the program genes—show a signal (Fig. 4c).

One piece of this puzzle is explained by considering patterns of co-regulation across the three programs: (1) regulators of the S phase and G2/M phase programs are shared but affect the programs in opposite directions (Fig. 4g); and (2) most G2/M and S phase regulators also affect autophagy, but the knockdown effect on autophagy is almost always positive (Fig. 4g and Extended Data Fig. 5a). These relationships are intuitive: S phase and G2/M phase are mutually exclusive components of the cell cycle; meanwhile, autophagy is suppressed during mitosis, and cell-cycle regulators are known to have a key role in that suppression[53].

To describe these patterns in a simple way, we defined two sets of regulator genes, denoted RA and RB, according to their effects on G2/M (Fig. 4g). To determine how the regulators of these three programs affect MCH, we fit their effects jointly in a multiple regression model. This analysis showed that G2/M and autophagy regulators both have independent repressive effects on MCH (Extended Data Fig. 5b). The opposite co-regulation of S and G2/M phase programs explains the opposite correlation of these regulators with γ (Fig. 4c). A summary of the joint model of regulator effects is shown in Fig. 4h.

One prediction of this model is that RA regulators should have stronger (more negative) genetic effects on MCH γs than RB regulators. This is because RA genes have a repressive effect on both G2/M and autophagy, and both programs have repressive effects on MCH; whereas for RB, the positive regulator effects on G2/M and the negative regulator effects on autophagy partially cancel the effects of each other on MCH. Indeed, consistent with this model, we saw that both RA and RB have significantly negative γs on average, but RA is much more strongly negative (Fig. 4i).

These observations emphasize the need for joint modelling of programs and show that the observed effect sizes of regulators on a trait can be modelled as sums of regulatory effects mediated through key pathways. A different form of crosstalk between programs, involving a negative-feedback loop affecting RDW, as well as the distinct relationships of program genes and their regulators with a trait, is discussed in detail in the Supplementary Note.

Validation with GWAS and trans-eQTL

Although the enrichment of GWAS hits to regulators was modest (Fig. 3b), we hypothesized that we might find consistent regulatory effects of GWAS variants on the core pathways if we take the direction of effect into account. We utilized trans-eQTL effects in peripheral blood[14] to test the directional regulatory effects of GWAS top hits on the programs identified by Perturb-seq (Fig. 4j, Supplementary Fig. 3 and Supplementary Note). Although the size of each trans-eQTL effect is small, MCH GWAS hits had directionally consistent regulatory effects on the haemoglobin synthesis and autophagy programs (P = 2 × 10−14 and 2 × 10−13, respectively). The direction of regulation by GWAS top alleles was concordant with what we inferred from our Perturb-seq and LoF burden test model. This indicates that GWAS and the LoF burden test converge on the regulation of shared core pathways.

Unified graphs from genes to programs to traits

We next aimed to build regulatory maps that link genes, programs and traits into coherent, unified models. Our goals in doing so are twofold: (1) we wanted to understand, in compact form, the main molecular processes that control a set of traits; and (2) we wanted to interpret, and even predict, the directions of effects of important trait-associated genes.

For each trait, we selected the top-ranked programs by program burden effects and, separately, in a joint regression model, the top ranked programs by regulator–burden correlations (Extended Data Fig. 6; Methods). On the basis of our analysis above, we allowed programs and program regulators to have independent effects in the model. After model selection, this procedure resulted in a graph that, for MCH, included five programs and three sets of program–regulators, as well as the inferred direction of effect of each program and regulator set on MCH (Extended Data Fig. 7).

A simplified representation of the MCH graph is depicted in Fig. 5a, showing haemoglobin synthesis, cell cycle and autophagy as critical controls of MCH. The direction of the genetic association of top genes on MCH was generally consistent with this model (43 out of 59 predicted correctly). The overall prediction accuracy was significantly higher than expected under a null model, using both leave-one-out cross-validation (P = 5 × 10−5) and permutation analyses for which we repeated the entire inference procedure (P < 5 × 10−5; Methods; Extended Data Fig. 8a,b). This approach allowed us to connect the gene-level top hits, identified solely from genetic association studies, to their functions in the pathway regulatory map.

Fig. 5: Association map of genes to programs to traits.

a, Regulatory map of MCH. Programs were selected by genome-wide association patterns of regulators or program genes with the trait (Methods). Top hits for MCH (|γ| > 0.1) whose effect directions were concordant with the model are placed onto the map. The colour of the genes indicate the direction of effects on the trait (sign(γ)); red denotes increase MCH with upregulation of the gene. The arrow with the asterisk was not selected in the initial program selection process. The P value is from the permutation test and is one-sided. b, Sharing of regulatory networks across traits. Here the arrows from gene to programs indicate the regulatory directions. Programs were selected if their regulators were found to be associated with at least one trait in the gene-to-program-to-trait map. The arrows from programs to traits were determined based on a joint regression model (Extended Data Fig. 9c). Regulatory directions on cell cycle pertain to G2/M phase. POLE is also a member of the S phase cell-cycle program. c, Programs identified in our model are associated with biological processes that are essential for erythrocyte maturation.

Examining the graph, we were intrigued that SUPT5H, which is involved in transcriptional elongation[37], has regulatory effects on all three programs. Perturb-seq shows that SUPT5H activates haemoglobin synthesis, and inhibits autophagy and the G2/M phase cell cycle, all of which result in increased MCH (Fig. 5a). Thus, our model predicts that SUPT5H is a master regulator for MCH, exerting same-direction effects via three different pathways. Indeed, the effect sizes of SUPT5H LoFs on MCH are among the largest of all genes (Fig. 2d), and LoFs in this gene can cause a thalassaemia phenotype[54]. Thus, this map can help us to interpret why genes are associated with a trait.

In addition to MCH, we also inferred gene-to-pathway-to-trait maps for RDW and IRF, revealing both shared and independent pathways of regulation across the three traits (Extended Data Fig. 8c–h). There were four programs whose regulators were significantly and independently associated with at least one trait (Fig. 5b): progenitor maintenance, haemoglobin synthesis, autophagy and cell cycle. Previous studies of haematopoiesis confirmed that all four pathways regulate essential aspects of erythrocyte maturation37,39,55,56 (Fig. 5c).

The multi-trait regulator graph (Fig. 5b) helps us to interpret the concordance and discordance of genetic associations across the traits. Genome-wide, MCH and RDW are negatively correlated in both GWAS data (rg = −0.39) and at significant burden loci (Fig. 2d). We can now interpret these observations as probably driven by opposite direction effects of both autophagy and cell cycle on these two traits. Conversely, RDW and IRF are positively correlated (rg = 0.15; Extended Data Fig. 9a), at least in part because both traits are positively regulated by progenitor maintenance.

We can also use the graph to understand how individual genes affect the different traits. For example, 16 genes in the graph have strong opposite-direction effects on MCH and RDW; our model correctly predicts opposite signs for 14 out of the 16, including SUPT5H, MED17 and ATR (Fig. 5b and Extended Data Fig. 9b). For instance, MED17 inhibits both the G2/M phase cell cycle and autophagy; both effects increase MCH and reduce RDW, with the result that MED17 increases MCH and reduces RDW.

By contrast, three genes in the graph differ from the genome-wide pattern, showing large same-direction effects on MCH and RDW. Our model correctly predicts two of these, and is suggestive for the third (POLE; Supplementary Note). Specifically, CAD and CALR both have repressive effects on RDW and MCH. Figure 5b suggests why: unlike most genes that affect both RDW and MCH through shared pathways, these genes affect the two traits via independent pathways: progenitor maintenance and haemoglobin synthesis. Both genes inhibit both pathways, but regulation of progenitor maintenance affects RDW and not MCH, whereas haemoglobin synthesis affects MCH but not RDW.

Extension of the model to other traits

Current availability of genome-wide Perturb-seq data in different cell types and cell conditions is limited. Nonetheless, we assessed the generalizability of our model by analysing Perturb-seq experiments with a limited number of perturbations in multiple additional cell lines—HepG2, Jurkat and RPE1 (refs. 2,57) (Supplementary Table 4)—along with additional complex traits (Extended Data Fig. 10, Supplementary Figs. 4–9 and Supplementary Note). We observed cell-type specificity of regulator–burden correlations, with burden effects for erythroid traits being more enriched in gene-regulatory effects in K562 cells than in other cell lines, whereas burden effects for HDL-cholesterol was more enriched in HepG2 cell-regulatory effects (Extended Data Fig. 10). In addition, we identified trait-specific patterns of regulator association (Supplementary Fig. 5). These findings, along with others (Supplementary Note), indicate that with more diverse and detailed gene regulation information, we can better understand the biology of a broad range of traits.

Discussion

Genetic associations serve a unique role in studies of human biology, as they can establish causal links from variants or genes to human traits and diseases. Yet, some 20 years after the first GWAS, we still lack genome-scale approaches for inferring interpretable, quantitative models of the biological pathways that connect genes to cellular functions to traits. Here we built on previous work in this area29,30,31,32,33 to develop the first approach to infer unified graphs linking directional effects of genes on traits via pathways of regulation and cellular functions. Although our work focuses on blood traits that underlie anaemia and related diseases, we anticipate that the principles learned here can be broadly applicable.

One essential feature of this paper is that we built graphs using quantitative gene effects estimated from LoF burden tests instead of unsigned enrichment of GWAS hits. We envisage LoFs and GWAS hits as reflecting the same underlying biological pathways41,42, but our results are both more significant, and more interpretable, when using LoFs. Unlike GWAS hits, LoF effect sizes are inherently directional, they are automatically linked to the correct genes, and their magnitudes are comparable across genes. Moreover, compared with common variants with tiny effects, LoFs are probably more functionally similar to CRISPR knockdowns, given the widespread non-linear and even non-monotonic relationships between gene expression and phenotypes58,59.

Although the model presented here is relatively simple, there will surely be value in future models that add complexity. Future versions could allow for more complex representations of gene-regulatory networks, more explicit modelling of regulatory crosstalk between programs and heterogeneity of gene functions within programs. Many traits are controlled by multiple cell types, and one can envision models in which genetic effects on traits are controlled by a superposition of effects across multiple cell-type-specific networks.

One unexpected result from our model was the finding that the effects of program regulators on a trait may be strongly discordant from the effect of program genes on the same trait. We hypothesize that some programs reflect downstream transcriptional consequences of cell biological processes, and that the genes within a program do not always lie on the causal pathway between the program–regulators and the trait (Supplementary Note). In such cases, the identification of genes in the program can provide useful clues about biological mechanism but the effects of program genes may differ dramatically from the effects of their regulators. Moreover, it is likely that some critical processes may not be detected or may not be interpretable from RNA readouts. Thus, it will be helpful in future analyses to augment Perturb-seq experiments with other types of cell phenotyping such as functional tests, protein measurements or cell painting60,61,62,63.

Finally, one critical challenge for using Perturb-seq to interpret association studies is how closely we need to match the cells used for Perturb-seq to the cells that determine trait variation[30]. Recent work has suggested that gene-regulatory relationships are often shared between closely related cell types, but generally not shared between more distant cell types57,64. Consistent with this, our results show that K562 serves as a suitable, although imperfect, model for erythrocyte development, but also that K562 is not suitable for modelling traits related to other blood cell lineages (Fig. 3d). We hypothesize that in general, Perturb-seq data will need to be closely matched to the trait-relevant cell types, but the matching does not need to be perfect.

Although our proof of principle here uses experimental data from K562 cells to model erythrocyte traits, we expect that the next generation of perturbation studies in cells, organoids and tissues63,65 will provide a critical interpretative framework for human genetics.

Methods

Datasets

GWAS data

We downloaded the publicly available GWAS summary statistics and SNP heritability estimates for traits in the UKB from Ben Neale’s laboratory (see the URL section below). We focused on traits with SNP heritability estimates exceeding 0.04.

LoF data

We used LoF burden test summary statistics from the UKB with 454,787 participants, as previously reported[1]. Specifically, we utilized the gene-level aggregated effect estimates from predicted LoF variants with a minor allele frequency of less than 0.01%. Data were downloaded from the GWAS Catalog[67].

Perturb-seq data

We utilized the genome-wide Perturb-seq dataset in K562 reported by Replogle et al.[2]. In this dataset, all expressed genes (n = 9,866) were targeted by a multiplexed CRISPRi sgRNA library in K562 cells engineered to express dCas9–KRAB. Single-cell RNA sequencing was performed to read out the sgRNAs together with the transcriptome. Only cells with a single genetic perturbation were used for the analysis, amounting to a median of 166 cells per gene perturbation and 11,499 unique molecular identifiers per cell. We downloaded the raw count data that the authors uploaded to figshare (see the URLs in the Code availability section).

For additional analyses, we utilized Perturb-seq data for essential genes in K562, RPE1 (ref. [2]), HepG2 and Jurkat[57] cell lines. Only cells with a single genetic perturbation were used for the analysis. The number of perturbations and the number of cells per perturbation are summarized in Supplementary Table 4. We downloaded the raw count data uploaded to figshare (see the URLs in the Code availability section) or the Gene Expression Omnibus (GSE264667).

ChIP–seq data

We utilized chromatin immunoprecipitation followed by sequencing (ChIP)–seq data in K562 for annotating gene programs. We downloaded 830 transcription factor ChIP–seq narrow peak files from the ENCODE project website[48] (see the URL in the Code availability section). All coordinates were mapped to hg19 with LiftOver[68].

Linkage disequilibrium score regression

To identify traits whose heritability is enriched in open chromatin regions in K562, we used S-LDSC[9]. All GWAS data were preprocessed with the ‘munge_sumstats.py’ script provided by the developers (see the URLs in the Code availability section). Variants in the HLA region were excluded from the analysis. The assay for transposase-accessible chromatin using sequencing (ATAC-seq) replicated narrow peak bed file in K562 was downloaded from ENCODE[48] (GSE170378, ENCFF590CPE), and the coordinates were mapped to hg19 using LiftOver[68]. Furthermore, we used narrow ATAC-seq peaks from 18 haematopoietic progenitor, precursor and differentiated cell populations previously reported[69].

For the additional analysis, replicated narrow peak files from ATAC-seq experiments for HepG2 and CD4+ T cells were downloaded from ENCODE[48] (ENCFF439EIO and ENCFF246KRE), and the coordinates were mapped to hg19 using LiftOver[68]. For RPE1 (ref. [70]) and Jurkat[71], as narrow peak files for ATAC-seq experiments were not available, we downloaded SRA files from the US National Institutes of Health NCBI Sequence Read Archive (SRR30621812 for RPE1, and SRR12368304 and SRR12368305 for Jurkat) and called the peaks. Specifically, we trimmed the adapter sequence with TrimGalore (v0.5.0)[72], aligned to the hg19 reference with Bowtie2 (v2.3.4.1)[73], filtered duplicates with MACS3 (v3.0.3)[74] and called narrow peaks with the MACS3 (v3.0.3) hmmratac command.

Linkage disequilibrium (LD) scores were calculated for each annotation using the 1000 G Phase 3 European population (ref. [75]). The heritability enrichment of each annotation for a given trait was computed by adding the annotation to the baseline LD score model (v1.1) and regressing against trait chi-squared statistics for HapMap3 SNPs. These analyses used v1.0.1 of the LDSC package (see the URL in the Code availability section).

Furthermore, we tested the genetic correlation between specific trait pairs using European LD scores with the LDSC package (v1.0.1).

Estimation of gene effect sizes with GeneBayes

Method overview

LoF burden tests are not well powered, especially for shorter or selectively constrained genes, as the likelihood of having LoF variants in these genes is low. We previously developed GeneBayes[44], an empirical Bayes framework aimed at addressing a similar challenge: the precise estimation of selective constraint on genes, which can be particularly challenging for short genes. Within GeneBayes, we used gene features in a machine learning-based empirical Bayes framework to improve the accuracy of constraint estimates. Diverse gene features, such as gene expression patterns and protein structure embeddings, can enhance the accuracy of these estimates. GeneBayes is a highly adaptable framework, easily extendable to various applications, as outlined in the original article[44]. In this instance, we utilized it to derive more precise effect size estimates for LoF burden tests.

To minimize overfitting when applying GeneBayes to LoF burden test estimates, we first performed feature selection using the BoostRFE function (boost recursive feature elimination) from the shap-hypetune package (see the URL in the Code availability section) to fit XGBoost[76] models on the sign and magnitude of \(\hat{\gamma }\), the estimated effect size from LoF burden test summary statistics. We used the predicted sign and magnitude as the features for GeneBayes, which we found to perform better than using the selected features directly; this may be due to differences in training dynamics between XGBoost and the gradient-boosted trees fit using GeneBayes.

Subsequently, we implemented the GeneBayes framework as previously described. Specifically, GeneBayes involves two steps: (1), learning a prior for the effect size of each gene through the utilization of gradient-boosted trees, as implemented in NGBoost[77], and (2), estimating gene-level posterior estimates of the effect sizes using a Bayesian framework. In our application of GeneBayes, we parameterize the prior as follows:

$$\mathrm{sign}(\gamma ) \sim \mathrm{Bernoulli}\,(p)$$

$$\mathrm{magnitude}(\gamma ) \sim \mathrm{Gamma}(\alpha ,\theta )$$

The parameter p is the probability that γ is positive or negative, and α, θ are the shape and scale parameters of the Gamma distribution, respectively. We learned the parameters of the prior using the following likelihood:

$$\hat{\gamma }|\gamma \sim \mathrm{Normal}(\gamma ,\,{\rm{s.e.}}(\hat{\gamma }))$$

The summary statistics \(\hat{\gamma }\) and \({\rm{s.e.}}(\hat{\gamma })\) are the estimated effect size and its standard error from the LoF burden tests, respectively.

Gene features

We compiled the following types of gene features from several sources: selective constraint of genes (Shet)[44], gene expression across cell types, protein embeddings and gene embeddings.

Shet refers to a reduction in fitness for heterozygous carriers of a LoF variant in any given gene. We utilized the Shet estimated in our previous work[44]. Gene expression across 79 single-cell types was downloaded from the Human Protein Atlas[78] (see the URL in the Code availability section). Protein embeddings were adopted from embeddings learned by an autoencoder (ProtT5) trained on protein sequences[79]. Gene embeddings were derived from GeneFormer, a pretrained deep learning model for single-cell transcriptomes[80]. Specifically, we used the Cell×Gene Discover census (see the URL in the Code availability section), and we extracted 1,000 cells per each of the cell types—‘erythroid progenitor cell’, ‘monocyte’, ‘erythrocyte’, ‘fibroblast’, ‘T cell’, ‘neutrophil’, ‘B cell’ and ‘haematopoietic stem cell’—and computed the average embeddings of each gene for the cellular classifier using the EmbExtractor module (see the URL in the Code availability section).

Finally, we used the posterior mean of the LoF burden test effect size as a point estimate for the following analyses.

Traits

As applying GeneBayes to all UKB traits is computationally intensive, we applied this to a subset of traits including all the blood cell-associated traits, blood biomarkers and some of anthropometric traits. A list of traits included in our analyses has been provided in Supplementary Table 5.

LoF burden test in the All of Us cohort

The All of Us dataset contains whole-genome sequencing together with various laboratory measurements[45]. On 5 February 2025, the values for MCH were reported for 213,787 sequenced individuals after filtering (UKB data-field 30050, AoU ID 3012030). For individuals with data from multiple visits, we took the latest visit, and we excluded outliers (more than 50 or less than 0 pg). Our previous analysis suggested that relatedness and population structure have a minimal effect on burden test results[58]. Therefore, we performed our tests on all individuals that passed our filtering criteria. We included the top 16 genotype principal components, which are provided in the data release. In addition, we generated 20 rare variant principal components using FlashPCA2 (ref. [81]) on variants sampled uniformly at random from the rare variant fraction (minor allele count (MAC) > 20, minor allele frequency (MAF) < 1%). We identified high-confidence LoF sites using the Variant Effect Predictor in Ensembl[82] with the LOFTEE plugin[83] and restricted our analyses to variants with MAF < 1%.

We performed burden tests using REGENIE[84], largely following the procedure previously described[58], which is based on ref. [1]. We used HapMap SNPs[85] extracted from the ACAF call set (a set of variants provided by All of Us filtered on MAC and MAF) to perform the whole-genome regression in the first step of REGENIE. We included age, sex, age-by-sex, age squared, 16 genotyping principal components and 20 rare variant principal components as covariates in both the first and the second steps. We used the rank-inverse-normal transform on the phenotypes in both steps. The burden mask aggregated all LoF sites with MAC > 5 and MAF < 1% into a single burden genotype for each gene. We added Gaussian noise to summary statistics generated from fewer than 20 individuals to remain in compliance with the All of Us Data User Code of Conduct. The noise was added to the effect size such that all burden tests with fewer than 20 individuals had the same standard error.

Pathway enrichment analysis of GWAS and LoF top hits

Clumping of GWAS top variants

To identify independently associated GWAS variants, we used PLINK (v1.90b5.3)[86] with the –clump flag, a P value threshold of 5 × 10−8, a linkage disequilibrium threshold of r^2 = 0.01 and a physical distance threshold of 10 Mb. In addition, we merged SNPs located within 100 kb of each other and selected the SNP with the minimum P value across all merged lead SNPs to avoid the false inclusion of genes that have neighbour genes with extremely large effects. This resulted in 634 independent variants associated with MCH. For each independent variant, we annotated the nearest protein-coding gene. To accomplish this, we used the bedtools (v2.30.0)[87] closest module to identify genes that overlap with the variant or have their transcription start site or transcription end site closest to the variant. Furthermore, we excluded genes in the HLA region due to extensive linkage disequilibrium. Finally, we obtained a list of 543 genes possibly associated with MCH GWAS signals.

Pathway enrichment analysis

We aimed to compare the pathways enriched in GWAS and LoF top hits for MCH. As pathways, we utilized all ontology terms in Gene Ontology[88] with a minimum of 20 genes and a maximum of 2,000 genes, as well as MsigDB hallmark genesets[89] that include the haem synthesis pathway. We utilized enrichGO and enricher functions in clusterProfiler[90] package in R for the analysis.

Among the enriched pathways, genes in the ‘positive regulation of macromolecule biosynthetic process’ pathway overlap significantly with those in the ‘autophagy’ pathway (P = 2 × 10−8), and thus its enrichment may reflect the relevance of autophagy pathway.

Enrichment analysis of GWAS and LoF top hits to HBA1 regulators

For the evaluation of the enrichment of GWAS top hits related to HBA1 regulators (Fig. 3b), we used the list of 543 closest genes to the independent GWAS hits defined above. We ranked the genes based on the P values of their regulatory effects on HBA1 expression. For each of the different thresholds for HBA1 regulators, we evaluated the enrichment using a two-sided Fisher’s exact test, using all the genes perturbed in the Perturb-seq as a background. Specifically, the columns of the 2 × 2 table for the test correspond to whether the genes are HBA1 regulators at each threshold, whereas the rows correspond to whether the genes are GWAS top hits.

In addition, for comparison, we evaluated the enrichment of 90 significant genes in the LoF burden test (FDR < 0.1) and the genes closest to the top 90 independent GWAS hits.

Estimation of gene-regulatory effects from Perturb-seq

We aimed to estimate gene-to-gene regulatory effects from Perturb-seq. We assessed the total effects of gene knockdown on gene expression by comparing perturbed and non-perturbed cells. After filtering out cells with fewer than 500 genes expressed and genes expressed in fewer than 500 cells, we compared the cells with perturbation of every gene versus the cells with non-targeting control gRNAs. Log-normalized counts of cells were used as input to the limma-trend pipeline[91], while accounting for gel bead-in-emulsion (GEM) group (batch effect), number of genes expressed and the percentage of mitochondrial gene expression as covariates. We utilized the log_2 fold change (logFC) of gene expression in perturbed cells compared with non-targeting cells as a point estimate of the perturbation effect on gene expression \(({\beta }_{x\to y})\).

Defining gene programs and the regulatory effects of genes

Identification of gene programs with cNMF

From a single-cell gene expression matrix, we identified the co-regulated set of genes. Intuitively, such a set of genes can correspond to genes that determine cellular identity or specific cellular processes, which we call programs. To identify gene programs and their activity in each cell, we applied the cNMF[47] method to the single-cell gene expression matrix from Perturb-seq.

Matrix factorization models the gene expression data matrix as the product of two lower rank matrices, one specifying the proportions in which the programs are combined for each cell, and a second encoding the relative contribution of each gene to each program[47] (Fig. 4a). We refer to the first matrix as a ‘usage’ matrix[47]. In cNMF, the usage matrix is normalized so that the usage values for each cell sum to 1. We used the normalized matrix as a usage of each program in each cell.

In cNMF, a meta-analysis of multiple iterations of NMF was performed to obtain a ‘consensus’ result. In cNMF, the number of programs (K) is a key model hyperparameter to tune. To determine it, we tested different values of K (30, 60, 90 and 120) and decided to proceed with K = 60 based on the error versus stability comparison (Extended Data Fig. 4a), as proposed by the authors. In addition, we used density threshold = 0.5 to filter out the outlier programs.

Annotation of programs to biological pathways

From the gene-by-program matrix produced by cNMF, we can obtain the non-negative loadings of each gene to the program. We ranked the genes based on the loadings and utilized the top-ranked genes for each program to characterize the biological pathways of the program.

Annotating the programs to specific biological processes is a multifaceted task. In this study, for each program, we considered three orthogonal lines of evidence for annotating biological pathways.

Gene Ontology enrichment of top genes

We examined the enrichment of the top 200 genes in the Gene Ontology categories and MsigDB hallmark gene sets using the enrichGO and enricher functions in the clusterProfiler[90] package in R. To calculate the enrichment, we utilized genes expressed in K562 cells as a background set to avoid bias.

We tested different thresholds for determining the top genes (100, 200, 300, 400 and 500). The Gene Ontology enrichment results were generally consistent, but we observed a trend: as the number of included genes increased, more categories were enriched in at least one program, with fewer categories specifically enriched for one program (Supplementary Fig. 10).

Capturing a wide range of biological categories, as well as annotating specific categories to the programs, is important for interpretation. Thus, we chose to use the top 200 genes for the Gene Ontology enrichment analysis.

Enrichment of transcription factor-binding sites

We can expect that for some programs, the genes within the same program are coordinately regulated by specific transcription factors. Such transcription factors can be used to characterize the programs. To this end, we utilized the ChIP–seq experiments of transcription factors in K562 from the ENCODE project. To convert the information on binding sites to a gene-level regulation score, we calculated the following score for each transcription factor (i) for each protein-coding gene (j), as adopted from ref. [92]:

$${S}_{i,j}(d)=\sum _{k}{P}_{i,k}\times {e}^{-{x}_{i,j,k}/d}$$

where Pi,k denotes the strength of peak k for transcription factor i (quantified by −log_10 q value for each peak, outputted by MACS2), xi,j,k denotes the distance from peak k to the transcription start site of gene j, and d represents the decay distance. The decay distance indicates the effective distance for the transcription factor and can vary depending on the transcription factors. Here we set the value to 1 kb, 5 kb, 10 kb, 50 kb, 100 kb, 500 kb or 1 Mb.

To determine which score was useful for the annotation of programs, we tested the correspondence of the score with differentially expressed genes (DEGs) after knockdown of the same transcription factor. Specifically, for each transcription factor, we listed positive or negative DEGs after knockdown in Perturb-seq (FDR < 0.1) and we compared the ChIP–seq score (Si,j(d)) between DEGs and non-DEGs by Mann–Whitney U-test.

As a natural consequence, we could annotate each transcription factor as an activator or inhibitor, according to the direction of effects after knockdown. We annotated a transcription factor as an activator if the downregulated DEGs after knockdown had significantly high ChIP scores (FDR < 0.05), and as an inhibitor if the upregulated DEGs after knockdown had significantly high ChIP scores (FDR < 0.05). As a result, ChIP scores for 167 transcription factors showed significant correspondence with their knockdown effects (FDR < 0.05) and were utilized for the annotation of programs. One best decay distance parameter was selected for each transcription factor based on the significance in the overlap with DEGs.

For each program, we compared the top 300 loading genes with other expressed genes in K562 with respect to the 167 ChIP scores using the Mann–Whitney U-test. This test evaluates the enrichment of binding sites of the transcription factors to each program genes. Furthermore, we compared the program activity of the transcription factor-knockdown cells with others to see whether the transcription factor had a direct effect on the activity of the program (Extended Data Fig. 4b).

Co-expression with marker genes

In addition, we manually confirmed the co-expression of marker genes for predefined cell types or pathways and the program activity of cells in the uniform manifold approximation and projection (UMAP)[93] space. Markers for red blood cells, myeloid cells and the integrated stress response pathway were adopted from the original Perturb-seq paper[2]. S phase and G2/M phase marker gene sets were adopted from ref. [94]. Markers for erythroid progenitors and megakaryocytes were determined from single-cell gene expression data of bone marrow haematopoietic progenitors[95], where we ranked the genes in each corresponding population based on expression specificity (Z-score) compared with other populations and selected the top 50 genes. This number of genes was determined to be roughly in the same range as the number of genes in the other gene sets.

After completing these three tests for each program, we defined the curated annotation of each program as follows: initially, when the program corresponded to specific cell types, including cellular marker genes as top-loading genes, it was annotated as the cell type. For the others, we considered them as programs reflecting cellular pathways. We prioritized the most significantly enriched Gene Ontology or MsigDB pathways from the top 10 enriched pathways while avoiding ambiguous pathways for interpretation (such as the ‘RNA binding’ pathway). In cases in which multiple programs were enriched for the same category, we attempted to distinguish them by their enriched transcription factors or colocalization with marker gene expression. Finally, we curated one annotation per program while considering these factors (Supplementary Table 3).

Estimation of the regulatory effects of genes on program activity

From the cell-by-program matrix produced by cNMF, we obtained the usage of each program in each cell. To obtain the effect size of each regulator on the program usage, we standardized the program usage to mean = 0 and s.d. = 1, and we compared perturbed cells with cells with non-targeting control gRNAs with a linear regression model, while accounting for GEM group (batch effect), number of expressed genes and percentage of mitochondrial gene expression as covariates. We utilized the point estimate of the effect size of perturbation on program usage as a regulatory effect of a gene \(({\beta }_{x\to P})\).

Comparison of program regulations with genetic associations

Definition of gene effects on traits and gene regulation

Unless specified, we utilized the posterior estimate of gene effect size on a trait with GeneBayes as the gene effect on a trait (γ). For gene-level regulatory effects, we used the logFC of gene expression in perturbed cells compared with non-targeting cells as a point estimate of the perturbation effect on gene expression, as described above \(({\beta }_{x\to y})\). For program-level regulatory effects, we utilized the effect size of perturbation on program usage as a regulatory effect of a gene, as described above \(({\beta }_{x\to P})\).

Correlation of gene regulatory effects with genetic associations

We started from a simple model in which the effect size of a peripheral gene x was determined by its regulatory effects on a limited set of core genes. In cases in which there was a single or a limited number of core genes y, the regulatory effect size of the peripheral gene on the core genes should correlate with the effect size of the peripheral gene on the trait.

We have previously observed a striking correlation between LoF burden test effect sizes and Shet on average across traits[41]. To avoid the confounding effects of selective constraint, we included Shet as a covariate in our linear regression model:

$${\gamma }_{x} \sim {\beta }_{x\to y}+{S}_{\mathrm{het},x}$$

where \({\beta }_{x\to y}\) corresponds to the regulatory effect of gene x on gene y. We excluded the effects of gene y itself, that is, \({\beta }_{y\to y}\), from the comparison because it does not reflect a trans-regulatory effect. For every expressed gene y, we evaluated the significance of the coefficient for the first term. In some of the plots, the significance level was multiplied by the sign of the coefficient.

Association of program genes with traits

In the program-level analysis, we quantified the average effects of program genes on traits, which we call program burden effect. Program burden effects are the average γ of the genes, which are representative of the program, as determined by the loading for the program in cNMF.

Of note, as a feature of cNMF, the loadings of the genes to the programs are always positive. Thus, the sign of the average γ provides interpretable directional information about the program association with the trait.

As selective constraints are positively correlated with |γ|[41], highly conserved programs, such as those essential for cellular survival, could have larger program burden effects. To avoid confounding, we divided the expressed genes in K562 into ten bins based on Shet. We then compared the average γ of the top loading genes with a 10,000 randomly chosen sets of the same number of genes, while matching for the Shet bin. To account for the directional association, we converted the rank of the observed value compared with the random distribution into two-sided P values, while adding the sign of the average γ to calculate the signed association P values.

Here the sign of program burden effects corresponds to the average effects of the LoF of program genes on the trait. Thus, positive program burden effects can be interpreted as a repressing association between program P and the trait.

The results were generally not affected by the choice of the number of top genes (100, 200 and 300). However, for some programs including the haemoglobin synthesis program, where the association with MCH was concentrated on a small number of haemoglobin genes, the association was more pronounced with a smaller number of top genes. Therefore, for visualization of program burden effects and regulator–burden correlation (for example, Fig. 4), we chose 100 for defining the top genes.

Correlation of program regulatory effects with genetic associations

Next, we aimed to quantify the correlation of regulatory effects of genes on the program with γ, which we call regulator–burden correlation.

We calculated the correlation of regulatory effects with trait association signals while accounting for Shet in the same way as the gene-level analysis:

$${\gamma }_{x} \sim {\beta }_{x\to P}+{S}_{\mathrm{het},x}$$

where \({\beta }_{x\to P}\) corresponds to the regulatory effect of gene x on program P.

For every program, we evaluated the significance of the coefficient for the first term. The significance level was multiplied by the sign of the coefficient for visualization.

Here the sign of \({\beta }_{x\to P}\) corresponds to the effect of the knockdown of gene x on the activity of program P. The sign of γx corresponds to the effect of the LoF of gene x on the phenotype. Thus, a positive regulator–burden correlation can be interpreted as a promoting association between program P and the trait.

Null distribution of burden effects

For visualization of the distribution of burden effects of regulators or program genes (Extended Data Fig. 5h), the expected distribution of burden effect sizes was determined by randomly picking up the same number of genes from non-associated genes 10,000 times and taking their average.

Estimation of causal relationships between programs

While examining the co-regulation patterns across programs, we noticed an asymmetric pattern of co-regulation between programs; that is, the regulators of program A also have effects on program B, but the regulators of program B do not have effects on program A (Extended Data Fig. 5c). Such asymmetry can be explained by a causal directional association from one program to the other. Biologically, this one-way association can be interpreted as positive or negative feedback from one program to the other.

A similar observation—that is, the asymmetric correlation of effects from explanatory variables between two traits—was reported in the GWAS literature[96]. For instance, when LDL cholesterol causally affects the risk of coronary artery disease, but not vice versa, the effect sizes for risk variants of LDL cholesterol show a strong correlation between the two traits, whereas those for risk variants of coronary artery disease do not show such correlation[96].

We adapted the analytic framework for causality from a previous GWAS[96] to our case. Specifically, for a pair of programs, P_1 and P_2, we identified significant regulators (FDR < 0.05) for each. We then calculate \({\rho }_{{P}_{1}}\), the Spearman’s rank correlation of effect sizes for P_1 and P_2, considering only the regulators of P_1. We also calculates \({\rho }_{{P}_{2}}\) for the regulators of P_2. Next, we modelled

$${\hat{Z}}_{{P}_{1}} \sim N\,\left({Z}_{{P}_{1}},\,\frac{1}{{N}_{{P}_{1}}-3}\right)$$

where \({Z}_{{P}_{1}}=\mathrm{arctanh}({\rho }_{{P}_{1}})\) and \({N}_{{P}_{1}}\) corresponds to the number of significant regulators for P_1.

Then, we considered four patterns of association, M1: P_1 causally associated with P_2 (\({Z}_{{P}_{2}}\) = 0); M2: P_2 causally associated with P_1 (\({Z}_{{P}_{1}}\) = 0); M3: no relationship between P_1 and P_2 (\({Z}_{{P}_{1}}\) = \({Z}_{{P}_{2}}\) = 0); and M4: correlation does not depend on how the regulators were ascertained (\({Z}_{{P}_{1}}\) = \({Z}_{{P}_{2}}\)).

We fit each model by maximizing the corresponding approximate likelihood. We then selected the model with the smaller Akaike information criterion from the two causal models (M1 and M2) and from the two non-causal models (M3 and M4). Finally, we calculated the relative likelihood of the best non-causal model compared with the best causal model.

$$r=\exp \,\left(\frac{{\mathrm{AIC}}_{\mathrm{causal}}-{\mathrm{AIC}}_{\mathrm{non}-\mathrm{causal}}}{2}\right)$$

We treated r < 0.01 as a threshold for causally associated programs. In the case of programs associated with RDW, the causal association from the haemoglobin synthesis program to the mitochondrial program showed r = 8.5 × 10−7, whereas other pairs of programs had r > 0.05 (also refer to Supplementary Note).

Validation with GWAS and trans-eQTL

We downloaded full trans-eQTL summary statistics for selected variants in peripheral blood from the eQTLGen[14] website (see the URL in the Code availability section). Here 10,317 trait-associated SNPs were tested for their effects on 19,960 genes that showed expression in blood. Only SNP–gene pairs with a distance greater than 5 Mb were tested. We selected SNPs with significant associations with MCH (P < 5 × 10−8) in the UKB, as well as variants with P > 0.05 as control variants.

Using the program genes defined from cNMF in K562 (the top 100 loading genes for each program), we asked whether GWAS hits for MCH have concordant regulatory effects on the program.

Specifically, for each SNP, we derived the MCH-increasing allele based on β coefficients from GWAS summary statistics, polarized the trans-eQTL Z scores of variants on program genes and calculated the average. We compared the values between GWAS significant variants and control variants using a two-sided Student’s t-test.

Validation of multiple program association with the trait

To test whether jointly modelling multiple programs can explain more of the genetic association signals than modelling with a single program, we conducted a cross-validation analysis. We randomly split 80% of the genes into a training set and 20% into a test set, and fitted regression models to explain the gene effects on the trait (γ) by gene-regulatory effects on the program (or programs) using the training set. We evaluated the variance of γ explained by the model using the test set.

We tested this with the set of multiple programs chosen from the regulator–burden correlations in gene-to-program-to-trait models for MCH and RDW, as well as with the same number of randomly chosen programs, and single program models. The selected multiple program model explained much more variance than any single program model or random combination of programs for MCH and RDW (Extended Data Fig. 6a–c). For IRF, only one program was chosen from the regulator–burden correlation in the gene-to-program-to-trait model, so we did not perform the comparison.

Construction of the gene-to-program-to-trait model

Prevalent co-regulation across programs, as well as feedback, suggested the need to jointly model multiple programs to identify those whose regulation independently explains the trait association signals. In addition, although program burden effects and regulator–burden correlation sometimes converge on the same program, we have observed cases where either only program content or only regulators are enriched in trait association signals, as well as cases in which both program content and regulators are enriched but through different mechanisms. Therefore, we treated program burden effects and regulator–burden correlation separately to identify trait-associated programs included in the model.

Step 1: selection of programs based on regulator–burden correlations

To select programs whose regulators are enriched for the trait association signals, we conducted a stepwise linear regression analysis using the ‘regsubsets’ function in the ‘leaps’ package[97] in R. In this analysis, we included gene-regulatory effects on 60 programs \(({\beta }_{x\to P})\), as well as levels of gene constraint (Shet; as defined in ref. [44]) as potential explanatory variables, with γx as the dependent variable.

We identified the combination of explanatory variables through exhaustive search to determine the best subsets for predicting γx in a multiple linear regression model with the given number of explanatory variables. Specifically, for MCH, we changed the number of explanatory variables from 1 to 6, and for each number of explanatory variables, we performed an exhaustive search for the combination of programs that explained the most variance of γ.

The number of variables to include in the final model was decided by assessing the variance explained in the model upon changing the number of variables (Supplementary Fig. 2a), along with the significance of the model fit in the subsequent permutation test (Supplementary Fig. 2b). For the MCH model, we opted to include three variables together with Shet: regulators for autophagy, haemoglobin synthesis and G2/M phase cell-cycle programs.

Step 2: selection of programs based on program burden effects

For selecting programs with enriched contents for the trait association signals, we followed the following process. First, for each program, we calculated the program burden effects. That is, we ranked the genes based on their loading and selected the top 200 genes and calculated the average of γ of these genes. This number was determined by the following test for the model fit. Then, we compared it with randomly selected 10,000 sets of genes expressed in K562 while matching for 10 bins of Shet to calculate two-sided enrichment P values. Subsequently, we ranked the programs based on these P values. To determine the number of programs to include in the final model, we varied the number of top programs included and evaluated the model fit in the subsequent permutation test (Supplementary Fig. 2b). Specifically, for the MCH model, five programs were selected: the haemoglobin synthesis program and four programs associated with different phases of cell cycle. These five programs largely corresponded to those that had significant program burden effects after Bonferroni correction in the previous test (Fig. 4c).

Step 3: predicting the signs of associations for the regulators and program genes in the model

After selecting programs from both regulator and program content associations with the trait, we assigned the predicted signs of effects to each gene in the model. Specifically, for regulators, we considered genes that exhibited significant regulatory effects on the selected programs (FDR < 0.05). In cases in which a regulator had regulatory effects on multiple programs, we calculated the total effects of a gene on the model by summing the product of the effect sizes of the selected programs on the trait in the multiple linear regression model (wP) and the gene effects on the program (\({\beta }_{x\to P}\); Extended Data Fig. 6d). The sign of this product was utilized as the regulatory direction of the gene to the trait predicted from the model.

For program contents, we assigned the sign of the association of the program (that is, the sign of the average γ of the top loading genes) to the top 200 loading genes. If a gene belongs to both program and regulator genes, although a such case was relatively rare, we assigned the sign from the program enrichment test because of the potentially larger effect sizes of program function on the trait (Supplementary Note).

Step 4: assessing the directional concordance of the associations of top hits with the model

To assess how well the predicted model can explain the directional genetic associations, we evaluated it in two ways: leave-one-out cross-validation and permutation testing.

For leave-one-out cross-validation, we left out one gene at a time, selected the programs based on program burden effects and regulator–burden correlation using the other genes, and predicted the sign of the left-out gene as described above. We then assessed the enrichment of correctly predicted genes among the top hits (genes with |γ| > 0.1), compared with genes with minimal associations (genes with |γ < 0.01), using Fisher’s exact test. In this test, the enrichment is influenced by both (1) the enrichment of the top genes among the genes selected in the model (significant regulators or program genes in the model), and (2) the accuracy of the predicted signs among the genes in the model. Our result for the MCH model showed that the top genes were enriched in both (1) selected genes in the model (OR = 1.8), and (2) sign concordance (OR = 1.9), with an overall enrichment of P = 5 × 10−5 and OR = 2.2. This result supported the use of Perturb-seq for predicting the directed gene associations.

For the permutation test, we created 20,000 sets of permuted γ by permuting gene labels. We then followed the same program selection and sign assignments processes, while fixing the number of selected programs from both the program burden effects and the regulator–burden correlation. In each permutation, we counted the number of top genes whose sign of association was correctly predicted by the model and evaluated the enrichment over other genes using Fisher’s exact test. Finally, we compared the Fisher’s test P value of the observed data to those of the permuted sets and calculated the permutation P value (Extended Data Fig. 8b,d,f). Similar to leave- one-out cross-validation, we observed that the observed genetic association data had many more concordant genes, along with a higher ratio of concordant to discordant predicted signs than the permuted data (Extended Data Fig. 8a,c,e). The permutation test can evaluate the fit of our model to the genetic association signals.

For the permuted dataset, we slightly modified the way for program selection. Here, instead of matching for Shet, we compared the distribution of γx between the top loading genes and randomly selected genes expressed in K562 using the Mann–Whitney U-test to calculate enrichment P values. Subsequently, we ranked the programs based on these P values and selected the same number of top programs. This helps to greatly speed up the process, although the resulting permutation P value for the model is potentially conservative.

We ran the permutation tests while differing the parameters for the modelling. The model fit to the data was robust to the choice of the number for defining program genes (100, 200 or 300) and to different thresholds for defining high-effect genes (|γ|; Supplementary Fig. 2c). Although the enrichment was not very sensitive to the number of top genes, 200 genes resulted in slightly more stable enrichment across a range of γ thresholds. On the basis of these results, we chose to use the top 200 genes for creating the gene-to-program-to-trait map. In addition, we chose the threshold for |γ| to be 0.1 based on the fit of the model.

Step 5: drawing the gene-to-program-to-trait map

Finally, we aimed to draw a map to interpret the functions of the trait-associated genes. Here we included all the top hits with |γ| > 0.1 whose direction of association was concordant with that predicted from the model into the map (Fig. 5a). When regulators have concordant regulatory effects on multiple programs, we included all paths in the map.

Reporting summary

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

Data availability

Data generated by or processed for this article are available on Zenodo[98] (https://doi.org/10.5281/zenodo.14751877). Public data used in this study are accessible via the URLs cited in the Code availability section.

Code availability

The codes used for this article are available on Zenodo[98] (https://doi.org/10.5281/zenodo.14751877). The following URLs can be accessed: Neale laboratory UKB data (http://www.nealelab.is/uk-biobank); Replogle et al. Perturb-seq data (https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387; linkage disequilibrium score regression software (https://github.com/bulik/ldsc); the ENCODE database (https://www.encodeproject.org/); the shap-hypertune package (https://github.com/cerlymarco/shap-hypetune); gene expression in single-cell types (https://www.proteinatlas.org/humanproteome/single+cell+type); the CellxGene Discover census (https://chanzuckerberg.github.io/cellxgene-census/); the GeneFormer embedding extractor module (https://geneformer.readthedocs.io/en/latest/geneformer.emb_extractor.html); and the eQTLGen trans-eQTL data (https://www.eqtlgen.org/trans-eqtls.html).

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Acknowledgements

This research has been conducted using the UKB resource under application number 52374. We utilized the All of Us resource under workspace ID aou-rw-c30ba93b. We thank V. G. Sankaran for valuable feedback on an earlier draft of the manuscript; J. Engreitz, K. Cromer, H. Mostafavi, R. Zhu, R. Lopez, N. Milind, C. J. Smith and the members of the Pritchard laboratory for helpful conversations; C. Theodoris for help with accessing GeneFormer gene embeddings; T. Tolpa for help with the figure design; and the reviewers for their constructive feedback. This work was funded by grants R01HG008140, R01HG011432, U01HG012069 and 1R01HG014005. A.M. received funding from the Simons Foundation, the Lloyd. J. Old STAR Award (Cancer Research Institute), the Parker Institute for Cancer Immunotherapy, the Innovative Genomics Institute, the Larry L. Hillblom Foundation (grant 2020-D-002-NET), the Northern California JDRF Center of Excellence, the Byers family, K. Jordan and the CRISPR Cures for Cancer Initiative. M.O. is supported by the Astellas Foundation for Research on Metabolic Disorder and the Chugai Foundation for Innovative Drug Discovery Science. E.D. is supported by an EMBO Post-doctoral Fellowship.

Author information

Authors and Affiliations

1. Department of Genetics, Stanford University, Stanford, CA, USA

Mineto Ota, Jeffrey P. Spence, Tony Zeng, Emma Dann, Nikhil Milind & Jonathan K. Pritchard

2. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA

Mineto Ota, Emma Dann & Alexander Marson

3. Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

Mineto Ota

4. Institute for Human Genetics (IHG), University of California San Francisco, San Francisco, CA, USA

Jeffrey P. Spence & Alexander Marson

5. Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA, USA

Jeffrey P. Spence

6. Department of Medicine, University of California San Francisco, San Francisco, CA, USA

Alexander Marson

7. UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA

Alexander Marson

8. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA

Alexander Marson

9. Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA

Alexander Marson

10. Innovative Genomics Institute, University of California Berkeley, Berkeley, CA, USA

Alexander Marson

11. Department of Biology, Stanford University, Stanford, CA, USA

Jonathan K. Pritchard

Contributions

M.O., A.M. and J.K.P. conceived and designed the study. M.O. performed all the data analyses. J.P.S. and E.D. contributed to the design and interpretation of the statistical analyses, and provided intellectual contributions to all aspects of the study. T.Z. contributed to the data analysis with GeneBayes. N.M. contributed to the data analysis with the All Of Us cohort. M.O. and J.K.P. wrote the manuscript, with critical input from all authors. J.K.P. and A.M. supervised the study and acquired funding.

Corresponding authors

Correspondence to Mineto Ota, Alexander Marson or Jonathan K. Pritchard.

Ethics declarations

Competing interests

A.M. is a cofounder of Site Tx, Arsenal Biosciences and Survey Genomics; serves on the boards of directors at Site Tx and Survey Genomics; is a member of the scientific advisory boards of network.bio, Site Tx, Arsenal Biosciences, Cellanome, Survey Genomics, NewLimit, Amgen and Tenaya; owns stock in network.bio, Arsenal Biosciences, Site Tx, Cellanome, NewLimit, Survey Genomics, Tenaya and Lightcast; has received fees from network.bio, Site Tx, Arsenal Biosciences, Cellanome, Spotlight Therapeutics, NewLimit, AbbVie, Gilead, Pfizer, 23andMe, PACT Pharma, Juno Therapeutics, Tenaya, Lightcast, Trizell, Vertex, Merck, Amgen, Genentech, GLG, ClearView Healthcare, AlphaSights, Rupert Case Management, Bernstein and ALDA; is an investor in and informal advisor to Offline Ventures; and is a client of EPIQ. The Marson laboratory has received research support from the Parker Institute for Cancer Immunotherapy, the Emerson Collective, Arc Institute, Juno Therapeutics, Epinomics, Sanofi, GlaxoSmithKline, Gilead and Anthem and reagents from Genscript, Illumina and Cellanome. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature thanks Hailiang Huang, Gosia Trynka who co-reviewed with Olivier Bakker, and George Vassiliou 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 Analysis of the heritability of multiple traits from GWAS, related to Fig. 1.

a) Heritability enrichment of UKB traits to 18 primary hematopoietic cell types[69] and K562. Heritability enrichment was estimated with S-LDSC by adding each annotation to the baseline model. Traits associated with the morphology or quantity of RBC, monocyte/granulocyte or platelet are labeled on top. Both cell types (rows) and traits (columns) are hierarchically clustered based on their patterns of enrichment. K562 showed the closest similarity to MEP. b) Similarity of open chromatin regions of primary cell types to K562. Plotted are Jaccard index, which captures the proportion of open chromatin regions that are shared with K562. c) Genetic correlation across traits which were enriched to K562 in S-LDSC analysis.

Extended Data Fig. 2 Evaluation of GeneBayes, related to Figs. 2 and 3.

a) Comparison of GWAS and LoF burden test associations for MCH. We took the minimum GWAS p-value within an LD block, and the minimum LoF burden test p-value for any gene that overlaps the LD block. Dotted lines indicate p = 5 × 10−8 for GWAS and p = 5 × 10−4, which corresponds to an FDR of 0.1 for the LoF burden test. Each dot corresponds to the LD block. Numbers of blocks in each quadrant are depicted on the top right corner. P-value is from a two-sided Fisher’s exact test. b) Correlation of burden test γ with All of Us. Plots are for the top 200 genes ranked by absolute values of raw γ (left) or GeneBayes posterior (right). c) Correlation of burden test γwith All of Us with different prior information. The result is for MCH. We ranked the genes based on absolute burden test effect size in UKB, either with or without applying GeneBayes with various patterns of prior information. d) Enrichment of GO and MsigDb hallmark pathways to top hits for MCH. The enrichment of the top 200 genes from the LoF burden test and GWAS is compared. For GWAS, the closest genes to the lead hits were ordered by p-values. For the LoF burden test, whether or not GeneBayes was applied, genes were ordered by absolute effect sizes. The GeneBayes posterior from various patterns of priors is also compared. e) Enrichment of top 200 genes from GWAS or LoF burden test with or without applying GeneBayes to representative pathways. The result is for MCH. f) Regulator-burden correlation for MCH is compared with their γ for MCH. Same comparison with Fig. 3c, but this time using γ before applying GeneBayes. Dotted lines indicate the same threshold with Fig. 3c. g) Correlation significance of HBA1 regulatory effects with gene effects across a variety of traits. Same comparison with Extended Data Fig. 3b, but this time using γ before applying GeneBayes. Dotted line indicates the same threshold with Extended Data Fig. 3b.

Extended Data Fig. 3 Relevance of gene regulatory effects on trait associations, related to Fig. 3.

a) Enrichment of hemoglobin metabolism gene set for GWAS and LoF lead hits. For both GWAS closest genes and the LoF burden test, genes were ranked by association p-values, and top gene enrichment for the gene set was assessed using Fisher’s exact test. Error bars indicate 95% confidence intervals. b) Correlation significance of HBA1 regulatory effects with gene effects (γ) across a variety of traits. c) Genome-wide QQ-plots for burden-regulator correlations for a wide variety of traits. Each dot indicates one gene. Black solid line indicates the median across each category of traits. For serum biomarker traits, 5 traits which showed extensive association with MKI67 regulatory effects are plotted separately. d) Correlation significance of MKI67 regulatory effects with gene effects across a variety of traits. Dotted line indicates the threshold for Bonferroni significance.

Extended Data Fig. 4 Annotation of programs by transcription factor binding sites, related to Fig. 4.

a) Number of cNMF components against solution stability measured by the Euclidean distance silhouette score of the clustering, and Frobenius error of the consensus solution, outputted by cNMF. b) Enrichment of transcription factor binding sites to program genes. Narrow peaks from ChIP-seq of transcription factors (TF) in K562 cells were used to calculate the enrichment (Methods). For significantly enriched TF-program pairs (FDR < 0.05), we tested the effect of knockdown of the TF on program activity and marked an asterisk if the KD also had an effect in the expected direction; that is, if the KD of an activator transcription factor decreased the program activity (p < 0.05), we marked it, and vice versa for repressor.

Extended Data Fig. 5 Association of programs and regulators with traits, related to Fig. 4.

a) Co-regulation pattern between S phase and autophagy programs. Each dot is a gene that has significant regulatory effects on S phase program. b) Correlation of regulatory effects on three programs with MCH γin the multiple regression model. Error bars indicate 95% CI. c) Co-regulation pattern between ATP dependent activity, hemoglobin synthesis and autophagy programs. Genes with regulatory effects on hemoglobin program activity also had effects on ATP activity, but the opposite was not true. d) Correlation of regulatory effects on three programs with RDW γ in the multiple regression model. Error bars indicate the 95% CI. Bottom: model that combines the co-regulation pattern and trait association of the programs. e) The fraction of cells in different cell cycles in the groups of cells with perturbations (left) and the model for explaining the cell cycle program association with MCH (right). Error bar indicates standard error estimated from Jackknife resampling. f) Effects of cell cycle program genes KD on cellular growth. Growth screening data were obtained from an independent experiment using K562 (ref. [99]). The effect size is a normalized measure of the impact of KD on cellular growth compared to wild type, denoted as gamma in the original manuscript. g) Effects of program genes KD on cellular growth[99]. Here, for each program, we created 100,000 sets of control genes matched for Shet and compared the mean effects on cellular growth. h) Distribution of burden test effect sizes for MCH (left) and RDW (right). The plots show significant regulators of the autophagy program (FDR < 0.05, divided into positive and negative regulators) and the top 100 genes for the autophagy program by loading weights. P-values are from the regulator-burden correlation test.

Extended Data Fig. 6 Multiple program association model.

a) We split the genes into a training set and a test set, and fitted multiple or single regression models to test the association between the gene regulatory effects on the program(s) and the gene effects on the trait (γ). We evaluated the variance explained by the model using the test gene set. b-c) Variance explained by the regression models for MCH (b) and RDW (c). “Programs, selected” refers to the programs selected from the regulator-burden correlations in the gene-to-program-to-trait map. “Programs, random” refers to the randomly selected sets of multiple programs. Single programs shown are the top 5 programs as to the variance explained. Error bars indicate 1.96 × standard errors. d) Schematics for making multiple program association model.

Extended Data Fig. 7 Programs selected for modeling MCH associations.

Programs were selected based on program burden effects (left) or regulator-burden correlations (right). For each program, top hits (|γ| > 0.1) for MCH that overlap with the top 200 loading genes (for program genes) or regulators (FDR < 0.05, for regulator genes) are listed. The color of genes correspond to the sign of γ. Genes in parentheses are discordant from the predicted directions from the overall model. Some of the genes are associated with multiple programs or regulators.

Extended Data Fig. 8 Gene to program to trait maps, related to Fig. 5.

a) Number of top hits (|γ| > 0.1) for MCH whose direction of associations were concordant or discordant with that predicted from the model. Grey points and their density plot are the results from 20,000 permutations. Red point shows the observed data. b) Distribution of top hits concordance p-values in permutation tests for MCH. In each permutation, we counted the number of top hits concordant with the model and evaluated its enrichment (Methods). The observed result showed the highest concordance compared to permuted sets. c-f) Same plots as (a) and (b), for RDW (c,d) and IRF (e, f). g-h) Gene to program to trait map for RDW (g) and IRF (h).

Extended Data Fig. 9 Cross-trait comparisons of gene effects, related to Fig. 5.

a) Comparison of LoF burden test effect sizes after GeneBayes between IRF and RDW. The solid line corresponds to the first principal component. b) Cross-trait directional relationships of gene effects in the predicted gene-to-program-to-trait model and raw data from the LoF burden test. The left table shows the comparison between MCH and RDW, while the right table shows the comparison between RDW and IRF. For each table, only genes that have strong effects in both traits (|γ| > 0.1) and selected in the predicted model for both traits are considered. For instance, +1 means that the gene has strong effects for both traits in the same direction. MCH and IRF share few genes with strong effects and could not be compared. c) Correlation of regulatory effects on four programs or Shet with γ. For each trait, correlation coefficients were estimated with the multiple regression model. Error bars indicate 95% CI.

Extended Data Fig. 10 Regulator-burden correlation of genes in different cell lines.

Each dot represents one of the genome-wide expressed genes. For essential gene Perturb-seq, the correlation between regulatory effects and burden effects across essential genes is plotted. For the genome-wide Perturb-seq, the correlation across all perturbed genes is plotted.

Supplementary information

Supplementary Information

This file contains Supplementary Notes and Supplementary Figures 1–12.

Reporting Summary

Supplementary Table 1

List of UKB traits for sLDSC analysis.

Supplementary Table 2

Heritability enrichment of model traits to cell line open chromatin regions.

Supplementary Table 3

Annotation of cNMF programs to biological pathways.

Supplementary Table 4

List of Perturb-seq datasets utilized in the analysis.

Supplementary Table 5

List of control traits for LoF burden test.

Peer Review File

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Ota, M., Spence, J.P., Zeng, T. et al. Causal modelling of gene effects from regulators to programs to traits. Nature 650, 399–408 (2026). https://doi.org/10.1038/s41586-025-09866-3

- Received: 28 January 2025

- Accepted: 05 November 2025

- Published: 10 December 2025

- Version of record: 10 December 2025

- Issue date: 12 February 2026

- DOI: https://doi.org/10.1038/s41586-025-09866-3

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Spatiotemporal cellular map of the developing human reproductive tract

Valentina Lorenzi,

Cecilia Icoresi-Mazzeo,

Charlotte Cassie,

Nadav Yayon,

Elias R. Ruiz-Morales,

Carmen Sancho-Serra,

Ryan Colligan,

Frederick C. K. Wong,

Magda Marečková,

Elizabeth Tuck,

Kenny Roberts,

Tong Li,

Marc-Antoine Jacques,

James Ashcroft,

Xiaoling He,

Berta Crespo,

Batuhan Cakir,

Simon Murray,

Yong Gu,

Alexander V. Predeus,

Martin Prete,

Iva Kelava,

Roger Barker,

Luz Garcia-Alonso,

John C. Marioni &

…

Roser Vento-Tormo

Nature volume 650, pages 428–437 (2026)

Abstract

The human reproductive tract is essential for species perpetuation and overall health. Its development involves complex processes of sex specification, tissue patterning and morphogenesis, the disruption of which can cause lifelong issues, including infertility1,2,3,4,5. Here we present an extensive single-cell and spatial multi-omic atlas of the human reproductive tract during prenatal development to provide insights beyond those that are possible with smaller-scale, organ-focused studies. We describe potential regulators of sexual dimorphism in reproductive organs and pinpoint previously unknown genes involved in Müllerian duct emergence and regression and urethral canalization of the penis. By combining histological features with gene expression and chromatin accessibility data, we define transcription factors and signalling events potentially involved in the regionalization of the Müllerian and Wolffian ducts. We also refine how the HOX code is established in distinct reproductive organs and reveal that the expression of thoracic HOX genes is increased in the rostral mesenchyme of the fallopian tube and epididymis. Our findings further indicate that epithelial regionalization of the fallopian tube and epididymis, which probably contribute to sperm maturation and capacitation, is established during development. By contrast, later events are necessary for regionalization of the uterocervical canal epithelium. Finally, on the basis of single-cell data and fetal-derived organoids, we show that the fetal uterine epithelium is vulnerable to oestrogen-mimicking endocrine disruptors. By mapping sex-specific reproductive tract regionalization and differentiation at the cellular level, our study provides valuable insights into causes and potential treatments of developmental reproductive disorders.

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Main

The development of the human reproductive tract is a complex morphogenetic process orchestrated by paracrine interactions[6] and hormonal signalling[7]. The internal genitalia (with the exception of the gonads) originate from the Müllerian and Wolffian ducts (derived from intermediate mesoderm) and the urogenital sinus (derived from endoderm). In genetically female individuals (XX), the Müllerian ducts develop into the fallopian tubes, uterus, cervix and upper vagina, whereas the urogenital sinus forms the lower vagina1,8. In genetically male individuals (XY), the Wolffian ducts give rise to the epididymis, vas deferens and seminal vesicles, whereas the urogenital sinus becomes the prostate2,9. The genital tubercle, derived from lateral plate mesoderm, endoderm and surface ectoderm, gives rise to the external genitalia: the clitoris in female individuals and the penis in male individuals[3].

Initially, embryonic reproductive tissue precursors (that is, Müllerian and Wolffian ducts, urogenital sinus and genital tubercle) comprise an undifferentiated epithelial inner layer and surrounding mesenchyme. As development progresses, the sexually dimorphic differentiation of the mesenchyme precedes and dictates the differentiation of the epithelium[6]. Müllerian and Wolffian duct differentiation is particularly complex, as precise spatial boundaries must be established between the resulting organs10,11.

Both Müllerian and Wolffian ducts are present in genetically female and male embryos until approximately 9–10 post-conceptional weeks (PCW). If the embryonic gonads differentiate into testes under the control of the Y chromosome-linked SRY gene[12], Sertoli cells in the testes produce anti-Müllerian hormone (AMH), which causes the Müllerian ducts to regress[13]. Leydig cells in the testes secrete testosterone, which promotes the development of the Wolffian ducts into the male upper reproductive tract[9], and which is further converted into dihydrotestosterone, leading to the development of the male lower reproductive tract[14]. In the absence of SRY and these hormones, as in XX embryos, the Wolffian ducts regress and the Müllerian ducts, urogenital sinus and genital tubercle develop into the female reproductive tract.

Genetic and environmental disruptions to reproductive tract development can lead to congenital anomalies, infertility and cancer4,5. For example, approximately 7% of women have congenital uterine anomalies, a figure that increases to around 17% among those who experience recurrent miscarriages[4]. However, the cellular and molecular mechanisms that mediate human reproductive tract development remain poorly studied and have primarily been inferred from rodent and chicken loss-of-function studies or human histological observations15,16,17. Recently, we and others have used single-cell transcriptomics to study the developing human reproductive tract, primarily focusing the gonads18,19 and, to a limited extent, the upper portions of the Müllerian and Wolffian ducts[20]. However, we lack a holistic study of development of the entire reproductive tract in both sexes. Notably, how the ducts are specified and patterned along their rostrocaudal axis in humans is largely unknown.

Here we generate a highly resolved, spatiotemporal, multi-omic map of the entire human reproductive tract (excluding the gonads) during development, profiling more than half a million cells spanning the first and second trimesters. We detail the cellular and molecular features of the female and male reproductive tracts throughout the critical stages of sexual differentiation and reveal how sex-specific signals drive the dimorphic development of reproductive organs and the regression of sexually unmatched ducts. We also resolve the cascade of gene-expression changes from tissue-wide gradients into lineage-specific compartments in the Müllerian and Wolffian ducts. Specifically, we define the key transcription factors and cell–cell communication events that drive their differentiation into final organ derivatives. Finally, we harness our atlas to pinpoint cell types and developmental windows that are probably affected by endocrine-disrupting chemicals (EDCs) and clinically approved drugs, and we validate the effect of two EDCs using fetal-derived human uterine organoids.

Single-cell resolved spatiotemporal map

We profiled 89 reproductive tract samples from fetuses aged 6–21 PCW, covering stages of sex specification and differentiation of the internal and external genitalia and the regression of the unmatched reproductive ducts. We used single-cell RNA sequencing (scRNA-seq; 538,742 cells), single-cell chromatin accessibility with sequencing (scATAC–seq; 226,668 cells), spatially resolved gene-expression profiling through in situ sequencing (ISS; 11 slides, 1,853,342 cells) and 10x Visium (36 slides) to generate the data (Fig. 1a,b and Supplementary Tables 1–3). By mapping the dissociated single-cell data onto stage-matched ISS and 10x Visium data, we enhanced the resolution and stringency of cell-type definitions, which led to the identification of 52 distinct reproductive-tract-specific cell types (Fig. 1c and Extended Data Fig. 1a–d). The integration of spatially resolved data was crucial for cell annotation, as unique markers for many of the identified cell types had not been previously described (Methods, Supplementary Note 1 (which also reports the references for markers previously reported in the literature and used to guide our annotations) and Supplementary Table 4).

Fig. 1: Single-cell resolved spatiotemporal atlas.

a, Schematic of human reproductive development showing the main anatomical structures in XX and XY embryos and fetuses. b, Diagram summarizing the stage and sex composition of our donors along with the technologies used to characterize them. c, Top, batch-corrected uniform manifold approximation and projection (UMAP) embedding of the scRNA-seq dataset (n = 538,742 cells) coloured by major developmental cell lineages (left) and chromosomal sex (right). Bottom, batch-corrected UMAP embedding of reproductive-specific scRNA-seq cells from the internal genitalia (left; n = 379,663 cells) and external genitalia (right; n = 70,027 cells) coloured by cell type. d, Left, Image of a representative 10 PCW male fetus stained with haematoxylin and eosin (H&E) and profiled by ISS (n =  3 biologically independent samples). Scale bar, 1,000 μm. Right, inferred cell-type labels for selected cell types from the scRNA-seq dataset in the ISS slide. e, Left, H&E-stained image of a representative 17 PCW female fetus profiled by ISS (n  =  4 biologically independent samples). Scale bar, 2 mm. Right, inferred cell-type labels for selected cell types from the scRNA-seq dataset in the ISS slide. f, Left, H&E-stained image of a representative 16 PCW male fetus profiled by ISS (n  =  1 biologically independent sample) Scale bar, 500 μm. Right, inferred cell-type labels for selected cell types from the scRNA-seq dataset in the ISS slide. CE, cervix; EP, epididymis; Epi, epithelium; FT, fallopian tube; Lig, ligament; Mese, mesenchyme; SmMu, smooth muscle; UT, uterus; VA, vagina; VD, vas deferens; WR, Wolffian remnants. Illustrations in a and b created by A. García.

In the early stages of development (until around 9 or 10 PCW), we identified coelomic epithelial (UPK3B+LRRN4+), Müllerian duct cells (WNT7A+SOX17+ epithelium and AMHR2+CNTN1+ mesenchyme) and Wolffian duct cells (WNT9B+GATA3+ epithelium and PLAC1+HTR2B+ mesenchyme) (Fig. 1c,d, Extended Data Fig. 1e–h and Supplementary Fig. 1a–c). We also detected cells from the urogenital sinus (FOXA1+SHH+ epithelium and GAP43+TNC+ upper and FOXF1+FENDRR+ lower mesenchyme) and genital tubercle (UPK1A+PSCA+ epithelium and TBX4+TBX5+ mesenchyme) (Fig. 1c,d, Extended Data Fig. 1e–h and Supplementary Fig. 1a–c). In samples from <8 PCW embryos, various cell types from adjacent kidney (TMEM52+ distal tubule, SLC12A1+ loop of Henle, TM4SF4+ S-shaped body, GLYAT+ proximal tubule and NPHS1+ podocyte) and adrenal glands (SHISA3+ adrenal cortex) were also present, as accurate tissue microdissection for this developmental stage is challenging (Extended Data Fig. 1f and Supplementary Fig. 1a–c).

As gestation progresses (9–21 PCW), female-specific cells emerged in the fallopian tubes (PNOC+ERP27+ non-ciliated and DNAH12+ ciliated epithelium and ITGBL1+CD36+ mesenchyme), uterocervix (UCA1+DLX5+ epithelium and ITGA4+RORB+ mesenchyme) and vagina (Müllerian-derived DLX5+TP63+ epithelium and SRD5A2+GAP43+ mesenchyme, urogenital sinus-derived FOXA1+PRAC1+ epithelium and SRD5A2+FENDRR+ mesenchyme) in XX fetuses (Fig. 1c,e, Extended Data Fig. 2a–i and Supplementary Fig. 2a–c). Notably, the uterus and cervix showed a highly similar cell-type composition, which indicated that further regionalization probably occurs after 21 PCW (Extended Data Fig. 2c). In the same time window (9–21 PCW), male-specific epididymis (SPAG11B+ non-ciliated and DNAH12+ ciliated epithelium and PLAC1+HTR2B+ mesenchyme), vas deferens (WNT9B+MUC6+ epithelium and RAI2+CHD7+ mesenchyme) and prostate (FOXA1+ epithelium[21] and SRD5A2+GAP43+ upper and SRD5A2+FENDRR+ lower mesenchyme) cells were detected in XY fetuses (Fig. 1c,f, Extended Data Fig. 3a–g and Supplementary Fig. 3a–c). Surrounding each organ of the developing female and male internal genitalia, there was also a layer of smooth muscle (MYH11+) and ligament (PTGER3+) (Extended Data Figs. 2d,h and 3f and Supplementary Note 1).

Our atlas further captured the remnants of sexually unmatched reproductive ducts that persisted in both sexes. Wolffian-like mesenchymal (PLAC1+) and epithelial (FXYD2+) cells were apparent near the fallopian tubes (epoophoron) in female fetuses until 21 PCW, whereas fallopian-like epithelial cells (PNOC+ERP27+) were observed in some male fetuses between 10 and 14 PCW. This finding indicates that Müllerian duct differentiation can occur in male individuals before its regression orchestrated by AMH concludes (Fig. 1c,e, Extended Data Fig. 2c,e and Supplementary Figs. 2a–c and 3a,b).

Consistent with studies of mice22,23, we did not identify sex-specific cell types in the developing penis or clitoris. Across all developmental stages and in both sexes, we identified the urethral epithelium (FOXA1+PSCA+), erectile tissues (corpus cavernosum (SOX9+PRR16+) and corpus spongiosum (FOXF1+SALL1+)), glans (SP9+DLX5+), prepuce (SIX1+SHOX2+), preputial lamina epithelium (KRT14+WNT3+) and surface genital epidermis (KRT14+KRTDAP+) (Extended Data Figs. 1h, 2c,f and 3c,d,g and Supplementary Figs. 1a–c, 2a–c and 3a–c).

In summary, our spatiotemporal, single-cell resource represents a highly comprehensive and unbiased characterization of the reproductive epithelia and surrounding mesenchyme during human prenatal development. It covers the progression from undifferentiated precursors to differentiated sex-specific organs, and is accessible at www.reproductivecellatlas.org.

Müllerian emergence and regression

The Müllerian ducts initially consist of simple mesoepithelial tubes that are specified from the extra-gonadal coelomic epithelium[24] around 6 PCW. According to studies in rodents, these cells migrate caudally in response to signalling from the Wolffian ducts[25] (which emerge earlier, around 4 PCW, a developmental stage difficult to access and therefore not included in our study), and eventually fuse at the urogenital sinus (Fig. 2a).

Fig. 2: Müllerian ontology, migration and regression.

a, Schematic of the major steps of Müllerian duct formation along with the cell types involved. b, Batch-corrected force directed graph (FDG) visualization of scRNA-seq data from 6–8 PCW fetuses, specifically the coelomic epithelium, Müllerian duct epithelium and Müllerian duct mesenchyme, coloured by cell type (left) and chromosomal sex (right). Reconstructed trajectories are overlaid on the embedding. c, Smoothed splines of key temporally variable genes involved in each differentiation trajectory of the Müllerian duct epithelium and mesenchyme (including male degenerating mesenchyme) from the coelomic epithelium. d, H&E-stained image and high-resolution, large-area images of a representative section of a Carnegie stage 19 embryo showing Müllerian epithelial emergence and migration. smFISH signals for FGF20 (red, migrating Müllerian duct epithelium), PNOC (yellow, rostral Müllerian duct epithelium), LYPD1 (cyan, rostral Müllerian duct epithelium) and GDNF (magenta, caudal migrating Müllerian duct epithelium) are highlighted (n = 3 biologically independent samples). Scale bars, 200 μm. G, gonad. e, High-resolution, large-area images of a representative section of a Carnegie stage 22 male fetus showing Müllerian mesenchymal regression (male-specific). smFISH signals for WNT7A (red, Müllerian duct epithelium), SP7 (yellow, regressing Müllerian duct mesenchyme), WIF1 (cyan, regressing Müllerian duct mesenchyme), WNT9B (yellow, Wolffian duct epithelium), NOTUM (green, regressing Müllerian duct mesenchyme) and NKD1 (cyan, regressing Müllerian duct mesenchyme) are highlighted (n = 2 biologically independent samples). Scale bars, 200 μm (left two panels), 500 μm (right two panels). Illustrations in a created by A. García.

We reconstructed cellular trajectories in the cell types that have key roles in the emergence, migration and initial regression of the Müllerian duct (between 6 and 8 PCW): the anterior mesonephric coelomic epithelium and the undifferentiated Müllerian epithelium and mesenchyme (Fig. 2a). We recovered two trajectories from the progenitor coelomic epithelium population to the Müllerian epithelium and mesenchyme. Notably, we also identified a male-specific degenerating mesenchymal lineage branching off the Müllerian mesenchyme (Fig. 2b).

In the Müllerian epithelial lineage, genes such as RXRG, PNOC and LYPD1 are transiently upregulated at the onset of mesothelial to epithelial cell differentiation (Fig. 2c and Supplementary Table 5). ALDH1A1 expressed by the Wolffian epithelium is the probable source of retinoic acid signalling through the RXRG–RARG axis (Extended Data Fig. 4a and Supplementary Table 6). As the trajectory progresses, migratory genes such as FGF20, SSTR2, GDNF, LGI1 and CALCA, which are known for their roles in neuronal migration and axonal outgrowth26,27, became upregulated (Fig. 2c and Supplementary Table 5). We validated the expression of PNOC, LYPD1, FGF20, GDNF and CALCA by multiplexed single-molecule fluorescence in situ hybridization (smFISH) (Fig. 2d and Extended Data Fig. 4b). smFISH imaging further revealed a coordinated patterning of these genes along the Müllerian duct epithelium. In detail, FGF20 marked the length of the migrating ductal epithelium, whereas the expression of PNOC and LYPD1 and of GDNF and CALCA were restricted to the rostral and caudal portions of the duct, respectively (Fig. 2d and Extended Data Fig. 4b).

The Müllerian mesenchymal lineage in turn was initially characterized by the upregulation of epithelial-to-mesenchymal transition markers such as CNTN1, ZEB2 and SNAI2[28] (Fig. 2c and Supplementary Table 5). The migratory genes PLXDC1, ZAP70 and TMEM163 were also upregulated, and TMEM163 was confirmed by ISS and smFISH analyses (in both male and female embryos) (Fig. 2c and Extended Data Fig. 4c,d). By contrast, the male-specific degenerating branch showed increased expression of the autophagy modulators ADRA2A, LAMP5 and GAL29,30, the WNT signalling inhibitors NOTUM and NKD1 and two previously reported markers from the mouse literature, SP7[31] and WIF1 (also a WNT inhibitor)[32] (Fig. 2c and Supplementary Table 5). The cell-type specificity of NOTUM, NKD1, SP7 and WIF1 was validated by ISS and smFISH, which revealed that these genes are not expressed in female fetuses (Fig. 2c,e and Extended Data Fig. 4e,f). The relevance of SP7 in the degenerating male Müllerian mesenchyme was further supported by computationally inferring transcription factor activities from scATAC–seq and scRNA-seq data (Extended Data Fig. 4g,h).

Overall, our multimodal approach shows that human Müllerian duct formation probably involves the coordinated expression of migration genes in both mesenchyme and epithelium, alongside male-specific upregulation of WNT inhibitors and autophagy markers in the mesenchyme during Müllerian degeneration (Extended Data Fig. 4i).

Müllerian and Wolffian patterning

We then investigated the differentiation and patterning of the Müllerian and Wolffian ducts into their final organ derivatives[8]. In rodents, once the migration process is complete, the Müllerian ducts in female animals are regionalized along the rostrocaudal axis, with cells in distinct segments acquiring specific identities to form the fallopian tubes, uterus, cervix and the upper part of the vagina[24]. Similarly, the Wolffian ducts regionalize along the rostrocaudal axis to give rise to the epididymis, vas deferens and seminal vesicle[9].

To explore how regional gene expression in Müllerian-derived and Wolffian-derived cells controls organ formation along the developing human reproductive tract, we used our spatially resolved transcriptomic data to generate a computational representation of the rostrocaudal axis[33] in female fetuses (from the fallopian fimbriae to the end of the upper vagina) and male fetuses (from the efferent ductules to the initial segment of the vas deferens) (Fig. 3a–d, Extended Data Fig. 5a,b and Supplementary Notes 2 and 3). For female fetuses, for which we have several ISS samples available, we also projected the spatial rostrocaudal axis values from ISS onto our scRNA-seq data, assigning pseudospace coordinates to dissociated cells (Extended Data Fig. 5c,d and Supplementary Note 2). Finally, we modelled gene-expression changes along each axis using ISS-imputed scRNA-seq data (only for Müllerian ducts) and 10x Visium data (for both Müllerian and Wolffian ducts; Extended Data Fig. 5e, Supplementary Notes 2 and 3 and Supplementary Table 7).

Fig. 3: Müllerian and Wolffian patterning.

a, Schematic of the fallopian and uterovaginal axes in >10 PCW female embryos. b, Left, H&E-stained image of a representative 15 PCW fallopian tube profiled with 10x Visium. Right, fallopian axis values overlaid per spot (n = 3 biologically independent samples). Scale bar, 500 μm. c, Left, stitched H&E-stained images of two consecutive sections from a representative 15 PCW uterovaginal canal profiled with 10x Visium. Right, uterovaginal axis values overlaid per spot (n = 2 biologically independent samples). Scale bar, 500 μm. d, Schematic of the Müllerian rostrocaudal axis. e, Smoothed splines of HOX transcription factors along the imputed Müllerian rostrocaudal axis in mesenchymal cells (scRNA-seq). f, High-resolution, large-area image of a representative 17 PCW female fetus with smFISH signals for HOXA7 (yellow), HOXA9 (red) and HOXA10 (cyan) (n = 2 biologically independent samples). Scale bar, 500 μm. g, Heatmap showing minimum and maximum (min–max) normalized expression of prioritized spatially variable mesenchymal transcription factors (beyond the HOX code) in scRNA-seq data (x axis) along the binned imputed Müllerian rostrocaudal axis (y axis). Dots mark transcription factors identified by scATAC–seq and RNA-seq analyses (Extended Data Fig. 6b). h, Heatmap showing min–max normalized expression of prioritized spatially variable mesenchymal ligands and receptors in scRNA-seq data (x axis) along the binned imputed Müllerian rostrocaudal axis (y axis). Arrows show interacting epithelial partners. In epithelial partners, _ marks multimeric or cofactor-dependent receptors, / marks cases where both protein family members interact with the mesenchymal partner, and * marks cases where more than two interact. Dots mark expression in ≥20% of epithelial cells per bin. i, Heatmap showing min–max normalized expression of selected spatially variable genes in 10x Visium data in non-ciliated epithelium (x axis) along the binned fallopian tube (FT) axis (y axis). j, High-resolution, large-area images of a representative 21 PCW female fetus with smFISH signals for DLX5 (red), PNOC (yellow) and TP63 (green) (n = 2 biologically independent samples). Scale bar, 1,000 μm. k, H&E-stained image of a fallopian tube sample from a 37-year-old individual, highlighting three regions profiled by 10x Visium (n = 1 biologically independent sample). Scale bar, 2 mm. l, Violin plot of rostral (top) and caudal (bottom) signature scores from i in the epithelial 10x Visium spots across regions from k. Significance, Jonckheere’s trend test (P = 5 × 10^4). Illustrations in a created by A. García.

We first examined the expression of the four lumbosacral HOX genes (HOXA9, HOXA10, HOXA11 and HOXA13) that orchestrate the rostrocaudal regionalization of the Müllerian and Wolffian mesenchyme in rodents[11]. In female fetuses, HOXA10 and HOXA11 were upregulated in the uterocervical mesenchyme, whereas HOXA13 was upregulated in the cervicovaginal mesenchyme, a result that aligns with previous studies[11] (Fig. 3e and Extended Data Fig. 5f). However, although literature suggests that Hoxa9 is upregulated throughout the fallopian tubes in mice[11], human HOXA9 exhibited increased expression in the caudal fallopian tube and uterocervical mesenchyme but was absent in the rostral fallopian tube mesenchyme (Fig. 3e and Extended Data Fig. 5f). This discrepancy prompted us to investigate other HOX genes that may be involved in patterning the rostral region of fallopian tubes in humans.

Thoracic HOX code members (including HOXA5, HOXC5, HOXC6 and HOXA7) showed increased expression in the rostral fallopian tube mesenchyme, with a gradual decrease along the caudal axis (Fig. 3e and Extended Data Fig. 5f). Integrative analysis of scRNA-seq and scATAC–seq data further confirmed the activity of thoracic HOX regulons in the fallopian tube mesenchyme, whereas the lumbosacral HOX regulons were active in the uterovaginal mesenchyme (Extended Data Fig. 5g). Moreover, the Wolffian mesenchyme, where the upper Müllerian ducts (corresponding to the region that gives rise to fallopian tubes) are embedded, seemed to be patterned by HOXA7 rostrally and HOXA9 caudally from the earliest stages of development (Extended Data Fig. 5h). Consistent with the finding that the Wolffian mesenchyme is already patterned early in development, in male embryos, the thoracic code later (around 10–21 PCW) marks the upper half of the epididymis, whereas HOXA9 is restricted to the lower half (Extended Data Fig. 5i,j). smFISH corroborated our single-cell and spatial transcriptomic findings. That is, HOXA7 is enriched in the rostral fallopian tube and epididymis, whereas HOXA9 is predominantly expressed in their caudal regions (Fig. 3f and Extended Data Fig. 5k,l).

We then explored transcription factors that might determine the regional specificity of the mesenchyme in the ducts beyond the HOX code. In the Müllerian mesenchyme, we observed a decreasing gradient of GATA6, PROX1, NFATC2 and FOXL2 expression and an increasing gradient of PBX3, PRRX2, EVX1, EVX2, LBX2, AHR, AR and ISL1 expression along the rostrocaudal axis, some of which were also found to be active by means of scATAC–seq (Fig. 3g and Extended Data Fig. 6a,b). These transcription factors included homeobox genes (GATA6, PBX3, PRRX2, EVX1, EVX2, LBX2 and ISL1) implicated in mesodermal rostrocaudal patterning of other organs in multiple species34,35,36. Moreover, the central portion of the Müllerian axis (corresponding to the uterus and cervix) was characterized by the upregulation of EMX2, ESR1, FOXO1, MEIS2 and RORB (Fig. 3g and Extended Data Fig. 6a,b).

We observed a distinct pattern of transcription factors in the Wolffian-derived mesenchyme, with FOXC2 and ALX1 marking the rostral and caudal portions of the epididymis (the male analogue of the fallopian tubes), respectively (Extended Data Figs. 5j and 6c,d). The portion of the Wolffian axis corresponding to the upper vas deferens (the male analogue of the uterus) showed specific expression of FOXC1 and upregulation of MEIS2 and RORB, consistent with its female uterine counterpart (Extended Data Figs. 5j and 6c,d). Owing to damage to the vas deferens during dissections, we could not define shared and specific transcription factors in the lower part of the vas deferens and seminal vesicles.

Altogether, our work refined the HOX code that underlies mesenchymal regionalization in the differentiating human Müllerian and Wolffian ducts (Extended Data Fig. 5m) and identified previously unknown spatially variable transcription factors, some shared between the sexes and others sex-specific.

Signals that guide ductal patterning

Heterotypic co-culturing of epithelial and mesenchymal cells of the reproductive tract has shown that mesenchymal cells in the ducts first acquire their regional identity and then instruct the adjacent epithelium to differentiate accordingly[6]. Hence, we next performed cell–cell communication analyses to identify specific interactions between the mesenchyme and epithelium along the Müllerian and Wolffian duct rostrocaudal axes (Extended Data Fig. 6e and Supplementary Notes 2 and 3).

We found increased activity of WNT and retinoic acid signalling (mediated by the mesenchymal-expressed ligands WNT4 and WNT5A and ALDH1A1) in the fallopian tubes and uterus (Fig. 3h and Extended Data Fig. 6f–h). This activity was opposed by an increasing gradient of WNT inhibition (driven by the upregulation of WIF1 and SFRP5 in the mesenchyme) in the upper vagina, results that corroborated existing mouse literature[37] (Fig. 3h and Extended Data Fig. 6f–h). Similarly, in the caudal portion of the Wolffian duct axis (corresponding to the vas deferens), there was an increase in WNT inhibition (driven by WIF1 expressed by the mesenchyme) (Extended Data Fig. 6i,j).

In the upper vagina, we observed increased signalling through the IGF1–IGF1R axis, integrin pathways involving TNC, and BMP activity mediated by GDF7, GDF10, BMP4 and BMP7 through BMPR, with each respective ligand being expressed in the mesenchyme. BMP signalling could induce the upregulation of RUNX1 and TP63 in the adjacent epithelium, as previously reported in mice[38] and in keeping with our analysis of spatially variable transcription factors in the differentiating Müllerian epithelium (Fig. 3h and Extended Data Figs. 6g,h,k and 7a,b). Although GDF7 and TNC expression peaked in the upper vagina, they were already expressed in the uterocervical mesenchyme (Fig. 3h). This expression pattern was mirrored in male embryos, for which BMP signalling via GDF7 and integrin signalling via TNC (both expressed by the mesenchyme) were upregulated in the initial segment of the vas deferens (Extended Data Figs. 6i,j and 7c,d).

Signalling between the mesenchyme and epithelium during ductal regionalization can be bidirectional, with signals from the epithelium also influencing the fate of the mesenchyme[6]. Consistent with this finding, we observed upregulation of the LGR5 receptor in fallopian tube mesenchyme, which may respond to its cognate ligand RSPO1 expressed in the adjacent epithelium (Fig. 3h). Moreover, co-expression of LGR5 and TSPAN8 in the fallopian mesenchyme suggests features reminiscent of a stem cell niche[39] (Extended Data Fig. 7e,f).

In summary, by investigating mesenchymal–epithelial cell interactions along the Müllerian and Wolffian rostrocaudal axes, we identified shared and sex-specific cell communication events that are probably pivotal in determining epithelial identity during the regionalization of the reproductive ducts (Extended Data Fig. 7g).

Fallopian and epididymal regionalization

In adulthood, the non-ciliated epithelia of the fallopian tubes and epididymis are functionally regionalized to support sperm capacitation and maturation, as reflected in marked gene-expression differences. However, it is unclear whether and when this regional differentiation occurs during fetal development[40]. To evaluate the in utero transcriptional gradients at the genome-wide level, we leveraged our Müllerian and Wolffian rostrocaudal axes analysis framework and examined intra-organ gene-expression changes in the developing fallopian tube and epididymis epithelia in fetuses between 10 and 21 PCW (Extended Data Fig. 7h,i, Supplementary Note 4 and Supplementary Table 8).

In the non-ciliated epithelial cells of the fetal fallopian tube, we identified genes (including PNOC, APOA1, CLDN6, ERP27 and ZBED2) for which expression decreased rostrocaudally from the fimbria to the isthmus, and genes for which expression peaked in the middle of the fallopian tube (for example, LYPD1, S100A1 and CRTAC1) (Fig. 3i,j and Extended Data Fig. 7j). Notably, PNOC and LYPD1 expression was already restricted to the rostral portion of the epithelium during Müllerian duct emergence, which indicated that some degree of regionalization begins early in development (Fig. 2d). We also observed upregulation of genes such as MUC6, WDR72 and KCNN4 in the fallopian isthmus (Fig. 3i). Orthologues of these genes are involved in isthmus-specific epithelial secretions in other species41,42.

Some genes are known to change their expression[43] along the rostrocaudal axis of the fallopian tubes in adults, but a comprehensive study is lacking. Thus, to determine whether the spatial gradient we identified in the fetus persists into adulthood, we generated 10x Visium spatial transcriptomic data from three regions of a human adult fallopian tube (fimbria, ampulla and isthmus) and scored epithelial spots in each region for the average expression of our fetal gene sets (Fig. 3k and Extended Data Fig. 7k). Both the rostral-biased and caudal-biased trends observed in fetal development were indeed maintained in adulthood (Fig. 3l).

In the non-ciliated epithelium of the fetal epididymis, we uncovered genes with a rostral bias, including ESR1, SALL1, VIL1, SPAG11A, PDZK1 and FXYD2, which are known regulators of fluid reabsorption and sperm maturation[44] in the adult epididymis (Extended Data Fig. 7l). Moreover, cell-adhesion genes such as the claudins (CLDN2 and CLDN10) and cadherins (CDH2 and CDH6) were enriched in the rostral portion of the epididymis, a result consistent with findings in adult tissues[45]. Moreover, several genes that exhibited increased expression towards the caudal epididymis—GATA3, WNT9B, TFAP2A, CPXM2 and BLNK—have also been reported in adults and are associated with immune response regulation[45] (Extended Data Fig. 7l,m).

Taken together, our findings indicate that the regional differentiation of the human fallopian tube and epididymis begins in utero and establishes transcriptional gradients that can persist into adulthood.

Sexual dimorphism in the genital tubercle

We next investigated how sexual dimorphism emerges in the external genitalia, where androgen action drives penile growth and canalization of the male urethra3,16. Although we did not identify sex-specific cell populations in the developing genital tubercle (Extended Data Fig. 8a), we observed stage-dependent differences in the mesenchymal erectile tissues that distinguished RFLNA+GAS2+ early corpus cavernosum and TTYH1+SCRG1+ late corpus cavernosum, as well as GRIDL2+FOXL2+ early corpus spongiosum and PDLIM3+TCF21+ late corpus spongiosum (Extended Data Fig. 8a,b). The masculinization programming window (MPW), which is estimated to occur between 8 and 14 PCW in humans, is the critical period during which disruptions in androgen signalling have the most significant phenotypic effects on newborn male individuals[46]. Studies in rodents suggest that during this window, the early corpus spongiosum, located adjacent to the invaginating urethral epithelium, has a crucial role in urethral canalization by moving medially and shaping the developing urethral canal in the penis[47].

To investigate the molecular underpinnings of urethral canalization in humans, we first validated the identity of the urethral epithelium and surrounding corpus spongiosum in both the penis and clitoris through spatial mapping (Fig. 4a and Extended Data Fig. 8c). In the developing penis, the early corpus spongiosum showed the highest activity of androgen receptor compared with all other cell types (Extended Data Fig. 8d–f).

Fig. 4: Sexual dimorphism in the genital tubercle.

a, H&E-stained image of a representative 14 PCW male fetus profiled with 10x Visium alongside spatial mapping of urethral epithelium and corpus spongiosum cells from the scRNA-seq dataset onto the corresponding 10x Visium slide (n = 2 biologically independent samples). Estimated cell-type abundance (colour intensity) in each 10x Visium spot is shown over the H&E image. Scale bars, 500 μm. b, Schematic of the process of urethral canalization that occurs in male external genitalia during the MPW (around 8–14 PCW). c, Volcano plot showing the log fold change (FC) (x axis) and adjusted P value (y axis) of the differential expression of genes (adjusted P = 0.05, |log[FC]| > 1) between male and female fetuses in the human early corpus spongiosum. Genes in bold were also identified as being significantly upregulated in the male mouse early corpus spongiosium. d, Volcano plot showing the log fold change (x axis) and adjusted P value (y axis) of the differential expression of genes (adjusted P = 0.05, |log[FC]| > 1) between male and female fetuses in the human urethral epithelium. e, Schematic summarizing the putative drivers of urethral canalization identified through our analyses. Illustrations in b and e created by A. García.

Source data

To identify candidate target genes downstream of androgen signalling that may be involved in urethral canalization, we performed differential expression analysis in the early corpus spongiosum between male and female fetuses during the MPW (Fig. 4b and Methods). We identified 18 genes with male-biased expression, including the known androgen targets CSRP2, CYP1B1, TMEM200A, SRD5A2 and NID1, as inferred by scATAC–seq data and supported by existing literature[48] (Fig. 4c and Extended Data Fig. 8g). To determine whether these genes are conserved across species, we re-analysed a mouse scRNA-seq dataset of male and female external genitalia during the MPW[23] and found that Mafb and Csrp2 also exhibited male-biased expression in the mouse equivalent of the early corpus spongiosum (Methods and Extended Data Figs. 8h–l and 9a). By contrast, the female early corpus spongiosum displayed upregulation of genes involved in organization of the extracellular matrix (for example, MFAP4, SEMA3C, LUM and FLRT2) and IGFBP3, which is downregulated by androgen signalling[49], potentially explaining its increased expression in females (Fig. 4c).

In addition to mesenchymal differences, we identified genes with sexually dimorphic expression in the human urethral epithelium. SCGB1A1 and PTPRD, the most upregulated genes in male individuals (Fig. 4d), have roles in the formation of canalized epithelial structures in other tissues. That is, SCGB1A1 has been implicated in the tubular organization of human-derived in vitro bronchioids[50], whereas PTPRD is recruited to epithelial adherens junctions at the time of cell–cell contact[51].

Knowing that mesenchymal differentiation directs epithelial differentiation[6], we next inferred sexually dimorphic cell–cell communication events between the early corpus spongiosum and the urethral epithelium in humans. Our analysis revealed a putative male-biased interaction between JAG1 (upregulated in the early corpus spongiosum) and its receptors NOTCH2 and NOTCH3 (expressed in the urethral epithelium), which implies that there is increased Notch signalling in male individuals (Extended Data Fig. 9b and Supplementary Table 9). This notion was further supported by the expression of the downstream Notch effector HES1. Moreover, we identified potential male-biased interactions between receptors involved in adherens junctions (NRP1, NRXN3 and PTPRD) expressed by the urethral epithelium and their ligands (SEMA3A, NLGN1, NLGN2, CLSTN1 and LRRC4B), which were upregulated in the early corpus spongiosum (Extended Data Fig. 9b). These findings, alongside the male-specific upregulation of SCGB1A1 and PTPRD in the urethral epithelium, provide support for the key role of adherens junction signalling in enabling urethral canalization in male individuals.

Altogether, our findings shed light on the establishment of sexual dimorphism in the genital tubercle by elucidating the genes and mesenchymal–epithelial interactions that potentially mediate urethral canalization in the penis (Fig. 4e).

Disruptions to reproductive development

Exogenous agents, including pharmaceutical and environmental chemicals, can interfere with developmental programs in utero and manifest as reproductive disorders later in life[52]. To identify reproductive cell types that may be susceptible to such disruptions, we focused on compounds with the potential to disrupt reproductive epithelia—a cellular compartment frequently implicated in disease—and identified 47 drugs with known anatomical therapeutic chemical (ATC) classification (Fig. 5a, Extended Data Fig. 9c–e and Supplementary Table 10).

Fig. 5: Disruptions to reproductive tract development.

a, Heatmap showing the z score enrichment of targets of clinically approved drugs (x axis) that specifically affect the epithelial compartment of early (≤10 PCW) reproductive tract organs among reproductive-specific cell types (y axis) identified in our scRNA-seq dataset through drug2cell predictions. The colour of each drug represents its ATC code (details in Extended Data Fig. 9e). b, Schematic of the experimental design for uterine epithelial organoid derivation and exposure to the endocrine-disrupting chemicals BPA and BBP. Dimethyl sulfoxide (DMSO) was used as the control. c, Dot plot showing the predicted probability from each epithelial in vivo cell type (x axis) of the uterovaginal canal from which the organoids were derived and the non-ciliated and ciliated cells of the control organoids (y axis). d, Immunofluorescence staining of representative uterine epithelial organoids derived from a 17 PCW female fetus (Hrv277 line) at day 4 following exposure to BPA, BBP or vehicle (DMSO; Control) for EPCAM (magenta, epithelial cell marker), ZO-1 (cyan, tight junction protein indicating apicobasal polarity) and F-actin (white, cytoskeletal filament) (n = 2 biologically independent samples). Scale bars, 100 μm. e, Bar plot showing the proportion of ciliated and non-ciliated cells in fetal-derived uterine epithelial organoids treated with vehicle control (n = 21,905 cells) or BBP (n= 23,338 cells). f, Volcano plot showing the log fold change (x axis) and adjusted P value (y axis) of the differential expression of genes (adjusted P = 0.05, |log[FC] | > 0.5) between differentially abundant neighbourhoods in the BBP-exposed condition and all other neighbourhoods in non-ciliated G1 cells. Genes in bold are also upregulated by BPA. Illustrations in b created by A. García.

Source data

The targets of monoclonal antibodies such as fremanezumab (which targets CALCA) and somatostatin agonists such as octreotide (which binds to SSTR2), which are used in the treatment of migraines and neuroendocrine tumours, respectively, were upregulated in the migratory Müllerian epithelium relative to all other cell types (Fig. 5a and Extended Data Fig. 9c). Conversely, the early Wolffian duct epithelium can be specifically targeted by drugs such as conivaptan (which acts on AVPR1A) and spironolactone (which targets PPARG), which are used for type 1 and 2 diabetes, respectively (Fig. 5a and Extended Data Fig. 9c). Both Müllerian and Wolffian duct epithelia, and their derivatives, particularly the uterocervical and vas deferens epithelia, also exhibited susceptibility to the antibiotic doxycycline (which binds to MMP7) (Extended Data Fig. 9c–e).

We then assessed the potential impact of EDCs, commonly found in plastics used for everyday objects, by evaluating the spatiotemporal dynamics of steroidogenic hormone receptor expression throughout gestation. Receptors for oestrogen (ESR1 and ESR2) and progesterone (PGR), the activities of which are affected by bisphenol A (BPA)[53] and phthalate esters, androgen receptor (AR), also disrupted by phthalate esters[54], and peroxisome proliferator-activated receptor gamma (PPARG), recently identified as a target of per- and polyfluoroalkyl substances (PFAS)[55], are all expressed in both male and female reproductive tracts. Before 10 PCW, expression of ESR1, ESR2 and PGR were not detected (Extended Data Fig. 9c). After 10 PCW, ESR1 was upregulated in most Müllerian duct derivatives, in the lower vaginal epithelium and in the caput epididymis epithelium, whereas PGR was expressed at low levels in the ciliated fallopian tube epithelium and in the smooth muscle of the upper vagina (Extended Data Fig. 9d,e). AR was highly expressed in the fallopian tube epithelium, the lower vaginal mesenchyme and the epithelium of the entire epididymis after 10 PCW (Extended Data Fig. 9d,e). Upregulation of AR was also observed in the urogenital sinus and genital tubercle derivatives throughout gestation. PPARG was upregulated in the epithelial cells of the Wolffian duct early in development and later in the caudal epididymis and vas deferens, and in the urethral epithelium across all stages (Extended Data Fig. 9c–e).

To experimentally validate these predictions, we tested the effects of BPA and benzyl butyl phthalate (BBP), a representative phthalate ester, on fetal uterine epithelial organoids derived from 12 and 17 PCW fetuses (Fig. 5b–d and Extended Data Fig. 10a–f). Label transfer from in vivo fetal data (Fig. 5c) and canonical marker expression of fetal uterine cells (Extended Data Fig. 10a) confirmed the fetal uterine identity of the organoids before exposure to the chemicals. Under basal conditions, the fetal uterine epithelium expressed ESR1 but lacked expression of PGR (Extended Data Fig. 9d), which suggested that either systemic oestrogen levels were too low to activate ESR1 or that ESR1-expressing cells in the fetal uterus are not yet competent to respond to oestrogen signalling. Following exposure to BPA and BBP, we observed an increase in ciliated cells in response to both compounds (Fig. 5e and Extended Data Fig. 10g), a result consistent with previous reports that oestrogen promotes ciliogenesis in female reproductive tissues[56]. Moreover, the organoids showed upregulation of PGR and several well-characterized oestrogen-responsive genes (for example, SCGB2A1, SCGB1D2, ASRGL1 and SLC39A6)57,58,59,60 (Fig. 5f and Extended Data Fig. 10h–j). Finally, we used the BPA-induced and BBP-induced gene signature to score adult endometrial organoids from our previous study[61]. There was higher enrichment in oestrogen-treated organoids than in oestrogen-deprived controls, a finding that further supports the oestrogen-like effects of these EDCs[58] (Extended Data Fig. 10k,l).

In summary, our integrated atlas enabled us to predict when and where external agents, such as clinically approved drugs and EDCs, have the potential to act during gestation.

Discussion

Congenital reproductive-tract disorders affect more than 3% of female[4] and 0.8% of male[5] newborns; however, our understanding of prenatal reproductive-tract development remains limited. In this study, we generated a developmental roadmap of the male and female reproductive tracts during key periods of sexual differentiation. This roadmap provides detailed temporal and spatial distributions of 52 reproductive-tract-specific cell types in 89 human samples spanning 6–21 PCW. Leveraging this dataset, we uncovered sex-specific cues that drive the divergent development of reproductive organs and the selective regression of sexually unmatched ducts. Moreover, by characterizing the progressive differentiation of epithelial and surrounding mesenchymal compartments, we provide new cellular and molecular insights into how early axial gradients are translated into defined cell lineages and distinct tissue structures (Supplementary Note 5). With this resource, researchers can contextualize known genetic variants linked to reproductive diseases by identifying when and in which cell types genes are expressed or chromatin regions are open. Moreover, our findings about gradients of transcription factors and morphogens activated during reproductive development pave the way for generating more complex in vitro models, which may facilitate the study of disease-causing perturbations.

Methods

Samples

Fetuses were obtained after voluntary terminations of pregnancy, which were performed either via medical or surgical procedures. The termination methods used did not compromise the integrity or morphology of the fetuses analysed in this study. Only well-preserved fetuses, without evidence of structural damage, were included. All tissue samples used for this study were obtained with written informed consent from all participants in accordance with the guidelines in The Declaration of Helsinki 2000. The human embryonic and fetal material was provided by the Joint MRC–Wellcome Trust (grant number MR/R006237/1 and MR/X008304/1) Human Developmental Biology Resource (HDBR, http://www.hdbr.org), with appropriate maternal written consent and approval from the Fulham Research Ethics Committee (REC reference 18/LO/0822 and 23-LO/0312) and Newcastle & North Tyneside 1 Research Ethics Committee (REC reference 18/NE/0290). The HDBR is regulated by the UK Human Tissue Authority (HTA; www.hta.gov.uk) and operates in accordance with the relevant HTA Codes of Practice. This research was also supported by the NIHR Cambridge Biomedical Research Centre (NIHR203312). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Assignment of developmental stage

Embryos up to 8 PCW were staged using the Carnegie staging method[62]. At stages beyond 8 PCW, age was estimated from measurements of foot length and heel-to-knee length and compared with the standard growth chart[63]. A piece of skin or, where this was not possible, chorionic villi tissue was collected from every sample for quantitative PCR analyses using markers for the sex chromosomes and the autosomes 13, 15, 16, 18, 21 and 22, which are the most commonly seen chromosomal abnormalities. All samples were karyotypically normal.

Tissue processing

All tissues for sequencing and spatial work were collected in HypoThermosol FRS Preservation solution (Sigma-Aldrich) and stored at 4 °C until processing. Tissue dissociation was conducted within 24 h of tissue retrieval with the exception of tissues that were cryopreserved and stored at −80 °C (Supplementary Table 1).

We used a previously described protocol optimized for gonadal dissociation and available at protocols.io[64]. In brief, tissues were cut into <1 mm^3 segments before digestion with a mix of trypsin–EDTA 0.25% and DNase I (0.1 mg ml–1) for 5–15 min at 37 °C with intermittent shaking. Samples >17 PCW were digested using a combination of collagenase and trypsin–EDTA using a previously described protocol64,65, but with modifications. In brief, samples were first digested with a mix of collagenase 1A (1 mg ml–1), DNase I (0.1 mg ml–1) and Liberase TM (50 µg ml–1) for 45 min at 37 °C with rotation. The cell solution was further digested with trypsin–EDTA 0.25% for 10 min at 37 °C with rotation. In both protocols, digested tissue was passed through a 100 µm filter and cells were collected by centrifugation (500g for 5 min at 4 °C). Cells were washed and resuspended in PBS–BSA 0.04% before cell counting.

Single-nucleus suspension

Single-nucleus suspensions were isolated from dissociated cells when performing scATAC–seq, following the manufacturers’ instructions, and from frozen tissue sections when performing multi-omic snRNA-seq and scATAC–seq. For the latter, thick (300 µm) sections were cryosectioned and kept in a tube on dry ice until subsequent processing. Nuclei were released by Dounce homogenization as described in detail in the methods at protocols.io (https://doi.org/10.17504/protocols.io.bp2l6n1xkgqe/v1).

Tissue cryopreservation

Fresh tissue was cut into <1 mm^3 segments before being resuspended with 1 ml ice-cold Cryostor solution (CS10, C2874-Sigma). Tissue was frozen at −80 °C, decreasing the temperature approximately 1 °C min–1. A detailed protocol is available at protocols.io (https://doi.org/10.17504/protocols.io.bgsnjwde).

Tissue freezing

Fresh tissue samples of the human developing reproductive tract were embedded in cold OCT medium and flash-frozen using a dry ice–isopentane slurry.

H&E staining and imaging

Fresh-frozen sections were removed from −80 °C storage and air dried before being fixed in 10% neutral-buffered formalin for 5 min. After rinsing with deionized water, slides were dipped in Mayer’s haematoxylin solution (QPath) for 90 s. Slides were completely rinsed in 4–5 washes of deionized water, which also served to blue the haematoxylin. Aqueous eosin 1% (Leica) was manually applied onto sections with a pipette and rinsed with deionized water after 1–3 s. Slides were dehydrated through an ethanol series (70%, 70%, 100% and 100%) and cleared twice in 100% xylene. Slides were coverslipped and allowed to air dry before being imaged on a Hamamatsu Nanozoomer 2.0HT digital slide scanner.

Multiplexed smFISH and high-resolution imaging

Large-tissue section staining and fluorescence imaging were conducted largely as previously described[66]. Sections were cut from fresh-frozen or fixed-frozen samples embedded in OCT at a thickness of 10 μm using a cryostat, placed onto SuperFrost Plus slides (VWR) and stored at −80 °C until stained. Tissue sections were then processed using a Leica BOND RX to automate staining with a RNAscope Multiplex Fluorescent Reagent kit v2 assay (Advanced Cell Diagnostics, Bio-Techne) according to the manufacturers’ instructions. Details of the probes used are provided in Supplementary Table 3. Before staining, human fresh-frozen sections were post-fixed in 4% paraformaldehyde in PBS for 15 min at 4 °C, then dehydrated through a series of 50%, 70%, 100% and 100% ethanol for 5 min each. Following manual pretreatment, automated processing included epitope retrieval by protease digestion with protease IV for 30 min before probe hybridization. Subsequently, the automated processing for these sections included heat-induced epitope retrieval at 95 °C for 5 min in buffer ER2 and digestion with protease III for 15 min before probe hybridization. Tyramide signal amplification with Opal 520, Opal 570 and Opal 650 (Akoya Biosciences) and TSA-biotin (TSA Plus Biotin kit, Perkin Elmer) and streptavidin-conjugated Atto 425 (Sigma Aldrich) was used to develop RNAscope probe channels.

Stained sections were imaged with a Perkin Elmer Opera Phenix High-Content Screening system in confocal mode with 1-μm z step size, using a ×20 (NA 0.16, 0.299 μm pixel–1), ×40 (NA 1.1, 0.149 μm pixel–1) or ×63 (NA 1.15, 0.091 μm pixel–1) water-immersion objective. The following channels were used: DAPI, excitation (ex.) of 375 nm and emission (em.) of 435–480 nm; Atto 425, ex. of 425 nm and em. of 463–501 nm; Opal 520 ex. of 488 nm and em. of 500–550 nm; Opal 570, ex. of 561 nm and em. of 570–630 nm; and Opal 650, ex. of 640 nm and em. of 650–760 nm.

Image stitching

Confocal image stacks were stitched as two-dimensional maximum-intensity projections using proprietary Acapella scripts provided by Perkin Elmer.

10x Genomics Chromium GEX library preparation and sequencing

For the scRNA-seq experiments, cells were loaded according to the manufacturer’s protocol for the Chromium Next GEM Single Cell 5′ v2 (DUAL) kit (10x Genomics) to attain between 2,000 and 10,000 cells per reaction. Library preparation was carried out according to the manufacturer’s protocol. Libraries were sequenced, aiming at a minimum coverage of 20,000 raw reads per cell, on Illumina HiSeq 4000 or Novaseq 6000 systems using the following sequencing format: read 1, 26 cycles; i7 index, 8 cycles; i5 index, 0 cycles; read 2, 98 cycles.

For the scATAC–seq and multimodal snRNA-seq and scATAC–seq experiments, cells were loaded according to the manufacturer’s protocol for the Chromium Single Cell ATAC v2 and Chromium Next GEM Single Cell Multiome ATAC+Gene Expression (10x Genomics) to attain between 2,000 and 10,000 cells per well. Library preparation was carried out according to the manufacturer’s protocol. Libraries for scATAC–seq were sequenced on an Illumina NovaSeq 6000 system, aiming at a minimum coverage of 10,000 fragments per cell, with the following sequencing format: read 1, 50 cycles; i7 index, 8 cycles; i5 index, 16 cycles; read 2, 50 cycles.

10x Genomics Visium library preparation and sequencing

Cryosections (10 µm) were cut and placed on Visium slides. These were processed according to the manufacturer’s instructions. In brief, sections were fixed with cold methanol, stained with H&E and imaged on a Hamamatsu NanoZoomer 2.0HT before permeabilization (24–30 min), reverse transcription and cDNA synthesis using a template-switching protocol. Second-strand cDNA was liberated from the slide and single-indexed libraries were prepared using a 10x Genomics PCR-based protocol. Libraries were sequenced (1 per lane on a HiSeq4000), aiming for 300 million raw reads per sample, with the following sequencing format: read 1, 28 cycles; i7 index, 8 cycles; i5 index, 0 cycles; read 2, 91 cycles.

10x Genomics Visium CytAssist library preparation and sequencing

Cryosections (10 µm) were collected onto SuperFrost Plus slides (VWR) and processed according to the 10x CytAssist protocol (CG000614 and CG000495). In brief, sections were fixed in methanol, H&E stained and imaged on a Hamamatsu Nanozoomer 2.0HT. After destaining, human whole transcriptome probe pairs were hybridized and ligated to the tissue RNA. The ligation products were then released and captured onto Visium slides using a CytAssist instrument. The probes were then extended to incorporate the spatial barcodes from the Visium slide, eluted and prepared into a dual-indexed library. Libraries were sequenced (4 samples per Illumina Novaseq SP flow cell) aiming for a minimum 25,000 read pairs per spot, with the following sequencing format: read 1, 28 cycles; i7 index, 10 cycles; i5 index, 10 cycles; read 2S, 90 cycles.

Customized ISS pipeline

ISS was performed using a 10x Genomics CARTANA HS Library Preparation kit (1110-02, following protocol D025) and an In Situ Sequencing kit (3110-02, following protocol D100), which comprise a commercialized version of HybISS[67]. A reproductive tract section was fixed in 3.7% formaldehyde (Merck 252549) in PBS for 30 min, washed twice in PBS for 1 min each, permeabilized in 0.1 M HCl (Fisher 10325710) for 5 min and washed twice again in PBS, all at room temperature. Following dehydration in 70% and 100% ethanol for 2 min each, a 50, 100 or 150 μl volume (depending on the size of the section) SecureSeal hybridization chamber (Grace Bio-Labs GBL621505-20EA) was adhered to the slide and used to hold subsequent reaction mixtures. Following rehydration in buffer WB3, probe hybridization in buffer RM1 was conducted for 16 h at 37 °C. The 171-plex probe panel included 5 padlock probes per gene, the sequences of which are proprietary (10x Genomics CARTANA). The section was washed with PBST (PBS with 0.05% Tween-20) twice, then with buffer WB4 for 30 min at 37 °C, and thrice again with PBST. Probe ligation in RM2 was conducted for 2 h at 37 °C and the section washed thrice with PBST, then rolling circle amplification in RM3 was conducted for 18 h at 30 °C. Following PBST washes, all rolling circle products (RCPs) were hybridized with LM (Cy5 labelling mix with DAPI) for 30 min at room temperature, the section was washed with PBST and dehydrated with 70% and 100% ethanol. The hybridization chamber was removed and the slide mounted with SlowFade Gold Antifade mountant (Thermo, S36937).

Imaging of Cy5-labelled RCPs at this stage acted as a quality-control step to confirm RCP (‘anchor’) generation and served to identify spots during decoding. Imaging was conducted using a Perkin Elmer Opera Phenix Plus High-Content Screening system in confocal mode with 1-μm z step size using a ×63 (NA 1.15, 0.097 μm pixel–1) water-immersion objective and 7% overlap between adjacent tiles. The following channels were used: DAPI, ex. of 375 nm and em. of 435–480 nm); Atto 425, ex. of 425 nm and em. of 463–501 nm; Alexa Fluor 488, ex. of 488 nm and em. of 500–550 nm; Cy3, ex. of 561 nm and em. 570–630 nm; and Cy5, ex. of 640 nm and em. of 650–760 nm.

Following imaging, the slide was de-coverslipped vertically in PBS (gently, with minimal agitation such that the coverslip fell off to prevent damage to the tissue). The section was dehydrated with 70% and 100% ethanol, and a new hybridization chamber was secured to the slide. The previous cycle was stripped using 100% formamide (Thermo AM9342), which was applied fresh each minute for 5 min, then washed with PBST. Barcode labelling was conducted using two rounds of hybridization: first, with an adapter probe pool (AP mixes AP1–AP6, in subsequent cycles), then a sequencing pool (SP mix with DAPI, customized with Atto 425 in place of Alexa Fluor 750), each for 1 h at 37 °C with PBST washes in between and after. The section was dehydrated, the chamber removed and the slide mounted and imaged as described above. This process was repeated another five times to generate the full dataset of seven cycles (anchor and six barcode bits).

Derivation and maintenance of fetal uterine organoids

Fetal uterine organoids were derived from 12 PCW (Hrv276-ORG) and 17 PCW (Hrv277-ORG) fetal reproductive tract samples, (developing uterus, cervix and vagina) following tissue dissociation as described above. The single-cell suspensions were washed in Advanced DMEM/F12 (Gibco, 12634-010), centrifuged and the cell pellet resuspended in Matrigel (Corning, 356231) at around a 1:3 ratio (pellet volume to Matrigel volume). The organoids were cultured as previously described[68], forming in 25 µl Matrigel domes in 48-well tissue treated plates covered by 250 µl basal endometrial organoid medium (Advanced DMEM/F12 (Gibco, 12634-010), 1% GlutaMAX (Gibco, 35050061), 1% insulin–transferrin–selenium (ITS-G) (Gibco, 41400045), 100 µg ml–1 primocin (Invivogen, ant-pm-1), 1× B27-vitamin A (Life Technologies, 12587010), 1× N2 (Life Technologies, 17502048), 1.25 mM N-acetyl-l-cysteine (Sigma-Aldrich, A9165-5G), 2 mM nicotinamide (Sigma, N0636-100G), 2 ng ml–1 recombinant human FGF-basic (154 amino acids) (Peprotech, 100-18B), 500 ng ml–1 recombinant human R-spondin-1 (R&D Systems, 4645-RS-01M/CF), 10 µM SB202190 (p38i) (StemCell Technologies, 72632), 500 nM A83-01 (Tocris, 2939), 50 ng ml–1 recombinant human EGF (Peprotech, AF-100-15), 10 ng ml–1 recombinant human FGF-10 (Peprotech, 100-26) and 100 ng ml–1 recombinant human Noggin (Peprotech, 120-10 C))[68]. The medium was supplemented with 10 µM of the ROCK inhibitor Y-27632 (Millipore, 688000) for the first 2 days of organoid line establishment, with full medium changes every 2–3 days.

All organoid lines were split and passaged approximately every 5–7 days depending on their size and density. TrypLE Express Enzyme (Gibco, 12604013) was added to each well, and domes were detached with either a 1,000 µl tip or cell scraper before being transferred to a 15 ml Falcon tube. The organoids were dissociated into cell clumps by forcefully pipetting the solution 15–30 times using a 1,000 µl tip, followed by incubation at 37 °C for 6–8 min. Advanced DMEM/F12 was added at 1:1 ratio to quench TrypLE Express Enzyme and pipetted up and down 10 more times with the 1,000 µl tip. Cell suspensions were centrifuged at 800g for 2 min at 4 °C. The supernatant was removed as close to the pellet as possible. Next, 30 µl cold Matrigel per desired dome were added and the pellet was slowly resuspended to evenly distribute the cells. A volume of 30 µl domes was seeded into 6-well, 12-well or 24-well tissue culture-treated plates depending on whether the organoids were being expanded or set-up for drug treatment. The domes were placed in an incubator for 10 min at 37 °C, followed by the addition of basal endometrial organoid medium supplemented with 10 µM Y-27632.

Treatment of fetal uterine organoids with BPA or BPP

For all drug treatment experiments, organoids were passaged 48 h before addition of the compound. Organoids were plated in 30 µl domes as described above in two technical replicates, each containing two organoid domes. For testing the effect of endocrine disruptors on fetal reproductive organoids, Hrv276-ORG and Hrv277-ORG lines were treated with either 10 μM of BPA (Sigma, 239658) or 100 μM BBP (Sigma, 308501), with controls receiving the same volume of DMSO as the compound administered. For the endocrine-disrupter experiments BPA, BBP or DMSO were added to basal endometrial organoid medium (as described above), but with phenol-red-free DMEM/F12 as the base medium (Merck, D6434). Organoids were treated with BPA or BBP for a total of 96 or 144 h, with full medium change every 48 h. After 96 or 144 h of drug treatment, organoids were dissociated into a single-cell suspension. In brief, organoids were collected and washed in ice-cold phenol-red-free DMEM/F12 (BPA, BBP or DMSO control) and centrifuged at 600g for 6 min at 4 °C. The supernatant was removed and replaced with TrypLE Express Enzyme at a ratio of 500 µl per 30 µl dome, and pipette-mixed with a p1,000 tip for 30 times. Organoid suspensions were incubated at 37 °C for 15–25 min, with manual shaking every 2 min. Cells were checked at 15 min then every 5 min until an adequate single-cell suspension was achieved of about 70% single cells. Once the cells were sufficiently digested, TrypLE was quenched with phenol-red-free DMEM/F12. Cells were re-centrifuged and medium was aspirated to leave around 50 µl of medium. The cell suspensions were then vigorously pipette-mixed with a p20 tip 30–60 times. To this suspension, 200 μl of 1% PBS–BSA was added, thoroughly mixed and passed through a 70 μm filter. Live cells were counted using Trypan blue. The cells were loaded into a 10x Genomics Chromium chip as described in the Chromium Next GEM Single Cell 5′ v2 (DUAL) kit.

Immunofluorescence of fetal uterine organoids

Fetal uterine organoids were grown and treated as described above in µ-Slide 8 Wells (ibidi, 80801). Organoids were fixed in 4% paraformaldehyde for 45 min at room temperature and washed several times in PBS. Cells were permeabilized and blocked for 2 h in 2% Triton-X + 5% FBS in PBST. Organoids were washed in PBS before incubation with primary antibodies. Antibodies were incubated in an antibody dilution buffer (0.25% Triton-X + 1% FBS in PBST) at 4 °C overnight. Organoids were stained with TRITC-conjugated phalloidin (Thermo Fisher Scientific, R37112, dilution according to the manufacturer’s instructions), Alexa 488-conjugated ZO-1 (Invitrogen, 339188; 1:200 dilution) and APC-conjugated EpCAM (BD biosciences, 347200; 1:200 dilution). Organoids were washed 3 times for 45 min each in PBS and then mounted in ibidi mounting medium (ibidi, 50011).

Organoids were imaged with a Perkin Elmer Opera Phenix High-Content Screening system in confocal mode with 10-μm z step size, using a ×20 (NA 0.16, 0.299 μm pixel–1) water-immersion objective. The following channels were use: DAPI, ex. of 375 nm and em. of 435–480 nm); Alexa 488, ex. of 488 nm and em. of 500–550 nm; TRITC, ex. of 561 nm and em. of 570–630 nm; and APC, ex. of 640 nm and em. of 650–760 nm.

Analysis of scRNA-seq data

Per-library analyses

For each sequenced scRNA-seq library, we performed read alignment to the human reference genome (GRCh38 2020-A), and mRNA quantification and initial quality control using STARsolo[69] with default parameters. Ambient RNA contamination was inferred and subtracted from the original expression matrix using the deep generative model CellBender[70]. For multiplexed libraries, Souporcell[71] was then applied to deconvolve the genotypes and to assign cells to their respective donors. Owing to the scarcity of human cell-type markers, each library was first analysed independently before integrating them to have a way of formally evaluating the quality of the integration. In brief, we used Scrublet[72] for cell-doublet calling with a two-step diffusion doublet identification, as previously described[73]. Genes expressed by fewer than 3 cells were excluded, as were cells expressing fewer than 1,500 genes, more than 20% mitochondrial genes or with more than 40% of the scrublet score.

After converting the expression space to log [CPM/100 + 1], the anndata object was transposed to the gene space to identify cell cycling genes in a data-driven manner, as previously described73,74. Principal component analysis, neighbour identification and partition-based Leiden clustering[75] were performed on the gene space, and then the members of the gene cluster, including known cycling genes (CDK1, MKI67, CCNB2 and PCNA), were flagged as the data-derived cell cycling genes and discarded in the downstream analysis. Back in the cell space, we identified highly variable genes, performed principal component analysis, computed the neighbourhood graph, Leiden clustering[75] and UMAP[76] for visualization in two dimensions. The per-library computational analysis workflow described so far was wrapped in a Nextflow[77] pipeline with two processes to enable parallelization and reproducibility.

To identify genes characteristic of each cluster, we performed term frequency–inverse document frequency, a method borrowed from natural-language processing that reflects how important a word (gene) is to a document (cluster) in a corpus (dataset), as implemented in the R library SoupX[78]. Annotations were only finalized when analysing spatially resolved transcriptomics data (both 10x Visium and ISS). A detailed explanation of the cell types identified alongside their marker genes is provided in Supplementary Note 1.

Integration of scRNA-seq libraries

After having annotated each sample separately and realizing the significant differences in cell-type composition across samples, we generated three integrated views that best preserved the biological heterogeneity of this system: ≤10 PCW male and female samples together (when the sexually unmatched ducts are still present and the first signs of regionalization of the ducts appear); >10 PCW male samples; >10 PCW female samples. The variational autoencoder-based method scVI[79], trained on the 7,500 most highly variable genes and with 30 latent variables and 2 hidden layers, was then applied to mitigate batch effects across donors in each of the three views. To evaluate the integrated manifold, we then overlaid the per-sample annotations and confirmed that the biological signal was preserved while correcting for donor-specific effects. Moreover, for a more quantitative evaluation of the integration results, we computed the Shannon entropy per Leiden cluster of the per-sample cell-type annotations as well as the donor and sex labels, following a previously described method[80]. Clusters with a donor label entropy equal to 0 (that is, donor-specific clusters) were removed from further analysis. Each cluster was then annotated on the basis of majority voting (≥40%) of the cell-type labels. Clusters showing high cell-type label entropy (that is, <40% of cells in the cluster having the same cell-type label) were further investigated and annotated according to their most expressed term frequency–inverse document frequency markers.

Finally, for visualization purposes only, all libraries were integrated with scVI (7,500 highly variable genes, 60 latent variables), and cell-type labels were overlaid from the per-view annotations. Variations in cell-type proportions across developmental time were visualized with an area plot.

Per-organ analyses

The cellular and molecular features of the establishment of sexual dimorphism in each organ of the developing human reproductive tract were investigated by performing subanalyses on the following relevant cell types:

- All cell types in male and female external genitalia during the MPW (8–14 PCW)

- Coelomic epithelium, Müllerian duct epithelium and mesenchyme during the period of Müllerian duct emergence (6–8 PCW)

- Mesenchymal and epithelial cells from the differentiating Müllerian and Wolffian ducts (>10 PCW), resulting in four subanalyses (Müllerian-derived mesenchyme, Müllerian-derived epithelium, Wolffian-derived mesenchyme and Wolffian-derived epithelium).

In all these per-organ analyses, preprocessing was carried out analogously to what is described in the per-library analysis section, and Harmony[81] (theta = 0) was used to correct for batch effects.

Human–mouse comparison of external genitalia

We leveraged the availability of an annotated scRNA-seq dataset of mouse genital tubercle (comprising two male and female samples for each of three developmental stages: embryonic day 14.5 (E14.5), E16.6 and and E18.5)[23] to identify the transcriptional regulators underpinning sexual dimorphism in the corpus spongiosum in each species. To define a shared feature space, we first took the set of orthologous genes expressed in at least ten cells in both species. From the set of common orthologous genes, we then computed the top 4,000 highly variable genes in each species and identified their intersection (around 2,700 genes). Using the intersection of highly variable genes, batch effects owing to different donors or mice were corrected by means of Harmony[81], and a Milo[82] object was computed on each species’ batch-corrected embedding. Each neighbourhood from a species was matched to its k (k = 30) closest neighbourhoods in the other species (in both the mouse-to-human and human-to-mouse directions). The final matches were formed by identifying the mutually nearest pairs of neighbourhoods that appeared in both directions[83]. Each matched neighbourhood was then annotated by majority voting of the labels of the cells in the neighbourhood. Matching across cell-type labels was visualized with an alluvial plot. We then considered the unique combination of matching cell-type labels as the harmonized cell-type annotations across species and focused on the mouse cells that matched the human corpus spongiosum label for differential expression analysis between male and female individuals.

Differential expression analysis in the human and mouse genital tubercle

We conducted differential expression analysis between male and female samples in the human and mouse corpus spongiosum and human urethral epithelium using PyDESeq2[84]. Only samples between 8 and 14 PCW (the so-called MPW) were included in the analysis. Genes mapping to the Y chromosome were excluded from differential expression testing. Results of the differential expression analysis were visualized with a Volcano plot showing the genes with |log[FC > 1]| and adjusted P < 0.05.

Cell–cell interaction analysis from scRNA-seq data

Sexually dimorphic cell–cell interactions between the mesenchymal corpus spongiosum and the urethral epithelium during the MPW (8–14 PCW) were inferred using CellphoneDB (v.5.1)[85] using method 3 (differential expression analysis). After splitting both cell clusters into male and female, differentially expressed genes for each cell type–sex combination were identified using the FindAllMarkers() function in Seurat[86], with only positive markers being considered. Only genes expressed in at least 10% of the cells in a cell type–sex combination were considered for this analysis. The search for interaction was restricted to cell types in the same sex (which was passed as input to the method in the ‘microenvironments’ file).

Wolffian-to-Müllerian duct signalling during the developmental time window of Müllerian duct emergence (6–8 PCW) was also explored with CellphoneDB (v.5.1)[85] using method 3. Differentially expressed genes per cell type (that is, Wolffian mesenchyme, Wolffian epithelium, Müllerian mesenchyme and Müllerian epithelium) were similarly computed using the FindAllMarkers() function in Seurat[86] (with only positive markers being considered), and only genes expressed in at least 10% of the cells in a cell type were considered for this analysis. All cell types belong to the same ‘microenvironment’.

Trajectory inference and differential expression along trajectories

The trajectory inference method Slingshot[87] was applied to recover the lineages originating from the coelomic epithelium during Müllerian duct emergence (6–8 PCW). The pseudotime ordering of the cells along with the weighted assignment of each cell to the three lineages were then used as input for TradeSeq[88] to extract genes that were differentially expressed along a lineage or between lineages with the associationTest() function.

Inference of clinically approved drugs potentially disrupting reproductive epithelia

Using drug2cell[89], we derived drug scores for compounds in the CHEMBL database by averaging the expression levels of target genes in each cell on the three views of the dataset independently. We then performed a Wilcoxon rank-sum test to identify significant differences in drug scores between each reproductive epithelium (that is, Müllerian duct epithelium, Wolffian duct epithelium, urogenital sinus epithelium and urethral epithelium in <10 PCW female and male embryos; fallopian tube epithelium, uterocervix epithelium, upper vagina epithelium, vaginal plate epithelium and urethral epithelium in >10 PCW female fetuses; epididymis epithelium, vas deferens epithelium, prostate epithelium and urethral epithelium in >10 PCW fetuses) and all other reproductive-specific cells in the dataset. Results were filtered based on adjusted P values (<0.01), log fold changes (>2) and rank scores to select the most significant drugs associated with the target cell type. An additional filtering step was performed to exclude drugs for which target genes were not specific to the target cell type and required that the targets were expressed in at least 10% of cells in the target cell type.

In vivo–in vitro comparison

In vivo epithelial cells from the uterovaginal canal were used to train a CellTypist[90] model with the top 200 genes per cell type (as ranked by their absolute regression coefficients associated with each cell type) as features. The trained model was then used to predict the cell-type labels in the untreated organoids, and the predicted probabilities were visualized using a dot plot.

To assess changes in cellular abundance following perturbation, Milo[82] was used to construct a k-nearest neighbour (kNN) graph on embeddings integrated using Harmony[81] for untreated versus BPA-treated and untreated versus BBP-treated organoids. Differential abundance testing was performed by assigning cells to neighbourhoods and applying a generalized linear model to compare the proportion of BPA-treated or BBP-treated cells relative to untreated controls, accounting for differences in cellular sampling. Differential expression analysis was conducted by comparing transcriptomic profiles of cells in differentially abundant neighbourhoods to all remaining neighbourhoods in each dataset. Genes with |log[FC]| > 0.5 and adjusted P < 0.05 were considered significant, and results were visualized using a volcano plot.

Gene set enrichment analysis was performed using EnrichR[91] to identify biological pathways associated with BPA-induced or BBP-induced gene expression changes. Upregulated genes were compared against the MSigDB Hallmark 2020[92] gene sets, and significantly enriched pathways (adjusted P < 0.05) were visualized using a bar plot.

Analysis of scATAC–seq data

Per-library analyses

scATAC–seq libraries were processed (read filtering, alignment, barcode counting and cell calling) with 10x Genomics Cell Ranger ATAC pipeline v.2 using the pre-built 10x GRCh38 genome (v.3.1.0) as a reference. Similar to RNA, we analysed each ATAC library independently until cell-type annotation to evaluate the quality of the subsequent integrations using the ArchR framework[93]. Cells with fewer than 3,500 fragments or a minimum transcription start site enrichment of 10 were filtered out, as were cells deemed as doublets. Dimensionality reduction on the tile matrix was performed with Latent Semantic Indexing, and the low-dimensional variables were then used to compute the neighbourhood graph, partition-based Leiden clustering[75] and UMAP visualization[76].

To annotate cells, we used canonical correlation analysis to match the gene activity score matrix of each scATAC–seq library with the integrated gene-expression space of the corresponding view (for example, if the scATAC–seq library came from a female sample older than 10 PCW, we used the >10 PCW female-integrated scRNA-seq dataset). For optimal reproducibility and parallelization, the per-sample scATAC–seq analyses were also wrapped in a Nextflow script.

Integration of scATAC–seq libraries

The individually annotated samples were then integrated by re-computing latent semantic indexing on the concatenated tile matrices and using mutual nearest neighbours[83] to correct for batch effects. Mutual nearest neighbours has proven to be highly effective for scATAC–seq (for which we do not have as many samples as scRNA–seq), as it enables us to specify the order of integration and hence mitigate batch effects in a more biologically informed fashion.

Integrative analysis of scRNA-seq and scATAC–seq data

Combining information from transcriptomics and chromatin accessibility data enables the prioritization of transcription factors that are likely to be active in each cell type, along with the identification of putative regulatory relationships between transcription factors and target genes. We sought to do this when investigating the regulatory landscape underlying the process of urethral canalization in males, Müllerian duct emergence from the coelomic epithelium, and patterning of the mesenchyme and epithelium during Müllerian and Wolffian duct differentiation.

Using the fragment files and cell annotations obtained through label transfer from the scRNA-seq data, pseudobulks were generated per cell type. Cell-type-specific peaks were called using MACS2[94] as implemented in the pycistopic workflow[95]. A set of consensus peaks was derived through iterative overlapping, and the resulting matrix of cells by consensus peaks was used as input to topic modelling with latent dirichlet allocation. Latent dirichlet allocation models were selected according to the metrics provided in pycistopic. Harmony[81] was used to correct for the donor effect. The manifold was clustered with the Leiden algorithm[75] and differentially accessible regions were computed between the Leiden clusters. The union of the differentially accessible regions and the regions contained in topics (obtained by binarizing the region-topic probabilities) served as the set of regions used to find transcription factor-binding motifs with pycistarget[95].

To infer enhancer-driven gene regulatory networks, scRNA-seq and scATAC–seq data were eventually combined into a pseudo-multiome dataset using SCENIC+[95]. In essence, SCENIC+ generates metacells that contain cells from both data modalities by randomly sampling cells of the same cell-type label. Within a 150-kb region around each gene, gradient-boosting machines and correlation analysis were used to infer the relationships between enhancers and genes, as well as between transcription factors and genes. This approach enabled the identification of enhancers that are associated with the regulation of specific target genes and the inference of transcription factors that potentially contribute to the regulation of these genes.

Analysis of 10x Visium data

Per-library analyses

Visium data consist of FASTQ sequencing files and a bright-field microscopy image stained with H&E per capture area. The Space Ranger (v.2.0.0) software provided by 10x Genomics was used to align the barcoded spot pattern to the H&E tissue image and to differentiate tissue from background. The resulting spot-by-transcript abundance matrix was analysed using the package squidpy.

Estimation of cell-type abundances per Visium spot using matched scRNA-seq data

To deconvolve the transcriptional signal coming from each Visium spot into an estimated abundance of each cell type present in the >10 PCW female and male views of the scRNA-seq dataset, we applied the Bayesian model cell2location[96]. In brief, cell2location first estimates reference cell-type signatures from the dissociated scRNA-seq data using negative binomial regression. It then decomposes the spatially resolved Visium RNA count matrices into the reference cell-type signatures.

Annotation of anatomical and histological features

We used the microscopy H&E images to annotate anatomical structures and histological features independently of gene expression. Using the package TissueTag[33], anatomical features were manually labelled, whereas histological features were inferred using a random forest classifier trained on a few manually labelled points. Estimated cell-type abundances per spot in each anatomical structure (or histological feature) were then averaged, and an enrichment score of cell type per anatomical structure (or histological feature) was computed.

Scoring of the fetal fallopian tube decreasing signature in adult data

To evaluate whether the rostrocaudal decreasing pattern of expression of the 15 genes identified in the fetal fallopian tube was preserved into adulthood, we generated 10x Visium spatial transcriptomics data from three areas of an adult fallopian tube (fimbriae, ampulla and isthmus) and scored the epithelial spots in each capture area for the average expression of this gene signature. In brief, we used the scanpy[97] function sc.tl.score_genes() to compute the score and then performed the Jonckheere’s trend test[98] with the alternative hypothesis ‘decreasing’ (2000 permutations) to quantify the significance of the trend. The same approach was used to test the rostrocaudal increasing pattern of expression.

Analysis of ISS data

Probe selection

To locate cell types and states as they appear and disappear during development of the reproductive tract (especially during early stages of development, which cannot be assayed with spot-based spatial transcriptomics), we designed a panel of 171 genes. The most unique marker genes per cell type were selected using term frequency–inverse document frequency, and the resulting panel was evaluated for completeness using geneBasis[99]. To evaluate completeness at the cell level, geneBasis checks for preservation of a cell’s neighbourhood in the kNN graph built with all the highly variable genes and the kNN graph constructed with the gene panel by comparing the distance between each cell and these two sets of nearest neighbours. geneBasis was applied to evaluate the same gene panel on the three views of the scRNA-seq data (≤10 PCW male and female embryos, >10 PCW male fetuses and >10 PCW female fetuses) separately. However, as thoracic HOX genes are not cited in the literature as relevant to the differentiation of the reproductive tract, these were not included in the original gene panel. We therefore decided to swap four of the genes in the original panels (DPP4, DPP6, CRISP3 and DPP10) for four HOX genes (HOXC4, HOXC6, HOXA7 and HOXC10) and ran ISS on some samples with this resulting panel instead (Supplementary Table 3).

Preprocessing

ISS data were preprocessed with a computational pipeline implemented by T.L. in Nextflow. In brief, the first step of the pipeline involved integrating the tiles along the z axis using maximal projection for each channel and then stitching them together along the remaining x and y spatial axes. Because the tissue moves slightly between sequencing rounds, image registration was required to correct for spatial misalignment of the fluorescent spots. This registration was achieved using nonlinear optical flow to align the small, Gaussian-like spots in the images. Once the images were stitched and corrected for misalignment, we used the DAPI signal to segment nuclei with CellPose[100]. For computational efficiency, images were first sliced into smaller 10,000 × 10,000 pixels patches. Even though there was no membrane protein staining, cell segmentation could be obtained by expanding the segmented nuclei by about 10 or 15 pixels to mimic the cytoplasm. Finally, spots appearing across registered coding channels were detected and their intensities were extracted. The intensity values for each spot over imaging cycles were then decoded on the basis of the collection of barcodes in the codebook provided by CARTANA with the PoSTcode algorithm[101]. To minimize false positives, PoSTcode inflates the codebook with an additional background barcode. The stacks of image values, representing the intensities across different channels and imaging cycles, were modelled using a matrix-variate Gaussian mixture model, assuming correlations between channels and imaging cycles. Decoded spots were ultimately assigned to segmented cells, which resulted in a gene-expression matrix used for downstream analyses.

Cell-type annotation of ISS data based on matched scRNA-seq data

Although the ISS gene panel was designed to maximize cell-type recovery, the throughput of the assay was still too limited to confidently assign cell-type identities solely on the basis of examining the measured gene expression. We therefore leveraged the full transcriptome resolution of the scRNA-seq dataset to increase the confidence in cell-type assignment using an approach based on kNN graphs implemented in the iss-patcher library[102]. Using this approach, we annotated the cells of ISS samples in a per-view fashion (for example, ISS samples for ≤10 PCW were annotated using the ≤10 PCW scRNA-seq reference). In early (≤10 PCW) samples, ISS cells for which the anatomical annotation was ‘gonad’ were excluded from the label-transfer algorithm because there are no gonad cells in the scRNA-seq reference data (by design).

Annotation of anatomical structures

The experimental workflow of ISS currently does not involve the acquisition of a H&E image of the sample. We overcame the limitation of not having a H&E image by generating a ‘virtual’ RGB image from the gene-expression profiles of three highly expressed markers (one per R, G and B channel). Major anatomical landmarks were therefore annotated on the virtual RGB image based on the morphology and the H&E image of the consecutive section. Histological landmarks were not annotated, as they necessitate the texture information captured by H&E staining only. By combining the information about the cell-type label of each ISS cell and its annotated anatomical location in the tissue architecture, we then computed an enrichment score (z score) of each cell type in each anatomical location (separately per view of the data). Such z score enrichment was computed using only the ISS cells with a cell-type label fraction above 0.8 (meaning that 80% of the scRNA-seq neighbours are annotated with the same cell-type label). The per-view enrichment scores were visualized using a heatmap.

Computational representation of the Müllerian and Wolffian rostrocaudal axes

Detailed information about the rationale and implementation for the computational representation of the Müllerian and Wolffian rostrocaudal axes can be found in Supplementary Notes 2, 3 and 4; here, we provide a summary.

To study the spatial gene-expression patterns along the developing female reproductive tract, we constructed the Müllerian rostrocaudal axis by measuring distances from key anatomical landmarks in spatially resolved transcriptomics data (10x Visium Cytassist and ISS), as described in the OrganAxis framework[33]. The axis spans from the fallopian fimbriae to the Müllerian vagina–vaginal plate junction, thereby capturing the differentiation of the Müllerian ducts. In cases when the full length of the uterovaginal canal could not be captured in a single section owing to the limitations of the 10x Visium Cytassist platform’s capture area, consecutive tissue sections were computationally stitched together[103]. This stitching involved manually overlapping consecutive sections using the image processing software Fiji (https://imagej.net/plugins/trakem2/) and applying affine transformations to align the sections, which were then concatenated into a single dataset. The resulting Müllerian rostrocaudal axis was normalized and rescaled, which enabled consistent comparison of gene-expression patterns across different samples of the female reproductive tract.

To extend our spatial analysis to single-cell resolution, we projected the Müllerian rostrocaudal axis onto scRNA-seq data by leveraging the single-cell resolution provided by ISS technology. We restricted our analysis to mesenchymal and epithelial compartments, which are key to understanding the development of the female reproductive tract. Using a modified kNN approach implemented in the iss-patcher[102] library, each cell in the scRNA-seq dataset was assigned a position along the Müllerian rostrocaudal axis based on its proximity to cells in the ISS data. We then used the TradeSeq[88] framework to model gene expression along the measured (10x Visium Cytassist) and imputed (scRNA-seq) Müllerian rostrocaudal axis, treating the axis analogously to pseudotime. Genes that showed significant changes in expression along the axis were identified using stringent criteria (P < 0.001, log[FC] > 0.5), and we prioritized those with specific expression in mesenchymal or epithelial cells.

In parallel, to investigate gene-expression patterns along the male reproductive tract, we constructed the Wolffian rostrocaudal axis, spanning the length of the epididymis and the initial segment of the vas deferens. This axis was derived using data exclusively from 10x Visium Cytassist, as we lacked sufficient ISS male samples for robust imputation of the axis onto scRNA-seq. The axis was similarly defined by calculating the normalized distance between the efferent ductules and the vas deferens. It was then rescaled to maintain consistency with the Müllerian rostrocaudal axis to facilitate comparative analyses. We used the TradeSeq[88] framework to model gene expression continuously along the Wolffian rostrocaudal axis and used the same significance thresholds as for the Müllerian rostrocaudal axis to prioritize genes for which expression changes along the differentiating Wolffian ducts.

Prioritized spatially variable mesenchymal and epithelial genes in the imputed Müllerian rostrocaudal axis and measured Wolffian rostrocaudal axis were then used to filter the transcription factors and cell–cell communication events that probably drive Müllerian and Wolffian regionalization during fetal development.

Reporting summary

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

Data availability

All raw and processed sequencing and imaging data generated in this study have been deposited in public repositories. Sequencing data are available at ArrayExpress under the following accessions: E-MTAB-15475 for scRNA-seq; E-MTAB-15457 for scRNA-seq from organoids; E-MTAB-15479 for scATAC–seq; and E-MTAB-15471 for 10x Visium spatial transcriptomics. Imaging data, including ISS, RNAscope, immunofluorescence and H&E, are available through the BioImage Archive (accession S-BIAD2224). All datasets are publicly accessible. scRNA-seq data used to generate the figures in this paper can also be accessed and downloaded via our web portal: www.reproductivecellatlas.org. Publicly available datasets used in this study were downloaded from ref. [23] (Gene Expression Omnibus identifier GSE174712). Source data are provided with this paper.

Code availability

The code used to perform the analyses presented in the paper can be found at GitHub (https://github.com/ventolab/Human-ReproductiveTract-Development-Atlas).

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Acknowledgements

This research was funded by Wellcome Trust Grant 220540/Z/20/A and by the European Union’s Horizon 2020 research and innovation programme HUGODECA under grant agreement number 874741. The publication is part of the Human Cell Atlas (www.humancellatlas.org/publications/). The human embryonic and fetal material was provided by the Joint MRC–Wellcome Trust (grant number MR/R006237/1 and MR/X008304/1) Human Developmental Biology Resource (HDBR, http://www.hdbr.org). We thank staff at the Spatial Genomics Platform Core Facility and the Sanger Core Sequencing pipeline for support with sample processing and sequencing library preparation; A. García (https://www.bio-graphics.es/) for his help with conceptualizing and making the illustrations that are part of this paper; K. Hua for his advice on statistical testing; A. Maartens for proofreading and providing advice on the narrative of the paper; and the IBSA Foundation for scientific research for supporting V.L. and E.R.R.-M. with their yearly fellowship for early career scientists.

Author information

Author notes

1. These authors contributed equally: Valentina Lorenzi, Cecilia Icoresi-Mazzeo

Authors and Affiliations

1. Wellcome Sanger Institute, Cambridge, UK

Valentina Lorenzi, Cecilia Icoresi-Mazzeo, Charlotte Cassie, Nadav Yayon, Elias R. Ruiz-Morales, Carmen Sancho-Serra, Ryan Colligan, Frederick C. K. Wong, Magda Marečková, Elizabeth Tuck, Kenny Roberts, Tong Li, James Ashcroft, Batuhan Cakir, Simon Murray, Yong Gu, Alexander V. Predeus, Martin Prete, Iva Kelava, Luz Garcia-Alonso, John C. Marioni & Roser Vento-Tormo

2. European Bioinformatics Institute–European Molecular Biology Laboratory, Cambridge, UK

Valentina Lorenzi, Nadav Yayon, Marc-Antoine Jacques & John C. Marioni

3. Wellcome–MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK

Nadav Yayon, Marc-Antoine Jacques, Xiaoling He, Roger Barker & Roser Vento-Tormo

4. John van Geest Centre for Brain Repair, Cambridge University, Cambridge, UK

Xiaoling He & Roger Barker

5. HDBR, Great Ormond Street Institute of Child Health, University College London, London, UK

Berta Crespo

Contributions

V.L., L.G.-A., J.C.M. and R.V.-T. conceived and designed the experiments and analyses. V.L. analysed the data with contributions from N.Y., T.L., M.-A.J., B. Cakir, S.M., Y.G., A.V.P. and M.P. C.I.-M., C.S.-S. and R.C. performed sample processing. C.I.-M. and E.T. performed the imaging experiments with help from K.R. C.C. and R.C. derived the organoids and conducted the perturbation experiment with help from E.R.R.-M., M.M. and I.K. X.H. and B. Crespo performed sample dissections. R.B. provided samples. J.A. provided data. V.L., L.G.-A., J.C.M. and R.V.-T. interpreted the data. V.L., L.G.-A., J.C.M. and R.V.-T. wrote the manuscript with help from C.I-M., M.M. and F.C.K.W. L.G.-A., J.C.M. and R.V.-T. supervised the work. All authors read and approved the manuscript.

Corresponding authors

Correspondence to Luz Garcia-Alonso, John C. Marioni or Roser Vento-Tormo.

Ethics declarations

Competing interests

J.C.M. has been an employee of Genentech since September 2022 and M.M. has been an employee at Emm since January 2024. These affiliations are not related to the work presented in this paper. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature thanks Martin Estermann, Dominic Gruen, Vincent Harley 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 Computational workflows and spatial mapping of cell types in early (<= 10 PCW) female and male reproductive tract samples.

a, Schematic representation of the computational workflow used to analyse scRNA-seq data. b, Schematic representation of the computational workflow used to analyse scATAC-seq data. c, Schematic representation of the computational workflow used to analyse 10x Visium data and integrate it with scRNA-seq data. d, Schematic representation of the computational workflow used to analyse In Situ Sequencing (ISS) data and integrate it with scRNA-seq data. e, Confusion matrix evaluating the completeness of the gene panel selected for ISS at the cell type level for early (<= 10 PCW) male and female samples in our scRNA-seq data. f, (top left) Hematoxylin and eosin (H&E) stained image of a representative Carnegie stage (CS) 19 sample profiled with ISS alongside (top right) annotations of the anatomical structures and (bottom left) inferred cell type labels for selected cell types from the early (<= 10 PCW) scRNA-seq dataset in the ISS slide (n = 3 biologically independent samples). Scale bar: 500 μm. g, (left) H&E stained image of a representative 10 PCW male sample profiled with ISS alongside (right) annotations of the anatomical structures (n = 3 biologically independent samples). Scale bar: 1000 μm. h, Heatmap showing the z-score enrichment of each epithelial, mesenchymal, smooth muscle, and ligament cell type annotated in early male and female scRNA-seq samples <= 10 PCW (x-axis) in ISS cells corresponding to each anatomical annotation (y-axis). AG: adrenal gland; G: gonad; K: kidney; UGS: urogenital sinus.

Extended Data Fig. 2 Spatial mapping of cell types in late (>10 PCW) female reproductive tract samples.

a, Confusion matrix evaluating the completeness of the gene panel selected for ISS at the cell type level for late (>10 PCW) female samples in our scRNA-seq data. b, (left) Representative section of a 17 PCW female sample profiled with In Situ Sequencing (ISS) showing the annotations of the anatomical structures and (right) the inferred cell type labels for mesenchymal cell types from the scRNA-seq dataset in the ISS slide (n = 4 biologically independent samples). Scale bar: 2 mm. c, Heatmap showing the combined z-score enrichment of each epithelial, mesenchymal, smooth muscle and ligament cell type annotated in female scRNA-seq samples > 10 PCW (x-axis) in 10x Visium spots and ISS cells corresponding to each anatomical annotation (y-axis). d, Heatmap showing the z-score enrichment of each epithelial, mesenchymal, smooth muscle and ligament cell type annotated in female scRNA-seq samples >10 PCW (x-axis) in 10x Visium spots corresponding to each histological annotation (y-axis). e, (left) Hematoxylin and eosin (H&E) stained image of a representative 15 PCW female fallopian tube sample profiled with ISS alongside (right) the inferred cell type labels for epithelial cell types from the scRNA-seq dataset in the ISS slide (n = 3 biologically independent samples). Scale bar: 500 μm. f, (left) H&E stained image of a representative 13 PCW female external genitalia sample profiled with ISS alongside (right) the inferred cell type labels for selected cell types from the scRNA-seq dataset in the ISS slide (n = 1 biologically independent sample). Scale bar: 500 μm. g, (left) H&E stained image of a representative 15 PCW female uterovaginal canal sample profiled with ISS alongside (right) the inferred cell type labels for epithelial cell types from the scRNA-seq dataset in the ISS slide (n = 3 biologically independent samples). Scale bar: 500 μm. h, (left) H&E stained image of a representative 19 PCW female sample profiled with 10x Visium. (middle) Annotations of the histological compartments and (right) spatial mapping of fallopian tube smooth muscle and ligament cells from the scRNA-seq dataset onto the corresponding 10x Visium slide. Estimated cell type abundance (colour intensity) in each 10x Visium spot is shown over the H&E image (n = 6 biologically independent samples). Scale bar: 500 μm. i, (left) H&E stained image of a representative 17 PCW female sample profiled with 10x Visium alongside (right) spatial mapping of fallopian tube, uterine, cervical and upper vagina mesenchymal cells from the scRNA-seq dataset onto the corresponding 10x Visium slide. Estimated cell type abundance (colour intensity) in each 10x Visium spot is shown over the H&E image (n = 6 biologically independent samples). Scale bar: 500 μm.

Extended Data Fig. 3 Spatial mapping of cell types in late (>10 PCW) male reproductive tract samples.

a, Confusion matrix evaluating the completeness of the gene panel selected for ISS at the cell type level for late (>10 PCW) male samples in our scRNA-seq data. b, (left) Hematoxylin and eosin (H&E) stained image of a representative section of a 16 PCW male epididymis and vas deferens sample profiled with In Situ Sequencing (ISS) alongside (right) annotations of the anatomical structures (n = 1 biologically independent sample). Scale bar: 500 μm. c, Heatmap showing the combined z-score enrichment of each epithelial, mesenchymal, smooth muscle and ligament cell type annotated in male scRNA-seq samples > 10 PCW (x-axis) in 10x Visium spots and ISS cells corresponding to each anatomical annotation (y-axis). d, (left) H&E stained image of a representative 16 PCW male external genitalia sample profiled with ISS alongside (right) the inferred cell type labels for selected cell types from the scRNA-seq dataset in the ISS slide (n = 1 biologically independent sample). Scale bar: 1000 μm. e, (left) H&E stained image of a representative 14 PCW male epididymis and vas deferens sample profiled with 10x Visium alongside (right) spatial mapping of ciliated epithelial cells from the scRNA-seq dataset onto the corresponding 10x Visium slide. Estimated cell type abundance (colour intensity) in each 10x Visium spot is shown over the H&E image (n = 3 biologically independent samples). Scale bar: 500 μm. f, H&E stained image of a representative 20 PCW male epididymis and vas deferens sample profiled with 10x Visium alongside (right) spatial mapping of mesenchymal and ligament cells from the scRNA-seq dataset onto the corresponding 10x Visium slide. Estimated cell type abundance (colour intensity) in each 10x Visium spot is shown over the H&E image (n = 3 biologically independent samples). Scale bar: 500 μm. g, H&E stained image of a representative 21 PCW male external genitalia sample profiled with 10x Visium alongside (right) spatial mapping of selected cell types from the scRNA-seq dataset onto the corresponding 10x Visium slide. Estimated cell type abundance (colour intensity) in each 10x Visium spot is shown over the H&E image (n = 2 biologically independent samples). Scale bar: 500 μm.

Extended Data Fig. 4 Ontology, migration and regression of the Müllerian duct.

a, Dotplot showing the log-transformed, min-max normalised expression of Wolffian ligands and Müllerian receptors (y-axis) in each cell type (x-axis). Interacting ligands and receptors are connected with an arrow. b, Haematoxylin and eosin (H&E) stained image and high-resolution, large-area imaging of a representative section of a Carnegie Stage (CS) 23 sample with smFISH signal for KLK11 (green, coelomic epithelium), FGF20 (red, migrating Müllerian duct epithelium), GDNF (cyan, caudal migrating Müllerian duct epithelium), CALCA (yellow, caudal migrating Müllerian duct epithelium) (n = 3 biologically independent samples). Scale bar: 1000 μm. c, H&E image and ISS data of a representative section of a CS19 sample showing the measured expression of TMEM163 in the Müllerian duct mesenchyme (n = 3 biologically independent samples). Scale: 500 μm. d, High-resolution, large-area imaging of a representative section of a (left) CS22 male sample and (right) CS23 female sample with smFISH signal for WNT7A (red, Müllerian duct epithelium) and TMEM163 (green, migrating Müllerian duct mesenchyme) (n = 4 biologically independent samples, 2 males and 2 females). Scale bar: 200 μm (left panel), 500 μm (right panel). e, H&E image and ISS data of a representative section of a 10 PCW male sample showing the measured expression of WNT7A, WNT9B, SP7, WIF1, NOTUM, and NKD1 (n = 1 biologically independent sample). Scale bar: 1000 μm. f, High-resolution, large-area imaging of a representative section of a CS23 female sample with smFISH signal for WNT7A (red, Müllerian duct epithelium), SP7 (yellow, regressing Müllerian duct mesenchyme), WIF1 (cyan, regressing Müllerian duct mesenchyme), WNT9B (yellow, Wolffian duct epithelium), NOTUM (green, regressing Müllerian duct mesenchyme), NKD1 (cyan, regressing Müllerian duct mesenchyme) (n = 2 biologically independent samples). Scale bar: 500 μm. g, Batch corrected t-distributed Stochastic Neighbour Embedding (t-SNE) embedding of downsampled 6-8 PCW scATAC-seq cells (n = 1220) from the coelomic epithelium, Müllerian duct epithelium and Müllerian duct mesenchyme coloured by developmental stage (measured in PCW) and cell type. h, Heatmap showing the inferred regulon activity (from both scRNA-seq and scRNA-seq data 6-8 PCW samples) of the TFs (x-axis) involved in the formation of the Müllerian duct in each relevant cell type (y-axis). The colour scale is proportional to the expression of the TF while the size of the dot reflects the importance of the TF regulon in each cell type. i, Schematic illustration summarising the putative mechanisms underpinning the formation of the human Müllerian duct as found in our analysis. AG: adrenal gland; G: gonad, K: kidney; UGS: urogenital sinus. Illustrations in i created by A. García.

Extended Data Fig. 5 Mesenchymal patterning of the Müllerian and Wolffian ducts.

a, Schematic representation of the computational workflow used to construct the Müllerian and Wolffian rostro-caudal axes from 10x Visium and In Situ Sequencing (ISS) samples > 10 PCW. b, (left) Schematic illustration of the Wolffian rostro-caudal axis, which spans the developing epididymis and vas deferens and is computed from the haematoxylin and eosin (H&E) images of 10x Visium male samples > 10 PCW. (middle) H&E image of a representative 16 PCW male sample profiled with 10x Visium with annotated epididymis and vas deferens and with (right) Wolffian rostro-caudal axis values overlaid in each 10x Visium (n = 3 biologically independent samples). Scale bar: 500 μm. c, Schematic representation of the computational workflow used to impute the Müllerian rostro-caudal axis in scRNA-seq data from ISS female samples > 10 PCW based on a shared k-nearest neighbours embedding (Supplementary Note 3). d, Batch-corrected Uniform Manifold Approximation and Projection (UMAP) embedding of epithelial and mesenchymal cells (n = 58,938) in female scRNA-seq samples > 10 PCW coloured by (left) cell type and (right) imputed Müllerian rostro-caudal axis value. e, (left) Venn diagram showing the intersection of genes that are identified as spatially-variable along the measured (in 10x Visium) and imputed (in scRNA-seq) Müllerian rostro-caudal axes for mesenchymal spots. (right) Distribution of cosine similarities between the smoothing splines of the genes that are identified by both data modalities for mesenchymal spots/cells. f, Smoothed splines of HOX TFs along the measured Müllerian rostro-caudal axis in mesenchymal spots (10x Visium). g, Inferred regulon activity (from scRNA-seq/scATAC-seq data) of the HOX TFs (x-axis) in the differentiating Müllerian duct mesenchyme (y-axis). Colour reflects TF expression; dot size reflects regulon importance per cell type. h, (left) H&E image of a representative 10 PCW male sample profiled with ISS alongside (right) the measured expression of HOXA7 and HOXA9 in the Wolffian mesenchyme (n = 3 biologically independent samples). Scale bar: 1000 μm. i, H&E stained image and 10x Visium data of a representative section of a 14 PCW male sample showing the measured expression of HOXC5, HOXA7, HOXA9, HOXA10, HOXA11 (n = 3 biologically independent samples). Scale bar: 500 μm. j, Heatmap showing the min-max normalised expression measured in 10x Visium data of prioritised spatially variable mesenchymal TFs (x-axis) along the binned measured Wolffian rostro-caudal axis (y-axis). TFs marked with a dot are also identified by our scATAC/RNA-seq analysis (as reported in Extended Data Fig. 6d). k, High-resolution, large-area imaging of a representative section of a 17 PCW female sample with smFISH signal for HOXA7 (yellow, rostral fallopian tube mesenchyme), HOXA9 (red, caudal fallopian tube mesenchyme), HOXA10 (cyan, uterocervical mesenchyme) (n = 2 biologically independent samples, also shown in Fig. 3f). Dashed-lined boxes highlight fallopian tube regions magnified at the bottom of the panel. Scale bar: 500 μm (top panel) and 100um for figure zoom-ins. l, High-resolution, large-area imaging of a representative section of a 14 PCW male sample with smFISH signal for HOXA7 (yellow, rostral epididymis), HOXA9 (red, caudal epididymis), HOXA10 (cyan, vas deferens) (n = 2 biologically independent samples). Scale bar: 500 μm. m, Schematic illustration summarising the mesenchymal patterning by HOX code genes along the differentiating Müllerian and Wolffian ducts found by our analysis. Illustrations in b and m created by A. García.

Extended Data Fig. 6 Mesenchymal patterning and mesenchymal-epithelial interaction along the differentiating Müllerian and Wolffian ducts.

a, Batch corrected t-distributed Stochastic Neighbour Embedding (t-SNE) of downsampled Müllerian-derived mesenchymal cells from female samples > 10 PCW profiled with scATAC-seq (n = 5370 cells after downsampling) coloured by stage (measured in PCW) and cell type. b, Heatmap showing the inferred regulon activity (from both scRNA-seq and scRNA-seq data > 10 PCW female samples) of the TFs (x-axis) involved in the establishment of cellular diversity in the Müllerian duct-derived mesenchymal cells (y-axis). The colour scale is proportional to the expression of the TF while the size of the dot reflects the importance of the TF regulon in each cell type. c, Batch corrected t-SNE of downsampled Wolffian-derived mesenchymal cells from male samples > 10 PCW profiled with scATAC-seq (n = 5469 cells after downsampling) coloured by stage (measured in PCW) and cell type. d, Heatmap showing the inferred regulon activity (from both scRNA-seq and scRNA-seq data > 10 PCW male samples) of the TFs (x-axis) involved in the establishment of cellular diversity in the Wolffian duct-derived mesenchymal cells (y-axis). The colour scale is proportional to the expression of the TF while the size of the dot reflects the importance of the TF regulon in each cell type. e, Schematic illustration showing the bidirectional cell-cell communication between the mesenchyme and epithelium of the differentiating Müllerian and Wolffian ducts along their rostro-caudal axes in female and male samples > 10 PCW. f, (left) Hematoxylin and eosin (H&E) image of a representative 13 PCW female sample profiled with In Situ Sequencing (ISS) alongside (right) measured expression of ALDH1A1 and WIF1 (n = 3 biologically independent samples). Scale bar: 100 μm. g, Heatmap showing the min-max normalised expression measured in scRNA-seq data of epithelial partners of the spatially variable mesenchymal ligand/receptors (x-axis) along the binned imputed Müllerian rostro-caudal axis (y-axis). h, Network representation of the gene regulatory network for HOX code TFs only including targets that were identified as spatially-variable in the Müllerian-derived mesenchyme. i, Heatmap showing the min-max normalised expression measured in 10x Visium data of prioritised spatially variable mesenchymal ligands or receptors (x-axis) along the binned measured Wolffian rostro-caudal axis (y-axis). Interacting partners for each spatially variable mesenchymal ligand/receptor are reported on the x-axis below the activatory or inhibitory arrows. A dot in a bin indicates that the interacting partner of the spatially variable mesenchymal ligand/receptor is expressed in at least 20% of epithelial cells in that bin. j, Heatmap showing the min-max normalised expression measured in 10x Visium data of epithelial partners of the spatially variable mesenchymal ligand/receptors (x-axis) along the binned measured Wolffian rostro-caudal axis (y-axis). k, Heatmap showing the min-max normalised expression measured in scRNA-seq data of prioritised spatially variable epithelial TFs (x-axis) along the binned imputed Müllerian rostro-caudal axis (y-axis). CE: cervix; FT: fallopian tube; LV: lower vagina; UT: uterus; UV: upper vagina. Illustrations in e created by A. García.

Extended Data Fig. 7 Epithelial regionalisation along the differentiating Müllerian and Wolffian ducts and within the fallopian tube and epididymis.

a, Batch corrected t-SNE embedding of downsampled Müllerian-derived epithelial cells (n = 2502 cells after downsampling) from female samples > 10 PCW profiled with scATAC-seq coloured by stage (measured in PCW) and cell type. b, Heatmap showing the inferred regulon activity (from both scRNA-seq and scRNA-seq data > 10 PCW female samples) of the TFs (x-axis) involved in the establishment of cellular diversity in the Müllerian duct-derived epithelial cells (y-axis). The colour scale is proportional to the expression of the TF while the size of the dot reflects the importance of the TF regulon in each cell type. c, Batch corrected t-SNE embedding of Wolffian-derived epithelial cells (n = 1502 cells after downsampling) from male samples > 10 PCW profiled with scATAC-seq coloured by stage (measured in PCW) and cell type. d, Heatmap showing the inferred regulon activity (from both scRNA-seq and scRNA-seq data > 10 PCW male samples) of the TFs (x-axis) involved in the establishment of cellular diversity in the Wolffian duct-derived epithelial cells (y-axis). The colour scale is proportional to the expression of the TF while the size of the dot reflects the importance of the TF regulon in each cell type. e, (left) Hematoxylin and eosin (H&E) image of a representative 17 PCW fallopian tube sample profiled with 10x Visium alongside (right) measured expression of LGR5 and TSPAN8 in each spot (n = 6 biologically independent samples). Scale bar: 500 μm. f, High-resolution, large-area imaging of a representative section of a 13 PCW female sample with smFISH signal for EPCAM (red, epithelium), LGR5 (yellow, fallopian tube mesenchyme and uterocervical epithelium), LYPD1 (magenta, middle fallopian tube epithelium), FOXL2 (cyan, fallopian and uterocervical mesenchyme) (n = 3 biologically independent samples). Scale bar: 1000 μm. g, Schematic illustration summarising the mesenchymal-epithelial signalling present along the differentiating Müllerian and Wolffian ducts found in our analysis. h, Schematic illustration of the derivation of the fallopian axis in female samples > 10 PCW (this is part of the Müllerian rostro-caudal axis shown in Fig. 3a–d). i, Schematic illustration of the derivation of the epididymal axis in male samples > 10 PCW (this is part of the Wolffian rostro-caudal axis shown in Extended Data Fig. 5b). j, High-resolution, large-area imaging of a representative section of a 21 PCW female sample with smFISH signal for EPCAM (blue, epithelium), LGR5 (red, fallopian tube mesenchyme and uterine epithelium), LYPD1 (yellow, middle fallopian tube epithelium), FOXL2 (cyan, fallopian tube and uterine mesenchyme) (n = 3 biologically independent samples). Scale bar: 1000 μm. k, Normalised, log-transformed expression of (left) PNOC and (right) MUC6 in each 10x Visium spot of capture areas in Fig. 3k. Scale bars: 500 μm. l, Heatmap showing the min-max normalised expression measured in 10x Visium data of selected spatially variable non-ciliated epithelial genes (x-axis) along the binned epididymal axis (y-axis). m, High-resolution, large-area imaging of a representative section of a 21 PCW male sample with smFISH signal for WNT9B (red, caudal epididymal epithelium), GATA3 (yellow, caudal epididymal epithelium), WIF1 (cyan, epididymal mesenchyme) (n = 3 biologically independent samples). Scale bar: 1000 μm. Illustrations in g–i created by A. García.

Extended Data Fig. 8 Sexual dimorphism in the external genitalia and comparison with mice.

a, Batch-corrected Uniform Manifold Approximation and Projection (UMAP) embedding of the epithelial and mesenchymal cells in the external genitalia profiled with scRNA-seq (n = 51,304 cells) coloured by cell type, chromosomal sex, and developmental stage (measured in PCW). b, Dot plot showing the variance-scaled, log-transformed expression of genes (x-axis) characteristic of the annotated cell types (y-axis) detected in the external genitalia. Top-layer groups marker genes by lineage (epithelial or mesenchymal). c, Hematoxylin and eosin (H&E) stained image of a representative 12 PCW female sample profiled with 10x Visium alongside spatial mapping of urethral epithelium and corpus spongiosum cells from the scRNA-seq dataset onto the corresponding 10x Visium slide. Estimated cell type abundance (colour intensity) in each 10x Visium spot is shown over the H&E image (n = 2 biologically independent samples). Scale bar: 500 μm. d, Scatter plot showing the area under the curve of the inferred activity of the chromatin region-based cistrome for AR (y-axis) as a function of its expression (x-axis) in each scATAC/RNA-seq metacell derived from the male external genitalia. Each metacell is coloured by cell type. e, Batch corrected t-distributed Stochastic Neighbour Embedding (t-SNE) embedding of downsampled scATAC-seq cells (n = 10,966 cells after downsampling) from the male external genitalia coloured by developmental stage (measured in PCW) and cell type. f, Heatmap showing the inferred regulon activity (from both scRNA-seq and scRNA-seq data of male samples) of the TFs (x-axis) involved in the establishment of cellular diversity in the external genitalia (y-axis). The colour scale is proportional to the expression of the TF while the size of the dot reflects the importance of the TF regulon in each cell type. g, Network representation of the gene regulatory network for AR only including targets that were identified as male-biased in the early corpus spongiosum. Nodes in green correspond to genomic regions which contain an AR binding motif, while nodes in red are the inferred target genes. h, Schematic representation of the computational workflow used to compare human and mouse external genitalia during the masculinisation programming window. i, Sankey plot representing the matching neighbourhoods between the mouse and human external genitalia labelled by the most abundant cell type in the neighbourhood. j, Batch corrected UMAP embedding of embryonic day (E) 14.5, E16.5, E18.5 (corresponding to the masculinisation programming window in mice) scRNA-seq cells (n = 60,832 cells) from the mouse external genitalia[23] coloured by cell type. The cell type annotations are taken from the authors of the original study. k, Batch corrected UMAP embedding of embryonic day (E) 14.5, E16.5, E18.5 scRNA-seq cells from the mouse external genitalia highlighting the neighbourhoods that match the human corpus spongiosum. l, Volcano plot showing the log fold-change (x-axis) and adjusted p-value (y-axis) of the differential expression test (adjusted p-value = 0.05, |logFC| > 1) between male and female samples within the mouse corpus spongiosum.

Source data

Extended Data Fig. 9 Sexual dimorphism in the external genitalia and potential disruptions to epithelial reproductive development.

a, Batch corrected Uniform Manifold Approximation and Projection (UMAP) embedding of E14.5, E16.5, E18.5 scRNA-seq cells from the mouse external genitalia showing the expression of early corpus spongiosum markers Foxf1, Sall1, Foxl2. b, Dotplot showing the log-transformed, min-max normalised expression of early corpus spongiosum ligands and urethral epithelium receptors (y-axis) in each cell type (x-axis), separated by sex. Interacting ligands and receptors are connected by an arrow. c, Dot plots showing the log-transformed, variance-scaled expression of steroidogenic receptors (x-axis) and target genes (x-axis) of the clinically approved drugs in Fig. 5a in each reproductive-specific cell type (y-axis) identified in our early (<= 10 PCW) scRNA-seq dataset. d, (left) Heatmap showing the z-score enrichment of targets of clinically approved drugs (x-axis) found to be specifically impacting the epithelial compartment of late female (>10 PCW) reproductive tract organs among reproductive-specific cell types (y-axis) identified in our scRNA-seq dataset. (right) Dot plots showing the log-transformed, variance-scaled expression of the steroidogenic receptors (x-axis) and target genes (x-axis) of the clinically approved drugs in (right) in each reproductive-specific cell type (y-axis) identified in our late female (>10 PCW) scRNA-seq dataset. e, (left) Heatmap showing the z-score enrichment of targets of clinically approved drugs (x-axis) found to be specifically impacting the epithelial compartment of late male (>10 PCW) reproductive tract organs among reproductive-specific cell types (y-axis) identified in our scRNA-seq dataset. (right) Dot plots showing the log-transformed, variance-scaled expression of the steroidogenic receptors (x-axis) and target genes (x-axis) of the clinically approved drugs in (right) in each reproductive-specific cell type (y-axis) identified in our late male (>10 PCW) scRNA-seq dataset.

Extended Data Fig. 10 Exposure of fetal-derived uterine epithelial organoids to endocrine disrupting chemicals.

a, Dot plot showing the log-transformed, variance-scaled expression of uterine, ciliated, and cervicovaginal epithelial markers (x-axis) in the non-ciliated and ciliated cells of the control organoids (y-axis). b, (left) Bright-field microscopy images of uterine epithelial organoids derived from a 12 PCW female sample at day 4 following exposure to BPA, BBP, or vehicle control (DMSO). Scale bar: 250 μm. (right) Immunofluorescence staining of representative organoids from the same experimental conditions for EPCAM (magenta, epithelial cell marker), ZO-1 (cyan, tight junction protein indicating apico-basal polarity), and F-actin (white, cytoskeletal filament) (n = 2 biologically independent samples). Scale bar: 100 μm. c, Same as b, but for the 12 PCW-derived organoid line at day 6 following exposure to BPA, BBP, or vehicle control (DMSO) (n = 2 biologically independent samples). Scale bar: 250 μm for brightfield and 100 μm for immunofluorescence. d, Bright-field microscopy images of uterine epithelial organoids derived from a 17 PCW female sample at day 4 following exposure to BPA, BBP, or vehicle control (DMSO) (n = 2 biologically independent samples). Scale bar: 250 μm. e, Same as b, but for the 17 PCW-derived organoid line at day 6 following exposure to BPA, BBP, or vehicle control (DMSO) (n = 2 biologically independent samples). Scale bar: 250 μm for brightfield and 100 μm for immunofluorescence. f, (top) Batch-corrected Uniform Manifold Approximation and Projection (UMAP) embedding of control and BPA-exposed organoids derived from 12 and 17 PCW samples profiled with scRNA-seq (n = 44,678 cells) coloured by cell type and condition. (bottom) UMAP embedding of control and BBP-exposed organoids derived from 12 and 17 PCW samples profiled with scRNA-seq (n = 45,243 cells) coloured by cell type and condition. g, Bar plot showing the proportion of ciliated and non-ciliated cells in control (n = 21,909 cells) and BPA-exposed (n = 22,769 cells) fetal-derived uterine epithelial organoids. h, Volcano plot showing the log fold-change (x-axis) and adjusted p-value (y-axis) of the differential expression test (adjusted p-value = 0.05, |logFC| > 0.5) between differentially abundant neighbourhoods in the BPA exposed condition and all other neighbourhoods. Genes colored in red are also upregulated by BBP. i, Gene Set Enrichment Analysis of the upregulated genes upon BPA exposure. j, Gene Set Enrichment Analysis of the upregulated genes upon BBP exposure. k, Dot plot showing the variance-scaled, log-transformed expression of the genes (x-axis) which are upregulated by both BPA and BBP in non-ciliated cells (y-axis) in in vivo fetal uterine epithelium and adult uterine epithelium in the proliferative phase of the menstrual cycle[104]. l, Violin plot showing the combined expression of genes upregulated by both BPA and BBP identified in fetal-derived uterine organoids in the adult endometrial organoids before (NH_d0) and after (Estrogen_induced_PGR+) the addition of estrogen in the media.

Source data

Supplementary information

Supplementary Information

Supplementary Note Figures, Supplementary Table legends and Supplementary Notes 1–5: 1, cell-type annotations; 2, Müllerian rostrocaudal axis; 3: Wolffian rostrocaudal axis; 4, intra-organ regionalization of fallopian tube and epididymis; and 5, extended discussion.

Reporting Summary

Supplementary Fig. 1

Cell-type characterization of early (≤10 PCW) female and male reproductive tract samples.

Supplementary Fig. 2

Cell-type characterization of late (>10 PCW) female reproductive tract samples.

Supplementary Fig. 3

Cell-type characterization of late (>10 PCW) male reproductive tract samples.

Supplementary Tables

Supplementary Tables 1–10.

Source data

Source Data Fig. 4

Source Data Fig. 5

Source Data Extended Data Fig. 8

Source Data Extended Data Fig. 10

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Lorenzi, V., Icoresi-Mazzeo, C., Cassie, C. et al. Spatiotemporal cellular map of the developing human reproductive tract. Nature 650, 428–437 (2026). https://doi.org/10.1038/s41586-025-09875-2

- Received: 22 August 2024

- Accepted: 06 November 2025

- Published: 17 December 2025

- Version of record: 17 December 2025

- Issue date: 12 February 2026

- DOI: https://doi.org/10.1038/s41586-025-09875-2

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Human assembloids recapitulate periportal liver tissue in vitro

Lei Yuan,

Sagarika Dawka,

Yohan Kim,

Anke Liebert,

Fabian Rost,

Robert Arnes-Benito,

Franziska Baenke,

Christina Götz,

David Long Hin Tsang,

Andrea Schuhmann,

Anna Shevchenko,

Roberta Rezende de Castro,

Seunghee Kim,

Aleksandra Sljukic,

Anna M. Dowbaj,

Andrej Shevchenko,

Daniel Seehofer,

Dongho Choi,

Georg Damm,

Daniel E. Stange &

…

Meritxell Huch

Nature volume 650, pages 438–449 (2026)

Abstract

The development of complex multicellular human in vitro systems holds great promise for modelling disease and advancing drug discovery and tissue engineering[1]. In the liver, despite the identification of key signalling pathways involved in hepatic regeneration2,3, in vitro expansion of human hepatocytes directly from fresh patient tissue has not yet been achieved, limiting the possibility of modelling liver composite structures in vitro. Here we first developed human hepatocyte organoids (h-HepOrgs) from 28 different patients. Patient-derived hepatocyte organoids sustained long-term expansion of hepatocytes in vitro and maintained patient-specific gene expression and bile canaliculus features and function of the in vivo tissue. After transplantation, expanded h-HepOrgs rescued the phenotype of a mouse model of liver disease. By combining h-HepOrgs with portal mesenchyme and our previously published cholangiocyte organoids4,5,6, we generated patient-specific periportal liver assembloids that retain the histological arrangement, gene expression and cell interactions of periportal liver tissue, with cholangiocytes and mesenchyme embedded in the hepatocyte parenchyma. We leveraged this platform to model aspects of biliary fibrosis. Our human periportal liver assembloid system represents a novel in vitro platform to investigate human liver pathophysiology, accelerate drug development, enable early diagnosis and advance personalized medicine.

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Main

Each year, chronic and end-stage liver diseases account for over 2 million human deaths worldwide[7]. Rodent models have advanced understanding of liver biology. However, species-specific differences (for example, in metabolism and toxicity) impact understanding of which concepts are universal and which are species-specific, making the translation of potential therapeutic targets into effective human therapies a substantial challenge. Human liver single-cell and spatial transcriptomics have unveiled human cellular heterogeneity8,9,10,11,12,13. However, the static nature of these analyses does not provide information about the highly dynamic processes occurring in disease initiation and progression. Primary hepatocytes cannot be expanded in culture[14], and, although cancer cell lines have been informative, they suffer from genetic drift. Reprogrammed hepatocytes (ProliHHs) are proliferative but have bi-phenotypic and progenitor features[15]. Additionally, none of these models recapitulates the three-dimensional (3D) bile canaliculus structures (thin and elongated lumina) observed in tissue[16], making it difficult to model complex disease states or recapitulate patient-specific traits, both of which are essential for precision medicine and early diagnosis.

Organoids have emerged as promising models to better predict therapeutic outcomes[1]. Human intestinal organoids effectively model the structure and function of human tissue[17]. However, recapitulating in vitro the architecture and cellular interactions of complex tissues such as the human liver remains an unmet challenge. We have described liver organoid models4,5,6 (recently renamed as intrahepatic cholangiocyte organoids[18]) in which cholangiocytes/ductal cells can be expanded long term in culture. We19,20 and others21,22 have demonstrated that these models enable the study of mouse liver regeneration in vitro. Small modifications to this system allowed the generation of branching organoids[23], akin to the morphogenesis of the developing tissue24,25, and organoids could be transplanted into animals to reconstruct the bile duct in vivo[26]. Mouse adult hepatocyte organoids have been developed27,28. Additionally, mouse[29] and human27,30 liver hepatoblast organoids were successfully generated from fetal tissue. However, expanding human adult hepatocytes from patient tissue has remained a challenge[31]. Regrettably, all these models consist only of epithelial cells and lack the ability to fully replicate the cellular interactions and architecture of in vivo adult human liver tissue. Similarly, liver organoids derived from human pluripotent stem cells, although they contain stromal and epithelial populations, do not replicate native adult liver periportal cell interactions or architecture18,32. By co-culturing mouse cholangiocyte organoids with mouse liver portal mesenchyme, we obtained cholangiocyte–portal mesenchyme organoids that retain the binary cell–cell interactions present in the mouse liver[20]. Chimeric epithelial co-cultures of mouse cholangiocyte and two-dimensional (2D) human hepatocyte-like cells have been reported[33]. However, a complex 3D multicellular model that captures human liver portal cellular interactions does not yet exist for liver tissue from human adults.

Here we developed an adult human hepatocyte organoid model (h-HepOrgs) that allows the long-term serial expansion (passaging for >3 months at 1:2 splits) of human adult hepatocytes directly from fresh patient liver tissue. h-HepOrgs retained the gene expression and function of in vivo human adult hepatocytes in a patient-specific manner and formed bile canaliculus structures akin to the ones in human tissue. As we expand and cryopreserve organoids from fresh tissue, we have been able to generate a living biobank of hepatocyte organoids from 28 donors. We combined these novel patient-derived hepatocyte organoids with primary human portal mesenchyme and human cholangiocyte organoids (h-CholOrgs) from the same patient to generate human periportal assembloids that recapitulate the functional cell interactions and architecture of the in vivo tissue. Finally, we exploited the potential of this system to model aspects of human biliary fibrosis.

YAP and WNT promote h-HepOrg growth

To recapitulate the epithelial–stromal interactions and architecture of human liver, we sought to obtain an expandable source of adult hepatocytes, cholangiocytes and mesenchyme from the same individual. A prerequisite was to first identify methods to expand human adult hepatocytes. Hence, we obtained hepatocytes from patient tissue by perfusion[34] (Methods) and cultured them in our reported hepatoblast organoid culture medium (MM)[29]. However, the cultures rapidly filled with cholangiocyte organoids, preventing further analysis (Extended Data Fig. 1a, no MACS). We adapted the isolation protocol by including a step of EpCAM magnetic activated cell sorting (MACS), which allowed us to obtain viable hepatocytes from the EpCAM-negative fraction while generating cholangiocyte organoids by culturing the EpCAM-positive cholangiocyte fraction in our reported cholangiocyte medium[4] (Fig. 1a, Extended Data Fig. 1a–c, Methods and Supplementary Table 1). Although we detected minimal growth for 7–14 days (Fig. 1c, MM), hepatocytes rapidly died thereafter.

Fig. 1: Primary human hepatocytes can be expanded long term when grown as hepatocyte organoids.

a–f, Liver tissues were obtained from patients undergoing surgery and processed for cell isolation as described in the Methods. Isolated PHHs were used to generate h-HepOrgs that would self-renew in vitro and could be expanded long term. a, Schematic depicting the protocol for generating h-HepOrgs. See Methods for details. b, IPA of several publicly available datasets (lists 1–3; generated from refs. 27,35) used to identify signalling pathways involved in hepatocyte proliferation. Bar plots show the IPA pathway activity z score for each selected pathway. Circles represent the different datasets. See Supplementary Data 1_S5 for details. Bold represents pathways related to EM1 and EM2 conditions. DEGs, differentially expressed genes. c, Representative bright-field images of primary h-HepOrgs cultured in the indicated media (Methods) at day 10 of culture (passage 0). Scale bars: 500 μm (top); magnification, 100 μm (bottom). d, Representative bright-field images of patient-derived primary h-HepOrgs serially expanded and cultured long term in EM2. n = 3 independent donors. Scale bars: 100 μm (left images), 1 mm (right-most image). P, passage; D, day. e, Organoid formation efficiency of h-HepOrgs cultured with the indicated media. Bars represent mean ± s.e.m. from n = 4 (MM, MM + WntS, MM + TRULI) or n = 5 (MM + WntS + TRULI, MM + WntS + TRULI – Nic) independent donors. Dots are coloured by donor. One-way analysis of variance (ANOVA) followed by Tukey’s multiple-comparison test. f, Serial expansion (with 1:2 splits) of h-HepOrgs from the indicated donors. The graph shows the expansion potential of h-HepOrgs in the indicated media. We checked the potential for organoids to be expanded beyond ten passages. As detailed in the graph and in Supplementary Table 2, under EM1 the cultures exhibited lower expansion potential, with none of them expanding beyond passage 10. For the donors for whom h-HepOrgs were expanded in EM2 and reached passage 10 (PHH29, DSD40, PHH27), we stopped culturing the h-HepOrgs at the time of submission. Panel a created in BioRender. Yuan, L. (2025) https://BioRender.com/hem14cv.

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Hence, we sought to identify culture conditions for the long-term expansion of human adult hepatocytes as hepatocyte organoids. We hypothesized that signalling pathways involved in cancer progression or tissue regeneration could activate hepatocytes and promote their exit from quiescence. To explore this, we analysed expression profiles from human liver cancer organoids[35] and mouse partial hepatectomy[27] and compared them to those for human healthy and cancer tissues (Supplementary Data 1 and Methods). Ingenuity Pathway Analysis (IPA) revealed that several pathways, such as AMPK, EGF, mTOR and IGF-1, were consistently differentially expressed across at least two datasets (Extended Data Fig. 1d and Supplementary Data 1). WNT, MAPK and FGFR2 signalling pathways were active, whereas IL-6, HIPPO and NOTCH pathways appeared inactive (Fig. 1b). Among the predicted active upstream regulators, we found YAP and CTNNB1, suggesting YAP and WNT activation (Extended Data Fig. 1e).

Both WNT and YAP are established drivers of liver regeneration3,36,37 and cancer38,39. Therefore, we activated WNT and YAP signalling by supplementing our MM hepatoblast medium[29] with WNT surrogate (WntS)[40] and a LATS1/LATS2 (LATS1/2) inhibitor (TRULI or TDI-011536)[41]. Combining these enabled serial passaging (5–6 passages) of h-HepOrgs as solid structures with no lumina (Fig. 1c–f and Extended Data Fig. 1g). TRULI-treated cultures showed superior morphology compared with TDI-011536 (Extended Data Fig. 1h), so we continued with the MM + WntS + TRULI combination, hereafter termed h-HepOrg expansion medium 1 (EM1). The other tested pathways did not result in consistent or quantifiable organoid growth (Extended Data Fig. 1f).

We further optimized the EM1 medium by testing the need for each component. Notably, removing nicotinamide improved the efficiency of organoid formation nearly tenfold and enabled long-term culture for over 3 months (>10 passages with a 1:2 split each week) (Extended Data Fig. 1i and Supplementary Table 2). These results were in line with our IPA analysis showing inactivity of NAD signalling (Fig. 1b) and previous reports of nicotinamide hepatotoxicity in humans[42]. Using these optimized conditions (EM1 without nicotinamide, or MM + WntS + TRULI – Nic, hereafter called EM2), we successfully generated expandable h-HepOrgs from 28 patients (11–85 years old, 30% female) with 100% efficiency, including 5 samples from cryopreserved hepatocytes (Supplementary Table 2). No other tested conditions supported robust expansion (Extended Data Fig. 1j, Supplementary Table 3 and source data for Extended Data Fig. 1). h-HepOrgs maintained stable chromosome numbers over time and could be frozen and thawed without loss of expansion capacity, enabling the creation of a living biobank from a total of 28 different donors (Extended Data Fig. 1k,l).

Together, these results demonstrate that combination of WNT and YAP activation allows the long-term expansion of adult h-HepOrgs.

h-HepOrgs mimic liver structure and function

To characterize the expanded h-HepOrgs, we first performed RNA sequencing (RNA-seq) analysis on early (passages 1–3) and late (passage 10) cultures and compared their expression patterns with those of freshly isolated primary human hepatocytes (PHHs) and h-CholOrgs from the same donors (when possible). Gene expression in expanded h-HepOrgs closely correlated with that in PHHs, whereas h-CholOrgs segregated separately (Extended Data Fig. 2a,b). Gene expression and enrichment analyses revealed that, compared with PHHs, the matching h-HepOrgs exhibited a proliferative signature that was maintained until late passages (greater than passage 10) and resembled regenerating tissue after hepatectomy (Extended Data Fig. 2c–e). These results were in agreement with positive staining for the proliferation marker Ki-67 (Fig. 2a, top) and negligible staining for the apoptosis marker cleaved caspase 3 (Extended Data Fig. 2f). h-HepOrgs exhibited elevated WNT and YAP target gene expression, compared with PHHs, consistent with WNT pathway activation and LATS1/2 inhibition (Extended Data Fig. 2g,h). Immunofluorescence confirmed nuclear YAP localization in TRULI-treated cultures (Fig. 2b and Extended Data Fig. 2i). Quantitative PCR (qPCR) confirmed these results (Extended Data Fig. 2j). However, we cannot exclude the possibility that off-target effects may also contribute to h-HepOrg growth, as TRULI can inhibit kinases other than LATS1/2.

Fig. 2: h-HepOrg gene expression resembles that of in vivo hepatocytes.

a–c, h-HepOrgs expanded in EM2 or differentiated in DM or liver tissue was analysed for expression of the indicated markers. Representative images are shown from at least two independent donors from three independent experiments. a, Immunofluorescence staining for Ki-67 (magenta) in h-HepOrgs in EM2 (top) and DM (bottom). DAPI (cyan) stained nuclei. Scale bars, 50 μm. b, Immunofluorescence staining for YAP (magenta), nuclei (DAPI; cyan) and cell borders (F-actin; grey) in liver tissue (top) and h-HepOrgs in EM2 with (middle) and without (bottom) TRULI. Scale bars, 50 μm. White arrowheads indicate cells with nuclear YAP. Yellow arrowheads indicate YAP-negative cells. c, HNF4A (magenta), bile canaliculus (BC) marker CD13 (green) and F-actin (grey) immunofluorescence staining of organoids in EM2 (left) or DM (right). h-HepOrgs in DM exhibit thinner and more interconnected bile canaliculi. Scale bars: 50 μm. d,e, RNA-seq analysis of h-HepOrgs in EM2 or DM, h-CholOrgs and PHHs either freshly isolated (primary PHHs) or cultured for 1 day (1d-PHH monolayer). d, Heat map representing scaled gene expression. BA, bile acid; Cholest., cholesterol; met., metabolism. e, GSEA of h-HepOrgs in DM compared with EM2. Full list in Supplementary Data 2. Results are shown as a dot plot, where dot colour corresponds to the adjusted P value (permutation test implemented in clusterProfiler, adjusted using the Benjamini–Hochberg method). Dot size corresponds to the gene ratio (the number of core enrichment genes divided by the total number of genes in the pathway). KEGG, Kyoto Encyclopaedia of Genes and Genomes; NES, normalized enrichment score. f, Human liver tissue (left) and an h-HepOrg in DM (right) stained for pericentral (GS; magenta, top) and periportal (HAL; yellow, bottom) markers. Nuclei were stained with DAPI (cyan). Fire LUT images (first column) show fluorescence intensities for GS and HAL. CV, central vein; PV, portal vein. Scale bars, 100 μm (tissue) and 50 μm (organoids). Representative images from three independent experiments.

Analysis of marker gene expression showed that the expanded h-HepOrgs expressed hepatocyte markers such as HNF4A and ALB, several apolipoproteins (APOC2 and APOA4) and cytochromes (CYP3A4 and CYP3A7), albeit at lower levels than in freshly isolated hepatocytes (Fig. 2d, Extended Data Fig. 2c and Supplementary Data 2). Expression of cholangiocyte markers such as SOX9, KRT19 and KRT7 was markedly reduced compared with in h-CholOrgs, whereas expression of the embryonic liver marker AFP suggested incomplete maturation (Fig. 2d, Extended Data Fig. 2k and Supplementary Data 2). qPCR and immunofluorescence analyses confirmed high expression of HNF4A and the apical and polarity marker CD13 (ANPEP) (Fig. 2c,d, Extended Data Fig. 2k and Supplementary Data 2). However, detailed analysis of the distribution of CD13 expression showed the presence of wide, disconnected round lumina, which does not reflect the morphology of the bile canaliculus network formed by hepatocytes in vivo[43] (Fig. 2c, compare CD13 in h-HepOrgs in EM2 to the tissue panel in Fig. 3b). Taken together, these results indicate that expanding h-HepOrgs in EM2 may represent an immature hepatocyte state. Therefore, we sought to define a differentiation medium.

LATS1/2 inhibition was recently shown to promote cholangiocyte growth[44], while it is well established that YAP activation drives hepatocyte de-differentiation and its inactivation facilitates re-differentiation[45]. Therefore, we reasoned that reducing YAP activation would facilitate the maturation of h-HepOrgs. Following several iterations, we developed a hepatocyte differentiation medium (referred to as DM) that removed YAP and FGFR2 activation, maintained WNT signalling and added dexamethasone (Extended Data Fig. 3a and Methods). Under DM, the cellular morphology improved: hepatocytes (HNF4A+) had reduced proliferation, acquired a significantly higher cytoplasm to nucleus ratio and had improved bile canaliculi (CD13+), which presented with a thinner and more elongated morphology (Fig. 2a,c, compare EM2 with DM, and Extended Data Fig. 3b). Combined, these features suggested enhanced hepatocyte maturation. To assess the extent of the maturation, we performed RNA-seq analysis. In principal-component analysis (PCA), differentiated h-HepOrgs were closer to freshly isolated hepatocytes and farther away from h-CholOrgs, when compared with h-HepOrgs in EM (Extended Data Fig. 3c). Differentiated cells had increased expression of many markers of mature cells, some to similar levels as in freshly isolated human hepatocytes, including ALB, several apolipoproteins (APOE and APOA1), bile acid transporters (ABCC2 (MRP2)), and cholesterol and bile acid metabolic genes (ABCG8 and CYP27A1). Additionally, several detoxifying enzymes, including CYP2C9, CYP3A5, CYP3A4 and MAOA, some of which are pericentrally zonated10,11,46,47, were also upregulated (Fig. 2d and Extended Data Fig. 3f–i). In line with this, we observed strong positive enrichment for gene signatures related to hepatocyte functions, including cholesterol, fatty acid and drug metabolism, phase II conjugation, clot formation and bile secretion, among others, whereas cell cycle and proliferation signatures were negatively enriched (Fig. 2e and Extended Data Fig. 3e). Similarly, expression of the embryonic marker AFP and cholangiocyte makers KRT7 and KRT19 was reduced (Fig. 2d and Extended Data Fig. 3i).

Notably, some pericentrally zonated genes, such as CYP2E1 and GLUL (encoding glutamine synthase, GS), as well as some periportally zonated genes, such as ALDOB and SCD, were highly upregulated (Fig. 2d and Extended Data Fig. 3h). In immunofluorescence analysis for pericentral (GS) or periportal (histidine ammonia lyase, HAL) markers, some organoids had a gradient of expression, with some cells positive and others negative for the markers (Fig. 2f). Dual staining for CYP2E1 (pericentral marker) and E-cadherin (enriched in the periportal region) highlighted the heterogeneity and spatial distribution of hepatocyte function within the same h-HepOrg, at least for the genes tested (Extended Data Fig. 3j).

Upon differentiation, h-HepOrgs recapitulated the complex cell polarity of in vivo hepatocytes[14], with the tight junction and apical polarity marker ZO-1 localized to the apical surface of adjacent hepatocytes, resembling the morphology of bile canaliculi in human liver tissue (Fig. 3a). Immunofluorescence staining for the apical marker CD13 followed by image analysis and reconstruction revealed that differentiated h-HepOrgs had longer and more branched bile canaliculus networks within each organoid, when compared with the same organoids in EM, and resembled in vivo tissue (Fig. 3b). Additionally, the connectivity of the bile canaliculi network was also significantly improved, coming closer to that of tissue (Fig. 3c). Notably, we observed that different patients had fine-detailed differences in bile canaliculus morphology, with some patients having thin and homogenous bile canaliculi, some having wider and inhomogeneous bile canaliculi and others having bile canaliculi full of branchlets (Extended Data Fig. 4a–c). We found similar variation in bile canaliculi architecture across our different organoid cultures, suggesting that our model could capture the different types of bile canaliculus morphology observed in human tissue (Extended Data Fig. 4d).

Fig. 3: h-HepOrgs maintain in vivo function and patient-specific features.

a, Illustration of the apical–basal polarity of hepatocytes. Right, representative images of E-cadherin (magenta), radixin (tissue; green, top) and ZO-1 (h-HepOrg in DM; green, bottom) staining (n = 1 donor). Nuclei are stained with DAPI (cyan). Scale bars: 25 μm; magnification, 10 μm. b, Representative images (n = 3 donors) of CD13 (bile canaliculi; green) and F-actin (grey) in an h-HepOrg in EM2 (top), an h-HepOrg in DM (middle) and human liver tissue (bottom). DAPI marks nuclei (cyan). Right, segmented bile canaliculi in 3D, depicting local thickness in Fire LUT (blue, thinner; red, thicker). Scale bars for all images and magnifications, 50 µm. c, Total number of triple junctions (a proxy for connectivity). For tissue, dots correspond to one field of view. For organoids, dots correspond to one organoid and colours correspond to donors. The graph shows the mean ± s.e.m. Two-way ANOVA (factors: condition and donor) with Tukey’s multiple-comparison test; P values shown in the figure. d, Heat map showing scaled expression of donor-specific genes computed across primary PHHs (purple) and matching h-HepOrgs in DM (yellow). Hierarchical clustering was performed on both samples and genes. Full list in Supplementary Table 4. e,f, Cytochrome activity (relative luciferase units, RLU) (e) and albumin secretion (ng ml–1) (f) for h-HepOrgs in EM2 (dark purple) or DM (light purple), h-CholOrgs (green) and freshly isolated PHHs cultured in 2D for 1 (light pink) or 7 (orange) days. Results are normalized by cell count. Graphs show the mean ± s.e.m. from the indicated number of independent donors: 7d-PHH (n = 4), h-HepOrgs in EM2 (n = 7 for e and 9 for f), h-HepOrgs in DM (n = 7 for e and 8 for f), h-CholOrgs (n = 8) and 1d-PHH (n = 9). One-way ANOVA followed by Tukey’s multiple-comparison test. g, Norverapamil production (pmol h–1 per 10^4 cells) detected by mass spectrometry in h-HepOrgs in DM and PHHs. Unpaired two-tailed Student’s t test with Welch’s correction (n = 3 donors). h, Kaplan–Meier survival curves for Fah−/−Rag2−/−Il2rg−/− mice sham operated or intrasplenically injected with 500,000 cells from h-HepOrgs in EM2 or DM or fresh PHHs. Log-rank test, P = 0.0127. Scheme from panel a adapted from ref. [51], Springer Nature Limited.

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Given that our h-HepOrgs are derived directly from patient tissue, we next assessed whether they retain patient-to-patient variability in culture, thus enabling patient-specific modelling of hepatocyte-related liver diseases. For this, we analysed the transcriptomes of primary hepatocytes at the time of isolation and their matching h-HepOrgs under DM to identify the specific gene signatures of each patient. We found strong correlation (R^2 = 0.7–0.9) between the organoids and the original primary hepatocytes from which they were derived (Extended Data Fig. 4e). Interestingly, many of the patient-specific genes we found expressed in organoids and their source cells had been associated with susceptibility to several liver diseases, including hepatitis virus infection (IL1RL1 and ERAP2), liver cancer (GPC3) and cholestasis during pregnancy (GABRP). More notably, some genes were involved in metabolic pathways, including the glutathione-related gene GSTM3, the lactate dehydrogenase LDHC and the lipid metabolism-related genes APOA4, FAR2 and ACSM1, among others (Fig. 3d and Supplementary Table 4). These results indicated that h-HepOrgs could preserve patient-specific signatures, with important implications for modelling human liver diseases.

Next, we compared the functional performance of differentiated h-HepOrgs to that of PHHs. Differentiated h-HepOrgs exhibited hepatic functions, including robust albumin secretion and moderate cytochrome P450 activity, comparable to 7-day PHHs (Fig. 3e,f). Specifically, differentiated h-HepOrgs displayed CYP2C9 activity equivalent to that of 7-day PHHs and modestly reduced CYP3A4 activity, whereas 1-day PHHs had superior activity for both enzymes. Mass spectrometry analysis revealed that differentiated h-HepOrgs significantly outperformed 1-day PHHs in converting the antiarrhythmic and antihypertensive drug verapamil into its primary metabolite norverapamil (Fig. 3g). This suggests more robust or sustained expression and coordination among multiple CYP enzymes relevant to verapamil metabolism, including the metabolizing enzymes CYP2C8, CYP3A4 and CYP3A5, all of which are responsible for verapamil N-demethylation and were highly expressed in h-HepOrgs in DM (Extended Data Fig. 3g,i). Furthermore, we observed inter-donor variability in verapamil metabolism among h-HepOrg lines (Fig. 3g), reflecting patient-specific metabolic phenotypes and underscoring the potential of this platform for personalized drug metabolism studies.

Notably, both expanded and differentiated hepatocyte organoids readily engrafted and maintained their hepatic function in vivo, following xenotransplantation in the mouse model of tyrosinemia type I liver disease (Fah−/−Rag2−/−Il2rg−/− mice)[48], with grafts distributed throughout the liver parenchyma. Importantly, the engrafted cells were able to rescue the lethal phenotype (Fig. 3h and Extended Data Fig. 4f).

In summary, we have developed a novel h-HepOrg model that enables the expansion of functional adult human hepatocytes directly from patient tissue and preserves hepatocyte polarity and bile canaliculus organization while retaining some aspects of patient-to-patient variability.

Assembloids model periportal tissue

We next aimed to reconstruct the periportal region of the liver lobule by reproducing the cellular interactions among hepatocytes, cholangiocytes and portal mesenchyme, specifically portal fibroblasts. PDGFRA, which is exclusively expressed in liver mesenchyme and absent in other stromal cells8,10,11,13,49, was used to isolate liver mesenchymal cells (Extended Data Fig. 5a–f and Methods). To enrich for portal fibroblasts, we examined publicly available datasets8,49 and found that CD90 (THY1) is exclusively expressed in human portal fibroblasts (Extended Data Figs. 5g and 6a). Immunofluorescence analysis confirmed its restricted expression in the periportal region (Extended Data Fig. 6c). Thus, by sorting for CD90+PDGFRA+ cells and culturing them in defined conditions, we enriched for human portal fibroblasts (Extended Data Fig. 6b,d). RNA-seq and qPCR analysis confirmed that CD90+PDGFRA+ cells expressed portal fibroblast markers (for example, DCN and THY1), some VSMC markers (MYH11) and several growth factors (HGF and WNT5A, among others), all enriched in portal mesenchyme in vivo, and were negative for markers of other liver stromal populations, such as hepatic stellate cells (for example, LRAT) and mesothelia (for example, OGN) (Extended Data Fig. 6e–h). Immunofluorescence for vimentin (mesenchyme) and CD90 (portal mesenchyme) confirmed that the majority of the expanded cells were portal fibroblasts (Extended Data Fig. 6i).

Next, we aimed to generate human periportal assembloids using cells from healthy human tissue. To facilitate visualization of integration of cells in the structures, cholangiocyte organoids and portal mesenchymal cells were tagged with nuclear fluorescent proteins (Extended Data Fig. 6j) while leaving hepatocytes unlabelled. To determine the proportions of cells to assemble into composite structures, we first quantified the physiological proportions of the three cell types in vivo, finding an average of 15% cholangiocytes, 8% portal fibroblasts and 77% hepatocytes (Extended Data Fig. 7a). Following several iterations to induce self-assembly of the three populations into a single structure, we found that mixing one h-HepOrg structure with a defined number of portal fibroblasts and cholangiocytes (from h-CholOrgs), from the same donor when possible, in 96-well low-adhesion U-bottom plates, readily generated structures in which the three cell types were together, with cholangiocytes and portal fibroblasts embedded inside the h-HepOrg structure. We called these structures periportal assembloids (Fig. 4a,b). The ratio of 1 h-HepOrg to 25 portal fibroblasts and 100 cholangiocytes better captured the tissue cell ratios at day 6 after assembloid formation and was selected for further experiments (Extended Data Fig. 7b,c). To upscale the assembloids generated, we used AggreWell plates (Fig. 4b and Extended Data Fig. 7d). Notably, both methods generated assembloids with high efficiency (approximately 80%) (Fig. 4c) and the resulting assembloids closely mirrored the cellular composition and proportions of tissue (Fig. 4e). Therefore, we only used AggreWell plates from this point on. Notably, by day 3, assembloids reproducibly established key periportal architectural features, with cholangiocytes (KRT19+nuclear green fluorescent protein (nGFP)+) forming bile duct-like structures containing open lumina in close proximity to portal fibroblasts (nuclear red fluorescent protein (nRFP)) and both cell types embedded within the hepatocyte (HNF4A+) parenchyma. This architectural organization, in which ductal cells form an apical lumen, basally contacted by mesenchymal cells and embedded in the hepatocyte structure, was observed in approximately 80% of the assembloids and across donors. These results were independent of the donor source of healthy liver mesenchyme, indicating minimal impact of mesenchyme origin under healthy conditions (Fig. 4d–f and Extended Data Fig. 7d–h). Portal fibroblasts consistently extended long cellular processes towards the basal side of cholangiocytes, leading to physical contacts and reminiscent of the interactions observed in human tissue, although these processes did not completely wrap cholangiocytes as in portal tracts in vivo (Fig. 4f and Extended Data Fig. 7h). Under these conditions, the assembloids could be maintained for at least 2 weeks with no evidence of cell death or proliferation (Extended Data Fig. 7i–l).

Fig. 4: Human periportal assembloids recapitulate in vivo liver periportal tissue.

a, Experimental approach. Chol, cholangiocytes; PFs, portal fibroblasts. b, Day 6 periportal assembloids. Scale bar, 100 µm. c, Aggregation efficiency at 24 h. Mean ± s.e.m. of n = 3 independent experiments. Results are presented relative to the number of HepOrgs and are expressed as a percentage. Not assembloid, structures that do not containe all 3 cell types or structures with 2 or more hepatocyte organoids per structure. d, Representative images of AggreWell-derived assembloids (bottom) and liver tissue (top). Portal fibroblasts (magenta), cholangiocytes (green), nuclei (blue) and membranes (grey) were visualized using the indicated markers. Scale bars, 50 μm. PanCK, pan-cytokeratin. e, Cellular composition at day 6. Mean ± s.e.m. from ≥3 independent experiments. Dots correspond to the percentages of hepatocytes (Hep), cholangiocytes and portal fibroblasts per structure. f, Representative images of day 3 assembloids showing cholangiocytes (nGFP) and portal fibroblasts (nRFP). Staining was performed for KRT19 (white, left), HNF4A (yellow, middle), vimentin (white, right), nuclei (DAPI; blue) and membranes (phalloidin; green (left) or grey (middle)). Scale bars, 50 μm. g,h, scRNA-seq analysis of assembloids. g, Correlation between assembloid cells (this study) and matching cells in liver cell atlases. References used for comparison were refs. 8,11,12,13,49. h, Dot plot showing marker expression in assembloids and liver atlases. i, Heat map of liver zonation genes in h-HepOrgs in DM and hepatocytes from assembloids (pseudobulk; Methods). j, Urea synthesis by day 5 assembloids (brown), h-HepOrgs in DM (purple), PHHs (1-day culture; pink) and control h-CholOrgs (green). Mean ± s.e.m., n = 3 donors. Dot colours correspond to donors. One-way ANOVA with Tukey’s test for multiple comparisons. k, Gluconeogenesis by day 6 assembloids (brown) and h-HepOrgs in DM (purple). Mean ± s.e.m., n = 4 independent donors. Dot colours correspond to donors. Two-tailed paired t test. l, Human albumin secretion by assembloids at 1, 6 or 10 days (brown). Data for h-HepOrgs reproduced from Fig. 3f and shown for comparison: data are also shown for h-CholOrgs (green), PHHs (1-day culture, light pink; 7-day culture, orange), and h-HepOrgs in DM (light purple) or EM2 (dark purple). Bars show the mean ± s.e.m. from n = 4 (7d-PHH, assembloids), n = 8 (h-CholOrgs, h-HepOrgs in DM) or n = 9 (h-HepOrgs in EM2, 1d-PHH) donors. One-way ANOVA followed by Tukey’s multiple-comparison test. In j–l, results are normalized to total cell number. Panel a created in BioRender. Yuan, L. (2025) https://BioRender.com/nf4r0g6.

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Next, we used single-cell RNA-seq (scRNA-seq) analysis to benchmark our model to in vivo human liver tissue. In clustering, PCA and correlation analysis, the assembloid cells mostly overlapped with the corresponding cells in human liver cell atlases8,11,12,13,49 (Fig. 4g and Extended Data Fig. 8a). Hepatocytes, cholangiocytes and mesenchymal cells from assembloids expressed classical markers of their in vivo counterparts (hepatocytes: ALB, HNF4A; cholangiocytes: KRT7, KRT19; mesenchymal cells: VIM, THY1) (Fig. 4h). Gene set enrichment analysis (GSEA) confirmed that mesenchymal cells were highly enriched for signatures of extracellular matrix (ECM) organization and cell adhesion, cholangiocytes were enriched for cytoskeleton and cell–cell communication, and hepatocytes were enriched for fatty acid metabolism, complement and drug metabolism, similar to human liver tissue (Extended Data Fig. 8c,d).

Interestingly, we observed heterogeneous expression of classical zonated hepatocyte markers, with a fraction of hepatocytes expressing periportal markers (SAA1, SAA2 and APOA1) and others expressing pericentral markers (CYP2E1) (Fig. 4h and Extended Data Fig. 8b). To investigate whether the periportal assembloid microenvironment and the interaction with portal ductal and mesenchymal populations could promote a more portalized identity, we compared the gene expression profile of hepatocytes from h-HepOrgs cultured in DM with that of assembloids (also cultured in DM). Notably, hepatocytes within assembloids exhibited higher expression of periportal markers, including SAA1, SAA2, HAMP and APOA1, whereas pericentral genes such as CYP2E1, CYP3A4 and GLUL were downregulated relative to h-HepOrgs cultured alone (Fig. 4i). Staining for SAA1 and SAA2 confirmed the spatially heterogenous expression of these portal markers, with positive cells overlapping with regions of E-cadherin-high cells (Extended Data Fig. 9a), in agreement with our scRNA-seq results (Fig. 4h,i).

Notably, periportal assembloids outperformed differentiated h-HepOrgs in both urea production and gluconeogenesis (both of which are portal functions), while the drug-metabolizing capacity associated with pericentral hepatocytes was less pronounced than in hepatocyte organoids, in line with their more portal-like nature (Fig. 4j,k and Extended Data Fig. 9b). As expected, periportal assembloids retained core hepatocyte functions, with albumin secretion increasing over time to levels matching those for hepatocyte organoids and exceeding those of 1-day 2D primary hepatocyte cultures (Fig. 4l).

These findings suggest that the periportal microenvironment within assembloids could promote acquisition of a more portal-like hepatocyte identity. In line with this hypothesis, we noted that some hepatocyte membranes joined the lumen of the bile ducts, similar to what we observed in tissue in vivo and suggestive of physiological cell–cell contact between these cell types (Extended Data Fig. 9c,d).

Taking these data together, our human liver periportal assembloid model captures the gene expression, the cell interactions and aspects of the tissue architecture of the native human liver periportal region.

Assembloids model features of biliary fibrosis

Portal mesenchyme often contributes to myofibroblast populations in human fibrosis[50]. Hence, we next investigated whether we could use our human assembloid model containing portal fibroblasts to recapitulate aspects of human liver disease in vitro, specifically biliary fibrosis. Interestingly, increasing mesenchymal cell numbers (20-fold) while keeping the other cell numbers constant (even from the same source tissue) altered the assembloids’ composition, with increased cholangiocyte (GFP+KRT19+) numbers while hepatocyte (HNF4A+) numbers decreased (Fig. 5a–e). Ki-67 staining indicated that cholangiocytes exhibited early proliferative responses to fibrotic cues, and cleaved caspase 3 staining revealed that the reduction in hepatocyte numbers was associated with increased cell death occurring, at least in part, through apoptosis (Extended Data Fig. 10a–c). This finding was consistent with our observations in mouse assembloids[51], suggesting a conserved mechanism across species.

Fig. 5: Periportal assembloids mimic aspects of human biliary fibrosis.

a–h, Assembloids were generated by assembling h-HepOrgs, cholangiocytes/ductal cells derived from h-CholOrgs (nGFP+) and portal fibroblasts (nRFP+) at a ratio of 1 h-HepOrg to 25 portal fibroblasts and 100 cholangiocytes (homeostatic-like assembloids) or 1 h-HepOrg to 500 portal fibroblasts (20 times more mesenchymal cells) and 100 cholangiocytes (fibrotic-like assembloids) (a); 24 h or 7 days later, the cultures were collected and processed for immunofluorescence (b–e) or RNA-seq (f–h) analysis. a, Experimental design. b, Representative bright-field image of an assembloid at 24 h in an AggreWell plate. Scale bar, 100 µm. c, Representative bright-field images of a homeostatic-like assembloid (left) and a matching fibrotic-like assembloid with 20-fold excess mesenchymal cells (right) at 7 days. Scale bars, 100 µm. d, Cell composition of homeostatic-like and fibrotic-like assembloids. Dots correspond to the percentages of hepatocytes, cholangiocytes and portal fibroblasts in each structure. The graph shows the mean ± s.e.m. for assembloids from three independent experiments. Two-sided paired (by donor) Student’s t test; P values are shown in the figure. e, Representative images of homeostatic-like (top) and fibrotic-like (bottom) assembloids stained for hepatocyte (HNF4A; yellow, magenta arrow) and cholangiocyte (KRT19; white and nGFP, white arrow) markers. Mesenchyme is marked by nRFP (magenta). The low-magnification views also show phalloidin (membrane; blue) and DAPI (nuclei; blue) channels. Scale bars, 50 µm. f, Correlation between the three different populations in fibrotic assembloids (this study) and corresponding cells in diseased human liver cell atlases. g, Dot plot for hepatocyte, cholangiocyte and mesenchyme markers in fibrotic-like assembloids (this study) and liver tissue datasets. References used for comparison are refs. 8,13. h, GSEA comparing fibrotic and homeostatic assembloids. Dot plots show selected enriched terms in fibrotic versus homeostatic assembloids. Dot colour corresponds to the adjusted P value (permutation test in clusterProfiler, adjusted using the Benjamini–Hochberg method). Dot size, gene ratio (the number of core enrichment genes divided by the total number of genes in the pathway). Orange, KEGG pathways; brown, Gene Ontology (GO) terms. Full list in Supplementary Data 4. Panel a created in BioRender. Yuan, L. (2025) https://BioRender.com/ejby6iy.

Source Data

scRNA-seq clustering and correlation analyses revealed that the cells from assembloids with excess mesenchyme recapitulated the transcriptional signatures of diseased human livers8,13 (Fig. 5f). The top markers identifying the three cell populations in the corresponding patient datasets were also highly expressed in the corresponding assembloid cells (Fig. 5g), and GSEA revealed that mesenchyme and cholangiocytes from fibrotic, but not homeostatic, assembloids had increased expression of proteins involved in collagen and matrix deposition processes (Fig. 5h, Extended Data Fig. 10f,g and Supplementary Data 4). Similarly, cholangiocytes, but not mesenchyme, exhibited signatures of proliferation (Fig. 5h and Extended Data Fig. 10f), in agreement with the increased number of GFP+ cholangiocytes detected (Fig. 5c,d). These gene signatures (increased matrix and cholangiocyte numbers) are reminiscent of the fibrotic tissue from human patients with biliary fibrosis and primary sclerosing cholangitis (PSC)13,50. We therefore refer to assembloids with excess mesenchyme as ‘fibrotic-like’, to distinguish them from the ‘homeostatic-like’ assembloids with homeostatic numbers of mesenchymal cells.

Notably, hepatocytes from fibrotic-like assembloids were positively enriched for gene sets related to inflammatory reactions, including tumour necrosis factor (TNF) signalling, several interleukins (IL-4 and IL-6), and NF-κB, JAK–STAT and Toll-like receptor cascades (Fig. 5h and Extended Data Fig. 10d,e). Conversely, cell cycle signatures and hepatocyte functions such as bile secretion and lipid and drug metabolism were negatively enriched (Fig. 5h). Both hepatocytes and cholangiocytes from fibrotic assembloids were also highly enriched in transforming growth factor-β (TGFβ) signalling signatures (Fig. 5h and Extended Data Fig. 10d–f), mirroring the transcriptional changes in patients with biliary fibrosis[13].

Morphologically, we observed that fibrotic-like assembloids, but not matching homeostatic assembloids, exhibited a cystic-like phenotype reminiscent of cholangiocyte organoids (Extended Data Fig. 10h,i). This observation was in line with the immunofluorescence analysis, which indicated that in fibrotic-like assembloids some hepatocytes (HNF4A+GFP−) were positive for the cholangiocyte marker KRT19, and opened lumina, resembling the polarity of simple ductal epithelium, suggestive of potential hepatocyte-to-duct transdifferentiation (Extended Data Fig. 10j). Interestingly, all these phenotypes, including (1) enrichment of gene signatures for TNF, IL-4 and IL-6 signalling; (2) increased hepatocyte apoptosis; and (3) increased expression of cholangiocyte markers, have been reported in patients with fibrosis as well as in recent liver cell atlases of patients with PSC and primary biliary cirrhosis (PBC)13,52. These results combined suggest that our assembloid model with excess mesenchyme mimics some aspects of human biliary fibrosis as seen in cholangiopathies, including PSC and PBC.

Discussion

Failure in maintaining the intricate cellular organization and multidirectional interactions of the cells within the liver leads to chronic disease, often presenting with cholestasis and fibrosis, which progresses to cirrhosis and cancer53,54. Despite being reductionist by nature, ex vivo systems offer powerful tools to dissect disease mechanisms. We recently showed that mouse periportal assembloids model key architectural features of the in vivo tissue and can serve as a tractable in vitro model to investigate universal principles of bile canaliculi formation, cholestatic injury or fibrogenesis[51]. However, species-specific differences in drug metabolism, toxicity or pathophysiology necessitate the development of complementary human models that capture patient-specific features to better understand disease mechanisms and identify therapeutics.

Recent advances in human liver models underscore the ongoing efforts and broad interest in developing physiologically relevant in vitro systems. These include induced pluripotent stem cell-derived hepatocyte organoids harbouring liver sinusoidal endothelial cell (LSEC)-like cells[55] or exhibiting dual zonation[56], functional hepatocyte organoids derived from cryopreserved hepatocytes[57], mass generation of hepatobiliary organoids[58], co-cultures of dermal fibroblasts with hepatocyte spheroids[59], and mouse fibroblasts aggregated with hepatocyte spheroids and cholangiocyte organoids[60]. However, a model capable of recapitulating the multicellular periportal liver tissue organization and cellular interactions ex vivo—while it would enable inter-individual comparative studies and investigation of patient-specific disease traits—has not yet been developed.

Here we overcome this challenge by establishing long-term-expandable h-HepOrgs from adult patient liver tissue and combining them with h-CholOrgs and human portal mesenchyme to form complex periportal liver assembloids. These assembloids recapitulate essential structural and functional features of the native human periportal region and, upon manipulation, model aspects of human biliary fibrosis. Our h-HepOrg model enables long-term expansion while preserving functional drug-metabolizing capabilities and capturing patient-to-patient variability, including differences in metabolic enzymes and disease-predisposing genes. At both the cellular and mesoscale levels, h-HepOrgs mimic fine architectural features such as bile canaliculus morphology and display heterogeneous expression of zonated hepatocyte genes. Although we observed variability in bile canaliculi morphology among organoids derived from different donors, whether this reflects true patient-to-patient differences will require further investigation.

Interestingly, assembloids exhibited increased portal-region functional features. Whether direct interactions between hepatocytes, cholangiocytes and portal mesenchyme are sufficient to instruct portal-specific hepatocyte identity remains an open question. Likewise, the possibility that hepatocyte subpopulations at the onset of culture influence differential responses to microenvironmental cues cannot be excluded. Our modular, ‘self-organized Lego-like’ assembloid platform provides a unique system to systematically manipulate individual cellular components and begin to dissect, in a controlled setting, how specific microenvironmental signals or cell–cell interactions contribute to human hepatocyte identity and zonation.

Of note, by increasing the number of portal mesenchymal cells, we generated assembloids that recapitulated several aspects of human cholestatic disease and biliary fibrosis. One caveat, though, is the lack of other mesenchymal cells, immune cells and portal vasculature (portal vein and hepatic artery), which limits the formation of a true periportal triad. Incorporating these will be crucial to reproduce all aspects of liver disease.

In summary, the patient-derived hepatocyte organoids and periportal assembloid models we present here hold the potential to initiate a new era in diverse areas of liver research, including in diagnostics, toxicology, personalized drug efficacy screening and cellular transplantation therapy.

Methods

Human specimens

All human liver tissues used in this study were obtained after informed consent was obtained from patients undergoing operations at either the Department of Visceral, Thoracic and Vascular Surgery (VTG), University Hospital Carl Gustav Carus Dresden (UKD) or Leipzig University Medical Center. Informed consent was obtained from all participants. Use of the human samples for this study was approved by the corresponding institutional review boards of either the University Hospital Carl Gustav Carus Dresden (ethical vote BO-EK-57022020, ratified on 10 March 2020) or the Leipzig University Hospital (ethical vote: registration number 322/17-ek, date 10 June 2020 ratified 30 November 2021 and registration number 450/21-ek, date 21 November 2021 ratified on 4 October 2024). Five samples (F-PHH1–F-PHH5) were obtained from cryopreserved hepatocytes from Lonza Pharma&Biotech-Bioscience Solutions. Resected liver specimens were obtained from patients undergoing partial hepatectomy for benign or malignant conditions (for example, colorectal liver metastases, hepatocellular carcinoma or benign focal lesions). Only histologically normal, non-tumorous tissue adjacent to the resection site was used for organoid derivation. Clinical background information (sex, age, diagnosis/surgical indication) is provided in Supplementary Tables 1 and 2. Commercially obtained cryopreserved PHHs were derived from the livers of healthy donors deemed unsuitable for transplantation. Commercial number and supplier are given in Supplementary Table 2.

All procedures involving human material were conducted in accordance with the Declaration of Helsinki and institutional ethical guidelines.

Isolation of primary human hepatocytes and cholangiocytes

PHHs were isolated using a two-step collagenase perfusion method as described in refs. 34,61. The human liver tissue received from UKD was perfused with solution A (composed of 10 mM HEPES and 2.5 mM EGTA in HBSS) at 39 °C for at least 20 min, with a rate of 15 ml per 20 s. Subsequently, the perfusion solution was switched to solution B (containing 100 mM HEPES, 4.8 mM CaCl_2 and 1 g l–1 collagenase P, in HBSS) and perfused at 37 °C for 5–15 min, also at a rate of 15 ml per 20 s. The digestion process was halted by adding cold William’s E medium supplemented with 1% HEPES, 1% GlutaMAX and 1% penicillin/streptomycin. PHHs were detached from the tissue by shaking using forceps and combing the cells out of the tissue. Afterwards, they were filtered through a 100-µm nylon cell strainer. Cells were then spun at 50g for 5 min, and the resulting pellet was resuspended in cold William’s E medium supplemented with 1% HEPES, 1% GlutaMAX and 1% penicillin/streptomycin. The cell suspension was kept cold and centrifuged again at 50g for 5 min.

For samples obtained from Leipzig University Hospital, the perfusion procedure differed slightly: solution A (composed of 10 mM HEPES (Carl Roth), 143 mM NaCl, 6.7 mM KCl, 2.4 mM EGTA, 5 mM N-acetyl-l-cysteine, 11 mM d-glucose (all provided by Sigma-Aldrich) and 32 U l–1 human insulin (Eli Lilly) in double-distilled water (pH 7.4)) at 39 °C with a rate of 25 ml per minute for at least 20 min. The perfusion solution was then switched to solution B (composed of 67 mM NaCl, 6.7 mM KCl, 10 mM HEPES, 0.5% BSA, 4.8 mM CaCl_2 × 2H_2O (all provided by Sigma-Aldrich), and 1 g l–1 collagenase P (Roche) in ddH_2O (pH 7.6), diluted 1:2 in stop solution (composed of DPBS with Ca2+, Mg2+ (Gibco), supplemented with 16.7% FBS (Merck)) and perfused at 39 °C for 5–15 min at a rate of 25 ml min–1. The digestion was stopped by adding cold stop solution. Hepatocytes were filtered through a funnel with gauze (Hartmann) and centrifuged at 51g for 5 min. Cell pellets were washed in DPBS with Ca2+, Mg2+, centrifugated at 51g for 5 min and resuspended in William’s E medium supplemented with 10% FBS (Merck), 15 mM HEPES, 1 mM sodium pyruvate, 1% penicillin/streptomycin, 1% MEM NEAA (all provided by Gibco), 1 µg ml–1 dexamethasone (Jenapharm) and 32 U l–1 human insulin (Eli Lilly). The isolated PHHs were shipped overnight in ChillProtec plus medium (Biochrom).

Cryopreserved hepatocytes (F-PHH1–F-PHH5; Supplementary Table 2), commercially available from Lonza, were defrosted using human hepatocyte thawing medium (Lonza) following the manufacturer’s instructions.

The isolated PHH preparations (either from fresh tissue from Dresden or Leipzig Hospital or commercially available frozen hepatocytes) were enriched for both EpCAM-negative (hepatocytes) and EpCAM-positive (cholangiocytes) by MACS using an anti-human CD326 antibody (BioLegend) and anti-biotin microbeads (Ultra Pure, Miltenyi) following the manufacturer’s instructions. The EpCAM-negative fraction with a viability of >50% (Supplementary Table 1) was used to generate hepatocyte organoids as described below (see ‘Hepatocyte organoid culture’). The EpCAM-positive fraction, formed by human cholangiocytes, was used to generate h-CholOrgs as described previously4,5 and in ‘Cholangiocyte organoid culture’. A digestion method without perfusion, as the one detailed in ref. [4], only generated h-CholOrgs. h-HepOrgs were not formed under non-perfused protocols.

The complete list of patients used and the comparative between digestion and perfusion are provided in Supplementary Tables 1 and 2.

Flow cytometry validation of PHH purity following MACS enrichment

Freshly isolated PHHs and MACS-enriched EpCAM-negative PHHs (as described above) were centrifuged at 80g for 5 min. Pellets were resuspended in HBSS containing 2% FBS and incubated on ice for 10 min (blocking). After centrifugation (80g, 5 min), cells were resuspended in HBSS with 1% FBS, stained with EpCAM-Alexa 488 (5 μl per test; BioLegend), and incubated for 45 min on ice. Cells were then washed twice with HBSS containing 1% FBS, centrifuged and resuspended in 200 μl HBSS with 1% FBS, DAPI (1:1,000) and DNase I (1:1,000) for flow cytometry analysis.

Cholangiocyte organoid culture

For cholangiocyte organoid cultures, EpCAM-positive cholangiocytes were mixed with Matrigel growth factor reduced (Matrigel, Corning) or Cultrex basement membrane extract 2 (BME2) (Cultrex-RGF basement membrane extract type 2, BME2 (AMSBIO) at 50,000 cells per 50 μl in each well of a 24-well plate and cultured at 37 °C and 5% CO_2 in h-CholOrg EM medium as described in refs. 4,5: AdDMEM/F12 medium containing 1% HEPES, 1% penicillin/streptomycin, 1% GlutaMAX, 1× B27 and 1.25 mM N-acetylcysteine (Sigma) supplemented with 10 nM gastrin (Merck/Sigma), 50 ng ml–1 hEGF (Peprotech), 10% RSPO1 conditioned medium (homemade), 100 ng ml–1 FGF10 (Peprotech), 10 mM nicotinamide (Merck/Sigma) and 25 ng ml–1 HGF (Peprotech)], 5 μM A8301 (Tocris) and 10 μM forskolin (Tocris, 1099). For the first 3–5 days in culture, this medium was supplemented with 30% WNT3a conditioned medium (Wnt-CM) (homemade), 25 ng ml–1 Noggin (Peprotech) and 10 μM ROCK inhibitor (Ri) (Y-27632, Merck/Sigma). The grown cholangiocyte organoids were passaged at a 1:3 ratio once a week as described in ref. [4]. Organoid lines were routinely tested for mycoplasma.

Hepatocyte organoid culture

For hepatocyte organoid cultures, the isolated PHHs (EpCAM-negative fraction) were mixed with Matrigel (Corning) or BME2 (AMSBIO), and 12,500–50,000 cells were seeded in 50-μl domes per well in 24-well plates and incubated at 37 °C and 5% CO_2. After gel solidification, culture medium was added. The culture medium was based on the medium from ref. [29] for hepatoblasts (MM) with modifications and the addition of WNT and YAP activation. The medium was composed of AdDMEM/F12 (Invitrogen) supplemented with 1% HEPES, 1% GlutaMAX (ThermoFisher), 1% penicillin/streptomycin (ThermoFisher), 1× B27 without retinoic acid (Gibco), 1.25 mM N-acetylcysteine (Sigma), 10 nM gastrin (Sigma) and the following growth factors: 50 ng ml–1 hEGF (Peprotech), 15% RSPO1 conditioned medium (home-made), 100 ng ml–1 FGF10 (Peprotech), 100 ng ml–1 FGF7 (Peprotech), 50 ng ml–1 HGF (Peprotech), 10 mM nicotinamide (Sigma, for EM1 medium only), 2 µM A83-01 (Tocris), 3 µM CHIR99021 (Tocris), 10 µM Y-27632 (Tocris), 0.5 nM Wnt surrogate Fc fusion protein as in ref. [40] (IPA, N001) and 10 µM TRULI (Axon) or 10 µM TDI-011536 (Selleckchem).

After 1 week to 10 days, the organoids were removed from the Matrigel or BME2, mechanically dissociated into small fragments using TrypLE Express (Gibco) and transferred to fresh Matrigel or BME2. Passaging was performed once per week at a 1:2 split ratio for at least 3 months. For preparation of frozen stocks, the organoid cultures were dissociated, mixed with Recovery cell culture freezing medium (Gibco) and frozen following standard procedures.

For the optimization of culture conditions, medium component screening experiments were performed in which each of the components Amphiregulin (AREG; 100 ng ml–1; R&D Systems), dexamethasone (1.6 µM; Sigma), G-CSF (50 ng ml–1; R&D Systems), IL-6 (2 ng ml–1; R&D Systems), M-3m3FBS (phospholipase C activator; 25 µM; Tocris), TGFα (100 ng ml–1) and TRULI (Axon) was added to our previously published mouse hepatoblast medium (MM[29]) with minor modifications: AdDMEM/F12 (Invitrogen) supplemented with 1% HEPES, 1% GlutaMAX, 1% penicillin/streptomycin, 1× B27 without retinoic acid, 1.25 mM N-acetylcysteine, 10 nM gastrin, 50 ng ml–1 hEGF, 15% RSPO1 conditioned medium, 100 ng ml–1 FGF10, 100 ng ml–1 FGF7, 50 ng ml–1 HGF, 10 mM nicotinamide, 2 µM A83-01, 3 µM CHIR99021, 10 µM Y-27632 and 0.5 nM Wnt surrogate Fc fusion protein. Note that addition of TRULI alone resulted in a significant increase in organoid formation efficiency (Fig. 1c,e). However, after 1–2 splits, the cultures rapidly deteriorated and could not be expanded further (Fig. 1f).

For h-HepOrg hepatic differentiation, h-HepOrgs were expanded in EM2 medium above, split, seeded and cultured for 2–5 days under EM1 culture medium, after which the medium was changed to DM medium composed of AdDMEM/F12 supplemented with 1% HEPES, 1% GlutaMAX, 1% penicillin/streptomycin, 1× B27 without retinoic acid, 1.25 mM N-acetylcysteine, 50 ng ml–1 hEGF, 15% RSPO1 conditioned medium, 50 ng ml–1 HGF, 2 µM A83-01, 3 µM CHIR99021, 10 µM Y-27632, 0.5 nM Wnt surrogate Fc fusion protein, 100 ng ml–1 FGF19 (R&D Systems) and 1.6 µM dexamethasone (Sigma). DM was changed every 2–3 days for 7 days.

For organoid formation efficiency, primary hepatocytes were isolated and cultured in different media as described above. To prevent organoids from fusing, 25,000 (for EM2 medium) or 50,000 (all other media) viable hepatocytes (viability of >80%) were plated in 50 μl Matrigel or BME2 and cultured as described above. After 12–14 days, organoid numbers were counted and results were expressed as a percentage relative to the initial seeding cell numbers. Organoid lines were routinely tested for mycoplasma.

Isolation of human liver portal fibroblasts

Human liver portal fibroblasts were isolated from human liver tissues by collagenase digestion. In brief, human liver tissue was minced and rinsed with cold DMEM (Gibco) supplemented with 1% HEPES, 1% GlutaMAX, 1% penicillin/streptomycin and 1% FBS. Minced tissues were incubated with a collagenase solution consisting of 2.5 mg ml–1 collagenase D (Roche), 0.1 mg ml–1 DNase I (Sigma), 1× B27 without retinoic acid, 1.25 mM N-acetylcysteine, 5% RSPO1 conditioned medium and 10 µM Y-27632 in DMEM supplemented with 1% HEPES, 1% GlutaMAX and 1% penicillin/streptomycin. Incubation was carried out for 30–60 min at 37 °C on a shaker set at 120 rpm. The digestion was halted by adding cold DMEM supplemented with 1% HEPES, 1% GlutaMAX, 1% penicillin/streptomycin and 1% FBS. The suspension was then filtered through a 70-µm cell strainer and centrifuged for 5 min at 300g. After removing the supernatant, the cell pellet was resuspended in cold DMEM supplemented with 1% HEPES, 1% GlutaMAX, 1% penicillin/streptomycin and 1% FBS. The suspension was centrifuged again for 5 min at 300g, and the resulting pellet was resuspended in cold DMEM supplemented with 1% HEPES, 1% GlutaMAX, 1% penicillin/streptomycin and 20% FBS. For sorting, portal fibroblasts were stained with anti-human CD90 (THY1) conjugated to APC, anti-human CD140a (PDGFRα) conjugated to PE, anti-CD11b/CD31/CD45 conjugated to PECy7 and anti-EpCAM conjugated to Alexa 488 for 30 min on ice and washed twice. THY1-positive portal fibroblasts were sorted using a BD FACSAria Fusion and cultured in DMEM supplemented with 1% HEPES, 1% GlutaMAX, 1% penicillin/streptomycin and 20% FBS at 37 °C and 5% CO_2 until used for assembloid formation or frozen for biobanking. Portal fibroblast cultures were routinely tested for mycoplasma.

Viral infection

For portal fibroblast infections, cultures (passage 0 or 1) grown in DMEM+++ supplemented with 20% FBS (Merck/Sigma, F7524) were washed with PBS and dissociated to single cells by incubation with 1× TrypLE for 6 min at 37 °C. The cell concentration was determined by manual counting in a haemocytometer, and 10,000 cells were plated into each well of a 48-well plate and the medium mixed with nRFP- or nGFP-encoding lentivirus (LVP360-R and LVP360-G, GenTarget) to achieve a multiplicity of infection (MOI) of 10–30, then replaced after 12 h and the solution was changed after 72 h.

For cholangiocyte organoid infection, duct cells (passage 0 or 1) were extracted from Matrigel and digested with TrypLE to prepare single-cell suspensions as described in ref. [5], which were then manually counted using a haemocytometer to determine cell concentration. In a 48-well plate, 150 µl of cells and 50 µl of virus suspension from nRFP- or nGFP-encoding lentivirus (LVP360-R and LVP360-G, GenTarget) were added to achieve a MOI of 10–30, mixed thoroughly, centrifuged at 600g for 60 min at 32 °C and incubated for 6 h at 37 °C in 5% CO_2. Cells were collected in 1.5-ml tubes and centrifuged at 600g for 5 min, the virus-containing medium was discarded and cells were resuspended in 25 µl of Matrigel, followed by the addition of cholangiocyte medium (supplemented with 30% WntCM, 25 ng ml–1 noggin and 10 µM Y-27632 for the first 3 days).

Periportal assembloids

To generate liver periportal assembloids comprising hepatocytes, cholangiocytes and portal fibroblasts, we first prepared the cellular components as follows: nGFP-labelled cholangiocyte organoids (passage 5–11), grown in cholangiocyte expansion medium (h-CholOrg-EM) as detailed above, were collected from Matrigel using cold AdDMEM/F12 (Invitrogen, 12634010) containing 1% HEPES (ThermoFisher, 15630-056), 1% penicillin/streptomycin (ThermoFisher, 15140-122) and 1% GlutaMAX (ThermoFisher, 35050038). Matrigel was removed and organoids were dissociated to single cells using prewarmed 1× TrypLE (Gibco) for 7–12 min at 37 °C. nRFP-labelled portal fibroblast cultures (passage 5–12) grown in DMEM+++ with 20% FBS (Merck/Sigma, F7524) were washed with PBS and dissociated to single cells by incubation with 1× TrypLE for 6 min at 37 °C. Both single-cell suspensions were spun at 200g for 5 min, resuspended in DM medium as described above but without A8301, and then manually counted with a haemocytometer to determine cell concentration. Cultured h-HepOrgs from EM2 medium were split and transferred to EM1 medium for 2 days and then to DM medium for 3 days. Hepatocyte organoids were then collected and washed using cold AdDMEM/F12 supplemented with 1% HEPES, 1% penicillin/streptomycin and 1% GlutaMAX and incubated for 10 min on ice using cold cell recovery solution (Corning, 354253) to remove the ECM. h-HepOrgs were then resuspended using DM without A8301 and placed into a low-attachment six-well plate; differentiated organoids (with bubbly morphology) were selected and hand-picked under a stereomicroscope.

To define an approach for human periportal liver assembloid formation, several iterations were performed. First, we sought to identify a medium that would support assembloid formation, that is, the culture of all three cell types: hepatocytes, cholangiocytes/ductal cells and portal mesenchyme without overgrowth of any of them, we tested several media and found that a minor adaptation of the DM medium used for h-HepOrgs differentiation without A8301 (assembloid medium) supported culture of the three cell types while preventing their overgrowth. To determine the optimal quantities of the three cell types required for periportal assembloid formation, we first investigated the proportions of portal fibroblasts and ductal cells in healthy human periportal liver tissue. We observed that the ratio varies from donor to donor from 1:1 to 4:1 ductal cells per fibroblast. Therefore, we tested this range of ratios in vitro by varying the proportions of mesenchyme and ductal cells that were mixed with a single h-HepOrg (~200-µm diameter). In short, in 96-well low-attachment U-bottom plates (Corning), we assembled (as described below) 1 h-HepOrg with 25 portal fibroblasts and 25, 50, 100 or 200 cholangiocytes, or with 100 cholangiocytes and 50 or 100 portal fibroblasts. We selected the proportion of 25 portal fibroblasts per 100 cholangiocytes/ductal cells. In AggreWell plates (AggreWell 800, Stem Cell Technologies), we scaled up proportionally, taking into account that the AggreWell 800 plate has 300 microwells in each well and used 7,500 portal fibroblasts, 30,000 cholangiocytes and 100 h-HepOrgs (proportion of 1 h-HepOrg to 75 portal fibroblasts and 300 cholangiocytes).

For non-healthy/non-physiological ratios, we used 500 portal fibroblasts, 100 cholangiocytes and 1 h-HepOrg for 96-well low-attachment U-bottom plates, and 15,0000 portal fibroblasts, 30,000 cholangiocytes and 50 h-HepOrgs for AggreWell plates.

For assembly in MW96, we mixed fibroblasts and cholangiocytes in 96-well low-adhesion U-bottom plates using 150 μl DM (without A8301) with 2.4 mg ml–1 methylcellulose (MeC; Sigma, M6385) and spun at 50g for 5 min. Individual h-HepOrgs were then added to the well and the mixture was incubated for 18–24 h at 37 °C and 5% CO_2. For assembly in AggreWell plates, plates were first pretreated as recommended by the manufacturer. Then, ductal and mesenchymal cells together with h-HepOrgs were mixed in 1.5 ml DM (without A8301) with 2.4 mg ml–1 methylcellulose, spun down for 5 min at 50g and incubated for 18–24 h at 37 °C and 5% CO_2. After 18–24 h in suspension in the 96-well/AggreWell plate, the cell suspension was collected with a 1-ml pipette and transferred to a low-attachment 6-well plate. The structures were manually picked under a stereomicroscope and seeded in 25 μl Matrigel dome in prewarmed 48-well plates. The Matrigel was allowed to solidify for 30 min at 37 °C in 5% CO_2, and the wells were overlayed with an additional 300 μl of DM (without A8301). The medium was changed every 3–4 days. Under these conditions, 70% of the initial cholangiocytes formed a lumen. Raw data were incorporated into the quantification of periportal-like spatial organization in assembloids (source data for Extended Data Fig. 7e).

Immunostaining of organoids and assembloids

For immunofluorescence staining, organoids and assembloids were first extracted from Matrigel with ice-cold Cell Recovery solution and then fixed for 30 min with 4% paraformaldehyde (PFA) at 4 °C. Fixed organoids were washed and transferred to µ-Slide 8-well chamber slides (glass bottom; Ibidi). Blocking and permeabilization were performed for 1 h at room temperature in PBS containing 2% BSA and 0.1%, 0.2%, 0.5% or 1% Triton X-100 depending on the antigen (Supplementary Data 5). The samples were incubated with primary antibodies overnight at 4 °C in blocking solution. After that, the antibody was washed with three washes with PBS and the samples were incubated overnight at 4 °C or for 8 h at room temperature with secondary antibodies diluted in blocking solution and, if required, also phalloidin and DAPI were added to the secondary antibody mix. The samples were washed three times with PBS and subsequently cleared using fructose-glycerol clearing solution (25 ml glycerol, 5.3 ml dH_2O and 22.5 g fructose–60% glycerol and 2.5 M fructose). The samples were stored in PBS until they were cleared for imaging as described above. The antibodies and dilutions used are listed in Supplementary Data 5.

For haematoxylin and eosin (H&E) staining, organoids were collected in cold DPBS (Gibco) and fixed with 4% PFA for 30 min and dehydrated and embedded in paraffin using standard methods. Paraffin sections (8 μm) were cut and stained for H&E using standard protocols.

Immunostaining of thin and thick tissue sections

For thin tissue sections (8–12 μm) and staining, human liver tissues were fixed in 10% formalin overnight with rolling at 4 °C. After fixation, tissues were washed with PBS and incubated with 10% sucrose for 1–2 h, then transferred to 30% sucrose in PBS for 24 h and subsequently embedded in OCT compound (VWR, 361603E) to generate OCT cryopreserved tissue blocks. Tissue blocks were cryosectioned on a CryoStar NX70 cryostat (ThermoScientific). Sections were blocked in PBS with 10% donkey serum (DS) and 0.1% Triton X-100 for 2 h at room temperature, incubated with primary antibodies diluted in PBS with 3% donkey serum and 0.1% Triton X-100 overnight at 4 °C and subsequently washed and incubated with secondary antibodies diluted in 0.05% BSA in PBS and DAPI for 2 h at room temperature. Sections were mounted in Vectashield. The list of antibodies used is available in Supplementary Data 5.

For thick tissue sections and staining, the protocol from ref. [62] was used. Immediately after surgical resection, liver tissue samples were cut into smaller pieces and fixed in 4% PFA for 24 h on a rotator at 4 °C and washed three times with PBS, followed by quenching with 50 mM ammonium chloride solution (NH_4Cl) for 24 h and again washed three times with PBS. For storage, liver pieces were kept in PBS at 4 °C. For sectioning, livers were embedded in moulds with 4% low-melting agarose (Bio-Rad, 1613111) in PBS and cut into 50- or 100-μm-thick sections on a vibratome (Leica, VT1200S). For deep tissue imaging, if antigen retrieval was required, tissue sections were placed in Eppendorf tubes with prewarmed 1× citrate buffer (Sigma-Aldrich, C9999), pH 6, at 80 °C for 30 min in a shaking heating block and then washed three times with PBS. Tissue sections were permeabilized with 0.5% Triton X-100 in PBS for 1 h at room temperature. The primary antibodies were diluted in Tx buffer (0.2% gelatin, 300 mM NaCl and 0.3% Triton X-100 in PBS) and incubated for 48 h at room temperature. After washing three times for 15 min each with 0.3% Triton X-100 in PBS, the sections were incubated with secondary antibodies, DAPI (1 mg ml–1; 1:1,000) and phalloidin for another 48 h. After washing three times for 15 min each with 0.3% Triton X-100 in PBS and three times for 1 min each with PBS, the optical clearing started by incubating the slices in 25% fructose for 4 h, continued in 50% fructose for 4 h, 70% fructose overnight, 100% fructose (100% wt/vol fructose, 0.5% 1-thioglycerol and 0.1 M phosphate buffer, pH 7.5) overnight, followed by a final overnight incubation in SeeDB solution (80.2% (wt/wt) fructose, 0.5% 1-thioglycerol and 0.1 M phosphate buffer)[63]. The samples were mounted in SeeDB. A list of antibodies and dyes used is available in Supplementary Data 5.

For immunohistochemistry of tissue sections from xenotransplanted mice, mouse liver tissue samples were cut into smaller pieces and fixed in 10% formalin overnight. Sections (4 μm) were subjected to immunohistochemical staining, which was performed using a Dako REAL EnVision detection system (Dako, K5007). Anti-human GAPDH antibody (Abcam) (Supplementary Data 5) was used as the primary antibody and nuclei were counterstained with haematoxylin. Stained tissues were viewed under a Virtual Slide System (Leica, ScanScope CS2).

The immunohistochemistry analysis for PDGFRA, DCN and ASPN in healthy human liver tissue was obtained from the publicly available image dataset from Human Protein Atlas (HPA)[64] (version 24proteinatlas.org). The corresponding URL is indicated in the figure legend.

Imaging of organoids, assembloids and tissues

Bright-field images of organoids were obtained with a Leica DMIL LED inverted microscope and Leica DFC 450C camera or with a Leica M80 stereoscope and MC170HD camera and Leica LAS software. H&E staining of organoids was obtained with a Leica DM4B microscope and DMC5400 camera and Leica LAS X software.

Confocal images of organoids and thick tissue sections were acquired on an inverted single-photon point scanning confocal microscope (Zeiss Cell Discoverer 7 with LSM 900 and Airyscan 2) using a Zeiss APOCHROMAT ×20/0.95-NA Autocorr air objective, with a tube lens of ×0.5 or ×1, and a voxel size of 0.4 × 0.4 × 0.5 μm or 0.5 × 0.5 × 0.5 μm for organoids and 0.3 × 0.3 × 0.3 μm for thick tissue sections. Laser lines 405, 488, 561 and 640 were used for excitation of fluorophores, and GaAsP-PMT detectors were used for detection. High-resolution Airyscan images were acquired using this system for imaging polarity in detail for the tissue sections with a voxel size of 0.0823 × 0.0823 × 0.3 μm. Image processing was done using Zen software or ImageJ/Fiji.

Imaging of assembloids and thin tissue sections was performed using an inverted multiphoton laser-scanning microscope (Zeiss LSM 780 NLO). To improve the resolution, image denoising was performed with deconvolution using HuygensPro. Raw image stacks were imported into the software, and a point spread function (PSF) was either estimated based on the imaging conditions (numerical aperture, wavelength and refractive index) or obtained from PSF calibration images. The HuygensPro classic maximum likelihood estimation (CMLE) algorithm was applied for deconvolution, with an iteration stop criterion based on optimal signal-to-noise ratio and minimal change in successive iterations.

Image analysis

Quantification of the percentage of YAP-positive and YAP-negative nuclei was performed using Arivis 4D Pro software (version 4.2.0). The steps of the analysis pipeline included background correction, denoising, nuclear segmentation based on DAPI and quantification of the fluorescence intensity of YAP immunofluorescent staining in the nuclei. The total number of nuclei and the number of YAP-positive nuclei were quantified, and, subsequently, the number of YAP-negative nuclei was calculated by subtracting the number of YAP-positive nuclei from the total number of nuclei. Finally, the percentages of YAP-positive and YAP-negative nuclei were calculated.

Quantification of cytoplasmic to nuclear area was performed using Arivis 4D Pro software (version 4.2.0). For this, a representative 2D z slice was taken from each organoid. The analysis pipeline included preprocessing steps of background correction on the phalloidin channel (marking cell borders) and normalization and denoising on the DAPI channel (marking nuclei). To obtain the nuclear area, nuclear segmentation was done based on DAPI, followed by quantification of the total nuclear area. For the cytoplasmic area, segmentation was done based on phalloidin to obtain the outline of the area occupied by the cytoplasm. Finally, the ratio of cytoplasmic area to nuclear area was calculated.

For 3D visualization of bile canaliculi, high-resolution images were obtained as described above. Segmentation was performed on CD13 (for bile canaliculi) and F-actin (cell borders) staining with phalloidin. Analysis of bile canaliculus morphology and bile canaliculus network properties was performed using a custom-made Fiji script publicly available at https://git.mpi-cbg.de/huch_lab/assembloid-paper. A description of the script can be found in ref. [51] In brief, immunofluorescence images from several conditions were used in this analysis: EM2, DM and liver tissue, from hereon referred as ‘structure’. We refer to individual bile canaliculus networks as ‘network’. We determined the connectivity of the network by analysing the total number of branching points (number of triple junctions) per structure. We determined the length of the network per structure by analysing the total length of all branches in the structure. To compare structures in different conditions, we plotted these values as dot plots in which each dot was one structure. In the case of tissue, each dot was one field of view. The features extracted from Fiji were exported as .csv files and plotted using Prism.

For assembloids, to visualize the structure from different angles, immunofluorescence images were visualized in 3D using MotionTracking (http://motiontracking.mpi-cbg.de)[43]. For this, Gaussian blurring was applied to the channels of interest and then visualized in 3D.

For quantification of cholangiocytes and portal fibroblasts in assembloids, Arivis 4D software (Zeiss) was used. For the analysis, nuclei were segmented based on diameter, probability threshold and split sensitivity to align with the expected morphology in the fluorescence images. When segmentation was incomplete due to weak fluorescence signals, missing nuclei were manually added. This approach was used to determine the number of nuclei per cell and the number of cells per organoid. All segmentation results were manually reviewed and corrected as necessary.

Isolation of mRNA and RT–qPCR analysis

RNA was extracted from organoid cultures or freshly isolated tissue using the RNeasy Mini RNA Extraction Kit (Qiagen) with DNase treatment and reverse-transcribed using Moloney murine leukaemia virus reverse transcriptase (Promega). All targets were amplified (40 cycles) using gene-specific primers (Key Resource Table) and PowerUp SYBR Green master mix (ThermoFisher) or iQ SYBR Green Supermix (Bio-Rad) and run on a qPCR instrument (Thermo Fisher QuantStudio 7 Pro or GeneAmp PCR System 9700; Applied Biosystems respectively). Data were analysed using Design & Analysis 2.7.0 software (ThermoFisher).

Karyotyping

Mitotic metaphases for karyotyping were obtained by subculturing hepatocyte organoids in the active growth phase. The following day, cells were exposed to 0.2 μg ml–1 colcemid (Gibco) for 60 min at 37 °C to arrest them in metaphase. Organoids were dissociated into single cells using TrypLE Express (Gibco). After centrifugation and removal of the supernatant, cells were subjected to hypotonic treatment with a solution of 0.075 M KCl for 30 min at 37 °C, followed by fixation in a 3:1 methanol to acetic acid solution. The preparation was washed three times with the fixative before slide preparation. Chromosomes were stained with Giemsa (Merck) diluted in Gurr buffer (pH 6.8; Gibco). Images were taken with a Zeiss Axio Imager.Z2 upright motorized stand with an ApoTome.2 for improved z contrast.

Functional assays

For functional assays, h-HepOrgs were cultured in EM and DM media and assembloids in DM media as described above. As negative controls, we used h-CholOrgs grown as described above. As positive controls, we used freshly isolated PHHs cultured in standard 2D hepatocyte monolayer culture or in sandwich culture[65]. In brief, for the positive control of 2D hepatocytes, fresh isolated PHHs were plated onto collagen (1.8 mg ml–1; RatCol collagen, Advanced Biomatrix)-coated 24-well plates at 500,000 or 250,000 cells per well in William’s E medium (PAN Biotech) supplemented with 10% FBS, penicillin/streptomycin and 100 nM dexamethasone for 3 h for attachment. For the monolayer culture (1d-PHH monolayer control), the cells were cultured on William’s E medium supplemented with 1% HEPES + 1% GlutaMAX + 1% penicillin/streptomycin and 100 nM dexamethasone for 18 h (or 24 h, for albumin assays) and then processed for the functional assays. For sandwich cultures, fresh isolated PHHs were plated onto collagen as above and overlayed with a second collagen layer (1.2 mg ml–1; RatCol collagen, Advanced Biomatrix) and cultured for 7 days in William’s E medium supplemented with CM4000 cell maintenance supplement (ThermoFisher Scientific).

To determine albumin secretion, supernatant from 24 h was collected and the amount of albumin was determined using a human-specific albumin ELISA kit (Assay Pro) following the manufacturer’s instructions on an ELISA plate reader (Tecan Spark 20M). To measure cytochrome P450 activity, on the day of the experiment cholangiocyte and hepatocyte organoids in EM2 or DM were removed from Matrigel using Cell Recovery solution (Corning). Organoids, 2D hepatocyte monolayers or 2D sandwich cultures were then all cultured in William’s E medium supplemented with 1% HEPES + 1% GlutaMAX + 1% penicillin/streptomycin supplemented with luciferin-H substrate (100 µM) or luciferin-IPA (3 µM) for 6 h. Cytochrome activity was measured using the P450-Glo Assay Kit (Promega) according to the manufacturer’s instructions on a plate reader (PerkinElmer Envision). Results were normalized to total viable cell counts per well.

Urea synthesis assay

To determine urea secretion, cell culture supernatants were collected from 48-well plate after 12 h of culture. The concentration of secreted urea was measured by Urea Assay Kit (Abnova) according to the manufacturer’s instructions.

Measurement of gluconeogenesis

Gluconeogenesis was assessed using a Glucose-Glo Assay (Promega). Organoids/assembloids were first washed twice with PBS to remove residual glucose and then incubated for 24 h in glucose-free medium (Gibco) to deplete intracellular glucose stores. Subsequently, the organoids were stimulated for 24 h in gluconeogenesis-inducing medium (glucose-free medium supplemented with 10 mM lactate; Sigma-Aldrich, L7022) to promote hepatic glucose production.

After incubation, 25 µl of supernatant from each well was transferred to a 96-well assay plate and mixed with an equal volume of glucose detection reagent. Following incubation for 60 min at 37 °C, luminescence was measured using a luminometer.

Cell counting

h-HepOrgs were dissociated into single cells using 10× TrypLE (Gibco) after 10 and 15 days of culture in specified media. Cell counts were determined using a Countess II FL automated cell counter (ThermoFisher Scientific).

Quantification of xenobiotic metabolism by mass spectrometry

h-HepOrgs were cultured in DM as previously described. Assembloids were maintained under the same conditions for 6 days. Freshly isolated PHHs were cultured in a monolayer for 24 h, also as described above. Following culture, all cells were washed twice with PBS. The medium was then replaced with 100 μl of William’s E medium supplemented with 1% HEPES, 1% GlutaMAX, 1% penicillin/streptomycin and verapamil (Merck) at a final concentration of 4 µM. Cells were incubated for 6 h, after which the supernatant was collected and analysed by mass spectrometry.

Organoids and assembloids were dissociated into single cells using 10× TrypLE and manually counted using a haemocytometer. The resulting cells were washed twice with PBS and stored at –20 °C.

Metabolites were separately extracted from the supernatant and from the cells by isopropanol:methanol:chloroform mixture (4:2:1, v/v/v) containing 7.5 mM ammonium formate (termed MS mix). A supernatant aliquot of 100 μl was diluted 20-fold (v/v) with MS mix, vortexed, centrifuged for 7 min at 13g and the pellet was discarded. Cells suspended in 100 μl PBS were first lysed using ~25 stainless steel beads of 0.5 mm in size (Next Advance, USA, 152034) in the Qiagen Ratsch Tissue Lyser at 30 Hz for 8 min and metabolites were extracted as above. Each sample was prepared in three biological replicates and analysed by mass spectrometry immediately after extraction.

Mass spectrometry analysis was performed on a Q Exactive hybrid quadrupole Orbitrap tandem mass spectrometer (ThermoFisher Scientific) in positive ion mode by direct infusion of total extracts. Prior analyses, the internal standard verapamil-13C3 hydrochloride (Merck) was dissolved in methanol and spiked into samples to a final concentration of 200 nM. Aliquots of 40 μl of each sample were then placed on twin.tech PCR Plate 96 (Eppendorf, 0030128.648) and infused into the mass spectrometer via TriVersa NanoMate robotic ion source (Advion Interchim Scientific) using nanoflow chips with a nozzle diameter of 4.1 μm. The ion source was controlled using Chipsoft 8.1.0 software. Spraying voltage and gas back pressure were set to 1.25 kV and 0.95 psi, respectively. The ion transfer capillary temperature was set to 200 °C and the S-lens RF level was set to 50%. A target mass resolution (Rm/z) of 200 was set to 140,000 (full width at half maximum, FWHM) for both Fourier transform mass spectrometry (FT MS) and FT MS/MS spectra. To acquire FT MS spectra, the automated gain control (AGC) was set to 3 × 10^6, the maximum injection time was set to 500 ms, the acquired mass range m/z was 50–700, the lock masses m/z 445.12003 and m/z 338.34174. The acquisition cycle consisted of recording FT MS^1 spectra for 1.2 min followed by two FT MS/MS^2 spectra for 1.8 min from the precursors with m/z 455.291 (for verapamil [M + H]+) and m/z 441.275 (for norverapamil [M + H]+); precursor m/z isolation width was 3 Th.

Spectra were averaged in Xcalibur Qual Browser v.3.0 (ThermoFisher Scientific) over a 30-s time range corresponding to stable spray; peaks of metabolites and standard extracted with 5 ppm mass accuracy. The absolute amount of norverapamil was calculated from its molecular ion intensity normalized to the intensity of the standard. For calibration, aliquots of William’s E medium containing verapamil (Merck, V-002-1ML) with the concentration ranging from 2 μM to 8 nM were diluted 20-fold with MS mix, spiked with the internal standard and analysed as described above. The determined abundance of norverapamil in supernatant and in cellular pellets was summed up, normalized to 10^4 cells and its production rate was expressed in pmol/h.

Xenotransplantation in Fah −/− Rag2 −/− Il2rg −/− (FRG) mice

Male and female Fah−/−Rag2−/−Il2rg−/− (FRG) mice were obtained from Jackson Laboratory. Mice were housed and maintained under specific-pathogen-free conditions in accordance with the principles of laboratory animal care and the guide set by the HYU Industry-University Cooperation Foundation. All animal experiments were conducted under protocols approved by the Institutional Animal Care and Use Committee (IACUC) of Hanyang University (2024-0148B). Experimental groups were not predetermined based on the sex of the mice, and all animals were randomly assigned to experimental procedures. Male mice accounted for approximately 25% of the total cohort. FRG mice 8–16 weeks old were used for all experiments. For their maintenance, mice were administered ad libitum NTBC (2-(2-nitro-4-trifluoromethylbenzoyl)-1,3-cyclohexanedione) in their drinking water.

Mice aged 8–16 weeks of both sexes were kept on NTBC in drinking water until 3 days before the experiment, when NTBC was withdrawn. h-HepOrgs expanded in EM2 and differentiated in DM were dissociated into single cells and prepared for injection. For transplantation experiments, commercially available frozen PHHs were used (F-PHH2; Supplementary Table 2). Organoids cultured under EM2 medium as well as isolated hepatocytes (PHHs) from the same donors were used as controls. Following dissociation, 500,000 dissociated organoid cells or 800,000 PHHs were resuspended in 100 μl AdDMEM/F-12 medium and injected into the spleen. The non-injected negative-control group received 100 μl PBS instead of cells. Mice were cycled in and out of NTBC treatment for 3 days every time their body weight dropped below 80% of the initial weight.

IPA

We performed IPA (Qiagen) to identify potential candidate signalling pathways. For this, we first generated three DEG lists as DEGs between liver cancer organoids and liver healthy (list 1) or cancer (list 2) tissue (Supplementary Data 1_S1) and DEG list between partial hepatectomy and healthy tissue (list 3). Gene lists were generated as follows: lists 1 and 2: gene expression matrices from hepatocellular carcinoma (HCC)-derived organoids, HCC liver tissue and liver tissue from healthy donors were obtained from the Gene Expression Omnibus (GEO) under accession number GSE84073 (ref. [35]). DEGs were identified using DESeq2 (ref. [2]), applying a threshold of |log2 fold change| > 1 and an adjusted P value of <0.1 (Supplementary Data 1_S1). For list 3, DEGs comparing partial hepatectomy and undamaged liver hepatocytes in mouse were sourced from the supplementary tables in ref. [27]. Additionally, a list of genes mutated in both HCC-derived organoid lines was derived from the whole-exome sequencing (WES) results in ref. [35] (list 4). The full list of DEGs from lists 1–4 is provided in Supplementary Data 1_S1.

The three DEG lists and the mutated gene list (lists 1–4) were analysed using IPA, using the canonical pathway analysis and upstream regulator prediction functions (QIAGEN Inc., https://digitalinsights.qiagen.com/ipa). In brief, the significance of the association between the dataset and canonical pathways was determined using a right-tailed Fisher’s exact test, followed by Benjamini–Hochberg correction for multiple testing. For analyses in which log fold changes were available, an activity z score was computed to predict the activation or inhibition likelihood of specific pathways base. Upstream regulator analysis used a computational algorithm to identify upstream regulators potentially responsible for the observed gene expression changes. From the IPA canonical pathway analysis, pathways were filtered based on an adjusted P value of <0.05 and the presence of the keyword ‘signalling’ in the pathway name (Supplementary Data 1_S2). Selected pathways of interest with a mean adjusted P value and frequency of pathway significance across comparisons are plotted in Extended Data Fig. 1c (Supplementary Data 1_S3). Activity z scores from the selected pathways were individually plotted as well as their corresponding mean values in Fig. 1b (Supplementary Data 1_S4,5). Next, results from the upstream regulator analysis were filtered for (1) an adjusted P value of <0.1 as upstream regulator and (2) the molecules from the two selected signalling pathways (Supplementary Data 1_S6). Key components of the signalling pathways and their adjusted P value in upstream regulator analysis are plotted in Extended Data Fig. 1d (Supplementary data 1_S7).

Bulk RNA-seq library preparation

mRNA was isolated from on average 270 ng total RNA by poly(dT) enrichment using the NEBNext Poly(A) mRNA Magnetic Isolation Module (NEB) according to the manufacturer’s instructions. Samples were then directly subjected to the workflow for strand-specific RNA-seq library preparation (Ultra II Directional RNA Library Prep, NEB). For ligation, NEB Next Adapter for Illumina of the NEB Next Multiplex Oligos for Illumina Kit was used. After ligation, adapters were depleted by XP bead purification (BeckmanCoulter) adding the bead solution in a ratio of 0.9:1 to the samples. Unique dual indexing was done during the following PCR enrichment (12 cycles) using amplification primers carrying the same sequence for i7 and i5 index (i5: AATGATACGGCGACCACCGAGATCTACACNNNNNNNNACATCTTTCCCTACACGACGCTCTTCCGATCT; i7: CAAGCAGAAGACGGCATACGAGATNNNNNNNNGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT). After two more XP bead purification steps (0.9:1), libraries were quantified using the Fragment Analyzer (Agilent). Libraries were sequenced on an Illumina NovaSeq 6000 in 100-bp paired-end mode to a depth of 40 million read pairs per library.

RNA-seq data processing

Raw bulk RNA-seq data were processed using nf-core/rnaseq v3.18.0 (https://doi.org/10.5281/zenodo.1400710) of the nf-core collection of workflows[66], using reproducible software environments from the Bioconda[67] and Biocontainers[68] projects. The pipeline was executed with Nextflow (v24.10.5)[69]. The reference genome used was Homo sapiens GRCh38 (Ensembl release 111). The pipeline was run with custom parameters for trimming (extra_trimgalore_args: ‘--nextseq 20 --length 15’), alignment (extra_star_align_args: ‘--outFilterMismatchNmax 999 --outFilterMismatchNoverLmax 0.1 --alignMatesGapMax 200000 --chimSegmentMin 20 --twopassMode Basic --alignIntronMin 20 --alignIntronMax 200000’) and quantification (extra_salmon_quant_args: ‘--seqBias --gcBias --posBias’). The resulting MultiQC report was inspected to ensure overall sequencing quality and pipeline performance.

Transcript-level abundance estimates were imported using the tximeta package[70] to generate a gene-level count matrix. Next, variance stabilizing transformation (VST) from DESeq2 (refs. 71,72) was used to normalize the data. Euclidean distance matrices, principal-component analysis (PCA) and heat map visualizations were computed on the VST-transformed values. On some heat maps, minimum–maximum scaling was applied. In Extended Data Fig. 2a,b, batch correction was performed on the VST-transformed values using limma’s removeBatchEffect, with sample material type (tissue versus organoid) treated as the batch variable[73]. For differential expression analysis, DESeq2 was used. For comparison between MM + WntS + TRULI and primary (fresh isolated PHHs), the design formula ~ donor + condition_l3 was applied (Extended Data Fig. 2). Log-fold changes were shrunken using lfcShrink with the ashr method (type = ‘ashr’), applying a fold-change threshold of 1.5 and a significance threshold of α = 0.05 (ref. [74]). For the comparison between DM and EM2 (Fig. 2e), the design formula ~ batch + donor + condition_l1 was applied. Log-fold changes were shrunken using lfcShrink with the ashr method (type = ‘ashr’), applying a fold-change threshold of 1.5 and a significance threshold of α = 0.05. For the comparison between h-HepOrgs and portal fibroblasts (Extended Data Fig. 6h), the design formula ~sex + cell_type was applied. Log-fold changes were shrunken using lfcShrink with the ashr method (type = ‘ashr’), applying a fold-change threshold of 4 and a significance threshold of α = 0.05. Gene set enrichment analysis (GSEA) was conducted using the clusterProfiler package, leveraging gseKEGG, gseGO and gsePathway for pathway enrichment analysis[75].

The zonated gene list (Extended Data Fig. 3h) was obtained by manually curating genes that have been confirmed to be portally or centrally zonated from human spatial transcriptomic datasets10,11,46,47 (a full list is provided in Supplementary Data 2_S6). We then intersected this refined zonated gene list with our list of differentially expressed genes in the DM versus EM2 comparison.

Donor-specific genes were identified separately for batches Y1/Y2 and S1/S2 using a likelihood ratio test (LRT) with the full model ~donor and the reduced model ~1. Genes with an adjusted P value of <0.05 were retained, and the resulting gene lists from the two batches were merged. Pairwise correlations between organoids and primary cells were computed using the donor-specific genes. For the heat map shown in Extended Data Fig. 4e, sex-specific genes were excluded.

The complete software stack for downstream analysis is available as a Docker container (rnaseq-notebook:2025-04-21) archived at https://quay.io/repository/fbnrst/rnaseq-notebook and archived on Zenodo (https://doi.org/10.5281/zenodo.17704466).

Single-cell transcriptomics with 10x Genomics

For scRNA-seq analysis, assembloids were generated by assembling h-HepOrgs, cholangiocytes/ductal cells derived from cholangiocyte organoids (n-GFP) and portal fibroblasts (n-RFP) at a ratio of 1 h-HepOrg to 25 portal fibroblasts and 100 cholangiocytes. At 5–6 days after aggregation, assembloids were collected as follows: periportal assembloids were dissociated to single cells using 10× TrypLE for 5 min at 37 °C. The cells were resuspended in DM and 10 μg ml–1 DNase in BSA-coated tubes and filtered through a 100-μm strainer. Cell suspensions (30,000–50,000 cells) were concentrated by centrifugation (50g, 5 min, 4 °C) and the volume was reduced to ~55 µl. Cells were carefully resuspended and visually inspected under a light microscope to determine cell concentration and quality. The concentrations of the single-cell suspensions were adjusted to 138–912 cells per microliter and carefully mixed with the reverse transcription mix before loading cells on the 10x Genomics Chromium system[76] in a Chromium Single-Cell G Chip targeting 3,000–10,000 cells per reaction. Following the guidelines of the 10x Genomics Chromium Single-Cell Kit v3.1 user manual, the droplets were directly subjected to reverse transcription, the emulsion was broken and cDNA was purified using Dynabeads MyOne Silane (10x Genomics). cDNA was first amplified with 12 cycles, and then purified with 0.6× SPRIselect beads (BeckmanCoulter) to enrich cDNA fragments (>400 bp). A quality and quantity control of cDNA on the Fragment Analyzer (using the DNF-473 NGS Fragment Kit, Agilent) was eventually performed to obtain its concentration. The 10x Genomics scRNA-seq library preparation–involving fragmentation, dA tailing, adapter ligation and 11 or 12 cycles of indexing PCR, was performed based on the manufacturer’s protocol. After quantification, the libraries were sequenced on an Illumina NovaSeq 6000 in paired-end mode (R1/R2, 100 cycles; I1/I2, 10 cycles), generating 230–370 million fragment pairs.

scRNA-seq data analysis

The raw scRNA-seq data were processed using nf-core/scrnaseq v3.0.0 (https://doi.org/10.5281/zenodo.3568187) of the nf-core collection of workflows[66], using reproducible software environments from the Bioconda[67] and Biocontainers[68] projects. The pipeline was executed with Nextflow (v24.10.5)[69]. STARSOLO was used as the aligner. The reference genome was set to Homo sapiens GRCh38 (Ensembl release 111) with custom additions for RFP and GFP transgenes, obtained from SnapGene (DsRed1 and EGFP, respectively). Outputs were inspected for quality control, and one sample with poor quality control was excluded from further analysis. Within nf-core/scrnaseq, technical artefacts were eliminated using CellBender[77]. The CellBender output was used for data visualization. Doublet detection for each sample was performed using scrublet[78].

Further analysis was performed using scanpy[79]. Quality control was applied with the following thresholds: minimum total counts of 5,000, minimum detected genes of 2,000, the maximum percentage of counts in the top 50 genes set at 50%, the maximum percentage of mitochondrial counts set at 15% and a maximum doublet score of 0.15. Gene filtering was performed to retain genes expressed in at least ten cells. After filtering, the data underwent normalization, log transformation and identification of the top 3,000 most highly variable genes. PCA was performed, and batch correction was implemented through Harmony integration[80]. UMAP visualization and Leiden clustering were used to identify the three expected cell types81,82.

To compare homeostatic-like and fibrotic-like organoids, pseudobulk aggregation was performed using decoupleR for each cell type[83]. Pseudobulk data were generated by summing raw counts for each sample and cell type, with a minimum requirement of ten cells per group and 1,000 total counts. Differential expression analysis was conducted using pyDESeq2 (ref. [84]). For each cell type, DESeq2 datasets were created with design factors that included ‘donor’ and ‘condition’, using the ‘homeostatic-like’ condition as the reference. Differentially expressed genes between the homeostatic-like and fibrotic-like conditions were ranked on the basis of the test statistic. Subsequently, gene set enrichment analysis (GSEA) was performed on the ranked lists using clusterProfiler, focusing on KEGG, Reactome and GO terms.

The complete software stack for downstream analysis is available as a Docker container (singlecell-notebook:2025-04-21) archived at https://quay.io/repository/fbnrst/singlecell-notebook and archived on Zenodo (https://doi.org/10.5281/zenodo.17704461).

Comparison to public datasets

Data from refs. 12,13,49 were downloaded in h5ad format from https://cellxgene.cziscience.com/. Additionally, data from ref. [11] were obtained from https://data.mendeley.com/datasets/yp3txzw64c/1, and the dataset from ref. [8] was downloaded from https://datashare.ed.ac.uk/bitstream/handle/10283/3433/tissue.rdata and converted to h5ad format using the sceasy package.

These public datasets were merged with the raw count matrix of our quality control-filtered organoid data. Subsequently, the combined dataset underwent normalization, followed by log transformation and detection of the top 4,000 most highly variable genes. We performed PCA and integrated the dataset using Harmony, specifying concatenation of the paper and donor as batch variables, with a maximum of 20 iterations and a theta value of 1.5. Selected genes were visualized in a dot plot (Fig. 4h).

Pseudobulk analyses were then conducted using the decoupleR package to summarize gene expression by cell type. This involved generating a pseudobulk dataset in which raw counts were summed by sample and cell type, ensuring a minimum of 30 cells per group. Following pseudobulk aggregation, the data were normalized and log transformed, with the top most highly variable genes identified on the basis of mean expression and dispersion. Additionally, the ‘paper’ variable was regressed out to mitigate batch effects. Next, PCA was performed on the pseudobulk data, using 50 principal components for subsequent analyses. Hierarchical clustering was executed using the Pearson correlation metric, and Pearson correlation matrices were plotted, as shown in Figs. 4g and 5f.

Marker genes for the three major cell types were computed separately for our organoid data and the merged public data using scanpy’s rank_genes_groups function. For each dataset, the top 300 marker genes for each cell type were selected. Subsequently, GSEA was performed using the gseapy package, leveraging the Enrichr method[85]. The analysis focused on the KEGG 2021 Human and Reactome 2022 gene sets, with a P-value cut-off of 0.05. Shared enriched pathways between the organoid and tissue datasets were identified, and the combined enrichment scores for selected terms were plotted (Extended Data Fig. 8c,d).

Data statistical analysis

The specific statistical test is specified in the legend. P < 0.05 was considered statistically significant. In all cases, data from at least three independent experiments were used. Calculations were performed using the Prism 9 software package. All P values are given in the corresponding figure legends or in the corresponding figure or in the corresponding source data file. Dispersion and precision measures (for example, mean, median, s.d., s.e.m.) are specified in the figure legends. No statistical methods were used to predetermine sample size. All scRNA-seq statistics are described above in the corresponding section.

Reporting summary

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

Data availability

Ethical approval for the generation of organoid lines was granted under the condition of restricted access. Organoid lines may be obtained upon request to the corresponding author, signing of a project-specific material transfer agreement and receiving ethical approval covering the planned work. Approval will be granted for work covered by the consent granted by patients. Depending on the requested organoid line, the ethical approval will be handled by the institutional review boards of the University Hospital Carl Gustav Carus Dresden or the University of Leipzig, according to where the patient was consented.

The raw scRNA-seq (EGAD50000001453) and bulk RNA-seq (EGAD50000001454) data (FASTQ files) generated in this study have been deposited in the European Genome-phenome Archive (EGA) under accession number EGAS50000000994. Following EGA regulations, access to these data is controlled to protect the privacy and identity of study participants. Access requests can be submitted via the EGA website. Requests will be evaluated by the Data Access Committee (DAC) EGAC50000000112 to ensure that the proposed data use is consistent with the consent provided by participants. Approved users will be required to sign a data access agreement (DAA) that specifies the permitted uses of the data. A template of the DAA is available at https://edmond.mpg.de/api/access/datafile/250610. Applications are normally reviewed within 10 working days.

Count matrices and fully processed data, together with the source code for the sequencing data analysis, are publicly available via Zenodo (https://doi.org/10.5281/zenodo.17251198)[86].

Comprehensive lists of DEGs, GSEA terms, and marker genes are provided in Supplementary Data 2–4.

All other images as well as qPCR and measurement data are presented in the manuscript, and data used to plot the graphs are provided as supplementary information with this paper.

The URLs used to generate the UMAP plots from the data in ref. [8] for Extended Data Fig. 5 and the histology images from the Human Protein Atlas database for Extended Data Fig. 5 are all provided in the Extended Data Fig. 5 legend.

The raw data from mass spectrometry analyses are publicly available at the following repository: https://doi.org/10.17617/3.Z9GMJE. Source data are provided with this paper.

Code availability

The source code for bulk RNA-seq and scRNA-seq data analysis is available at https://git.mpi-cbg.de/huch_lab/yuan_dawka_kim_liebert_et_al_2025_sequencing (https://doi.org/10.5281/zenodo.17251198)[86].

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Acknowledgements

M.H. is supported by the Max Planck Gesellschaft and is the recipient of an Allen Distinguished Investigator Award, a Paul G. Allen Frontiers Group advised grant of the Paul G. Allen Family Foundation, which supported A.L. and R.R.d.C. This project was partially supported by the European Research Council under the European Union’s Horizon Europe research and innovation programme (grant agreement no. 101088869) awarded to M.H. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. Part of this work was also funded by the LiSyM grant from the Bundesministerium für Bildung und Forschung (BMBF Federal Ministry of Education and Research) awarded to M.H. (031L0258C and 031L0315B) and to G.D. and D.S. (031L0258E and 031L0315D). This project was also partially supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, 514150034 and 513752256). D.L.H.T. was supported by a Croucher Foundation fellowship. D.E.S. was supported by a German Cancer Aid Max Eder Grant (70113745). We thank F. Ahmed for help with chromosome counting. We thank J. Jarrells and J. Hernandez (MPI-CBG) for assistance with fluorescence-activated cell sorting (FACS), the light microscopy facility for imaging troubleshooting and training (J. Peychl and R. Maraspini), the Technology Development Studio facility for the high-throughput imaging and image analysis (R. Barsacchi and M. Stöter) and the Dresden Concept Genome Center for the RNA-seq and scRNA-seq library (S. Reinhardt and J. Bläsche at the DcGC Dresden-concept Genome Center, a core facility of the CMCB and Technology Platform of the TUD (Technische Universität Dresden). We thank J. Pöche for assistance with hepatocyte isolation and the surgical research laboratory and especially S. Hübner as well as the operative team of the Department of Visceral, Thoracic and Vascular Surgery, University Hospital Dresden, for assistance in liver tissue processing. We thank the whole team of the Department of Hepatobiliary Surgery and Visceral Transplantation, Leipzig University Medical Center, for their support in patient acquisition and liver tissue logistics. We thank G. Schicht for her assistance in the hepatocyte isolations at Leipzig University Medical Center. We thank M. Marass for insightful comments and discussions on the manuscript.

Funding

Open access funding provided by Max Planck Society.

Author information

Author notes

1. These authors contributed equally: Lei Yuan, Sagarika Dawka, Yohan Kim, Anke Liebert

Authors and Affiliations

1. Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

Lei Yuan, Sagarika Dawka, Yohan Kim, Anke Liebert, Fabian Rost, Robert Arnes-Benito, David Long Hin Tsang, Andrea Schuhmann, Anna Shevchenko, Roberta Rezende de Castro, Aleksandra Sljukic, Anna M. Dowbaj, Andrej Shevchenko & Meritxell Huch

2. Department of MetaBioHealth, Sungkyunkwan University, Suwon, South Korea

Yohan Kim

3. Department of Visceral, Thoracic and Vascular Surgery, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany

Franziska Baenke & Daniel E. Stange

4. National Center for Tumor Diseases Dresden (NCT/UCC), a partnership between DKFZ, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Technische Universität Dresden and Helmholtz-Zentrum Dresden–Rossendorf (HZDR), Dresden, Germany

Franziska Baenke & Daniel E. Stange

5. Department of Hepatobiliary Surgery and Visceral Transplantation, Clinic for Visceral, Transplant, Thoracic and Vascular Surgery, Leipzig University Medical Center, Leipzig, Germany

Christina Götz, Daniel Seehofer & Georg Damm

6. Department of Surgery, Hanyang University College of Medicine, Seoul, South Korea

Seunghee Kim & Dongho Choi

7. Center for Systems Biology (CSBD), Dresden, Germany

Meritxell Huch

Contributions

M.H. designed the study. L.Y., S.D., Y.K., A.L. and R.A.-B. performed most of the experiments and, together with M.H., interpreted the results. F.R. performed the scRNA-seq analysis. F.R. and D.L.H.T. performed the bulk RNA-seq analysis. R.R.d.C. performed the chromosome analysis. A. Schumann, Anna Shevchenko and Andrej Shevchenko applied direct-infusion mass spectrometry to characterize drug metabolism capacity. F.B., D.E.S. and C.G. assisted with tissue processing and hepatocyte isolation. D.S., G.D. and D.E.S. obtained patient consent for the tissue samples used in the study. S.D. performed image analysis and, with A. Sljukic, the bile canaliculus reconstructions. A.M.D. and A. Sljukic contributed to the first phases of the project. Y.K. and D.C., with assistance from S.K., conducted the xenotransplantation experiments. L.Y., S.D., Y.K., A.L., F.R. and M.H. wrote the manuscript. All authors read and commented on the manuscript.

Corresponding author

Correspondence to Meritxell Huch.

Ethics declarations

Competing interests

The Max Planck Institute of Molecular Cell Biology and Genetics is an applicant for a patent on the hepatocyte organoids and assembloids described in this work (EP23207475 and EP25169082.2). M.H. is an inventor on several patents on organoid technology. Y.K., S.D., R.A.-B. and M.H. are inventors on a patent on human hepatocyte organoids (EP23207475). M.H., L.Y., S.D., A.M.D. and A. Sljukic are co-inventors on a patent on human assembloids (EP25169082.2). The remaining authors declare no competing interests.

Peer review

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Extended data figures and tables

Extended Data Fig. 1 Establishment of the culture conditions to expand human hepatocyte organoids long-term while retaining their genetic stability over time.

a-j, Refinement of the isolation procedure and media conditions for expanding patient-derived human hepatocyte organoids. To remove cholangiocytes from the digested prep, the perfused digested tissue was processed for EpCAM-negative selection by MACS (a-c). To identify culture conditions, Ingenuity Pathway analysis was performed (d-e) and several signalling pathways found differentially expressed were screened for hepatocyte organoid growth (f-j). a, Representative brightfield images from n > 10 different experiments showing primary human hepatocytes (PHHs) seeded directly after isolation in MM medium (no-MACS, left) or after exclusion of EpCAM-positive cholangiocytes by MACS (right). The EpCAM-negative fraction, enriched for PHHs was seeded in MM (EpCAMneg fraction), while the EpCAM-positive fraction, enriched in human cholangiocytes, was seeded in Chol-media (EpCAMpos fraction). See Methods and Supplementary Table 1 for details. Scale bar, 1 mm (left), 100 μm (right). b, Representative brightfield images of seeded PHHs with either high or low viability after isolation following the indicated procedure. Scale bar, 200 μm. c, Efficiency of cholangiocyte removal following EpCAM+ MACS-based separation. Flow cytometry analysis of EpCAM+ cells on the liver cell preparation before MACS and on the EpCAM neg fraction after MACS. The absence of EpCAM+ cells in the ‘after-MACS EpCAM-neg fraction’ confirms the efficient depletion of cholangiocytes from the liver cell preparation. Graph presents the quantification of EpCAM− cell percentages before and after MACS. Data are represented as mean ± SD from n = 3 donors, from n = 3 independent experiments. d, Ingenuity Pathway Analysis (IPA) was performed on n = 4 comparisons using datasets from Broutier et al.[35] and Hu et al.[27] (Supplementary Dataset 1_S1). Shown are pathways enriched in ≥2 comparisons (adj. p < 0.05, grey line). Bars represent mean –log10 (adj. p), error bars the SD; bar colour indicates the number of significant comparisons (Supplementary Datasets 1_S2–S3). e, IPA upstream regulator analysis for the different datasets used (Lists 1–3, see Methods). The heatmap plots selected members from the 2 indicated signalling pathways. Gradient colour bar, -log (adjusted p-value) are plotted for prediction of the upstream regulators from different DEG comparison list (Supplementary Dataset 1_S7). f, Screen for activation of a subset of signalling pathways identified in ‘d’ using the indicated components. Representative brightfield images are shown. Scale bar, 200 μm. g, H&E staining of h-HepOrgs grown in h-HepOrgs-EM2 medium shows that the h-HepOrgs grow as solid structures in vitro. Representative images from n = 3 independent experiments are shown. h, h-HepOrgs cultured in the presence of Wnt-ligand (WntS) and either TRULI or TDI-011536 show lumen formation after several passages under TDI-011536 treatment. Representative images of n = 3 independent experiments are shown. Scale bar, 1 mm (left), 50 μm (middle), 200 μm (right). P, passage. i, Graph showing the expansion potential of h-HepOrgs from multiple donors in EM1 (complete) and in EM1 with the removal of individual components as indicated. Note that removal of Nic increased the longevity of the cultures for all the donors tested. This medium is subsequently called h-HepOrgs-EM2. Dot, passage. j, Comparison of expansion potential between h-HepOrgs grown in h-HepOrgs-EM2 medium and other media conditions. Following hepatocyte isolation, 12,500 cells were seeded in Matrigel and cultured in our h-HepOrgs-EM2 or EM1 medium or in the medium published by Peng et al.[28], or Hu et al.[27], or in MM medium supplemented or not with Wnt or TRULI. Liver cell preparations obtained from the same perfused tissue but not-MACS as well as from the same tissue but not perfused (no perfusion) were also analysed. EpCAM+ cholangiocyte fraction cultured in our cholangiocyte medium was used as control. Total cell count was measured at the indicated time points. Graph presents the mean ± SEM of n = 3 different donors with n = 3 technical replicates from n = 3 independent experiments. Statistical significance was determined by two-way ANOVA with Dunnett’s test for multiple comparisons, comparing each condition to the EM2 group at the last time point (day15); p < 0.0001 for all comparisons. The colour coding indicates the comparison of our h-HepOrgsEM2 medium to the respective medium in the legend. k, Chromosome analysis of h-HepOrgs expanded for long or short time in culture. Representative image of a chromosome spread is shown (top). Graph shows the number of chromosomes from h-HepOrgs-EM2 at different passages from multiple donors indicating the maintenance of genetic stability over time. P, passage. l, Cryopreserved h-HepOrgs grown in EM2 can be recovered from cryopreservation without exhibiting any signs of loss of expansion potential. Representative brightfield images of h-HepOrgs after freezing and thawing are shown. Scale bar, 500 μm (top), 100 μm (bottom). P, passage.

Source Data

Extended Data Fig. 2 Serially expanded human hepatocyte organoids exhibit gene expression programs of regenerating livers.

a-h, h-HepOrgs organoids were cultured for several passages (from P1 to P10) in the presence of Wnt-ligand (WntS) and LATS1/2 inhibitor (TRULI or TDI-011536). At the indicated time point, serially expanded cultures were collected and processed for either RNA (a-e, g-h, j-k) or immunofluorescence (f) analysis. a-b, PCA (a) and correlation analysis (b) for fresh isolated human primary hepatocytes (primary, pink) and cholangiocyte organoids (h-CholOrg, green) and h-HepOrgs (purple) at early (P1) and late (P10) passage. Both, PCA and correlation analysis were batch corrected for tissue and culture. c, Heatmap showing the expression of the most DEG in regenerating mouse livers after partial hepatectomy (PHx) in h-HepOrgs under specific culture conditions, as well as in fresh isolated hepatocytes (primary). The top100 DEG from Hu et al.[27] were used from which 89 direct orthologs as annotated by ENSMBL were identified and are displayed. Column, donor. Passage number is indicated with colour according to the legend in d. d, Heatmap displaying the expression of the top 100 cell cycle genes described in Tirosh et al.[87], in h-HepOrgs under specific culture conditions, as well as in fresh isolated hepatocytes (primary). Column, donor. Passage number is indicated with colour. e, Selected list of gene sets significantly enriched in h-HepOrgs under MM+WntS+TRULI compared to fresh isolated hepatocytes. The full list is presented in Supplementary Dataset 2. The results are presented as a dot plot, where dot colour represents the adjusted p-value (permutation test implemented in clusterProfiler, adjusted using the Benjamini–Hochberg method), and dot size corresponds to the GeneRatio (number of core enrichment genes divided by the total number of genes in the pathway). NES, normalized enrichment score. Brown, gene sets enriched in KEGG database. Orange, gene sets enriched in Reactome database. f, Immunofluorescent staining for cleaved caspase 3 (yellow), F-actin (Phalloidin, white) and DAPI (nuclei, blue) in h-HepOrgs serially expanded under h-HepOrgs-EM2 medium. n = 3 independent experiments. Scale bar, 50 µm. g, Heatmap displaying the expression of YAP targets from Wang et al.[88], in h-HepOrgs under specific culture conditions, as well as in fresh isolated hepatocytes (primary). Column, donor. Passage number is indicated with colour. h, Heatmap displaying the expression of liver specific WNT targets according to the TCF4 ChIP seq dataset described in Boj et al.[89], in h-HepOrgs under specific culture conditions, as well as in fresh isolated hepatocytes (primary). Column, donor. Passage number is indicated with colour according to the legend in g. i, Graph showing mean ± SEM of the percentage of YAP-positive and YAP-negative nuclei in h-HepOrgs cultured in presence of TRULI (+TRULI, EM2 condition) or after TRULI removal for 1 week (-TRULI). Each dot represents one organoid, colours indicate different donors (n = 3 donors). Ordinary two-way ANOVA (factors: condition and donor) was used for statistical analysis, p < 0.0001. j, Quantitative RT-PCR analysis of the YAP downstream target genes ANKRD1, CYR61, and AMOTL2 in cultures treated for 12 h with TRULI (+TRULI, orange) or without TRULI (−TRULI, grey). Gene expression levels are normalized to HPRT and presented as 2^−ΔCt. Data are shown as mean ± SEM (n = 6 donors). Significance was assessed using an unpaired two-tailed Student’s t-test. p-values are indicated. k, qPCR analysis of the indicated genes in human cholangiocyte organoids (h-CholOrg, green) and h-HepOrgs cultured in EM2 medium (purple) and fresh isolated hepatocytes (Primary PHHs, pink). Gene expression levels are normalized to HPRT and presented as 2^−ΔCt. Graph represents the mean ± SEM of n ≥ 3 independent donors, Dot, donor. Significance was assessed using one-way ANOVA followed by Tukey’s multiple comparison test, p-values are indicated on the graph.

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Extended Data Fig. 3 Differentiated human hepatocyte organoids retain gene expression of mature in vivo hepatocytes.

a, Experimental design. b, Graph showing the mean ± SD of cytoplasmic to nuclear ratio measured as area (CA/NA) in h-HepOrgs in EM2 and in DM. Dot, individual organoid. Shape, independent donor (n = 3 donors). Ordinary two-way ANOVA (factors: medium and donor) was used for statistical analysis, p < 0.0001. c-i, h-HepOrgs organoids were cultured for several passages in h-HepOrgs-EM2 medium and then differentiated in DM as indicated in methods. At day 7 of the differentiation protocol the organoids were harvested and processed for RNAseq analysis. c, PCA analysis showing the PC1 and PC2 components from the RNAseq analysis from fresh isolated primary hepatocytes (PHHs, pink), hepatocytes cultured for 1 day in standard monolayer culture (1d-PHH monolayer, light pink), cholangiocyte organoids (h-CholOrg, green) and h-HepOrgs in EM2 (dark purple) or DM (light purple). d, Heatmap showing all DEG between h-HepOrgs grown in DM compared to EM2 medium, and their expression in fresh isolated hepatocytes (Primary, PHHs), hepatocytes cultured for 1 day in standard monolayer culture (1d-PHH monolayer) and in cholangiocyte organoids (h-CholOrg). Note the similarity between DM, 1d-PHHs and primary hepatocytes. Column, donor. e, Selected list of gene sets from the GO-terms database that are significantly enriched in h-HepOrgs under DM compared to EM2. The full list is presented in Supplementary Dataset 2. Results are presented as dot plot, where dot colour represents the adjusted p-value (permutation test implemented in clusterProfiler, adjusted using the Benjamini–Hochberg method), and dot size corresponds to the GeneRatio (number of core enrichment genes divided by the total number of genes in the pathway). NES, normalized enrichment score. Note that many of positively enriched gene sets are related to classical hepatocyte functions such as lipid and drug metabolism and bile secretion and transport. Conversely, negatively enriched gene sets are related to cell cycle. f-g, Heatmap showing the expression of the full list of genes from the KEGG cholesterol dataset (e) and drug metabolism (f) datasets from the GSEA analysis between h-HepOrgs grown in DM compared to EM2 medium, and their expression in fresh isolated hepatocytes (Primary, PHHs), hepatocytes cultured for 1 day in standard monolayer culture (1d-PHH monolayer, light pink) and in cholangiocyte organoids (h-CholOrg). Note the similarity between DM, 1d-PHHs, and primary hepatocytes. Column, donor. h, Heatmap showing the expression of DEG between DM and EM2 that intersect with the gene list of pericentrally or periportally zonated genes 1. Details are found in Supplementary Table 2. i, CPM values from the RNAseq for the hepatocyte marker Albumin (ALB), the mature hepatocyte marker and metabolizing enzymes CYP3A4, CYP3A5, CYP2C8, CYP2C9, the bile salt transporter MRP2, the cholangiocyte marker KRT19 and the hepatoblast marker AFP. Note that upon DM the expression of mature hepatocyte markers increases while the expression of the cholangiocyte and foetal marker decreases. Bars represent mean ± SEM for n = 4 (h-CholOrg) or n = 3 (all others) biologically independent samples (donors). j, h-HepOrgs in DM stained for the pericentral marker CYP2E1 (magenta) and periportally enriched marker ECAD (green) (n = 3 donors). CYP2E1 and ECAD shown in Fire LUT for enhanced visualisation. Nuclei were stained with DAPI (cyan). Scale bars, 50 μm. Panel a adapted from ref. [51], Springer Nature Limited.

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Extended Data Fig. 4 h-HepOrgs present bile canaliculi architectural heterogeneity similar to patient cohorts and capture patient-specific gene expression programs.

a, Illustration showing the heterogeneity in the BC morphologies observed. Compare the illustration to the bottom magnified panels in figures in ‘b’ and ‘d’. Note that both, tissues and organoids from different patients present fine detailed differences in BC morphology, with some being thin and homogenous (white arrowhead), others wider and inhomogeneous (orange arrowheads) and some full of branchlets (blue arrowheads). b, Immunofluorescent images for bile canaliculi (CD13, green), cell borders (F-actin, grey) and nuclei (DAPI, cyan) (top) showing variation in bile canaliculi (BC) networks between tissue samples from different donors (n = 3). White arrowheads, thin BC; orange arrowheads, wide BC; blue arrowheads, BC with branchlets. Scale bar, 50 μm (top three panels); 10 μm (bottom). c, Graph showing the differences in the total bile canaliculi branch length for the largest network (network with the maximum number of branches) between the different tissue samples from different donors (n = 3 donors). Each dot represents one field of view. Refers to data from Fig. 3c. d, Immunofluorescent images for bile canaliculi (CD13, green), cell borders (F-actin, grey) and nuclei (DAPI, cyan) (top) showing variation in bile canaliculi (BC) networks between h-HepOrgs from different donors cultured in DM (n = 3 donors). White arrowheads, thin BC; orange arrowheads, wide BC; blue arrowheads, BC with branchlets. Scale bar, 50 μm (top three panels); 10 μm (bottom). e, Spearman correlation heatmap showing pairwise similarities between primary hepatocyte samples and differentiated hepatocyte organoids. Donor-specific genes were first identified from fresh primary hepatocytes, and these genes were then used to calculate pairwise correlations with gene expression profiles from organoids. Warmer colours indicate higher correlation coefficients, representing stronger transcriptomic similarity. P, passage. f, Human GAPDH immunohistochemical results of a representative liver section of h-HepOrgs-DM (bottom) and fresh isolated primary human hepatocytes (PHH, top) transplanted in Fah−/−Rag2−/−Il2rg−/− (FRG) mice at day 90 and 92 after transplantation, respectively. Representative images from X number of independent images from n = 2 mouse (HepOrg-DM) and n = 1 mouse (PHH). Scale bar, 1 mm (left); 200 μm (right).

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Extended Data Fig. 5 Expression profile of human primary mesenchymal (MSC) populations.

a-b, d and g, scRNA-seq analysis of human hepatic MSC populations reported in Ramachandran et al.[8] (a-b, d) and Guilliams et al.[49] (g). The datasets were explored for the expression of specific mesenchymal markers in human healthy liver using the interactive sites https://shiny.igc.ed.ac.uk/livercellatlas/[8] and https://www.livercellatlas.org/umap-ststmouseFibro.php[49] provided by the authors. a, tSNE plots show the clustering analysis of the distinct human liver mesenchymal sub-populations in healthy (right) and cirrhotic (middle) human livers reported in Ramachandran et al.[8]. b, Violin plot indicates the data point distribution of gene expression for THY1 (CD90) in the indicated mesenchymal subpopulations of healthy human liver, reported in Guilliams et al.[49]. c, Immunohistochemistry for PDGFRA, DCN and ASPN in healthy liver tissue from cohorts of patients from the human protein atlas database (HPA). Scale bars, 50 µm. d, Gene tSNE [t-distributed stochastic neighbour embedding] (left) and violin plots (right) show the mRNA expression levels and distribution for PDGFRa across different mesenchymal subpopulations. e, RT-qPCR analysis of the gene expression of immune, endothelia, epithelia and mesenchyme markers in isolated human mesenchyme (hMSC) and human liver (h-liver). Graph presents mean ± SD from n = 3 independent human donors. Gene expression was normalized to the house-keeping gene HPRT. f, Gene expression levels for VSMC, HSC and PF markers in serially expanded human portal fibroblasts (hPFs, magenta) and human liver (h-liver, black). Graph presents mean ± SD (n = 3). g, Violin plots show the data point distribution of gene expression in the distinct mesenchymal subpopulations for the indicated genes in healthy and cirrhotic human livers reported in Ramachandran et al.[8]. PF: portal fibroblasts /(myo)fibroblasts, HSC: hepatic stellate cells. VSMC vascular smooth muscle cell. Panels a and d reproduced from ref. [8], Springer Nature Limited. Images in panel c reproduced from the Human Protein Atlas (HPA), available under a CC BY-SA 4.0 license (https://creativecommons.org/licenses/by-sa/4.0/deed.en). PDGFRA, https://www.proteinatlas.org/ENSG00000134853-PDGFRA/tissue/liver#img; DCN, https://www.proteinatlas.org/ENSG00000011465-DCN/tissue/liver#img; ASPN, https://www.proteinatlas.org/ENSG00000106819-ASPN/tissue/liver#img.

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Extended Data Fig. 6 Human portal fibroblasts from patient donors expand in vitro while retaining the expression profile of in vivo portal fibroblasts.

a, Mesenchymal cell populations in human healthy and cirrhotic livers from the publicly available scRNAseq data from Ramachandran et al.[8]. The dataset was explored for the expression of specific portal fibroblast markers in human healthy liver using the interactive site https://shiny.igc.ed.ac.uk/livercellatlas/ provided by the authors. tSNE map (left) and corresponding violin plots (right) depict THY1 (CD90) transcript distribution and expression levels across mesenchymal subpopulations. b, FACS gating strategy for isolating PFs from human liver tissue. Immune and endothelial cells are excluded through negative gating for CD31, CD45, and CD11b markers. hPFs are isolated based on EpCAMneg/THY1pos sorting. c, Immunofluorescence staining of human liver tissue reveals the distribution of different liver cell types (CD90, PF marker, magenta; pan cytokeratin, PanCK, bile duct marker, green). Membranes are marked with phalloidin (white) nuclei with DAPI (blue). Dotted boxes, magnifications showing midzonal tissue parenchyma (1) and liver portal tract (2) region. Note that CD90 is exclusively expressed in the portal region. n = 3 independent experiments. Scale bar, 50 µm. d, Representative images of PFs monoculture, cultured in Basal medium, Basal medium with 3% FBS and Wnts, Basal medium with 3% FBS and DMEM with 20% FBS. n = 2 independent experiments. Scale bar, 100 µm. e, RT-qPCR gene expression analysis for portal fibroblast (THY1, PDGFRA, MFAP4, COL1A1, COL3A1), HSC (RGS5, LRAT), cholangiocytes (EPCAM) and VSMC (ACTA2, MYH11) markers in cultured PDGFRA+CD90+ cells at passage 1 and 3 (P1-P3) and human liver tissue (h-liver). Graph presents mean ± SD (n = 3). Values are presented relative to the house-keeping gene HPRT. f, Heatmap showing the expression of hepatocyte, cholangiocyte and mesenchymal markers from the indicated subpopulations (PFs, HSC, mesothelia, VSMC) in cultured portal fibroblasts (PF, magenta) and fresh isolated hepatocytes (primary, brown) and cholangiocytes (h-Chol, green) from different donors. PF, portal fibroblast; HSC, hepatic stellate cell; VSMC, vascular smooth muscle cell. Column, donor. g, Gene expression levels for the indicated secreted molecules in cultured PDGFRA+CD90+ cells and h-liver analyzed by RT-qPCR. Data are expressed as mean ± SD (n = 3). h, Heatmap of all differentially expressed genes in cultured hPFs and freshly isolated hepatocytes (PHH). Note that genes showing expression of mesenchymal markers are highly expressed in the PF population. i, Immunofluorescence staining for the portal fibroblast marker, CD90 (yellow) and the pan-mesenchymal marker, Vimentin (magenta) in cultured portal fibroblasts, n = 3 independent experiments in n = 1 donor. Note that all the cells are positive for the PF marker, CD90. Scale bar, 50 µm. j, To identify the PFs and cholangiocyte cells after assembly in periportal assembloids, PFs and human cholangiocytes were isolated from human donors (matching whenever possible) and cultured as described in Methods. After several passages, the cells were infected with lentiviral particles containing nuclear-GFP or nuclear-RFP to generate reporter fibroblast lines. Left, schematic diagram of viral infection for h-CholOrg (top) and hPFs (bottom). Representative images of h-CholOrg (top) and PFs (bottom) following transduction with specific nuclear-GFP or nuclear-RFP viral vectors. n ≥ 3 independent experiments. Scale bars, 100 µm. Panel j created in BioRender. Yuan, L. (2025) https://BioRender.com/g8uci15.

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Extended Data Fig. 7 Optimization and characterization of periportal assembloids.

a, To determine the ratio of hepatocytes, ductal cells/cholangiocytes to mesenchyme to test for the generation of assembloids, human liver tissue was stained for the cholangiocyte marker pan-cytokeratin (PanCK) and mesenchymal marker (CD90). A representative image is presented in Fig. 4d. The numbers of pan-cytokeratin positive ductal cells (PanCK+) and CD90 positive portal mesenchyme were determined and used to calculate the ratio of both populations in human liver tissue. The results are presented as violin plots showing the ratio of PanCK+ to CD90+ cells from n = 3 donors. Dot, one independent portal region. Dot colour, independent donor. b-d, f-j, Periportal assembloids were generated by mixing a defined number of human hepatocyte organoids (h-HepOrgs) with n-RFP tagged portal fibroblasts (PFs, magenta) and n-GFP tagged cholangiocytes from cholangiocyte organoids (Chol, green). b-c, Representative bright field and epifluorescence images (b) and confocal immunofluorescence images (c) of day 6 assembloids showing different ratios of hPFs (Red) and Chol (Green) in periportal assembloids. In (c) nuclei were stained with DAPI (blue) and membranes with F-actin (Phalloidin, white). Scale bars, 100 µm (b) and 50 µm (c). d, Representative immunofluorescence images of assembloids after 24 h in AggreWellTM demonstrating that h-PFs (Red) and h-Chol (Green) have already assembled with h-HepOrgs (not labelled) to generate composite structures. Nuclei were stained with DAPI (blue) and membranes with F-actin (Phalloidin, white). Scale bars, 50 µm. e, Donut chart illustrating the proportion of correctly (blue) and incorrectly (grey) organized periportal-like structures amongst assembloids (n = 20). f, Representative confocal images of periportal assembloids cultured for 3 days. Nuclei are stained with DAPI (blue); cell membranes with Phalloidin (white). Scale bars, 50 μm. g, Representative confocal images showing the proliferative (right, Ki-67, white) and apoptotic (left, Cleaved Caspase 3, yellow) states on day-6 assembloids. Nuclei are stained with DAPI (blue) and in the left panel membranes are stained with F-actin (Phalloidin, white). Scale bars, 50 µm. h, Left to right, representative confocal images of periportal assembloids cultured for 3 days and stained for cholangiocyte (KRT19, white, left), hepatocyte (HNF4A, yellow, middle) and hPF (vimentin, white, right) markers. Nuclei are stained with DAPI (blue) and in the middle panel, membranes are stained with F-actin (Phalloidin, white). Scale bars, 50 µm. i-j, Representative bright field images of human liver assembloids cultured for 2 weeks indicating that the three populations persist in the composite structures. Scale bars, 100 μm. k, Representative confocal images showing the proliferative (left, Ki-67, white) and apoptotic (right, Cleaved Caspase 3, yellow) states on long-term cultured assembloids (16 days). n = 3 independent experiments. Scale bars, 50 μm. l, Quantification of Ki-67+ and Cleaved Caspase 3+ cells in assembloids cultured for 6 (the ‘short term’ condition, as described in Extended Data Fig. 7f, for comparison) or 16 days. Results are presented as mean ± SD of n = 3 donors, biological replicates. p-values were calculated using two-sided unpaired Student’s t-test.

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Extended Data Fig. 8 scRNAseq analysis of human periportal assembloids.

a-c, Assembloids were generated by assembling h-HepOrgs, cholangiocytes/ductal cells derived from cholangiocytes organoids (nuclear-GFP) and portal fibroblasts (PFs, nuclear-RFP) at a ratio 1 h-HepOrgs: 25 PFs: 100 Cholangiocytes and 5-6 days later the cultures were collected and submitted for scRNAseq analysis (see Methods for details). a, PCA after batch correction with Harmony showing the integration of scRNAseq data from assembloids (This study) with human liver cell atlas datasets. b, UMAPs showing the 3 different clusters of cells in assembloids and the expression of the hepatocyte marker (ALB, albumin), the pericentral markers (CYP3A4 and CYP2E1) and the periportal markers (APOA1 and CDH1). The full list of markers for each population can be found in Supplementary Dataset 3. c, GSEA for Reactome (orange) and KEGG (brown) pathways in cholangiocytes (top) and Mesenchyme (bottom) from periportal assembloids (orange bar) and the corresponding enrichment in in vivo human liver tissue (blue bar). Data is presented as combined score. The full list can be found in Supplementary Dataset 3. d, GSEA for Reactome (orange) and KEGG (brown) pathways in hepatocytes from periportal assembloids (orange bar) and the corresponding enrichment in in vivo human liver tissue (blue bar). Data is presented as combined score. The full list can be found in Supplementary Dataset 3.

Extended Data Fig. 9 Hepatocytes, cholangiocytes and mesenchyme in assembloids express markers and establish cell-cell interactions similar to the in vivo portal tissue.

a, Immunofluorescence staining of day 4 periportal assembloids using the periportal markers SAA1 and SAA2 (yellow) and the epithelial and periportal enriched marker ECAD (white). Hepatocytes with positive (white arrow, region 1) and negative (magenta arrow, region 2) SAA1/SAA2 staining are shown. Left, robust expression of the portal markers SAA1 and SAA2 (top) and ECAD (bottom) in hepatocytes from assembloids. FIRE-LUT illustrates the different intensity of the corresponding markers. Right, co-localization of SAA1 and SAA2 staining with cholangiocytes (Chol-nGFP, green) and portal Mesenchyme (PFs-nRFP, magenta) highlights that the hepatocytes with strongest expression of portal markers SAA1 and SAA2 localize nearby cholangiocytes surrounded by portal fibroblasts (PFs-nRFP, magenta), confirming the periportal-like nature of assembloids. Portal identity is further contextualized by co-localization with ECAD (E-cadherin; epithelial marker) (bottom panels). Representative images from n = 2 samples from n = 1 donor. Scale bars, 50 µm. b, Mass spectrometry analysis was used to detect Norverapamil, the primary metabolite of Verapamil, in assembloids (6-day culture) and differentiated hepatocyte organoids (h-HepOrgs-DM). Unpaired two-tailed Student’s t-test with Welch’s correction, comparing biological replicates (n = 3). Values for h-HepOrgs correspond to the data in Fig. 3 and are shown here for comparison against assembloids. c, Immunofluorescent images showing the similarity between human assembloid (top) and human liver tissue (bottom). In assembloids, mesenchymal cells are labelled in magenta, Cholangiocytes labelled in green, F-actin and KRT19 are stained in green as well, and nuclei with DAPI (blue), n = 3 independent experiments. The assembloid image corresponds to the one presented in Fig. 5f where KRT19 in white is shown. In human liver tissue, staining was done for cholangiocytes (PCK, magenta), cell borders (F-actin, grey) and nuclei (DAPI, cyan), n = 3 independent experiments from n = 2 independent donors. Yellow arrowheads indicate the presence of ductal structures amidst the hepatocytes in both assembloids and tissue. Scale bar, 50 μm. d, Top, images showing magnified areas and maximum intensity projection of the assembloids (from panel c, top) with hepatocytes (DAPIpos, KRT19neg), cholangiocytes (KRT19pos, nuclei white), and the interface between the two cell-types - indicated by the white arrowhead, n = 3 independent experiments. The images are immunofluorescent images visualized in 3D to enable viewing from another angle. Bottom, immunofluorescent images showing magnified areas of human liver tissue (from panel c, bottom) with hepatocytes (PCKneg), cholangiocytes (PCKpos), and the interface between the two cell-types - indicated by the white arrowhead, n = 3 independent experiments from n = 2 biologically independent donors. Scale bar, 50 μm, 25 μm.

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Extended Data Fig. 10 Modelling hepatocyte transdifferentiation and epithelial responses to excess portal mesenchyme in human periportal assembloids.

a, Representative immunofluorescence images of periportal assembloids with homeostatic (top) or fibrotic (bottom). Ki-67 (white) and Cleaved Caspase-3 (yellow) mark proliferating and apoptotic cells, respectively. Phalloidin (blue) marks membranes; DAPI (blue), nuclei. Scale bars, 50 μm. b, Quantification of Ki-67+ in cholangiocytes and PFs in 24 h (day 0) and day 7-homeostatic (light pink) and fibrotic (dark pink) assembloids. Note the significantly higher proliferation of cholangiocytes under fibrotic-like conditions compared to homeostatic-like at day 0. Mesenchymal cells showed a trend toward higher proliferation although was not significant. At day 7 proliferation rates decreased. Results are presented as mean ± SD of n = 3 independent donors. p-values were calculated using two-sided paired (by donor) Student’s t-test. c, Quantification of total number of cleaved Caspase-3+ cells per assembloids at day 7 in homeostatic-like and fibrosis-like assembloids. Data is expressed as a percentage of + cells per total cell numbers. Dot, organoid per donor (n = 3 donors). Bars represent mean ± SD. p-value was calculated using a two-sided paired (by donor) Student’s t-test. d-g, Additional gene sets enriched in each of the three cell types in fibrotic-like vs homeostatic-like assembloids. Note that fibrotic-like hepatocytes and cholangiocytes are positively enriched for inflammatory signatures. Additionally, fibrotic-like hepatocytes are negatively enriched for functional pathways and cell cycle functions. Results are presented as dot plot, where dot colour represents the adjusted p-value (permutation test implemented in clusterProfiler, adjusted using the Benjamini–Hochberg method), and dot size corresponds to the GeneRatio (number of core enrichment genes divided by the total number of genes in the pathway). NES, normalized enrichment score. The full list can be found in Supplementary Dataset 4. h-j, Assembloids with homeostatic numbers (homeostatic-like) or excess amount of mesenchyme (fibrotic-like) were generated as described in Methods and cultured for 28 days in assembloids medium. h, Representative bright field images of homeostatic-like (left) and fibrotic-like (right) assembloids cultured for 28 days containing cholangiocytes/ductal cells (green), mesenchyme (magenta) and hepatocytes (unlabelled). Note that only fibrotic assembloids opened big lumina (arrow) and acquired a ductal/cholangiocyte cystic-like organoid shape similar to the organoids presented in Extended Data Fig. 1a and published in Huch et al.[4] Scale bars, 100 µm. i, Quantification of the number of organoids with cystic phenotypes (with lumen) vs solid organoids (without lumen). Graph presents the mean ± SD of n = 3 independent experiments with n = 2 independent donors. Dot colour, independent experiment. j, Representative confocal images of homeostatic (top) and fibrotic-like (bottom) periportal assembloids stained for hepatocyte (HNF4A, yellow) and cholangiocyte (KRT19, white) markers. Nuclei and membranes were also stained with DAPI (blue) and phalloidin (blue, left and middle), respectively. Note that in homeostatic assembloids the lumen (asterisk) is formed by nGFP+KRT19+ ductal cells (white arrow), while in fibrosis-like assembloids the lumen (asterisk) is also formed by cells that are double positive for HNF4A+ KRT19+ (magenta arrow).

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Supplementary information

Supplementary Table 1

Summary of human liver tissues used for testing hepatocyte isolation conditions. Summary of clinical data from human liver tissues used for testing isolation methods. Hepatocytes were cultured in our published mouse hepatoblast MM medium (Prior et al., 2019^29, see methods). Organoid-like structures were generated only when perfusion was used and cell viability exceeded 50%. However, as described in Fig. 1c, under MM conditions growth was minimal and hepatocytes rapidly died thereafter.

Reporting Summary

Supplementary Table 2

Summary of human liver tissues used for hepatocyte organoid formation and expansion under h-HepOrg-EM1 and h-HepOrg-EM2 conditions. Summary of patient information, isolation methods, viability, hepatocyte organoid formation and hepatocyte organoid expansion under EM1 (MM +WntS +TRULI) and EM2 (MM +WntS +TRULI w/o Nicotinamide) culture conditions. Long-term expansion (>10 passages) was achieved in 6 of 9 donors under EM2, but not in any of the 7 EM1 donors tested. n/a, not applicable.

Supplementary Table 3

Comparison with previous research of generation of hepatocytes organoids vs our work. Summary of key parameters in recent studies generating hepatocyte organoids from tissue-derived human or mouse hepatocytes, in comparison to this study. Parameters include cellular origin, donor age, biobank size, culture method, long-term expansion potential, hepatocyte maturity, patient specificity, functional characteristics, and disease modelling capabilities. Only human cell-derived cultures are considered for maturity, precision, functionality, and disease modelling columns. n/a, not applicable.

Supplementary Table 4

Donor-specific gene signatures recapitulated in hepatocytes organoids, associated with metabolism, viral response, and liver disease susceptibility. A curated list of donor-specific genes whose expression is recapitulated in human liver organoids. The genes are annotated based on their involvement in metabolism, lipid processing, antiviral responses (HBV/HCV), and susceptibility to liver diseases including cirrhosis and cancer. Each gene entry includes symbol, full name, functional annotation, and supporting references when applicable (PubMed ID or DOI). Genes are color-coded by functional category: metabolic enzymes, lipid metabolism-related genes, interferon-stimulated genes, and disease-associated markers. This dataset highlights how liver organoids retain individualized transcriptional features relevant to hepatic function and disease. Data related to Figure 3d.

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Supplementary Datasets

Supplementary Datasets 1-5.

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Yuan, L., Dawka, S., Kim, Y. et al. Human assembloids recapitulate periportal liver tissue in vitro. Nature 650, 438–449 (2026). https://doi.org/10.1038/s41586-025-09884-1

- Received: 22 October 2024

- Accepted: 07 November 2025

- Published: 17 December 2025

- Version of record: 17 December 2025

- Issue date: 12 February 2026

- DOI: https://doi.org/10.1038/s41586-025-09884-1

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Alberto Arrè,

Sajaysurya Ganesh,

Francesca Giordano,

Richard Davies,

Jonathan Wolf,

Kate M. Bermingham,

Sarah E. Berry,

Tim D. Spector &

…

Nicola Segata

Nature volume 650, pages 450–458 (2026)

Abstract

The incidence of cardiometabolic diseases is increasing globally, and both poor diet and the human gut microbiome have been implicated[1]. However, the field lacks large-scale, comprehensive studies exploring these links in diverse populations[2]. Here, in over 34,000 US and UK participants with metagenomic, diet, anthropometric and host health data, we identified known and yet-to-be-cultured gut microbiome species associated significantly with different diets and risk factors. We developed a ranking of species most favourably and unfavourably associated with human health markers, called the ‘ZOE Microbiome Health Ranking 2025’. This system showed strong and reproducible associations between the ranking of microbial species and both body mass index and host disease conditions on more than 7,800 additional public samples. In an additional 746 people from two dietary interventional clinical trials, favourably ranked species increased in abundance and prevalence, and unfavourably ranked species reduced over time. In conclusion, these analyses provide strong support for the association of both diet and microbiome with health markers, and the summary system can be used to inform the basis for future causal and mechanistic studies. It should be emphasized, however, that causal inference is not possible without prospective cohort studies and interventional clinical trials.

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Main

Cardiometabolic diseases (CMDs) are the leading causes of morbidity and mortality in Western countries and constitute a heavy burden on global healthcare systems[1]. The most predominant CMDs are cardiovascular disease (CVD) and type 2 diabetes (T2D)[3], which are connected with the increased consumption of calorie-dense, high-risk processed foods observed over the past few decades[4]. Habitual diet is not only among the known risk factors associated with CMDs, but also the primary modifiable target for prevention and treatment[5]. Well established anthropometric and intermediary measures of CMDs, ranging from clinical measurements (for example, blood pressure) to lipid profiles (such as triglycerides, cholesterol and lipoproteins), glucose levels (for example, fasting and postprandial glucose, and haemoglobin A1c (HbA1c)), inflammatory markers (for example, glycosylated proteins, the systemic inflammation marker GlycA[21] and high-sensitivity C-reactive protein), and known risk factors such as body mass index (BMI), can be used to study the diet–CMD axis6,7,8 but do not consider the biochemical mechanisms occurring in the human gut.

The human gut microbiome has emerged as a cofactor on the same axis as it is associated with diet and cardiometabolic conditions9,10,11,12 and is a modifiable element13,14,15. A change in dietary patterns can shift the species-level composition of the microbiome, with knock-on effects on host health[16]. However, individual responses to dietary interventions vary, and precision nutrition aims at identifying host-specific factors that modulate the interaction between diet and host health[17], but it is currently not possible to disentangle the effects diet plays to improve cardiometabolic health via the microbiome. Furthermore, the composition of the gut microbiome displays high individuality and variation depending on different demographics, ethnicity, sex and age; hence, defining or identifying universal biomarkers of a healthy gut microbiome has proven difficult18,19,20.

Nutritional intervention studies usually involve low sample-size cohorts at the population level and are often limited by their statistical power and specificity to local lifestyle and dietary habits, which are all critical aspects, especially given the microbiome’s complexity and variability. Large-scale comprehensive studies with multi-national populations can help disentangle some of the complex interplays between dietary patterns and the gut microbiome to develop personalized interventions to prevent and treat CMDs. Accordingly, we collated, sampled and analysed five of the largest metagenomic cohorts available to date, comprising more than 34,000 people and spanning two continents, paired with dietary data, detailed anthropometric and health markers. We identified microbiome species consistently associated with more favourable and (inversely) unfavourable health markers across continents. These species were organized into two microbiome rankings, representing host health and diet quality, respectively, that can be the basis for future causal and mechanistic studies.

Metagenomics of the ZOE PREDICT cohorts

We used four large-scale microbiome cohorts from the ZOE PREDICT studies (n = 33,596; Fig. 1a, Supplementary Fig. 1, Supplementary Table 1 and Methods) to assemble an extensive microbiome dataset of people with detailed dietary records along with anthropometric measures. Together with the previously available ZOE PREDICT 1 cohort[9] (n = 1,098), the PREDICT cohorts comprise 34,694 participants from both the USA (n = 21,340) and the UK (n = 13,354; Methods). Collected data comprise common health risk factors such as BMI, triglycerides, blood glucose and HbA1c, as well as several dietary indices and clinical markers that are intermediary measures of cardiometabolic health, such as the atherosclerotic CVD (ASCVD) risk, high-density lipoproteins (HDL) and GlycA[21] (Supplementary Table 2 and Methods).

Fig. 1: The ZOE PREDICT studies comprise over 34,000 healthy people from five cross-sectional studies from the UK and the USA with gut microbiome samples, detailed individual information and dietary habits.

a, In this study, we considered and harmonized five cross-sectional ZOE PREDICT cohorts with participants from the UK and the USA (Supplementary Fig. 1). For each cohort, sample size and the percentage of female participants (% F) are reported in the upper bar plots, with sequencing depth (left-hand columns, darker colour, average size in gigabases) and the total number of detected species (right-hand columns, lighter colour) are reported in the middle bar plots, showing that cohorts with lower sequencing depths do not have fewer total numbers of detected species. Bottom box plots, distributions of age (left-hand columns, darker colour) and BMI (right-hand columns, lighter colour) in the five PREDICT cohorts (the PREDICT 1 (P1) cohort is split into its UK and US parts, but considered as a single cohort). Box plots show first and third quartiles (boxes) and the median (middle line); whiskers extend up to 1.5 × interquartile range (IQR). b, Random forest classification (discriminating the first three quartiles against the fourth quartile) and regression machine learning models (Methods) trained on the whole microbiome SGB-level relative abundance values with a cross-validation approach, show moderately strong and consistent associations with different categories of clinical data available across the five cross-sectional ZOE PREDICT cohorts (full machine learning results are reported in Extended Data Fig. 1 and Supplementary Tables 2 and 3). HEI, healthy eating index; PDI, plant-based diet index; hPDI, healthful PDI; oPDI, overall PDI.

A systematic machine learning validated approach9,22 (Methods) revealed strong associations consistent across the five ZOE PREDICT cohorts between the microbiome and surrogate health markers and nutrition (Fig. 1b, Supplementary Table 2 and Methods). Markers that were classified accurately by the gut microbiome included glycemia, blood cholesterol, triglycerides and inflammation (both fasting and postprandial; Extended Data Fig. 1), with age, BMI, the healthy eating index[23] and the healthful plant-based diet index (PDI)[24] also correlated with microbiome machine learning regression estimates (Spearman’s correlation > 0.4; Fig. 1b and Supplementary Table 2). The top predicted markers from both machine learning regression and classification showed consistent associations across PREDICT cohorts, with average area under the receiver operating characteristic curve (AUC) ranging from 0.64 to 0.73, and an average Spearman’s correlation ranging from 0.30 to 0.46 for regression (Fig. 1b and Supplementary Table 3).

Ranking gut species to host and health

We next set out to identify which gut microbial species were most responsible for the microbiome’s associations with host markers. To do so, we grouped the 37 markers into three categories: (1) anthropometric-derived and accessible health-related measures (hereafter called ‘personal’ and including, for example, ASCVD and blood pressure), (2) fasting (for example, GlycA, triglycerides, HDL, cholesterol and glucose) and (3) postprandial markers, which are surrogate measures of cardiometabolic health. As expected, some markers tended to correlate quantitatively (Supplementary Table 4 and Methods).

We considered 661 non-rare microbial species (greater than 20% prevalence; Methods) according to the definition of species-level genome bins (SGBs)19,20, and computed the partial Spearman’s correlations (corrected for sex, age and BMI) between the relative abundance of each micro-organism and the value of each marker. Correlations were ranked, and correlations’ ranks were averaged within each category and then averaged among the three categories in each cohort (Methods). The five resulting cohort-level average rankings were averaged to derive a single ranking that we called the ‘ZOE Microbiome Health Ranking 2025’ (ZOE MB health-rank). This resulted in a ranking for 661 microbial species in which the lowest ranking (closer to 0) species are the most positively associated with the considered panel of host markers and vice versa for the highest ranking (closer to 1) species (Fig. 2, Extended Data Figs. 2 and 3, Supplementary Fig. 2 and Supplementary Tables 5 and 6).

Fig. 2: The 15 top and bottom health-ranked SGBs show consistent associations across the five PREDICT cohorts.

a, Average percentiles for the 15 most favourably (top) and unfavourably (bottom) ranked SGBs (selected for visualization purposes) across all five PREDICT cohorts. Percentiles are computed from the ranking of the correlations between SGBs and the different markers in each clinical data category. Percentiles close to 0 reflect SGBs consistently correlated positively with positive markers and negatively with negative markers, and vice versa for percentiles close to 1. For each cohort, the average percentiles for three clinical data categories are shown (personal, fasting and postprandial) and the cohort-level average. The rightmost column of the heatmap reports the ZOE MB health-ranks with the distribution of their relative abundance values when present (derived from n = 34,694 participants spanning the five PREDICT cohorts). Box plots as in Fig. 1. b,c, Detailed percentiles for the 15 most favourably and unfavourably ranked SGBs against the markers of the three clinical data categories of the PREDICT 1 (UK) (b) and PREDICT 3 US22A (US) (c) cohorts. Detailed panels of the percentiles for the other three cohorts can be found in Extended Data Fig. 2. iAUC, incremental area under the curve; PUFA, polyunsaturated fatty acid; QUICKI, quantitative insulin sensitivity check index; THR, total-cholesterol-to-HDL ratio; VLDL, very-low-density lipoprotein.

Most SGBs ranked within the 50 most favourably or unfavourably linked to host anthropometry belong to the Firmicutes phylum (92 out of 100) and, in particular, to the Clostridia class (n = 80; Supplementary Table 7). Within this class, in the ZOE MB health-ranks, most SGBs belonged to the Clostridiales order, with 32 unfavourably ranked SGBs (of which n = 27 Lachnospiraceae out of 50) and 31 favourably ranked SGBs (n = 13 Lachnospiraceae and n = 12 Ruminococcaceae) assigned to this order. Collectively, the average total relative abundance of the 50 most favourably ranked SGBs is 5.98%, whereas the 50 most unfavourably ranked SGBs account for 13.64% (Supplementary Table 7).

Uncharacterized health-linked bacteria

A large portion of the 50 most favourably ZOE MB health-ranked SGBs are unknown (n = 22), meaning that these are uncultured species represented solely by microbial genomes reconstructed from metagenomic data. Of the 28 known SGBs (with available isolate genomes), 24 are still uncharacterized species without phenotypic descriptions and recognized taxonomic names (Supplementary Table 7). Eubacterium siraeum (SGB4198) and Faecalibacterium prausnitzii (SGB15317) are among the few exceptions with previous support for their favourable role9,25.

By contrast, the 50 unfavourably ZOE MB health-ranked SGBs are generally species with cultured isolates and established taxonomic labels (Supplementary Table 7). Among the 44 known SGBs, several species were already linked with detrimental effects on the host, including Ruminococcus gnavus[26], Flavonifractor plautii[27], Ruminococcus torques28,29 and Enterocloster bolteae[30]. Overall, the most prevalent favourably ranked health-associated micro-organisms in the human gut belong to under-investigated species, highlighting gaps in our knowledge of the potential beneficial role of the human microbiome in promoting and maintaining non-pathogenic conditions.

Gut species ranked by diet quality

Similarly to the ZOE MB health-ranks, we defined a species ranking on the basis only of dietary markers across all five PREDICT cohorts, which we called the ‘ZOE Microbiome Diet Ranking 2025’ (ZOE MB diet-rank; Supplementary Table 5). As markers of a generally healthier diet, we adopted five validated indices (Methods) computed starting from validated food frequency questionnaires (FFQs) or logged diet data (logged using a mobile phone app), reflecting long- and short-term dietary habits, respectively (Extended Data Figs. 4 and 5 and Methods).

The ZOE MB health- and diet-rankings showed, as expected, general concordance (Spearman’s ρ = 0.72; Extended Data Fig. 6a and Supplementary Table 5). Although the large majority of the SGBs highlighted by high or low ZOE MB health-ranks and diet-ranks belong to unknown taxa, reported phenotypic characteristics of known species agree with our analysis. For example, R. torques (SGB4608) and F. plautii (SGB15132), discussed previously as unfavourable species according to the ZOE MB health-ranks, were also concordantly unfavourably ranked in the ZOE MB diet-ranks (0.991–0.904 and 0.981–0.901, respectively). On the other hand, the favourably ranked Blautia glucerasea (SGB4816) was described to reduce visceral fat accumulation, blood glucose and triglycerides in mice[31] (ZOE MB health-ranks and diet-ranks of 0.267 and 0.062, respectively). As another example, in a dietary fibre supplementation trial involving individuals with T2D, Lachnospira eligens (SGB5082) was increased selectively and associated negatively with postprandial glucose and insulin, body weight and waist circumference[32] (ZOE MB health-ranks and diet-ranks of 0.276 and 0.115, respectively), indicating that precise dietary interventions aimed at stimulating beneficial bacterial growth can contribute to treating or managing metabolic disorders symptoms.

Despite the overall agreement between the ZOE MB health- and diet-rankings, 65 out of the 661 ranked SGBs showed discordant rankings (absolute rank difference at least 0.3; Extended Data Fig. 6a and Supplementary Table 8). Generally, the different trends may be due to the different capacities of certain bacteria (for example, generalists) to use a variety of substrates, including those derived from unhealthy diets, while releasing functional metabolites with protective or health-promoting effects. Among these, for example, Harryflintia acetispora (SGB14838) was found associated with favourable cardiometabolic markers and unfavourable diets (ZOE MB health-rank = 0.363 and ZOE MB diet-rank = 0.879) in this study. This strict anaerobe can use readily available monosaccharides such as maltose, glucose and fructose, but can also produce short-chain fatty acids[33], which are regulatory and anti-inflammatory mediators[34].

Across the US and UK populations, the ZOE MB health-rankings showed high consistency (Spearman’s ρ = 0.61; Extended Data Fig. 6b), whereas country-specific ZOE MB diet-rankings were more heterogeneous (Spearman’s ρ = 0.26; Extended Data Fig. 6c). The intraclass correlation coefficients (ICC)[35] also suggest that the ZOE MB health-ranks are more consistent across countries than the ZOE MB diet-ranks (ICC = 0.5929 and 0.2623, respectively; Extended Data Fig. 6b,c). Across cohorts, we obtained an ICC = 0.63 and 0.46 for the ZOE MB health-ranks and diet-ranks, respectively, indicating that health rankings were more able to capture cohorts and countries differences, whereas the most favourably ranked species appeared to match across populations with similar levels of industrialization and lifestyle.

Species rankings stratify by BMI

BMI is an imperfect but widely adopted and easy-to-obtain anthropometric marker of health risk. As BMI was not included among the markers of the ZOE MB health- and diet-rankings, and we corrected for it in the partial correlation analysis, we set out to evaluate how the two rankings can stratify people according to their BMI to assess how health signatures in the gut microbiome are reflected in body mass.

We correlated the 661 ZOE MB health-ranked species with BMI (corrected for sex and age), in each PREDICT cohort, and found that, overall, the ranks were associated positively with BMI (Spearman’s ρ = 0.72), with the favourably ranked SGBs correlated negatively with BMI, whereas unfavourably ranked SGBs correlated positively with BMI (Fig. 3a). These results were confirmed when considering the ZOE MB diet-ranks and discrete BMI categories (Extended Data Fig. 7a–c; all intra-dataset comparisons statistically significant at Q < 0.2 and all 30 except 7 at Q < 0.01) as well as the cumulative abundance of the species in the two 50-species sets (Fig. 3b,c; all intra-dataset comparisons statistically significant at Q < 0.2 and all 30 except 5 at Q < 0.01).

Fig. 3: ZOE MB health- and diet-ranked species show significant and reproducible associations with BMI and diseases.

a, Concordance of ZOE MB health-ranks with partial Spearman’s correlations against BMI (corrected for sex and age) across PREDICT cohorts. Favourably ranked SGBs correlate negatively with BMI; unfavourably ranked SGBs correlate positively (ZOE MB diet-ranks in Extended Data Fig. 7a). Shading represents 95% confidence interval. b,c, Cumulative relative abundance of favourably (b) and unfavourably (c) ranked SGBs across BMI categories. As BMI increases, reflecting higher health risks, the abundance of favourable SGBs decreases whereas that of unfavourable SGBs increases. Similar patterns were seen for SGB richness (Extended Data Fig. 7b,c). Only non-significant (NS) false discovery rate (FDR)-corrected P values (Q > 0.01, two-sided Mann–Whitney U-test) are annotated. Box plots as in Fig. 1. d, Meta-analysis of the 50 most favourable and unfavourable SGBs comparing participants of healthy weight with those with obesity from public cohorts. Lower BMI is associated with more favourable SGBs; people with higher BMI carry more unfavourable SGBs. Meta-analysis on ranks defined on UK and US participants shows reproducibility across countries. Other comparisons are in Extended Data Fig. 8 and the diet-ranked SGBs meta-analysis in Extended Data Fig. 9. Country codes: ARG, Argentina; AUT, Austria; DEU, Germany; DNK, Denmark; FRA, France; GBR, United Kingdom of Great Britain and Northern Ireland; IRL, Ireland; ISR, Israel; KAZ, Kazakhstan; NLD, Netherlands; USA, United States of America. e, Meta-analysis of disease group (adjusted by sex, age and BMI) on standardized mean differences (SMD) of cumulative relative abundance of the 50 most favourable (left) and unfavourable (right) SGBs from both rankings (meta-analysis on SGB richness in Supplementary Fig. 5a; Methods). f, Meta-analysis of normalized ZOE MB health-ranks and diet-ranks, weighted by arcsin square-root of relative abundance values (right, weighted score sum; left, score sum (unweighted)). Horizontal lines in meta-analysis plots represent 95% confidence intervals. CRC, colorectal cancer; IBD, inflammatory bowel disease; IGT, impaired glucose tolerance.

To generalize these associations, we leveraged a total of 5,348 healthy individuals from 27 public cohorts divided into three BMI categories, healthy weight (n = 2,837), overweight (n = 1,562) and obese (n = 949; Supplementary Table 9 and Methods). In 47 pairwise comparisons, 34 had a higher median richness for the 50 most favourably ranked ZOE MB health SGBs in lower BMI groups versus higher BMI groups (binomial P = 0.003; Supplementary Table 10 and Supplementary Fig. 3a), and this was not dependent on country effects or sequencing depth (Supplementary Table 11), highlighting the generalization of the identified ranks. Meta-analysis based on linear regression on single cohorts (Methods) showed that individuals with healthy weight carried, on average, 5.2 more of the 50 favourably ZOE MB health-ranked SGBs than people with obesity (P = 0.0003; Fig. 3d and Supplementary Table 12), which corresponded to a normalized difference in the cumulative abundances of unfavourably and favourably ranked SGBs of Cohen’s d = −0.59 (P < 0.0001; Supplementary Tables 10 and 13 and Methods). Correspondingly, individuals with obesity carried, on average, 1.95 more of the unfavourably ranked SGBs than people of healthy weight (P = 0.0005; Fig. 3d, Supplementary Tables 14 and 15; Cohen’s d on cumulative relative abundances = 0.29; P = 0.0001). Pairwise analysis of the other BMI categories confirmed these results (Extended Data Fig. 8 and Supplementary Tables 10 and 12–15).

Similarly, we tested the association of the 50 most favourably and unfavourably ZOE MB diet-ranked SGBs with BMI, and found similar but milder signals compared with the ZOE MB health-ranks (average Spearman’s correlations between the two ranks and BMI of 0.61 and 0.72, respectively; Fig. 3a and Extended Data Fig. 7a). Using public datasets, 36 intra-dataset comparisons out of 47 showed a higher median cumulative abundance and a higher median richness of the 50 most favourable SGBs in lower BMI classes compared with higher BMIs (binomial P = 0.0003; Supplementary Fig. 3b). Conversely, 36 comparisons showed a higher median count of the least favourable 50 SGBs for the higher BMI classes compared with the lower BMI groups (binomial P = 0.0003; Supplementary Table 10). The contribution of diet-ranked SGBs in different BMI categories similarly showed a decreasing number and cumulative relative abundance of favourably ranked SGBs and an increase in unfavourably ranked SGBs (Extended Data Fig. 7d–g). In meta-analysis, healthy weight and overweight participants carried 3.5 and 1.5 more favourable diet-ranked SGBs, and 1.25 and 0.88 fewer unfavourable ZOE MB diet-ranked SGBs than obesity participants, respectively (Extended Data Fig. 9, Supplementary Fig. 3 and Supplementary Tables 16–19). All these analyses were confirmed when rankings were computed without adjusting for BMI (Extended Data Fig. 7h–k) and, altogether, these results suggest that the ZOE MB health- and diet-ranks can stratify people based on their obesity status regardless of geography.

Species rankings and host diseases

Next, we assessed whether the ZOE MB health-ranked SGBs had a differential presence or abundance in control participants compared with participants with a defined disease condition, exploiting 25 case–control, publicly available microbiome studies (4,816 samples in total with n = 2,707 controls and n = 2,109 cases; Methods) investigating five diseases with variable levels of association with the gut microbiome (Supplementary Table 20). The number of the 50 most favourably ZOE MB health-ranked SGBs was higher in controls than cases for 21 of the 25 cohorts, whereas the count of the 50 most unfavourably ranked SGBs was correspondingly higher in cases for the same number of cohorts (binomial P = 0.0004).

We performed a meta-analysis on the count of the 50 most favourable and unfavourable SGBs from the ZOE MB health- and diet-rankings. Control samples carried, on average, 3.6 more favourably ranked SGBs than participants with disease (random-effect model, P = 0.0002; Methods) and 1.6 fewer unfavourable SGBs (P = 0.0004; Supplementary Fig. 5a and Supplementary Table 21). Similarly, for the ZOE MB diet-ranked SGBs, controls carried, on average, 3.8 more favourable SGBs and 1.3 fewer unfavourable SGBs, P = 9.5×10−6 and P = 0.0006, respectively; Supplementary Fig. 5a and Supplementary Table 21). Furthermore, meta-analyses of the cumulative abundance of the 50 most favourable and unfavourable SGBs confirmed a greater contribution from favourable species in control groups and of unfavourable SGBs in the corresponding disease groups (meta-analysis Cohen’s d = −0.29, P = 7.1 × 10−6 and d = 0.21, P = 0.054 for the ZOE MB health-ranks; d = −0.24, P = 3.1 × 10−6 and d = 0.28, P = 0.0002 for the ZOE MB diet-ranks; Fig. 3e and Supplementary Table 22).

To assess how informative the rankings are in summarizing the health-associated status of a single sample, we scored all metagenomes from diseased and control participants by summing the normalized ZOE MB health-ranks of the SGBs present in the sample (Methods). We found a strong separation between diseased and control participants (meta-analysis Cohen’s d = −0.37, P = 8.3 × 10−8), improving over the simple counting of the number of most favourable and unfavourable SGBs (Fig. 3f). Notably, T2D showed the strongest disease-specific association (meta-analysis Cohen’s d = −0.47, P = 6.78 × 10−5; Fig. 3f and Supplementary Table 23) with the weighted version of this score showing an even stronger effect for T2D (meta-analysis Cohen’s d = −0.51, P = 0.0002). People were also scored using the ZOE MB diet-ranks, and similar links with their health status emerged (Fig. 3f and Supplementary Table 23). Notably, standard alpha diversity measures such as gut SGBs richness and Shannon’s entropy measures showed weaker and less consistent associations, with significant links only in the IBD and T2D comparisons (Supplementary Fig. 5b and Supplementary Table 24).

Although the ranking-based scoring of single samples cannot have the same predictive power for host phenotypes compared with condition-specific supervised learning approaches relying directly on labelled training data, our results showed how embedding the ranking system into a simple one-dimensional microbiome index provides a meaningful evaluation of microbiome health conditions.

Diet changes effects on ranked species

To validate the effect of dietary changes on the presence and abundance of gut microbial species according to their ZOE MB health-rankings, we analysed two dietary intervention studies, namely ZOE METHOD[36] and BIOME[37] (ClinicalTrials.gov registrations, NCT05273268 and NCT06231706, respectively). In brief, the ZOE METHOD cohort comprised n = 347 people assigned to a personalized dietary intervention programme (PDP; n = 177) arm versus an arm with general diet advice following the US Department of Agriculture recommendations (control, n = 170). People assigned to the PDP group showed lower energy intake and a significant decrease in triglycerides, HbA1c, weight and waist circumference after 18 weeks[36]. The ZOE BIOME cohort comprised n = 349 healthy adults (intention-to-treat) randomized into the primary intervention group (receiving a defined prebiotic blend, n = 116), the functional control group (receiving bread croutons to match the calories in the control group, n = 120) and the daily probiotic group (supplemented with 15 billion colony-forming units of Lacticaseibacillus rhamnosus per day, n = 113). Overall, weight, waist circumference, metabolites and gastrointestinal symptoms did not differ significantly between groups[37].

We identified which microbiome species were impacted significantly by the dietary interventions in the two cohorts. In the ZOE BIOME cohort, 57, 4 and 14 prevalent SGBs showed significant changes at the endpoint (Q < 0.01) for the prebiotic blend, probiotic and control arm, respectively (Fig. 4a). Among the species with a significant change in the prebiotic arm were beneficial fibre-degrading Bifidobacterium adolescentis (SGB17244), Bifidobacterium longum (SGB17248) and Blautia obeum (SGB4811)38,39,40, as well as butyrate-producing Agathobaculum butyriciproducens (SGB14993), Anaerobutyricum hallii (SGB4532) and Coprococcus catus (SGB4670)41,42. By contrast, the species Dysosmobacter welbionis (SGB15078), among the top unfavourably associated SGBs in our study, was decreased significantly by the same dietary intervention (Supplementary Table 25). In the ZOE METHOD cohort, we found 46 SGBs differed significantly in their relative abundance in the PDP arm, and only two in the control arm (Fig. 4b and Supplementary Table 25; Wilcoxon signed-rank test Q < 0.1). Of note, the prominent butyrate producers Roseburia hominis (SGB4936) and A. butyriciproducens (SGB14993) were also found to increase in the PDP intervention.

Fig. 4: Dietary interventions have a large impact on microbiome composition.

a,b, Pre–post dietary intervention variations in prevalent gut microbial SGBs (at least 10% at both time points). The plots show the effect size (log_2-transformed ratio of mean relative SGB abundance at endpoint over baseline) against the significance (Q values, FDR–Benjamini–Hochberg-corrected P values). a, BIOME cohort (ClinicalTrials.gov NCT06231706) with n = 321 healthy adults from the UK (n = 106 prebiotic blend, n = 106 probiotic and n = 109 control), significance threshold set to Q < 0.01. b, METHOD cohort (ClinicalTrials.gov NCT05273268) with n = 347 US individuals (n = 177 PDP, n = 170 control), and significance threshold set to Q < 0.1. c, Change in relative abundance for the significant SGBs in the intervention arms of BIOME (prebiotic blend, n = 57). d, Change in relative abundance of METHOD (PDP, n = 46), separated into those that increase from those that decrease from baseline (B) to endpoint (E). Extended Data Fig. 10a–d reports the change in relative abundance and prevalence of the control and prebiotic arms. Two-sided Wilcoxon test; box plots as in Fig. 1).

The dietary intervention groups of both clinical trials that aimed at improving diet using different approaches (prebiotic blend for BIOME and PDP for METHOD) showed the highest number of significantly changing SGBs (Fig. 4a and Supplementary Table 25). Focusing on the most significant gut microbial SGBs with largest change in relative abundance after dietary interventions, we found increasing Bifidobacterium animalis (SGB17278)—a bacterium present in dairy-based foods and in the microbiome of people consuming larger amounts of them43,44 (Fig. 5a,b and Supplementary Table 25), an unknown Lachnospiraceae bacterium (SGB4953, BIOME; Fig. 5a) and R. hominis (SGB4936, METHOD; Fig. 5b) both previously associated with a vegan diet[43], and another unknown Lachnospiraceae bacterium (SGB5200, BIOME; Fig. 5a) linked to a vegetarian diet[43]. Butyricimonas paravirosa (SGB1785, METHOD; Fig. 5b), Phocea massiliensis (SGB14837), a currently uncharacterized Ruminococcaceae species (SGB14899) and Candidatus Pararuminococcus gallinarum (SGB63327) (all found in the BIOME cohort; Fig. 5a), were instead decreasing in the intervention and reported to be associated with a mixed diet[43]. The Streptococcus salivarius (SGB8007, METHOD; Fig. 5b) species was also found in food microbiomes[45] and, together with an unknown Ruminococcaceae species (SGB14899, BIOME; Fig. 5a), were found associated with non-vegans[43].

Fig. 5: Gut microbial SGBs that increase after dietary interventions are linked to more favourable ZOE MB health- and diet-ranks.

a, The 20 most significant gut microbial SGBs with the greatest effect sizes following the BIOME dietary intervention from Fig. 4a (left), paired with their ZOE MB health-ranks and diet-ranks, if available (right). b, The 20 most significant gut microbial SGBs with the greatest effect sizes following the METHOD personalized dietary intervention programme from Fig. 4b (left), paired with their ZOE MB health-ranks and diet-ranks, if available (right). The x axis shows the log_2-transformed ratio of mean relative abundance SGB values at endpoint over baseline. All values are reported in Supplementary Table 25. c,d, The distributions of the ZOE MB health-ranks (c) and diet-ranks (d) for the prebiotic blend arm of the BIOME cohort (n = 57 of tested SGBs). e,f, The distributions of the ZOE MB health-ranks (e) and diet-ranks (f) for the PDP arm of the METHOD cohort (n = 46 of tested SGBs). The distributions show that SGBs increasing in relative abundance have significantly more favourable ranks, whereas decreasing SGBs have more unfavourable ranks (two-sided Mann–Whitney U-test, P = 7.78 × 10−3, P = 3.00 × 10−5, P = 5.20 × 10−5 and P = 2.03 × 10−5, respectively). Distributions of the ZOE MB health-ranks and diet-ranks for the other arms are reported in Extended Data Fig. 10e,f. Box plots as in Fig. 1.

We found that the SGBs with increased relative abundance at endpoint in the prebiotic blend arm of the BIOME trial (Fig. 4a and Supplementary Table 25) showed significantly more favourable ZOE MB health-ranks and diet-ranks than the decreasing SGBs (Fig. 5c,d, Mann–Whitney U-test P = 7.78 × 10−3 and P = 3.00 × 10−5, respectively). This was confirmed for the PDP arm of the METHOD cohort (Fig. 5e,f; Mann–Whitney U-test P = 5.20 × 10−5 and P = 2.03 × 10−3, respectively). No significant enrichment for ZOE MB health- and diet-rankings was instead detected for the significantly changing SGBs for the probiotic group of the BIOME cohort or the control groups of both BIOME and METHOD cohorts (Extended Data Fig. 10e,f).

Together, these results show how dietary interventions or tailored prebiotic blends, both aiming at improving diet quality, positively modulate the microbiome composition. The SGBs’ rankings (ZOE MB health and diet), which were defined on cross-sectional independent cohorts, were strongly and consistently predictive of the SGBs most associated with the dietary interventions in independent cohorts and countries, supporting the direct, reproducible and actionable link between diet and microbiome composition.

Conclusions

Defining the baseline composition of the human gut microbiome in ‘healthy’ host conditions has been a long-standing challenge. This area has several problems, including defining general host health across age, as well as the inter-population microbiome variability, the existence of several distinct health-associated microbiome configurations19,46, the personalized nature of diet’s impact on the gut microbiome16,47, the diversity of dietary regimes48,49 and the effect of social interaction on microbiome transmission[44]. To address this challenge, we reformulated the question of what a health-associated microbiome is by scoring gut microbiome species for their tendency to be correlated with healthy diet scores and with the continuum of a panel of intermediate markers of cardiometabolic health, in large and generally healthy populations. By leveraging diet scores such as the healthy eating index or the healthful PDI, and health estimators such as blood glucose, HDL and triglycerides, we identified species that are expected to characterize hosts in healthier conditions, as well as other species that are enriched in hosts with more unfavourable health risk factors. Most of the key health-associated species were from previously uncharacterized species, underlining the wide knowledge gap of the microbiome composition in non-diseased conditions. These rankings, named ZOE MB health-ranks and diet-ranks, are released and maintained publicly (Supplementary Table 5 and ‘Data availability’ section) and can be adopted by the research community to evaluate whether a given human gut microbiome sample is characterized by a more favourable or unfavourable diet and health-associated species.

Several factors were crucial in the robust definition of the proposed microbiome species ranking systems. First, the scale of our combined cohorts with consistent experimental protocols for metagenomic sequencing and analysis is unprecedented. Second, the geographic diversity spanning all US states and UK regions, although confined to typical Westernized lifestyles and diets, allowed us to overcome local lifestyle-associated microbiome configurations. Third, consistent long- and short-term diet logging data processed in an integrative quantitative approach and validated markers that are intermediary measures of cardiometabolic health, and more advanced postprandial metabolomic-derived markers, enabled a fine-grained definition of relative health gradients across the surveyed populations. Fourth, publicly available datasets that were processed and curated uniformly, permitted independent validation and generalization of the results and showed the relevance of the species rankings toward additional conditions and diseases not evaluated in the original populations. We acknowledge that the demographic composition of the cohorts may influence some associations, and we are continuing to expand in both population scale and precision of each host-associated readout.

Our microbiome species ranking system proved accurate in reflecting changes induced by large-scale dietary intervention trials with associated host marker improvements (Figs. 4 and 5). Indeed, the cross-sectional associations were reflected in a significant and substantial increase of health-associated microbiome species and a reduction or depletion of unfavourably ranked species. Many health-associated host markers are co-correlated because they are nutritional indicators, and disentangling their direct interactions from those mediated by the microbiome will remain elusive until large-scale microbiome interventions become possible in humans. In this respect, one key limitation of our study design is that it does not allow directly disentangling of the effect that diet exerts on the microbiome to improve cardiometabolic health from the impact of diet only. This is particularly important as diet-based ranks were more dependent on country-related differences compared with health ranks, and further studies should explore food-specific links with gut microbial species and cardiometabolic outcomes in greater detail[50]. This would entail designing large-scale interventions in which both the introduction of single foods and alterations of specific microbiome characteristics (for example, by administration of specific microbiome members) are tested, which are ultimately required to provide causal evidence that personalized nutritional interventions targeting the microbiota have a robust and reproducible impact on cardiometabolic health. Nonetheless, the confirmation of the cross-sectional patterns along the diet–microbiome–health axis in longitudinal nutrition intervention trials not only increases the intrinsic value of the rankings but confirms that the human gut microbiome can be modulated successfully by dietary intervention and that the effects on the microbiome of such interventions are both predictable and reproducible. By providing the full list of ranked microbial species, this work can be exploited in future research on microbiome-powered precision nutrition and can be expanded in the future to more diverse populations and lifestyles that are currently underrepresented in microbiome, nutritional and health studies.

Methods

ZOE PREDICT cohorts definition

The ZOE PREDICT programme comprises several distinct studies that together constitute one of the largest multi-omic health initiatives, linking diet, person-specific metabolic responses to foods, and the gut microbiome. In this work, we considered and harmonized five ZOE PREDICT cohorts: PREDICT 1, PREDICT 2, PREDICT 3 US21, PREDICT 3 US22A, and PREDICT 3 UK22A. The PREDICT 1 cohort (NCT03479866) was described previously9,51. In brief, PREDICT 1 enrolled 1,098 participants (n = 1,001 from the UK and n = 97 from the USA) who underwent a clinical visit to collect anthropometric information and blood samples, followed by an at-home phase during which postprandial responses to both standardized tests and ad libitum meals were recorded. Stool samples were collected at home before the in-person clinical visit. The PREDICT 2 study (NCT03983733) had a similar collection protocol to PREDICT 1 but was conducted entirely remotely and included data from 975 people from 48 US states (including the federal District of Columbia and without participants from North Dakota and Hawaii). The PREDICT 3 cohorts (US21, US22A and UK22A) are research cohorts (NCT04735835) embedded within the ZOE commercial product. Participants provide informed written consent for their data to be used for scientific research purposes. In total, 32,621 samples (n = 11,798 for US21, n = 8,470 for US22A and n = 12,353 UK22A) were collected and retrieved. The studies were fully remote, participants completed health and food questionnaires at baseline, and self-collected and shipped stool samples. Cardiometabolic markers were collected as described below. Furthermore, we considered and analysed two registered clinical nutritional intervention studies, namely METHOD[36] (NCT05273268) and BIOME[37] (NCT06231706), focusing on the microbiome changes and their links with the two derived SGB-level rankings (ZOE MB health-ranks and diet-ranks). All study protocols are registered and available on clinicaltrials.gov through the clinical trials number and link affiliated with each trial.

Sample collection, DNA extraction and sequencing

For the PREDICT 1 cohort, sample collection, DNA extraction and sequencing were described previously[9]. The PREDICT 2 samples were collected in Zymo buffer, DNA extraction was performed at QIAGEN Genomic Services using DNeasy 96 PowerSoil Pro, and sequencing was performed on the Illumina NovaSeq 6000 platform using the S4 flow cell and targeting 7.5 Gb per sample. The PREDICT 3 samples were self-collected into tubes containing the DNA-Shield Zymo buffer. Sample processing was performed by Zymo and Prebiomics. In brief, DNA extraction by Zymo used the ZymoBIOMICS-96 MagBead DNA kit, whereas Prebiomics used the DNeasy 96 PowerSoil Pro kits. Sequencing libraries were prepared using the Illumina DNA Prep Tagmentation kit, following the manufacturer’s guidelines. Whole-genome shotgun metagenomic sequencing on the Illumina NovaSeq 6000 platform used the S4 flow cell and targetted 3.75 Gb per sample.

All raw sequenced data were quality controlled using the preprocessing pipeline available at https://github.com/SegataLab/preprocessing, which comprises three steps: (1) removal of reads with low-quality (Q < 20), too short (length under 75 nt), or with more than two ambiguous bases; (2) removal of host contaminant DNAs (Illumina’s spike-in phiX 174 and human genomes, hg19); and (3) synchronization of paired-end and unpaired reads.

Dietary data processing

In the PREDICT cohorts, we assessed long-term food intakes using FFQs, which were largely consistent across cohorts. Specifically, for PREDICT 1 participants (UK), we used a modified 131-item European Prospective Investigation into Cancer and Nutrition (EPIC) FFQ[52]. Participants in PREDICT 2 (USA) were surveyed using a similarly validated Diet History Questionnaire-III FFQ, including 135 items about food and beverages, as well as 26 questions about dietary supplements[53]. In PREDICT 3 UK22A and US22A, we developed and used a 264-item FFQ adapted from the EPIC-Norfolk Study FFQ and the Diet History Questionnaire-III. Consequently, there is a large overlap between the food items collected across the FFQs; for example, 90% of questions in the EPIC FFQ are included in the PREDICT 3 FFQ. This FFQ also includes additional food items to accurately capture modern eating habits—a limitation of older FFQ versions[54]. In the PREDICT 3 US21 cohort, FFQs were not collected, and only short-term logged dietary data collected using the ZOE mobile phone app were used instead.

Starting from both long- and short-term dietary data, we computed three versions of the PDI[55], namely, the overall PDI, the healthful PDI (measuring the adherence to a healthier plant-based foods diet) and the unhealthy PDI (measuring the intake of unhealthful plant-based foods), as well as the healthy eating index[23] (measuring how consumed foods align with dietary guidelines), the alternative Mediterranean diet score (measuring the adherence to a Mediterranean diet)[56] and the Healthy Food Diversity (HFD) index (measuring the number, distribution and health value of consumed foods)[57]. Specifically, to calculate PDIs and the healthy eating index, food items were first assembled into food groups by mapping them onto a ‘food tree’ consisting of a database of nutrient information arranged according to a hierarchical tree structure: level 1 (9 food groups), level 2 (52 food groups) and level 3 (195 food groups). UK foods were mapped onto the Composition of Foods Integrated Dataset (CoFID)[58] using food categories or sub-group codes, whereas US foods were similarly mapped onto the US Department of Agriculture Food and Nutrient Database for Dietary Studies database. Level 3 foods were aggregated and harmonized by nutrition scientists to allow for comparisons across cohorts. The Mediterranean diet and HFD scores were calculated as described previously[9].

Host health and anthropometric marker collection

In PREDICT 1, sex and age were self-reported, whereas height, weight and blood pressure were measured at a clinic visit (day 0). At the clinic visit, participants were also fitted with wearable continuous glucose monitor CGM) devices (Abbott Freestyle Libre Pro (FSL)), visceral fat mass was measured using dual-energy X-ray absorptiometry scans following standard manufacturer’s recommendations (DXA; Hologic QDR 4500 plus) and fasting GlycA was measured using a high-throughput NMR metabolomics (Nightingale Health) 2016 panel. Fasting and postprandial venous blood samples were also collected at the clinic; plasma glucose and serum total cholesterol, HDL-C and triglycerides were measured using Affinity 1.0, and whole blood HbA1c% was measured using Viapath. The ten-year ASCVD risk was calculated as per the 2019 American College of Cardiology (ACC) and American Heart Association (AHA) clinical guidelines[59]. Additional data were collected over the subsequent 13-day period at home; postprandial responses to eight standardized meals (seven in duplicate) of differing macronutrient (fat, carbohydrate, protein and fibre) content were measured using CGMs and dried-blood-spot analysis as described previously[13]. T2D and hyperlipidemia were self-reported via health questionnaires. The PREDICT 2 and PREDICT 3 studies were fully remote. Sex, age, height, weight and blood pressure were self-reported, and fasting and postprandial responses for total cholesterol, HDL-C, triglycerides and HbA1c were assessed using whole blood finger-prick samples collected at home using dried-blood-spot analysis by commercial laboratories (CRL, Eurofins Biomnis). CGMs were fitted at home by participants. A selection of standardized meals smaller than in PREDICT 1 was tested in PREDICT 2 and PREDICT 3 (a metabolic challenge meal, and medium-fat and carbohydrate breakfast and lunch meals). Some of the considered markers represent the same metabolic function over time and showed positive correlations between their fasting and postprandial measurements, whereas others represent opposite types of the same biomolecular pathway and showed negative correlations among them (Supplementary Table 4).

Public human microbiome datasets

We leveraged 27,011 public metagenomic samples from 107 cohorts available through the curated MetagenomicData 3 (cMD3) resource60,61 to define the cohorts used for the meta-analyses on BMI and healthy–diseased comparison (‘Statistical and meta-analyses’). For the meta-analysis on BMI, we selected cohorts with stool microbiome samples from healthy participants (self-assessed, not reporting a diagnosis), aged at least 16 years, BMI ≥ 18.5 and sex information available. Cohorts with fewer than 30 people were excluded. Furthermore, the ThomasAM_2018_c and LeChatelier_2013 cohorts were excluded as duplicates in the YachidaS_2019 and NiesenHB_2014 cohorts, respectively. Overall, 6,182 samples from 34 different cohorts and 20 countries were retrieved. Participants were classified into three categories: healthy weight (BMI ≥ 18.5 and <25), overweight (BMI ≥ 25 and <30) and obese (BMI ≥ 30). Then, each combination of country, dataset and two BMI categories was tested if at least 15 samples were retained. These led to analysing a total of 5,348 samples from 27 cohorts (2,837 healthy weight, 1,562 overweight and 949 obese participants; Supplementary Table 9). For the health–diseased meta-analyses, we selected from cMD3 participants aged at least 16 years, BMI ≥ 18.5 and the sex information available that were part of a case–control study of one of the following diseases: CRC, IBD (including ulcerative colitis and Crohn’s disease), T2D, IGT and ASCVD. Studies with fewer than 30 people were excluded. In total, we considered ten datasets of CRC (650 cases and 645 controls), two datasets of IGT (273 cases and 492 controls), five datasets of T2D (775 cases and 900 controls), three datasets of IBD (103 controls, 59 of which used in two different comparisons, 60 individuals with Crohn’s disease and 68 individuals with ulcerative colitis) and three datasets of CVD (283 cases and 508 controls). Notably, German and French participants of the MetaCardis cohort were separated, and this led to a set of 449 controls used in both the T2D and the IGT analyses, whereas only the 176 controls from France were used in the CVD analysis. Overall, the total number of samples analysed was N = 4,816 (2,707 controls and 2,109 cases) from 25 cohorts and 10 countries (Supplementary Table 20). The cohort selection for the two analyses used the script https://github.com/waldronlab/curatedMetagenomicDataAnalyses/blob/main/python_tools/meta_analysis_data.py available in cMD3.

Microbiome taxonomic profiling

All microbiome samples from the PREDICT cohorts were profiled using MetaPhlAn 4 (v.4.beta.2, database vJan21_CHOCOPhlAnSGB_202103), without performing read subsampling, as the benefit of occasionally detecting a few additional low-abundance species in samples with a higher number of reads outweighs the potential noise from uneven sequencing depth. Samples retrieved from cMD3 (described in ‘Public human microbiome datasets’) were profiled with MetaPhlAn 4 (v.4.beta.1, database vJan21_CHOCOPhlAnSGB_202103) using default parameters in both cases (among default parameters, the stat_q is set to 0.2 by default, which defines the quantiles for the robust average coverage calculation), which precludes the necessity for additional prevalence filters considering its default parameters are tailored for the taxonomic profiling of human microbiome samples[19]. MetaPhlAn 4 is a publicly available taxonomic profiler for metagenomic samples (Github repository: https://github.com/biobakery/MetaPhlAn) that leverages medium and high-quality genomes from isolates and metagenome-assembled genomes (MAGs). Isolate genomes and MAGs are clustered at 95% average nucleotide identity to define SGBs, as described previously[20]. If an SGB cluster contains a genome isolate, then it is referred to by that isolate’s taxonomic label. If an SGB contains only MAGs, then it represents an unknown species cluster and is assigned the taxonomic label of a genus, family or phylum, according to which is the genomically closest to a taxonomic label from isolate genomes. As the taxonomic classification of MetaPhlAn depends on species-specific marker genes, sometimes there are several SGBs of very closely related genomes for which the identification of SGB-specific markers is not feasible. In this case, more than one SGB can be considered together, and the label ‘_group’ is appended to the representative SGB ID. In this way, MetaPhlAn 4 improves the resolution of the taxonomic profiling task[62].

Rankings definition

We first identified a subset of prevalent SGBs to ensure a minimum number of non-zero relative abundance values. In each PREDICT cohort, we selected markers that are intermediary measures of host health or diet health, and they were organized into four categories: personal, dietary, fasting and postprandial (Supplementary Table 2). Second, we calculated the partial Spearman’s correlation between each SGB and health markers, adjusting for sex, age and BMI, using the ‘pingouin’ Python package (v.0.5.4, https://github.com/raphaelvallat/pingouin) (Extended Data Figs. 3 and 5). The relative abundance values of SGBs (including zeros) were used as input for the correlations. Third, the SGB-marker partial correlations were sorted ascending if the marker was considered as positive with respect to health, or descending if the marker was considered as negative. These sorted partial correlations were ranked and normalized according to cohort sample sizes into percentiles ranging from 0 to 1 (function pandas.DataFrame.rank with param pct=True from pandas v.2.1.3) (Fig. 2b,c and Extended Data Figs. 2 and 4). Fourth, for each category of markers, we computed the average percentiles across markers (Fig. 2a and Extended Data Fig. 4). SGBs were retained in the overall rankings if they were ranked in at least two different cohorts, leading to a final ranking of 661 SGBs. Finally, the ZOE MB health-rank 2025 was defined by first averaging the personal, fasting and postprandial category percentiles within each cohort, and then averaging these cohort-specific averages. The ZOE MB diet-rank 2025 instead was defined by averaging the dietary percentiles across all cohorts (Fig. 2a, Extended Data Fig. 4 and Supplementary Table 5). The ZOE Microbiome Rankings are also available at https://zoe.com/our-science/microbiome-ranking.

Machine learning

To assess the link to the human gut microbiome composition, we developed and used a machine learning framework based on random forest classification and regression algorithms from the scikit-learn (v.1.3.2) Python package (as implemented in the RandomForestClassifier and RandomForestRegressor functions, respectively), both with ‘n_estimators=1000’ and ‘max_features=sqrt’ parameters[63]. We trained random forest classifiers and regressors on MetaPhlAn 4-estimated SGB-level relative abundances (arcsine square-root transformed) to assess the extent to which the outcome variable was predictable from the microbiome as a proxy of the strength of the microbiome–variable association. This framework was used and described originally in ref. [9] and accounts for the presence of twin pairs in the data, which avoids biases due to identical values in twins. In brief, the framework uses a cross-validation approach, splitting the dataset randomly into training and testing folds with an 80:20 ratio, respectively, and repeated 100 times (as implemented in the StratifiedShuffleSplit function). Folds are also constructed to maintain a similar ratio of the two classes to predict as they appear in the full data. For target variables with continuous values, classification was performed by contrasting the first against the fourth quartile, the first three against the fourth quartile and the first against the last three quartiles. Performances were evaluated using the AUC for the classification task, whereas Spearman’s correlation between the real and predicted values was used for the regression task[22].

Statistical and meta-analyses

We performed a meta-analysis to determine the possible links between BMI (categorized into ‘healthy weight’, ‘overweight’ and ‘obese’) and our ranked SGBs across various publicly available studies comprising a total of 5,348 people who were not diagnosed with any specific disease. We first evaluated the ZOE MB health- and diet-ranks by assessing the cumulative relative abundance and richness of the 50 most favourable and the 50 least favourable SGBs in each dataset in each BMI category: healthy weight, overweight and obese (see ‘Public human microbiome datasets’ for the specific cut-offs). Specifically, we assessed the number of intra-dataset, between-BMI groups pairwise comparisons in which the group median abundance or the group median count was higher in the lower BMI group (when considering most favourable SGBs from both ranks) or higher in the higher BMI group (when looking at least favourable SGBs). Next, we fit linear models for each dataset and pair of BMI categories: healthy weight versus overweight, healthy weight versus obese, and overweight versus obese. In the first model, we looked at the count of the 50 most favourable and unfavourable ZOE MB health- and diet-ranked SGBs. A second model was fitted on the cumulative relative abundance (arcsine square-root transformed) of the 50 most favourable and unfavourable SGBs in the two rankings. All models were adjusted by sex and age. Cohen’s d was used to estimate the effect size of the normalized difference between unfavourable and favourable ranked SGBs when considering cumulative abundances. This quantifies the difference between the means of two groups in terms of standard deviations. Specifically, as originally defined, a ‘small’ effect size corresponds to d = 0.2, a ‘medium’ effect size to d = 0.5 and a ‘large’ effect size to d = 0.8 (ref. [64]). In these models, the lower BMI category of each comparison was used as the negative control, so negative coefficients reflect a higher count of SGBs in the lower BMI category, whereas positive coefficients reflect a higher count of SGBs in the higher BMI category. Effect sizes were summarized through meta-analysis, computed as a random-effect model using the Paule–Mandel heterogeneity on adjusted mean differences from the linear regression models (standardized for cumulative abundances). We assessed the presence of the 50 most favourable and most unfavourable SGBs from both the ZOE MB health- and diet-ranks among the countries considered in these analyses (18 in total) and when considering only people of healthy weight (n = 2,837). To link the ranked SGBs with the country, we fit a linear model on the count and cumulative relative abundance of the SGBs, and the models were adjusted by the sequencing depth of the study. We used ordinary least squares adjusted by sequencing depth when comparing two datasets from different countries, and linear mixed model blocked by dataset ID and adjusted by sequencing depth when comparing pairs of countries in which at least one country was represented by more than one dataset (country- and sequencing depth-adjusted P values are presented in Supplementary Table 11).

A second meta-analysis tested the associations between our ZOE MB health- and diet-ranked SGBs and five gut-associated diseases (CVD, T2D, IBD, CRC and IGT) across studies, for a total of 4,816 samples (‘Public human microbiome datasets’). Linear models were used to predict the binary disease outcome (healthy versus diseased) for each disease, using the cumulative abundances (arcsine square-root transformed) of the 50 most favourable or unfavourable SGBs, adjusting by sex, age and BMI. The betas of the linear models were converted into SMDs as described previously[65]. We also defined models to predict healthy versus diseased using the sum of the SGB ranks normalized between −1 and 1, considering all 661 SGBs for the ZOE MB health- and diet-ranks, once using the direct sum of the SGB ranks and once weighting ranks by the relative abundance of each SGB in each sample (transformed using the arcsine and square-root function to avoid overestimating the ranks of highly abundant species due to compositionality). SMDs were calculated similarly to those in the previous case. In all meta-analytical models, the set of cohorts considered comprised studies encompassing several diseases with a shared control group that we analysed separately. To account for the overlaps in the studies considered, we computed weights based on the inverse effect sizes variance-covariance matrix, as suggested previously66,67. Thus, five meta-analyses were performed, one for each disease: CVD (three datasets), T2D (six datasets), IBD (three datasets), CRC (ten datasets) and IGT (two datasets). Of note, in the comparisons of controls versus T2D, IGT and CVD, the MetaCardis French and German sub-cohorts were considered as different datasets, and their controls were meta-analysed as different cohorts. In particular, only French control samples were used in the CVD analysis, which included only French cases. Finally, meta-analysis summaries were computed using the same technique. Analyses we carried out with Python (v.3.12.0), using also the following libraries: numpy (v.1.26.2), scipy (v.1.11.4), statsmodels (v.0.14.0), and matplotlib (v.3.8.2) and seabron (v.0.11.2) for visualization.

Ethical compliance

All study protocols are registered on clinicaltrials.gov and procedures are compliant with all relevant ethical regulations. Ethical approval for the PREDICT 1 study was obtained in the UK from the King’s College London Research Ethics Committee (REC) and Integrated Research Application System (IRAS 236407), and in the USA from the institutional review board (Partners Healthcare Institutional Review Board (IRB) 2018P002078). Ethical approval for the PREDICT 2 study (Pro00033432) was obtained from Advarra IRB. Ethical approval for the PREDICT 3 study (Pro00044316, HR/DP-21/22-28300 and HR/DP-24/25-45829) was obtained from Advarra IRB and King’s College London REC. Ethical approval for the METHOD study (Pro00044316; protocol no. 00044316) was obtained from Advarra IRB. Ethical approval for the BIOME study (HR/DP-23/24-39673) was obtained through King’s College London REC. All participants provided written informed consent and all studies were carried out in accordance with the Declaration of Helsinki and Good Clinical Practice.

Reporting summary

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

Data availability

The human genome version used in the preprocessing of the microbiome samples is the GRCh37 genome assembly (hg19, GCA_000001405.1). Raw metagenomic samples, along with metadata information (sex, age, BMI and country) and microbiome profiles for all participants of the ZOE PREDICT Studies, are publicly available. Metagenomes from PREDICT 1 are publicly available as previously reported[9], whereas the PREDICT 2 and PREDICT 3 cohorts (US21, US22A and UK22A) are deposited in the European Nucleotide Archive (ENA) of the European Bioinformatics Institute (EBI) under accession numbers PRJEB75460, PRJEB75462, PRJEB75463 and PRJEB75464, and are publicly accessible. Sex, age, BMI, country and quantitative taxonomic profiles for each sample are publicly available within the curated MetagenomicData package[60] and at Zenodo (https://doi.org/10.5281/zenodo.15307999)[68]. The full list of species for the ZOE Microbiome Rankings are publicly available at https://zoe.com/our-science/microbiome-ranking, where future updates will also be made available. The version of the ZOE Microbiome Rankings discussed in the present work is reported in Supplementary Table 5. To protect participant privacy, individual participant clinical data are not publicly available and cannot be deposited in public repositories. Researchers may request access to the restricted data by submitting a research proposal via email to data.papers@joinzoe.com. All proposals will be reviewed by a sub-panel of the ZOE Scientific Advisory Board within 4 working weeks. Proposals, researchers or institutions requesting data will be approved if they meet the standard criteria related to ethics, privacy and data protection regulations. Approved researchers are required to enter into a data-sharing agreement with ZOE. The requested host parameters will be provided as ordered data points without loss of reproducibility, as the analysis of this work (including deriving the ranks) was performed using non-parametric statistics. These data are available at Zenodo (https://doi.org/10.5281/zenodo.17236382)[69] and are encrypted; access to the data will be granted to researchers whose proposals are approved. All data from non-PREDICT external public cohorts used to validate the rankings are available in full at Zenodo (https://doi.org/10.5281/zenodo.17236261)[70].

Code availability

The custom Python code developed for the meta-analyses performed on public data and included in this work is available at GitHub (https://github.com/SegataLab/inverse_var_weight) and at Zenodo (https://doi.org/10.5281/zenodo.17236261)[70]. The MetaPhlAn code for the taxonomic profiling is available at GitHub (https://github.com/biobakery/MetaPhlAn), Zenodo (https://doi.org/10.5281/zenodo.17236261)[70] and Bioconda (https://bioconda.github.io/recipes/metaphlan/README.html).

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Acknowledgements

We thank all the participants of the PREDICT programme. This work was supported by Zoe Ltd. and TwinsUK, which is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation (CDRF), the National Institute for Health Research (NIHR) Clinical Research Network (CRN) and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. It was also supported by the European Research Council (ERC-CoG microTOUCH-101045015) to N.S., by the European Union NextGenerationEU (Interconnected Nord-Est Innovation programme, INEST) to N.S., by the National Cancer Institute of the National Institutes of Health (1U01CA230551) to N.S. and by the Premio Internazionale Lombardia e Ricerca 2019 to N.S.

Author information

Author notes

1. Paolo Manghi

Present address: Research and Innovation Center, Fondazione Edmund Mach, San Michele all’Adige, Italy

2. These authors contributed equally: Sarah E. Berry, Tim D. Spector, Nicola Segata

Authors and Affiliations

1. Department CIBIO, University of Trento, Trento, Italy

Francesco Asnicar, Paolo Manghi, Gloria Fackelmann, Gabriel Baldanzi, Liviana Ricci, Gianmarco Piccinno, Elisa Piperni, Katarina Mladenovic & Nicola Segata

2. Zoe Ltd, London, UK

Elco Bakker, Federica Amati, Alberto Arrè, Sajaysurya Ganesh, Francesca Giordano, Richard Davies, Jonathan Wolf, Kate M. Bermingham, Sarah E. Berry & Tim D. Spector

3. IEO, Istituto Europeo di Oncologia IRCSS, Milan, Italy

Elisa Piperni & Nicola Segata

4. Department of Twins Research and Genetic Epidemiology, King’s College London, London, UK

Federica Amati, Tim D. Spector & Nicola Segata

5. Department of Nutritional Sciences, King’s College London, London, UK

Kate M. Bermingham & Sarah E. Berry

Contributions

F. Asnicar, S.E.B., T.D.S. and N.S. conceived and supervized the study. F. Asnicar, P.M., E.P., K.M. and A.A. performed the analyses: F. Asnicar collected the data, performed microbiome analyses and species rankings; P.M. performed meta-analyses on public data; E.P. supported the analysis of longitudinal data; K.M. helped with microbiome profiling; A.A. supported the analysis of the two clinical trial, nutritional intervention studies. E.B., S.G., F.G. and R.D. set up the interface to retrieve clinical data information. G.F., G.B., L.R., G.P., F. Amati and K.M.B. supported with results interpretation. J.W. is co-founder of ZOE Ltd. and made the assembly of these large cohorts possible. F. Asnicar, P.M. and N.S. drafted the manuscript. All authors reviewed and edited the manuscript.

Corresponding authors

Correspondence to Francesco Asnicar or Nicola Segata.

Ethics declarations

Competing interests

J.W. and T.D.S. are co-founders of ZOE Ltd.—a commercial initiative active in the field of personalized nutrition—and owners of the ZOE PREDICT studies. E.B., F. Amati, A.A., S.G., F.G., R.D., J.W. and K.M.B. are, or have been, employees of Zoe Ltd. F. Asnicar, S.E.B., T.D.S. and N.S. are consultants to ZOE Ltd. F. Asnicar, R.D., J.W., S.E.B., T.D.S. and N.S. receive options with ZOE Ltd. All other authors declare no competing interests. Zoe Ltd. holds the following patent applications on the SGBs ranking: PCT (World) patent pending applications PCT/EP2024/058262, PCT/EP2024/058286 and PCT/EP2024/058290.

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Extended data figures and tables

Extended Data Fig. 1 Microbiome predictive potential for personal information, dietary indices, fasting, and postprandial metabolic markers, via classification and regression random forest models.

Distributions of the random forest median AUCs (a) and median Spearman’s correlation coefficients (b) (Methods) in the five cross-sectional PREDICT studies for the different clinical data divided into four categories: ‘Personal’, ‘Dietary’, ‘Fasting’, and ‘Postprandial’. The AUC and Spearman’s index thresholds of 0.7 and 0.3, respectively, are indicated with a dashed line. a) Each point represents the median AUC value obtained in cross-validation for each cohort when testing the first versus the fourth quartile of the corresponding clinical marker values on the x-axis. b) Each point represents the median Spearman’s correlation coefficient for the predicted values by the regressor and the true values in the cross-validation setting for each cohort.

Extended Data Fig. 2 Detailed associations of the 15 top and bottom cardiometabolic-ranked SGBs in the PREDICT3 UK22A, PREDICT2, and PREDICT3 US21 cohorts.

The single-marker percentiles, divided into the three categories (‘Personal’, ‘Fasting’, and ‘Postprandial’) for the 15 most favorable and unfavorable ZOE MB Health-ranked SGBs the other three PREDICT cohorts not reported in Fig. 2 (a, PREDICT3 UK22A; b, PREDICT2, and c, PREDICT3 US21). Heatmaps with the single Spearman’s partial correlations for all PREDICT cohorts are available in Supplementary Fig. 4.

Extended Data Fig. 3 Spearman’s partial correlations of the 15 top and bottom cardiometabolic-ranked SGBs.

a-e) Spearman’s partial correlations (corrected for age, sex, and BMI) between SGB relative abundance and single marker values show consistency across the five PREDICT cohorts. These partial correlations were ranked and averaged first within and then across the three data categories (‘Personal’, ‘Fasting’, and ‘Postprandial’, reported in Supplementary Fig. 3) separately in each cohort. The cohorts’ averages were then used to define the cardiometabolic rank (for those SGBs analyzed in at least two cohorts).

Extended Data Fig. 4 Diet associations of the 15 top and bottom diet-ranked SGBs.

a-e) For each PREDICT cohort, we computed Spearman’s partial correlation between the SGBs’ relative abundances and different diet indexes. Associations were ranked and averaged in each cohort separately. f) The ZOE MB Diet-ranking was computed for SGBs ranked in at least two PREDICT cohorts. The raw Spearman’s partial correlations are available in Supplementary Fig. 7.

Extended Data Fig. 5 Spearman’s partial correlations of the 15 top and bottom diet-ranked SGBs.

a-e) Study-wise Spearman’s partial correlation coefficients (corrected for sex, age, and BMI) for the 15 most favorable and unfavorable ZOE MB Diet-ranked SGBs in different diet indexes. The associations appear consistent across cohorts.

Extended Data Fig. 6 Comparison of the ZOE MB Health and Diet ranks and with geography.

a) The ZOE MB Health and Diet ranks are overall in agreement (Spearman’s correlation = 0.72), albeit some SGBs show discordant rankings (absolute difference between the two ranks ≥ 0.3). These SGBs are highlighted in orange, and their ranks and taxonomy assignment are reported in Supplementary Table 6. b,c) Comparison of the ZOE MB Health (b) and Diet (c) ranks computed only on the PREDICT UK and US cohorts (Spearman’s correlations of 0.61 and 0.26, respectively). The top and right-side histograms depict the x and y-axis marginal distributions in each plot.

Extended Data Fig. 7 ZOE DIET ranks and their associations with BMI.

a) Comparison of the ZOE MB Diet-ranks (x-axis) with the Spearman’s partial correlations (corrected for sex and age, y-axis) for the 661 ranked SGBs in the five PREDICT cohorts. b) The number of the 50 most-favorably ranked SGBs (ZOE MB Health-rank, Richness) detected in different BMI categories, showed that increasing BMI, linked with increasing health risks, is reflected by a lower presence of favorable SGBs. On the other hand, c) unfavorably-ranked SGBs show an increasing count in higher-risk BMI categories. d,e) The box plots report the number of the 50 most favorable and unfavorable ZOE MB Diet-ranked SGBs of individuals stratified into three BMI categories (healthy-weight, overweight, and obese) in each PREDICT cohort. f,g) Similarly, the box plots represent the cumulative relative abundance of the 50 most favorable and unfavorable ZOE MB Diet-ranked SGBs in individuals categorized into the three BMI categories in each cohort. h,i) The box plots report the number of the 50 most favorably and most unfavorably ranked SGBs, ranked using the same markers and categories as in the ZOE MB Health-ranks (Methods), but partial correlations were corrected only for sex and age. j,k) Similarly, the box plots report the count of the 50 most favorable and unfavorable SGBs in the three BMI categories, with SGBs ranked according to their partial correlation with BMI, adjusted by sex and age. Only non-significant FDR-corrected P values (ns, P value > 0.01) from the Mann-Whitney U test are reported.

Extended Data Fig. 8 Meta-analysis of the 50 most favorably and unfavorably ZOE MB Health-ranked SGBs in overweight vs obese and healthy-weight vs overweight individuals.

a) Overweight individuals tend to carry a higher number of the 50 most favorably ZOE MB Health-ranked SGBs than obese individuals (left); the 50 most unfavorably ranked SGBs are increased in obese individuals vs overweight individuals (Methods). b) Healthy-weight individuals tend to carry a higher number of the 50 most favorably ZOE MB Health-ranked SGBs than overweight individuals (left); the 50 most unfavorably ranked SGBs are found in similar amounts in healthy-weight and overweight individuals (Methods). Error bars represent the 95% confidence interval.

Extended Data Fig. 9 Meta-analysis of the 50 most favorably and unfavorably ZOE DIET-ranked SGBs comparing individuals from different BMI categories.

a) Comparison of the number of the 50 most favorable Diet-ranked SGBs in pairs of BMI categories. Healthy-weight and overweight individuals tend to have a higher number of favorably-ranked SGBs than obese individuals (Methods). b) Comparison of the number of the 50 most unfavorably Diet-ranked SGBs in pairs of BMI categories. Obese individuals tend to have a higher number of unfavorably-ranked SGBs (Methods). Error bars represent the 95% confidence interval.

Extended Data Fig. 10 Significantly changing SGBs after dietary interventions show consistent patterns across cohorts in terms of relative abundance, prevalence, and ZOE MB Diet-ranks.

a) Distributions of the mean relative abundance of the significant SGBs for the probiotic and control arms of the BIOME cohort (relative to Fig. 4a). b) Distributions of the mean relative abundance of the significant SGBs for the control arm of the METHOD cohort (relative to Fig. 4b). c) Distributions of the prevalence of the significant SGBs of the BIOME cohort (relative to Fig. 4a) and d) of the METHOD cohort (relative to Fig. 4b). SGBs are separated into “increasing” and “decreasing”, depending on their trend in relative abundance values, showing that SGBs found to be increased in relative abundance are also more prevalent, while the opposite is observed for SGBs decreasing in relative abundance. e) Distributions of the ZOE MB Health ranks for the significant SGBs in the Probiotic and Control arms of the BIOME cohort and METHOD cohorts. f) Distributions of the ZOE MB Diet ranks for the significant SGBs in the Probiotic and Control arms of the BIOME and METHOD cohorts.

Supplementary information

Supplementary Information

Supplementary Figs. 1–5 and table legends.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–25.

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Asnicar, F., Manghi, P., Fackelmann, G. et al. Gut micro-organisms associated with health, nutrition and dietary interventions. Nature 650, 450–458 (2026). https://doi.org/10.1038/s41586-025-09854-7

- Received: 29 May 2024

- Accepted: 04 November 2025

- Published: 10 December 2025

- Version of record: 10 December 2025

- Issue date: 12 February 2026

- DOI: https://doi.org/10.1038/s41586-025-09854-7

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Potent neutralization of Marburg virus by a vaccine-elicited antibody

Amin Addetia,

Lisa Perruzza,

Kaitlin Sprouse,

Young-Jun Park,

Matthew McCallum,

Cameron Stewart,

Bianca Partini,

Jack T. Brown,

Alessia Donati,

Katja Culap,

Alessio Balmelli,

Bhavna Chawla,

Swagata Kar,

Michal Gazi,

Kendra Alfson,

Yenny Goez-Gazi,

Ricardo Carrion Jr,

Davide Corti,

Fabio Benigni &

…

David Veesler

Nature volume 650, pages 459–469 (2026)

Abstract

Marburg virus (MARV) is a filovirus that causes severe and often lethal haemorrhagic fever1,2. Despite the increasing frequency of MARV outbreaks, no vaccines or therapeutics are licensed for use in humans. Here we designed mutations that improve the expression, thermostability and immunogenicity of the prefusion MARV glycoprotein (GP) ectodomain trimer, which is the sole target of neutralizing antibodies and vaccines in development3,4,5,6,7,8. We discovered a fully human, pan-marburgvirus monoclonal antibody, MARV16, that broadly neutralizes all MARV isolates, Ravn virus and Dehong virus with 40–100-fold increased potency relative to previously described antibodies[9]. Moreover, MARV16 provided therapeutic protection in guinea pigs challenged with MARV. We determined a cryogenic electron microscopy structure of MARV16-bound MARV GP. The structure shows that MARV16 recognizes a prefusion-specific epitope spanning GP1 and GP2, which blocks receptor binding and prevents conformational changes required for viral entry. We further determined the architecture of the MARV GP glycan cap, which shields the receptor-binding site, and identified architectural similarities with distantly related filovirus GPs. MARV16 and previously identified antibodies directed against the receptor-binding site9,10,11 simultaneously bound MARV GP. These antibody cocktails required multiple mutations to escape neutralization by both antibodies, a result that paves the way for the development of MARV therapeutics resistant to viral evolution. MARV GP stabilization along with the discovery of MARV16 advance prevention and treatment options for MARV disease.

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Main

MARV belongs to the Filoviridae family and causes Marburg virus disease (MVD), which is characterized by a haemorrhagic fever with a case fatality rate ranging from 24 to 88%1,2,12. In recent years, multiple African countries have experienced MARV outbreaks, including Ghana in 2022, Equatorial Guinea in 2023, Rwanda in 2024 and Tanzania in 2023 and 2025 (refs. 8,13,14,15,16). Although recent outbreaks were contained, larger MARV outbreaks are likely to occur, similar to the 2013–2016 outbreak of the related Ebola virus (EBOV) in West Africa, which led to more than 28,600 infections and 11,325 casualties17,18. The recurring and frequent spillovers of MARV underscores the necessity for licensed vaccines and therapeutics, which are currently not available.By contrast, multiple countermeasures have been developed and approved for Ebola virus, including vaccines such as Ervebo (rVSV-ZEBOV) and Zabdeno/Mvabea (Ad26.ZEBOV/MVA-BN-Filo), as well as monoclonal antibody therapeutics including mAb114 (Ebanga) and the REGN-EB3 cocktail (Inmazeb). All of these therapeutics provide proof of concept for the successful development of filovirus countermeasures19,20,21,22.

Vaccines and monoclonal antibodies in development against MARV target the viral GP because GP-directed antibodies have been suggested to be the primary correlate of protection against MVD[3]. The MARV GP is a homotrimeric protein anchored in the viral membrane and is responsible for recognition of the host receptor, NPC1, and subsequent membrane fusion that leads to viral entry23,24. The MARV GP is proteolytically cleaved by furin during viral morphogenesis to produce the GP1 and GP2 subunits that remain covalently linked by a disulfide bond10,11,25. GP1 comprises three domains: the core, the glycan cap and the mucin-like domain. The GP1 core contains the receptor-binding site (RBS), which is shielded from neutralizing antibodies by the highly glycosylated glycan cap and mucin-like domain10,23,26,27. In contrast to the EBOV or Sudan virus (SUDV) GPs, an ordered glycan cap has not been visualized for MARV GP, which suggests that the RBS might be more exposed for MARV GP compared with the EBOV and SUDV GPs10,11,27,28. GP2 is also composed of three domains: the wing, the core and the transmembrane domain. The GP2 core is the fusion machinery that promotes fusion of the viral and host membranes29,30,31. The wing domain is unique to the Marburgvirus genus and wraps around the GP equator, which was proposed to limit recognition by neutralizing antibodies[11].

Several MARV investigational vaccines have advanced to phase I or II clinical trials, with one of them, cAd3-Marburg, being used during the MARV outbreak in Rwanda in 2024 (refs. 3,4,5,6,7,8). These vaccines either display MARV GP on a viral vector or encode for MARV GP. These vaccines may benefit from the identification and inclusion of mutations that increase GP expression and prefusion stability, similar to the stabilizing mutations that were incorporated into vaccines for SARS-CoV-2 and for respiratory syncytial virus32,33. Only one stabilizing mutation that promotes trimer formation of the MARV GP ectodomain has been identified so far[34]. By contrast, several stabilizing mutations have been identified for the EBOV and SUDV GPs, which suggests that further optimization of prefusion MARV GP may be possible34,35.

Monoclonal antibodies that target multiple MARV GP domains have been isolated from patients who recovered from MVD and from GP-immunized animals9,36,37,38. However, only antibodies that target the RBS had detectable, albeit weak, neutralizing activity against MARV[9]. Several of these RBS-directed antibodies have shown protective efficacy in animal models, with one of them, MR191, being the precursor to the investigational therapeutic monoclonal antibody MBP01 (refs. 9,39,40). Given that the neutralization potency of RBS-directed antibodies, including MR191, can be reduced by single mutations in the highly variable glycan cap[9], an antibody cocktail may prove to be more resistant to viral evolution.

Here we set out to identify MARV GP-stabilizing mutations to develop an immunogen that will enable subsequent discovery of vaccine-elicited antibodies that potently neutralize MARV. We identified two mutations in the MARV GP2 heptad repeat 1-C (HR1C) in the GP2 core domain that increase expression, thermostability and immunogenicity of the prefusion ectodomain trimer while retaining its native prefusion structure and antigenicity. Immunization of a humanized transgenic mouse with prefusion-stabilized MARV GP ectodomain trimer enabled isolation of a potent neutralizing antibody, designated MARV16, that targets a prefusion GP epitope spanning the GP1 and GP2 subunits. We further demonstrate that MARV16 potently neutralizes historical and contemporary MARV variants and the related Ravn virus (RAVV) and Dehong virus (DEHV). We show that MARV16 protects guinea pigs against MVD when administered after MARV exposure. Finally, we show that MARV16 can bind to MARV GP simultaneously with RBS-directed antibodies, thereby providing a path towards the development of a therapeutic antibody cocktail for MVD.

Expression of the MARV GPΔMuc ectodomain

To produce a soluble, prefusion MARV GP ectodomain trimer, we first designed a MARV GP construct that lacks the mucin-like domain (residues 257–425) and the transmembrane domain (residues 638–681) (Fig. 1a). The mucin-like domain was omitted from our construct because antibodies that target this domain are unlikely to be neutralizing41,42. We also incorporated three previously described mutations, W439A, F445G and F447N, which increase cleavage of precursor GP by furin[10], and the H589I mutation, which promotes the formation of monodisperse trimers in the absence of an exogenous trimerization domain[34]. Co-expression of this construct, termed MARV GPΔMucWT, with furin produced monodisperse trimers (Fig. 1b), as visualized by negative-stain electron microscopy. Binding to MR191 (Fig. 1c) confirmed proper folding and antigenicity of our MARV GPΔMucWT ectodomain, which we used for a subsequent antibody discovery campaign.

Fig. 1: Design of a prefusion-stabilized MARV GP.

a, Schematic of MARV GP domain organization and mutations included in MARV GPΔMuc ectodomain constructs. CD, cytoplasmic domain; FCS, furin cleavage site; GC, glycan cap; MLD, mucin-like domain; TM, transmembrane domain. SP, signal peptide. b, Negative-stain electron micrographs of MARV GPΔMuc constructs. Scale bar, 50 nm. Five micrographs were collected for each of the four biological replicates of each ectodomain. c, MARV GPΔMuc ectodomains binding to immobilized MR191 IgG assessed by BLI. d, Ribbon diagram of the RAVV GP (Protein Data Bank (PDB) identifier: 6BP2) highlighting the residues mutated in GP2 HR1c. GP1, purple; GP2, gold; N-linked glycans, light blue surfaces. e–g, Recombinant production yields (e), size-exclusion chromatograms (f) and differential scanning fluorimetry analysis (g) of MARV GPΔMucWT and MARV GPΔMuc mutants purified from Expi293 cells. The fold increase in yield relative to MARV GPΔMucWT is displayed above the plot (e). The bar represents the mean yield ± s.d. across four biological replicates. mAU, milli-absorbance units. Data in g are shown as the first negative derivative of the fluorescence intensity (dF) with respect to temperature (dT), and the Tm of MARV GPΔMucWT and MARV GPΔMucPV are indicated with dotted vertical lines. Data presented in c, f and g are from one biological replicate and representative of three other biological replicates. Six technical replicates were conducted and averaged for each biological replicate in g. h, Scheme of the immunogenicity study design of MARV GPΔMuc ectodomains in BALB/c mice (n = 10 per group; bleed 2 serum could not be collected for 1 animal in the 0.1 µg MARV GPΔMucWT group). i,j, MARV GPΔMucPV serum binding titre (i) and neutralizing titre against VSV pseudotyped with the MARV/Musoke GP (j) respectively, presented as average ED_50 (half-maximal effective dilution) or ID_50 (half-maximal inhibitory dilution) values obtained from two biological replicates conducted in technical duplicate and using distinct batches of protein or pseudovirus. The limit of detection (ED_50 of 33 or ID_50 of 10) is indicated with a dotted line. Black lines indicate the geometric mean titre. Statistics were assessed using the Kruskal–Wallis test with Dunn’s post-test comparing groups receiving identical doses of MARV GPΔMucWT or MARV GPΔMucPV.

Identification of stabilizing mutations in HR1C

Similar to other class I fusion proteins, MARV GP undergoes large-scale structural rearrangements to mediate membrane fusion with the GP2 HR1C region (residues 577–583), rearranging from a loop before fusion to a helix after fusion10,11,29,30. Previous studies have identified EBOV GP2 HR1C mutations that stabilize the prefusion conformation and improve GP expression yields with and without the addition of a trimerization domain34,35. However, porting the EBOV GP T578P stabilizing mutation to MARV GP did not produce the same enhancement in expression[34]. Following our previous success in stabilizing the Langya virus G and Epstein–Barr virus gB proteins43,44, we used ProteinMPNN[45] to assist the identification of stabilizing MARV GP HR1C mutations using the previously determined RAVV GP structure[11] as an input model. Identified mutations were visually inspected, and those that seemed compatible with the prefusion conformation were incorporated into the MARV GPΔMuc ectodomain (Fig. 1a,d). Individual substitution of T582P and F583V improved expression yields by 1.6-fold and 1.7-fold, respectively, compared with our original MARV GPΔMucWT, and led to a 2.5-fold enhancement when combined together (Fig. 1e). All three designed MARV GPΔMuc ectodomain constructs eluted as monodisperse species and at a similar retention volume to MARV GPΔMucWT when analysed by size-exclusion chromatography (Fig. 1f). Electron microscopy imaging of negatively stained samples confirmed the monodispersity of the constructs (Fig. 1b). Moreover, retention of MR191 binding (Fig. 1c) indicated that introduction of the HR1c mutations did not alter the conformational integrity or antigenicity of MARV GP. Both the T582P (mean melting temperature (Tm) ± s.d. of 69.7 ± 0.2 °C) and F583V (Tm of 71.2 ± 0.1 °C) mutations improved the thermostability of MARV GPΔMuc, increasing the Tm by 0.9 and 2.4 °C, respectively. The T582P/F583V double mutant (designated MARV GPΔMucPV) led to a 2.6 °C greater melting temperature than the original MARV GPΔMucWT (Tm of 71.4 ± 0.2 compared with 68.8 ± 0.3 °C), which indicated that the mutant exhibited improved stability of the prefusion state (Fig. 1g).

We next assessed the immunogenicity of the stabilized MARV GP ectodomains by immunizing BALB/c mice (n = 10 per group) with three doses spaced 4 weeks apart of 0.1, 1 or 10 µg of MARV GPΔMucWT or MARV GPΔMucPV ectodomain with Addavax as an adjuvant (Fig. 1h). Both the MARV GPΔMucWT and MARV GPΔMucPV ectodomains induced potent serum binding titres after two doses, with a geometric mean titre (GMT) ranging from 3.9 × 10^3 to 3.7 × 10^4 (Fig. 1i and Extended Data Fig. 1). After three doses, mice immunized with the MARV GPΔMucPV ectodomain exhibited serum binding titres (GMTs for 0.1, 1 and 10 µg of 2.1 × 10^4, 1.2 × 10^5 and 2.2 × 10^5, respectively) 1.9–4.0-fold higher than those for mice immunized with the MARV GPΔMucWT ectodomain (GMTs for 0.1, 1 and 10 µg of 1.1 × 10^4, 5.0 × 10^4 and 5.4 × 10^4, respectively). Most mice immunized with 1 or 10 µg MARV GPΔMucWT or MARV GPΔMucPV ectodomain had detectable serum-neutralizing antibody titres after three doses (Fig. 1j and Extended Data Fig. 1). The serum-neutralizing antibody titres were similar for mice immunized with the MARV GPΔMucWT ectodomain (GMTs for 0.1, 1 and 10 µg of 21, 84 and 58, respectively) or the MARV GPΔMucPV ectodomain (GMTs for 0.1, 1 and 10 µg of 28, 57 and 87, respectively). These data suggest that the MARV GPΔMucPV ectodomain is more immunogenic than the MARV GPΔMucWT ectodomain and a promising vaccine candidate, particularly as current MARV vaccines minimally elicit neutralizing antibodies3,7,46. Moreover, strategies such as multimerization of the stabilized GP on nanoparticles47,48,49 or delivering the stabilized GP as an mRNA vaccine42,50 will probably aid in inducing a more robust neutralizing antibody response.

Discovery of a potent MARV-neutralizing antibody

All monoclonal antibodies identified so far that target MARV GP display no or weak neutralization potency (half-maximal inhibitory concentration (IC_50) of >1 µg ml–1) against MARV GP pseudoviruses and even weaker, if any, activity (IC_50 > 100 µg ml–1) against authentic MARV9,36,37,38. To identify potent MARV-neutralizing antibodies, we used Alloy ATX transgenic mice that have human immunoglobulin loci encoding for the heavy chain and either the lambda (ATX-GL) or the kappa (ATX-GK) light chain. Two ATX-GL and two ATX-GK mice were immunized with MARV GPΔMucWT for a total of three doses (Fig. 2a). Mice were killed 6 days after the last boost, and peripheral blood, spleen and lymph nodes were collected and cells were freshly isolated. MARV GPΔMucWT-specific memory B cells were selected by fluorescence-assisted cell sorting (Extended Data Fig. 2), and variable domain (VH and VL) sequences were subsequently retrieved by PCR with reverse transcription PCR (RT–PCR).

Fig. 2: Discovery of a pan-marburgvirus antibody.

a, Schematic of the immunization schedule used to discover MARV GP-directed monoclonal antibodies (n = 4 mice). b, Dose–response neutralization curves for MARV16, MR78 and MR191 against VSV pseudotyped with the MARV/Musoke GP. Data are the mean ± s.e.m. from three technical replicates. Data are representative of 3–5 additional biological replicates. c, Neutralization potency of MARV16, MR78, MR191 and 4C2, a MERS-CoV antibody, against authentic MARV/Musoke. Data points reflect PRNT_50 values obtained from two biological replicates using distinct batches of IgGs. The black line indicates the mean PRNT_50 value. d, Binding affinity of the MARV16 Fab to immobilized MARV GPΔMucWT measured using BLI. e,f, MARV16, MR78 and MR191 IgG (e) or Fab (f) binding to immobilized MARV GPΔMucWT assessed by BLI. g, MARV16 IgG binding to immobilized MARV GPΔMucWT at variable pH values using BLI. h, Schematic highlighting GP mutations (black vertical lines) in MARV variants relative to MARV/Musoke. Residue numbers correspond to the MARV/Musoke GP. i, Neutralization potency of MARV16, MR78 and MR191 against VSV pseudotyped with the indicated MARV GP. j, Phylogenetic tree constructed using the amino acid sequences of related filovirus GPs with sequence identity relative to the MARV/Musoke GP shown to the right. k, Neutralization potency of MARV16, MR78, MR191 and EBOV-515 against VSV pseudotyped with the indicated filovirus GP. Data presented in i and k are averaged IC_50 values obtained from at least two biological replicates conducted in technical triplicate using distinct batches of IgG and pseudoviruses. l–o, MARV GPΔMucWT, EBOV GPΔMuc, SUDV GPΔMuc, thermolysin-cleaved EBOV GPΔMuc or thermolysin-cleaved SUDV GPΔMuc binding to immobilized EBOV-515 (l), MR191 (m), MR78 (n) or MARV16 (o) IgGs assessed with BLI. Data presented in d–g and l–o reflect one biological replicate and are representative of two biological replicates using distinct batches of proteins.

We recovered ten antibodies that bound to MARV GPΔMucWT (half-maximal effective concentration (EC_50) of 3.4–10.4 ng ml–1) (Extended Data Table 1 and Extended Data Fig. 2), and one of them, designated MARV16, potently neutralized vesicular stomatitis virus (VSV) pseudotyped with the vaccine-matched MARV/Musoke GP (IC_50 of 36.4 ng ml−1) (Extended Data Fig. 2). MARV16 is 42-fold and 39-fold more potent than the previously described RBS-directed neutralizing antibodies[9] MR78 (IC_50 of 1,520 ng ml–1) and MR191 (IC_50 of 1,407 ng ml−1), respectively, as measured side-by-side using MARV/Musoke GP VSV pseudovirus (Fig. 2b). Furthermore, MARV16 neutralized authentic MARV/Musoke (50% plaque reduction neutralization test (PRNT_50) of 2.2 µg ml–1), whereas no MR78-mediated or MR191-mediated neutralization was detected (PRNT_50 of >100 µg ml–1 corresponding to the limit of detection of the assay) (Fig. 2c and Extended Data Fig. 2). These results establish MARV16 as a best-in-class MARV-neutralizing antibody.

MARV16 displays broadly neutralizing activity

We assessed the kinetics and affinity of binding of the MARV16 antigen-binding fragment (Fab) to immobilized MARV GPΔMucWT by biolayer interferometry (BLI). The results revealed strong engagement characterized by single-digit nanomolar affinity (dissociation constant (Kd) of 1.35 nM) (Fig. 2d and Extended Data Table 2). Furthermore, MARV16 bound more strongly to MARV GPΔMucWT than MR78 and MR191 in both IgG and Fab formats (Fig. 2e,f). Given that MARV enters target cells through fusion with the endosomal membrane induced by the low pH of late endosomes[51], we assessed the influence of pH on binding between MARV GPΔMucWT and the three antibodies. MARV16 bound MARV GPΔMucWT at comparable affinities at all four pHs tested (Fig. 2g). By contrast, MR78 and MR191 binding was unaltered at pH 7.4, 6.5 and 5.5, but was enhanced at pH 4.5 (Extended Data Fig. 3). We propose that these results may reflect increased accessibility of the RBS at lower pH values.

We next evaluated the neutralization breadth of MARV16 against seven historical and contemporary MARV isolates using VSV pseudotyped with the corresponding MARV GPs. These differ from that of the vaccine strain (MARV/Musoke) by 6.3–8.7% at the amino acid level (Fig. 2h), with most substitutions mapping to the glycan cap or mucin-like domain. MARV16 potently neutralized all 7 vaccine-mismatched MARV isolates, with IC_50 values ranging from 151 to 310 ng ml–1 (Fig. 2i and Extended Data Fig. 4) and markedly outperformed MR78 and MR191 against all of these MARV isolates when assessed side by side.

Evaluation of MARV16-mediated neutralization breadth across the Filoviridae family revealed that MARV16 potently inhibited RAVV and DEHV VSV pseudoviruses, but not Měnglà virus (MLAV), EBOV or SUDV VSV pseudoviruses (Fig. 2j,k and Extended Data Fig. 4). Previous studies have shown that RBS-directed antibodies, including MR78 and MR191, recognize epitopes shared among all filovirus GPs but fail to neutralize Ebolaviruses owing to masking mediated by the glycan cap9,10. To determine whether MARV16 similarly recognizes a cryptic pan-filovirus epitope, we assessed whether MARV16 IgG binds the uncleaved and thermolysin-cleaved forms of the EBOV and SUDV GPΔMuc (that is, removing the glycan cap)[10] and compared it to EBOV-515, MR78 and MR191 using BLI. The Ebolavirus GP2-directed antibody EBOV-515 bound uncleaved and cleaved EBOV and SUDV GPΔMuc, but not MARV GPΔMucWT (Fig. 2l). MR78 and MR191 bound MARV GPΔMucWT and the cleaved EBOV and SUDV GPΔMuc, but not the uncleaved EBOV or SUDV GPΔMuc (Fig. 2m,n). MARV16 bound the MARV GPΔMucWT but not EBOV or SUDV GPΔMuc, irrespective of their cleavage. This finding indicates that MARV16 does not cross-react with GPs from the Ebolavirus genus, which is most likely due to their extensive genetic divergence (Fig. 2j,o).

Structural basis of MARV16-mediated neutralization

To understand the molecular basis of the potent neutralization of MARV by MARV16, we characterized the MARV GPΔMucWT ectodomain bound to the MARV16 Fab using single-particle cryogenic electron microscopy (cryo-EM) and determined a structure at 2.6 Å resolution (Extended Data Table 3 and Extended Data Fig. 5). MARV16 recognizes an epitope that spans GP1 and GP2 (Fig. 3a,b and Extended Data Table 4). An average surface area of 1,100 Å^2 is buried at the interface between the epitope and the paratope with the majority of contacts with MARV GP contributed by the Fab heavy chain. GP2 accounts for about 75% of the epitope buried surface area and is recognized by all three complementarity-determining regions (CDRs) of the MARV16 heavy chain. CDRH3 residues recognize MARV GP2 through hydrogen bonds, salts bridges and van der Waals interactions, including CDRH3 R99 and D105 forming salt bridges with GP2 E515 and K550, respectively, and hydrogenbonding of CDRH3 N101 with the GP2 N551 side chain and of the CDRH3 W102 indol with GP2 N554 (Fig. 3c). CDRH1 and CDRH2 form extensive interactions with GP2, such as hydrogenbonding between the CDRH2 S52 and S54 side chains and GP2 D513, CDRH2 Y57 and the backbone amide and carbonyl oxygen of GP2 R517, and the CDRH2 Y59 and GP2 R517 side chains. The CDRH1 T33 side chain interacts with the GP2 E515 side chain. The MARV16 light chain also interacts with GP2 primarily through CDRL3 involving D93 salt bridged to GP2 R517 and hydrogen bonding of the S91 and Y92 backbone carbonyls with the GP2 K550 side chain (Fig. 3d). MARV GP1 recognition is primarily mediated through MARV16 CDRH2 with the S54 and S56 backbone carbonyls hydrogen-bonded to the GP1 K90 and K120 side chains, respectively, and hydrogenbonding of the CDRH2 Y57 and GP1 E87 side chains (Fig. 3e). These extensive contacts explain the strong MARV16 binding affinity and the conservation of interface residues among MARV isolates, RAVV and DEHV explains the pan-marburgvirus neutralizing activity of this antibody (Fig. 2h–k and Extended Data Fig. 6). Multiple epitope residue substitutions explain the lack of EBOV and SUDV VSV neutralization mediated by MARV16 (Fig. 2j,k).

Fig. 3: Molecular basis of MARV16 neutralization.

a, Ribbon diagram of the cryo-EM structure of the MARV GPΔMucWT ectodomain in complex with three MARV16 Fab fragments. Only the Fab variable domains were modelled into the density. MARV GP1 and GP2 are shown in shades of purple and gold, respectively, MARV16 VH is shown in green and MARV16 VL is shown in light green. N-linked glycans are rendered as light blue surfaces. b, Ribbon diagram of a single MARV GP protomer in complex with one MARV16 Fab. c–e, Zoomed-in views of interactions between MARV16 CDRH1–CDRH3 and MARV GP2 (c), MARV16 CDRL3 and MARV GP2 (d) and MARV16 CDRH2 and MARV GP1 (e). Selected hydrogen bonds and salt bridges are denoted with black dashed lines. f, Binding modes of the NPC1 receptor (yellow) and MARV16 (green/light green) to MARV GP. The position of NPC1 was determined by superimposing the EBOV GP–NPC1 (PBD: 5JNX) and MARV GP–MARV16 Fab structures. The EBOV GP trimer and the region of the MARV GP glycan cap resolved in our structure (residues 191–219) are not shown for clarity. The red star denotes steric clashes. g, Ribbon diagrams of MARV GP1 and EBOV GP1 (PDB: 3CSY). The MARV GP1 core and glycan cap are shown in dark and light purple, respectively. The EBOV GP1 core and glycan cap are shown in dark and light blue, respectively. Hydrogen bonds between the glycan cap and core are indicated with black dashed lines, and glycan cap aromatic residues inserted in the RBS are shown in stick representation and labelled. The position of the experimentally determined RBS for the EBOV GP and that of the putative RBS for the MARV GP are indicated with red dashed circles.

In the previously determined RAVV GP–MR191 structure, the GP2 wing partially obstructs the MARV16 epitope[11]. In our structure, the GP2 wing is disordered and the amino-terminal residues of the GP2 core shift by up to 18 Å relative to their position in the RAVV GP–MR191 structure, which enables binding of the MARV16 Fab (Extended Data Fig. 6). These data indicate that the wing and N terminus of the GP2 core are flexible and do not completely shield the GP from neutralizing antibodies. Compared with structures of previously characterized anti-EBOV GP antibodies, MARV16 shares a similar binding mode to the EBOV GP-directed neutralizing antibodies ADI-15946, EBOV-515 and EBOV-520 (Extended Data Fig. 6), which have been suggested to neutralize EBOV by tethering GP1 and GP2 in the prefusion conformation52,53,54,55. Our structural data suggest that MARV16 locks the MARV GP1 and GP2 interface through contacts with residues that are rearranged during fusogenic conformational changes that lead to membrane fusion[30]. Furthermore, comparison with the NPC1-bound EBOV GP structure26,56 indicates that MARV16 would also interfere with receptor binding, as the heavy chain variable domain would sterically clash with the NPC1 N-terminal domain (Fig. 3f), as is also the case for ADI-15946, EBOV-515 and EBOV-520.

Resolving the MARV GP glycan cap

The discovery of neutralizing antibodies that target the MARV GP RBS from individuals infected with MARV suggests that the glycan cap might not be ordered and therefore does not shield the RBS9,36. Our cryo-EM map resolved density near the RBS that corresponded to residues 191–219 of the GP1 glycan cap, consistent with partial shielding of the RBS by the glycan cap in a way reminiscent of that observed for the EBOV GP (Extended Data Fig. 6). Indeed, the EBOV GP glycan cap interacts with the GP1 core through β-strand augmentation and insertion of F225 and Y232 into the RBS[27]. Our structure reveals that the MARV GP architecture is highly similar to that of the EBOV GP, sharing the β-strand augmentation and insertion of residue F212 into the MARV RBS (Fig. 3g). The possibly tighter anchoring of the EBOV GP glycan cap to the RBS, relative to MARV GP, may cause the latter region to be more mobile and easily displaced than the EBOV glycan cap. This finding explains why MR78 and MR191 neutralize MARV, but not EBOV.

Non-neutralizing antibodies bind the wing and HR2

We next mapped MARV GP epitopes recognized by the nine non-neutralizing monoclonal antibodies discovered through immunizing Alloy ATX mice. The antibodies clustered into four distinct binding groups on the basis of epitope binning performed using BLI (Extended Data Fig. 7). Group I consisted of MARV4, MARV12, MARV18, MARV21 and MARV23, whereas group II consisted of MARV11 and MARV14. MARV7 and MARV20 did not compete with the other antibodies analysed, which indicated that each of these two antibodies target distinct MARV GP antigenic sites. We then used electron microscopy of negatively stained samples to identify the MARV GP epitopes recognized by the four binding groups. Group I antibodies, represented by MARV18, binds HR2 (Extended Data Fig. 7). By contrast, group II antibodies, represented by MARV14, and MARV7 bind the GP2 wing (Extended Data Fig. 7). The epitope targeted by MARV20 could not be resolved, which may be due to MARV20 targeting a flexible region on the MARV GP (Extended Data Fig. 7).

MARV16 protects against MVD in vivo

To assess the protective efficacy of MARV16, we challenged guinea pigs (n = 6 per group) with 1,000 plaque-forming units (PFU) of guinea pig-adapted MARV/Angola and administered 10 mg of MARV16 (human IgG1) 1, 2 or 4 days post-infection (d.p.i.) (Fig. 4a). An additional six guinea pigs were administered an isotype control monoclonal antibody 1 d.p.i. All six animals from the control group died by 13 d.p.i. and exhibited high plasma viral loads, weight loss (≥5% decrease) and increasing clinical scores after infection (Fig. 4b–f). Moreover, 3 out of these 6 animals displayed increased body temperatures (≥1.1 °C increase) after infection. For the MARV16-treated guinea pigs, the following percentage of animals survived: 33% (2 out of 6) of animals that received MARV16 1 d.p.i.; 83% (5 out of 6) of animals that received MARV16 2 d.p.i.; and 50% (3 out of 6) of animals that received MARV16 4 d.p.i. (Fig. 4b). We did not detect MARV16 plasma binding titre 1 day after treatment for two guinea pigs that received MARV16 1 d.p.i. (and died at 7 and 10 d.p.i.) and for one animal that received MARV16 4 d.p.i. (and died at 10 d.p.i.). These results suggest that in these animals, MARV16 was sequestered at the injection site and cleared or ‘soaked up’ immediately by the challenge virus before reaching the blood stream. After excluding these animals from the analysis, 50% (2 out of 4) of animals that received MARV16 1 d.p.i. and 60% (3 out of 5) of animals that received MARV16 4 d.p.i. survived the MARV challenge (Fig. 4b). Sixty percent of the surviving MARV16-treated guinea pigs experienced weight loss before recovering, whereas 40% did not experience any weight loss. Furthermore, 7 out of the 10 surviving guinea pigs had transient elevation of body temperatures, and all surviving animals exhibited low clinical illness scores (Fig. 4d–f). We observed 2-log, 3-log and 1-log reductions in MARV viral loads at 5 d.p.i. for animals treated with MARV16 at 1, 2 or 4 d.p.i., respectively (geometric mean viral loads of 1.2 × 10^5, 9.6 × 10^3 and 1.9 × 10^6 genome equivalents (GE) per ml, respectively) compared with guinea pigs that received the isotype control (geometric mean viral loads of 2.8 × 10^7 GE per ml) (Fig. 4c). These data indicate that MARV16 provides therapeutic protection against MARV challenge.

Fig. 4: MARV16 protects guinea pigs against MVD.

a, Schematic of the MARV challenge study assessing the therapeutic efficacy of MARV16. i.p., intraperitoneal. b, Survival curves for guinea pigs (n = 6 per group) challenged with 1,000 PFU of MARV/Angola and treated with MARV16 or an isotype control antibody. Animals were monitored for 29 d.p.i. Survival curves for the groups administered with MARV16 1 d.p.i. and 4 d.p.i. excluding the animals with undetectable plasma MARV16 concentration 1 day after treatment are shown as boxes with dashed lines. Statistical differences in survival between groups were assessed using Kaplan–Meier survival analysis and excluding animals with undetectable plasma concentrations of MARV16. A two-sided log-rank test was used and Holm–Šídák correction was applied for multiple comparisons between the isotype control antibody-treated guinea pigs and MARV16-treated groups. c, MARV/Angola viral loads measured in the plasma of infected guinea pigs at 5 d.p.i. The black line indicates the geometric mean viral load for each group and the dotted line denotes the limit of detection (viral load of ≤2 × 10^3 GE per ml). Statistical differences in viral loads between groups were assessed using the Kruskal–Wallis test with Dunn’s post-test comparing the isotype control antibody-treated guinea pigs to the MARV16-treated groups (excluding animals with undetectable plasma concentrations of MARV16). d–f, Daily body weights (d), body temperatures (e) and clinical scores (f) for guinea pigs for the duration of the challenge study. Animals with undetectable plasma concentrations of MARV16 1 day after treatment are denoted with triangles, which indicates that in these animals, MARV16 was sequestered at the injection site and cleared or ‘soaked up’ by the challenge virus immediately before reaching the blood stream.

We next assessed the ability of MARV16 to trigger activation of FcγRIIa (H131) and FcγRIIIa (V158) as surrogate assays for antibody-dependent cellular phagocytosis (ADCP) and antibody-dependent cellular cytotoxicity (ADCC), respectively. MARV16 did not activate either FcγRIIa or FcγRIIIa (Extended Data Fig. 8). This result suggests that the observed protection induced by MARV16 originates solely from direct viral neutralization. We propose that the introduction of modifications to the crystallizable fragment (Fc), as done for other MARV antibodies37,39,40, to promote effector functions may further improve the therapeutic efficacy of MARV16.

Formulation of MARV-neutralizing antibody cocktails

Antibody cocktails composed of two or more monoclonal antibodies that target nonoverlapping neutralizing epitopes are frequently used as antiviral therapeutics as they promote greater resistance to viral evolution than single monoclonal antibodies[57]. As MARV16 binds to a distinct epitope on MARV GP to that of the RBS-directed MR78 and MR191, we reasoned that MARV16 and MR78 or MR191 may simultaneously bind the MARV GP. To examine this possibility, we performed a competitive binding assay with MARV16 and MR78 or MR191 and observed that MARV GPΔMucWT could bind MR78 or MR191 after binding MARV16 (Fig. 5a). Furthermore, three MARV16 Fab fragments and three MR78 or MR191 Fab fragments could simultaneously bind to the prefusion MARV GPΔMucWT ectodomain trimer, as visualized by single-particle electron microscopy analysis of negatively stained samples (Fig. 5b,c).

Fig. 5: Formulation of MARV antibody cocktails.

a, Competitive binding assay of MARV16, MR78 and MR191 IgG to the MARV16-bound MARV GPΔMucWT ectodomain using BLI. Data presented are from one biological replicate and are representative of data from two biological replicates using distinct batches of protein. b,c, Representative 2D class averages and 3D reconstruction of negatively stained MARV GPΔMucWT ectodomain bound to MR78 and MARV16 Fab fragments (b) or MR191 and MARV16 Fab fragments (c). The position of the MR78 (pink) or MR191 (blue) Fab fragments were determined by superimposing the RAVV GP–MR78 Fab (PDB: 5UQY) or RAVV GP–MR191 Fab (PDB: 6BP2) structures with our MARV GP–MARV16 Fab structure. Scale bar, 400 Å. d–f, Escape mutations identified for MARV16 alone (d), the MARV16–MR78 antibody cocktail (e) and the MARV16–MR191 antibody cocktail (f) using replication-competent VSV encoding the MARV/Musoke GP instead of the VSV G protein. Two selection experiments were performed using the separately plaque-purified VSV-MARV/Musoke GP isolates 2B2 and 2B4. The virus was passaged in the presence of increasing concentrations of antibody until observation of obvious cytopathic effects (>20% of the field of views) in the presence of 100 µg ml–1 of antibody. Mutations were identified by deep sequencing of the viral supernatant, and those that reached a frequency of at least 10% are displayed in the plots and coloured according to the isolate they were identified from. The V547G mutation that was also identified in 2B2 passaged without antibody is displayed in grey. g, Escape mutations (red) identified during the antibody-selection experiments mapped on the surface of MARV GPΔMucWT (grey). The MR78 position (pink) was determined by superimposing the RAVV GP–MR78 Fab (PDB: 5UQY) structure with our structure of MARV GP in complex with the MARV16 Fab (green). C226, F447S and L448P were not resolved in our structure and are not displayed on the MARV GP.

To evaluate the barrier to escape from antibody-mediated neutralization, we first passaged replication-competent VSV encoding the MARV/Musoke GP instead of the VSV G protein in the presence of MARV16 alone. Two independent selection experiments were performed using two separately plaque-purified VSV-MARV/Musoke GP isolates, designated 2B2 and 2B4. The input 2B2 virus contained two nonsynonymous GP mutations mapping to the signal peptide (K2E) and the mucin-like domain (I381R), whereas the input 2B4 virus solely contained the I381R GP mutation. Both selection experiments using MARV16 alone produced a single escape mutation, A514T, in the GP2 core (Fig. 5d). These data are consistent with previous work on monoclonal antibodies, including MR78 and MR191, which demonstrated that a single mutation in the target viral antigen can enable it to escape single monoclonal antibodies9,57.

To demonstrate that an antibody cocktail formulated with MARV16 and an RBS-directed antibody can prevent escape by a single MARV GP mutation, we next passaged the VSV-MARV/Musoke GP isolates in the presence of antibody cocktails composed of MARV16 and MR78 or MARV16 and MR191. In contrast to MARV16 alone, two or more mutations were required for the isolates to escape from the MARV16–MR78 and MARV16–MR191 antibody cocktails. Selection using the 2B2 isolate in presence of the MARV16–MR78 cocktail led to the identification of three escape mutations, Q128P, F447S and K550E, in the GP1 core, the mucin-like domain and the GP2 core, respectively (Fig. 5e). Using the 2B4 isolate for the MARV16–MR78 selection experiments produced two escape mutations, Y197C and A514E, in the glycan cap and GP2 core, respectively (Fig. 5e). For the MARV16–MR191 cocktail, three escape mutations were identified using the 2B2 isolate, which mapped to the glycan cap (C226R), the wing (L448P) and the GP2 core (K550E) (Fig. 5f). We identified two escape mutations, L451S and A514T, in the wing and the GP2 core, respectively, for the 2B4 isolate passaged in the presence of MARV16–MR191 (Fig. 5f). Our data indicate that mutations in the GP2 core probably affected recognition by CDRH1 and CDRH2 (A514T/E) or by CDRH3 and CDRL3 (K550E) (Fig. 5g) and led to MARV16 escape. Consistent with previous selection experiments[9], mutations in the RBS (Q128P or N129S in previous work) or mutations that probably prevented displacement of the glycan cap or wing (Y197C, C226R, L448P and L451S or S220P, C226Y and P455L in previous work[9]) led to MR78 and MR191 escape (Fig. 5g). These data indicate that MARV16 and an RBS-directed antibody can be used together in a therapeutic antibody cocktail that requires multiple GP mutations to escape neutralization by both antibodies, thereby creating a MARV therapeutic with increased resilience to viral evolution.

Discussion

The identification of stabilizing mutations is a key goal of vaccine design as the use of such mutations can markedly improve the immunogenicity of viral antigens by preferentially eliciting antibodies directed towards the desired conformation of a GP. Such success has been achieved for SARS-CoV-2 S and respiratory syncytial virus F vaccines, which incorporate prefusion-stabilizing mutations32,33. The stabilizing mutations identified here improved the expression, thermostability and immunogenicity of MARV GPΔMuc. However, as the serum-neutralizing antibody titres were modest in mice immunized with MARV GPΔMucWT or MARV GPΔMucPV, strategies such as multimeric presentation of the stabilized MARV GP on nanoparticles47,48,49 or inclusion of the stabilizing mutations into mRNA vaccines encoding for the full-length MARV GP42,50 will probably be necessary for inducing more potent neutralizing antibody titres. We also used ProteinMPNN[45] to identify stabilizing mutations in the MARV GPΔMuc, similar to approaches recently used for the Langya virus G and Epstein–Barr virus gB proteins43,44, thereby further demonstrating the utility of machine-learning-enabled vaccine design.

Previous studies have suggested that the MARV GP equator and base are shielded from neutralizing antibodies by the GP1 mucin-like domain and the GP2 wing10,11 given that all previously characterized MARV GP-neutralizing antibodies solely target the RBS9,36. As MARV16 binds to an epitope that spans GP1 and GP2, our data showed that the mucin-like domain and wing do not fully shield GP2. Instead, the wing is conformationally flexible, thereby enabling antibody binding. As a result, we anticipate that future antibody discovery campaigns will identify neutralizing antibodies that target multiple different GP2 epitopes. MARV16 recognizes a conserved Marburgvirus epitope and neutralizes filoviruses as distantly related as DEHV, which only shares 49.9% amino acid identity with MARV GP. Several Ebolavirus GP-directed antibodies that neutralize EBOV, SUDV and Bundibugyo ebolavirus, but not MARV, including ADI-15946, EBOV-515 and EBOV-520, recognize similar epitopes to MARV16 (refs. 52,53,54,55), which indicates that this epitope is a prime target for broad genus-specific neutralization. Therapeutics or vaccines that target this MARV GP antigenic site will therefore probably provide robust protection against pre-emergent MARV variants and MARV-related filoviruses, similar to those developed for EBOV[48].

For Ebolaviruses, a structured glycan cap blocks access to the RBS until GP cleavage mediated by cathepsin B or L in the endosomes27,28,51. This characteristic limits the elicitation of and potency of RBS-directed antibodies9,10. By contrast, the glycan cap had not been visualized for MARV GP, and RBS-directed antibodies mediate weak but detectable MARV neutralization, which suggested that the MARV GP RBS is more exposed than that of Ebolavirus GPs9,10,11,36. Our structure revealed that the MARV glycan cap shields the RBS in a similar manner to the glycan cap of Ebolaviruses. However, the increased conformational heterogeneity or looser tethering of the MARV GP glycan cap to the RBS might enable easier displacement, which explains why MR78 and MR191 neutralize MARV, but not Ebolaviruses. Accordingly, we observed an increase in binding for the RBS-directed antibodies MR78 and MR191 to the MARV GPΔMucWT ectodomain in acidic conditions. These data suggest that the glycan cap is less likely to mask the RBS in the acidic conditions of late endosomes.

As diagnosis and treatment of MVD is often delayed in humans, monoclonal antibodies have been evaluated on the basis of their capacity to provide therapeutic benefits in animal models when administered several days after MARV exposure39,40. We showed here that MARV16 provides significant protection against MARV infection in guinea pigs when administered as late as 96 h after infection, which indicates that MARV16 is a promising candidate for treating MVD. Moreover, therapeutic antibody cocktails consisting of multiple antibodies that target distinct epitopes on an antigen are favoured for viral pathogens as the targeted viral protein would typically need to accumulate multiple mutations to evade all the antibodies in the cocktail[57]. All previous neutralizing antibodies identified against the MARV GP target the RBS, which limited the development of an antibody cocktail for MARV9,36,37,38. Our demonstration that MARV16 can bind to the MARV GP concurrently with RBS-directed antibodies indicates that a therapeutic antibody cocktail against MARV, similar to ZMapp for EBOV[20], can be developed. We showed here that such cocktails require multiple GP amino acid substitutions to escape neutralization by both antibodies in the cocktail. These data indicate that antibody cocktails composed of MARV16 and a RBS-directed antibody can be used as MARV therapeutics to provide increased resistance to mutations. Indeed, such combination should retain efficacy during treatment even if the virus evolves.

In summary, our results will inform both vaccine and therapeutic development against MARV, providing improved treatment and prevention options for future MARV outbreaks.

Methods

Cells

HEK-293T (ATCC), Vero E6 (ATCC), BHK-21/WI-2 (Kerafast) and BS-C-1 cells (ATCC) were grown in DMEM supplemented with 10% fetal bovine serum (FBS) and 1% penicillin–streptomycin–l-glutamine (PS) at 37 °C and 5% CO_2. Expi293 (ThermoFisher) and ExpiCHO-S (ThermoFisher) were grown in Expi293 or ExpiCHO medium, respectively, at 37 °C and 8% CO_2 rotating at 130 rpm. Cell lines were not authenticated or tested for mycoplasma contamination.

In vivo animal studies

Sixty (30 male and 30 female) 6–8-week-old BALB/c mice (Mus musculus) were obtained from Inotiv. Mice were housed at the Bioqual vivarium facility (12-h light–dark cycle, temperature of 20–26.1 °C and relative humidity of 30–70%) with free access to sterilized water and chow. Mice were handled in accordance with the standards of the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) International’s reference resource: the eighth edition of the ‘Guide for the Care and Use of Laboratory Animals’, Animal Welfare Act as amended, and the 2015 reprint of the Public Health Service (PHS) Policy on Human Care and Use of Laboratory Animals. All experiments were performed under the Bioqual Institutional Animal Care and Use Committee (IACUC)-approved protocol number 23-054P. Animals were inspected before inclusion in experiments and monitored throughout the study by a veterinarian.

ATX-GK and ATX-GL female mice, 6–7 weeks old, were obtained from Alloy Therapeutics and housed for the immunization experiment at the Institute for Research in Biomedicine, Bellinzona, Switzerland. All animal experiments were performed in accordance with the Swiss Federal Veterinary Office guidelines and authorized by the Cantonal Veterinary (approval number 35554 TI-39/2023/2023). Animals were supervised by a licensed veterinarian, and proper steps were taken to ensure the welfare and to minimize the suffering of all animals in the studies. Animals were housed in ventilated cages in a 12-h light–dark cycle at 20 ± 2 °C and a relative humidity of 55 ± 8%, with free access to water and standard sterilized chow.

Twenty-four female, 4–8-week-old Hartley guinea pigs (Cavia porcellus) were obtained from Charles River Laboratories and housed in the Centers for Disease Control and Prevention (CDC)-accredited Biosafety Level 4 (BSL-4)/Animal Biosafety Level 4 (ABSL-4) containment facility at Texas Biomed (temperature of 18–28 °C and relative humidity of 25–75%) with free access to water and chow. All experiments were approved by Texas Biomed’s IACUC under protocol 1915C before the initiation of the study and performed in accordance with the Animal Welfare Act and the Guide for the Care and Use of Laboratory Animals. Animals were observed by a veterinarian at least once daily before exposure to the challenge virus and at least twice daily after administration of the challenge virus. Moribund animals were euthanized with the approval of a veterinarian.

Constructs

The construct encoding the MARV GPΔMucWT ectodomain (residues 1–256 and 426–637) with a carboxy-terminal 8× His tag was codon-optimized, synthesized and inserted into pcDNA3.1(+) by Genscript. Mutations neighbouring the furin-cleavage site (W439A, F445G and F447N) and the stabilizing mutation (H589I) were introduced using In-Fusion Cloning with overlapping mutagenesis primers. The HR1c-stabilizing mutations (T582P and F583V) were also introduced through In-Fusion Cloning using overlapping mutagenesis primers. Constructs encoding the EBOV GPΔMuc domain (residues 1–312 and 463–637) and the SUDV GPΔMuc domain (residues 1–313 and 474–637) both with a C-terminal T4 foldon and 8× His tag were codon-optimized, synthesized and inserted into pcDNA3.4 by Genscript. The constructs encoding the full-length MARV/Musoke GP (GenBank accession number: NC_001608) with a C-terminal Flag tag was codon-optimized, synthesized and inserted into pcDNA3.1(+) by Genscript. Constructs encoding the full-length MARV/Ci67 (GenBank accession: EF446132), MARV/Ozolin (GenBank accession: AY358025), MARV/Angola (GenBank accession: KY047763), MARV/Kakbat-SL-2017 (GenBank accession: MN258361), MARV/Kasbat-SL-2018 (GenBank accession: MN187403), MARV/Ghana-2022 (GenBank accession: OQ672470) and MARV/Equatorial Guinea-2023 (HS415030) were codon-optimized, synthesized and inserted into pHDM by Genscript. Constructs encoding furin and the MR78, MR191 and EBOV515 heavy and light chains were codon-optimized, synthesized and inserted into pcDNA3.1(+) by Genscript. Constructs encoding the MARV4, MARV7, MARV11, MARV14, MARV16, MARV18, MARV20, MARV21 and MARV23 heavy and light chains were generated by cloning variable regions into IgG1 and IgK expression vectors[58]. VH and VL amino acid sequences for the ten MARV antibodies are presented in Extended Data Table 1. Constructs encoding the MARV7, MARV14, MARV18 and MARV20 VH-CH1 sequence with an N-terminal CD5 leader sequence and C-terminal His tag were codon-optimized, synthesized and inserted into pcDNA3.4 by Genscript. Constructs encoding the MARV7, MARV14, MARV18 and MARV20 VL-CH1 sequence with an N-terminal CD5 leader sequence and C-terminal 3× Flag tag were codon-optimized, synthesized and inserted into pcDNA3.4 by Genscript. The native, full-length MARV/Musoke GP was synthesized and inserted between the VSV M and L proteins in pVSV eGFP dG (a gift from C. Cepko; Addgene plasmid 31842) by Genscript. Helper plasmids encoding the VSV N, P, L and G proteins were obtained from Kerafast (EH1012).

Recombinant protein expression and purification

To produce the MARV GPΔMuc, EBOV GPΔMuc and SUDV GPΔMuc ectodomains, Expi293 cells were grown to a density of 3 × 10^6 cells per ml and then transfected with constructs encoding the ectodomain and furin at a 3:1 mass ratio using an Expifectamine293 transfection kit following the manufacturer’s instructions. Five days after transfection, the supernatant was collected, clarified by centrifugation and incubated with Ni Sepharose Excel resin (Cytiva) for 1 h at room temperature. The resin was then collected in a gravity column and washed with buffer containing 25 mM sodium phosphate pH 8.0, 300 mM NaCl and 50 mM imidazole or 100 mM Tris pH 8.0, 300 mM NaCl and 40 mM imidazole. The proteins were then eluted using an elution buffer containing 25 mM sodium phosphate, 300 mM NaCl, 500 mM imidazole, pH 8.0 or 100 mM Tris, 300 mM NaCl, 300 mM imidazole and further purified into TBS (20 mM Tris pH 7.4 and 100 mM NaCl, or 50 mM Tris pH 7.4 and 150 mM NaCl) by size-exclusion chromatography using a Superose 6 Increase 10/300 GL column. The purified proteins were concentrated using a 100 kDa Amicon centrifugal filter, flash frozen and stored at −80 °C until use.

MARV7, MARV11, MARV14, MARV16, MARV18, MARV20, MARV21 and MARV23 were expressed recombinantly by transient transfection of ExpiCHO-S cells (ThermoFisher Scientific) using an ExpiFectamine CHO transfection kit (Thermo Fisher Scientific). After 8 days, cell culture supernatant was separated with a Sartoclear Dynamics Lab V kit (Sartorius) and affinity purified by protein A chromatography using ÄKTA Xpress Fast Protein liquid chromatography (Cytiva) with HiTrap protein A columns (Cytiva) followed by buffer exchange to a buffer containing 20 mM histidine and 150 mM NaCl, pH 6.0 using HiPrep 26/10 desalting columns (Cytiva). The purified antibody concentrate was quantified using a Lunatic spectrophotometer (Unchained Labs) and stored at −80 °C until use.

MR78, MR191 and EBOV515 monoclonal antibodies were produced by transfecting Expi293 cells grown to a density of 3 × 10^6 cells per ml with the heavy chain and light chain constructs supplied at a 1:1 mass ratio using an Expifectamine293 transfection kit. Four to 5 days after transfection, the supernatant was collected, clarified by centrifugation and flowed over a protein A column. The column was then washed with at least ten column volumes of wash buffer containing 20 mM sodium phosphate, pH 8.0. The eluted antibodies were then exchanged into TBS and concentrated using a 100 kDa Amicon centrifugal filter.

MARV7, MARV14, MARV18 and MARV20 Fab fragments were produced by transfecting Expi293 cells with the heavy and light chain constructs at a 1:1 mass ratio using an Expifectamine293 transfection kit and following the manufacturer’s recommendations. Four to 5 days after transfection, the supernatant was collected, clarified by centrifugation and incubated with Ni Sepharose Excel resin (Cytiva) for 1 h at room temperature. The resin was collected in a gravity column and washed with buffer containing 25 mM sodium phosphate pH 8.0, 300 mM NaCl and 20 mM imidazole. The proteins were then eluted using elution buffer containing 25 mM sodium phosphate, 300 mM NaCl, 500 mM imidazole, pH 8.0, and further purified into TBS (20 mM Tris pH 7.4 and 100 mM NaCl) by size-exclusion chromatography using a Superdex 75 Increase 10/300 GL column, and concentrated using a 30 kDa Amicon centrifugal filter.

Biotinylation of MARV GPΔMucWT

The MARV GPΔMucWT was produced and purified as described above. After elution from Ni Sepharose Excel resin, the GP was exchanged into PBS (137 mM NaCl, 2.7 mM KCl, 10 mM Na_2HPO_4 and 1.8 mM KH_2PO_4, pH 7.4) and concentrated to 1 mg ml–1 using a 100 kDa Amicon centrifugal filter. The MARV GPΔMucWT was biotinylated using an EZ-Link Sulfo-NHS-SS-Biotinylation kit (ThermoFisher) with a 40-fold molar excess of Sulfo-NHS-SS-biotin and incubating the reaction mixture at room temperature for 30 min. The biotinylated MARV GPΔMucWT was then purified into TBS by size-exclusion chromatography using a Superose 6 Increase 10/300 GL column. The purified protein was then concentrated using a 100 kDa Amicon centrifugal filter, flash frozen and stored at −80 °C until use.

Cleavage of EBOV and SUDV GPΔMuc

The EBOV and SUDV GPΔMuc ectodomains were produced and purified as described above. After elution from Ni Sepharose Excel resin, the GP was exchanged into TBS and concentrated to 1 mg ml–1 using a 100 kDa Amicon centrifugal filter. Thermolysin (Sigma-Aldrich), resuspended in TBS, was added to a final concentration of 0.2 mg ml–1. The reaction mixture was incubated at 37 °C for 1 h, after which phosphoramidon (Sigma-Aldrich) was added to a final concentration of 500 µM to stop thermolysin. The cleaved GP was purified into TBS by size-exclusion chromatography using a Superdex 200 Increase 10/300 GL column.

Generation of Fab fragments

To generate Fab fragments from the purified monoclonal antibodies, LysC, resuspended in TBS, was added to 1 mg of MARV16, MR78 or MR191 IgG at 1:4,000 to 1:8,000 mass ratios and incubated at 37 °C overnight. The following day, Cytiva MabSelect resin was added to the digested IgG solution and incubated for 1 h at room temperature. The flow through was collected and run over a Superdex 75 Increase 10/300 GL column equilibrated into TBS. Fractions containing the Fab fragment were pooled and concentrated using a 30 kDa Amicon centrifugal filter.

Immunogenicity study with BALB/c mice

Mice were randomized to form six groups (n = 10 mice per group) with equal numbers of male and female mice. On study days 0, 28 and 56, 0.1 µg, 1 µg or 10 µg of MARV GPΔMucWT or MARV GPΔMucPV diluted in TBS was mixed with InvivoGen Addavax at a 1:1 volume ratio for a total injection volume of 100 µl. The mice were intramuscularly immunized in both quadriceps (50 µl per quadricep). On study days −7, 14, 42 and 70, blood was collected from each animal in serum-separator tubes. The resulting serum was stored at −80 °C until use.

MARV GPΔMuc immunizations and antibody discovery from Alloy mice

Pre-immune serum was obtained from each mouse 1 week before immunization. ATX mice were immunized with recombinant MARV GPΔMucWT diluted (1:1) in Magic Mouse adjuvant (CDN-A001E; CD Creative Diagnostics) and subcutaneously and intraperitoneally injected. On day 0, mice received prime immunization with 20 µg of MARV GPΔMucWT and were boosted on day 13 and day 64 with the same amount of antigen. On day 70, the mice were killed and peripheral blood, spleen and lymph nodes were collected and cells were freshly isolated. B cells from either freshly isolated or frozen splenocytes were enriched by positive selection using mouse CD19 microbeads and LS columns (Miltenyi) and subsequently stained with mouse anti-IgM (BioLegend, 406508; 2 µg ml–1), anti-IgD (BioLegend, 405706; 2 µg ml–1), anti-IgA (Fisher Scientific, 15208769; 2 µg ml–1) and biotinylated MARV GPΔMucWT labelled with both streptavidin-Alexa-Fluor 488 and streptavidin-Alexa-Fluor 647 (Life Technologies). MARV GPΔMucWT-specific IgG+ memory B cells were sorted by flow cytometry by gating out IgM+IgD+IgA+ B cells and positively baiting B cells with dual-labelled (Alexa-Fluor 488 and Alexa-Fluor 647) antigen using a SH800SFP cell sorter (Sony). Sorted IgG+ memory B cells were seeded at clonal dilution in 384-well plates on a monolayer of feeder mesenchymal cells in the presence of B cell survival factors. Clones positive for antigen binding were then isolated and cDNA was synthesized. Monoclonal antibody VH and VL sequences were obtained by RT–PCR. Genes encoding V, D and J of the IgH DNA sequences were identified using the IMGT database as a ref. [59]. Monoclonal antibodies were then produced recombinantly as human IgG1 (IgG1m3 allotype) in ExpiCHO cells and transiently transfected with heavy and light expression vectors as previously described[58].

ELISA

The MARV GPΔMucWT or MARV GPΔMucPV ectodomain was diluted to 0.003 mg ml−1 in TBS, added to Maxisorp 384-well plates (ThermoFisher) and incubated overnight at room temperature. The following day, the plates were tapped until dry and blocked with blocker casein for 1 h at 37 °C. The plates were tapped dry, and serum samples (starting concentration of 1:40) or monoclonal antibodies (starting concentration of 0.1 mg ml–1) were diluted in TBS supplemented with 0.1% Tween 20 (TBST) and serially diluted 1:3 in TBST, added to plates and incubated at 37 °C for 1 h. The plates were tapped dry and washed 4 times with TBST, after which a goat anti-mouse IgG (H+L) HRP-conjugated antibody (ThermoFisher) diluted 1:5,000 in TBST or a goat anti-human IgG (H+L) HRP-conjugated antibody (ThermoFisher) diluted 1:5,000 in TBST was added to each well. The plates were incubated for 1 h at 37 °C, tapped dry and washed 4 times with TBST. SureBlue Reserve TMB 1-Component Microwell Peroxidase substrate (SeraCare) was added to each well and developed for 90 s at room temperature. An equal volume of 1 N HCl was added to each well to quench the reaction, after which the absorbance at 450 nm was measured using a BioTek Synergy Neo2 plate reader. The resulting data were analysed using GraphPad Prism 10, using a four-parameter logistic curve to determine the ED_50 for each antibody. Two biological replicates performed in technical duplicate were performed using two distinct batches of protein.

Pseudotyped virus production

VSV pseudotyped with the full-length MARV, RAVV, DEHV, MLAV, EBOV or SUDV GP was produced as previously described60,61,62,63. In brief, HEK-293T cells were split into 10-cm poly-lysine-coated dishes and grown overnight at 37 °C and 5% CO_2 until they reach approximately 90–95% confluency. The cells were washed once with DMEM and left in DMEM supplemented with 10% FBS. The cells were transfected with 16–24 µg full-length GP construct using Lipofectamine 2000 following the manufacturer’s recommendations, after which the cells were incubated for 20–24 h at 37 °C and 5% CO_2. The cells were then washed 3 times with DMEM, infected with VSV∆G/luc and incubated at 37 °C and 5% CO_2. After 2 h, the cells were washed 5 times with DMEM and left in DMEM supplemented with an anti-VSV-G antibody (I1-mouse hybridoma supernatant diluted 1:25 for CRL-2700, American Type Culture Collection) for 20–24 h at 37 °C and 5% CO_2. Following this incubation, the supernatant was collected, clarified by centrifugation, filtered using a 0.45 µM filter and concentrated with a 100 kDa centrifugal filter (Amicon). The resulting pseudovirus was stored at −80 °C until use.

Pseudovirus neutralization assay

Neutralization assays were performed as previously described60,61,62,63. In brief, Vero E6 cells were split into white-walled, clear-bottom 96-well plates at a density of 18,000 cells per well. The cells were grown overnight at 37 °C and 5% CO_2 until they reached approximately 80–90% confluency. The serum samples (starting concentration of 1:10) or monoclonal antibodies (starting concentration of 200 µg ml–1) were diluted in DMEM and serially diluted 1:3 in DMEM. VSV pseudotyped with the GP was diluted 1:5 to 1:250 in DMEM, after which an equal volume of diluted pseudovirus was added to the diluted monoclonal antibody or serum. The pseudovirus–antibody mixture was incubated at room temperature for 30 min. Following this incubation, growth medium was removed from the Vero E6 cells and the pseudovirus–antibody mixture was added to cells. The cells were incubated for 2 h at 37 °C and 5% CO_2, after which an equal volume of DMEM supplemented with 20% FBS and 2% PS was added to each well and the cells were incubated for another 20–24 h at 37 °C and 5% CO_2. An equal volume of ONE-Glo EX (Promega) was added to each well and the cells were incubated 37 °C for 5 min with constant shaking. The luminescence values from each well were measured using a BioTek Synergy Neo2 plate reader.

Data were normalized using GraphPad Prism 10 using the relative light unit (RLU) values obtained from uninfected cells to define 100% neutralization and the RLU values obtained from cells infected with pseudovirus in the absence of antibody to define 0% neutralization. ED_50 values were determined from the normalized data using an [inhibitor] versus normalized response–variable slope model. At least two biological replicates using distinct batches of pseudoviruses and monoclonal antibodies were performed.

Plaque reduction neutralization assay with authentic MARV

Vero E6 cells were split into 6-well plates at a density of 3 × 10^5 cells per well and grown overnight in high-glucose DMEM supplemented with 10% FBS and 1% PS at 37 °C and 5% CO_2 until they reached 75–95% confluency. The following day, monoclonal antibodies were diluted in high-glucose DMEM with 2% FBS and 1% PS (DMEM-2) to a starting concentration of 100 µg ml–1 and serially diluted 1:4 in DMEM-2. Next, 100 µl of MARV/Musoke diluted in DMEM-2 to 1,000 PFU per ml was added to 100 µl of the diluted antibodies and the virus–antibody mixture was incubated for 60 min at 37 °C. Following this incubation, an additional 300 µl of DMEM was added to the virus–antibody mixture and 400 µl of this mixture was added to the Vero E6 cells. The cells were incubated with the virus–antibody mixture for 60 min at 37 °C with gentle rocking, after which the mixture was removed and the primary overlay consisting of 1% agarose mixed 1:1 with 2× MEM containing 2 mM sodium pyruvate, 1% PS and 4% FBS was added. The cells were incubated for 7 days at 37 °C with 5% CO_2. Next, the cells were stained with an overlay containing 1% agarose mixed 1:1 with 2× MEM containing 2 mM sodium pyruvate, 4% FBS and 8% neutral red solution and incubated for 1 day at 37 °C and 5% CO_2, after which the number of plaques were manually counted. The per cent infectivity for each well was determined by dividing the number of plaques in the well by the number of plaques counted in the well with 24 pg ml–1 of antibody. Two biological replicates with one to three technical replicates were conducted for each antibody, and the PRNT_50 values were determined from the averaged data from the two biological replicates using an [inhibitor] versus normalized response–variable slope model in GraphPad Prism 10.

BLI

Binding of the stabilized MARV GPΔMucWT ectodomains to MR191 was assessed by first dipping pre-hydrated AHC2 biosensors into MR191 IgG diluted to 10 ng µl−1 in 10x kinetics buffer to a 1 nm shift. The MR191-coated biosensors were then dipped into each MARV GPΔMuc construct diluted to 10 nM in 10x kinetics buffer for 300 s, after which the biosensors were dipped into 10x kinetics buffer. All steps were conducted at 30 °C. Data were baseline subtracted using Octet Data Analysis HT software (v.12.0) and visualized using GraphPad Prism 10.

To measure the affinity of the MARV16 Fab for MARV GPΔMucWT, biotinylated MARV GPΔMucWT was diluted to a concentration of 10 ng µl–1 in 10x kinetics buffers and loaded onto pre-hydrated streptavidin biosensor to a 1 nm shift. The MARV GPΔMucWT-coated biosensors were then dipped for 300 s into MARV16 Fab diluted in 10x kinetics buffer at a starting concentration of 100 nM and serially diluted 1:3. The biosensors were then dipped into 10x kinetics buffer for an additional 300 s. All steps were conducted at 30 °C. The resulting data were baseline subtracted and fit using Octet Data Analysis HT software (v.12.0) and visualized using GraphPad Prism 10.

Binding comparisons between the MARV16, MR78 and MR191 Fab and IgG fragments were conducted as described above. Following immobilization of the biotinylated MARV GPΔMucWT on the streptavidin biosensors, the tips were dipped for 300 s into 100 nM of Fab or IgG diluted in 10x buffer, after which the tips were dipped into 10x kinetics buffer for 300 s. All steps were conducted at 30 °C. Data were baseline subtracted using Octet Data Analysis HT software (v.12.0) and visualized using GraphPad Prism 10.

Binding of MARV GPΔMuc to MARV16, MR78 or MR191 IgG at variable pH values were conducted by loading biotinylated MARV GPΔMucWT diluted in 10x kinetics buffer, pH 7.4 onto streptavidin biosensors to a 1 nm shift. The loaded biosensors were then dipped for 300 s into IgG diluted in 10x kinetics buffer at pH 7.4, 6.5, 5.5 or 4.5. The resulting data were baseline subtracted using Octet Data Analysis HT software (v.12.0) and visualized using GraphPad Prism 10.

Binding of the MARV GPΔMucWT, EBOV GPΔMuc, cleaved EBOV GP, SUDV GPΔMuc and SUDV GPΔMuc to MARV16, MR78, MR191 and EBOV515 IgG was assessed as described above. IgG diluted to 10 ng µl–1 in 10x kinetics buffer was loaded on AHC2 biosensors to a 1 nm shift, after which the loaded biosensors were dipped for 300 s into GP diluted to approximately 10 nM in 10x kinetics buffer. All steps were conducted at 30 °C. Data were baseline subtracted using Octet Data Analysis HT software (v.12.0) and visualized using GraphPad Prism 10.

Competitive binding of MARV16 versus MR78 or MR191 to the MARV GPΔMucWT was assessed by loading biotinylated MARV GPΔMucWT diluted to 10 ng µl–1 onto pre-hydrated streptavidin biosensor, after which the loaded biosensors were dipped for 300 s into 200 nM of MARV16 IgG diluted 10x kinetics buffer. The biosensors were then dipped into 100 nM of MR78, MR191 or MARV16 IgG diluted in 10x kinetics buffer for 300 s and finally dipped into 10x kinetics buffer for 300 s. All steps were conducted at 30 °C. The resulting data were baseline subtracted using Octet Data Analysis HT software (v.12.0) and visualized using GraphPad Prism 10.

For epitope binding of the MARV antibodies discovered from Alloy ATX mice, biotinylated MARV GPΔMucWT was diluted to 10 ng µl–1 and loaded onto pre-hydrated streptavidin biosensors to a 1 nm shift. The loaded biosensors were dipped into 200 nM of the saturating antibody diluted in 10x kinetics buffer for 900 s and then dipped into 100 nM of the competing antibody and 25 nM of the saturating antibody diluted in 10x kinetics buffer for 300 s. All steps were conducted at 30 °C. The resulting data were analysed with Octet Data Analysis HT software (v.12.0), in which the response for the saturating antibody was calculated by subtracting the average response measured for the last 30 s of the association step of the saturating antibody from the average response measured for the last 90 s of association step of the competing antibody. The resulting data were corrected by subtracting the response measured for self-blocking and the per cent binding was calculated by dividing the response for each competing–saturating antibody pair by the response measured for the saturating antibody binding to the MARV GPΔMucWT alone. Antibody pairs displaying reciprocal blocking relationships were considered to be a part of the same binding group.

In vivo challenge study

Twenty-four guinea pigs were randomly assigned to four groups (n = 6 animals per group). The animals were intraperitoneally infected with 1,000 PFU of guinea pig-adapted MARV/Angola diluted in 100 µl PBS. Twenty-four, 48 or 96 h after infection, the animals were intraperitoneally treated with 10 mg (approximately 20 mg kg–1) of MARV16 or an isotype control monoclonal antibody (MGH2). Blood samples were collected 24 h after administration of the antibody and 5 d.p.i. Body weights, body temperatures and clinical illness scores were recorded daily until the end of the study (29 d.p.i.). Clinical illness scores were assigned as follows. Weight loss: 0, decrease from baseline body weight between 0 and 4.99%; 1, decrease from baseline body weight equal or higher than 5 and less than 10.9%; 2, decrease from baseline body weight equal or higher than 11 and less than 19.9%; and 3, decrease from baseline body weight equal or higher than 20%. Temperature changes: 0, no change from baseline; 1, a change equal or higher than 1.1 °F; and 3, a change equal or higher than 2.2 °C. Dyspnoea: 0, normal respiration; 3, rapid respiration; and 12, laboured or agonal respiration. Responsiveness: 0, active; 2, mild unresponsiveness, becomes active when approached; 8, moderate unresponsiveness, lethargic, weakness; and 15, moribund or prostrate. Discoordination: 0, none; or 2, noticeable. Appearance: 0, active and alert; 1, rough hair coat; and 3, rough hair coat and hunched. Eye appearance: 0, normal; 1, discharge from eye; 2, squinty eye (or eyes); and 3, closed eyes. The total clinical illness scores were determined by adding up all clinical scores from the aforementioned categories. When animals reached euthanasia criteria (total clinical illness scores of 12–35), they were euthanized with the approval by the study’s veterinarian.

To measure MARV viral loads at 5 d.p.i., 100 µl of plasma collected from each animal was mixed with 150 µl PBS and inactivated with 750 µl TRIzol LS reagent. Next, 10 µg yeast tRNA and 10^3 PFU of MS2 bacteriophage were added to the sample. Next, 200 µl chloroform was added to each sample and the samples were centrifuged at 12,000g for 15 min at 2–8 °C. The aqueous phase was transferred to a Microtiter Deepwell 96 plate and RNA was extracted using a NucleoMag Pathogen kit (Macherey-Nagel) with a KingFisher Flex instrument. The extracted RNA was stored at −80 °C until use. RT–qPCR was performed using TaqPath 1-Step RT–qPCR master mix (ThermoFisher) using primers and a probe targeting the GP gene of MARV/Angola (MAGP forward primer: CCAAACGATGGGCCTTCA; MAGP reverse primer: TCCTCCCCTTCTGTATACTCAACAT; MAGP FAM/MGB probe: CAGGTGTACCTCCC). A standard curve was generated using a 1:10 serially diluted ssRNA standard (10^7 to 10^1 copies per 5 µl) and a MS2 phage assay was conducted as an internal extraction and detection control. Two technical replicates were conducted for each sample. Results are expressed as GE per ml of plasma.

To measure plasma concentrations of MARV16, plasma was collected from guinea pigs 1 day after antibody administration. MARV GPΔMucWT was diluted in PBS to 2 µg ml−1, added to Immulon 2 HB 96-well flat-bottom plates (ThermoFisher) and incubated overnight at 4 °C. The following day, the plates were washed 3 times with PBST, blocked for 1 h at 2–8 °C using Pierce Protein-Free (PBS) blocking buffer (ThermoFisher) and then washed 3 more times with PBST. Next, plasma samples diluted 1:10, 1:400 and 1:2,000 in PBS with 1% FBS and 0.2% Tween 20 were added to the wells and the plates were incubated for 1 h at 37 °C. The plates were washed 3 times with PBST and a goat anti-human IgG-HRP-conjugated secondary antibody (Millipore Sigma) diluted 1:6,000 in PBS with 1% FBS and 0.2% Tween 20 was added to each well. The plates were incubated 1 h at 37 °C and washed 3 times with PBST. TMB substrate (ThermoFisher) was added to each well, developed for 12 min and stopped with an equal volume of 2 N H_2SO_4. Optical densities at 450 nm were measured using a BioTek 800 TS spectrophotometer. A standard curve was generated using MARV16 diluted in PBS to 200 ng ml–1 and 1:2 serially diluted 7 times. The standard curve was fitted with a four-parameter logistic curve and used to calculate plasma concentrations of MARV16. Two technical replicates were conducted for each sample.

FcγRIIa or FcγRIIIa activation assays

Antibody-dependent activation of human FcγRIIIa and FcγRIIa was performed with a bioluminescent reporter assay. ExpiCHO cells transiently expressing membrane-anchored wild-type MARV/Musoke GP (target cells) were incubated with different amounts of monoclonal antibodies. After 25 min, Jurkat cells stably expressing FcγRIIIa receptor (V158 variant) or FcγRIIa receptor (H131 variant) and NFAT-driven luciferase gene (effector cells) were added at an effector to target ratio of 6:1 for FcγRIIIa assays and 5:1 for FcγRIIa assays. Signalling was quantified by the luciferase signal produced as a result of NFAT pathway activation. Luminescence was measured after 22 h of incubation at 37 °C with 5% CO_2 with a luminometer using Bio-Glo-TM luciferase assay reagent according to the manufacturer’s instructions (Promega).

Cryo-EM sample preparation and data collection

To generate MARV GPΔMucWT–MARV16 Fab complexes, 100 µg of MARV GPΔMucWT and 150 µg MARV16 Fab were incubated together at 37 °C for 30 min, after which the complexes were added to a 100 kDa centrifugal filter, washed 5 times with TBS and concentrated to 5.5 mg ml–1. Immediately before freezing, CHAPSO was added to the MARV GPΔMucWT–MARV16 Fab complexes to a final concentration of 5.45 mM. The complexes were added to freshly glow-discharged 2.0/2.0 UltraFoil grids (200 mesh)[64], after which the grids were plunge frozen using a Vitrobot MarkIV (ThermoFisher) with a wait time of 10 s, a blot force of 0 and a blot time of 5 s at 100% humidity and 23 °C. Data were acquired on a FEI Titan Krios transmission electron microscope operated at 300 kV and equipped with a Gatan K3 direct detector and Gatan Quantum GIF energy filter, operated in zero-loss mode with a slit width of 20 eV. Automated data acquisition was carried out using Leginon[65] at a nominal magnification of ×105,000 with a pixel size of 0.829 Å, a defocus range between −0.4 to −3.0 µm and a stage tilt of 0° or 25°. The dose rate was adjusted to 15 counts per pixel per s and each video was acquired in 75 frames of 40 s.

Cryo-EM data processing

Video frame alignment with a downsampled pixel size of 1.658 Å was carried out in WARP[66]. Estimation of microscope CTF parameters, particle picking and extraction (box size of 256 pixels^2) was conducted using cryoSPARC (v.4.6.2). Reference-free 2D classification to select well-defined particle images was performed in cryoSPARC[67]. Next, ab initio 3D reconstruction and heterogeneous refinement to select well-defined particle classes were performed in cryoSPARC. 3D refinements were then conducted using nonuniform refinement[68] with C3 symmetry and per-particle defocus refinement in cryoSPARC[68]. The particle images were then subjected to Bayesian polishing using Relion[69], during which the box size was adjusted to 512 pixels^2 and the pixel size was adjusted to 0.829 Å. Another round of nonuniform refinement with global and per-particle defocus refinement was performed in cryoSPARC. Next, focused 3D classification without particle alignment was conducted using a mask comprising GP residues 171–219 using a tau factor of 10 in Relion70,71. The particles with the best resolved local density were selected and subjected to local refinement with C3 symmetry in cryoSPARC using a mask comprising MARV GP and the Fab variable domains. Reported resolutions are based on the gold-standard Fourier shell correlation of 0.143 criterion, and Fourier shell correlation curves were corrected for the effects of soft masking by high-resolution noise substitution72,73.

Model building and refinement

USCF ChimeraX[74] and Coot[75] were used to fit into the map initial models of the MARV GP (PDB identifier: 6BP2) and MARV16 Fab, which was predicted using AlphaFold2 (ref. [76]). The model was then built and refined into the map using Coot, Rosetta77,78, ISOLDE[79] and Phenix[80]. Figures were generated using UCSF ChimeraX.

Differential scanning fluorimetry

The original and stabilized MARV GPΔMuc ectodomains were diluted in TBS and mixed with protein thermal shift buffer and dye (ThermoFisher) following the manufacturer’s recommendation such that the final concentration of protein in the reaction mix was 0.25 µg ml–1. The reaction mix was added to a 96-well qPCR plate (ThermoFisher) and sealed with MicroAmp optical adhesive film (ThermoFisher). The fluorescence intensity (λExcitation: 470 ± 15 nm; λEmission: 586 ± 10 nm) was measured from 25 °C to 99 °C using a QuantStudio 5 Real-Time PCR system (ThermoFisher). The data were analysed and visualized using QuantStudio Design and Analysis Desktop software (ThermoFisher) and GraphPad Prism 10. Data are presented as the negative first derivative of fluorescence intensity with respect to temperature. The Tm was identified by taking the second derivative of the fluorescence intensity with respect to temperature and smoothing the resulting function across four neighbours points. Four biological replicates each with six technical replicates were performed using distinct batches of protein.

Negative-stain electron microscopy

Complexes of the MARV GPΔMucWT–MARV16 Fab-MR78 Fab, MARV GPΔMucWT–MARV16 Fab–MR191 Fab, MARV GPΔMucWT–MARV7 Fab, MARV GPΔMucWT–MARV14 Fab, MARV GPΔMucWT–MARV18 Fab and MARV GPΔMucWT–MARV20 Fab were generated as described above. Purified MARV GPΔMuc mutants or MARV GPΔMucWT–Fab complexes were diluted to 0.01 mg ml–1 in TBS, added to freshly glow-discharged carbon-coated copper grids and stained with 2% uranyl formate. Data were acquired with a 120 kV FEI Tecnai G2 Spirit with a Gatan Ultrascan 4000 4k × 4k CCD camera at a nominal magnification of ×67,000 using Leginon[65]. The defocus ranged from −3.0 to −1.0 µm and the pixel size was 1.6 Å. Micrographs were then processed in cryoSPARC[67] using PatchCTF to estimate microscope CTF parameters and Blob picker to pick particles. Following particle extraction, reference-free 2D classification was performed to select well-defined particle images. Ab initio 3D reconstruction and homogenous refinement were then performed with the selected particle images applying C3 symmetry. Figures were generated using UCSF ChimeraX[74].

Antibody escape studies using replication-competent VSV-MARV/Musoke GP

These experiments underwent evaluation by the biosafety committee of the University of Washington before approval. Replication-competent VSV-MARV/Musoke GP (lacking VSV G) was generated as previously described[81] but with several modifications. BHK-21/WI-2 cells were split into a 6-well plate and grown overnight at 37 °C and 5% CO_2 until they reached approximately 90% confluency. The cells were then infected with vaccinia virus strain vTF7-3 (American Type Culture Collection, VR-2153) at a multiplicity of infection of about 3 for 45 min, after which the virus was removed from the cells and fresh growth medium (DMEM, 10% FBS and 1% PS) was added. The cells were transfected with the VSV-MARV/Musoke GP anti-genome, VSV N, VSV P, VSV L and VSV G constructs at a 1:3:5:1:8 mass ratio using Lipofectamine 2000. After 4 days, the supernatant was collected, clarified by centrifugation and filtered using a 0.22 µm filter. The resulting supernatant was added to Vero E6 cells grown overnight to 90–95% confluency in a 6-well plate. Cytosine arabinoside was added to the viral growth medium (DMEM, 5% FBS and 1% PS) at a concentration of 25 µg ml–1 to inhibit growth of residual vaccinia virus. After 72 h, the wells were examined for evidence of VSV-MARV/Musoke GP replication by screening for GFP expression and viral cytopathic effect (CPE). The supernatant from a well showing GFP expression and viral CPE was collected, clarified by centrifugation, filtered using a 0.22 µm filter and stored at −80 °C until use. To increase the infectious titre of the rescued virus, the virus was serially passaged on Vero E6 cells as follows. Four million Vero E6 cells were split into a 10 cm plate and grown overnight at 37 °C and 5% CO_2 until they reached approximately 90–95% confluency. The rescued virus was added to the cells and incubated at 37 °C and 5% CO_2 for 2 h, after which the virus was removed and replaced with fresh viral growth medium. The cells were incubated for 72 h at 37 °C and 5% CO_2, after which the cells were examined for viral CPE and the supernatant was collected, clarified by centrifugation and filtered using a 0.45 µm filter. One-tenth of the resulting supernatant was added to new Vero E6 cells. The virus was passaged 5 times until significant viral CPE was observed, which indicated that the virus was efficiently replicating in the Vero E6 cells.

To isolate individual VSV-MARV/Musoke GP clones, the passaged virus was added to Vero E6 cells split into 6-well plates and grown overnight to 90–95% confluency. After 2 h, the virus was removed and an agarose overlay consisting of MEM, 5% FBS, 1% PS and 1% SeaPlaque agarose was added to the cells. The cells were incubated for 72 h, after which individual plaques were selected and the resulting virus was expanded on Vero E6 cells grown in 12-well plates to a confluency of 90–95%. Two individual clones, 2B2 and 2B4, were then further passaged twice on Vero E6 cells grown to 90–95% confluency in 15 cm plates as described above. After 2 passages, the resulting viral supernatant was collected, clarified by centrifugation, filtered using an 0.45 µm filter, concentrated 10-fold using a 100 kDa Amicon filter and stored at −80 °C until use. Infectious titre for the viral stocks were determined using plaque assays as follows. Vero E6 cells were split into 6-well plates and grown overnight to a confluency of 90–95%. The passaged virus was serially diluted in DMEM and added to the Vero E6 cells. The cells were incubated at 37 °C and 5% CO_2 for 2 h, after which the virus was removed and replaced with an agarose overlay. The cells were incubated for 72 h at 37 °C and 5% CO_2. Next, the cells were fixed with 2% paraformaldehyde for 15 min at room temperature and the agarose overlay was then removed. Cells were stained with an 0.1% crystal violet solution and subsequently washed 3 times with PBS, after which plaques were manually counted.

To select for escape mutations, Vero E6 cells were split into 12-well plates and grown overnight at 37 °C and 5% CO_2 until they reach 90–95% confluency. MARV16 alone, MARV16 and MR78, or MARV16 and MR191 were diluted to a starting concentration of 100 µg ml–1 total antibody in DMEM and serially diluted 1:5 to a final concentration of 1.28 ng µl–1. Approximately 10^6  PFU of VSV-MARV/Musoke GP isolate 2B2 or 2B4 were added to the serial diluted antibodies and the virus–antibody mixtures were incubated for 1 h at 37 °C. The virus–antibody mixtures were then added to the Vero E6 cells and incubated for 72 h at 37 °C and 5% CO_2. The cells were examined for CPE and the supernatant from the well with the highest antibody concentration that showed >20% CPE was collected, clarified by centrifugation and filtered using a 0.45 µm filter. The process was repeated using the collected viral supernatant until the well containing 100 µg ml–1 of antibody showed >20% CPE, after which the supernatant was collected, clarified by centrifugation, filtered using a 0.45 µm filter and stored at −80 °C until use.

To identify escape mutations, viral RNA was extracted from the stored viral supernatant using a Zymo Quick-Viral RNA kit. cDNA was generated as previously described82,83 using Turbo DNase to remove gDNA, Superscript IV with random hexamers to generate single-stranded cDNA, Sequenase 2.0 polymerase to generate double-stranded cDNA and AMPure XP beads to purify the resulting cDNA. Sequencing libraries were generated using an Illumina DNA prep kit and sequenced on a 2 × 150 bp run on an Element Aviti. Sequencing reads were adapter-trimmed and quality-trimmed using Trimmomatic (v.0.39)[84] and mapped to the VSV-MARV/Musoke GP genome using Geneious Prime[85]. Variants present at a frequency of 10% or greater were identified using Geneious Prime.

Reporting summary

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

Data availability

The cryo-EM maps and atomic coordinates were deposited to the Electron Microscopy Data Bank and the PDB with accession numbers and EMD-49486 and 9NJL, respectively. Sequencing reads are available under NCBI BioProject PRJNA1336301. Other data and materials generated in this study are available on request and may require a materials transfer agreement.

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Acknowledgements

This study was supported by the National Institute of Allergy and Infectious Diseases (DP1AI158186 and 75N93022C00036 to D.V.), an Investigators in the Pathogenesis of Infectious Disease Awards from the Burroughs Wellcome Fund (D.V.) and the University of Washington Arnold and Mabel Beckman cryo-EM center. D.V. is an Investigator of the Howard Hughes Medical Institute and the Hans Neurath Endowed Chair in Biochemistry at the University of Washington. Sequencing data reported in this publication were obtained from the Northwest Genomics Center at the University of Washington.

Author information

Authors and Affiliations

1. Department of Biochemistry, University of Washington, Seattle, WA, USA

Amin Addetia, Kaitlin Sprouse, Young-Jun Park, Matthew McCallum, Cameron Stewart, Jack T. Brown & David Veesler

2. Humabs Biomed, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland

Lisa Perruzza, Bianca Partini, Alessia Donati, Katja Culap, Alessio Balmelli, Davide Corti & Fabio Benigni

3. Howard Hughes Medical Institute, Seattle, WA, USA

Kaitlin Sprouse, Young-Jun Park & David Veesler

4. BIOQUAL, Rockville, MD, USA

Bhavna Chawla & Swagata Kar

5. Texas BioMedical Research Institute, San Antonio, TX, USA

Michal Gazi, Kendra Alfson, Yenny Goez-Gazi & Ricardo Carrion Jr

Contributions

A.A., L.P., D.C., F.B. and D.V. conceived the project. A.A., J.T.B. and C.S. recombinantly expressed glycoproteins. A.A. designed MARV GP constructs and evaluated their antigenicity and stability. B.C. and S.K. performed the BALB/c mice immunogenicity study. L.P. and A.D. performed ATX-GX mice immunization, monoclonal antibody isolation and ELISAs. K.C. and A.B. produced the recombinant antibodies. M.G. and R.C.J. carried out authentic virus neutralization assays. M.G., K.A., Y.G.-G. and R.C.J. performed the challenge study. A.A. conducted BLI binding experiments. A.A. and K.S. conducted the pseudovirus neutralization assays. B.P. performed the FcγRIIIa and FcγRIIa activation assays. A.A. conducted the antibody escape experiments. A.A. carried out negative-stain and cryo-EM sample preparation, data collection and processing with help from Y.-J.P. and M.M. A.A. and D.V. built and refined the structures. A.A. and D.V. wrote the manuscript with input from all authors. A.A., L.P., D.C., F.B. and D.V. analysed the data.

Corresponding authors

Correspondence to Fabio Benigni or David Veesler.

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Competing interests

L.P., B.P., A.D., K.C., A.B., D.C. and F.B. are employees of Vir Biotechnology and may hold shares. Vir Biotechnology (L.P., D.C. and F.B.) and the University of Washington (A.A. and D.V.) filed a provisional patent application describing these results.

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Extended data figures and tables

Extended Data Fig. 1 Analysis of serum binding and neutralization titres for MARV GPΔMucWT- and GPΔMucPV-immunized mice.

a,b, Dose-response curves for the ELISAs against the MARV GPΔMucPV ectodomain (a) and for the neutralization assays against VSV pseudotyped with MARV/Musoke GP (b) for the sera collected from MARV GPΔMucWT- or GPΔMucPV-immunized mice. Serum was unable to be collected at bleed 2 for mouse D5120. Two biological replicates were performed for the ELISAs using distinct batches of proteins. Two technical replicates were performed per biological replicate. Data presented are from one representative biological replicate and presented as mean ± standard error from the two technical replicates. Two to six biological replicates were performed for the neutralization assays using distinct batches of antibodies and pseudoviruses. Three technical replicates were performed per biological replicate. Data from all biological replicates are shown and presented as mean ± standard error from the three technical replicates.

Extended Data Fig. 2 Analysis of binding and neutralization titres for the monoclonal antibodies discovered from ATX-Gx mice.

a, Flow cytometry gating strategy used for sorting MARV GPΔMucWT-reactive memory B cells. b,c, Dose-response curves for the ELISAs against the MARV GPΔMucWT ectodomain (b) and neutralization assays against VSV pseudotyped with MARV/Musoke GP (c) for the 10 antibodies discovered from the immunization study using the ATX-Gx mice. Two biological replicates were performed for the ELISAs using distinct batches of proteins and antibodies. Two technical replicates were performed per biological replicate. Data presented are from one representative biological replicate and presented as mean ± standard error from the two technical replicates. Two to six biological replicates were performed for the neutralization assays using distinct batches of antibodies and pseudoviruses. Three technical replicates were performed per biological replicate. Data from all biological replicates are shown and presented as mean ± standard error from the three technical replicates. d, Dose-response curves for plaque reduction neutralization tests for MARV16, MR78, MR191, and the MERS-CoV 4C2 IgG, conducted using authentic MARV/Musoke. Two biological replicates were performed with one to three technical replicates using distinct batches of monoclonal antibodies. Data are shown as the mean ± standard error of the technical replicates.

Extended Data Fig. 3 MR78 and MR191 binding to MARV GPΔMucWT at different pHs.

a,b, Binding of MR78 (a) and MR191 (b) IgGs at a concentration of 100 nM at the indicated pH to immobilized MARV GPΔMucWT, as measured by biolayer interferometry. Data shown are one representative out of two biological replicates using distinct batches of protein and antibodies.

Extended Data Fig. 4 Analysis of neutralization breadth of MARV monoclonal antibodies.

a–m, Neutralization dose-response curves for MARV16, MR78, MR191, and EBOV515 against VSV pseudotyped with the MARV/Musoke GP (a), MARV/Angola GP (b), MARV/Ci67 GP (c), MARV/Equatorial Guinea-2023 GP (d), MARV/Kakbat-SL-2017 GP (e), MARV/Kasbat-SL-2018 GP (f), MARV/Ozolin GP (g), MARV/Ghana-2022 GP (h), RAVV GP (i), DEHV GP (j), MLAV GP (k), EBOV GP (l), or SUDV GP (m). Each of the two to six biological replicates used distinct batches of pseudoviruses and antibodies and data are shown as the mean ± standard error of technical triplicates.

Extended Data Fig. 5 Cryo-EM data processing of the MARV GPΔMucWT-MARV16 Fab complex.

a,b, Representative cryo-EM micrograph (a) and 2D class averages (b) obtained for MARV GPΔMucWT in complex with MARV16 Fabs. Scale bar: 100 nm. c, Cryo-EM data processing workflow. NUR (per-particle defocus): non-uniform refinement with per-particle defocus refinement. d, Gold-standard fourier shell correlation curves for the locally refined MARV GPΔMucWT-MARV16 Fab complex (using a mask comprising the GP trimer and MARV16 Fab variable domains). e, Local resolution calculated with cryoSPARC for the locally refined MARV GPΔMucWT-MARV16 Fab complex. f, Heat map of angular distribution for the particles contributing to the final reconstruction. g, Conical fourier shell correlation plot[86].

Extended Data Fig. 6 Analysis of the MARV16-bound MARV GPΔMucWT complex.

a, Sequence conservation of MARV GP residues comprising the MARV16 epitope across the Marburgvirus isolates (MARV variants and RAVV) assessed in this study. Conservation scores were assigned using ConSurf[87]. b, Superimposition of the MR191 Fab-bound RAVV GP (PBD: 6BP2; orange) and MARV16 Fab-bound MARV GP (blue) comparing the N-terminus of the GP2 core domain of the two models. The arrow indicates the shift of the equivalent residues in each model. The MR191 and MARV16 Fabs are not shown for clarity. c, MARV GP2 residues 504–510 modelled into the cryo-EM map (semi-transparent grey surface). d, Comparison of the binding modes of MARV16 (green) and the EBOV antibody EBOV-515 (orange). The EBOV GP trimer from the EBOV GP-EBOV-442-EBOV-515 complex structure (PDB: 7M8L) was superimposed with the MARV GP trimer from the MARV GPΔMucWT-MARV16 structure to compare the EBOV-515 and MARV16 binding poses. MARV GP1 and GP2 are shown in different shades of purple and beige, respectively. N-linked glycans are rendered as light blue surfaces. e, The MARV GP1 glycan cap (residues 191–219) modelled into the cryo-EM density (semi-transparent grey surface). f, View of the putative RBS residues (shown in orange) that are shielded by the MARV glycan cap (light purple). The rest of GP1 is rendered purple.

Extended Data Fig. 7 Identification of MARV GPΔMucWT antigenic sites recognized by non-neutralizing monoclonal antibodies.

a,b, Percent binding (a) to the MARV GPΔMucWT and BLI traces (b) for antibody pairs evaluated in the epitope binning experiments. Antibody pairs showing reciprocal blocking relationships were classified as belonging to the same binding group. Data from one biological replicate are shown and representative of two biological replicates. c–e, 3D reconstructions, representative 2D class averages, and angular distribution plots obtained by single particle electron microscopy analysis of negatively stained MARV GPΔMucWT bound to the MARV18 (c), MARV14 (d), or MARV7 (e) Fabs. f, Representative 2D classes from an electron microscopy dataset of negatively-stained MARV GPΔMucWT-MARV20 Fab complex.

Extended Data Fig. 8 MARV16 does not activate FcγRIIa or FcγRIIIa.

a,b, In vitro evaluation of MARV16 mAb-mediated activation of human FcγRIIIa V158 (a) and FcγRIIa H131 (b) using a bioluminescent reporter assay as a surrogate assay for Fc-mediated effector functions. ExpiCHO cells transfected with MARV/Musoke GP and Jurkat-Fcγ cells were used as target and effector cells, respectively.

Extended Data Table 1 VH and VL amino acid sequences for the 10 MARV antibodies discovered in this study

Extended Data Table 2 Binding kinetics of the MARV16 Fab to immobilized MARV GPΔMucWT determined by BLI

Extended Data Table 3 Cryo-EM data collection, refinement and validation statistics

Extended Data Table 4 Residue contact table

Supplementary information

Reporting Summary

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Addetia, A., Perruzza, L., Sprouse, K. et al. Potent neutralization of Marburg virus by a vaccine-elicited antibody. Nature 650, 459–469 (2026). https://doi.org/10.1038/s41586-025-09868-1

- Received: 14 February 2025

- Accepted: 05 November 2025

- Published: 12 November 2025

- Version of record: 23 December 2025

- Issue date: 12 February 2026

- DOI: https://doi.org/10.1038/s41586-025-09868-1

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Transient hepatic reconstitution of trophic factors enhances aged immunity

Mirco J. Friedrich,

Julie Pham,

Jiakun Tian,

Hongyu Chen,

Jiahao Huang,

Niklas Kehl,

Sophia Liu,

Blake Lash,

Fei Chen,

Xiao Wang,

Rhiannon K. Macrae &

…

Feng Zhang

Nature volume 650, pages 481–489 (2026)

Abstract

Ageing erodes human immunity, in part by reshaping the T cell repertoire, leading to increased vulnerability to infection, malignancy and vaccine failure1,2,3. Attempts to rejuvenate immune function have yielded only modest results and are limited by toxicity or lack of clinical feasibility1,3,4,5. Here we show that the liver can be transiently repurposed to restore age-diminished immune cues and improve T cell function in aged mice. These immune cues were found by performing multi-omic mapping across central and peripheral niches in young and aged animals, leading to the identification of Notch and Fms-like tyrosine kinase 3 ligand (FLT3L) pathways, together with interleukin-7 (IL-7) signalling, as declining with age. Delivery of mRNAs encoding Delta-like ligand 1 (DLL1), FLT3L and IL-7 to hepatocytes expanded common lymphoid progenitors, boosted de novo thymopoiesis without affecting haematopoietic stem cell (HSC) composition, and replenished T cells while enhancing dendritic cell abundance and function. Treatment with these mRNAs improved peptide vaccine responses and restored antitumour immunity in aged mice by increasing tumour-specific CD8+ infiltration and clonal diversity and synergizing with immune checkpoint blockade. These effects were reversible after dosing ceased and did not breach self-tolerance, in contrast to the inflammatory and autoimmune liabilities of recombinant cytokine treatments6,7. These findings underscore the promise of mRNA-based strategies for systemic immune modulation and highlight the potential of interventions aimed at preserving immune resilience in ageing populations.

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Main

Ageing has a profound effect on the immune system, including the T cell repertoire, leading to reduced immune resilience1,2,3. Central to this decline in humans and most other mammals is the involution of the thymus. Thymic involution curtails naive T cell output, contracts T cell receptor (TCR) repertoire diversity and blunts primary responses, whereas peripheral T cells accrue dysfunctional states that heighten susceptibility to infection, vaccine failure and cancer.

Efforts to counter immune ageing have primarily focused on reversing thymic involution through hormones[8], cytokines[9], small molecules[10] and heterochronic parabiosis[11], or by directly modulating haematopoiesis[12]. Although these strategies have provided valuable insights into immune ageing, they have been limited by effect size, toxicity or clinical feasibility4,5.

Here we describe an approach for reconstituting thymus-derived factors in the liver to address age-related immune decline (Fig. 1a). We first identified signalling pathways in the thymus and peripheral blood T cells that decline with age. We then delivered mRNAs encoding these factors (DLL1, FLT3-L and IL-7) to the liver using lipid nanoparticles (LNPs). We found that this approach significantly improved immune response in ageing mice in both vaccination and cancer immunotherapy models with no adverse side effects or evidence of increased autoimmunity. These results highlight the potential of this approach to improve immune function and, more broadly, to use the liver as a transient ‘factory’ for replenishing factors that decline with age.

Fig. 1: Hepatic reconstitution of declining T cell signalling factors to restore immune signalling in ageing.

a, Overview of the approach to restore age-declining immune trophic cues by hepatic expression of Dll1, Flt3l and Il7 mRNAs. b, Spatial ligand–receptor interactions between thymic cortical epithelial cells (cTECs) and thymocytes decline with age (left), and ssGSEA shows reduced Notch pathway activity in circulating T cells (right). n = 47 spatial arrays and 96,683 blood T cell transcriptomes across 21 ages. Data are represented as violin plots with median + interquartile range. Statistical significance was determined by Mann–Whitney tests. c, cTEC–T cell IL-7 interaction (left) and downstream pathway in circulating T cell (right) activities are likewise diminished with age. n = 47 spatial arrays and 96,683 blood T cell transcriptomes across 21 ages. Data are represented as violin plots with median + interquartile range. Statistical significance was determined by Mann–Whitney tests. d, Thymus weight decreases with age (n = 18; 3 per timepoint). Interstitial FLT3-L levels are reduced in aged thymus by ELISA (n = 3 per group). Data are mean ± s.e.m.; statistical significance was determined by a two-tailed unpaired Student’s t-test. e, mRNA (DFI; Dll1, Flt3l and Il7) constructs formulated in SM-102 LNPs. f, Representative RIBOmap images 6 h post-DFI show robust ribosome-bound transcripts in the liver. A representative image from three imaged DFI-treated animals is shown. g, Single-cell quantification: translating Dll1, Flt3l and Il7 in the liver and spleen by RIBOmap (n = 1 for Luc and n = 3 for DFI). h, Immunofluorescence of DLL1 protein over 0–48 h after 5 µg DFI reveals transient induction in total liver and hepatocyte surface (phalloidin co-stain). Fold induction from baseline (0 h) is shown. n = 32 fields of view from n = 3 animals per time point per condition. Data are mean ± s.e.m. i, ELISA for FLT3-L levels in serum and the liver after 10 µg recombinant FLT3-L or 5 µg DFI at 3–48 h. Liver concentrations were normalized to liver weight; fold change from 0 h is shown. n = 3 animals per time point per compartment per condition. Data are mean ± s.e.m.; area under the cover (AUC) over 48 h compared by a two-tailed unpaired Student’s t-test. j, ELISA for IL-7 in serum and the liver after 10 µg recombinant IL-7 or 5 µg DFI at 3–48 h with liver normalization and fold change from 0 h. n = 3 animals per time point per compartment per condition. Data are mean ± s.e.m.; 48-h AUC compared by a two-tailed unpaired Student’s t-test. NS, not significant.

T cell support signals decline with age

A functional T cell repertoire depends on pro-survival and trophic cues: cytokines, hormones and self-peptide MHC, which are abundant in healthy individuals but diminish with age, contributing to immune decline13,14,15.

To nominate potentially immune restorative cues, we profiled trophic signalling across the mouse lifespan using single-cell RNA sequencing (scRNA-seq) or TCR-seq of peripheral blood T cells and spatially resolved Slide-TCR-seq on the thymus, the principal organ of T cell production[16]. After quality control, the dataset comprised approximately 97,000 circulating T cell transcriptomes (CD4+ and CD8+) and 47 spatial arrays covering approximately 1.26 million thymic positions spanning the day of birth to 90 weeks of age (Extended Data Fig. 1a–h). Ageing shifted T cell states, with loss of naive (Tcf7, Sell and Ccr7) and stem-like populations (Bcl11b, Lef1, Id3 and Sox4), and expansion of (virtual) memory (Cd44, Eomes, Gzmb and Tbx21) and exhaustion-like phenotypes (Pdcd1, Havcr2, Ctla4, Lag3, Tigit, Gzmk and Entpd1), consistent with previous mouse and human studies17,18 (Extended Data Figs. 1e,h,i and 2).

Using spatial proximity to infer bona fide cell–cell interactions (rather than co-regulation alone), we used permutation-based null models to identify age-dependent receptor–ligand pairs between thymocytes and thymic epithelial cells (TECs). Cortical TEC–T cell signalling declined markedly with age, whereas medullary TEC–T cell interactions were relatively preserved (Extended Data Fig. 3a,b). Integrating spatially informed interaction analysis with single-sample Gene Set Enrichment Analysis (ssGSEA) of peripheral blood-circulating T cells revealed age-linked attenuation of Notch1/3 and IL-7 signalling (Fig. 1b,c and Extended Data Fig. 3c,d). Although thymic stromal cells primarily act locally to support T cell maturation and selection, several of their soluble products accumulate in the thymus, enter the circulation and contribute to systemic immune homeostasis13,19. Consistently, intrathymic as well as circulating T cells from aged mice showed reduced expression of Notch, IL-7 and FLT3-L target genes and diminished downstream activity (Fig. 1b,c and Extended Data Fig. 3e,f). Soluble FLT3-L, produced in part by intrathymic fibroblasts and required to sustain T cell function in aged and post-transplant settings, also declined in thymic homogenates20,21 (Fig. 1d and Extended Data Fig. 3g).

These findings motivated us to transiently reconstitute age-diminished immune signalling using three immune trophic factors — DLL1 (to activate Notch), FLT3-L and IL-7 — in aged hosts. We selected DLL1 over DLL4 based on a more favourable safety and immunological profile: DLL4 is linked to angiogenesis and vascular remodelling and can overly restrict lymphoid differentiation, whereas we hypothesized that DLL1 supports T cell development without suppressing B cell output22,23.

Reconstituting immune cues in the liver

To bypass the structural and functional constraints of the involuted thymus, we reconstituted the identified signalling pathways ectopically in the liver. The unique haemodynamics and anatomical features of the liver enable priming and maintenance of adaptive immunity, and its protein-synthesis capacity is preserved even at advanced ages, making it a suitable site to modulate circulating T cells24,25,26.

We chose mRNA delivery over recombinant proteins because recombinant cytokines clear rapidly, necessitating frequent high-dose administrations that often result in significant toxicity6,7. By contrast, mRNA allows for more controlled, transient protein production, and recent advances have further optimized mRNA stability and functionality while minimizing immune-related side effects for in vivo applications6,27,28,29. In addition, canonical Notch ligands are transmembrane proteins and require cell–cell contact, precluding soluble delivery. We therefore packaged mRNAs encoding DLL1 (Dll1), FLT3-L (Flt3l) and IL-7 (Il7) (collectively, DFI), or firefly luciferase (Luc) or GFP controls, in an SM-102 LNP formulation (Fig. 1e, Extended Data Fig. 4a–e and Supplementary Fig. 1a–e). All mRNAs were m^1Ψ modified and 5′-m^7GpppNm capped. Primary hepatocytes expressed DLL1 on the surface and secreted biologically active IL-7 and FLT3-L into the supernatant after transfection (Extended Data Fig. 4a–c). Systemic LNP administration predominantly targeted the liver with minimal translation in other tissues (Fig. 1f,g, Extended Data Fig. 4d,e and Supplementary Fig. 1f,g). In situ profiling of ribosome-bound (rather than endosome-trapped) transcripts (RIBOmap) confirmed robust translation of all three DFI mRNAs in hepatocytes in vivo (Fig. 1f and Extended Data Fig. 4d,e), indicating hepatic expression and release with negligible off-target translation[30].

We next assessed pharmacokinetics and safety. Dll1 mRNA induced sustained DLL1 on the surface of hepatocytes for approximately 48 h (ref. [31]) (Fig. 1h and Extended Data Fig. 4f). mRNA-encoded FLT3-L achieved comparable serum half-life but higher single-dose systemic levels than recombinant FLT3-L (Fig. 1i). Intravenous recombinant IL-7 produced a sharp serum spike, whereas mRNA-encoded IL-7 yielded lower-amplitude, sustained levels with an approximately tenfold lower peak over 24 h (Fig. 1j). Because secreted IL-7 binds to heparan-sulfate proteoglycans, a substantial fraction of mRNA-delivered IL-7 remained in the hepatic extracellular matrix up to 24 h after serum levels normalized (Fig. 1j), potentially providing a compartmentalized source of IL-7 for cells traversing the sinusoids, where they also encounter hepatocyte-bound DLL1 (refs. 32,33).

Despite prolonged bioavailability of the three factors, we observed no changes in body weight, transaminases or liver function, and only minimal hepatic inflammation on histopathology after 4 weeks of DFI mRNA–LNPs in aged mice (Extended Data Fig. 4g–l), consistent with previous studies of SM-102 formulations6,34. By contrast, recombinant FLT3-L and IL-7 administered on the same schedule (two doses per week for 28 days) induced marked elevations of GM-CSF, IL-10, IL-12 and IL-1β, underscoring the greater risk of systemic inflammation with recombinant cytokines (Supplementary Tables 2 and 3 and Supplementary Fig. 1h,i).

DFI mitigates immune ageing phenotypes

Ageing is accompanied by a loss of naive T cells and accumulation of (virtual) memory and exhaustion-like states, most pronounced in CD8+ T cells and driven by thymic involution, chronic antigen exposure and inflammation1,17,18. We observed these shifts in our longitudinally profiled cohorts (Fig. 2a and Extended Data Figs. 1i and 2b,c). This bias towards the memory phenotype in ageing compromises responses to new antigens. We therefore asked whether DFI rebalances naive T cell representation.

Fig. 2: DFI treatment mitigates ageing-associated immune features.

a, Age-related decline in circulating naive T cells (Tcf7, Sell and Ccr7) and accumulation of exhausted-like T cells (Pdcd1, Ctla4, Lag3, Tigit, Entpd1 and Tnfrsf9) in mouse peripheral blood across age. Linear regression (red lines) shows Pearson’s R and significance versus age. b–d, Flow cytometry of spleens from adult (6 weeks) and aged (72 weeks) mice treated twice weekly with DFI or control (Luc) mRNA–LNPs for 28 days. Absolute numbers of naive T cells (CD44−CD62L+; b) and memory T cells (CD44+CD62L−/+; c), and naive-to-memory T cell ratio (d) are shown. n = 4 per group. Data are mean ± s.e.m.; one-way ANOVA with Dunnett’s post-hoc test. CM, central memory; EM, effector memory. e, TCR repertoire diversity from bulk V(D)J sequencing (n = 5 adult, 12 aged + Luc and 9 aged + DFI). Data are mean ± s.e.m. f, Rag2–eGFP tracing of thymocyte maturation (DN, double-positive (DP), single-positive (SP) CD4+ and SP CD8+) demonstrating increased thymopoiesis after DFI treatment. n = 3 mice per time point. Negative control refers to Rag2–eGFP−/−. Data are mean ± s.e.m. (error bands); repeated-measures two-way ANOVA with Dunnett’s post-hoc test was used. g, Quantification of TCR excision circles (TRECs) in peripheral blood indicating recent thymic emigrants (n = 12 adult, 12 aged + Luc and 9 aged + DFI); data are mean ± s.e.m.; one-way ANOVA with Tukey post-hoc test. h, Representative spatial STARmap projections of splenic dendritic cell (DC) subtypes from adult, Luc-treated and DFI-treated mice. i, Quantification of cDC1, cDC2 and other DC subsets per field of view (FOV; adult = 404, Luc = 386 and DFI = 408 FOVs from one animal per condition); data are represented as violin plots with median + interquartile range; one-way ANOVA with Tukey post-hoc test. j, Differential expression of activation markers (Cd40, Cd83, Cd86 and H2-K1) in the indicated DC subsets. FC, fold change. k, Spatial STARmap projections of splenic B cell subtypes. l, Relative abundance of mature (Cd19+, Ms4a1+, Cd22+ and Cd40+) and age-associated (Cd19+, Ms4a1+, Itgax+ and Tbx21+) B cells per FOV (adult = 404, Luc = 386 and DFI = 408 FOVs from one animal per condition); data are represented as violin plots with median + interquartile range; one-way ANOVA with Tukey post-hoc test.

Aged mice (72 weeks old) received DFI mRNA–LNPs or Luc mRNA–LNPs twice weekly for 28 days. DFI (but not individual factors) increased both frequency and absolute number of circulating naive (CD44−CD62L+) CD4+ and CD8+ T cells (Fig. 2b and Extended Data Fig. 5). Repeated administration of recombinant IL-7 has been shown to promote the proliferation of mature T cell subsets35,36,37. In line with this, Il7 mRNA alone expanded effector-memory (CD44+CD62L−) cells; however, full DFI did not increase memory subsets (Fig. 2c and Extended Data Fig. 5c–e). Consequently, the naive-to-memory ratio rose in the blood and spleen of DFI-treated animals, indicating complementary or moderating activity of the three signals when delivered in combination (Fig. 2d and Extended Data Fig. 5b).

To delineate the origin of these DFI-induced shifts in the T cell compartment of aged mice, we next analysed the spleen, peripheral blood, bone marrow and thymus of adult and aged mice. Deep bulk V(D)J sequencing showed no increase in clonality following DFI conditioning (Fig. 2e), arguing against homeostatic proliferation and favouring enhanced thymic output as a driver of the observed increase in naive T cell counts. Although aged thymuses displayed reduced mass and cellularity relative to young controls, 28 days of DFI partially restored both (Extended Data Fig. 6a–c). Although overall distributions of double-negative (DN), double-positive or single-positive thymocytes were preserved, DFI selectively expanded early DN1–DN3 thymocytes — the stages of early T lineage commitment — and induced Rag2 in thymocytes of Rag2–eGFP mice within 12 h, followed by increased mature single-positive CD4+ and CD8+ thymocytes towards youthful levels after 28 days of treatment (Fig. 2f and Extended Data Figs. 6d–f and 7a–c). Consistent with these data, TCR excision circles in peripheral blood were elevated (Fig. 2g), and Nur77 expression was elevated in circulating T cells (Extended Data Fig. 7d–f), indicative of an increase in recent thymic emigrants.

Because thymic output depends on progenitor supply, we examined haematopoiesis. With ageing, HSCs expand in number but increasingly adopt a myeloid-biased differentiation program12,38,39; we recapitulated HSC expansion and CD150high myeloid-biased HSC enrichment with age, which DFI did not reverse (Extended Data Fig. 8a–d). Multipotent progenitors were largely unchanged aside from an increase in lymphoid-primed multipotent progenitors after DFI (Extended Data Fig. 8b). By contrast, DFI robustly expanded common lymphoid progenitors (CLPs), which are markedly depleted with age, in bone marrow and increased the number of circulating CCR9+ (but not CCR7+) CLPs, which are associated with preferential thymus homing38,40 (Extended Data Figs. 8e–i and 9a–c). CCR9 surface levels on CLPs rose from the bone marrow to the thymus in DFI-treated mice, mirroring the dynamics found in young animals and absent in aged controls (Extended Data Fig. 9c–e), consistent with a peripheral priming of circulating CLPs that potentially augments thymic entry. Together, these data support a model in which liver-expressed DLL1, together with systemic FLT3-L and IL-7, boosts CLP production, survival and recruitment to the thymus, thereby increasing thymopoiesis. Thus, DFI enhances naive T cell output by amplifying committed lymphoid progenitors and facilitating intrathymic maturation, without reprogramming HSC composition.

Beyond T cells, DFI mitigated ageing-associated phenotypes in antigen-presenting and B cell compartments. DFI preferentially expanded splenic conventional type 1 dendritic cells (cDC1s), which are critical for antigen cross-presentation and decline in number and co-stimulatory function with age, probably by sustained FLT3-L-dependent differentiation from FLT3+ progenitors20,41,42 (Fig. 2h,i and Supplementary Fig. 2a–c). In situ-seq of spleens by STARmap revealed cDC1 enrichment within periarteriolar lymphoid sheaths and a concomitant upregulation of H2-K1 and co-stimulatory molecules (CD40, CD83 and CD86), consistent with improved priming capacity[43] (Fig. 2j and Supplementary Fig. 2a–c). In addition, we observed a reduced abundance of splenic age-associated B cells (CD19+, Ms4a1+, Itgax+ and Tbx21+) and an expansion of mature follicular-like B cells (CD19+, Ms4a1+, Cd22+ and CD40+) localized to B cell follicles following DFI treatment44,45 (Fig. 2k,l and Supplementary Fig. 2a–e).

DFI strengthens vaccine responses

On the basis of the above findings, we next tested whether DFI could restore adaptive immune function in aged animals. As a test case, we looked at vaccine response, a T cell-mediated process known to be blunted by ageing. In an adjuvanted ovalbumin (OVA) prime–boost model in adult (6 weeks) and aged (72 weeks) mice, aged cohorts showed fewer OVA-specific CD8+ T cells in the spleen and blood upon vaccination, impaired antigen-driven proliferation and reduced IL-2 and IFNγ production on recall, approaching levels of sham-vaccinated mice (Supplementary Fig. 3a–d). These deficiencies align with the impaired T cell-mediated vaccine responses commonly observed in older animals and humans1,46. Pre-conditioning aged mice with individual factors or the full DFI combination before vaccination showed that Il7 mRNA alone increased total T cell counts, whereas only DFI increased both total T cells and the frequency of OVA-specific CD8+ cells, yielding approximately twofold more antigen-specific CD8+ T cells in the spleen and a similar increase in blood35,36,37 (Fig. 3a–d and Supplementary Fig. 3e,f).

Fig. 3: Hepatic DFI reconstitution enhances vaccine-induced T cell responses in aged hosts.

a, Experimental design. Aged mice received DFI or control (Luc) mRNA–LNPs twice weekly for 28 days, then were immunized with adjuvanted OVA. b, Total splenic T cells (live CD45+CD3+) per spleen (n = 4 per group). Data are mean ± s.e.m.; one-way ANOVA with Tukey post-hoc test. c, Frequency of SIINFEKL–H-2K(b) tetramer+CD8+ T cells in the spleen (n = 4 per group). Data are mean ± s.e.m.; one-way ANOVA with Tukey post-hoc test. d, Absolute number of SIINFEKL–H-2K(b) tetramer+CD8+ T cells per spleen (n = 4 per group). Data are mean ± s.e.m.; one-way ANOVA with Tukey post-hoc test. e, Frequency of naive T cells (CD44−CD62L+) in the spleen (n = 4 per group). Data are mean ± s.e.m.; one-way ANOVA with Tukey post-hoc test. f, Frequency of exhausted-phenotype T cells (CD62L−PD1high) in the spleen (n = 4 per group). Data are mean ± s.e.m.; one-way ANOVA with Tukey’s post-hoc test. g, Proportions of naive (TN) and exhausted (TEX) T cells and the TN:TEX ratio after repetitive OVA or sham (vehicle) vaccination (n = 4 per treatment). h, Functional antigen recall: fold change in intracellular cytokines following 6-h SIINFEKL restimulation versus DMSO vehicle. IL-2 (left) and IFNγ (right) mean fluorescence intensity (n = 4 per group). Dotted lines indicate no change (fold change = 1.0); Data are mean ± s.e.m.; two-tailed unpaired t-tests. i, Age–response modelling. In a longitudinal cohort covering increasing ages, SIINFEKL–H-2K(b) tetramer frequencies were fit with a quadratic (second-order) polynomial regression with 95% confidence bands (d.f. = 29, R^2 = 0.9116). This standard curve was used to estimate an ‘immunological vaccination response age’ for numerically 52-week-old mice preconditioned with either DFI or Luc mRNA–LNPs before immunization (N = 40 total; n = 4 per age and treatment). The P value comparing the estimated vaccination response ages of the two treatment groups was calculated using a two-tailed unpaired t-test.

In addition to the reduced generation of new immune cells, ageing is characterized by the progressive shift to dysfunctional states by existing cells2,17. Aged mice were shown to accumulate PD1highCD62L− T cells, which we confirmed using scRNA-seq and flow cytometry in our longitudinal cohorts12,47 (Fig. 2a and Extended Data Fig. 1e,i). This phenotype expanded further with repeated vaccination, unlike in adults (Fig. 3f). DFI conditioning preserved a higher naive fraction post-vaccination and reduced PD1highCD62L− cells (with a reduced effect from IL-7 alone), together producing a more balanced T cell composition (Fig. 3e–g and Supplementary Fig. 3g). Functionally, CD8+ T cells from DFI-treated mice generated higher levels of IL-2 and IFNγ upon antigen-specific restimulation, whereas OVA-specific CD4+ frequencies and cytokines were largely unchanged (Fig. 3h and Supplementary Fig. 4a–e), suggesting that DFI preferentially supports CD8+ T cell responses.

In a longitudinal vaccination study across the lifespan of C57BL6/J mice, we found that DFI treatment increased vaccine-induced T cell counts in aged mice to levels comparable with those seen in much younger mice, effectively rejuvenating their response by approximately 24 weeks (Fig. 3i). Together, the full DFI combination, but not its single components, counteracts age-related defects in CD8+ vaccine responses, consistent with the broader principle that aged immunity retains the capacity to mount robust immune responses when sufficiently stimulated[48].

DFI rejuvenates antitumour responses

We next tested whether DFI improved responsiveness to tumour immunotherapy, which also diminishes with ageing. In B16-OVA melanoma and MC38-OVA colon carcinoma, we found that aged mice showed faster tumour progression and poorer survival than adults; immune checkpoint inhibition (ICI) with PDL1 blockade that controlled tumours in adults conferred little benefit in aged cohorts (Supplementary Fig. 5a–h), mirroring reports of age-dependent efficacy of PD1 or CTLA4 ICI48,49,50.

Pre-conditioning aged hosts with DFI for 28 days followed by a 72-h washout to control for direct antitumour effects improved endogenous control of MC38-OVA, increasing spontaneous rejection rates and prolonging survival (Supplementary Fig. 5e–h). In the more aggressive B16-OVA model, DFI drove complete rejection in 40% of aged mice, whereas all controls succumbed within 3 weeks despite anti-PDL1 treatment (Fig. 4a–d). Co-administration at treatment onset similarly delayed progression and improved survival over PDL1 blockade alone in aged animals with established tumours (Fig. 4e–h).

Fig. 4: Hepatic DFI reconstitution enhances antitumour T cell responses in aged hosts.

a, Experimental design. Aged mice received DFI or control (Luc) mRNA–LNPs for 28 days, followed by subcutaneous B16-OVA tumour challenge and two doses of anti-PDL1 checkpoint blockade. b, Tumour growth over time in the B16-OVA model (n = 8 per group). Three out of eight animals did not establish a measurable tumour (black arrow). c, Tumour size on day 12 (mean ± s.e.m.; two-tailed unpaired t-test). d, Kaplan–Meier survival analysis for the B16-OVA cohort (n = 8 per group; log-rank test). e, Design of adjuvant DFI or Luc LNP therapy combined with anti-PDL1 checkpoint blockade in established B16-OVA tumours. f, Tumour growth trajectories during combination therapy (n = 10 per group). g, Tumour size on day 14 (mean ± s.e.m.; two-tailed unpaired t-test). h, Kaplan–Meier survival curves for combination treatment (n = 10 per group; log-rank test). i, Schematic of a parallel adjuvant DFI + anti-PDL1 experiment terminated at day 12 for immune profiling with matched tumour sizes. j, Tumour size at day 12 (n = 5 per group). k, Absolute counts of live TILs in explanted tumours (n = 5 per group). l, CD8+ T cell frequency (left) and CD4:CD8 ratio (right) among TILs (n = 5 per group). m, Relative (left) and absolute (right) numbers of tumour-specific SIINFEKL–H-2K(b)+ TILs (n = 5 per group). For j–m, data are mean ± s.e.m.; statistical significance was determined by a two-tailed unpaired Student’s t-test. n, Uniform manifold approximation and projection (UMAP) of scRNA-seq of CD45+CD3+ TILs (n = 34,104 cells). TFH, follicular helper T; TH, helper T; Treg, regulatory T. o, CITE-seq feature plot showing CD8+SIINFEKL–H-2K(b)+ (Tet+) TILs. p, Expression of canonical markers of tumour recognition and exhaustion in Luc-treated versus DFI-treated TILs. q, Relative abundance of transcriptional clusters among CD8+Tet+ clonotypes. All comparisons were by two-tailed unpaired t-tests (o–q). r, Shannon diversity of bystander (Tet−) and tumour-specific (Tet+) TCR clonotypes across treatments.

In young mice, ICI confers antitumour immunity primarily through expansion and activation of tumour-specific CD8+ T cells. Ageing impairs this process, and elderly mice and humans show diminished CD8+ T cell infiltration and intratumoural effector function. To probe the underlying mechanism of the observed antitumour effects, we analysed tumour-infiltrating lymphocytes (TILs) on day 12 post-implantation, before significant tumour size divergence (Fig. 4i,j). Total TIL numbers were unchanged (Fig. 4k), but DFI increased the frequency of intratumoural CD8+ T cells and consequently lowered the CD4:CD8 ratio (Fig. 4l); SIINFEKL-loaded tetramers confirmed intratumoural enrichment of antigen-specific CD8+ cells (Fig. 4m and Supplementary Fig. 5i). In parallel, naive CD8+ cells expanded systemically (Supplementary Fig. 5j,k), suggesting that rejuvenated peripheral pools contribute to the TIL compartment.

scRNA–V(D)J profiling and CITE-seq using oligo-conjugated SIINFEKL–H-2Kb tetramers resolved eight canonical CD8+–CD4+ TIL states and delineated tumour antigen-specific T cells (Fig. 4n,o and Supplementary Fig. 5l–o). With DFI, both tumour-specific and bystander clonotypes showed lower expression of exhaustion-associated genes (Havcr2, Gzmk, Lag3, Pdcd1 and Tigit) and moderately higher Entpd1 (CD39) expression, consistent with increased tumour-antigen engagement or tissue residency rather than terminal dysfunction[51] (Fig. 4p). The naive-like fraction among tetramer+CD8+ TILs rose approximately 1.8-fold (Fig. 4q and Supplementary Fig. 5o), and clonal diversity (as assayed by Shannon index) increased in both tetramer+ and bystander repertoires (Fig. 4r and Supplementary Fig. 5p), indicating broader recruitment and intratumoural TCR repertoire breadth.

Together, DFI conditioning alone boosted endogenous antitumour control and synergized with PDL1 blockade to enhance therapeutic efficacy in aged hosts. Alongside recent work with mRNA-encoded cytokines[6], these data highlight liver-encoded delivery of immune modulators as a strategy to mitigate age-related immune dysfunction and overcome components of immunotherapy resistance.

Immunological safety of DFI treatment

To define durability and safety of DFI, aged mice received DFI or control mRNA–LNPs for 28 days and were then observed off-treatment for 28 days (Supplementary Fig. 6a). Thymic output rose during dosing, as evidenced by elevated TCR excision circles, and returned to baseline thereafter (Supplementary Fig. 6b,c), consistent with the short half-life of mRNA and the rapid decline of thymocyte Rag2 expression after a single dose (72 h or less; Fig. 2f and Extended Data Fig. 7c). STARmap of whole spleens showed that, despite waning thymic export, splenic T cell and dendritic cell numbers remained modestly elevated at day 28 post-cessation (Supplementary Fig. 6d–g), indicating partial persistence of peripheral remodelling. However, when vaccination occurred 4 weeks after stopping DFI, previously observed benefits such as higher vaccine-specific T cells, fewer exhaustion-like cells and favourable naive–effector-memory balance were no longer evident (Supplementary Fig. 6h–k). Thus, immune enhancement by our approach is largely confined to the dosing window and reverses upon withdrawal.

We next assessed whether DFI would exacerbate autoimmune disease. In NOD mice, which are prone to type 1 diabetes due to frequent generation of autoreactive TCRs, 4 weeks of DFI or control mRNA was followed by 6-month monitoring of glycaemia, glycosuria and disease onset (Fig. 5a). In addition, we quantified frequencies of CD8+ T cells specific to the NRP-V7 mimotope, a known target in NOD mice[52] (Extended Data Fig. 10a). DFI did not alter blood glucose levels, diabetes onset or autoreactive T cell frequencies versus control littermates (Fig. 5b,c), whereas NOD.Cg-Tg(TcraTcrbNY8.3)1Pesa/DvsJ (NY8.3) NOD mice with a genetically encoded NRP-V7-restricted TCR uniformly developed diabetes within 8 weeks, providing a positive-control threshold. We did not observe any treatment-related adverse effects over the course of this 6-month study.

Fig. 5: Immunological safety assessment of DFI.

a, Experimental design to evaluate autoimmune risk in NOD mice receiving DFI or control (Luc) mRNA–LNPs. NY8.3 mice, carrying an autoreactive TCR specific for NRP-V7 (KYNKANAFL), served as positive controls. Type 1 diabetes (T1D) onset was defined by blood glucose > 200 mg dl−1 in two consecutive measurements or by glucosuria. b, Longitudinal monitoring of blood glucose (top) and frequency of NRP-V7-specific TCRs in peripheral blood (bottom) of NOD mice treated as in panel a. NY8.3 (n = 5), NOD + Luc (n = 9) and NOD + DFI (n = 9). Data are mean ± s.e.m. c, Kaplan–Meier analysis of cumulative T1D incidence in NY8.3 (n = 5), NOD + Luc (n = 9) and NOD + DFI (n = 9) groups. Statistical significance was tested using log-rank (Mantel–Cox) tests. d, Experimental set-up to test antigen-specific T cell tolerance in Act-mOVA mice. OT-I (CD8+, OVA257–264) and OT-II (CD4+, OVA329–337) transgenic mice served as positive controls for OVA-targeted responses. e, Frequencies of SIINFEKL–H-2K(b) tetramer+CD8+ T cells in peripheral blood of Act-mOVA mice treated with Luc or DFI mRNA for 28 days. n = 5 per group. Data are mean ± s.e.m.; one-way ANOVA with Tukey’s post-hoc test; P values are shown for Act-mOVA comparisons. WT, wild type. f, Frequencies of AAHAEINEA–I-A(b) tetramer+CD4+ T cells in the peripheral blood of Act-mOVA mice treated as in panel e. n = 5 per group. Data are mean ± s.e.m.; one-way ANOVA with Tukey’s post-hoc test; two-tailed P values are indicated for Act-mOVA comparisons. g, Serum anti-OVA IgG ELISA (OD450–570) in Act-mOVA mice after Luc or DFI mRNA treatment followed by OVA–complete Freund’s adjuvant prime and OVA–incomplete Freund’s adjuvant boost (O) or sham vaccination (S). Wild-type (n = 5) vaccinated mice served as positive controls. Data are mean ± s.e.m.

To test safety in a model with intact central tolerance, we used Act-mOVA mice, which constitutively express germline-encoded OVA[53] (Fig. 5d). This allowed us to use the same potent model antigen as in our previous vaccination experiments; however, in this case, OVA was subject to central tolerance. Despite enhancing responses to exogenous OVA in aged wild-type mice, DFI neither induced OVA-specific CD4+ or CD8+ T cells after 4 weeks (Fig. 5e,f) nor broke humoral tolerance after adjuvanted OVA challenge in Act-mOVA mice, in contrast to wild-type controls (Fig. 5g).

Finally, in experimental autoimmune encephalitis, a polyclonal, antigen-driven central nervous system autoimmunity model, DFI was administered every 3 days following experimental autoimmune encephalitis induction by MOG35–55 peptide immunization and thereafter until symptom onset. Aged mice showed delayed disease onset, consistent with impaired T cell priming and previous reports, but worse subsequent clinical deterioration54,55 (Extended Data Fig. 10b–d). In line with our earlier vaccination experiments, DFI increased peripheral, but not central nervous system-infiltrating MHC class II I-Ab–MOG-specific CD4+ T cells after induction (Extended Data Fig. 10e,f) and did not worsen clinical scores or spinal cord inflammation and demyelination (Extended Data Fig. 10d,g,h). In adults, DFI neither increased MOG-reactive T cells in the periphery nor aggravated disease (Extended Data Fig. 10c,e–h), indicating correction of age-related deficits rather than indiscriminate immune amplification.

Together with the vaccination data, these results show that DFI transiently augments antigen-specific immunity in aged hosts while preserving self-tolerance across several models. The effects are reversible and temporally restricted, supporting liver-encoded systemic immune modulation as a novel strategy to improve immunity in older individuals.

Discussion

Here we have showed that immune function can be improved by repurposing the liver as a platform for ectopic production of immune factors. Our results show that delivery to the liver of mRNA encoding DLL1, IL-7 and FLT3-L in combination successfully enhanced immune function in aged mice. This approach provides a scalable alternative to more invasive methods of immune rejuvenation, such as thymic transplantation[56] or HSC manipulation[12], which are clinically challenging. In addition, our disease-agnostic strategy holds potential for synergizing with other immunostimulatory treatments, as we observed in the context of ICI treatment.

Although the biological roles of IL-7 and FLT3-L are well established, with both factors known to support thymopoiesis, dendritic cell expansion and peripheral T cell homeostasis20,21,35,36,37,41,42, combining them with DLL1 and delivering them to hepatocytes enables the creation of a transient, rejuvenated immune milieu in aged mice, distinct from conventional approaches based on recombinant protein infusion, which lack spatial control and tend to require chronic dosing or come with high toxicity. By using mRNA, we mitigated the systemic inflammation and autoimmune sequelae typically associated with recombinant cytokine-based therapies. However, the transient nature of mRNA delivery necessitates repeated administrations to sustain therapeutic effects. The long-term consequences of continuous exposure to these factors, especially in aged individuals should be analysed through extensive long-term safety studies.

Ageing affects the immune system beyond T cell biology, encompassing epigenetic[57] and metabolic[58] remodelling in lymphocytes, dysfunction in myeloid subsets[59] and structural alterations in stromal networks[60]. Future studies may therefore uncover additional strategies and factors to holistically target the hallmarks of immune ageing[1].

In summary, this study demonstrates that transiently repurposing the liver to express and secrete various therapeutic proteins could be a generalizable approach to engineering physiological processes. By mimicking specific signalling niches within the liver or secreting proteins for systemic circulation to restore homeostatic signals throughout the body, this strategy has the potential to improve health outcomes and address a wide range of human diseases and conditions.

Methods

Ethical statement

All experiments were performed in compliance with all relevant ethical regulations as approved by the Institutional Biosafety Committee (IBC) of the Broad Institute (protocol #IBC-2017-00146). All animal experiments were approved by the Institutional Animal Care and Use Committee of the Broad Institute (protocol ID 0017-09-14-2). Animal maintenance complied with all relevant ethical regulations and were consistent with local, state and federal regulations as applicable, including the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals.

Plasmid construction

G-blocks encoding the human α-globin 5′ untranslated region (UTR) and 3′ FI element were synthesized de novo by IDT. In vitro transcription vectors were cloned by inserting UTRs into a pET45 vector via Gibson assembly using Gibson Assembly Master Mix (E2611L, NEB) and transformation into chemically competent Stbl3 cells. A hard-coded A30LA70 polyA tail was added by PCR and ligation using the KLD enzyme mix (New England Biolabs). Subsequent coding sequences were inserted by digestion with NcoI and XhoI and Gibson assembly. Plasmid sequences were verified via next-generation sequencing, long-read sequencing (Primordium Labs) and PCR to verify the length of polyA tails.

In vitro transcription and LiCl purification of mRNA

Plasmids were linearized, and a T7-driven in vitro transcription reaction (Life Technologies) was performed to generate mRNA with 101 nucleotide long polyA tails. The 5′ UTR and the 3′ FI elements contained sequences from the human α-globin gene. Capping of mRNA was performed in concert with transcription through addition of a trinucleotide cap1 analogue CleanCap, and m1Ψ-5′-triphosphate (TriLink) was incorporated into the reaction instead of uridine-5′-triphosphate (UTP; Supplementary Fig. 1a). LiCl-based purification of mRNA was performed, mRNAs were then checked on an agarose gel and by a TapeStation RNA ScreenTape Analysis (Agilent; Supplementary Fig. 1b,c) before aliquoting at 1 µg µl−1 and storing at −80 °C.

DFI LNP production

We engineered a formulation of mRNA-encoded DFI within biodegradable lipopolyplexes, along with control formulations (Fig. 1e). To do this, we formulated LNPs by combining SM-102 as ionizable lipid, 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC), cholesterol and 1,2-dimyristoyl-rac-glycero-3-methoxypolyethylene glycol-2000 (DMG-PEG-2000) in a molar ratio of 50:10:38.5:1.5. These were formulated into LNPs along with mRNA using microfluidic mixing using a NanoAssemblr Ignite nanoparticle formulation system (Cytiva). In brief, an ethanol phase containing the above formulated lipidoid, phospholipid, cholesterol and DMG-PEG master mix was mixed with an aqueous phase (10 mM citrate buffer, pH 3) containing mRNA at a flow rate ratio of 1:3 and at a lipidoid:RNA weight ratio of 10:1. Upon formulation, mRNA–LNPs were diluted in sterile NaCl and the buffer was exchanged by concentrating with a 30-kDa spin filter (UFC9030, MilliporeSigma) to replace residual ethanol. NaCl-diluted mRNA–LNPs were stored at 4 °C until use. For all subsequent experiments, we utilized these SM-102 mRNA–LNPs encapsulating m1Ψ-5′-triphosphate-modified and m7GpppNm-capped DFI, Luc or GFP mRNA, respectively.

LNP characterization

The hydrodynamic size, polydispersity index (PDI) and zeta potential (ZP) of LNPs were measured using a DynaPro NanoStar II (Wyatt). The mRNA encapsulation efficiency of LNPs were determined using a modified Quant-iT RiboGreen RNA assay (Invitrogen) and found to be more than 85% on average (Supplementary Fig. 1d). LNP endotoxin levels were consistently found to be less than 1 endotoxin unit per ml. The average hydrodynamic diameter was approximately 75 nm with a polydispersity index of 0.02–0.03 (Supplementary Fig. 1e).

Cell culture and transfection

Unless otherwise stated, mammalian cells were maintained in T75 flasks (156499, Thermo Fisher) at 37 °C with 5% CO_2 in either DMEM-GlutaMAX (10569044, Thermo Fisher) or RPMI-GlutaMAX (61870127, Thermo Fisher). All media were supplemented with 10% FBS (97068-085, VWR) and 1× penicillin–streptomycin (15140122, Thermo Fisher). For growth of primary T cells, media were also supplemented with 50 µM 2-mercaptoethanol (21985023, Thermo Fisher). Images of transfected cells were acquired on a Leica DMI8 Confocal Microscope running Leica Application Suite X (1.4.3), equipped with a Lecia Stellaris 5 camera using an HC PL APO CS2 ×20/0.75 DRY objective and a pinhole setting of 1 Airy Unit. Images were processed using Fiji (https://imagej.net/software/fiji/downloads).

Flow cytometry

Cells were prepared and stained according to the staining protocol of each experiment outlined below, pelleted at 500g for 5 min and resuspended in 200 µl of flow cytometry buffer (PBS supplemented with 2% EDTA (15575020, Life Technologies) and 5% FBS (97068-085, VWR)). Samples were run on Beckman Coulter Cytoflex LX flow cytometers and analysis was performed using the FlowJo v10 software. Representative schemes for gating and threshold setting of each experiment are shown in the Extended Data and Supplementary figures.

Antigen-specific tetramer staining

All peptide–MHC tetramers were obtained from the NIH Tetramer Core Facility. Before viability dye and surface antibody staining, cells were incubated with appropriately titrated tetramer combinations. Unless stated otherwise, tetramer staining was performed in PBS for 20 min on ice, followed by washing and addition of antibody cocktails and/or fixation.

For OVA vaccination experiments, SIINFEKL–H-2Kb–PE and SIINFEKL–H-2Kb–APC tetramers were used at 1:100 dilution and AAHAEINEA–I-Ab–PE and AAHAEINEA–I-Ab–APC tetramers were used at 1:20 dilution. For central tolerance experiments in Act-mOVA mice, the same SIINFEKL–H-2Kb tetramers (1:100 dilution) were used in combination with AAHAEINEA–I-Ab–PE and AAHAEINEA–I-Ab–APC tetramers (1:20 dilution). Non-vaccinated wild-type T cells (negative) and OT-I and OT-II T cells (positive) were included as staining controls for MHC class I and class II tetramers, respectively.

For autoimmunity experiments in NOD mice, KYNKANAFL–H-2Kb–PE and KYNKANAFL–H-2Kb–APC tetramers were used at 1:50 dilution, with NY8.3 T cells serving as positive controls for staining.

For experimental autoimmune encephalitis (EAE) experiments in C57BL/6J mice, GWYRSPFSRVVH–I-Ab–PE and GWYRSPFSRVVH–I-Ab–APC tetramers were used at 1:25 dilution. Control tetramers consisted of I-Ab-restricted human CLIP87–101 (PVSKMRMATPLLMQA) conjugated to PE and APC, also at 1:25 dilution.

Animal experiments

All animal experiments were approved by the Institutional Animal Care and Use Committee of the Broad Institute (protocol ID 0017-09-14-2). Animal maintenance complied with all relevant ethical regulations and were consistent with local, state and federal regulations as applicable, including the NIH Guide for the Care and Use of Laboratory Animals. Animals were kept on a 12-h light–dark cycle between 68 °F and 79 °F and 30–70% humidity. Mice were acclimated at the animal facility for at least 7 days before performing any experiments. The sample size for vaccination experiments was decided based on a previous publication with similar experiments[48]. The sample size for tumour experiments was decided based on previous publications with similar experiments49,50. The sample size for NOD experiments was decided based on a previous publication with similar experiments[52]. The sample size for EAE experiments was decided based on a previous publication with similar experiments[61]. For all other exploratory experiments, no sample size calculations were performed. For all experiments, allocation of mice into experimental groups was randomized after stratifying for age and sex. Separate investigators performed treatment and data collection. Data-collecting investigators, for example, for tumour size measurements, were blinded to the treatment groups. Data-analysing investigators were not blinded to the treatment groups, as they involved internal controls, with the exception of pathologists for toxicity studies, who were blinded for the analyses. Experimental and control animals were treated equally and, when possible, housed in mixed cages.

In vivo delivery of mRNA

mRNA–LNPs (5 µg of each mRNA, equal molar, normalized to the control RNA) in a total volume of 100 μl sterile NaCl were injected through slow retro-orbital injection into each mouse. For Luc imaging, sterile NaCl injection was used as negative control. For all other experiments investigating DFI mRNA–LNPs, Luc mRNA–LNPs were used as negative control.

In vivo Luc imaging

In vivo activity of Luc following delivery of Luc mRNA–LNPs was measured using a Competent IVIS-Perkin Elmer IVIS Spectrum CT System (Perkin Elmer). Mice were injected through retro-orbital injection with Luc mRNA–LNPs at a dose of 5 μg mRNA per mouse, and bioluminescence imaging was performed on an IVIS imaging system (PerkinElmer). At 6 h post-injection, mice were anaesthetized with isoflurane and intraperitoneally injected with D-luciferin potassium salt (150 mg kg−1 (body weight)). Mice or dissected organs were imaged 10 min post-injection using auto-exposure settings. The luminescent activity was quantified using Aura 4.0 imaging software.

Quantification of in vivo DLL1 levels in the liver

To assess DLL1 protein induction following DFI, mice were intravenously injected with either 5 µg DFI mRNA–LNPs in 100 µl NaCl. The mice were then anaesthetized with isoflurane and rapidly decapitated. Liver tissue samples were harvested from the mediolateral lobe and placed in Tissue-Tek O.C.T. Compound. Then, the liver tissue in O.C.T. was frozen in liquid nitrogen and stored at −80 °C. Liver DLL1 levels were measured using immunofluorescence. In brief, liver tissue was cryo-sectioned at 15 µm thickness, fixed with 4% paraformaldehyde for 15 min and permeabilized with 0.5% Triton X-100 and 100 mmol l−1 glycine diluted in PBS for 10 min. Samples were blocked with blocking buffer (10% normal donkey serum (017-000-121, Jackson ImmunoResearch) in PBS–0.1% Tween-20) and stained with anti-DLL1 antibody (ab10554, Abcam; 1:800 dilution in blocking buffer) at 4 °C overnight. Samples were then washed with PBS–0.1% Triton X-100 for 3 × 10 min, stained with AF546-labelled secondary antibody (A10040, Invitrogen; 1:500 dilution in blocking buffer) at room temperature for 1 h and washed with PBS–0.1% Triton X-100 for 3 × 10 min. Samples were then stained with DAPI and AF488-phalloidin (A12379, Invitrogen) according to the manufacturer’s protocol to visualize the plasma membrane of hepatocytes. Liver DLL1 levels were quantified by taking the immunofluorescence signal intensity normalized to DAPI intensity at 6, 12, 24 and 48 h after injection (n = 3 mice per condition and time point). Uninjected littermates served as baseline controls (0 h). Tissue concentrations were expressed as fold change relative to baseline.

Quantification of in vivo IL-7 and FLT3-L levels

To assess cytokine induction following DFI or recombinant protein administration, mice were intravenously injected with either 5 µg DFI mRNA–LNPs in 100 µl NaCl or 10 µg recombinant mouse IL-7 (217-17, PeproTech) or 10 µg recombinant mouse FLT3-L (250-31L, PeproTech) in NaCl containing 0.1% BSA. Blood samples were collected by terminal cardiac puncture using EDTA-coated syringes and transferred to BD Microtainer tubes (365974, BD). Plasma was separated by centrifugation at 2,000g for 10 min at room temperature and snap frozen for later analysis.

Liver tissue samples were harvested from the mediolateral lobe, weighed for normalization and snap frozen. Frozen tissue fragments were homogenized on ice in 200 µl PBS containing protease inhibitor cocktail (P8340-1ML, Sigma) using a pre-chilled glass douncer with 15 strokes. Homogenates were clarified by centrifugation (10,000 rpm for 10 min at 4 °C), and supernatants were stored at −80 °C. IL-7 and FLT3-L levels in both the serum and liver were quantified using ELISA kits (mouse IL-7, EMIL7, Invitrogen; mouse FLT3-L, EMFLT3L, Invitrogen) according to the manufacturer’s protocol. Absorbance was measured at 450 nm with 570-nm background subtraction, and cytokine concentrations were calculated using logistic regression fitted to the standard curve. Samples were processed in technical triplicates, and the mean value was used for biological replicates.

Serum and liver cytokine levels were measured at 6, 12, 24 and 48 h after injection (n = 3 mice per condition and time point). Uninjected littermates served as baseline controls (0 h). Tissue concentrations were normalized to organ weight and expressed as fold change relative to baseline.

To assess age-dependent changes in thymic cytokine levels, thymus lobes were isolated from untreated C57BL/6J mice across a range of ages, including 3, 6, 12, 18 and 24 months (n = 3 per age group). Thymic tissue was weighed, snap frozen and processed identically to liver samples. FLT3-L concentrations were determined by ELISA, normalized to thymus weight and used to calculate total cytokine abundance across the lifespan.

Subcutaneous immunization

Adult (6 weeks) and aged (72 weeks) mice were immunized with 1 mg ml−1 full-length OVA protein emulsified in complete Freund’s adjuvant (EK-0301, Hooke Laboratories), followed by one booster dose of 1 mg ml−1 protein emulsified in incomplete Freund’s adjuvant (IFA; EK-0311, Hooke Laboratories). Mice were injected with antigen emulsified in complete Freund’s adjuvant (CFA) subcutaneously at two sites on the chest, injecting 0.1 ml at each site (total of 0.2 per mouse). The needle was kept inserted in the subcutaneous space for 10–15 s after each injection to avoid leakage of the emulsion. A booster injection of antigen emulsified in IFA was administered 14 days after immunization with antigen–CFA emulsion. The booster was given as a single subcutaneous injection with 0.1 ml of IFA emulsion, at one site on the sternum. Serum, peripheral blood or spleen samples were obtained 21 days after the initial immunization, unless otherwise specified.

Subcutaneous tumour implantation and treatment with ICIs

OVA-expressing melanoma B16 (B16-OVA) and MC38 (MC38-OVA) cell lines were provided by M. Kilian. Mice were inoculated subcutaneously with 1 × 10^5 B16-OVA or 5 × 10^5 MC38-OVA in Matrigel Matrix (Corning). Tumour growth was monitored daily by measuring with digital calipers using the two largest perpendicular axes until the area (0.5 × larger diameter × smaller diameter^2). The size of the tumours was assessed in a blinded, coded manner every day following treatment start and recorded as tumour volume. Mice were euthanized when tumours reached 2,000 mm^3 or upon ulceration. Of anti-PDL1 antibody (10 F.9G2, BioXCell), and/or control hamster and/or control rat IgG antibody (BioXCell), 100 μg was injected intraperitoneally every 3 days, as previously described, unless otherwise specified[49].

Isolation and staining of blood-circulating T cells

For all vaccination experiments, at the indicated time points post-vaccination, approximately 100 μl of blood was collected with EDTA-coated capillary tubes from each mouse and then transferred to an EDTA-coated tube. The collected blood samples were centrifuged at 2,000g for 10 min, followed by transferring the resulting plasma into another tube, and antibody staining was performed using the eBioscience one-step Fix/Lyse Solution (10X; 00-5333-54, Thermo Fisher). In brief, 50 or 100 µl of blood samples was incubated with 50 or 100 µl twofold concentrated antibody cocktails (1:100 final dilution) as well as TruStain FCX (anti-mouse CD16/32, clone 93; BioLegend; 1:50 final dilution) for 20 min at 4 °C in the dark, followed by the addition of 4 ml of one-step Fix/Lyse Solution and 15-min incubation at room temperature in the dark. Samples were then washed twice with 10 ml of PBS followed by centrifugation at 500g for 5 min and finally resuspended in 200 µl flow cytometry buffer. In the case of tetramer staining, samples were pre-incubated with the respective twofold concentrated tetramers (1:20 to 1:100 final dilution) for 20 min on ice in the dark, followed by incubation with twofold concentrated antibody cocktails (1:100 final dilution) as well as TruStain FCX (anti-mouse CD16/32, clone 93; BioLegend; 1:50 final dilution) for another 20 min at 4 °C in the dark.

Quantification of cellular vaccination responses

On day 21 following the initial subcutaneous immunization with OVA–CFA, mouse spleen single-cell suspensions were prepared in RPMI 1640 medium by mashing tissue against the surface of a 70-μm cell strainer (64752-00, BD Falcon). Then, the single-cell suspension was centrifuged at 500g for 5 min and the supernatant was removed. Red blood cells were lysed by adding 1 ml of ACK lysis buffer (Thermo Fisher) at 4 °C for 1.5 min, followed by centrifugation and removal of the supernatant. The cells were washed once with RPMI 1640 medium and then resuspended with RPMI 1640 medium (10% FBS and 1% penicillin-streptomycin antibiotic). Of splenocytes from each mouse, 4 × 10^6 were cultured in RPMI medium and stimulated with SIINFEKL peptide (synthesized at 99% purity by GenScript) at a final concentration of 1 μg ml−1 for each peptide for CD8+ T cell recall or 1× Cell Stimulation Cocktail containing phorbol 12-myristate 13-acetate and ionomycin (00-4970-03, eBioscience) for CD4+ T cell recall. The GolgiStop transport inhibitor cocktail (554724, BD) was added according to the manufacturer’s instruction 2 h later. Then, 6 h later, the cells were collected and washed with flow cytometry buffer (PBS with 2% FBS) before Fc block with TruStain FCX (anti-mouse CD16/32, clone 93; BioLegend; 1:50 final dilution) and surface antibody staining for 20 min at 4 °C. Cells were washed with a flow cytometry buffer and then fixed and permeabilized using a BD Cytoperm fixation/permeabilization solution kit (554714, BD) according to the manufacturer’s instructions. Cells were washed in perm/wash solution, followed by intracellular staining (for 45 min at 4 °C) using a cocktail of the respective cytokine or transcription factor antibodies. Finally, the cells were washed in perm/wash solution and suspended in a staining buffer. Samples were washed, resuspended in 200 µl flow cytometry buffer and acquired on a Beckman CytoFLEX LX Flow Cytometer. Analysis was performed using FlowJo v10 software.

TCRβ repertoire sequencing

Snap-frozen spleens were submitted to Adaptive Biotechnologies for deep TCRβ repertoire profiling. Genomic DNA was extracted using the DNeasy Blood & Tissue Kit (69504, Qiagen), followed by deep sequencing of rearranged Tcrb gene segments using the immunoSEQ mmTCRB Deep Sequencing platform (Adaptive Biotechnologies). Library preparation, high-throughput sequencing and initial data processing, including demultiplexing, quality filtering and V(D)J gene annotation, were performed by Adaptive Biotechnologies using their proprietary pipeline. Output files included productive clonotype frequencies and diversity metrics used for downstream analysis.

Quantification of TRECs

To quantify thymic output, signal joint T cell receptor excision circles (sjTRECs) were measured from peripheral blood. A total of 100 µl of cardiac blood was collected from mice following terminal anaesthesia and centrifuged to pellet cellular material. Genomic DNA was extracted using the DNeasy Blood & Tissue Kit (69504, Qiagen) according to the manufacturer’s instructions.

Analysis was performed using an Bio-Rad CFX Opus Real-Time PCR system, following previously published protocols for sjTREC detection in C57BL/6 mice62,63. The sjTREC-specific primers used were: forward, 5′-CCAAGCTGACGGCAGGTTT-3′; reverse, 5′-AGCATGGCAAGCAGCACC-3′. To control for input DNA variability, amplification of the constant region of the Tcra gene was used as an endogenous reference. The Tcra primer sequences were: forward, 5′-TGACTCCCAAATCAATGTG-3′; reverse, 5′-GCAGGTGAAGCTTGTCTG-3′. Cycle threshold (Ct) values were determined in technical duplicates for both targets. Relative sjTREC content was calculated as ΔCt = Ct_TREC − Ct_TCRA. For clarity, values are reported as −ΔCt, such that higher values reflect greater relative sjTREC abundance.

Spontaneous type 1 diabetes model and monitoring

NOD/ShiLtJ (NOD) mice were treated with DFI mRNA or Luc mRNA encapsulated in SM-102 LNPs twice per week for 4 weeks followed by clinical and molecular monitoring for the development of T1D. NY8.3 mice transgenic for the autoreactive TCR recognizing NRP-V7 were included as a positive control group. Experimental end point (onset of T1D) was reached in animals with blood glucose levels of more than 200 mg dl−1 in two independent measurements or in animals developing glucosuria52,64.

Experimental autoimmune encephalitis

EAE was induced as previously described[61]. All animals were of a C57BL/6J background. Mice were immunized subcutaneously with 100 μg of MOG35–55 peptide (110582, Genemed Synthesis) emulsified in CFA, which was freshly prepared by combining 20 ml of IFA (BD263910, BD Biosciences) with 100 mg of Mycobacterium tuberculosis H37Ra (231141, BD Biosciences) at a 1:1 ratio (v/v; 5 mg ml−1 final concentration). Each mouse received two subcutaneous injections of 100 μl of the MOG–CFA emulsion.

Pertussis toxin (180, List Biological Laboratories) was administered intraperitoneally at 320 ng per mouse (1.6 ng μl−1 in 200 μl PBS) on the day of immunization and again 48 h later. Mice were monitored twice daily and scored for EAE symptoms using the following clinical scoring system: 0 for no signs; 1 for limp tail; 2 for hindlimb weakness; 3 for hindlimb paralysis; 4 for forelimb paralysis; and 5 for moribund.

To assess the effects of DFI on disease progression, mice were randomly assigned to receive DFI or Luc control mRNA–LNPs after immunization until symptom onset. mRNA–LNPs were administered every 3 days starting on the day of immunization and continued until the onset of clinical symptoms in the first animals (day +15). To avoid repeated anaesthesia, intraperitoneal injection was used instead of retro-orbital delivery of mRNA–LNPs. All clinical scoring was performed in a blinded manner.

Quantification of MOG-specific CNS-infiltrating T cells

To isolate central nervous system (CNS)-infiltrating lymphocytes, mice were terminally anaesthetized and perfused transcardially with ice-cold PBS. Spleens were collected and weighed, and brains and spinal cords were harvested. Spinal cords were flushed by hydrostatic pressure. CNS tissues were minced and enzymatically digested in RPMI supplemented with Liberase (5401119001, Roche) for 30 min at 37 °C with gentle agitation. Following digestion, tissues were dissociated by 20 trituration cycles using a 10-ml serological pipette and filtered through a 70-μm cell strainer.

Mononuclear cells were enriched by 30% isotonic Percoll density gradient centrifugation at 800g for 30 min at room temperature without brake. The cell pellet was washed in PBS and resuspended in 200 μl PBS for downstream staining. For tetramer staining, 100 μl of the cell suspension was incubated for 1 h at room temperature with PE-conjugated or APC-conjugated I-Ab tetramers at a 1:25 dilution. The following tetramers were used: MOG38–49–I-Ab–PE and MOG38–49–I-Ab (GWYRSPFSRVVH; PE and APC; NIH Tetramer Core Facility) and control human CLIP87–101–I-Ab (PVSKMRMATPLLMQA, PE and APC; NIH Tetramer Core Facility). After tetramer incubation, cells were washed and stained for extracellular markers using standard flow cytometry antibodies. Following final washes, cells were resuspended in 200 μl flow cytometry buffer (PBS + 2% FBS + 2 mM EDTA) and immediately analysed.

Histopathological assessment of CNS tissue from EAE mice

Mice were perfused intracardially with ice-cold 1× PBS followed by ice-cold 4% paraformaldehyde (PFA). Spinal columns were harvested and post-fixed overnight in 4% PFA at 4 °C. For histopathological analysis of both meningeal and parenchymal compartments, spinal columns were subsequently transferred to 0.5 M EDTA (pH 7.4; S28291GAL, Thermo Fisher Scientific) and decalcified at 4 °C with continuous inversion for 7 days.

Following decalcification, tissues were processed in toto, paraffin-embedded, sectioned and mounted on glass slides. Serial sections were stained with haematoxylin and eosin and Luxol Fast Blue to assess inflammation and demyelination, respectively. All histological analyses were performed by a board-certified anatomical pathologist (IDEXX BioAnalytics).

Microscopic lesions were evaluated using established semiquantitative grading systems. Inflammatory changes were scored on a four-point scale as previously described65,66, ranging from no detectable inflammatory cells (score 0), to scattered infiltrates (score 1), perivascular clustering (score 2), and extensive perivascular cuffing with parenchymal extension or diffuse infiltration (score 3). Demyelination was graded based on Luxol Fast Blue staining from minimal subpial demyelination (score 1), through marked subpial and perivascular involvement (score 2), confluent subpial or perivascular demyelination (score 3), to extensive demyelination affecting one-half (score 4) or the entirety (score 5) of the spinal cord section, often accompanied by parenchymal immune infiltration.

All samples were well preserved with minimal or no autolytic or decalcification artefacts. Numerical scores were used to quantify total lesion burden and to compare the severity and prevalence of pathological changes across treatment groups.

STARmap protocol

STARmap padlock and primer probes were designed as previously described[67]. For the 64-gene STARmap data collection, 2–6 pairs of primer and padlock probes (Supplementary Table 4) were designed for each gene.

The mice used in this study were anaesthetized with isoflurane and rapidly decapitated. The mouse spleen tissue was collected and placed in Tissue-Tek O.C.T. Compound. Two biological replicates were collected for each condition (Luc, DFI and wild type). For the post-DFI-treatment analysis, three biological replicates were collected for each condition (NaCl, Luc, DFI and 4-weeks post-DFI). Then, the spleen tissue in O.C.T. was frozen in liquid nitrogen and stored at −80 °C. For mouse spleen tissue sectioning, the spleen tissue was transferred to cryostat (CM1950, Leica) and cut into 20-μm cross -ections at −20 °C. The slices were transferred and attached to glass-bottom 24-well plates pretreated with 3-(trimethoxysilyl)propyl methacrylate and poly-D-lysine.

The STARmap procedure was conducted as previously described[67]. In brief, the spleen slices were fixed with 400 μl 4% PFA in 1× PBS at room temperature for 15 min, then permeabilized with 600 μl pre-chilled methanol at −20 °C for 1 h. The samples were then taken from −20 °C freezer to room temperature for 5 min, then quenched with 400 μl quenching solution (0.1 mg ml−1 yeast tRNA, 0.1 U μl−1 SUPERase·In RNase inhibitor, 100 mM glycine and 0.1% Tween-20 in PBS) at room temperature for 10 min. After quenching, the samples were rinsed with 600 μl PBSTR (1× PBS supplemented with 0.1% Tween-20 and 0.02 U μl−1 SUPERase·In RNase Inhibitor) twice. Then, the samples were incubated with 200 μl of 1× hybridization buffer (2× SSC, 10% formamide, 20 mM ribonucleoside vanadyl complex, 0.1 mg ml−1 yeast tRNA, 0.1 U μl−1 SUPERase·In, 1% Triton X-100, pooled padlock and primer probes at a concentration of 10 nM per oligo) at 40 °C in a humidified oven with parafilm wrapping and shaking for 20 h. The samples were washed with 300 μl PBSTR twice and 300 μl high-salt washing buffer (4× SSC dissolved in PBSTR) once, for 20 min at 37 °C for each wash. Finally, the samples were rinsed once with 300 μl PBSTR at room temperature.

The samples were then incubated with a 200 μl ligation mixture (0.1 Weiss U μl−1 T4 DNA ligase, 0.5 mg ml−1 BSA and 0.2 U μl−1 of SUPERase·In RNase inhibitor in 1× T4 DNA ligase buffer) at room temperature for 3 h with gentle shaking. After the ligation reaction, the samples were washed twice with 300 μl PBSTR and then incubated with 200 μl rolling circle amplification mixture (0.2 Weiss U μl−1 Phi29 DNA polymerase, 250 μM dNTP, 20 μM 5-(3-aminoallyl)-dUTP, 0.5 mg ml−1 BSA and 0.2 U μl−1 of SUPERase·In RNase inhibitor in 1× Phi29 buffer) at 4 °C for 30 min then 30 °C for 3 h with gentle shaking. The samples were washed twice with 0.1% Tween-20 in PBS (PBST) before being temporarily stored at 4 °C overnight.

The next day, the samples were treated with 300 μl freshly prepared modification mixture (20 mM methacrylic acid NHS ester in 100 mM sodium bicarbonate buffer) at room temperature for 1 h and then washed once by PBST for 5 min. The samples were rinsed once and then incubated with 200 μl monomer buffer (4% acrylamide, 0.2% bis-acrylamide and 0.2% tetramethylethylenediamine in 2× SSC) at 4 °C for 15 min. Then, the buffer was aspirated, and 50 μl polymerization mixture (0.2% ammonium persulfate dissolved in pre-cooled monomer buffer) was added to the centre of the sample and immediately covered by Gel Slick-coated glass coverslip (72226-01, Electron Microscopy Sciences). The polymerization reaction was performed for 1 h at room temperature in an N2 box, then washed by PBST twice for 5 min each. The samples embedded in hydrogel were digested with 300 μl proteinase K mixture (0.5 mg ml−1 proteinase K and 1% SDS in 2× SSC) at 37 °C for 3 h, then washed by PBST three times for 5 min each. Subsequently, the samples were treated with 200 μl dephosphorylation mixture (0.25 U μl−1 Antarctic phosphatase, 0.5 mg ml−1 BSA in 1× Antarctic phosphatase buffer) at 37 °C overnight and washed by PBST three times for 5 min each.

For SEDAL sequencing, each sequencing cycle began with treating the sample with 800 μl stripping buffer (60% formamide and 0.1% Triton X-100 in H_2O) at room temperature twice for 10 min each, followed by washing with 1 ml PBST three times for 5 min each. Then, the samples were incubated with a 250 μl sequencing mixture (0.1875 U μl−1 T4 DNA ligase, 0.5 mg ml−1 BSA, 10 μM reading probe and 5 μM decoding probes in 1× T4 DNA ligase buffer) at room temperature for at least 5 h. The samples were washed with 900 μl washing and imaging buffer (10% formamide in 2× SSC buffer) three times for 10 min each, then immersed in washing and imaging buffer for imaging. Images were acquired using Leica TCS SP8 confocal microscopy with ×40 oil immersion objective (NA 1.3) and a voxel size of 194 nm × 194 nm × 350 nm (x × y × z). DAPI staining was performed before the first cycle using 5× DAPI in PBST for 3 h at room temperature. The DAPI signal was collected at the first cycle of imaging. Four cycles of imaging were performed to decode 64 genes.

After SEDAL sequencing, the samples were treated with 800 μl of stripping buffer three times at room temperature for 10 min each. The samples were then washed with 1 ml PBST three times for 5 min each. To visualize the plasma membrane and aid cell segmentation, the samples were treated with 300 μl Flamingo staining mixture (1× Flamingo fluorescent gel stain and 5× DAPI in PBS) at room temperature overnight, then washed with 300 μl PBST three times for 5 min each. The Flamingo signal was collected and the DAPI signal was reimaged while the samples were immersed in PBST with 0.1× Flamingo fluorescent gel stain.

STARmap data analysis

STARmap data analysis was performed as previously described43,67. In brief, image deconvolution was achieved with Huygens Essential (v21.04; Scientific Volume Imaging (http://svi.nl)), using the CMLE algorithm, with SNR:10 and 10 iterations. Image registration, spot calling and barcode filtering were performed as previously described.

For 3D cell segmentation, a synthetic image with improved contrast between cell nuclei was generated by multiplying the inverted Flamingo staining image and DAPI staining image after enhancing contrast using Fiji for each field of view (FOV). A StarDist 3D segmentation model was then trained using a manually labelled training dataset created from the synthetic data[68]. Subsequently, the model was applied to predict segmentation for each FOV.

For tissue region identification, to isolate low-frequency (that is, large length scale) transcriptional patterns over the tissue, Laplacian smoothing over a spatial Delaunay triangulation (that is, a nearest neighbour mesh of cells) was performed. The specific low-pass filter used was a heat kernel with time t = 10. Principal component analysis was then performed on the resulting low-pass-filtered cell-by-gene matrix to identify region features, followed by clustering in the principal component space to produce categorical region labels. K-means was used for clustering to avoid smoothing-induced spatial autocorrelation artefacts[69].

For quality control and cell-type classification, cells with less than two reads and expressed fewer than two genes were excluded. Gene expression profiles were normalized and scaled using standard Scanpy procedures[70]. A hierarchical clustering approach was then utilized to create a three-level cell-type annotation (Supplementary Fig. 2a). Initially, 24 clusters were identified through k-means clustering of the preprocessed gene expression profile containing 19 genes, which were further categorized into four level 1 cell types (T cells, B cells, macrophages and dendritic cells; Supplementary Fig. 2b). Cells lacking expression of any of the selected 19 gene markers were removed from subsequent analysis. Each level 1 cell-type underwent additional k-means clustering to establish level 2 annotations (Supplementary Fig. 2c). Level 2 CD4+ or CD8+ T cells then underwent a level 3 k-means clustering using a panel of 15 markers for further refinement in classification (Supplementary Fig. 2d).

RIBOmap procedure

Sixteen genes were profiled to validate the expression pattern of the three therapeutic mRNAs on liver and spleen tissues. The three therapeutic mRNAs, Dll1, Il7 and Flt3l, were profiled using RIBOmap, whereas the 13 cell-type marker genes were profiled using STARmap. RIBOmap enables the quantification of translation levels of Dll1, Il7 and Flt3l, which is a better reflection of protein production than mRNA levels. RIBOmap also excludes endosomal and extracellular mRNA signals, which are commonly found in mRNA-injected tissue samples. For both RIBOmap and STARmap, 5–6 pairs of primer and padlock probes were designed as described in Zeng et al.[30] (Supplementary Tables 1 and 4). The mice used in this experiment were anaesthetized with isoflurane and rapidly decapitated. The mouse liver and spleen tissues were collected and placed in Tissue-Tek O.C.T. Compound. Three biological replicates with therapeutic mRNA injections were collected, and one control sample with Luc mRNA injection was collected. Then, the liver and spleen tissues in O.C.T. were frozen in liquid nitrogen and stored at −80 °C.

The RIBOmap procedure was conducted similarly to the STARmap procedure described above[30]. The only difference is that RIBOmap splint probes targeting the 18S rRNA were included in the hybridization mixture. Data analysis was performed as described in the STARmap data analysis section. The translation levels of Dll1, Il7 and Flt3l were quantified at a single-cell level.

Serum toxicity analyses

Heart blood was aspirated by cardiac puncture using EDTA-precoated syringes and collected in BD Microtainer Tubes with potassium EDTA additive (365974, BD). Serum was isolated by centrifugation at 2,000g for 10 min at room temperature and snap frozen on dry ice. All samples were received by IDEXX BioAnalytics and stored securely at −80 °C before analysis. Serum AST, ALT, CK, albumin, triglycerides and GGT were measured by an Olympus AU5400 (IDEXX BioAnalytics). Samples demonstrating haemolysis were excluded from the analysis.

Serum cytokine analyses

Heart blood was aspirated by cardiac puncture using EDTA-precoated syringes and collected in BD Microtainer Tubes with potassium EDTA additive (365974, BD). Serum was isolated by centrifugation at 2,000g for 10 min at room temperature and snap frozen on dry ice. All samples were received by IDEXX BioAnalytics and stored securely at −80 °C before analysis. Samples were tested on the Milliplex MAP mouse cytokine/chemokine magnetic bead panel (MCYTOMAG-70K-PMX, Millipore) according to the kit protocol as qualified. Data were collected by xPONENT 4.3 (Luminex) and data analysis was completed using BELYSA 1.1.0 software. The data collected by the instrument software are expressed as median fluorescence intensity (MFI). MFI values for each analyte were collected per each individual sample well. Analyte standards, quality controls and sample MFI values were adjusted for background. Calibrator data were fit to either a five-parameter logistic or four parameter logistic model depending on best fit to produce accurate standard curves for each analyte. Quality control and sample data were interpolated from the standard curves and then adjusted according to the dilution factor to provide calculated final concentrations of each analyte present in the sample. Samples demonstrating haemolysis were excluded from the analysis.

Histopathological assessment of liver tissue

To evaluate the hepatic safety of LNP administration, liver tissue was harvested from female C57BL/6J mice. Twelve mice (n = 4 per group) comprising adult untreated controls, aged mice treated for 28 days with Luc–LNPs and aged mice treated for 28 days with DFI–LNPs were intracardially perfused under terminal anaesthesia with ice-cold 1× PBS, followed by 4% PFA. Livers were dissected, post-fixed in 4% PFA overnight at 4 °C and submitted to IDEXX BioAnalytics for blinded histopathological assessment.

Upon receipt, liver tissues were trimmed, paraffin embedded, sectioned and mounted onto glass slides. Sections were routinely stained with haematoxylin and eosin to assess general histology and hepatocellular integrity. Serial sections were stained with Masson’s Trichrome to visualize connective tissue and assess the presence of fibrosis. Microscopic evaluation was performed by board-certified veterinary pathologists blinded to treatment groups. Histopathological changes were graded for severity using a standardized semi-quantitative scale: 0 for no significant findings, 1 for minimal, 2 for mild, 3 for moderate, and 4 for severe. Evaluation criteria adhered to the International Harmonization of Nomenclature and Diagnostic Criteria standards (https://www.toxpath.org/inhand.asp). Numerical lesion scores were used to quantify both prevalence and severity of histological changes within and across experimental groups.

Dissociated scRNA-seq and data preprocessing

Heart blood was obtained through cardiac puncture in mice following cervical dislocation and stored in 0.5% EDTA for subsequent processing. Red blood cells were lysed using 1 ml ACK lysis buffer (Thermo). Freshly isolated immune cells post-lysis of red blood cells were blocked with rat anti-mouse CD16/32 (0.5 μg per well, eBioscience). Subsequently, respective antibodies in PBS were added in a total volume of 50 μl and stained for 30 min. eFluor 780 fixable viability dye (eBioscience) was used per the manufacturer’s protocol to exclude dead cells.

For scRNA-seq, cells were divided into eight aliquots per animal sample and pre-incubated for 10 min with titrated amounts of TotalSeq hashtag antibodies (C0301-C0308, BioLegend). Cells were sorted on a BD Aria Fusion cell sorter using a 100-μM nozzle and four-way purity mode. From peripheral blood, viable T cells (live, CD45+ and CD3+) were sorted in 20 μl 0.04% BSA in PBS and kept on ice until processing.

Single-cell capture, reverse transcription and library preparation were conducted on the Chromium platform (10X Genomics) with the single-cell 5′ reagent v2 kit (10X Genomics) following the manufacturer’s protocol, using 40,000 cells as input per channel. Each pool of cells underwent library quality testing, and library concentration was assessed. The final library for each pool was subjected to paired-end sequencing (26 bp and 92 bp) on one Illumina NovaSeq 6000 S2 lane. Raw sequencing data were processed and aligned to the mouse genome (GRCm39 — mm39) using the CellRanger pipeline (10X Genomics, v7.1.0).

scRNA-seq analysis of circulating T cells

Seurat datasets were generated for blood T cells at each time point. Singlets were identified per the published Seurat vignette (https://satijalab.org/seurat/articles/hashing_vignette.html) and used for downstream analyses. In addition, only cells with more than 500 and less than 4,000 unique features were detected, and less than 5% of mitochondrial counts were considered for further analysis. Blood datasets for each age group underwent merging and integration using the harmony package, following the guidelines in the published vignette (https://portals.broadinstitute.org/harmony/SeuratV3.html). The integration process involved the application of the following arguments: NormalizeData(), FindVariableFeatures(selection.method = “vst”, nfeatures = 2000),ScaleData(), RunPCA(), RunHarmony(“orig.ident”, plot_convergence = TRUE, dims.use = 1:20), RunUMAP(reduction = “harmony”, dims = 1:20), FindNeighbors(reduction = “harmony”, dims = 1:20), FindClusters(resolution = 0.5). After removing cells failing quality control, integration was reiterated with the same settings, and final transcriptional clusters for downstream analyses were identified using the FindClusters function (resolution = 0.7).

Annotation of mouse peripheral blood datasets was carried out manually, based on the final transcriptional clustering post-quality control and integration, using canonical marker genes and differential gene expression analyses through the FindAllMarkers function in Seurat. For subsequent visualization subsets for CD8 T cells (clusters: CD8_T activated, CD8_T cytotox, CD8_T effector-like, CD8_T exhausted, CD8_T IFN-responsive, CD8_T memory-like and CD8_T naive-like; 56,967 cells) and CD4 T cells (clusters: CD4_T activated, CD4_T IFN-responsive, CD4_T memory-like/naive, CD4_T naive and Treg; 39,716 cells) were built and a new UMAP embedding based on the harmony components was calculated.

The statistical significance of changes in cell-type abundance over time in the single-cell data was assessed using Pearson correlation through the cor.test(…, method= “pearson”) function. For visualization, log_2 fold changes of cell-type proportions at each time point relative to the 4-week time point were computed and graphically represented. Cell types exhibiting a significant correlation with increasing age (P < 0.05) were colour annotated. Density UMAP visualization was performed using the ggplot2 stat_density_2d() function.

scRNA-seq and V(D)J-seq analyses of B16-OVA TILs

B16-OVA were explanted at day +12 post-injection so that tumour sizes remained comparable between treatment groups. Tumour tissues were minced and enzymatically digested in RPMI supplemented with Liberase (5401119001, Roche) for 30 min at 37 °C with gentle agitation. Following digestion, tissues were filtered through a 70-μm cell strainer and centrifuged. The resulting cell pellet was resuspended in a 40% Percoll–PBS solution and layered on top of a 80% Percoll–PBS solution in 50 ml followed by centrifugation at 1,260g for 20 min at room temperature with the acceleration at the lowest setting and no break. The middle interface layer containing TILs was isolated and stained with a fixable viability dye (eFluor780) respective fluorophore-labelled antibodies, as well as an oligo-conjugated and PE-labelled H-2Kb SIINFEKL dextramer (JD02163DXG PE 25, Immudex). Cells were sorted on a Sony MA900 cell sorter using a 100-μM nozzle. Viable T cells (live, CD45+ and CD3+) were sorted in 20 μl 0.04% BSA in PBS and kept on ice until processing. Single-cell capture, reverse transcription and library preparation were conducted on the Chromium platform (10X Genomics) with the single-cell 5′ reagent v2 kit (10X Genomics) following the manufacturer’s protocol, using 20,000 cells as input per channel. Each pool of cells underwent library quality testing, and library concentration was assessed. The final library for each pool was subjected to paired-end sequencing (10 bp, 10 bp and 90 bp) on one Illumina NovaSeq X 25B lane. Raw sequencing data were processed and aligned to the mouse genome (GRCm39 — mm39) using the CellRanger pipeline (10X Genomics, v7.1.0).

Single-cell transcriptomic and V(D)J data were generated from four parallel runs, each consisting of sorted TILs isolated and pooled from two animals. Raw gene expression and antibody-derived tag (ADT) matrices using the conjugated oligo sequence (CCCATATAAGAAA) of the H-2Kb SIINFEKL dextramer were processed in R (v4.3.2) using the Seurat package (v5.3.0)[71]. Cells were retained if they expressed at least 200 but no more than 7,000 genes and exhibited less than 5% mitochondrial gene content. Genes detected in fewer than three cells (CreateSeuratObject(…,min.cells = 3)) were excluded. ADT assays were normalized using centred log-ratio transformation. TCR contig annotations were added to the scRNA dataset per sample using scRepertoire (v2.0.7)[72]. Clones were called based on the CDR3 amino acid sequence throughout the study. Clonal proportion was calculated per run. For downstream analyses, only cells with annotated TCR were used. T cells were projected onto the default TIL reference atlas using ProjecTILs (v3.5.2) to enable canonical cell-type annotation[73]. For visualization, the reference atlas UMAP embedding was used. H-2Kb-SIINFEKL dextramer staining was analysed from ADT assays. Cells with a centred log-ratio-normalized tetramer signal of more than 1.7 were classified as SIINFEKL specific (Tet+). Thresholding was guided by ridge plot distribution and cell-type distribution of the signal. Clonal diversity was calculated using scRepertoire function clonalDiversity(…,cloneCall = “aa”). Treemap plots of the tetramer-positive TCR repertoire were generated using the treemap package (v2.4-4). To this end, cells per treatment condition were downsampled to the same number of cells and used for visualization.

Quantifying spatial organization via interaction count statistic

Upstream data generation and analysis have been published elsewhere[16]. For each pair of cell types at each time point, a Z-score was computed by comparing the observed frequency of cell interactions for the pair within a 50-μm radius to the null hypothesis of the frequency of cell interactions for the same pair within a 150-μm radius. Subsequently, these Z-scores were plotted over time, and Pearson’s r was used to assess the strength of the correlation between changing Z-scores and age.

Receptor–ligand analysis

Upstream data generation and analysis have been published elsewhere[16]. We used the Squidpy[74] integration of CellPhoneDB[75] and Omnipath[76] to identify shifts in receptor–ligand interactions at each time point. Subsequently, the mean values of each receptor–ligand cell–cell pair across time were used to calculate Spearman’s R correlation and a P value. After correcting the P values through Bonferroni correction, unique interactions were categorized into those decreasing and increasing with age for visualization.

Chemicals and enzymes

Chemicals and enzymes are listed as name (catalog number, vendor): Tissue-Tek O.C.T. Compound (4583, SAKURA); glass bottom 24-well plates (P24-1.5H-N, Cellvis); 3-(trimethoxysilyl) propyl methacrylate (M6514, Sigma-Aldrich); poly-D-lysine (A-003-M, Sigma-Aldrich); 16% PFA (15710-S, Electron Microscopy Sciences); UltraPure DNase/RNase-free distilled water (10977023, Invitrogen); methanol (34860-1L-R, Sigma-Aldrich); PBS (10010-023, Gibco); Tween-20, 10% solution (655206, Calbiochem); yeast tRNA (AM7119, Thermo Fisher Scientific); SUPERase·In RNase inhibitor (AM2696, Thermo Fisher Scientific); UltraPure SSC, 20× (15557044, Invitrogen); formamide (655206, Calbiochem); ribonucleoside vanadyl complex (S1402S, New England Biolabs); T4 DNA ligase, 5 Weiss U μl−1 (EL0012, Thermo Fisher Scientific); Phi29 DNA polymerase (EP0094, Thermo Fisher Scientific); deoxynucleotide (dNTP) solution mix (N0447L, New England Biolabs); UltraPure BSA (AM2618, Thermo Fisher Scientific); 5-(3-aminoallyl)-dUTP (AM8439, Thermo Fisher Scientific); methacrylic acid NHS ester, 98% (730300, Sigma-Aldrich); DMSO, anhydrous (D12345, Invitrogen); acrylamide solution, 40% (161-0140, Bio-Rad); Bis solution, 2% (161-0142, Bio-Rad); ammonium persulfate (A3678, Sigma-Aldrich); N,N,N′,N′-tetramethylethylenediamine (T9281, Sigma-Aldrich); Gel Slick Solution (50640, Lonza Bioscience); OminiPur SDS, 20% (7991, Calbiochem); proteinase K solution, RNA grade (25530049, Thermo Fisher Scientific); Antarctic phosphatase (M0289L, New England Biolabs); DAPI (D1306, Molecular Probes); 10% Triton X-100 (93443, Sigma-Aldrich); and Flamingo fluorescent protein gel stain (1610490, Bio-Rad).

Antibodies

For immunohistochemistry, immunofluorescence and STARmap analyses of DLL1: anti-DLL1 antibody 1:800 (ab10554, Abcam) and AF546-labelled secondary donkey anti-rabbit IgG antibody 1:500 (A10040, Invitrogen) were used.

For mouse T cell analyses, anti-mouse IL-2 PE 1:100 (clone JES6-5H4, lots B351622 and B377599; 503808, BioLegend), anti-mouse CD45 BV510 1:100 (clone 30-F11, lots B386738, B360620 and B384034; 103138, BioLegend), anti-mouse IFNγ APC 1:100 (clone XMG1.2; lots B354911, B370994 and B396246; 505810, BioLegend), anti-mouse/human CD44 PE 1:100 (clone IM7, lot B343363; 103024, BioLegend), anti-Mo CD8a eBioscience eFluor 450 1:100 (clone 53-6.7, lot 2527379; 48-0081-82, Invitrogen), rat anti-mouse CD4 PerCP 1:100 (clone RM4-5, lots 2279727, 3201956 and 1334056; 553052, BD Bioscience), anti-mouse CD62L APC 1:100 (clone MEL-14, lot B371017; 104412, BioLegend), anti-mouse CD3 FITC 1:100 (clone 17A2; lots B388315, B388061 and B406287; 100204, BioLegend), hamster anti-mouse TCRβ APC 1:100 (clone H57-597; lot 3030398 and 2076848; 553174, BD Bioscience), TruStain FcX anti-mouse CD16/32 1:50 (clone 93; lots B398113, B380119, B368516, B372578 and B419152; 101320, BioLegend), eBioscience Fixable Viability Dye eFluor 780 1:1,000 (lot 2752774; 65-0865-14, Invitrogen), anti-mouse CD4 PE-Cy5.5 1:500 (clone RM4-5, lot B398342; 100514, BioLegend), anti-mouse CD8a BV510 1:100 (clone 53-6.7, lot B427044; 100752, BioLegend), anti-mouse/human CD44 PE 1:200 (clone IM7, lot B343363; 103024, BioLegend), anti-mouse CD62L APC 1:100 (clone MEL-14, lot B371017; 104412, BioLegend), anti-mouse CD3 Pacific Blue 1:100 (clone 17A2, lot B427533; 100214, BioLegend) and anti-mouse CD279 (PD1) APC 1:100 (clone 29F.1A12, lot B376789; 135210, BioLegend).

For mouse thymus analyses, anti-mouse CD4 PE-Cy5.5 1:500 (clone RM4-5, lot B398342; 100514, BioLegend), anti-mouse CD8a BV510 1:100 (clone 53-6.7, lot B427044; 100752, BioLegend), anti-mouse CD25 PerCP 1:200 (clone PC61, lot B378943; 102028, BioLegend), anti-mouse/human CD44 PE 1:200 (clone IM7, lot B417002; 103007, BioLegend), rat anti-mouse CD117 APC 1:100 (clone 2B8, lot 3199305; 553356, BD Bioscience), anti-mouse CD28 FITC 1:100 (clone E18, lot B373960; 122008, BioLegend), anti-mouse CD24 Pacific Blue 1:500 (clone M1/69, lot B385383; 101820, BioLegend), anti-mouse TCRβ chain PE/cyanine7 1:400 (clone H57-597, lot B394527; 109222, BioLegend), rat anti-mouse CD45R/B220 BUV661 1:100 (clone RA3-6B2, lot 3292032; 612972, BD Bioscience), TruStain FcX anti-mouse CD16/32 1:50 (clone 93, lots B398113, B380119, B368516, B372578 and B419152; 101320, BioLegend) and eBioscience Fixable Viability Dye eFluor 780 1:1,000 (lot 2752774; 65-0865-14, Invitrogen).

Reporting summary

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

Data availability

All single-cell profiling data of circulating T cells and B16-OVA TILs in ageing mice are available on Zenodo[77] (accession number 17385897). All spatial profiling (RIBOmap and STARmap PLUS) data of liver and spleen samples are available on Zenodo[78] (accession number 13858686). All spatial profiling (Slide-TCR-seq) data of thymuses across age are available on Zenodo[79] (accession number 17525729). The mouse genome assembly (GRCm39) was used for data alignment (NCBI RefSeq assembly; GCF_000001635.27).

Code availability

All codes necessary to reproduce all spatial thymus RNA-seq analyses (https://github.com/immunoliugy/aging_mouse_thymus) and all RIBOmap and STARmap PLUS analyses (https://github.com/wanglab-broad/hepatic_recon) are available on GitHub.

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Acknowledgements

We thank all members of the Zhang laboratory and M. Kilian for their support and useful discussions; and the Broad Comparative Medicine and the Broad Flow Cytometry Core staff for their technical support and guidance. F.Z. is supported by the Howard Hughes Medical Institute; the K. Lisa Yang Brain-Body Center at MIT; the Broad Institute Programmable Therapeutics Gift Donors; the Pershing Square Foundation, William Ackman and Neri Oxman; and by the Phillips family and J. and P. Poitras. M.J.F. was funded by the Deutsche Forschungsgemeinschaft (German Research Foundation; 512475649), supported by an EMBO Postdoctoral Fellowship (ALTF 689-2023) and acknowledges the support from the Deutsche José Carreras Leukämie-Stiftung (DJCLS 01ZI/2022), the Dr. Rolf M. Schwiete Stiftung (Project ID 2025-018) and the Else Kröner-Fresenius-Stiftung (2025_EKMS.52). X.W. acknowledges support from the Merkin Institute Fellowship, the Packard Fellowship, the Sloan Research Fellowship and the NIH DP2 New Innovator Award. S.L. acknowledges support from Impetus Grants, the Mark & Lisa Schwartz AI Initiative, the Margaret Q. Landenberger Research Foundation and BroadIgnite.

Author information

Authors and Affiliations

1. Howard Hughes Medical Institute, Cambridge, MA, USA

Mirco J. Friedrich, Julie Pham, Blake Lash, Rhiannon K. Macrae & Feng Zhang

2. Broad Institute of MIT and Harvard, Cambridge, MA, USA

Mirco J. Friedrich, Julie Pham, Jiakun Tian, Hongyu Chen, Jiahao Huang, Niklas Kehl, Sophia Liu, Blake Lash, Fei Chen, Xiao Wang, Rhiannon K. Macrae & Feng Zhang

3. McGovern Institute for Brain Research at MIT, Cambridge, MA, USA

Mirco J. Friedrich, Julie Pham, Blake Lash, Rhiannon K. Macrae & Feng Zhang

4. Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA, USA

Mirco J. Friedrich, Julie Pham, Blake Lash, Rhiannon K. Macrae & Feng Zhang

5. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Mirco J. Friedrich, Julie Pham, Blake Lash, Rhiannon K. Macrae & Feng Zhang

6. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA

Jiakun Tian, Hongyu Chen, Jiahao Huang & Xiao Wang

7. Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA

Sophia Liu

8. Harvard Stem Cell Institute, Cambridge, MA, USA

Fei Chen

9. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

Fei Chen

10. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Xiao Wang

Contributions

M.J.F. and F.Z. conceived the project. M.J.F. designed all the experiments with input from F.Z., R.K.M. and other authors. M.J.F. and J.P. performed and analysed all the experiments relating to identification and in vivo reconstitution of immune trophic factors. J.T., H.C. and J.H. performed the STARmap and RIBOmap experiments and analyses. M.J.F., J.P. and S.L. performed the longitudinal scRNA–TCR-seq and Slide-TCR-seq experiments. N.K. and S.L. performed the longitudinal scRNA–TCR-seq and Slide-TCR-seq analyses. B.L. provided assistance and guidance in cloning and production of mRNA–LNPs. F.C. and X.W. provided critical mentorship and guidance in technical procedures. F.Z. supervised this research and experimental design with support from R.K.M. M.J.F. and R.K.M. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Feng Zhang.

Ethics declarations

Competing interests

M.J.F. and F.Z. are co-inventors on US provisional patent application no. 63/530,465 filed by the Broad Institute and MIT relating to this work. F.Z. is a scientific advisor and cofounder of Beam Therapeutics, Pairwise Plants, Arbor Biotechnologies, Aera Therapeutics and Moonwalk Biosciences; and is a scientific advisor for Octant. M.J.F. reports speaker honoraria and consulting fees from Pfizer, Roche, Kerna Ventures and Moonwalk Biosciences. X.W. is a scientific cofounder and consultant of Stellaromics and Convergence Bio. All other authors declare no competing interests.

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Nature thanks Maria Mittelbrunn, Marcel van den Brink 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 Single-cell analysis reveals age-related shifts in peripheral and thymic T cell composition.

(A) Experimental overview for longitudinal profiling of peripheral and thymic T cells. Circulating T cells were isolated from peripheral blood of adult (6 weeks), middle-aged (36 weeks), and aged (72 weeks) mice and subjected to 10x Genomics 5′ single-cell RNA-sequencing. Thymuses were explanted for spatial transcriptomic profiling using Slide-seq v2. (B) Representative flow cytometry gating strategy for sorting peripheral blood CD3+ T cells for single-cell RNA-sequencing. (C) UMAP of CD8+ T cells (CD45+ CD3+ CD8+) across all age groups with cell-type annotations. Total of n = 56,967 CD8+ T cells. (D) Expression feature plots of canonical gene markers overlaid on UMAP from (C). (E) Proportional changes in CD8+ T cell subsets with age. Data shown as fold change relative to 4-week-old mice. Statistically significant changes highlighted in color (methods). (F) UMAP of CD4+ T cells (CD45+ CD3+ CD4+) across all age groups with cell-type annotations. Total of n = 39,716 CD4+ T cells. (G) Expression feature plots of canonical gene markers overlaid on UMAP from (F). (H) Proportional changes in CD4+ T cell subsets with age. Data shown as fold change relative to 4-week-old mice. Statistically significant changes highlighted in color (methods). (I) Heatmap showing expression of exhaustion-associated genes across circulating T cells stratified by age. Arrowhead denotes the average time point of overt thymic involution in C57BL/6 J mice.

Extended Data Fig. 2 Age-associated transcriptional reprogramming in peripheral CD4+ and CD8+ T cells.

(A) Dot plot of gene expression across annotated T cell subsets identified in single-cell RNA-seq data from peripheral CD45+ CD3+ T cells. (B) Left: UMAP of CD8+ T cells across all age groups annotated by subset. Right: same UMAP colored by mouse age. Total of n = 56,967 CD8+ T cells. (C) Left: UMAP of CD4+ T cells across all age groups annotated by subset. Right: same UMAP colored by mouse age. Total of n = 39,716 CD4+ T cells.

Extended Data Fig. 3 Age-driven decline in Notch, FLT3, and IL-7 signaling in intrathymic and peripheral T cells.

(A-B) Age-associated decline in spatial interaction strength between thymic cortical epithelial cells (cTEC) and thymocytes (A), and medullary epithelial cells (mTEC) and thymocytes (B). Interaction scores derived from ligand-receptor inference models. Pearson’s correlation (R) and P values shown. Arrowheads indicate time of thymic involution. Violin plots display mean interaction scores before and after involution; significance assessed by two-tailed unpaired t-test. (C-D) Receptor-ligand interaction heatmaps showing cell-cell communication pathways that decrease (C) or increase (D) with age across thymic cell populations. Interactions grouped by signaling axis and stratified by age category: neonatal (n = 7 spatial arrays from 3 mice), adult (n = 15 from 7 mice), middle-aged (n = 13 from 6 mice), and aged (n = 10 from 4 mice). (E) Pathway-level enrichment of Notch, FLT3, and IL-7 signaling in peripheral T cells. Violin plots of average enrichment scores per mouse. Data from n = 96,683 peripheral blood T cells. (F) Differential expression of Notch, FLT3, and IL-7 target genes in circulating T cells of adult vs. aged mice. Shown as log_2 fold change. Data from n = 96,683 peripheral blood T cells. (G) Absolute FLT3-L levels in explanted thymuses from mice across age (n = 3 animals per age group).

Extended Data Fig. 4 Translation and toxicity of DLL1-FLT3L-IL7 (DFI) mRNA–LNPs.

(A-B) Verification of DFI factor production by primary hepatocytes. (A) Confocal immunofluorescence of primary murine hepatocytes stained for DLL1 surface expression and β-catenin (membrane marker). Left panel: representative image following DFI transfection; right panel: mock-transfected control. Scale bar, 100 µm. (B) Extracellular concentrations of biologically active IL-7 and FLT3-L secreted by mRNA-transfected immortalized hepatocytes, as measured by IL-7 and FLT3-L ELISA. Data are mean ± s.e.m. n = 3 independent transfection experiments. Statistical significance was tested using two-tailed unpaired t-tests. (C) Supernatant cytokine levels of FLT3L and IL-7 from primary hepatocytes following DFI mRNA-LNP treatment, quantified by ELISA. (D) Representative RIBOmap deconvolved images of ribosome-bound transcripts in liver and spleen 6 h post-DFI injection. Note liver image (bottom) is repeated from Fig. 1f for ease of comparison. Representative image from three imaged DFI-treated animals. (E) Venn diagram showing overlap of hepatocytes expressing ribosome-bound transcripts for each of the three DFI mRNAs. n = 3 mice. (F) Representative immunofluorescence staining of liver tissue for DLL1 protein (yellow), F-actin (Phalloidin, magenta), and nuclei (DAPI, cyan) following DFI administration. See Fig. 1g for quantification. (G) Body weights of 72-week-old C57BL/6 J mice over 28 days of treatment with either Luc control or DFI mRNA–LNPs. n = 5 mice per group. (H) Serum liver function parameters including ALT, AST, ALP, and bilirubin measured in aged animals treated with vehicle (NaCl), DFI mRNA–LNPs, or recombinant IL-7/FLT3L protein. n = 10 (NaCl), n = 9 (DFI), n = 10 (recombinant cytokines). Data are presented as mean ± s.e.m. Statistical analysis was performed using one-way ANOVA followed by Tukey’s multiple comparison test. (I-J) Representative hematoxylin and eosin (H&E) and Masson’s trichrome stainings of liver sections from aged mice treated with NaCl (C) or DFI mRNA–LNPs (D). Representative images from one out of 12 imaged animals. (K) Histopathological scoring of liver lesions based on inflammation, hepatocellular degeneration, and fibrosis. Criteria followed INHAND nomenclature guidelines. n = 4 mice per group. (L) Cumulative pathology scores across all assessed features in aged NaCl-, Luc-, and DFI-treated mice. n = 4 mice per group.

Extended Data Fig. 5 DFI treatment enhances peripheral naïve T cell populations in aged mice.

(A) Representative flow cytometry gating strategy for the identification and quantification of CD45+ CD3+ T cells in spleens from adult (6 weeks) and aged (72 weeks) mice. (B) Quantification of total CD45+ CD3+ T cell frequency following 28 days of biweekly DFI or Luc mRNA-LNP treatment. n = 4 mice per group. Data are shown as mean ± s.e.m. Statistical significance was assessed using one-way ANOVA followed by Tukey’s post hoc test. (C-E) Frequencies of naïve (CD44− CD62L+), central memory (CM; CD44+ CD62L+), and effector memory (EM; CD44+ CD62L−) T cells (C), CD8+ T cells (D), and CD4+ T cells (E). n = 4 mice per group. Data represent mean ± s.e.m. Statistical comparisons were performed by one-way ANOVA with Tukey’s post hoc test.

Extended Data Fig. 6 DFI promotes thymopoiesis and restores thymocyte developmental stages in aged mice.

(A) Weights of explanted thymuses from adult (6 weeks), aged + Luc mRNA, and aged + DFI mRNA-treated (72 weeks) mice. n = 5 mice per group. Statistical significance was determined by one-way ANOVA followed by Tukey’s multiple-comparison post hoc test. (B) Representative H&E-stained thymic tissue sections from each group, highlighting cortical and medullary organization. (C) Quantification of thymic cellularity (×10³ cells/mm²) from explanted thymuses. n = 3 mice per group. One-way ANOVA with Tukey’s post hoc test was used to assess statistical significance. (D) Representative flow cytometry gating strategy for quantification of thymocyte subsets from explanted thymuses. (E) Quantification of major thymocyte subsets: double-negative (DN), double-positive (DP), and single-positive (SP) CD4+ and CD8+ thymocytes. Absolute counts were normalized to thymus weight (mg). n = 5 mice per group. Data are shown as mean ± s.e.m., one-way ANOVA with Tukey’s post hoc test. (F) Absolute counts of DN1-DN4 subsets. Normalized to thymus weight (mg). n = 5 mice per group. Data are shown as mean ± s.e.m., one-way ANOVA with Tukey’s post hoc test.

Extended Data Fig. 7 Analysis of Rag2- and Nur77 expression in thymocytes and T cells.

(A) Overview of the Rag2-EGFP experiment. DFI was injected at 0 h. (B) Representative flow cytometry gating strategy to quantify Rag2 expression levels in double- negative (DN), double-positive (DP), single-positive CD4+ (SP CD4+) and single-positive CD8+ (SP CD8+) thymocytes. (C) Mean fluorescence intensity (MFI) of Rag2-EGFP at indicated timepoints. n = 3 mice per timepoint. n = 3 Rag2-EGFP−/− mice were used as negative control. Data are shown as mean ± s.e.m., repeated measures two-way ANOVA with Dunnett’s post hoc test. (D) Overview of the Nur77-GFP experiment. DFI was injected at 0 h and 72 h. (E) Representative flow cytometry gating strategy to quantify Nur77 expression levels in CD4+ and CD8+ T cells in peripheral blood. (F) Mean fluorescence intensity (MFI) of Nur77-GFP at indicated timepoints in CD4+ (left) and CD8+ (right) T cells in peripheral blood. n = 3 mice per timepoint. n = 3 Nur77-GFP−/− mice were used as negative control. Data are shown as mean ± s.e.m., one-way ANOVA with Dunnett’s post hoc test.

Extended Data Fig. 8 Modulation of committed progenitors in aged bone marrow by DFI.

(A) Representative flow cytometry gating (left) and quantification (right) of total hematopoietic stem cells (HSCs; Lin− c-Kit+ Sca-1+ FLT3− CD34− CD150+), multipotent progenitor a (MPPa; Lin− c-Kit+ Sca-1+ FLT3− CD34+ CD150+), and multipotent progenitor b (MPPb; Lin− c-Kit+ Sca-1+ FLT3− CD34+ CD150−), as described previously[17]. (B) Gating (left) and quantification (right) of multipotent progenitor c (MPPc; Lin− c-Kit+ Sca-1+ FLT3+ CD34+ CD150−), as described previously[17]. (C) Gating (left) and quantification (right) of myeloid-biased HSCs (my-HSCs; Lin− c-Kit+ Sca-1+ FLT3− CD34− CD150high) and balanced HSCs (bal-HSCs; Lin− c-Kit+ Sca-1+ FLT3− CD34− CD150low). Gate to define my-HSC vs. bal-HSC was set as described previously[17]. (D) Ratio of my-HSCs to bal-HSCs across experimental groups. (E) Gating (left) and quantification (right) of common myeloid progenitors and granulocyte–macrophage progenitors (CMP/GMP; Lin− c-Kit+ Sca-1− CD34+ CD41−) (F) Gating (left) and quantification (right) of common lymphoid progenitors (CLPs; Lin− c-Kitint Sca-1int IL-7Rα+ FLT3+). (G) Schematic illustration of the hierarchical hematopoietic stem and progenitor cell (HSPC) lineage tree used for comparative analysis. (H) Fold changes in average frequencies of each HSPC subset in aged relative to adult bone marrow. (I) Fold changes in average frequencies of each HSPC subset in aged DFI-treated relative to aged Luc-treated bone marrow. For (A-F), quantifications were performed on n = 30 mice total (n = 10 adult, n = 10 aged + Luc, n = 10 aged + DFI). Data represent mean ± s.e.m. Statistical significance was assessed by one-way ANOVA followed by Tukey’s multiple-comparison post hoc test.

Extended Data Fig. 9 DFI induces CCR9-expressing common lymphoid progenitors (CLPs) in aged mice.

(A) Representative flow cytometry gating strategy to identify circulating CLPs (Lin− IL-7Rα+ Sca-1+ c-Kit+) in peripheral blood. (B) Left: Weights of explanted thymuses from each group. Right: Frequencies of intrathymic CLPs. n = 5 mice per group. (C) Left: Representative histograms showing CCR9 surface expression on intrathymic CLPs. Right: Frequencies of CCR9+ CLPs in thymus. n = 5 per group. (D) Top: Mean fluorescence intensity (MFI) of CCR9 on CLPs from bone marrow and thymus. Bottom: Fold change in CCR9 expression (thymus:bone marrow). n = 5 mice per group. Data analyzed by repeated measures two-way ANOVA and Tukey’s post hoc test. (E) Top: MFI of CCR7 on CLPs from bone marrow and thymus. Bottom: Fold change in CCR7 expression (thymus:bone marrow). n = 5 mice per group. Statistical analyses as in (D).

Extended Data Fig. 10 DFI treatment does not exacerbate autoimmune responses in T1D and EAE models.

(A) Gating strategy for tetramer staining of KYNKANAFL-H-2d-specific CD8+ T cells in the peripheral blood of NOD mice. (B) Schematic of the experimental design for experimental autoimmune encephalomyelitis (EAE) induction in adult and aged mice treated with DFI or Luc mRNA. Mice were immunized with MOG35–55 peptide and pertussis toxin, treated with mRNA–LNPs every 3 days until symptom onset, and monitored over 28 days. (C-D) Longitudinal EAE clinical scores in (C) adult and (D) aged mice. Mice were scored twice daily using a 0–5 scale: 0, no signs; 1, limp tail; 2, hindlimb weakness; 3, hindlimb paralysis; 4, forelimb paralysis; 5, moribund. (E-F) Frequencies of MOG38–49-I-Ab tetramer-binding CD4+ T cells in spleens. Control tetramer: CLIP87–101-I-Ab. n = 4 per group. (G) Representative spinal cord sections stained with H&E (top) and Luxol Fast Blue (bottom) to evaluate inflammatory infiltrates and demyelination. Arrows indicate areas of demyelination; arrowheads mark inflammatory foci. (H) Quantification of demyelination severity in spinal cords. n = 4 per group. P values calculated using one-way ANOVA with Tukey’s multiple-comparison post hoc test.

Supplementary information

Supplementary Information

This file contains Supplementary Figs. 1–6.

Reporting Summary

Supplementary Table 1

RIBOmap targets and probe sequences.

Supplementary Table 2

Raw values of serum cytokine measurements performed from peripheral blood of DFI mRNALNP, recombinant protein, or NaCl-treated animals.

Supplementary Table 3

Raw values of serum cytokine measurements performed from peripheral blood of DFI mRNA-LNP, Luc mRNA-LNP, single DFI factor mRNA LNP, or NaCl-treated animals.

Supplementary Table 4

STARmap targets and probe sequences.

Peer Review File

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Friedrich, M.J., Pham, J., Tian, J. et al. Transient hepatic reconstitution of trophic factors enhances aged immunity. Nature 650, 481–489 (2026). https://doi.org/10.1038/s41586-025-09873-4

- Received: 04 October 2024

- Accepted: 05 November 2025

- Published: 17 December 2025

- Version of record: 17 December 2025

- Issue date: 12 February 2026

- DOI: https://doi.org/10.1038/s41586-025-09873-4

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Temporal tissue dynamics from a spatial snapshot

Jonathan Somer,

Shie Mannor &

Uri Alon

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Abstract

Physiological and pathological processes such as inflammation and cancer emerge from interactions between cells over time[1]. However, methods to follow cell populations over time within the native context of a human tissue are lacking because a biopsy offers only a single snapshot. Here we present one-shot tissue dynamics reconstruction (OSDR), an approach to estimate a dynamical model of cell populations based on a single tissue sample. OSDR uses spatial proteomics to learn how the composition of cellular neighbourhoods influences division rate, providing a dynamical model of cell population change over time. We apply OSDR to human breast cancer data2,3,4, and reconstruct two fixed points of fibroblasts and macrophage interactions5,6. These fixed points correspond to hot and cold fibrosis[7], in agreement with co-culture experiments that measured these dynamics directly[8]. We then use OSDR to discover a pulse-generating excitable circuit of T and B cells in the tumour microenvironment, suggesting temporal flares of anticancer immune responses. Finally, we study longitudinal biopsies from a triple-negative breast cancer clinical trial[3], in which OSDR predicts the collapse of the tumour cell population in responders but not in non-responders, based on early-treatment biopsies. OSDR can be applied to a wide range of spatial proteomics assays to enable analysis of tissue dynamics based on patient biopsies.

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Main

Physiological processes involve cell populations that change over time[1]. Some cell populations expand and others are removed, as occurs in development and in the immune response to pathogens[9]. A clinically important example of changing cell populations is the evolution of the cancer microenvironment, in which the growing tumour recruits stromal and immune cells that are crucial for tumour survival10,11. Understanding cell population dynamics and the underlying cell–cell communication circuits is a major goal of tissue biology, and can enable new treatment strategies based on sculpting cell populations in desired ways7,12.

However, measuring the dynamics of cell populations in human tissues is currently very difficult. Biopsies provide a single snapshot, and taking multiple biopsies from a single patient is infeasible and does not provide longitudinal evidence from the same cells.

Current strategies to measure tissue dynamics do not apply to human biopsies. For example, cell lines, mice, organ-on-a-chip[13] or organoid models[14] are treated as replicas and are analysed at different time points. Ex vivo tissues can be followed over time but lack the native physiological context. Intravital fluorescence microscopy has made it possible to follow living cells within animal models15,16. Recent advances use synthetic biology to engineer cells to record their activity or lineage17,18. These approaches are restricted to animal models or in vitro settings, limiting their ability to capture the complexity of in vivo dynamics in humans.

The emergence of single-cell technologies offers new opportunities for studying tissues at high resolution. Notable approaches use single-cell data for understanding cell–cell communication at a single time point19,20,21,22. Other approaches attempt to infer dynamics of processes such as transcription within individual cells on a timescale of hours. Examples include RNA velocity, ergodic rate analysis and Zman-seq23,24,25. These methods do not address the challenge of understanding how cell populations change on the tissue level, processes that could take days to weeks.

Here we present an approach to estimate cell population dynamics based on a spatial biopsy snapshot (Fig. 1a). This approach, OSDR, is based on using a cell division marker to determine division rates as a function of neighbourhood composition, providing a dynamical model of how cell populations change over time. We applied OSDR to human breast cancer spatial proteomics samples from three large cohorts2,3,4. OSDR reconstructs a fibroblast–macrophage circuit with two steady states of hot and cold fibrosis5,7, in agreement with co-culture experiments that measure dynamics directly[8]. We then used OSDR to discover a pulse-generating excitable circuit of T and B cells, suggesting that cancer surveillance in the tumour microenvironment operates in temporal flares, as opposed to the steady-state picture implicit in current literature. Finally, we validated OSDR using longitudinal data from patients with triple-negative breast cancer who received either chemotherapy or chemotherapy and immunotherapy. In both treatment regimes, OSDR predicted the collapse of the tumour cell population in responders but not in non-responders, based on biopsies at treatment initiation. The present approach opens the way to infer cell population dynamics from spatial snapshots of patient biopsies.

Fig. 1: Overview of OSDR.

a, OSDR uses spatial proteomics of a biopsy to infer cell population dynamics. The illustration was created by Nigel Orme. b, The division probability is learned based on the number of cells of each type in the neighbourhood. c, Cell division rates are inferred from a cell division marker (Ki67). d, Rate of change in the number of cells \(X\) is the difference between divisions and removals as described by a stochastic model. e, Simulations can take an initial spatial arrangement of cells and propagate the populations over time. f, OSDR also provides a phase portrait showing the direction of change of cell populations as a function of their neighbourhoods, revealing fixed points.

Tissue dynamics from a spatial snapshot

The aim of this study was to introduce a method for inferring a dynamical model of cell populations based on a single tissue sample (Fig. 1a). We considered biopsy sections with cell-type markers and cell division markers. From this data, we generated a list of cell coordinates and division status for each cell type. We used imaging mass cytometry (IMC) samples2,3,4 and the division marker Ki67 (Methods), but in principle, other spatial proteomic or transcriptomic assays can be used (Methods).

The key idea is to use a spatial omics snapshot to estimate the division and removal rates of cells as a function of the composition of the neighbourhood of the cell. We call this approach OSDR. Its essential advance is the use of division rates measured at the level of single cells at one time point to produce dynamics at the tissue level.

The change in cell counts within a tissue is a balance between division, death and migration. We separated the dynamics into two parts: one part that results from cell division and death within a tissue, and a second part that results from influx of cells from circulation. Our work focused on inferring cell division and death. We then used this estimated component to quantify the net contribution of migration from external sources of cells (Methods; Supplementary Figs. 3m,n and 4m,n). In our analyses, local proliferation and removal were sufficient to explain the cell population dynamics, but this might not be the case in settings with a massive influx of cells.

To estimate the effect of each cell type on the growth and removal of other cells, we assumed that the neighbourhood surrounding each cell contains growth factors secreted by nearby cells. We thus tabulated, for each cell in a slide, its type, as well as the number and types of cells within a radius \(r\) (Fig. 1b). We used r = 80 µm based on measurements of in vivo cell–cell interaction ranges[26] (Methods). The ability of neighbourhood composition to predict division rates, as well as the inferred dynamics, are not sensitive to this choice of radius \(r\) (Supplementary Figs. 2e, 3f and 4g).

We defined division events using Ki67 thresholds based on experiments in human cell lines by Uxa et al.[27] (Methods; Fig. 1c). By using data on many different neighbourhoods, available from the spatial heterogeneity of the samples, we obtained the probability of division as a function of neighbourhood cell composition (Methods; Fig. 1d).

This approach is precise when cells across the sample obey the same underlying dynamics. In reality, varying proximity to blood vessels or tumour mass can create zones with different levels of metabolites, hypoxia, inflammation and other factors that can affect the dynamics[29]. To control for these effects, we show below that the estimated dynamics are preserved across such zones by including terms for density of endothelial or tumour cells in our model (Supplementary Figs. 3h and 4h). We also show that the estimated dynamics are preserved across various patient subgroups defined by external factors such as tumour genetics or stage (Supplementary Figs. 3i and 4i,j).

Ideally, one would also like a marker for cell death, but currently available markers are not considered to be sufficient to quantify the diverse forms of cell death[28]. To make progress, we bypassed the death marker by assuming that the death rate is a constant for each cell type, namely, that the removal rate is not affected by the composition of the neighbourhood of the cell. We found that the results are robust to wide variations in the value of this constant removal rate (Supplementary Figs. 3g and 4e). We therefore approximate the removal rate by the mean division rate for each cell type, an assumption that is exact in the case of (quasi) steady-state tissues in which mean removal and division rates are equal.

We used the estimated models for cell division and death to produce dynamics of cell populations at the scale of a tissue. To produce a trajectory of tissue composition over time, we performed the following steps. We computed the probabilities for division and death for each cell in the tissue. We then used the computed probabilities to sample division and death events for each cell. We added a new cell next to each dividing cell and removed cells that died. This produces the composition of the tissue after one time step ∆T. By repeating this process, we obtained a trajectory of tissue composition over time (Fig. 1e).

In addition to these detailed trajectories, our approach allowed a second perspective to study the circuitry of cell interactions in a manner that does not depend on the spatial organization of a specific tissue. To do so, we analysed the dynamical system of neighbourhood composition dynamics (Fig. 1f). Each neighbourhood composition was mapped to a location in a state-space where each axis corresponds to the number of cells of one cell type (Fig. 2a). Plotting the direction of change for each possible neighbourhood composition produced a phase portrait (Fig. 2b,c), in which arrows mark the direction and magnitude of the rate of change. The phase portrait provides an overview of the dynamics, including its stable and unstable fixed points. Figure 2a–c denotes two cell types for ease of visualization, yet this approach can provide phase portraits with numerous cell types as shown below.

Fig. 2: OSDR recovers known dynamical systems from simulations.

a, Neighbourhood composition maps to a location in state-space. b, The phase portrait shows the change in neighbourhood composition. c, The phase portrait corresponds to a set of ordinary differential equations, which are derived from the stochastic model using the mean direction of change. d, OSDR accurately reconstructs neighbourhood dynamics from simulations of known dynamical circuits based on simulated data of 10,000 cells of each type. ‘Known’ panels (left) display the ground-truth phase portrait of the system used to generate the simulated spatial data. The arrows indicate directions of change, and the coloured lines are nullclines in which only one cell type changes. The black and white dots are stable and unstable fixed points, respectively. ‘Inferred’ panels (right) display the estimated OSDR fixed points from ten simulations, for the four two-cell phase-portrait topologies, as well as the inferred phase portrait from the first simulation of the ten.

We thus obtained two outcomes: stochastic simulation starting from the tissue sample initial condition and phase-portrait analysis, which provides a general view of the dynamics. The phase portrait describes the cell-circuit ‘rules’ that govern local neighbourhoods. The simulations propagate these rules starting from a given initial condition. The two views complement each other. The phase portrait allows an overview of the fixed points of the system and basins of attraction; it does not include the effects of initial spatial conditions, which can influence the dynamics (Methods; Supplementary Fig. 1h–m). By contrast, the simulations provide a detailed account of a given tissue sample initial condition, but if the sample is small or not representative of other parts of the tissue, simulation might miss the larger picture provided by the phase portrait.

To test the feasibility of the OSDR approach, we began with simulated data. We asked how many cells are required to reconstruct a known dynamical system. In each simulation, we specified a certain dynamical system in which cells affect the growth rate of each other. We then simulated experimental spatial data by running the dynamics from various initial conditions of cell density, for a period of time that results in distributions of spatial cell concentrations that resemble experimental data. This created various cell compositions. We then fit OSDR to the simulated data and compared the estimated phase portrait with the phase portrait of the known dynamical system used in each simulation.

We simulated all possible phase-portrait topologies of two cell types, in which each cell type can be stable on its own or stable only in the presence of the other cell type. This resulted in four phase-portrait topologies (Fig. 2d). A few thousand cells of each type were sufficient to reliably reconstruct the fixed points and basins of attraction (Supplementary Fig. 2g–o). We conclude that the OSDR algorithm can reconstruct simple 2D dynamics from simulated cell data and recover the underlying phase portrait given enough cells.

OSDR infers fibroblast–macrophage dynamics

To test ODSR, we analysed an extensive spatial proteomics dataset by Danenberg et al.[2] (IMC; Methods). Data included biopsies from 715 patients with breast cancer (Methods). The samples included various breast cancer genotypes and stages. Each sample was an approximately 500 µm × 500 µm tissue section. The samples included a total of 859,710 cells. Cell types were identified using standard markers and include fibroblasts, macrophages, endothelial cells, adaptive immune cells and epithelial cells. We adopted the cell-type definitions from the original publication[2] (Supplementary Fig. 2a). The data included the Ki67 division marker.

As OSDR infers division probability (Ki67 > threshold) based on the identity of neighbouring cells, we first asked whether variation in Ki67 is indeed explained by the composition of the neighbourhoods of the cells in the data. This is not obvious, because this variation may stem from unmeasured factors such as gradients of nutrients, hypoxia or inflammation[29]. We performed logistic regression with cell counts as features (number of neighbouring cells of each type) and Ki67 as target (Ki67 over threshold; Methods). We found that cell counts accurately capture a wide range of division rates (Fig. 3a and Supplementary Fig. 2b). All fits were significant (log-likelihood ratio test P < 10−13; Supplementary Fig. 2c,d). We conclude that the counts of neighbouring cells are strong predictors of division rates.

Fig. 3: OSDR reconstructs breast cancer fibroblast and macrophage dynamics in agreement with in vitro experiments.

a, Statistical inference using IMC data from 715 human breast cancer biopsies[2] captures a wide range of division probabilities based on cell counts in the neighbourhood. Each dot corresponds to a subset of 4,000 cells (859,710 cells in total). b, The breast cancer microenvironment includes interactions between cancer-associated fibroblasts and macrophages. The illustration was created by Nigel Orme. c, The in vitro co-culture experiments of Mayer et al.[8] followed fibroblast and macrophage cells in breast cancer conditioned medium (red arrows) or standard medium (grey arrows) to establish a phase portrait with several fixed points (coloured circles).The arrows are changes in cell concentration over 4 days. Panel c was reproduced from ref. [8], Springer Nature Ltd, under a Creative Commons licence CC BY 4.0. d, Ki67 marker in fibroblasts as a function of neighbourhood composition (Methods). Each point corresponds to one fibroblast (69,873 cells), and its position is the composition of the neighbourhood of that cell. The blue line separates inferred regions of rising and falling cell numbers (inferred nullcline). The black contours represent density. e, Same as d but for macrophages (3,761 cells). f, OSDR inferred phase portrait for breast cancer-associated fibroblasts and macrophages. Black, black–white and white circles are stable, semi-stable and unstable fixed points, respectively. g, Phase portraits of fibroblasts and macrophages in an OSDR model that includes tumour cells. The panels show sections of the 3D phase portrait at low (left) or high (right) tumour density (tumour cells fixed at zero or at their mean density of 64 cells per neighbourhood, respectively). Circles are as in g. h, Kaplan–Meier curves for patients whose biopsies indicate hot or cold fibrosis (defined by a macrophage density of more than the halfway point between hot and cold fibrosis fixed points). The hot-fibrosis state is associated with poor prognosis (log-rank test P = 0.0046, n = 607 patients with associated clinical data). The shading indicates 95% confidence intervals based on Greenwood’s exponential formula[38].

The predictive ability of neighbouring cell counts could be explained by a number of factors (Supplementary Fig. 2f). Growth factor concentrations are influenced by the number of secreting and consuming cells in a neighbourhood26,30. Neighbour counts also reflect the chance of a cell to directly contact another cell, capturing processes such as contact inhibition and signalling via direct contact. Some cell types influence tissue hypoxia (endothelial cells and cancer cells) and inflammation (for example, macrophages and lymphocytes), processes that affect cell proliferation. Counts of such cell types might reflect the extent of hypoxia or inflammation in a region.

In some systems (for example, rapidly proliferating immune or developmental contexts), the assumption might not hold that the proliferation dynamics only depend on the current observed neighbourhood composition, and not on the history of neighbourhood compositions. We therefore recommend testing whether the division rate of each cell is predicted accurately based only on the current number of cells in its neighbourhood (as in Fig. 3a and Supplementary 2b–d). If this is not the case, the current approach is not relevant.

We compared OSDR to recent work that established in vitro dynamics for fibroblasts and macrophages in a breast cancer medium[8], supplying a reference phase portrait for the dynamics of these two cell populations. The in vitro phase portrait was obtained by seeding mice mammary fibroblasts and bone marrow-derived macrophages at different initial concentrations in co-cultures and following the changes in the cell populations over several days (Fig. 3b). This is a measurement of the population-level dynamics. In the presence of breast cancer-conditioned medium, fibroblast and macrophage cells showed a phase portrait with several distinct fixed points (Fig. 3c). Fibroblasts and macrophages supported each other in a fixed point called ‘hot fibrosis’, in the sense that both fibroblasts and macrophages coexist. Fibroblasts alone could support themselves in a ‘cold fibrosis’ fixed point. Macrophages were induced by the cancer-conditioned medium to form a macrophage-only fixed point at high macrophage densities.

The OSDR phase portraits from breast cancer samples provide dynamics for fibroblasts and macrophages (Fig. 3f). We included all fibroblast and macrophage subsets defined by Danenberg et al.[2] (Supplementary Fig. 2a). Similar results are found when considering neighbourhoods without adaptive immune cells, which were not present in the in vitro experiments, or including all neighbourhoods and adding terms for adaptive immune cells in the model (Supplementary Fig. 4k).

Of note, the reconstructed phase portrait shares most of the key features of the in vitro portrait (Fig. 3c,f). There is a stable fixed point in which fibroblasts and macrophages support each other, namely, a hot fibrosis fixed point. There is a cold fibrosis fixed point with fibroblasts only. There is a third semi-stable fixed point with high numbers of macrophages without fibroblasts.

We tested the robustness of the OSDR phase portrait. The fixed-point structure of the phase portrait is robust to resampling of cells and samples (Supplementary Fig. 3b–d). It is also consistently found in a wide range of parameter choices (neighbourhood radius and Ki67 threshold; Supplementary Fig. 3e,f) and death rates (Supplementary Fig. 3g), inclusion of endothelial or tumour cells in the statistical model (Supplementary Fig. 3h), or when considering only patient subgroups with specific cancer stage, tumour size, breast cancer genotype, patient survival time or type of treatment (Supplementary Fig. 3i).

We conclude that the reconstructed phase portrait captures the salient features of the in vitro phase portrait. Whereas the in vitro phase portrait used dynamical measurements of cell populations over days, the reconstructed portrait uses a single snapshot with cell density and division information.

We then asked whether cancer cells control the transition between cold and hot fibrosis. We used OSDR to fit a 3D model of cancer cell–fibroblast–macrophage dynamics. We then plotted 2D fibroblast–macrophage phase portraits at increasing cancer cell densities (Fig. 3g). Increasing tumour cell density pushes the stable fixed point towards a ‘hot’ state with abundant macrophages (Fig. 3g).

To ask whether hot fibrosis is clinically important, we partitioned the cohort from Danenberg et al.[2] into two groups according to the presence of hot or cold fibrosis (macrophage density > halfway point between hot and cold fibrosis fixed points), and fit a Kaplan–Meier survival curve to each group. The hot fibrosis state was associated with poor prognosis (Fig. 3h; log-rank test P = 0.0046), with a decrease in median survival from 192 to 132 months. This result remained significant when the number of tumour cells was controlled for (Wald test P = 0.01; Supplementary Fig. 3j).

To further test OSDR, we analysed two additional IMC breast cancer datasets, with an additional 1,012 patients and 2.1 million cells3,4. Similar results were found in all three IMC breast cancer datasets (Supplementary Fig. 3k,l).

Excitable dynamics of T and B cells

We next considered two other cell types in the breast cancer microenvironment, T cells and B cells. These cells are components of the adaptive immune system that have a major role in the tumour microenvironment and are crucial for immunotherapy31,32.

We analysed all neighbourhoods in the Danenberg dataset with at least one T and/or B cell. This included a total of 73,961 T cells and 27,642 B cells, with 1,067 and 213 cell division events, respectively. Thus, about 1.4% of the T cells and 0.7% of the B cells showed division events, consistent with previous breast cancer data[33].

We estimated the dynamics between T and B cells using OSDR. The inferred phase portrait has a striking feature called excitability. It shows a stable fixed point at zero cells (Fig. 4c). However, if T cells are raised above a threshold, they generate a pronounced pulse in which T cells rise, followed by B cells, and then both populations reduce to zero (Fig. 4d). This pulse is similar to pulses observed in autoimmune diseases such as relapsing–remitting multiple sclerosis[34].

Fig. 4: OSDR infers a pulse-generating excitable circuit of T and B cells in the breast cancer microenvironment.

a, Ki67 marker as a function of neighbourhood composition. Each point corresponds to one T cell (73,961 cells) from the IMC dataset of Danenberg et al.[2]. b, Same as a but for B cells (27,642 cells). c, OSDR inferred phase portrait indicates an excitable system. The blue highlighted trajectory displays a large pulse of adaptive immune cells. d, Spatial simulation of a 2 mm × 2 mm tissue based on inferred population dynamics shows that a high enough number of T cells triggers a pulse of T cells followed by B cells. The y axis displays the number of cells, normalized to the area of a 80-µm neighbourhood. e, Each pulse is followed by a refractory period, as evidenced in simulations in which additional T cells are introduced at different times (vertical arrows). A new pulse is generated only when T cells are introduced after B cells from the previous pulse have declined. f, 3D phase portrait with CD4 T cells, CD8 T cells and B cells shows an immune flare when the CD4 T cell density crosses threshold. The 2D projections are in grey. g, Distributions of CD4 T cells, CD8 T cells and B cells in the 100 most-proliferative and least-proliferative states of each cell type. The most-proliferative states are characterized by a high density of CD4 T cells and a low density of B cells.

Similar to the fibroblast–macrophage analysis above, the estimated dynamics were robust to resampling at the level of both cells and patients (Supplementary Fig. 4b–d) and were consistent under a wide range of death rates, neighbourhood sizes and Ki67 thresholds (Supplementary Fig. 4e–g). The dynamics are not confounded by unmodelled cell types (Supplementary Fig. 4h).

The pulsatile adaptive immune response in the inferred model is shown in Fig. 4c, in which T cells rise, followed by B cells that inhibit them, and then both cell populations decline. The model further indicates that after a pulse, there is a refractory period, in which B cells are still high and T cells are low. During this period, a new pulse cannot be generated due to the inhibitory effects of the B cells (Fig. 4e). A new pulse is possible only after the recovery period (Fig. 4e).

To study the role of different T cell subsets, we fit a 3D model including CD4 and CD8 T cell subsets as well as B cells. The excitable dynamics apparent in the 2D model were reproduced in the 3D model. The 3D model revealed that a pulse is initiated when the density of CD4 T cells crosses a threshold (Fig. 4f). For all three cell types, proliferation was highest in neighbourhoods with a high density of CD4 T cells and a low density of B cells (Fig. 4g). Proliferation was lowest at a high density of B cells and low density of CD4 cells (Fig. 4g). We conclude that CD4 cells initiate the immune pulse, whereas B cells provide the negative feedback required to terminate the pulse.

We next considered subtypes of breast cancer from the Danenberg cohort. We compared ER+, HER2+, PR+ and triple-negative breast cancer. Fibroblast–macrophage dynamics displayed hot and cold fibrosis fixed points in the phase portrait of all four subtypes (Supplementary Fig. 4j). The T–B immune pulse was similar in the three receptor-positive subtypes: ER+, PR+ and HER2+. However, the triple-negative breast cancer phase portrait showed a difference where the pulse does not end in zero T and B cells, but instead in a fixed point where B cells remain stable (Supplementary Fig. 4j). This result is consistent across breast cancer cohorts, with mixed cohorts displaying a collapse of both cells, and the triple-negative cohort displaying B cell stability. This result is consistent with the high rates of lymphocyte infiltration in triple-negative breast cancer, including tumour-infiltrating B cells at early stages of the disease35,36.

We also asked whether a full OSDR model with all cell types displays the same dynamics as the 2D models. We thus developed a model with the six major cell types in the Danenberg dataset: fibroblasts, macrophages, T cells, B cells, cancer cells and endothelial cells. We found that the 6D model recapitulates the dynamics of the 2D models in which other cell types are at their mean concentrations (Supplementary Fig. 4k). We conclude that the OSDR approach allows reconstruction of the dynamics of the full cell–cell interaction network in the samples.

OSDR predicts response to treatment

Patients with cancer can go through many months of treatment without knowing whether the tumour is growing or shrinking. To adjust treatment when it is ineffective, we need early signs of response. To address this challenge, we applied OSDR to a clinical trial with three longitudinal biopsies: the NeoTRIP trial. A cohort of 279 patients with triple-negative breast cancer were randomly assigned to chemotherapy (n = 141) or chemotherapy + anti-PDL1 immunotherapy (n = 138; Fig. 5a). Treatment consisted of eight 3-week cycles. Biopsies were collected at three time points: before treatment, 3 weeks into treatment (day of the second treatment cycle) and post-treatment (at surgical excision of the tumour following eight 3-week treatment cycles). Pathologists labelled the post-treatment biopsies as pathological complete response or residual disease.

Fig. 5: OSDR predicts the collapse of the tumour cell population in responders based on early-treatment biopsies.

a, Longitudinal triple-negative breast cancer dataset by Wang et al.[3] includes a total of 141 patients in the chemotherapy arm and 138 patients in the chemotherapy + immunotherapy arm. The illustration was created by Nigel Orme. b, Longitudinal predictions for six cell types. OSDR models were fit separately to early-treatment biopsies from responders (47 and 49 patients in the chemotherapy and chemotherapy + immunotherapy arms, respectively) and non-responders (54 and 57 patients in the chemotherapy and chemotherapy + immunotherapy arms, respectively) in each treatment arm (a total of 140,118 and 157,347 cells in responders and non-responders, respectively, in the chemotherapy arm; 135,559 and 144,261 cells in the chemotherapy + immunotherapy arm, respectively). The tumour cell population collapses in responders (dark grey line) but is stable in non-responders (red line). To isolate the effect of the tumour microenvironment dynamics, as opposed to initial tissue composition, trajectories were computed starting from the composition of all biopsies taken at the beginning of treatment. Plots display the average cell count over all trajectories. The shading denotes 95% confidence intervals of the mean (bootstrap)[39]. c, Fraction of dividing tumour cells in all neighbourhoods (n = 41,117 and 17,932 cancer cells from non-responders and responders, respectively) and in neighbourhoods with the top 10% of T cells. The error bars display 95% confidence intervals of the mean (bootstrap)[39]. We compared the fraction of Ki67+ cancer cells in responders versus non-responders using chi-squared tests of independence in all neighbourhoods (left) and in high T cell neighbourhoods (right). Both cases had P < 10−7. d, Zoomed in view of the first 90 days of predicted chemotherapy + immunotherapy treatment response dynamics. The T cell density increases in both responders and non-responders, and the tumour cell population collapses only in responders. The shading denotes 95% confidence intervals of the mean (bootstrap)[39].

We hypothesized that a response or lack of response to treatment should be evident in the early-treatment dynamics. We applied OSDR to week 3 biopsies from responders and non-responders in each treatment arm (total of four groups). We modelled dynamics of the six major cell types: fibroblasts, macrophages, tumour cells, endothelial cells, T cells and B cells. We then predicted the composition of the tissue over time. To detect differences resulting from dynamics, as opposed to differences resulting from initial tissue composition or tumour burden, we applied both responder and non-responder models to the same initial states, namely, we applied both models to every early-treatment biopsy. Figure 5b shows the predicted cell density over time, averaged over all starting states. In both treatment arms, OSDR predicts the collapse of the tumour cell population in responders but not in non-responders. These results are robust to the choice of patients used to fit each model (Supplementary Fig. 5a; Mann–Whitney U-test P < 10−5 for both treatment arms).

Our analysis showed that the collapse of tumour cells in responders is not a result of the initial tissue state, as we applied the models to all initial states. Moreover, the average proliferation rate of tumour cells at week 3 does not separate between responders and non-responders (Supplementary Fig. 5b). In fact, the mean division rate of tumour cells in responders was higher than non-responders in the immunotherapy treatment arm (Fig. 5c; chi-squared P < 10−7). Thus, the tumour cell population collapses as a result of interactions between cells over time and not initial tissue composition or average proliferation rates.

The predicted trajectories in immunotherapy show a sharp rise in T cell number (Fig. 5b). This rise appears in both responders and non-responders receiving immunotherapy, but not in patients in the chemotherapy arm. Because T cell density increases in both responders and non-responders under immunotherapy, we hypothesized that T cells have a different effect on tumour cells in these two groups. When considering all neighbourhoods, the fraction of dividing cancer cells is higher in responders than in non-responders (Fig. 5c, left; chi-squared P < 10−7). However, when we focus on neighbourhoods with many T cells (top 10%), the fraction of dividing cancer cells is twofold lower in responders than in non-responders (chi-squared P < 10−7; Fig. 5c).

We conclude that OSDR can predict treatment response using an early biopsy in both treatment arms (Fig. 5b). It also captures the rise in T cell numbers in immunotherapy but not in chemotherapy (Fig. 5d).

Discussion

We developed OSDR, an approach for inferring cell population dynamics from a tissue spatial omics snapshot. OSDR models cell division rate as a function of neighbourhood composition, and uses this to construct phase portraits and spatial simulations of a tissue. We validated the approach with longitudinal biopsies from breast cancer, identifying responders to chemotherapy and immunotherapy. We have discovered the potential for pulsatile T cell responses inhibited by B cells[37]. This can transform a steady-state concept of cancer immunity to a dynamic picture more similar to the flares observed in autoimmune disease[34].

Limitations and caveats

Application of OSDR requires both domain knowledge and awareness of model limitations. First, OSDR estimates dynamics at a given point in time, but can not model changes in the dynamics themselves. As such, OSDR will be inaccurate if dynamics appreciably change. For example, in the cohort from ref. [3], cell division rates drop considerably over the 24 weeks of treatment (Supplementary Fig. 5c). This could be a cumulative effect of chemotherapy. Prior knowledge or samples from multiple time points can be used to establish the timescale of the changes in dynamics. Rapid changes in tissue composition could also have an effect that history rather than current neighbourhoods dominate the dynamics (Supplementary Fig. 2f). In all cases, plots such as in Fig. 3a and Supplementary Fig. 2b,c should be used to test that neighbourhood compositions predict proliferation rates.

The current implementation approximates the death rate for each cell type as independent of the neighbourhood, and equal to the mean division rate in the sample. Establishing reliable markers for cell death rate may improve model estimates.

When sampling tissues, it is important to obtain a large variance of neighbourhood compositions. We estimate that a model can be built for an individual patient given on the order of 0.2 cm^2 of tissue, perhaps from multiple sections of a biopsy. By sampling a large tissue section or multiple biopsies, one can observe different tissue regions in different stages of the dynamic processes (Supplementary Fig. 5d).

Tissue dynamics naturally bias the distribution of observed neighbourhood compositions. As a result, some state-space regions could have limited data, making inferences more sensitive within these regions (Supplementary Fig. 2l–o). Plots of neighbourhood composition distribution as in Figs. 3d,e and 4a,b can help to recognize regions with limited data.

To use OSDR, one needs to consider confounders. First, when using data from multiple patients, one needs to check that a result is stable across different subsets of patients (for example, stage, genotype and tumour size). Reproducing the dynamics across patient subgroups ensures that we do not overinterpret multiple events from different tissues as a trajectory. A second type of confounder could occur spatially within the tissue, for example, gradients of hypoxia or inflammation. This can be tested by including terms for endothelial, immune and tumour cells, which serve as proxies for hypoxia and inflammation. These terms are automatically included in full OSDR using all cell types. If key cell subtypes are crucial for the dynamics but are not detected by the experimental markers, it might be impossible to test this type of confounder.

OSDR captures cell division and removal within the tissue, so predictions can deviate from observations when transdifferentiation or migration has a dominant role. We can identify cases in which transdifferentiation or migration terms are required to explain observations by plotting the sources and sinks of unexplained components of the dynamics, as in Supplementary Fig. 4m,n. For example, in the case of T and B cells, OSDR predicts a single stable point with zero cells of each type. An external source of cells is required to explain the observation of tissues with positive densities of each cell type. Accordingly, Supplementary Fig. 4m,n suggests that the influx of lymphocytes drives tissues with no lymphocytes to the point of initiation of an immune flare. Thus, observed high densities of lymphocytes are explained by local proliferation (that is, by OSDR) rather than external influx. In Fig. 4d,e, we simulated this migration component by adding cells to the tissue. Future implementations of OSDR could directly model transdifferentiation or migration components.

In the present work, we compared OSDR predictions against pre-therapy and post-therapy biopsies from human patients, over a 24-week course of treatment (Fig. 5). Direct measurement of tumour microenvironment dynamics in vivo can test the dynamics on a finer timescale in animal models. Possible methods include mouse models of tumour development (which have less inter-sample heterogeneity), pulse-chase experiments and intravital microscopy[16].

In summary, OSDR opens up exciting opportunities for studying how interactions between cells in the tumour microenvironment contribute to changes in the tumour over time. Tissue dynamical models provided by OSDR can inform mechanisms of pathology and provide targets for treatments aimed at changing tissue composition to reach therapeutic goals.

Methods

The number of cells of a given type in a tissue section can change by division or death, moving in or out of the section (flux) or transdifferentiation[40]. If we restrict ourselves to modelling cell types that do not transdifferentiate at appreciable rates, such as T cells and macrophages, and analyse tissues in which flux is negligible relative to rates of death or division (or where we can add flux terms post hoc), we remain with the following equation for the rate of change of a population of cells of type \(i\):

$$\frac{d{X}_{i}}{{dt}}=\frac{{\rm{\#}}\mathrm{Divisions}}{{dt}}-\frac{{\rm{\#}}\mathrm{Deaths}}{{dt}}$$

OSDR aims to transition from static observations of cell division or death in a tissue into rates. The key insight is: if we obtain a marker for cell division (or death), and in each cell division the marker remains above a defined threshold for a time period \({dt}\), then all observed divisions occurred within the last \({dt}\) hours. Thus:

$$\frac{d{X}_{i}}{{dt}}=\frac{{\rm{\#}}\mathrm{Divisions}}{\mathrm{Time}\,{\rm{a}}\,\mathrm{division}\,\mathrm{remains}\,\mathrm{observable}}-\frac{{\rm{\#}}\mathrm{Deaths}}{\mathrm{Time}\,{\rm{a}}\,\mathrm{death}\,\mathrm{remains}\,\mathrm{observable}}$$

The rate of division or death of a cell is influenced by the signals that it receives from its environment, its access to nutrients, its genetics and factors such as physical contact with other cells. We call this complete set of factors the ‘neighbourhood’ of the cell. This definition sets an ideal, and we denote the particular set of features used to approximate this ideal as \(N(x)\), where \(x\) is some cell in the tissue.

If we consider cells with identical neighbourhoods, a fraction of them will be dividing. This fraction is higher if the neighbourhood induces a high rate of division. We thus viewed the observations of division or death as random events whose probabilities are determined by the neighbourhood of the cell. Thus, for cell \(x\) of type \(i\), the distribution of the observation \({O}_{i,t}(x)\) of a division or death event is modelled as:

$${O}_{i,t}(x)=\left\{\begin{array}{cc}+1/d{t}^{+} & \text{with probability}\,{p}_{i}^{+1}(N(x))\\ -1/d{t}^{-} & \text{with probability}\,{p}_{i}^{-1}(N(x))\\ 0 & \text{remaining}\end{array}\right..$$

Where pi+1 and pi−1 are the statistical inference models for division or death, and dt+ and dt− are the durations of observed division or death markers, respectively. In this study, dt+ is defined as 1 time unit (roughly a few hours; ref. [27]), and the approximation of the death rate as the mean division rate is defined using the same time units. Thus, in this implementation dt+ = dt− = 1. We divided by the durations so that the (stochastic) change in the number of cells in each timestep is:

$$\frac{d{X}_{i}}{{dt}}=\sum _{x\in {X}_{i}}{O}_{i,t}(x)$$

Tissues are heterogenous, and the diverse cellular compositions in different regions result in various directions of change. To analyse the change at a certain state, rather than the change in complete tissues, we computed the expected change with respect to an initial condition where cells share the same neighbourhood:

$${E}_{x}\left[\frac{d{X}_{i}}{{dt}}\right]\,=\,E\,\left[\sum _{x\in {X}_{i}}{O}_{i,t}(x)\right]\,=\,{\rm{\#}}{X}_{i}\,[{{p}_{i}}^{+1}(N(x))-{{p}_{i}}^{-1}(N(x))]$$

In this study, features are based on the number of neighbouring cells of each type within a predefined radius. For further discussion on this choice of features, see Supplementary Fig. 2f. The neighbourhoods can thus be represented as a vector of cell densities:

$$X:= {({X}_{1},{X}_{2},\ldots ,{X}_{k})}^{T}$$

As a result, we can interpret the inferred statistical models as components of a set of ordinary differential equations (ODEs) defined over a state-space of cell densities:

$$\frac{\text{d}}{\text{d}t}X=\left(\begin{array}{c}{X}_{1}\,({p}_{1}^{+1}(X)-{p}_{1}^{-1}(X))\\ {X}_{2}\,({p}_{2}^{+1}(X)-{p}_{2}^{-1}(X))\\ \vdots \\ {X}_{k}\,({p}_{k}^{+1}(X)-{p}_{k}^{-1}(X))\end{array}\right)=f(X)$$

Note that OSDR is inference-model agnostic; although we used logistic regression here, other statistical inference models can be adopted within this framework. In addition, this approach can be applied to data acquired by any technology that enables classification and spatial localization of discrete cells, together with reliable measurement of markers for division (and possibly death).

Model inference algorithm

For input:

Cell-level annotations: \(({B}_{i},{T}_{i},{\vec{x}}_{i}{,O}_{i})\) for cell \(i=1,\ldots \,N\)

- \({B}_{i}\) denotes an ID for the biopsy sample cell \(i\) came from. \({B}_{i}\) is used to formalize that a cell can only influence cells from the same tissue.

- \({T}_{i}\in T\) indicates the cell type for cell \(i\) (for example, \({T}_{i}\,=\) ‘fibroblast’), and let \(T\) be the set of types.

- For any \(t\in T\), let \({n}_{t}\) be the total number of cells of type \(t\). We also assumed some order on the cells of type \(t\), such that: \(t[\,j]=i\) for any index \(j\in \{1,\ldots {n}_{t}\}\), and the original index \(i\in \{1,\ldots ,N\}\).

- \(\vec{{x}_{i}}\in {R}^{2}\) denotes the 2D spatial coordinates for cell \(i\).

- Oi ∈ {0,1} refers to the binary observation of division.

For the algorithm:

For each cell type \(t\in T\):

1. (1)
Compute a table of features (neighbour counts of each cell type) \({X}_{t}\in {R}^{{n}_{t}\times T}\).

$${X}_{t}[i,{t}^{{\prime} }]:= \mathop{\sum }\limits_{j=1}^{N}1\,[{B}_{j}={B}_{t[i]},{T}_{j}={t}^{{\prime} },\Vert {\overrightarrow{x}}_{t[i]}-\overrightarrow{{x}_{j}}\Vert < r]$$

Here \(t[i]\) is the original index of the i-th cell of type \(t\). The value at row \(i\) and column \(t{\prime} \) is a count of all cells that came from the same tissue as cell \(t[i]\), that have type \(t{\prime} \) and are within a distance \(r\).

Row \(i\) in \({X}_{t}\) is our representation for the neighbourhood of the cell \(t[i]\): the counts of all cell types in its proximity. Denote this vector by \(N(t[i]):= {X}_{{T}_{i}}[i,:]\).

2. (2)
Perform feature transformations on \({X}_{t}\), such as adding interaction terms (transformations are selected through a separate process of cross validation).

3. (3)
Define the binary cell-division labels \({y}_{t}\in \{\mathrm{0,\; 1}{\}}^{{n}_{t}}\):

$${y}_{t}[i]={O}_{t[i]}$$

4. (4)
Fit a multivariate logistic regression model \({p}_{t}^{+}\) for the division rate based on the features and labels \({X}_{t},{y}_{t}\).

5. (5)
Define the death rate:

$${p}_{t}^{-}={\mathrm{mean}(y}_{t})$$

For output:

$$(({p}_{t}^{+},\,{p}_{t}^{-}):t\in T)$$

Ki67 thresholds

We used previous data to establish a Ki67 threshold for cell division events. Uxa et al.[27] have demonstrated that Ki67 levels peak towards the G2/M phase of the cell cycle, with preserved kinetics (up to scale) across two human and one mouse cell line. We adjusted Ki67 levels to correct for different scaling and division rates between cell lines (Supplementary Fig. 1a,b) by selecting Ki67 values above a noise threshold \({T}_{n}\), subtracting \({T}_{n}\) and dividing by the Ki67 standard deviation. We choose \({T}_{n}=0.5\) mean isotopic counts because this is the typical magnitude of experimental noise in this dataset (Supplementary Fig. 1c). This produces distributions with similar shape (Supplementary Fig. 1d) in accordance with the cell line results from Uxa et al. We defined a cell division by the normalized Ki67 above a division threshold \({T}_{d}\). Example fractions of dividing cells are provided in Supplementary Fig. 1e. The resulting model estimates are robust to \({T}_{d}\) values between 0 and 1 on this adjusted scale (Supplementary Figs. 3f and 4g).

Choice of spatial proteomics technology

Out of the currently available spatial-omics technologies, multiplexed protein imaging41,42,43,44 was the most suitable for our setting. Current barcode-based spatial transcriptomics45,46 aggregate cells in spots of 55 µm in diameter so that they do not associate transcript levels with single discrete cells. In addition, because the distance between spot centres is 100 µm, if we place a cell in the centre of a spot, we measure less than one-ninth of the area immediately surrounding it (area of a circle of radius 27.5 µm, within another of radius 72.5). Fluorescent-based approaches such as MERFISH allow modelling single cells but they only recover a small fraction of every transcript in the tissue. This is a barrier for basing the analysis on levels of a single transcript: Ki67. IMC provides measurements within well-defined cells, reliable measurement of Ki67 and, in terms of cost, makes datasets in the order of magnitude of hundreds of thousands of cells feasible. Classical staining methods should also be feasible for analysing specific pairs of cells. For example, for analysing fibroblasts and macrophages, we could use four markers: fibroblast and macrophage markers, a cell nucleus marker and Ki67.

Implementing OSDR using spatial transcriptomics would allow more fine-grained definition of cell types based on transcriptional profiles. Cell division and death events could be defined based on multiple gene expression markers, rather than Ki67 used here, enhancing the accuracy of the method. In principle, OSDR can compute dynamics of many cell types and subtypes, beyond the six studied here. We expect the required number of cells to increase with the number of cell subtypes considered. Future work can add computation of transitions between cell subtypes or states to more fully capture cell population dynamics.

Data exclusion

We excluded outlier samples that deviated by more than six standard deviations in both cell density and number of cell divisions. These excluded outliers amounted to 3 samples out of 718 in the Danenberg dataset[2], and 2 samples out of 771 in the Fischer dataset[4].

Enforcing maximal density

We assumed there cannot be a net positive flux at the maximal observed density of a cell type. We applied a correction in the form of:

$${{p}_{i}}^{\mathrm{correction}}={c}_{i}\cdot {{d}_{i}}^{n}$$

Where di is the density of cell I, and the power n controls the steepness: high n implies that the correction applies primarily to higher densities (Supplementary Fig. 1f,g). The constant term ci is defined as the minimal correction ensuring non-positive flux at maximum density (Di). We compute division minus death rates at the maximal density of one cell, over all possible values of the second cell:

$${c}_{i}\cdot {{D}^{n}}_{i}=\mathop{\max }\limits_{N(x):{d}_{i}={D}_{i}}{{p}^{+}}_{i}(N(x))-{{p}^{-}}_{i}(N(x))$$

In practice, the state-space region near maximal density for both cell types is unpopulated by cells, so we used values up to the 95% quantile density for each type. This is more robust to extrapolation errors in areas with minimal data. For the fibroblast–macrophage model, this correction is trivial because the estimated net flux is negative at high densities; for the T and B cell model, the correction makes a difference only to T cells (Supplementary Fig. 1g).

Population versus neighbourhood dynamics

In OSDR, we first estimated the cell-level models for division and death probabilities (see ‘Model inference algorithm’ in Methods). We then used the cell-level models to analyse tissue dynamics in one of two ways.

We called the first approach ‘population-level dynamics’. We started with a spatial tissue section. We then produced a temporal sequence of tissue snapshots as follows. We first computed the probabilities of division and death for each cell, taking into account the composition of the neighbourhood of each cell. We then used the probabilities to sample for each cell: division, death or none. We placed each new cell in the neighbourhood of the dividing cell. We removed cells that died (Supplementary Fig. 1h).

One nuance in this process relates to cells at the edge of the tissue. When a cell near the edge of the tissue divides, its daughter cell might be placed outside the tissue. To keep tissue size fixed, we removed cells placed out of bounds. This biases the neighbourhoods of cells near the edge of the tissue. To correct this bias, we rescaled the cell counts of cells near the edge of the tissue: \(\mathrm{Number}\,\mathrm{of}\,\mathrm{cells}\,\mathrm{in}\,\mathrm{neighbourhood}\cdot \frac{1}{\mathrm{Neighbourhood}\,\mathrm{fraction}\,\mathrm{within}\,\mathrm{tissue}}\). This is an unbiased estimator of neighbourhood composition, as the ‘neighbourhood fraction within the tissue’ is also the probability of keeping a daughter cell following division.

We can also add cell movement to this sampling procedure. We implemented a random walk by sampling a Gaussian translation to each cell at each time step. Note that a large diffusion coefficient can disperse cells out of tissue boundaries (Supplementary Fig. 1i), whereas a small diffusion coefficient can cause regions in the tissue to be effectively isolated (Supplementary Fig. 1j). More elaborate modes of cell movement include attraction towards a chemokine source, adhesion to nearby cells and migration of cells from outside the tissue. Studying the effects of different modes remains outside the present scope.

To study a sequence of tissue sections, we plotted the number of cells over time (for example, Fig. 4e,f). Because tissues can have different sizes, we found it useful to rescale the number of cells to the area of a neighbourhood. Thus, the y axis displays: \(\mathrm{Number}\,\mathrm{of}\,\mathrm{cells}\,\mathrm{in}\,\mathrm{tissue}\cdot \frac{\mathrm{Neighbourhood}\,\mathrm{area}}{\mathrm{Tissue}\,\mathrm{area}}\).

One limitation of plotting the average density is that we do not observe the complete distribution. Of note, the average density does not necessarily coincide with the mode. Supplementary Fig. 1k shows a simulation in which the mode locates the stable fixed point of the system, rather than average density.

Thus, to analyse population dynamics, we needed to specify an initial spatial tissue configuration and a diffusion coefficient. We had to also consider the distribution of neighbourhood densities across the tissue. The second approach for analysing tissue dynamics did not require these choices.

We called the second approach ‘neighbourhood-level dynamics’. Here we analysed an ODE obtained by computing the average direction of change of each cell type (Methods). To analyse this ODE, it was convenient to plot phase portraits for 2D models (for example, Figs. 2b–d, 3f and 4c) and trajectories for higher dimensions (for example, Fig. 5b).

Generally, population and neighbourhood dynamics do not have to agree. The population model is stochastic and discrete, whereas the neighbourhood model is deterministic and smooth. We constructed examples to highlight two important differences between the population and neighbourhood approaches.

The first type of discrepancy results from collapse of a cell population. In the deterministic neighbourhood dynamics, a cell population will always flow in the average direction of change. Thus, if a cell population has a positive stable fixed point, it will deterministically flow towards it. Conversely, in the stochastic population dynamics, a cell population can move against the average direction of change. If due to a random fluctuation, all cells in a population die, the population will not recover. This can produce large differences between neighbourhood and population dynamics. A collapse is more likely to occur for cells with a low-density fixed point. It is also more likely without diffusion. Adding even small diffusion allows neighbourhoods with higher densities to ‘rescue’ neighbourhoods that had collapsed. Supplementary Fig. 1l shows that the macrophage population eventually collapses without diffusion. Adding diffusion produces similar steady states under both models (Supplementary Fig. 1l).

A second type of discrepancy results from the initial tissue configuration. Population dynamics depend on the initial tissue configuration, whereas neighbourhood dynamics do not. Certain particularly ‘adversarial’ tissue configurations, coupled with low cell motion, can produce large discrepancies. For example, under neighbourhood dynamics, the T and B cell model produces a flare from a location of (4,1) on the TB phase portrait (Fig. 4c). Under population dynamics, a flare also occurs when we initialize a tissue by placing cells at random (Supplementary Fig. 1m, first example). However, we could organize the same number of cells such that no flare occurred. For example, we can place T cells at one side of the tissue and B cells at the other. In this case, the B cell population collapses because the B cells do not have T cell neighbours (Supplementary Fig. 1m, second example). Another option is placing all B cells in the same place, creating a high density of B cells locally. In this case, the low density of B cells at the tissue level does not reflect their high density locally (Supplementary Fig. 1m, third example). In these examples, we used our understanding of neighbourhood dynamics to design a tissue configuration that produces a discrepancy.

Simulations of known dynamical models

To determine the accuracy of the inferred dynamical model as a function of sample size, we simulated spatial data based on a known dynamical model (that is, predefined functions p+ and p−) using the following procedure:

1. (1)
Sample a random initial number of cells for each cell type.

2. (2)
Sample a random spatial position in the tissue for each cell.

3. (3)
For n steps:

1. (i)
Compute for each cell the probability of division or death based on its current neighbourhood.

2. (ii)
Sample an event of division, death or none based on the computed probabilities.

3. (iii)
Remove dead cells and place a new cell next to each dividing cell (here the location of new cells is sampled uniformly within the neighbourhood. It is straightforward to incorporate knowledge about cell motility at this stage, if available).

This produces a tissue section whose density and spatial distribution is produced by the dynamics, as we assume is the case for real tissues. We repeated the procedure 100 times from various initial densities and sample with replacement 50,000 cells evenly from all tissues. From this pool, we then sampled 1,000, 5,000, 10,000 or 25,000 cells for the model fits.

Because empirical distributions in our data had tails towards lower cell densities, we sampled the initial cell densities from a β-distribution biased towards lower densities (parameters 2,4 scaled to a maximal value of 7). Model parameters were selected to produce division rates in ranges that resemble real data (mostly within 1–6%). The fraction of divisions was approximately 2% for all models. We selected the number of simulation time steps as that required to produce distributions qualitatively similar to real data.

To evaluate model fits, we tested whether the correct location and type of stable or semi-stable fixed points were recovered (Supplementary Fig. 2g–k), as well as accuracy of reconstructed basins of attraction (Supplementary Fig. 2l–o). To account for discretization error, if a stable point on an axis was recovered, and an unstable point was located within less than one cell from that point, we considered it as semi-stable. Such a point has effectively no basin of attraction. The same precaution should apply to interpretation of model fits in general.

For a detailed analysis of the simulations of each ground truth model, see Supplementary Fig. 2g–o.

Plotting Ki67 levels as a function of neighbourhood composition

Figures 3d,e and 4a,b display the measured Ki67 > threshold as a function of neighbourhood composition. To transform the cell-level binary division events into rates, we computed a local average and subtracted the mean division rate. For the local average, we used a Gaussian smoothing kernel over the cell-density state-space, providing a non-parametric plot of the division rate. The gamma parameter of the kernel controls the degree of smoothing. We plotted a value of gamma that produces contours of similar complexity as those of the estimated parametric models.

Magnitude of error due to unmodelled terms

To estimate the effect of unmodelled processes we applied a Fokker–Planck approach. Each tissue is analogous to a particle moving through a space whose coordinates are defined by the densities of each cell type. For example, the location \(x\) could be the density of fibroblasts and macrophages (that is, \(x=(F,M)\)). The velocity through this space is determined by the rate of change in each cell type. This velocity has a deterministic component composed of \(\vec{v}(x,y)\), the division minus removal rates that we previously estimated using division markers, and \(\vec{u}(x,y)\), which includes unmodelled terms such as cell migration and differentiation from stem cells. The velocity also includes a stochastic component, which reflects fluctuations in velocity of each cell type.

Applying the Fokker–Planck equation to our setting:

$$\frac{{\rm{\partial }}}{{\rm{\partial }}t}\rho ({\boldsymbol{x}})=-\,{\rm{\nabla }}\cdot [\rho ({\boldsymbol{x}})\cdot (\overrightarrow{{\boldsymbol{v}}}({\boldsymbol{x}})+\overrightarrow{{\boldsymbol{u}}}({\boldsymbol{x}}))]+\mathop{\sum }\limits_{i,j=1}^{N}\frac{{{\rm{\partial }}}^{2}}{{\rm{\partial }}{{\boldsymbol{x}}}_{i}{\rm{\partial }}{{\boldsymbol{x}}}_{j}}[{D}_{i,j}({\boldsymbol{x}})\rho ({\boldsymbol{x}})]$$

For the notation: \(\nabla \cdot \) is the divergence operator; \(\rho ({\boldsymbol{x}})\) is the density of tissues at location \({\boldsymbol{x}}\) in the state-space; \(\overrightarrow{{\boldsymbol{v}}}({\boldsymbol{x}})+\overrightarrow{{\boldsymbol{u}}}({\boldsymbol{x}})\) is the velocity of a neighbourhood in the state-space; \(\overrightarrow{{\boldsymbol{v}}}({\boldsymbol{x}})\) is the division minus the death rate field, estimated from data; \(\overrightarrow{{\boldsymbol{u}}}({\boldsymbol{x}})\) is the velocity field due to unmodelled terms such as cell migration (will be estimated now); and \(D({\boldsymbol{x}})\) is the diffusion coefficient matrix. We assumed that fluctuations are proportional to the number of cells (for example, changes in nutrient availability modify growth rates of existing cells, rather than adding or removing cells at a constant rate).

Formally, \(dX=[{\boldsymbol{v}}({\boldsymbol{x}})+{\boldsymbol{u}}({\boldsymbol{x}})]dt+{\sigma }({\boldsymbol{x}})\,\circ \,d{W}_{t}\). Where \(d{W}_{t}\) is an N-dimensional Brownian motion and \({\sigma }({\boldsymbol{x}})={\sigma }\cdot {({{\boldsymbol{x}}}_{1},\ldots ,{{\boldsymbol{x}}}_{N})}^{T}\) multiplies each component independently. Overall, for \(i\ne j\) \({D}_{i,j}({\boldsymbol{x}})=0\) and for \(i=j\) \({D}_{i,i}({\boldsymbol{x}})={{\sigma }}^{2}{{{\boldsymbol{x}}}_{i}}^{2}\). The diffusion term becomes: \({{\sigma }}^{2}{\sum }_{i=1}^{N}\frac{{{\rm{\partial }}}^{{\bf{2}}}}{{{\rm{\partial }}}^{2}{{\boldsymbol{x}}}_{i}}[{{{\boldsymbol{x}}}_{i}}^{2}\rho ({\boldsymbol{x}})]\). We henceforth denote the scalar \({\sum }_{i=1}^{N}\frac{{{\rm{\partial }}}^{2}}{{{\rm{\partial }}}^{2}{{\boldsymbol{x}}}_{i}}[{{{\boldsymbol{x}}}_{i}}^{2}\rho ({\boldsymbol{x}})]\) as \(D({\boldsymbol{x}})\). The equation becomes:

$$\frac{{\rm{\partial }}}{{\rm{\partial }}t}\rho (x)=-\,{\rm{\nabla }}\cdot [\rho ({\boldsymbol{x}})\cdot \overrightarrow{{\boldsymbol{v}}}({\boldsymbol{x}})]-{\rm{\nabla }}\cdot [\rho ({\boldsymbol{x}})\cdot \overrightarrow{{\boldsymbol{u}}}({\boldsymbol{x}})]+{{\sigma }}^{{\bf{2}}}D({\boldsymbol{x}})$$

For a large sample of patients, we can assume that sampling the same tumours within a number of days would produce approximately the same distribution. This implies that \(\frac{\partial \rho }{\partial t}\approx 0\). Thus:

$${\rm{\nabla }}\cdot \,[\rho ({\boldsymbol{x}})\cdot \overrightarrow{{\boldsymbol{v}}}({\boldsymbol{x}})]=-\,{\rm{\nabla }}\cdot [\rho ({\boldsymbol{x}})\cdot \overrightarrow{{\boldsymbol{u}}}({\boldsymbol{x}})]+{{\sigma }}^{2}D({\boldsymbol{x}})$$

The left-hand side is inferred from data for all \({\boldsymbol{x}}\). Recall, \(\overrightarrow{{\boldsymbol{v}}}({\boldsymbol{x}})\) is estimated from division markers and we estimated \(\rho (x)\) by computing the location of tissue of each patient in the state-space followed by kernel density estimation. We then numerically evaluated the divergence \({\rm{\nabla }}\cdot (\rho ({\boldsymbol{x}})\cdot \overrightarrow{{\boldsymbol{v}}}({\boldsymbol{x}}))\) using finite differences.

By plotting \({\rm{\nabla }}\cdot (\rho \cdot \overrightarrow{{\boldsymbol{v}}})\), we can visualize the net contribution (in units of change in density per unit time) of the missing terms. In the special case where \(\sigma \approx 0\), we directly obtained the divergence of the error (for example, migration) field as \({\rm{\nabla }}\cdot (\rho \cdot \overrightarrow{{\boldsymbol{u}}})=-\,{\rm{\nabla }}\cdot (\rho \cdot \overrightarrow{{\boldsymbol{v}}})\). Plotting \({\rm{\nabla }}\cdot (\rho \cdot \overrightarrow{{\boldsymbol{v}}})\) thus identified the sources and sinks of the error field, as well as the magnitude of error. Note, for interpretability, we divided the rates by the maximal \(\rho \) so that units are in [fractions of maximal density/∆t].

To quantify the contribution of diffusion versus deterministic terms (migration, and so on) we solved an ordinary least squares problem with a single parameter \(\sigma \):

$$\hat{{\sigma }}=\mathop{\text{arg min}}\limits_{{\sigma }}\sum _{x}({\rm{\nabla }}\cdot (\rho ({\boldsymbol{x}})\cdot \overrightarrow{{\boldsymbol{v}}}({\boldsymbol{x}}))-{{\sigma }}^{2}D{({\boldsymbol{x}}))}^{2}$$

The variable \({\boldsymbol{x}}\) is taken over a discrete 2D grid. Solving for \(\sigma \) this way implies that the deterministic error velocity \(\overrightarrow{{\boldsymbol{u}}}({\boldsymbol{x}})\) should not include components that could be explained by stochastic diffusion.

Reporting summary

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

Data availability

IMC datasets analysed in this study were previously published by Danenberg et al.[2] (https://zenodo.org/records/7324285, associated clinical data on https://www.cbioportal.org/), Fischer et al.[4] (https://zenodo.org/records/7494509) and Wang et al.[3] (https://zenodo.org/records/7990870).

Code availability

Analysis was performed using Python (tested on versions 3.10+). Code (https://github.com/JonathanSomer/osdr) and documentation (https://jonathansomer.github.io/osdr/) are available on GitHub.

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Acknowledgements

We thank A. Aharoni-Somer, I. Kessler, I. Itzhak, R. Scherz-Shouval, S. Miyara, I. Ben-Shalom, Y. Lebel, A. Mayo, S. Mayer, T. Milo, L. Keren, N. Yosef, S. Itzkovitz, U. Shalit, R. Kishony, S. Shen-Orr, R. Meir and all members of our laboratories for discussions. J.S. was supported by the Adams Fellowship of the Israel Academy of Sciences and Humanities. This study was further supported by the Sagol Institute for Longevity Research, Cancer Research UK grant C19767/A27145 and the European Research Council under the European Union’s Horizon 2020 research and innovation program (grant agreement no.856487) to U.A.

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Authors and Affiliations

1. Department of Electrical & Computer Engineering, Technion Israel Institute of Technology, Haifa, Israel

Jonathan Somer & Shie Mannor

2. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

Jonathan Somer & Uri Alon

Contributions

J.S., U.A. and S.M. conceived the study together. J.S. led the analyses and method development. S.M. and U.A. jointly supervised the research. All authors contributed to writing and revising the manuscript.

Corresponding authors

Correspondence to Jonathan Somer, Shie Mannor or Uri Alon.

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Competing interests

J.S., U.A. and S.M. are inventors on a patent application related to this work (PCT/IL2025/050139).

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Nature thanks Laleh Haghverdi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Reporting Summary

Supplementary Table 1

Raw data of velocities at all neighborhood compositions

Supplementary Table 2

Raw data of velocities at all neighborhood compositions

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Somer, J., Mannor, S. & Alon, U. Temporal tissue dynamics from a spatial snapshot. Nature 650, 490–499 (2026). https://doi.org/10.1038/s41586-025-09876-1

- Received: 30 April 2024

- Accepted: 06 November 2025

- Published: 21 January 2026

- Version of record: 21 January 2026

- Issue date: 12 February 2026

- DOI: https://doi.org/10.1038/s41586-025-09876-1

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Heterotrimeric G proteins transduce information to intracellular partners by modulating GTP binding and hydrolysis[3]. Through their interaction with G-protein-coupled receptors (GPCRs) and effectors, G proteins provide the transducer function that is necessary for the conveyance of extracellular information4,5. Heterotrimeric G proteins consist of an α subunit bound to a β and γ subunit dimer; they remain a trimer while the α subunit is bound to GDP[6]. Receptors provide the transmembrane conduit for a signal between the extracellular agonist and the intracellular G-protein transducer[7]. Specifically, GPCRs undergo a conformational change that acts to catalyse a reaction between the receptor and the Gα protein8,9,10. This interaction shifts the affinity for Gα binding to GDP to conditions that favour GDP release and GTP binding[3]. Thus, the receptor acts as a GEF and this reaction is considered to be primarily unidirectional[11] (Fig. 1a). However, there have been observations that the GTP loading function of the receptor is reversible—that is, the receptor may facilitate the release of GTP from Gα. Early examples of this reversible interaction used nonhydrolysable forms of GTP such as GTPγS3,12, wherein the dissociation of radiolabelled GTPγS could be observed upon agonist binding to the receptor. One such study examined the kinetics of the release of 35S-GTPγS in cells expressing the mu opioid receptor (MOR) and found that the rate of release of nucleotide was increased as a function of a single saturating concentration of agonist and that partial and full agonists maintained their rank order efficacy in both exchange reactions[13] (35S-GTPγS binding and 35S-GTPγS release).

Fig. 1: GPCRs induce both GTP binding and GTP release from Gα proteins and the process is agonist-mediated.

a, Schematic of the proposed model, showing the conventional pathway of GDP-to-GTP exchange (left) and the expanded model to allow for both GTP and GDP release (right) as detailed in the linked Article[14]. A, agonist; R, receptor (asterisks indicate different active states); Ka, affinity constant; G protein; Gapo, unbound G protein (blue); GGDP, GDP-bound G protein (green); GGTP, GTP-bound G protein (red); α_1 and α_2, active state affinities. b, DAMGO-stimulated binding and release in CHO-MOR cells presented as raw data (in disintegrations per minute (dpm)) and the normalization to baseline and maximum response. c–g, Normalized binding and release with indicated agonists in CHO-K1 cells expressing MOR (c; n = 3 binding, 3 release), KOR (d; n =3 binding, 5 release), 5-HT1AR (e; n = 8 binding, 7 release), M_2R (f; n = 3 binding, 3 release) and SST_2R (g; n = 4 binding, 4 release). The raw data are presented in Extended Data Fig. 1. MPE, maximum possible effect. b–g, Data are mean ± s.e.m. and potency is presented as mean with 95% confidence interval. h, Comparison of potency in 35S-GTPγS binding versus release by unpaired, two-tailed t-test for each receptor comparing the individual potency (pEC_50, where EC_50 is half-maximal effective concentration) values measured per experiment. Data are mean with 95% confidence interval. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; NS, not significant (P > 0.05).

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Here we investigate the significance of the release mechanism as a function of agonist concentration and how it can influence drug responsiveness in vivo. In a linked Article[14], we present extensive pharmacological and biochemical characterizations of the release reaction, which we summarize in the three-state coupling model (Fig. 1a). In the study, we show that the GTP-release function of the receptor adheres to the pharmacological principles that pertain to the GTP-binding function of the receptor. In summary, the release function is dependent on agonist concentration and can be reversed by antagonists, and competitive interactions are preserved between orthosteric agonists and antagonists. Moreover, we demonstrate that the effect is due to activation of the receptor population and not merely a function of receptor occupancy. We provide experimental evidence and a functional state model that establishes that the efficacy and potency of an agonist to promote GTP release can differ from its efficacy and potency to induce GTP binding. Therefore, an agonist may demonstrate selectivity for affecting the equilibrium of the functional active state of the G protein and can show a preference for one state over the other. We also provide evidence that an agonist may have a different rank order potency and efficacy for the two states of the exchange function of a GPCR.

In the present study we show that agonists can induce both GTP binding and GTP release from Gα in a concentration-dependent manner, and that this can be observed for several different GPCRs (Fig. 1). GTP binding is assessed using a conventional method that entails incubating isolated cell membranes in the presence of 35S-GTPγS and increasing concentrations of agonists15,16. To observe GTP release, we use a ‘pulse-chase’ paradigm, which entails first loading the membrane preparations with 35S-GTPγS. Since many GPCRs are negatively regulated by sodium ions, removal of sodium allows for the constitutive activation of all sensitive receptors and the subsequent loading of 35S-GTPγS binding to G proteins. After the pulse, the chase entails dilution of the membranes and inclusion of an excess of unlabelled (cold) GTPγS in the presence of sodium (see Methods).

The two reactions are compared in Fig. 1b using membranes prepared from cells overexpressing mouse MOR. The data are presented as radioactivity counts for both the binding and the release assay; to facilitate comparison of the potencies, the data are also normalized to the baseline (0%) and the highest concentration used in each response (100%), and the curve is inverted for the release function. For the MOR, the potency of DAMGO ([D-Ala_2, N-MePhe_4, Gly-ol]-enkephalin), an enkephalin analogue, is conserved for both assays and the same is true for met-enkephalin (Fig. 1c). The exchange effect can also be observed for dynorphin A (1–17) at the kappa opioid receptor (KOR), serotonin (5-HT) at the serotonin 1A receptor (5-HT_1AR), carbachol at the muscarinic 2 receptor (M_2R) and somatostatin-14 (SST-14) at the somatostatin 2 receptor (SST_2R) (Fig. 1d–g). Notably, agonist potencies at MOR and M_2R are conserved in the two states, whereas dynorphin, serotonin and somatostatin are significantly more potent at their cognate receptors for promoting the release of GTP (Fig. 1h; individual curves are shown in Extended Data Fig. 1).

There are multiple clinically relevant opioid agonists that span a broad range of pharmacological characteristics (including partial agonists and biased agonists); therefore, we used these tool compounds to determine whether the release function and binding function could be dissociated at the MOR. In addition, we tested two new compounds, which were selected on the basis of their scaffold variation from biased MOR agonists introduced by our laboratory (the SR series—for example, SR-17018) and for their characteristics as full agonists that are less potent than morphine in cellular assays. The latter consideration was based on a desire to not introduce more potent opioid agonists to the scientific literature. For each drug, DAMGO was assayed in parallel to serve as a reference, since DAMGO maintains the same potency in both responses and serves to define the maximum efficacy in both assays in this cell line. Not unexpectedly, several agonists perform similarly to DAMGO, preserving the potency in both responses; however, some agonists show a differential preference for potency (Fig. 2a) and/or efficacy (Fig. 2b) for one state over the other (see Extended Data Fig. 2 for curves and Extended Data Table 1 for parameters). Since the two effects were measured in parallel with DAMGO, we also determined the difference in transduction efficiencies (ΔΔlogR; Fig. 2c and Extended Data Table 1) for each agonist in the release assay and the binding assay. This representation permits normalization between responses to directly compare agonist activity[17]. The two new agonists show significant gains in the release function, having nearly a hundred-fold gain in selectivity for the release active state, as measured by the difference in transduction efficiencies; we have named these compounds muzepan1 and muzepan2 (Fig. 2d), as they are mu opioid receptor-acting compounds containing an ‘azepane’ ring.

Fig. 2: Opioid agonists exhibit differential preferences for GTP binding and release in CHO-MOR cell membranes.

a–c, Comparisons of the mean of the individual 35S-GTPγS binding and release: potencies (a), maximum efficacies (Emax normalized to DAMGO (100% versus baseline (0%)) (b), and difference in transduction efficiencies (ΔΔlogR, relative to DAMGO) (c).Data are mean with 95% confidence interval. Unpaired t-test was used for comparing binding and release parameters for each compound. Extended Data Fig. 2 shows concentration–response curves and Extended Data Table 1 presents parameters and number of individual replicates (n ≥ 3). d, Chemical structures of muzepan1 and muzepan2 with binding affinities (pKi with s.e.m., n = 6; Ki is the inhibition constant) determined from competition binding assays with 3H-naloxone.

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Several of the agonists that show a state preference have previously been identified as biased agonists that prefer G-protein signalling over β-arrestin2 recruitment (for example, oliceridine[18], PZM21[19], herkinorin[20], buprenorphine21,22,23 and SR-17018[24]). When tested in the cellular assays expressing the human MOR that were used to evaluate the biased agonism of SR-17018[24], both muzepan1 and muzepan2 show no preference between GTPγS binding and β-arrestin2 recruitment (Extended Data Fig. 3a and Extended Data Table 2). Moreover, the exchange selectivity for GTP release over binding is maintained at the human receptor (Extended Data Fig. 3b, Extended Data Table 3). Therefore, whereas many of the compounds that showed selectivity for release over binding also show preference for G-protein binding over β-arrestin2 recruitment, the correlation is not absolute.

To demonstrate the physiological significance of agonist-induced GTP release, the experiment was repeated in mouse spinal cord membranes. In the binding experiment, DAMGO promotes only a 40% stimulation in the native tissue ((1.4 ± 0.01)-fold; P < 0.001, paired t-test versus baseline; Extended Data Fig. 4a). We determined that the sodium-free conditions lead to very high levels of GTPγS binding, making it difficult to see an effect of DAMGO on release. This is not unexpected, as there are relatively low levels of MOR in the system, as reflected by the approximately 40% stimulation in the binding studies (Extended Data Fig. 4a). Therefore, to isolate the MOR-accessible G-protein pool, we used DAMGO in the presence of sodium and 35S-GTPγS in the pulse phase and diluted 100-fold as part of the chase (Methods and ref. [14]). We demonstrate that this is feasible in the CHO-mMOR cell line, where the potencies of DAMGO are similar to those in the sodium-free loading conditions, although the potency for the release function is slightly decreased (19 nM binding versus 43 nM release; P < 0.05, t-test; Extended Data Fig. 4b). In mouse spinal cord membranes, when 100 nM DAMGO is included in the pretreatment period, there is an increase of about 10% in 35S-GTPγS loading (P < 0.001; paired t-test), but no change is evident in the spinal cord of MOR-knockout mice (Extended Data Fig. 4c). We therefore took this modest stimulation as representative of MOR-mediated GTPγS loading in the mouse spinal cord.

In mouse spinal cord membranes, DAMGO is more potent in stimulating 35S-GTPγS binding than muzepan1 and muzepan2, whereas all agonists are full agonists (Fig. 3a). In the release paradigm, DAMGO loses potency, whereas muzepan1 and muzepan2 gain potency (Fig. 3b). Notably, the efficacy obtained by muzepan1 and muzepan2 reach the 10% maximal effect anticipated in the pulse loading (Extended Data Fig. 4c). This is in contrast to DAMGO, which does not reach this plateau, suggesting that in spinal cord, the enkephalin-like agonist may be selective against release. No significant effects were observed in spinal cord membranes from MOR-knockout mice (Extended Data Fig. 4d); therefore, the effects are likely to be due to MOR activation.

Fig. 3: Bidirectional GTP exchange in mouse spinal cord membranes.

a, 35S-GTPγS binding and release induced by indicated agonists. Data points show mean ± s.e.m.; potencies are presented as pEC_50 with 95% confidence interval. Binding: DAMGO: n = 8; muzepan1: n = 12; muzepan2: n = 4. Release: DAMGO: n = 8; muzepan1: n = 9; muzepan2: n = 4. b, Comparison of potencies between the binding and release exchange function for each agonist shown in a. Data are mean with 95% confidence interval. Unpaired, two-tailed, t-test of the mean of individual experiments.

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In mice, muzepan1 and muzepan2 are brain penetrant as they can be detected in brain 1 h following intraperitoneal injection of 3 mg kg−1 of compound (muzepan1: 463 ± 83 nM, muzepan2: 493 ± 187 nM, n = 3; Extended Data Fig. 5a). We therefore tested the compounds for antinociceptive efficacy in the hot plate and warm water tail immersion assays and compared them with morphine (Fig. 4a). The potencies determined by the effect at 1 hour correlate with their rank order potency in GTPγS binding (as well as release), although we note that at higher doses, muzepan1 remains nearly maximally efficacious for the 4 h duration of the test (Fig. 4b). Notably, none of the agonists have effects in MOR-knockout mice (Fig. 4b,c; for morphine see ref. [24]).

Fig. 4: Potency in mouse hot plate and warm water tail immersion nociceptive assays.

a–c, Morphine (a), Muzepan1 (b) and Muzepan2 (c) were tested at the indicated doses (in mg kg−1, intraperitoneal injection (IP)) in the hot plate (left) and tail flick (middle) assays in wild-type and MOR-knockout (MOR-KO) mice. Right, potency was determined by comparing the response at 1 h based on the percentage of MPE calculated from the baseline (BL) and a cut-off time of 20 s for the hot plate and 30 s for the tail flick. Potency curves are extended to convey 0% (determined by baseline) and 100% (determined by maximum cut-off time used to estimate the ED_50); potencies are presented with 95% confidence interval in the legends. a, Morphine: n = 6 (3 mg kg−1 and 24 mg kg−1), n = 10 (6 mg kg−1 and 12 mg kg−1). b, Muzepan1: n = 8 (3 mg kg−1), n = 5 (6 mg kg−1 and 12 mg kg−1), n = 7 (24 mg kg−1); MOR-KO: n = 5. c, Muzepan2: n = 4 (6 mg kg−1), n = 10 (12 mg kg−1), n = 6 (24 mg kg−1); MOR-KO: n = 3. HP, hot plate assay; TF, tail flick assay.

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Since muzepan1 and muzepan2 promote GTP release more potently than GTP binding, we considered whether a sub-efficacious dose would alter responsiveness to conventional opioid analgesics. When co-administered with 12 mg kg−1 morphine, a low dose of muzepan1 (3 mg kg−1) enhances and prolongs morphine-induced antinociception, and exceeds the calculated additive individual effects of each drug in both nociceptive tests (Fig. 5a). Furthermore, the inclusion of muzepan1 at varying doses of morphine produces the same enhancement, significantly improving the potency (median effective dose (ED_50)) of morphine by about two-fold (P < 0.0001; Fig. 5b and Extended Data Fig. 6) in both assays. Muzepan2 (12 mg kg−1), which alone produces very little antinociception, also enhances and prolongs the response of morphine in a manner that is greater than additive (Fig. 5c).

Fig. 5: GTP-release-selective agonists enhance and prolong opioid-induced antinociception in mice.

a, Hot plate (top) and tail flick (bottom) assays with muzepan1 (Muze1; 3 mg kg−1, intraperitoneal) and morphine (Mor; 12 mg kg−1, intraperitoneal) alone and combined. The calculated sum effect of both drugs (Σ) is shown for comparison. Right, mean (± s.e.m.) area under the curve (AUC). One-way ANOVA. b, AUC for 4 h hot plate (top) and tail flick (bottom) assays following treatment with different doses of morphine (Extended Data Fig. 6). Legend indicates the ED_50 (with 95% confidence interval). c, Muzepan2 (Muze2; 12 mg kg−1, intraperitoneal) also enhances morphine (12 mg kg−1, intraperitoneal)-induced antinociception (analysis as in a). d, Muzepan1 (3 mg kg−1, intraperitoneal) prolongs fentanyl (Fent; 0.3 mg kg−1)-induced antinociception in male and female mice (analysis as in a). See Extended Data Table 4 for two-way repeated measures ANOVA analyses of time course data for a–d and the number and sex of mice in each assay (n = 6–14; individual mouse data are shown as symbols in the bar charts) as well as the results of post hoc ANOVA analyses for drug effect over time.

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To assure that the compounds are not indirectly enhancing morphine effects by competing with morphine metabolism, which occurs via glucuronidation[25], we also used fentanyl, which is metabolized by CYP3A4[26]. Notably, neither compound competes with CYP3A4 as determined by in vitro competition studies (muzepan1: 11%, muzepan2: <10% inhibition at 10 µM; Extended Data Fig. 5b). Whereas only male mice were tested with morphine owing to sex-dependent differences in morphine metabolism and sensitivity[27], both male and female mice were tested with fentanyl, since the responses that we measured are comparable between the sexes24,28. When combined, muzepan1 dramatically prolongs the efficacy of fentanyl over time in a manner that is greater than the predicted additive effects of both compounds (Fig. 5d; see Extended Data Table 4 for statistical analyses of all time course data in Fig. 5).

Notably, the effects of muzepans on opioid-mediated antinociception resemble those of adding a positive allosteric modulator to an MOR agonist. Therefore, we tested whether muzepan1 could act allosterically by measuring its concentration-dependent effect on fentanyl-induced GTPγS binding in spinal cord membranes. Since we do not observe a leftward shift in fentanyl potency, we cannot conclude that muzepan1 behaves as a positive allosteric modulator at MOR (Extended Data Fig. 7).

Whereas it is desirable to enhance the antinociceptive effects of analgesic opioids, it is not desirable to enhance the respiratory suppression and bradycardia associated with these agonists. Therefore, we tested the effect of muzepan1 alone and in combination with fentanyl in mouse pulse oximetry and heart rate monitoring studies (Fig. 6). Alone, muzepan1 produces respiratory suppression and bradycardia at 24 mg kg−1 and 48 mg kg−1 but not at 3 mg kg−1; by contrast, fentanyl at lower doses (0.3 mg kg−1 and 2 mg kg−1) produces marked decreases in arterial oxygen saturation and heart rate (two-way repeated measures ANOVA, see Extended Data Table 5 for all comparisons). The response to the combination of 3 mg kg−1 muzepan1 with 0.3 mg kg−1 or 2 mg kg−1 fentanyl does not differ from the effect produced by fentanyl alone in either assay. Furthermore, increasing the dose of muzepan1 to 24 mg kg−1, a dose that produced mild but significant effects alone (two-way repeated measures ANOVA; Fig. 5 and Extended Data Table 5), is also not additive with fentanyl. Therefore, inclusion of muzepan1 with fentanyl prolongs fentanyl-induced antinociception, whereas respiratory and cardiac effects are not enhanced.

Fig. 6: Muzepan1 does not enhance fentanyl-induced respiratory suppression or bradycardia in mice.

a,b, Muzepan1 produces respiratory suppression (a) and bradycardia (b) at intraperitoneal doses of 24 mg kg−1 and 48 mg kg−1 but not at 3 mg kg−1 in male and female mice. Neither dose alters fentanyl (0.3 mg kg−1 or 2 mg kg−1, intraperitoneal) effects when combined. c,d, The AUC for the 1.5 h after drug treatments (doses in mg kg−1, intraperitoneal, indicated in brackets) for oxygen saturation (c) and heart rate (d). One-way ANOVA with Tukey’s post hoc test. See Extended Data Table 5 for two-way repeated measures ANOVA for time course data in a,b and number and sex of mice in each assay (n = 4–8; individual mouse data are shown as symbols in the bar charts).

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Discussion

The state-dependent interaction between GPCRs and G proteins that we reveal here implies an ongoing cycle that would affect the availability of the G proteins and their ability to engage with different effectors. Moreover, steric hindrance produced by a constitutive G-protein cycle may prevent the receptor from interacting with other effectors, particularly with direct binders, such as β-arrestins. It is attractive to speculate that persistence of the cycle could prevent the recruitment of β-arrestins. This is consistent with the observation that many of the agonists that produce state-dependent increases in potency and/or efficacy of GTP release, have been characterized as G-protein-signalling-biased agonists (that is, those that are biased against recruiting β-arrestins). However, here we present compounds that diverge from this correlation. Specifically, we focus on two full agonists that do not show bias against β-arrestin recruitment, and demonstrate that the release state selectivity cannot be the only contributor to G-protein signalling bias. Although the exceptions can be identified, it remains possible that changes in the G-protein cycling kinetics will affect GPCR signalling bias.

Our study demonstrates that all opioid agonists tested in the transfected cell system promote GTP release and that the potencies for both release and binding are similar for many agonists. In mouse spinal cord membranes, agonists are less potent in the binding assays compared with in the transfected cell system; this is probably due to the low representation of MOR-expressing neurons in the gross dissection of the spinal cord. In the same preparations, DAMGO-induced release is significantly diminished, and this decrease may be amplified by the low number of receptors in the preparation. Regardless of the concentration of receptor, the release induced by muzepans remains efficacious, and their potency is significantly improved, suggesting that the robust effect on release is preserved even in this heterogenous endogenous system.

In this system, we have utilized a nonhydrolysable form of GTP (GTPγS) to demonstrate that the GEF function of the GPCR is reversible and that agonists that show a preference for the reversal can have effects at sub-efficacious doses in vivo. It is important to acknowledge that, in the endogenous setting, we must consider that the GTP bound to the G protein is not stabilized and is still subject to hydrolysis by the intrinsic GTPase activity of the G protein. Further, this can be acted upon by other endogenous GAPs such as regulator of G-protein signalling (RGS) family proteins. However, if the receptor can promote GTP binding and release independent of the energy expenditure of hydrolysis, it may present a means to affect G-protein coupling kinetics which would change the effect of such GAPs in the system.

Our findings show that the receptor can facilitate GTP release; however, the context of the environment and the nature of the ligand will determine the ultimate consolidation of signalling. Therefore, the bidirectionality of the exchange function of a GPCR will affect the overall kinetics and energy landscape of GPCR activation. Thus, we propose a modification of the two-state model of receptor activation (as we present in Fig. 1a). We believe the net effect of an agonist on the fate of the G protein is a sum of the hydrolysis and the exchange, and not simply the forward reaction limited to energy-consuming hydrolysis.

The nature of the GTPγS binding assay favours the detection of coupling to inhibitory G proteins owing to their abundance in cells; moreover, the actions at the MOR can be blocked by pertussis toxin, further implicating the Gαi/o class of proteins[14]. It remains to be determined whether this occurs for other G proteins (such as, Gs or Gq proteins). In addition to having a role in determining preference for G-protein signalling, the release cycle of one G protein could shift the preference of the receptor away from another G protein. Effectively, the release cycle of a previously coupled G protein would also compete with the coupling of another class of G protein (that is, the inability to change coupling from Gi to Gq). One benefit of this approach is that it allows for the simultaneous assessment of two active states of the receptor in a single receptor population and that it can be amenable to testing in tissue. In the spinal cord, we note that DAMGO, an MOR-selective enkephalin analogue, is more potent in promoting GTP binding than release, whereas in the CHO overexpression cell lines, the DAMGO effects are nearly equivalent in both assays. Other GPCRs examined in the CHO cell lines may also prove to have differential affinities for one active state over the other when tested in the endogenous setting.

Our studies have shown that compounds that prefer the release state of the MOR are able to enhance and prolong the effects of opioid analgesics in mouse thermal nociception assays. We favour a model in which the receptor engages in multiple exchange events, keeping the G protein nearby to rapidly exchange nucleotide, and allowing another cycle to begin limiting the expenditure of energy through GTPase activity. This would enable the receptor-mediated G-protein activation to be reset without waiting for the completion of the GTPase cycle and, further, it keeps the proteins in close proximity. Of note, SR-17018 is also release-preferring, as well as a partial agonist, and a G-protein-signalling-biased agonist (over β-arrestin2 recruitment). In mice, it produces antinociception without respiratory suppression; moreover, it does not lead to tolerance in several pain assays24,29,30. In addition to being a G-protein-signalling-biased partial agonist, SR-17018 has also been shown to be a noncompetitive agonist that stabilizes G-protein signalling[16]. Moreover, treatment of morphine-tolerant mice with SR-17018 leads to a reversal of morphine tolerance while suppressing signs of withdrawal and restoring morphine sensitivity in mice[29]. It is not yet clear what exact properties lead to SR-17018 restoration of sensitivity but the identification of this property and the further generation of probe compounds that preserve or eliminate each property independently will aid our studies to disentangle desirable opioid effects from unwanted side effects.

Notably, muzepan1 produces respiratory suppression and bradycardia at 24 and 48 mg kg−1, which may not be unexpected, as it is a full agonist that does not show a preference for G-protein signalling over β-arrestin2 recruitment. However, whereas it is not surprising that the inactive dose of 3 mg kg−1 muzepan1 does not produce an additive effect with 0.3 or 2 mg kg−1 fentanyl, it is surprising that 24 mg kg−1 dose of muzepan1, which alone does suppress activity, does not enhance or prolong the effects of 2 mg kg−1 fentanyl on respiratory suppression or bradycardia. There remains the potential that we have reached a ceiling effect with the mice when combining the higher doses of each drug. However, we do note that the median lethal dose (LD_50) for fentanyl has been recorded at 113 mg kg−1 (intraperitoneal) in C57BL/6 mice, suggesting that there should be potential to detect an additive decrease in oxygen saturation and heart rate[31]. Notably, the muzepans have been explored here for their robust ability to produce GTP release, and they may not be considered safer opioids at this time, as we clearly show that significant respiratory suppression can be observed with muzepan1 at high doses. Moreover, these probe compounds have not been evaluated for other opioid side effects, such as addiction liability, tolerance or dependence, nor have they been evaluated for general safety or toxicity. However, developing agonists that preserve the release preference, while also implementing other favourable properties, such as slow onset pharmacokinetics, partial agonism and fine-tuning preference for different signalling partners (β-arrestin versus G protein) is underway to improve the safety profile of such ligands.

With the examples provided here, we can conclude that at nearly inactive doses, muzepan1 enhances fentanyl-induced antinociception but does not enhance fentanyl-induced respiratory suppression in mice. We speculate that by favouring a release state of the receptor, either the receptor or the G proteins may have less opportunity to engage with secondary effectors that promote the cardiovascular side effects while perpetuating signalling that leads to antinociception. For example, a G protein that is cycling between GTP-bound and GDP-bound states may have more or fewer opportunities to interact with the Gβγ subunits, thereby preventing their further interaction with ion channels or other effectors, such as regulatory kinases or scaffolding proteins. Alternatively, the perpetual engagement between the receptor and G protein could sterically prevent engagement with other proteins, such as β-arrestins or other G-protein types. In this manner, one downstream signalling pathway may become more efficient while another is disengaged. In the case of SR-17018, a preference for promoting G-protein binding over β-arrestin2 recruitment as well as a preference over GIRK activation have been demonstrated24,32,33. It remains to be determined what preferences will be desirable; however it is evident that the environment of the receptor in the tissue in which it mediates its response will dictate any preference for a secondary cascade.

By demonstrating that there are changes in both the rank order potency and efficacy of agonists, we demonstrate that there is a change in activity of the receptor for the GTP binding versus GTP release functions. Further, we conclude that the GTP binding and release events are independently regulated by receptor activity and that an agonist has the opportunity to not only promote G-protein activation but also to modulate G-protein activity in a state-selective manner. We demonstrate that a GTP-releasing agonist can enhance and prolong the effects of conventional opioid agonists in antinociception without enhancing respiratory suppression or bradycardia in mice. Therefore, the selective modulation of potency and efficacy in the release function can change the dynamics of the drug effects in vivo. More broadly, since a ligand can induce a preference for exchange selectivity at a particular receptor, the agonist could thereby effectively regulate the availability of the Gα subunit for subsequent protein interactions and downstream signalling events. Overall, these findings demonstrate that for the opioid receptors, and possibly for all GPCRs, drug action will be a composite of its ability to promote both GTP binding and GTP release from the G protein.

Methods

Chemicals

Compounds were obtained from the following vendors: Sigma-Aldrich: oxymorphone (O-004-1ML), loperamide (1448005), methadone hydrochloride (M0267), morphine sulfate pentahydrate (M8777), sufentanil citrate (SML0535), herkinorin (5.08018.0001), buprenorphine (B9275), serotonin hydrochloride (H9523), carbachol (C4382), naloxone hydrochloride (N7758) and GDP (G7127). Cayman Chemical: PZM21 (20576-10), fentanyl citrate (22659) and GTPγS (35098). DAMGO (11711) was from Tocris Bioscience. Oliceridine (TRV-130; 510256) was from MedKoo Biosciences. Dynorphin A 1-17 (3195) was from Fisher Scientific. Somatostatin-14 was custom synthesized by CPC Scientific. Met-enkephalin (30-0-10) was from American Peptide Company. SR-17018, muzepan1 and muzepan2 were made in house. 35S-GTPγS (NEG030H001MC) was from Revvity. 3H-(-)naloxone was provided by the National Institute on Drug Abuse (NIDA) Drug Supply Program.

Animals

Male and female C57BL6/J (JAX:000664) and male MOR-KO (JAX:007559) mice were purchased from The Jackson Laboratory. MOR-KO mice were maintained by homozygous breeding. Mice were housed in groups of 2–5 and maintained on a 12 h:12 h light:dark cycle with food and water ad libitum. All adult mice were naive and at least ten weeks old prior to injection. For thermal antinociception tests, investigators were blinded to drugs and doses being administered. Mice were administered drugs intraperitoneally at a volume of 10 µl per g; for combinations of drugs, a single solution was prepared. All mice were used in accordance with the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals with approval by The Herbert Wertheim UF Scripps Institute of Biomedical Technology and Innovation Animal Care and Use Committee.

Cell lines

Chinese Hamster Ovary (CHO-K1) cells were purchased from ATCC. Human MOR (hMOR), mouse MOR (mMOR) and human KOR (hKOR) cells have been described previously[24]. For the other cell lines, receptor constructs were purchased from cDNA Resource Center including SST_2R (SSTR20TN00), 5-HT1AR (5TR01ATN00) and M_2R (MAR020TN00). The SSTR_2R, 5-HT1AR and M_2R cell lines were produced by electroporation of a pcDNA3.1 vector containing the N-terminally HA-tagged receptor into the parental cell line. A BD FACSAria3 flow cytometer was utilized to select for individual cells expressing receptor utilizing an anti-HA AlexaFluor 488 conjugate antibody (1:200). hMOR, hKOR, SST_2R, 5-HT1AR and M_2R cell lines were maintained under geneticin selection (500 µg µl−1). The mMOR cell line was maintained under puromycin selection (500 µg µl−1). Cells were maintained in 1:1 DMEM:F12 media supplemented with 10% heat-inactivated fetal bovine serum (HI-FBS) at 37 °C with 5% CO_2. Prior to use in assays, cells were serum-starved for 30 min (hMOR, hKOR, 5-HT1AR and M_2R) or 2 h (mMOR and SST_2R) then removed from the plate with 5 mM EDTA in PBS with a scraper. Pellets were rinsed with PBS and frozen in 1.5 ml tubes at −80 °C until use. All cell lines were verified to be mycoplasma-free by monthly testing.

35S-GTPγS binding and release

35S-GTPγS binding

35S-GTPγS binding in cell lines was performed similarly as previously described16,24. In brief, for CHO-hMOR cells, pellets were homogenized with a Potter–Elvehjem Teflon-on-glass Dounce homogenizer in homogenization buffer (10 mM Tris (pH 7.4), 100 mM NaCl, 1 mM EDTA) then pelleted (20,000g, 4 °C, 30 min). All other cell lines (CHO-mMOR, CHO-hKOR, CHO-hSST_2R, CHO-h5-HT1AR and CHO-hM_2R) were homogenized in 10 mM Tris (pH 7.4), 1 mM EDTA. All reactions were performed with 0.1% DMSO and 0.1 nM 35S-GTPγS (specific acitivity = 1,250 Ci mmol−1; Revvity) in 50 mM Tris (pH 7.4), 100 mM NaCl, 5 mM MgCl_2, 1 mM EDTA with differing quantities of protein and GDP. For CHO-hMOR, CHO-mMOR and CHO-5-HT1AR cells, 10 µg protein and 10 µM GDP was used. CHO-M_2R cell reactions were performed with 3 µg protein and 3 µM GDP. CHO-SST_2R cell reactions were performed with 10 µg protein and 20 µM GDP. CHO-hKOR cell reactions were performed with 15 µg protein and 3 µM GDP. Reactions were terminated by rapid filtration through GF/B filters with cold water after a 1 h incubation. Filters were punched into 96-well Opti-plates (Revvity) and dried overnight. Radioactivity was measured with 100 µl per well MicroScint-20 on a MicroBeta 2 (Revvity). For determination of bias between GTPγS and β-arrestin2 recruitment, 35S-GTPγS binding was performed in CHO-hMOR cells exactly as described[24] and is shown in Extended Data Fig. 3a.

For 35S-GTPγS binding in C57BL/6J and MOR-KO spinal cord, tissue was homogenized via a Polytronic Tissue Tearor (BioSpec Products, 985370) and then a glass-on-glass Dounce homogenizer in homogenization buffer (10 mM Tris (pH 7.4), 1 mM EDTA). Homogenate was pulled through a 28G insulin needle before pelleting at 20,000g, 4 °C, 30 min. Reactions were performed with 10 µg, 10 µM GDP, 0.1% DMSO and 0.1 nM 35S-GTPγS and incubated for 1 h at 25 °C. Reactions were terminated as described above.

35S-GTPγS release in sodium-free conditions

Membranes were prepared in 10 mM Tris (pH 7.4), 1 mM EDTA as described for 35S-GTPγS binding in (10 mM Tris (pH 7.4), 1 mM EDTA). For 35S-GTPγS loading (the ‘pulse’) of CHO-hMOR, CHO-mMOR and CHO-5-HT1AR cells, 1 mg of protein was incubated with 1 nM 35S-GTPγS and 10 µM GDP in 20 ml of 50 mM Tris (pH 7.4), 5 mM MgCl_2, 1 mM EDTA for 1 h at 25 °C. Identical conditions were used for CHO-SST_2R cells except GDP was increased to 20 µM. For CHO-M_2R cells, 0.3 mg of protein was incubated in the same conditions with 3 µM GDP. For CHO-hKOR cells, 1.5 mg of protein was incubated with 3 µM GDP. Release was performed by diluting tenfold into 50 mM Tris (pH 7.4), 100 mM NaCl, 5 mM MgCl2, 1 mM EDTA supplemented with 1 µM cold GTPγS and GDP corresponding to the receptor utilized. Release was performed for 1 h at 25 °C then terminated as described for 35S-GTPγS binding. See Supplementary Fig. 1 for a schematic.

35S-GTPγS release following 100 nM DAMGO-stimulated loading

For CHO-mMOR cells and C57BL/6J spinal cord studies relying on 100 nM DAMGO for loading, membranes were prepared as described for the sodium-free loading conditions. Then, 1 mg of protein was incubated with 1 nM 35S-GTPγS, 10 µM GDP, and 100 nM DAMGO in 2 ml of 50 mM Tris (pH 7.4), 100 mM NaCl, 5 mM MgCl_2, 1 mM EDTA for 1 h at 25 °C. Release was performed in large-volume 96-well plates with 10 µg protein, 10 µM GDP, 0.1% DMSO, at 2 ml final volume and incubated for 1 h at 25 °C. Reactions were terminated as described above for 35S-GTPγS binding. See Supplementary Fig. 2 for a schematic.

Radioligand binding

3H-(-)naloxone binding studies were performed as previously described[16]. Membranes were prepared via homogenization with a Polytronic Tissue Tearor then glass-on-glass Dounce homogenization in homogenization buffer (50 mM Tris (pH 7.4), 1 mM EDTA). Homogenate was pulled through a 28G insulin needle before pelleting at 20,000g, 4 °C, 30 min. Binding was performed with 10 µg membrane in 10 mM Tris (pH 7.4) containing 1% DMSO and approximately 2 nM 3H-naloxone (1.50–2.04 nM; specific acitivity = 48.19 Ci mmol−1) at a final volume of 200 µl. Reactions were incubated for 1 h at 25 °C then filtered through GF/B fiberglass filters with cold 10 mM Tris (pH 7.4) by rapid filtration over GF/B filters and washed with cold 10 mM Tris buffer. Filters were punched into white, 96-well OptiPlate and dried overnight. Radioactivity was quantified using 100 µl per well MicroScint-20 on a MicroBeta2. The Kd of 3H-(-)naloxone determined by homologous competition in these studies is 0.83 (0.33–1.1) nM, n = 6.

β-arrestin2 recruitment

β-arrestin2 recruitment was performed as previously described[24]. In brief, U2OS-β-arrestin2-hMOR PathHunter cells were plated at a density of 5,000 cells per well in a 384-well, white-walled assay plate in OptiMEM supplemented with 1% HI-FBS and incubated at 37 °C with 5% CO_2 for 16–20 h. Drug was prepared in PBS and cells were treated for 90 min at 37 °C. β-arrestin2 recruitment was determined using the PathHunter Detection Kit and luminescence was measured using a BioTek Synergy Neo2 multimode plate reader (BioTek).

Cytochrome P450 inhibition

Inhibition studies were carried out with 10 µM compound incubated with human liver microsomes and selective marker substrates (1A2, phenacetin demethylation to acetaminophen; 2C9, tolbutamide hydroxylation to hydroxytolbutamide; 2D6, bufuralol hydroxylation to 4′-hydroxybufuralol; 3A4, midazolam hydroxylation to 1′-hydroxymidazolam). After a 10 min incubation, the reaction was terminated and the percent inhibition was determined as previously described[34].

Antinociception

Thermal antinociception was performed as previously described[24]. Prior to testing, mice were habituated to the testing room for 1 h. The tail flick test was determined as the amount of time until a mouse rapidly flicked its tail when placed 2–3 cm into a 49 °C water bath with a cut-off applied at 30 s. The hot plate test was measured using a 52 °C hot plate analgesia meter (Columbus Instruments) and forepaw or hindpaw licking or flicking were observed with a maximum latency of 20 s to prevent tissue damage.

Respiration and heart rate measures

Respiratory and heart rate parameters were simultaneously measured using the MouseOx Plus pulse oximeter (Starr Life Sciences) as previously described16,24. Two days prior to testing, mice were shaved around the neck and habituated for 30 min to the collars and 50 ml conical tubes which were modified to restrain the mice. The following day, the mice were habituated to the collars and restraint for 30 min. On testing day, the basal vital signs of the mice were determined for 30 min then animals were injected and monitored for 90 min.

Pharmacokinetics

Male C57BL6/J mice were injected intraperitoneally with muzepan1 or muzepan2 at the doses indicated and blood was collected at indicated time points. Brains were collected following cervical dislocation, and snap frozen in liquid nitrogen. Samples were subjected to liquid chromatography (Shimadzu)–tandem mass spectrometry from AB Sciex. Pharmacokinetic parameters were calculated using a noncompartmental model[24] (Phoenix WinNonlin, Pharsight).

Data analysis

Concentration response studies were analysed by nonlinear regression analysis following normalization (baseline = 0 and maximum response = 100%). For all of the studies, we used the mean of the individual experiments to generate the potency (logEC_50) and efficacy (Emax) values as presented as pEC_50 with 95% confidence interval in the figures and graphs and as pEC_50 with s.e.m. in the table. Both potency and efficacy parameters were produced using three-parameter nonlinear regression with adaptation of the equation:

$$\mathrm{Response}=\mathrm{basal}+\frac{{E}_{\max }-\mathrm{basal}}{{10}^{({\mathrm{logEC}}_{50}-X)}+1}$$

where X is the agonist concentration in log molar units and logEC_50 is agonist potency in log molar units. Statistical analyses comparing binding and release parameters, for each compound, was performed by unpaired t-test. For the MOR studies, DAMGO was tested in parallel for all compounds and was used for normalization (baseline = 0, DAMGO at 10 µM = 100%). Statistical comparisons between binding and release parameters were performed by unpaired t-test comparing the individual parameters determined in each experiment; the number of replicates are indicated in the table.

In addition, a form of the operational model frequently applied to bias analysis was employed as the binding and release assays were considered independent measures of agonist activity17,35. The equation takes the form:

$$\mathrm{Response}=\mathrm{basal}+\frac{{E}_{\max }-\mathrm{basal}}{1+{\left(\frac{1+{10}^{(X+\log K)}}{{10}^{(X+\log {R}_{\text{reference}}+\Delta \log R)}}\right)}^{n}}$$

where basal and Emax describe the system limits, and n defines the transducer slope. For the reference agonist DAMGO, logK and ΔlogR are held constant at zero. In this case, the logRreference for the reference agonist reduces to the plogEC_50. For full test agonists the logRreference is held constant, from the fit of the reference agonist, and the ΔlogR is permitted to float. The logK is held constant at zero for all full agonists. For partial test agonists, the logRreference is again held constant and the ΔlogR and logK are permitted to float.

For the determination of the transduction efficiency, the ΔlogR was determined for each individual assay with DAMGO serving as the reference agonist. The ΔΔlogR was determined by unpaired t-test between the ΔlogR from the G-protein release assay and the ΔlogR of the G-protein binding assay. The same approach was used to determine the bias factor comparing the ΔlogR in G-protein binding versus the ΔlogR in β-arrestin2 recruitment in the CHO-hMOR cells.

In radioligand binding studies, naloxone competition was fit to the homologous (naloxone) or heterologous (muzepan1, muzepan2) competition equation:

$$\mathrm{Binding}=\mathrm{bottom}+\frac{{B}_{\max }\times [{}^{3}\text{H-naloxone}]}{[{}^{3}\text{H-naloxone}]+{10}^{\log (X)}+{K}_{\mathrm{naloxone}}}$$

where, for 3H-naloxone binding, ‘bottom’ and Bmax are the non-specific and maximum binding, [3H-naloxone] is the radioligand concentration, Knaloxone is the naloxone equilibrium dissociation constant, and X is the cold naloxone concentration in molar units. For muzepan1 and muzepan2, competition data were fit to the heterologous competition equation:

$$\mathrm{Binding}=\mathrm{bottom}+\frac{\mathrm{Top}\,-\,\mathrm{bottom}}{1+\frac{{10}^{\log (X)}}{{10}^{{K}_{i}\times \left(1+\frac{[{}^{3}{\rm{H}}-\mathrm{naloxone}]}{{K}_{\mathrm{naloxone}}}\right)}}}$$

where parameter definitions are shared between the two equations. In the heterologous competition equation, Top is the maximum observed binding, Ki is the molar affinity constant of the competitive ligand, and X is the concentration of the competitive ligand. Experiments were run together and both [3H-naloxone] and Knaloxone are held constant for the analysis.

Antinociception

A maximum possible effect (%MPE) was calculated as 100% × [(baseline response − test response)/(cut-off time − baseline response)]. For the determination of potency, the %MPE was compared at the 1 h time point by nonlinear regression analysis in GraphPad Prism (v.10.4), sharing the Hill slope and constraining the bottom to 0 and the top to 100%. For the comparison of morphine potency with and without muzepan1, the AUC was determined from the %MPE over the 4 h testing period; this was normalized to the maximum possible effect (the AUC if all points reached 100%) and fit the nonlinear regression analysis. Statistical comparisons of the logED_50 were made between two curves in Prism using an extra-sum-of-squares F test.

Respiration and heart rate

A two-way repeated measures ANOVA was used to compare drug effects (35–120 min) as a function of time and the results are presented in Extended Data Table 5. In addition, the AUC was determined by normalizing to the first 30 min of habituation for the following drug effect over 1 h. These values were then compared by one-way ANOVA comparing to vehicle with a Dunnett’s post hoc test, or between drug treatments (fentanyl versus fentanyl plus 3 mg kg−1 or 24 mg kg−1 muzepan1; 3 groups, Tukey’s post hoc test).

Reporting summary

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

Data availability

All data shown in graphs are provided as a Source Data file.

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Acknowledgements

We acknowledge Klaus-Peter Hofmann for critical and constructive discussion of our preliminary findings. Funding was provided by the National Institute on Drug Abuse/NIH to L.M.B. (DA038964), and L.M.B. and T.D.B. (DA033073). Pharmacokinetic data were collected using a mass spectrometer funded by NIH grant 1 S10OD030332-01.

Author information

Authors and Affiliations

1. Department of Molecular Pharmacology and Physiology, Morsani College of Medicine, University of South Florida, Tampa, FL, USA

Edward L. Stahl, Matthew A. Swanson & Laura M. Bohn

2. The Skaggs Graduate School of Chemical and Biological Sciences at Scripps Research, La Jolla, CA, USA

Matthew A. Swanson & Laura M. Bohn

3. Department of Molecular Medicine, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation and Technology, Jupiter, FL, USA

Vuong Q. Dang, Michael D. Cameron, Nicole M. Kennedy & Thomas D. Bannister

Contributions

L.M.B. and E.L.S. conceived and designed the studies, supervised the experiments, analysed data and wrote the manuscript. E.L.S. developed the experimental design for the in vitro studies and performed both in vivo and in vitro experiments. E.L.S. defined the models. E.L.S., M.A.S. and V.Q.D. performed the biochemistry experiments and contributed to analysis. L.M.B. developed the experimental design for the in vivo studies. M.A.S. and E.L.S. performed the animal studies with the assistance of L.M.B. and V.Q.D. for extensive blinding and drug preparation. M.D.C. provided the pharmacokinetic studies. T.D.B. and N.M.K. designed and synthesized SR-17018, muzepan1 and muzepan2.

Corresponding authors

Correspondence to Edward L. Stahl or Laura M. Bohn.

Ethics declarations

Competing interests

The University of Florida holds a patent on SR-17018 with L.M.B. and T.D.B. (US10751335B2) and has filed for patent protection of muzepan1 and muzepan2 (with L.M.B., N.M.K. and E.L.S.). The other authors declare no competing interests.

Peer review

Peer review information

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

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Extended data figures and tables

Extended Data Fig. 1 GPCRs induce both GTP binding and release from Gα proteins and the process is ligand dependent.

Shown are the radioactivity counts (dpm) without normalization. Means with s.e.m are plotted, and the parameters are presented with 95% CI in Fig. 1, MOR: n = 6 binding, 5 release; KOR: n = 3 binding, 5 release; 5HT1AR: 8 binding, 7 release; M2 n = 3 binding, 3 release; SST2: n = 4 binding, 4 release. Accompanies Fig. 1.

Source Data

Extended Data Fig. 2 Different opioid agonists show state selectivity for GTP binding or release.

Shown are the responses to each agonist normalized to baseline (0%) and DAMGO max response (100%) assessed concurrently with a DAMGO concentration response curve. Means with s.e.m are plotted. Accompanies Fig. 2 and Extended Data Table 1 which includes the number of replicates (n ≥ 3) and curve parameters. DAMGO is the mean of all DAMGO run in parallel with each test opioid (n = 56 binding, n = 45 release).

Source Data

Extended Data Fig. 3 Characterization of agonists at the human MOR.

a. Determination of GTPγS binding in CHO-K1 cells expressing human MOR and βarrestin2 recruitment to the human MOR in U2OS PathHunter® cells from Millipore. See Extended Data Table 2 for EC_50, Emax and ΔΔlogR values with n. b. Comparison of GTP binding and release following sodium free loading of 35S-GTP binding in hMOR CHO cell membranes with graphic presentation of the mean of the pEC_50 and Emax from individual experiments with 95% CI. The mean of the ΔΔlogR with 95% CI is presented and determined by comparing ΔlogR values from binding and release using an upaired, two-tailed t-test. See Extended Data Table 3 for EC_50, Emax and presentation of ΔΔlogR with n.

Source Data

Extended Data Fig. 4 Adaptation of the method to spinal cord and verification in mMOR-CHO cells.

a. Spinal cord from C57BL6/J male mice stimulated with 10 µM DAMGO induces a 40% increase in GTPγS binding over baseline (0) (left, p < 0.001, paired t-test, n = 3 mice as shown). Removing sodium from the system leads to an ~4-fold increase in baseline binding of 35S-GTPγS (middle, **p < 0.01, paired t-test, n = 3 mice). Under these conditions, DAMGO-induced release cannot be detected (right, p > 0.05 paired t-test, n = 3 mice). b. In CHO cells expressing mouse MOR cells, the inclusion of 100 nM DAMGO in the preloading condition, in the presence of sodium, is sufficient to load the 35S-GTPγS and that DAMGO-mediated release can still be observed; the purple line and diamonds plots the curve with the consideration of the residual 1 nM remaining DAMGO in the chase (*p < 0.05 unpaired t-test comparing individual pEC_50; pEC_50 presented with 95%CI in figure legend). c. Use of 100 nM DAMGO in the preloading of spinal cord membranes results in a 10.5% increase in labeling that does not occur in membranes from MOR-KO mice (*p < 0.05 paired t-test, n = 3 mice). d. Neither 35S-GTPγS binding nor release is detected in spinal cord membranes from MOR-KO mice (plotted is the mean with s.e.m.; n = 3 mice per point). Accompanies Fig. 3.

Source Data

Extended Data Fig. 5 Pharmacokinetic properties of Muzepan1 and Muzepan2.

a. Brain and plasma levels measured in male C57BL6/J mice at the indicated time points; means with SD are shown (muzepan1: 3 mg/kg, n = 6 plasma, 3 brain; 24 mg/kg n = 3 plasma, 4 brain; muzepan2: 3 mg/kg: n = 3 plasma, 3 brain; 24 mg/kg, n = 4 plasma, 4 brain). The conversion of tissue levels to molarity assumes 1 gram = 1 mL. b. Single point (10 µM) inhibition of cytochrome P450 enzymes presented as % inhibition and comparison to known standard inhibitors performed in duplicate.

Source Data

Extended Data Fig. 6 Hot plate (top) and Tail flick (bottom) responses of C57BL6/J male mice at indicated doses of morphine (mg/kg, i.p.) as indicated in the figure legends with 3 mg/kg i.p. Muzepan1.

The sum of the mean effect of the two drugs is shown as Σ. Statistical comparisons over time are in Extended Data Table 2. Bar charts present the mean with s.e.m. of the area under the curve (AUC) calculated over the 4-hour assay. Statistical comparisons are made by ordinary one-way ANOVA comparison within doses of morphine comparing morphine alone, the combination, and the calculated sum, *p < 0.05, ***p < 0.001, ****p < 0.0001). Mouse numbers are shown as circles in the AUC plots and also in Extended Data Table 4 along with 2-way RM-ANOVA comparing the effects over time with post-hoc analysis. Accompanies Fig. 5b.

Source Data

Extended Data Fig. 7 Competitive Schild analysis of fentanyl-stimulated GTPγS binding in the presence of increasing concentrations of muzepan1 in mouse spinal cord.

Data are normalized to 10 µM point of fentanyl as 100% and to vehicle alone (0%). The potencies of fentanyl are shown in the table inset. Muzepan1 alone is a full agonist, therefore, it stimulates binding at 10 and 100 nM when given alone (left x axis). Values for pEC_50 and Emax are presented with 95% CI from analysis of the curves using GraphPad Prism (v. 10.4), (n = 7 individual mice with all three treatment groups assayed in parallel).

Source Data

Extended Data Table 1 Pharmacological parameters from Fig. 2 presented as the mean pEC_50 and EMAX with s.e.m. determined from the mean of individual curves

Extended Data Table 2 Determination of GTPγS binding in CHO-K1 cells expressing human MOR and βarrestin2 recruitment to the human MOR in U2OS PathHunter® cells from Millipore

Extended Data Table 3 Determination of GTP binding and release in CHO-K1 cells expressing human MOR

Extended Data Table 4 Statistical analysis of the hot plate (HP) and tail flick (TF) studies as a function of dose and time presented in the indicated panels in Fig. 5

Extended Data Table 5 Statistical analysis of the respiratory (% arterial oxygen saturation, %O2) and heart rate (beats per minute) measures as a function of dose and time over 1.5 h

Supplementary information

Supplementary Information

Reporting Summary

Peer Review File

Source data

Source Data Figs. 1–6, Source Data Extended Data Figs. 1–7

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Stahl, E.L., Swanson, M.A., Dang, V.Q. et al. GTP release-selective agonists prolong opioid analgesic efficacy. Nature 650, 500–508 (2026). https://doi.org/10.1038/s41586-025-09880-5

- Received: 02 April 2025

- Accepted: 06 November 2025

- Published: 17 December 2025

- Version of record: 17 December 2025

- Issue date: 12 February 2026

- DOI: https://doi.org/10.1038/s41586-025-09880-5

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Amendments & Corrections

- Author Correction: Nutrient-sensing nuclear receptors coordinate autophagy

- Author Correction: Relatively warm deep-water formation persisted in the Last Glacial Maximum

Author Correction Published: 27 January 2026

Author Correction: Nutrient-sensing nuclear receptors coordinate autophagy

Jae Man Lee,

Martin Wagner,

Rui Xiao,

Kang Ho Kim,

Dan Feng,

Mitchell A. Lazar &

…

David D. Moore

Nature volume 650, page E8 (2026)

The Original Article was published on 12 November 2014

Correction to: Nature https://doi.org/10.1038/nature13961 Published online 12 November 2014

In the originally published version of this article, Extended Data Fig. 4 contained inadvertent duplications introduced during figure assembly: panel 4c (the bottom of the second column) erroneously reused images from panel 4a (the bottom of the third column); panel 4c (the upper panel of the third column) erroneously reused images from panel 4a (the upper panel of the rightmost column); panel 4b (the middle of the leftmost column) erroneously reused images from panel 4b (the bottom of the leftmost column); panel 4b (the upper of the second column) erroneously reused images from panel 4b (the bottom of the third column). We provide a corrected Extended Data Fig. 4 (Fig. 1, below) in which the appropriate images corresponding to the stated conditions have been restored from the original source data (Supplementary Figs. 1–4). This correction does not affect the results, statistical analyses, or conclusions of the paper.

Fig. 1

Corrected Extended Data Fig. 4.

Supplementary information is available in the online version of this amendment.

Author information

Author notes

1. Martin Wagner & Dan Feng

Present address: Laboratory of Experimental Hepatology, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Medical University of Graz, Auenbruggerplatz 15, A-8036 Graz, Austria (M.W.); Stanford University School of Medicine, Palo Alto, 94305, California, USA

Authors and Affiliations

1. Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, 77030, Texas, USA

Jae Man Lee, Martin Wagner, Rui Xiao, Kang Ho Kim & David D. Moore

2. Division of Endocrinology, Diabetes, and Metabolism and the Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, 19014, Pennsylvania, USA

Dan Feng & Mitchell A. Lazar

Corresponding author

Correspondence to David D. Moore.

Source data

Source Data Extended Data Fig. 4

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Lee, J.M., Wagner, M., Xiao, R. et al. Author Correction: Nutrient-sensing nuclear receptors coordinate autophagy. Nature 650, E8 (2026). https://doi.org/10.1038/s41586-026-10119-0

- Published: 27 January 2026

- Version of record: 27 January 2026

- Issue date: 12 February 2026

- DOI: https://doi.org/10.1038/s41586-026-10119-0

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Author Correction Open access Published: 27 January 2026

Author Correction: Relatively warm deep-water formation persisted in the Last Glacial Maximum

Jack H. Wharton,

Emilia Kozikowska,

Lloyd D. Keigwin,

Thomas M. Marchitto,

Mark A. Maslin,

Martin Ziegler &

…

David J. R. Thornalley

Nature volume 650, page E9 (2026)

The Original Article was published on 21 January 2026

Correction to: Nature https://doi.org/10.1038/s41586-025-10012-2 Published online 21 January 2026

In the version of the article initially published, the label “35.50” on the Fig. 1b colour scale bar should have read “35.00” and has now been corrected in the HTML and PDF versions of the article.

Author information

Authors and Affiliations

1. Department of Geography, University College London, London, UK

Jack H. Wharton, Emilia Kozikowska, Mark A. Maslin & David J. R. Thornalley

2. Department of Earth Sciences, Utrecht University, Utrecht, Netherlands

Emilia Kozikowska & Martin Ziegler

3. Woods Hole Oceanographic Institution, Woods Hole, MA, USA

Lloyd D. Keigwin & David J. R. Thornalley

4. Department of Geological Sciences and INSTAAR, University of Colorado, Boulder, CO, USA

Thomas M. Marchitto

Corresponding author

Correspondence to Jack H. Wharton.

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Wharton, J.H., Kozikowska, E., Keigwin, L.D. et al. Author Correction: Relatively warm deep-water formation persisted in the Last Glacial Maximum. Nature 650, E9 (2026). https://doi.org/10.1038/s41586-026-10186-3

- Published: 27 January 2026

- Version of record: 27 January 2026

- Issue date: 12 February 2026

- DOI: https://doi.org/10.1038/s41586-026-10186-3

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Amendments & Corrections

Author Correction: Nutrient-sensing nuclear receptors coordinate autophagy

Author Correction: Relatively warm deep-water formation persisted in the Last Glacial Maximum