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This Week

- A plastics treaty is urgently needed, but getting it right will take time

- Advising governments about science is essential but difficult. So train people to do it

- US academics: look after foreign students

EDITORIAL 03 December 2024

A plastics treaty is urgently needed, but getting it right will take time

Although the latest talks have ended without a final agreement, countries have pledged to continue talking — and that is no small achievement.

Demonstrators demand action against plastics pollution ahead of the fifth round of talks on an international treaty. The talks took place in Busan, South Korea.Credit: Daewoung Kim/Reuters

More than two years ago, Nature urged the international community to put science front and centre as nations embarked on talks to agree a treaty to end plastics pollution. Such an agreement is needed urgently. Around 400 million tonnes of plastics are produced each year, and this figure is expected to double by 2040. Of all the plastics that have ever been produced, only about 9% have been recycled. If left unchecked, and if the world is to limit global warming to 1.5 °C above pre-industrial temperatures, plastics production and disposal are projected to be responsible for 15% of permitted carbon emissions by 2050.

But the effort to curb plastics pollution is, at best, a work in progress. The latest round of talks in Busan, South Korea, broke up on 1 December without a treaty being formally agreed. Campaign groups and representatives of many countries — especially island states, which see the effects of plastics pollution every day, as plastics wash up on their coastlines — were visibly angry and frustrated.

The meeting’s outcome was not a surprise. International agreements can take years to finalize. This is particularly true of complex accords that involve the regulation of individual chemicals and chemical products. Talks on the United Nations Chemical Weapons Convention took more than a decade, from start to finish, before the agreement opened for signatures in 1993.

The makings of a text for a plastics treaty are now in place and negotiators will reconvene within a year to continue talks. Although delegates’ frustrations are justified, the commitment to continuing the discussions and the ambition of most participating countries to secure a strong agreement are positive, says Samuel Winton, a researcher at the Global Plastics Policy Centre at the University of Portsmouth, UK, who is studying the negotiations as they unfold.

There have been five rounds of negotiations since talks on an agreement got the green light at the UN Environment Assembly in March 2022. The proposed agreement will cover polymers and microplastics — particles less than five millimetres long — and products that contain them.

It will include a list of named products to be regulated. There will also be provision for chemicals and products to be exempt from the treaty, but the criteria for these are yet to be defined.

The treaty will have a “financial mechanism” — that is, some form of funding attached. Who will contribute, what the fund should total and what it is intended to be used for all remain to be agreed. Some of these questions might end up being parked until after the treaty text has been finalized and the first conference of the parties (or COP meeting) is held. We know from COP meetings on other topics that it is extremely difficult for participants to reach an agreement when there are too many issues in contention.

One area on which the latest meeting reached, at best, a fragile consensus was the part of the text that says that any eventual agreement must cover the “full life cycle” of plastics. This was in no small part because what is meant by full life cycle still needs to be defined. Most countries interpret it as including both the production and disposal of plastics. However, some 30 countries that extract and sell fossil fuels — including Kuwait, Russia and Saudi Arabia — oppose the idea of setting limits on plastics production and would prefer the treaty to concentrate on regulating waste flows.

This is an area in which a role for scientists will be key, both to help define terms according to a consensus of the evidence and to undertake research to bridge any knowledge gaps.

That said, scientists have found it hard to get access to talks on the treaty. This is partly down to logistics — there are around 2,000 accredited observers allowed to attend the talks, out of a total of 3,300 participants, and meeting rooms cannot accommodate these numbers. Another reason, according to researchers at the Centre for Science and Environment, a think tank in New Delhi, is that some decisions were made in closed groups of countries that did not permit observers to attend. This is not a positive development, because it risks undermining trust in what should be a transparent process.

Observers represent industry, non-governmental groups and academic research. So far, each negotiating session has seen more representatives from the fossil-fuel industry than the last, according to the Center for International Environmental Law, a non-governmental organization in Washington DC (see go.nature.com/3zgjzba). These voices cannot be allowed to dominate. The UN Environment Programme, based in Nairobi, is the overall host for the talks, and has still not announced how it will formally incorporate independent scientific advice. It should delay no further in addressing this: a formal role for scientists is essential. Both the treaty’s text, including its definitions, and the post-treaty implementation plans must be based on an accepted consensus of the research evidence.

It is clear that the majority of countries do not want a weak treaty and are not going to compromise just to get a text over the line. And although all nations are looking to protect their own interests, no country doubts the need for an agreement to end plastics pollution. The status quo cannot continue — and scientists must guide the way, both during the negotiations and to ensure that the treaty, once finalized, is observed.

Nature 636, 7 (2024)

doi: https://doi.org/10.1038/d41586-024-03932-y

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EDITORIAL 04 December 2024

Advising governments about science is essential but difficult. So train people to do it

A great scientist doesn’t necessarily make an effective science adviser — but schooling and practice can help to bridge the gap.

Institutions including the Blavatnik School of Government at the University of Oxford, UK, are offering courses in science and evidence in public policy.Credit: Hufton+Crow/View Pictures/Universal Images Group/Getty

Earlier this year, Nature asked science-policy specialists which country is particularly good at ensuring science is factored into government decisions. The question mystified many respondents. “Not aware of any,” wrote one. “None have enviable systems,” wrote another. “Very hard to say,” said a third.

That sums up the poor opinion that many have of science advice to governments. Nature’s survey was sent to several thousand people worldwide, most of whom are affiliated to the International Network of Government Science Advice (INGSA), a global association of researchers and policymakers, based in Auckland, New Zealand. Some 80% of the nearly 400 respondents rated their country’s science-advice system as being patchy, poor or very poor. Blame lay on both sides: 77% said that policymakers and politicians ignore and undervalue science advice, and 73% said that researchers fail to understand policy.

That dismal verdict is worrying. It has been nearly five years since the start of the COVID-19 pandemic, during which many scientists had difficulty giving governments advice and politicians struggled to implement it. And yet, it seems that only a few countries have stopped to make repairs. Governments today are wrestling with climate change, infectious diseases, artificial intelligence and wars. To address these issues effectively, they need knowledge from research. More than 70% of survey respondents said that misinformation and disinformation obscure science advice.

Having political leaders who are receptive to science is one essential ingredient for effective science advice. Another is having people who can deliver it well. It is a misconception to think that someone successful in research will also succeed in politics and policymaking, in which events move faster than in academia and the style of delivery is as important as what is said. Improving global science advice — and therefore improving the world — needs people with better training and skills for the job.

That brilliant scientists often prove ineffectual in government has been clear since at least the mid-1960s, when the first cross-government chief scientific adviser was appointed in the United Kingdom. “Having gained access to the corridors of power, scientists could not find their way to the men’s room,” wrote science journalist Peter Ritchie Calder, in his 1975 memoir Scientists in the British Government. Politicians weren’t much better, he added: they were “deferential, even gullible, but, by and large, they did not know the right questions to ask”.

A lot has changed in the intervening years. Today, many countries have well-established formal systems in which governments call on chief science advisers, national academies and committees of scientists to inform policy with evidence from research. Beyond this, research shapes policy in a spectrum of ways, from think-tank reports to scientist–politician meet-and-greets.

Aside from an understanding of science, a core attribute for science advisers is a good grasp of: how governments and their departments work; the goals and deadlines of policymakers; and how evidence can be appropriately added to the mix. That experience can be gained, for example, through fellowships or internships in government agencies. In the United States, the American Association for the Advancement of Science runs one well-known fellowship in Washington DC. There are many more.

Another requirement is a repertoire of people skills: the ability to communicate complex ideas in succinct, everyday language; the capacity to build trusting relationships, so that politicians have faith in the information they receive and that their confidences will not be breached; being able to respectfully understand others’ views and priorities, however different. “Only in this way can you hope to convey the evidence to them in a way that helps them understand and appreciate it,” says Mark Ferguson, who was Ireland’s chief science adviser between 2012 and 2022.

Knowledge brokers

Training scientists in policy is one approach. Also important is to nurture an emerging group known as knowledge brokers — people specialized in incorporating research evidence into the machinery of government.

A growing number of institutions worldwide offer training to both scientists and knowledge brokers. One is the International Institute for Science Diplomacy and Sustainability in Kuala Lumpur, which was founded last year by Zakri Abdul Hamid, a former science adviser to Malaysia’s prime minister. The institute trains people to bridge science and international diplomacy, preparing them for United Nations climate meetings, for instance.

INGSA offers training too, but wants to do more, says Rémi Quirion, chief scientist of Québec, Canada, and INGSA’s president. Research funders and employers need to incentivize researchers to do science-advice training and work. Some 60% of survey respondents said that funders’ failure to do so was an impediment to science advice.

These efforts need to be informed by evidence. A study in 2022 identified more than 1,900 initiatives worldwide aimed at promoting greater engagement between policymakers and researchers, from the collaborative production of policy briefs to networking events (K. Oliver et al. Evid. Policy 18, 691—713; 2022). Only 6% had been evaluated to assess how well they worked.

Today’s challenges are galvanizing interest in science policy among young people who see it as a more direct way to have an impact than pure research. That’s good news. It is easy for researchers to gripe that politicians don’t understand science. A better response is to learn about politics and policy, and to get involved.

Nature 636, 8 (2024)

doi: https://doi.org/10.1038/d41586-024-03910-4

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WORLD VIEW 03 December 2024

US academics: look after foreign students

The first Trump term took a toll on international scholars and collaborations — we can avoid similar pitfalls.

By

Christina W. Yao

I paid close attention to the 2024 US presidential election — both as a US citizen and as a researcher who studies international-student mobility and racial equity in science education. In the past few weeks, dozens of students have shared with me their fears and apprehensions about possible changes in policies around immigration and science funding after the election of Donald Trump to a second term. They worry, for example, about obtaining and retaining visas for themselves and their families, or about the continuity of grant-sponsored funding.

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Nature 636, 9 (2024)

doi: https://doi.org/10.1038/d41586-024-03912-2

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Competing Interests

The author declares no competing interests.

News in Focus

- Antimatter to be transported outside a lab for first time — in a van

- Fossilized poo and vomit show how dinosaurs rose to rule Earth

- ‘A place of joy’: why scientists are joining the rush to Bluesky

- AI’s computing gap: academics lack access to powerful chips needed for research

- Is the COP29 climate deal a historic breakthrough or letdown? Researchers react

- Evidence of oldest known alphabet unearthed among Syrian tomb treasures

- Limits on foreign students are harming research, universities warn

- These two ancient human relatives crossed paths 1.5 million years ago

- How close is AI to human-level intelligence?

- Science could solve some of the world’s biggest problems. Why aren’t governments using it?

NEWS 26 November 2024

Antimatter to be transported outside a lab for first time — in a van

The volatile substance will be driven across the CERN campus in trucks to different facilities, giving scientists greater opportunities to study it.

By

Elizabeth Gibney

The antiProton Unstable Matter Annihilation, or PUMA, is a compact experiment designed to carry antiprotons from CERN’s Antimatter Factory to the laboratory’s ISOLDE facility. Credit: Maximilien Brice/CERN (CC-BY-4.0)

Two teams of CERN physicists are racing to perform an extraordinary feat: transporting antimatter for the first time. Antimatter — matter’s mirror image — is difficult to create and extremely short-lived, because on contact with matter it instantly annihilates. One team wants to move the antimatter so that it can be studied with greater precision, and the other will use it to probe materials in the first experiments of their kind.

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Nature 636, 13-14 (2024)

doi: https://doi.org/10.1038/d41586-024-03841-0

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NEWS 27 November 2024

Fossilized poo and vomit show how dinosaurs rose to rule Earth

Analysis of 200-million-year-old digested foods reveals how the animals became dominant.

By

Helena Kudiabor

Dinosaurs rose to prominence over millions of years.Credit: Arthur Dorety/Stocktrek Images via Alamy

Faeces and vomit fossils from dinosaurs reveal how the animals evolved to rule Earth. The study, which was published in Nature on 27 November, analysed hundreds of pieces of fossilized digestive material, called bromalites, to reconstruct what dinosaurs ate and how this changed[1]. The fossils reveal that the rise of the dinosaurs, over millions of years during the Triassic period, was influenced by factors including climate change and other species’ extinction.

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Nature 636, 14-15 (2024)

doi: https://doi.org/10.1038/d41586-024-03889-y

Read the related News & Views ‘Faeces and vomit record the rise of dinosaurs’.

References

1. Qvarnström, M. et al. Nature https://doi.org/10.1038/s41586-024-08265-4 (2024).

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NEWS 21 November 2024 Update 22 November 2024 Correction 22 November 2024

‘A place of joy’: why scientists are joining the rush to Bluesky

Researchers say the social-media platform — an alternative to X — offers more control over the content they see and the people they engage with.

By

Smriti Mallapaty

Bluesky has been growing rapidly since 2023.Credit: Anna Barclay/Getty

Researchers are flocking to the social-media platform Bluesky, hoping to recreate the good old days of Twitter.

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Nature 636, 15-16 (2024)

doi: https://doi.org/10.1038/d41586-024-03784-6

Updates & Corrections

- Update 22 November 2024: This story has been updated to include public comments from Elon Musk on X, as well as a source for data on Bluesky’s Science feed.

- Correction 22 November 2024: A previous version of this story incorrectly stated the date that Musk posted about bots on X.

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NEWS 20 November 2024

AI’s computing gap: academics lack access to powerful chips needed for research

Survey highlights disparity between academic and industry scientists’ access to computing power needed to train machine-learning models.

By

Helena Kudiabor

Tech giant NVIDIA’s H100 graphics-processing unit is a sought after chip for artificial-intelligence research.Credit: NVIDIA

Many university scientists are frustrated by the limited amount of computing power available to them for research into artificial intelligence (AI), according to a survey of academics at dozens of institutions worldwide.

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Nature 636, 16-17 (2024)

doi: https://doi.org/10.1038/d41586-024-03792-6

References

1. Khandelwal, A. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2410.23261 (2024).

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NEWS 25 November 2024

Is the COP29 climate deal a historic breakthrough or letdown? Researchers react

Rich countries will take the lead in finding US$300 billion annually in climate finance for poor countries.

By

Ehsan Masood

Activists protest during at the COP29 climate meeting in Baku, Azerbaijan.Credit: Dominika Zarzycka/SOPA via ZUMA/Alamy

An eleventh-hour deal that rescued the COP29 climate talks in Baku, Azerbaijan, is a “fragile consensus”, researchers who study climate finance have told Nature.

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Nature 636, 17-18 (2024)

doi: https://doi.org/10.1038/d41586-024-03875-4

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NEWS 26 November 2024

Evidence of oldest known alphabet unearthed among Syrian tomb treasures

Cylinders discovered in 2004 are inscribed with the earliest known examples of letters, say archaeologists.

By

Miryam Naddaf

The small clay cylinders are engraved with symbols thought to be letters.Credit: Glenn Schwartz, Johns Hopkins University

Clay cylinders unearthed from a tomb in Syria and dated to 4,400 years ago are inscribed with traces of the earliest known alphabetic writing system, an analysis suggests.

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Nature 636, 18 (2024)

doi: https://doi.org/10.1038/d41586-024-03876-3

References

1. Schwartz, G. M. Pasiphae 15, 255—266 (2021).

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NEWS 27 November 2024

Limits on foreign students are harming research, universities warn

The UK, Canadian and Australian governments have introduced immigration restrictions for students.

By

Smriti Mallapaty

Governments in several countries are restricting visas for foreign students. Credit: Chu Chen/Xinhua via Alamy

Immigration rules restricting the number of foreign students in some countries are putting universities under pressure. From the United Kingdom and Canada to Australia, universities say that border restrictions have led to large declines in international-student enrolments, and that these are crunching their budgets, global reputations and ability to contribute to global science.

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Nature 636, 19-20 (2024)

doi: https://doi.org/10.1038/d41586-024-03807-2

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NEWS 28 November 2024

These two ancient human relatives crossed paths 1.5 million years ago

Remarkably preserved footprints of Homo erectus and Paranthropus boisei offer direct evidence that extinct hominin species coexisted.

By

Miryam Naddaf

Footprints made by two species of ancient human relative were found in Kenya. This one is thought to have been left by Paranthropus boisei.Credit: Kevin G. Hatala

Some 1.5 million years ago, two ancient species crossed paths on a lake shore in Kenya. Their footprints in the mud were frozen in time and lay undiscovered until 2021.

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Nature 636, 20 (2024)

doi: https://doi.org/10.1038/d41586-024-03907-z

References

1. Hatala, K. G. et al. Science 386, 1004–1010 (2024).

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NEWS FEATURE 03 December 2024

How close is AI to human-level intelligence?

Large language models such as OpenAI’s o1 have electrified the debate over achieving artificial general intelligence, or AGI. But they are unlikely to reach this milestone on their own.

By

Anil Ananthaswamy

Illustration: Petra Péterffy

OpenAI’s latest artificial intelligence (AI) system dropped in September with a bold promise. The company behind the chatbot ChatGPT showcased o1 — its latest suite of large language models (LLMs) — as having a “new level of AI capability”. OpenAI, which is based in San Francisco, California, claims that o1 works in a way that is closer to how a person thinks than do previous LLMs.

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Nature 636, 22-25 (2024)

doi: https://doi.org/10.1038/d41586-024-03905-1

References

1. Bubeck, S. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2303.12712 (2023).

2. Valmeekam, K., Stechly, K. & Kambhampati, S. Preprint at arXiv https://doi.org/10.48550/arXiv.2409.13373 (2024).

3. Goldblum, M., Finzi, M., Rowan, K. & Wilson, A. G. Preprint at arXiv https://doi.org/10.48550/arXiv.2304.05366 (2024).

4. Villalobos, P. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2211.04325 (2024).

5. Gurnee, W. & Tegmark, M. Preprint at arXiv https://doi.org/10.48550/arXiv.2310.02207 (2024).

6. Li, K. et al. In Proc. Eleventh International Conference on Learning Representations (ICLR, 2023).

7. Vafa, K., Chen, J. Y., Rambachan, A., Kleinberg, J. & Mullainathan, S. Preprint at https://doi.org/10.48550/arXiv.2406.03689 (2024)

8. Gundawar, A. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2405.20625 (2024).

9. Ha, D. & Schmidhuber, J. Preprint at arXiv https://doi.org/10.48550/arXiv.1803.10122 (2018).

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NEWS FEATURE 04 December 2024

Science could solve some of the world’s biggest problems. Why aren’t governments using it?

A Nature global survey finds that most specialists are unhappy with systems to provide science advice to policymakers.

By

Helen Pearson

Illustration: Barbara Gibson. Images: Getty/Alamy

Killer viruses. Artificial intelligence. Extreme weather. Microplastics. Mental health. These are just a few of the pressing issues on which governments need science to inform their policies. But the systems that connect scientists with politicians are not working well, according to a Nature survey of around 400 science-policy specialists around the world. Eighty per cent said their country’s science-advice system was either poor or patchy, and 70% said that governments are not routinely using such advice.

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Nature 636, 26-30 (2024)

doi: https://doi.org/10.1038/d41586-024-03906-0

Additional data analysis by Jeffrey Perkel.

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Supplementary Information

1. Survey questions

2. Survey data

Books & Arts

- Great power and great responsibility: how consciousness changes the world

- The contentious genius who made black holes real

- DNA need not apply: Books in brief

BOOK REVIEW 02 December 2024

Great power and great responsibility: how consciousness changes the world

A vivid account of the evolution of minds will fill readers with wonder — and challenge how they think about their moral responsibility to protect the planet.

By

Alan C. Love

People have a moral imperative to value the lives of the animals they control, and so should stop factory farming, argues Peter Godfrey-Smith. Credit: Chaideer Mahyuddin/AFP/Getty

Living on Earth: Life, Consciousness and the Making of the Natural World Peter Godfrey-Smith William Collins (2024)

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Nature 636, 32-34 (2024)

doi: https://doi.org/10.1038/d41586-024-03916-y

Competing Interests

The author declares no competing interests.

BOOK REVIEW 02 December 2024

The contentious genius who made black holes real

Roger Penrose, now 93, never shied away from wild ideas and the ensuring debates. The first full-length biography reveals more about the mathematician and the man.

By

Davide Castelvecchi

Roger Penrose was one of the pioneers who established the theory of black holes. Credit: APA-PictureDesk/Alamy

The Impossible Man: Roger Penrose and the Cost of Genius Patchen Barss Basic (2024)

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Nature 636, 34-36 (2024)

doi: https://doi.org/10.1038/d41586-024-03917-x

References

1. Penrose, R. Bull. Inst. Math. App. 10, 266–271 (1974).

2. Penrose, R. Phys. Rev. Lett. 14, 57–59 (1965).

3. Penrose, L. S. & Penrose, R. Br. J. Psychol. 49, 31–33 (1958).

Competing Interests

The author declares no competing interests.

BOOK REVIEW 22 November 2024

DNA need not apply: Books in brief

Andrew Robinson reviews five of the best science picks.

By

Andrew Robinson

Good Nature

Kathy Willis Bloomsbury (2024)

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Nature 636, 35 (2024)

doi: https://doi.org/10.1038/d41586-024-03863-8

Competing Interests

The author declares no competing interests.

Opinion

- Leon Cooper obituary: Nobel laureate who developed theory of superconductivity

- How provinces and cities can sustain US–China climate cooperation

- Why the EU must reset its Green Deal – or be left behind

OBITUARY 19 November 2024

Leon Cooper obituary: Nobel laureate who developed theory of superconductivity

The condensed-matter physicist also turned his attention to neural networks, improving our understanding of the human brain and helping to develop machine learning.

By

Georgina Ferry

Credit: Keystone Press/Alamy

Leon Cooper, who has died aged 94, helped to solve a problem that had stumped many of the greatest minds in twentieth-century physics. With his colleagues John Bardeen and Robert Schrieffer, he deciphered the dance of electrons that causes superconductivity, or the sudden drop in electrical resistance experienced by certain materials, such as mercury, when they reach temperatures only a few degrees above absolute zero. This phenomenon has since served to generate, for example, the very high magnetic fields needed to operate technology such as magnetic resonance imaging body scanners. The Bardeen–Cooper–Schrieffer (BCS) theory of superconductivity won them the Nobel Prize in Physics in 1972.

Having worked out one of the hardest problems in physics, Cooper turned his attention to neuroscience. With his graduate students Elie Bienenstock and Paul Munro, he developed a model — inevitably dubbed the Bienenstock–Cooper–Munro (BCM) theory to mirror the BCS theory — of changes in the strength of the neuronal connections in the brain as individuals learn.

His pioneering theoretical work on neural networks places him in the company of other physicists such as John Hopfield and Geoffrey Hinton — winners of the 2024 Nobel Prize in Physics who developed algorithms that could represent the process of learning in a model of a very small volume of brain tissue.

Cooper (originally Kupchik) was born in New York City, the son of Jewish immigrants from Belarus and Poland. After his mother died, he and his sister spent part of their childhood in care. In 1947, he graduated from the Bronx High School of Science, which has produced six other Nobel laureates in physics. He then studied physics at Columbia University, New York City, completing a PhD in 1954.

He joined the Institute for Advanced Study in Princeton, New Jersey, for a year, before Bardeen recruited him to the University of Illinois at Champaign–Urbana, to work together on problems of condensed-matter theory and specifically on superconductivity. “The long and very imposing list of physicists (among them [Niels] Bohr, [Werner] Heisenberg and [Richard] Feynman) who had tried or were trying their hand at superconductivity should have given me pause,” recalled Cooper in a later memoir. Feynman had even said that anyone trying to tackle superconductivity would soon discover that they were “too stupid to solve the problem”.

Superconductivity was first observed in 1911, but by the mid-1950s it was still unknown how electrons could flow seemingly without limit in supercooled mercury. Some suggested that new physics — an undiscovered kind of particle, for example — might be needed to account for the phenomenon. Working for more than a year with every theoretical tool at his disposal, Cooper proposed that faint vibrations in lattices of atoms at low temperatures prompted electrons to pair up instead of repelling one another. All of these ‘Cooper pairs’ could in turn operate as a single entity and pass through the lattice unopposed.

Mathematical analysis by Schrieffer supported the theory, which Bardeen refined. Their paper caused a sensation (J. Bardeen et al. Phys. Rev. 108, 1175; 1957); it was subsequently confirmed by experiment. The same phenomenon also underlies the performance of more recently discovered ‘high-temperature superconductors’, which reach a resistance-free state at up to 100 degrees above absolute zero. “The great thing that Bardeen, Cooper and Schrieffer showed was that no new particles or forces had to be introduced to understand superconductivity,” wrote Steven Weinberg, a theoretical physicist who won the Nobel Prize in Physics in 1979.

In 1958, Cooper moved to Brown University in Providence, Rhode Island, where he remained for the rest of his working life. In 1974, he founded the Institute for Brain and Neural Systems there, recruiting a team to research the connectivity underlying cognitive function. The group built on the work of pioneers such as brain researchers Warren McCulloch and Walter Pitts of the University of Illinois at Chicago, who had published a mathematical model of a neural network in 1943 (W. S. McCulloch and W. Pitts, Bull. Math. Biophys. 5, 115–133; 1943), and of the Canadian psychologist Donald Hebb, whose 1949 book The Organization of Behavior proposed that, if neurons repeatedly fire — that is, send electrical signals — to the body and brain at the same time, the synapses between them become stronger.

The BCM theory, published in 1982, addressed how specific cells in the visual cortex become selective for certain stimuli, such as an edge at a particular angle, depending on previous visual experience. It proposed that, the more often incoming signals activated a neuron, the higher the threshold for its activation would become, and vice-versa. This sliding threshold has the effect of stabilizing the responsiveness of the neuron, making it either more or less active. Cooper’s goal was to model how living brains work, and the BCM theory has stood the test of time. It has also played a part in the development of machine learning. Cooper and his collaborators went on to apply insights from machine learning to many other fields, such as sonar detection.

Although he formally retired in 2014, Cooper never lost his curiosity. That year, he published a collection of essays, Science and Human Experience, based on his lively interest in physics, neuroscience, philosophy and how these fields interacted with each other. He argued that science emerged from a uniquely human desire to explain the world that surrounds us, and that imagination was a crucial part of the process. “Our imagination is marvellously free,” he wrote, “capable of any juxtaposition, unbounded by logic or experience.”

Nature 636, 37 (2024)

doi: https://doi.org/10.1038/d41586-024-03798-0

Competing Interests

The author declares no competing interests.

COMMENT 02 December 2024

How provinces and cities can sustain US–China climate cooperation

National politics is set to drastically shift in both countries. But avenues for joint action still exist.

By

Fan Dai,

Jerry Brown,

Zhenhua Xie,

Yi Wang &

…

Peng Gong

In 2017, the governor of California Jerry Brown (left) met with China’s President Xi Jinping to sign a series of climate agreements.Credit: Imago/Alamy

The relationship between the United States and China stands at a crucial juncture. Given Donald Trump’s recent victory in the US election, the slowdown in China’s economy and rising tensions around trade and technology, productive cooperation between the two countries is far from guaranteed.

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Nature 636, 39-42 (2024)

doi: https://doi.org/10.1038/d41586-024-03919-9

References

1. The White House. U.S.-China Joint Announcement on Climate Change (2014).

2. The White House. U.S.-China Joint Presidential Statement on Climate Change (2015).

3. The White House. U.S.-China Joint Presidential Statement on Climate Change (2016).

4. CCCI. U.S., China Cities and States Strengthen Ties to Advance Climate Action (2024).

5. California Energy Commission. 2022 Total System Electric Generation (2023).

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Competing Interests

The authors declare no competing interests.

COMMENT 03 December 2024

Why the EU must reset its Green Deal – or be left behind

The world has changed since Europe’s ambitious climate package was designed. Here’s how the agreement should evolve.

By

Rabah Arezki,

Jean-Pierre Landau &

Rick van der Ploeg

Tractors blocked the streets of Brussels in March as farmers protested over the European Union’s agricultural policies. Credit: Shutterstock

Through its Green Deal, the European Union shows its ambition to be a world leader in the fight against climate change. Approved in early 2020, this comprehensive package of policies — spanning clean energy, buildings, farms, transport, industry and more — aims to achieve ‘net zero’ for EU greenhouse-gas emissions by 2050.

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Nature 636, 42-44 (2024)

doi: https://doi.org/10.1038/d41586-024-03918-w

References

1. European Commission. The European Green Deal: Delivering the EU’s 2030 Climate Targets (EC, 2023).

2. Rennert, K. et al. Nature 610, 687–692 (2022).

3. Bilal, A. & Kaenzig, D. R. The Macroeconomic Impact of Climate Change: Global vs. Local Temperature. Working Paper 32450 (US National Bureau of Economic Research, 2024).

4. Darvas, Z., Welslau, L. & Zettelmeyer, J. How Demographic Change Will Hit Debt Sustainability in European Union Countries. Policy Brief 22/24 (Bruegel, 2024).

5. Draghi, M. The Future of European Competitiveness: Part A. A Competitiveness Strategy for Europe (European Commission, 2024).

6. Acemoglu, D., Aghion, P., Bursztyn, L. & Hemous, D. Am. Econ. Rev. 102, 131–166 (2012).

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Work

- A step-by-step guide to landing your next job in science

- Notching up a win: fresh tools for activating Notch

- I help researchers to measure methane at a local level so that we can make global changes

CAREER FEATURE 04 December 2024

A step-by-step guide to landing your next job in science

A road map to success based on Nature’s hiring-in-science survey, with advice for every stage from application to offer.

By

Linda Nordling

Find a new job

Illustration: Tiago Galo

Nature’s 2024 hiring in science survey

This article is the fourth in a short series discussing the results of Nature’s 2024 global survey of hiring managers in science. The survey, created in partnership with Thinks Insights & Strategy, a research consultancy in London, launched in June and was advertised on nature.com, in Springer Nature digital products and through e-mail campaigns. It received 1,134 self-selecting respondents from 77 countries, based in academia, industry and other sectors, including industry responses provided in partnership with Walr, a market-research panel. The full survey data sets are available at go.nature.com/3bgpazn.

The e-mail arrives on a Friday afternoon. It’s a job alert. The research focus matches your expertise perfectly. The laboratory group has funding. The city works for your family. But then the familiar doubts surface. Your past four applications received no reply. How can you make this one succeed?

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Nature 636, 259-262 (2024)

doi: https://doi.org/10.1038/d41586-024-03926-w

TECHNOLOGY FEATURE 21 November 2024

Notching up a win: fresh tools for activating Notch

Synthetic protein agonists could boost T-cell biomanufacturing and therapeutic strategies.

By

Stephanie Melchor

T cells surrounding a cervical cancer cell. Credit: Steve Gschmeissner/SPL

From roundworms to humans, signalling through the Notch protein-receptor family drives embryonic development, cellular differentiation and tissue homeostasis. It’s also absolutely essential for the transformation of immature human immune cells into T cells — cellular warriors that target viruses and tumours.

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Nature 636, 263-264 (2024)

doi: https://doi.org/10.1038/d41586-024-03822-3

References

1. Mout, R. et al. Preprint at bioRxiv https://doi.org/10.1101/2024.08.07.607099 (2024).

2. Perez, D. H. et al. Preprint at bioRxiv https://doi.org/10.1101/2024.08.06.606897 (2024).

3. Smyrlaki, I. et al. Nature Commun. 15, 465 (2024).

WHERE I WORK 02 December 2024

I help researchers to measure methane at a local level so that we can make global changes

Ioannis Binietoglou makes sure that the latest technology reaches areas that need it.

By

Oscar Allan

Ioannis Binietoglou is a remote-sensing policy manager at the Clean Air Task Force and is based in Athens, Greece. Credit: Giacomo d’Orlando for Nature

“I work for the Clean Air Task Force, a non-profit organization in Boston, Massachusetts, which aims to reduce the emissions that cause climate change by promoting and distributing the latest energy technology. It also pushes for policy changes in organizations and governments around the world. I focus on methane, which can be 80 times more damaging to the environment than carbon dioxide.

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Nature 636, 266 (2024)

doi: https://doi.org/10.1038/d41586-024-03927-9

This interview has been edited for length and clarity.

Research

- Combining quantum processors with real-time classical communication

- Observation of Hilbert space fragmentation and fractonic excitations in 2D

- Mineralized collagen plywood contributes to bone autograft performance

- Coupling of unactivated alkyl electrophiles using frustrated ion pairs

- New particle formation from isoprene under upper-tropospheric conditions

- Isoprene nitrates drive new particle formation in Amazon’s upper troposphere

- Global potential for natural regeneration in deforested tropical regions

- Exaptation of ancestral cell-identity networks enables C4 photosynthesis

- A subcortical feeding circuit linking an interoceptive node to jaw movement

- Long-term lineage commitment in haematopoietic stem cell gene therapy

- Adult skull bone marrow is an expanding and resilient haematopoietic reservoir

- Anti-viral defence by an mRNA ADP-ribosyltransferase that blocks translation

- Leptin-activated hypothalamic BNC2 neurons acutely suppress food intake

- Rhythmic IL-17 production by γδ T cells maintains adipose de novo lipogenesis

- RAS-mutant leukaemia stem cells drive clinical resistance to venetoclax

- Nucleosome flipping drives kinetic proofreading and processivity by SWR1

Article Open access Published: 20 November 2024

Combining quantum processors with real-time classical communication

Almudena Carrera Vazquez,

Caroline Tornow,

Diego Ristè,

Stefan Woerner,

Maika Takita &

…

Daniel J. Egger

Nature volume 636, pages 75–79 (2024)

Abstract

Quantum computers process information with the laws of quantum mechanics. Current quantum hardware is noisy, can only store information for a short time and is limited to a few quantum bits, that is, qubits, typically arranged in a planar connectivity[1]. However, many applications of quantum computing require more connectivity than the planar lattice offered by the hardware on more qubits than is available on a single quantum processing unit (QPU). The community hopes to tackle these limitations by connecting QPUs using classical communication, which has not yet been proven experimentally. Here we experimentally realize error-mitigated dynamic circuits and circuit cutting to create quantum states requiring periodic connectivity using up to 142 qubits spanning two QPUs with 127 qubits each connected in real time with a classical link. In a dynamic circuit, quantum gates can be classically controlled by the outcomes of mid-circuit measurements within run-time, that is, within a fraction of the coherence time of the qubits. Our real-time classical link enables us to apply a quantum gate on one QPU conditioned on the outcome of a measurement on another QPU. Furthermore, the error-mitigated control flow enhances qubit connectivity and the instruction set of the hardware thus increasing the versatility of our quantum computers. Our work demonstrates that we can use several quantum processors as one with error-mitigated dynamic circuits enabled by a real-time classical link.

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Main

Quantum computers process information encoded in quantum bits with unitary operations. However, quantum computers are noisy and most large-scale architectures arrange the physical qubits in a planar lattice. Nevertheless, current processors with error mitigation can already simulate hardware-native Ising models with 127 qubits and measure observables at a scale where brute-force approaches with classical computers begin to struggle[1]. The usefulness of quantum computers hinges on further scaling and overcoming their limited qubit connectivity. A modular approach is important for scaling current noisy quantum processors[2] and for achieving the large numbers of physical qubits needed for fault tolerance[3]. Trapped ion and neutral atom architectures can achieve modularity by physically transporting the qubits4,5. In the near term, modularity in superconducting qubits[6] is achieved by short-range interconnects that link adjacent chips7,8.

In the medium term, long-range gates operating in the microwave regime may be carried out over long conventional cables9,10,11. This would enable non-planar qubit connectivity suitable for efficient error correction[3]. A long-term alternative is to entangle remote QPUs with an optical link leveraging a microwave to optical transduction[12], which has not yet been demonstrated, to our knowledge. Moreover, dynamic circuits broaden the set of operations of a quantum computer by performing mid-circuit measurements (MCMs) and classically controlling a gate within the coherence time of the qubits. They enhance algorithmic quality[13] and qubit connectivity[14]. As we will show, dynamic circuits also enable modularity by connecting QPUs in real time through a classical link.

We take a complementary approach based on virtual gates to implement long-range interactions in a modular architecture. We connect qubits at arbitrary locations and create the statistics of entanglement through a quasi-probability decomposition (QPD)15,16,17. We compare a Local Operations (LO) only scheme[16] to one augmented by Classical Communication (LOCC)[17]. The LO scheme, demonstrated in a two-qubit setting[18], requires executing multiple quantum circuits with local operations only. By contrast, to implement LOCC, we consume virtual Bell pairs in a teleportation circuit to create two-qubit gates19,20. On quantum hardware with sparse and planar connectivity, creating a Bell pair between arbitrary qubits requires a long-range controlled-NOT (CNOT) gate. To avoid these gates, we use a QPD over local operations resulting in cut Bell pairs that the teleportation consumes. LO do not need the classical link and is thus simpler to implement than LOCC. However, as LOCC only requires a single parameterized template circuit, it is more efficient to compile than LO and the cost of its QPD is lower than the cost of the LO scheme.

Our work makes four key contributions. First, we present the quantum circuits and QPD to create multiple cut Bell pairs to realize the virtual gates in ref. [17]. Second, we suppress and mitigate the errors arising from the latency of the classical control hardware in dynamic circuits[21] with a combination of dynamical decoupling and zero-noise extrapolation[22]. Third, we leverage these methods to engineer periodic boundary conditions on a 103-node graph state. Fourth, we demonstrate a real-time classical connection between two separate QPUs thereby demonstrating that a system of distributed QPUs can be operated as one through a classical link[23]. Combined with dynamic circuits, this enables us to operate both chips as a single quantum computer, which we exemplify by engineering a periodic graph state that spans both devices on 142 qubits. We discuss a path forward to create long-range gates and provide our conclusion.

Circuit cutting

We run large quantum circuits that may not be directly executable on our hardware because of limitations in qubit count or connectivity by cutting gates. Circuit cutting decomposes a complex circuit into subcircuits that can be individually executed15,16,17,24,25,26. However, we must run an increased number of circuits, which we call the sampling overhead. The results from these subcircuits are then classically recombined to yield the result of the original circuit (Methods).

As one of the main contributions of our work is implementing virtual gates with LOCC, we show how to create the required cut Bell pairs with local operations. Here, multiple cut Bell pairs are engineered by parameterized quantum circuits, which we call a cut Bell pair factory (Fig. 1b,c). Cutting multiple pairs at the same time requires a lower sampling overhead[17]. As the cut Bell pair factory forms two disjoint quantum circuits, we place each subcircuit close to qubits that have long-range gates. The resulting resource is then consumed in a teleportation circuit. For instance, in Fig. 1b, the cut Bell pairs are consumed to create CNOT gates on the qubit pairs (0, 1) and (2, 3) (see section ‘Cut Bell pair factories’).

Fig. 1: Local operations and classical communication.

a, Depiction of an IBM Quantum System Two architecture. Here, two 127 qubit Eagle QPUs are connected with a real-time classical link. Each QPU is controlled by its electronics in its rack. We tightly synchronize both racks to operate both QPUs as one. b, Template quantum circuit to implement virtual CNOT gates on qubit pairs (q_0, q_1) and (q_2, q_3) with LOCC by consuming cut Bell pairs in a teleportation circuit. The purple double lines correspond to the real-time classical link. c, Cut Bell pair factories C_2(θi) for two simultaneously cut Bell pairs. The QPD has a total of 27 different parameter sets θi. Here, \(U(\theta ,\phi )=\sqrt{X}{R}_{z}(\theta )\sqrt{X}{R}_{z}(\phi )\).

Periodic boundary conditions

We construct a graph state |G⟩ with periodic boundary conditions on ibm_kyiv, an Eagle processor[1], going beyond the limits imposed by its physical connectivity (see section ‘Graph states’). Here, G has ∣V∣ = 103 nodes and requires four long-range edges Elr = {(1, 95), (2, 98), (6, 102), (7, 97)} between the top and bottom qubits of the Eagle processor (Fig. 2a). We measure the node stabilizers Si at each node i ∈ V and the edge stabilizers formed by the product SiSj across each edge (i, j) ∈ E. From these stabilizers, we build an entanglement witness \({{\mathcal{W}}}_{i,j}=(1-\langle {S}_{i}\rangle -\langle {S}_{j}\rangle -\langle {S}_{i}{S}_{j}\rangle )/4\), which is negative if there is bipartite entanglement across the edge (i, j) ∈ E (ref. [27]) (see section ‘Entanglement witness’). We focus on bipartite entanglement because this is the resource we wish to recreate with virtual gates. Measuring witnesses of entanglement between more than two parties will measure only the quality of the non-virtual gates and measurements making the impact of the virtual gates less clear.

Fig. 2: Periodic boundary conditions.

a, The heavy-hexagonal graph is folded on itself into a tubular form by the edges (1, 95), (2, 98), (6, 102) and (7, 97) highlighted in blue. We cut these edges. b, The node stabilizers Sj (top) and witnesses \({{\mathcal{W}}}_{i,j}\), (bottom), with 1 standard deviation for the nodes and edges close to the long-range edges. Vertical dashed lines group stabilizers and witnesses by their distance to cut edges. c, Cumulative distribution function of the stabilizer errors. The stars indicate node stabilizers Sj that have an edge implemented by a long-range gate. In the dropped edge benchmark (dash-dotted red line), the long-range gates are not implemented and the star-indicated stabilizers thus have unit error. The grey region is the probability mass corresponding to node stabilizers affected by the cuts. d–f, In the two-dimensional layouts, the green nodes duplicate nodes 95, 98, 102 and 97 to show the cut edges. The blue nodes in e are qubit resources to create cut Bell pairs. The colour of node i is the absolute error ∣Si − 1∣ of the measured stabilizer, as indicated by the colour bar. An edge is black if entanglement statistics are detected at a 99% confidence level and violet if not. In d, the long-range gates are implemented with SWAP gates. In e, the same gates are implemented with LOCC. In f, they are not implemented at all.

We prepare |G⟩ using three different methods. The hardware-native edges are always implemented with CNOT gates but the periodic boundary conditions are implemented with (1) SWAP gates, (2) LOCC and (3) LO to connect qubits across the whole lattice. The main difference between LOCC and LO is a feed-forward operation consisting of single-qubit gates conditioned on 2n measurement outcomes, where n is the number of cuts. Each of the 22n cases triggers a unique combination of X and/or Z gates on the appropriate qubits. Acquiring the measurement results, determining the corresponding case and acting based on it is performed in real time by the control hardware, at the cost of a fixed added latency. We mitigate and suppress the errors resulting from this latency with zero-noise extrapolation[22] and staggered dynamical decoupling21,28 (see section ‘Error-mitigated quantum circuit switch instructions’).

We benchmark the SWAP, LOCC and LO implementations of |G⟩ with a hardware-native graph state on G′ = (V, E′) obtained by removing the long-range gates, that is, E′ = E\Elr. The circuit preparing |G′⟩ thus requires only 112 CNOT gates arranged in three layers following the heavy-hexagonal topology of the Eagle processor. This circuit will report large errors when measuring the node and edge stabilizers of |G⟩ for nodes on a cut gate because it is designed to implement |G′⟩. We refer to this hardware-native benchmark as the dropped edge benchmark. The swap-based circuit requires an additional 262 CNOT gates to create the long-range edges Elr, which drastically reduces the value of the measured stabilizers (Fig. 2b–d). By contrast, the LOCC and LO implementation of the edges in Elr does not require SWAP gates. The errors of their node and edge stabilizers for nodes not involved in a cut gate closely follow the dropped edge benchmark (Fig. 2b,c). Conversely, the stabilizers involving a virtual gate have a lower error than the dropped edge benchmark and the swap implementation (Fig. 2c, star markers). As an overall quality metric, we first report the sum of absolute errors on the node stabilizers, that is, ∑i∈V∣Si − 1∣ (Extended Data Table 1). The large SWAP overhead is responsible for the 44.3 sum absolute error. The 13.1 error on the dropped edge benchmark is dominated by the eight nodes on the four cuts (Fig. 2c, star markers). By contrast, the LO and LOCC errors are affected by MCMs. We attribute the 1.9 additional error of LOCC over LO to the delays and the CNOT gates in the teleportation circuit and cut Bell pairs. In the SWAP-based results, \({{\mathcal{W}}}_{i,j}\) does not detect entanglement across 35 of the 116 edges at the 99% confidence level (Fig. 2b,d). For the LO and LOCC implementation, \({{\mathcal{W}}}_{i,j}\) witnesses the statistics of bipartite entanglement across all edges in G at the 99% confidence level (Fig. 2e). These metrics show that virtual long-range gates produce stabilizers with smaller errors than their decomposition into SWAPs. Furthermore, they keep the variance low enough to verify the statistics of entanglement.

Operating two QPUs as one

We now combine two Eagle QPUs with 127 qubits each into a single QPU through a real-time classical connection. Operating the devices as a single, larger processor consists of executing quantum circuits spanning the larger qubit register. Apart from unitary gates and measurements running concurrently on the merged QPU, we use dynamic circuits to perform gates that act on qubits on both devices. This is enabled by a tight synchronization and fast classical communication between physically separate instruments required to collect measurement results and determine the control flow across the whole system[29].

We test this real-time classical connection by engineering a graph state on 134 qubits built from heavy-hexagonal rings that wind through both QPUs (Fig. 3). These rings were chosen by excluding qubits plagued by two-level systems and readout issues to ensure a high-quality graph state. This graph forms a ring in three dimensions and requires four long-range gates that we implement with LO and LOCC. As before, the LOCC protocol thus requires two additional qubits per cut gate for the cut Bell pairs. As in the previous section, we benchmark our results to a graph that does not implement the edges that span both QPUs. As there is no quantum link between the two devices, a benchmark with SWAP gates is impossible. All edges exhibit the statistics of bipartite entanglement when we implement the graph with LO and LOCC at a 99% confidence level. Furthermore, the LO and LOCC stabilizers have the same quality as the dropped edge benchmark for nodes that are not affected by a long-range gate (Fig. 3c). Stabilizers affected by long-range gates have a large reduction in error compared with the dropped edge benchmark. The sum of absolute errors on the node stabilizers ∑i∈V∣Si − 1∣, is 21.0, 19.2 and 12.6 for the dropped edge benchmark, LOCC and LO, respectively. As before, we attribute the 6.6 additional errors of LOCC over LO to the delays and the CNOT gates in the teleportation circuit and cut Bell pairs. The LOCC results demonstrate how a dynamic quantum circuit in which two subcircuits are connected by a real-time classical link can be executed on two otherwise disjoint QPUs. The LO results could be obtained on a single device with 127 qubits at the cost of an additional factor of 2 in run-time as the subcircuits can be run successively.

Fig. 3: Two connected QPUs with LOCC.

a, Graph state with periodic boundaries shown in three dimensions. The blue edges are the cut edges. b, Coupling map of two Eagle QPUs operated as a single device with 254 qubits. The purple nodes are the qubits forming the graph state in a and the blue nodes are used for cut Bell pairs. c,d, Absolute error on the stabilizers (c) and edge witnesses (d) implemented with LOCC (solid green) and LO (solid orange) and on a dropped edge benchmark graph (dotted-dashed red) for the graph state in a. In c and d, the stars show stabilizers and edge witnesses that are affected by the cuts. In c and d, the grey region is the probability mass corresponding to node stabilizers and edge witnesses, respectively, affected by the cut. In c and d, we observe that the LO implementation outperforms the dropped edge benchmark, which we attribute to better device conditions as these data were taken on a different day from the benchmark and LOCC data.

Discussion and conclusion

We implement long-range gates with LO and LOCC. With these gates, we engineer periodic boundary conditions on a 103-node planar lattice and connect two Eagle processors in real time to create a graph state on 134 qubits, going beyond the abilities of a single chip. Here, we chose to implement graph states as an application to highlight the scalable properties of dynamic circuits. Our cut Bell pair factories enable the LOCC scheme presented in ref. [17]. Both the LO and LOCC protocols deliver high-quality results that closely match a hardware-native benchmark. Circuit cutting increases the variance of measured observables. We can keep the variance under control in both the LO and LOCC schemes as indicated by the statistical tests on the witnesses. An in-depth discussion of the measured variance is found in the Supplementary Information.

The variance increase from the QPD is why research now focuses on reducing the sampling overhead. It was recently shown that cutting multiple two-qubit gates in parallel results in optimal LO QPDs with the same sampling overhead as LOCC but requires an additional ancilla qubit and possibly reset30,31. In LOCC, the QPD is required only to cut the Bell pairs. This costly QPD could be removed, that is, no shot overhead, by distributing entanglement across multiple chips32,33. In the near to medium term, this could be done by operating gates in the microwave regime over conventional cables10,34,35 or, in the long term, with an optical-to-microwave transduction36,37,38. Entanglement distribution is typically noisy and may result in non-maximally entangled states. However, gate teleportation requires a maximally entangled resource. Nevertheless, non-maximally entangled states could lower the sampling cost of the QPD[39] and multiple copies of non-maximally entangled states could be distilled into a pure state for teleportation[40] either during the execution of a quantum circuit or possibly during the delays between consecutive shots, which may be as large as 250 μs for resets[41]. Combined with these settings, our error-mitigated and suppressed dynamic circuits would enable a modular quantum computing architecture without the sampling overhead of circuit cutting.

In an application setting, circuit cutting could benefit Hamiltonian simulation[42]. Here, the cost of circuit cutting is exponential in the strength of the cut bonds times the evolution time. This cost may thus be reasonable for weak bonds and/or short evolution times. Furthermore, the LO scheme presented in ref. [42] requires ancilla qubits in a Hadamard test, which would require a reset through a dynamic circuit if the same bond is cut multiple times in a Trotterized time evolution.

Circuit cutting can be applied to both wires and gates. The resulting quantum circuits have a similar structure making our approach applicable to both cases. Our real-time classical link implements long-range gates and classically couples disjoint quantum processors. The cut Bell pairs that we present have values beyond our work. For example, these pairs are directly usable to cut circuits in measurement-based quantum computing, which relies on dynamic circuits[14]. This could also be accomplished with LO; the result would be an execution setting identical to ours with dynamic circuits. Furthermore, the combination of staggered dynamical decoupling with zero-noise extrapolation mitigates the lengthy delays of the feed-forward operations, which enables a high-quality implementation of dynamic circuits. Our work sheds light on the noise sources, such as ZZ cross-talk occurring during the latency, that a transpiler for distributed superconducting quantum computers must consider[43]. In summary, we demonstrate that we can use several quantum processors as one with error-mitigated dynamic circuits enabled by a real-time classical link.

Methods

Circuit cutting

The gates in a quantum circuit are quantum channels acting on density matrices ρ. A single quantum channel \({\mathcal{E}}(\rho )\) is cut by expressing it as a sum over I quantum channels \({{\mathcal{E}}}_{i}(\rho )\) resulting in the QPD

$${\mathcal{E}}(\rho )=\mathop{\sum }\limits_{i=0}^{I-1}{a}_{i}{{\mathcal{E}}}_{i}(\rho ).$$

(1)

The channels \({{\mathcal{E}}}_{i}(\rho )\) are easier to implement than \({\mathcal{E}}(\rho )\) and are built from LO[16] or LOCC[17] (Fig. 1). As some of the coefficients ai are negative, we introduce γ = ∑i∣ai∣ and Pi = ∣ai∣/γ to recover a valid probability distribution with probabilities Pi over the channels \({{\mathcal{E}}}_{i}\). Here, γ can be seen as the amount by which the QPD deviates from a true probability distribution and is thus a cost to pay to implement the QPD. Without a QPD an observable is estimated by \(\langle O\rangle ={\rm{Tr}}\,\{O{\mathcal{E}}(\rho )\}\). However, when using this QPD, we build an unbiased Monte Carlo estimator of O as

$${\langle O\rangle }_{{\rm{QPD}}}=\gamma \mathop{\sum }\limits_{i=0}^{I-1}{P}_{i}{\rm{sign}}({a}_{i})\,\text{Tr}\,\{O{{\mathcal{E}}}_{i}(\rho )\}.$$

(2)

The variance of the QPD estimator ⟨O⟩QPD is a factor of γ^2 larger than the variance of the non-cut estimator ⟨O⟩ (ref. [44]). When cutting n > 1 identical channels, we can build an estimator by taking the product of the QPDs for each individual channel, resulting in a γ2n rescaling factor22,45. This exponential increase in variance is compensated by a corresponding increase in the number of measured shots. Therefore, γ2n is called the sampling overhead and indicates that circuit cutting must be used sparingly. Details of the LO and LOCC quantum channels \({{\mathcal{E}}}_{i}\) and their coefficients ai are provided in sections ‘Virtual gates implemented with LO’ and ‘Virtual gates implemented with LOCC’, respectively.

Virtual gates implemented with LO

Here, we discuss how to implement virtual CZ gates with LO16,18. We follow ref. [16] and, therefore, decompose each cut CZ gate into local operations and a sum over six different circuits defined by

$$\begin{array}{l}2{\rm{CZ}}\,=\sum _{\alpha \in \{\pm 1\}}{R}_{z}\left(\alpha \frac{\pi }{2}\right)\otimes {R}_{z}\left(\alpha \frac{\pi }{2}\right)\\ \,\,\,-\sum _{{\alpha }_{1},{\alpha }_{2}\in \{\pm 1\}}{\alpha }_{1}{\alpha }_{2}{R}_{z}\left(-\frac{{\alpha }_{1}+1}{2}\pi \right)\otimes \left(\frac{I+{\alpha }_{2}Z}{2}\right)\\ \,\,\,-\sum _{{\alpha }_{1},{\alpha }_{2}\in \{\pm 1\}}{\alpha }_{1}{\alpha }_{2}\left(\frac{I+{\alpha }_{1}Z}{2}\right)\otimes {R}_{z}\left(-\frac{{\alpha }_{2}+1}{2}\pi \right),\end{array}$$

(3)

where \({R}_{z}(\theta )=\exp \left(-{\rm{i}}\frac{\theta }{2}Z\right)\) are virtual Z rotations[46]. The factor 2 in front of CZ is for readability. Each of the possible six circuits is thus weighted by a 1/6 probability (Extended Data Fig. 1). The operations (I + Z)/2 and (I − Z)/2 correspond to the projectors |0⟩ ⟨0| and |1⟩ ⟨1|, respectively. They are implemented by MCMs and classical post-processing. More specifically, when computing the expectation value of an observable ⟨O⟩ = ∑iai⟨O⟩i with the LO QPD, we multiply the expectation values ⟨O⟩i by 1 and −1 when the outcome of an MCM is 0 and 1, respectively.

In the experiments that implement graph states with LO in the main text, we implement the CZ gate with six circuits built from Rz gates and MCMs[16]. Cutting four CZ gates with LO thus requires I = 6^4 = 1,296 circuits. However, as the node and edge stabilizers of the graph states are at most in the light cone[47] of one virtual gate, we instead implement two QPDs in parallel, which requires I = 6^2 = 36 LO circuits per expectation value. In general, sampling from a QPD results in an overhead of \({({\sum }_{i=0}^{I-1}| {a}_{i}| )}^{2}\), where I is the number of circuits in the QPD and the ai are the QPD coefficients[44]. However, as the LO QPDs in our experiments have only 36 circuits, we fully enumerate the QPDs by executing all 36 circuits. The sampling cost of full enumeration is \(I({\sum }_{i=0}^{I-1}| {a}_{i}{| }^{2})\). Furthermore, as ∣ai∣ = 1/2 ∀ i = 0, …, I − 1, sampling from the QPD and fully enumerating it both have the same shot overhead.

The decomposition in equation (3) with γ^2 = 9 is optimal with respect to the sampling overhead for a single gate[17]. Recently, refs. 30,31 found a new protocol that achieves the same γ overhead as LOCC when cutting multiple gates in parallel. The proofs in refs. 30,31 are theoretical demonstrating the existence of a decomposition.

Virtual gates implemented with LOCC

We now discuss the implementation of the dynamic circuits that enable the virtual gates with LOCC. We first present an error suppression and mitigation of dynamic circuits with dynamical decoupling (DD) and zero-noise extrapolation (ZNE). Second, we discuss the methodology to create the cut Bell pairs and present the circuits to implement one, two and three cut Bell pairs. Finally, we propose a simple benchmark experiment to assess the quality of a virtual gate.

Error-mitigated quantum circuit switch instructions

All quantum circuits presented in this work are written in Qiskit. The feed-forward operations of the LOCC circuits are executed with a quantum circuit switch instruction, hereafter referred to as a switch. A switch defines a set of cases in which the quantum circuit can branch depending on the outcome of a corresponding set of measurements. This branching occurs in real time for each experimental shot, with the measurement outcomes being collected by a central processor, which in turn broadcasts the selected case (here corresponding to a combination of X and Z gates) to all control instruments.

As quantum computing scales, the control electronics become tailored to its QPU and are no longer built from off-the-shelf components. Recent IBM devices have a single QPU with a rack of dedicated and tailored control electronics, as shown in refs. 29,48. The realization of the feed-forward we present builds upon the work in ref. [29] and advances its scalability in two main ways. First, our development enables the synchronization and inter-communication between separate experimental setups. Not only are the control instruments for the two sub-QPUs located in different racks, but they are also configurable in software to operate on them independently for the LO experiments and recombined for LOCC. This architecture is extensible to multiple racks and QPUs. It overcomes several of the challenges in operating a distributed control system as pointed out in ref. [23]. Second, the duration of the conditional operation is independent of the measurement results, of which qubits are measured, and which qubits are subject to the conditional operations (apart from minor differences due to cable lengths). This enables the scheduling and execution of programs equally across the combined QPU as if it were a single one.

The feed-forward process results in a latency of the order of 0.5 μs (independent of the selected case) during which no gates can be applied (Extended Data Fig. 2a, red area). Free evolution during this period (τ), often dominated by static ZZ cross-talk in the Hamiltonian, typically with a strength ranging from about 10^3 Hz to 10^4 Hz, substantially deteriorates results. To cancel this unwanted interaction and any other constant or slowly fluctuating IZ or ZI terms, we precede the conditional gates with a staggered DD X–X sequence, adding 3τ to the switch duration (Extended Data Fig. 2a). The value of τ is determined by the longest latency path from one QPU to the other and is fine-tuned by maximizing the signal on such a DD sequence. Furthermore, we mitigate the effect of the overall delay on the observables of interest with ZNE[22]. To do this, we first stretch the switch duration by a factor c = (τ + δ)/τ, where δ is a variable delay added before each X gate in the DD sequence (Extended Data Fig. 2a). Second, we extrapolate the stabilizer values to the zero-delay limit c = 0 with a linear fit. In many cases, an exponential fit can be justified[1]; however, we observe in our benchmark experiments that a linear fit is appropriate (Extended Data Fig. 2). Without DD, we observe strong oscillations in the measured stabilizers that prevent an accurate ZNE (see the XZ stabilizer in Extended Data Fig. 2c). As seen in the main text, this error suppression and mitigation reduce the error on the stabilizers affected by virtual gates.

The error suppression and mitigation that we implement for the switch also apply to other control flow statements. The switch is not the only instruction capable of representing control flow. For instance, OpenQASM3[49] supports if/else statements. Our scheme is done by (1) adding DD sequences to the latency (possibly by adding delays if the control electronics cannot emit pulses during the latency); (2) stretching the delay; and (3) extrapolating to the zero-delay limit.

Cut Bell pair factories

Here, we discuss the quantum circuits to prepare the cut Bell pairs needed to realize virtual gates with LOCC. To create a factory for k cut Bell pairs, we must find a linear combination of circuits with two disjoint partitions with k qubits each to reproduce the statistics of Bell pairs. We create the state ρk of the Bell pairs following ref. [50] such that \({\rho }_{k}=(1+{t}_{k}){\rho }_{k}^{+}-{t}_{k}{\rho }_{k}^{-}\), where tk = 2k − 1. Here, \({\rho }_{k}^{\pm }\) are mixed states separable with respect to the partitions A and B. Note that ρk entangles the qubit partitions A and B, shown in Fig. 1c, but \({\rho }_{k}^{\pm }\) do not. The total cost of this QPD with two states is determined by γk = 2tk + 1. Next, we realize \({\rho }_{k}^{\pm }\) from a probabilistic mixture of pure states \({\rho }_{k,i}^{\pm }\), that is, valid probability distributions. The state \({\rho }_{k}^{-}\) is easily implemented by a uniform mixture of all basis states that correspond to a 0 entry on the diagonal of the density matrix ρk. The basis states themselves do not appear in ρk. We thus implement \({\rho }_{k}^{-}\) as a diagonal density matrix of \({n}_{k}^{-}={4}^{k}-{2}^{k}\) basis states. The state \({\rho }_{k}^{+}\) is harder to engineer. It requires a probabilistic mixture of intricate states with entanglement within each partition A and B but not between them. To engineer \({\rho }_{k}^{+}\), we thus build a parametric quantum circuit Ck(θi) with parameters θi in which no two-qubit gate connects qubits between A and B. Following ref. [50], we need \({n}_{k}^{+}={2}^{{2}^{k}}-1\) pure states to realize \({\rho }_{k}^{+}\). The exact form of \({\rho }_{k}^{+}\), omitted here for brevity, is given in Appendix B of ref. [50]. Therefore, the total number of parameter sets \(I={n}_{k}^{+}+{n}_{k}^{-}\) required to implement one, two and three cut Bell pairs is 5, 27 and 311, respectively. Finally, the coefficients ai,k of all the circuits in the QPD in equation (1) that implement \({\rho }_{k}^{\pm }\) are

$${a}_{i,k}=\frac{1+{t}_{k}}{{n}_{k}^{+}},\,\,{\rm{for}}\,\,i\in \{0,...,{n}_{k}^{+}-1\},\,{\rm{and}}$$

(4)

$${a}_{i,k}=-\frac{{t}_{k}}{{n}_{k}^{-}},\,\,{\rm{for}}\,\,i\in \{{n}_{k}^{+},...,{n}_{k}^{+}+{n}_{k}^{-}-1\}.$$

(5)

For k = 2, the resulting weights, ∣ai,k∣/γk are approximately all equal. There is thus no practical difference between sampling and enumerating the k = 2 QPD when executing it on hardware. More precisely, for the factories with two cut Bell pairs that we run on hardware, the cost of sampling the QPD is \({({\sum }_{i=0}^{I-1}| {a}_{i,2}| )}^{2}={\gamma }_{2}^{2}(1+1.6\times 1{0}^{-7})\) and the cost of fully enumerating the QPD is \(I({\sum }_{i=0}^{I-1}| {a}_{i,2}{| }^{2})={\gamma }_{2}^{2}(1+1.0\times 1{0}^{-3})\), where γ_2 = 7.

We construct all pure states \({\rho }_{k,i}^{\pm }\) from the same template variational quantum circuit Ck(θi) with parameters θi, where the index i = 0, …, I − 1 runs over the I elements of the probabilistic mixtures defining \({\rho }_{k}^{\pm }\). The parameters θi in the template circuits Ck(θi) are optimized by the SLSQP classical optimizer[51] by minimizing the L_2-norm with respect to the I pure target states needed to represent \({\rho }_{k}^{+}\), where the norm is evaluated with a classical state vector simulation. After testing various approaches, we find that those provided in Fig. 1c and Extended Data Fig. 3 enable us to achieve an error, based on the L_2 norm, of less than 10−8 for each state while having minimal hardware requirements. To enable rapid execution of the QPD with parametric updates, all the parameters are the angles of virtual Z rotations[46] (Fig. 1c). As \({\rho }_{k}^{-}\) is built from basis states, we analytically derive the parameters. Therefore, we could also significantly simplify the ansatz Ck(θi), for example, by cancelling CNOT gates. However, we keep the same template for compilation and execution efficiency. On first inspection, the parameters entering \({\rho }_{k}^{+}\) do not have any usable structure. We thus leave it up to future research to further investigate whether these parameters have any structure that could be leveraged to simplify the cut Bell pair factories.

A single-cut Bell pair is engineered by applying the gates U(θ_0, θ_1) and U(θ_2, θ_3) on qubits 0 and 1. Here, and in the figures, the gate U(θ, ϕ) corresponds to \(\sqrt{X}{R}_{z}(\theta )\sqrt{X}{R}_{z}(\phi )\). The QPD of a single-cut Bell pair requires five sets of parameters given by {[π/2, 0, π/2, 0], [π/2, −2π/3, π/2, 2π/3], [π/2, 2π/3, π/2, −2π/3], [π, 0, 0, 0], [0, 0, π, 0]} which could also be derived analytically. The circuits to simultaneously create two and three cut Bell pairs are shown in Fig. 1c and Extended Data Fig. 3, respectively. The circuits and the values of the parameters as obtained by the optimizer are available on GitHub (www.github.com/eggerdj/cut_graph_state_data).

In the experiments that implement graph states with LOCC in the main text, we construct two QPDs in parallel with I = 27 circuits, each QPD implementing two long-range CZ gates. This execution is similar to the LO execution in which we also execute two QPDs in parallel.

Benchmarking qubits for LOCC

The quality of a CNOT gate implemented with dynamic circuits depends on hardware properties. For example, qubit relaxation, dephasing and static ZZ cross-talk all negatively affect the qubits during the idle time of the switch. Furthermore, measurement quality also affects virtual gates implemented with LOCC. Errors on MCMs are harder to correct than errors on final measurements as they propagate to the rest of the circuit through the conditional gates[52]. For instance, assignment errors during readout result in an incorrect application of a single-qubit X or Z gate. Given the variability in these qubit properties, care must be taken in selecting those to act as cut Bell pairs. To determine which qubits will perform well as cut Bell pairs, we develop a fast characterization experiment on four qubits that does not require a QPD or error mitigation. This experiment creates a graph state between qubits 0 and 3 by consuming an uncut Bell pair created on qubits 1 and 2 with a Hadamard and a CNOT gate. We measure the stabilizers ZX and XZ which require two different measurement bases. The resulting circuit, shown in Extended Data Fig. 4a, is structurally equivalent to half of the circuit that consumes two cut Bell pairs, for example, Fig. 1c. We execute this experiment on all qubit chains of length four on the devices that we use and report the mean squared error (MSE), that is, [(⟨ZX⟩ − 1)2 + (⟨XZ⟩ − 1)2]/2 as a quality metric. The lower the MSE is the better the set of qubits act as cut Bell pairs. With this experiment we benchmark, ibm_kyiv (the device used to create the graph state with 103 nodes), and ibm_pinguino-1a and ibm_pinguino-1b (the two Eagle QPUs combined into a single device, named ibm_pinguino-2a, used to create the graph state with 134 nodes). We observe more than an order of magnitude variation in MSE across each device (Extended Data Fig. 4b).

The qubits we chose to act as cut Bell pairs are a tradeoff between the graph we want to engineer and the quality of the MSE benchmark. For example, the graphs with periodic boundary conditions presented in the main text were designed first based on the desired shape of |G⟩ and second based on the MSE of the Bell pair quality test.

Graph states

A graph state |G⟩ is created from a graph G = (V, E) with nodes V and edges E by applying an initial Hadamard gate to each qubit, corresponding to a node in V, and then CZ gates to each pair of qubits (i, j) ∈ E (refs. 53,54). The resulting state |G⟩ has ∣V∣ first-order stabilizers, one for each node i ∈ V, defined by Si = Xi∏k∈N(i)Zk. Here, N(i) is the neighbourhood of node i defined by E. These stabilizers satisfy Si|G⟩ = |G⟩. By construction, any product of stabilizers is also a stabilizer. If an edge (i, j) ∈ E is not implemented by a CZ gate, the corresponding stabilizers drop to zero, that is, ⟨Si⟩ = ⟨Sj⟩ = 0. This effect can be seen in the dropped edge benchmark, see, for example, Fig. 2b.

Entanglement witness

We now define a success criterion for the implementation of a graph state with entanglement witnesses[55]. A witness \({\mathcal{W}}\) is designed to detect a certain form of entanglement. As we cut edges in the graph state, we focus on witnesses \({{\mathcal{W}}}_{i,j}\) over two nodes i and j connected by an edge in E. An edge (i, j) of our graph state |G⟩ presents entanglement if the expectation value \(\langle {{\mathcal{W}}}_{i,j}\rangle < 0\). The witness does not detect entanglement if \(\langle {{\mathcal{W}}}_{i,j}\rangle \ge 0\). The first-order stabilizers of nodes i and j with (i, j) ∈ E are

$${S}_{i}={Z}_{j}{X}_{i}\prod _{k\in N(i)\backslash j}{Z}_{k}\,\text{and}\,{S}_{j}={X}_{j}{Z}_{i}\prod _{k\in N(j)\backslash i}{Z}_{k}.$$

(6)

Here, N(i) is the neighbourhood of node i, which includes j because (i, j) ∈ E. Thus, N(i)\j is the neighbourhood of node i excluding j. Following refs. 55,56, we build an entanglement witness for edge (i, j) ∈ E as

$${{\mathcal{W}}}_{i,j}=\frac{1}{4}{\mathbb{I}}-\frac{1}{4}(\langle {S}_{i}\rangle +\langle {S}_{j}\rangle +\langle {S}_{i}{S}_{j}\rangle ).$$

(7)

This witness is zero or positive if the states are separable. Alternatively, as in ref. [27], a witness for bi-separability is also given by

$${{\mathcal{W}}}_{i,j}^{{\prime} }={\mathbb{I}}-\langle {S}_{i}\rangle -\langle {S}_{j}\rangle .$$

(8)

Here, we consider both witnesses. The data in the main text are presented for \({{\mathcal{W}}}_{i,j}\). As discussed in ref. [56], \({{\mathcal{W}}}_{i,j}\) is more robust to noise than \({{\mathcal{W}}}_{i,\,j}^{{\prime} }\). However, \({{\mathcal{W}}}_{i,j}\) requires more experimental effort to measure than \({{\mathcal{W}}}_{i,\,j}^{{\prime} }\) because of the stabilizer SiSj.

For completeness, we now show how a witness can detect entanglement by focusing on \({{\mathcal{W}}}_{i,j}\). A separable state satisfies \(\langle {P}_{1}...{P}_{n}\rangle ={\prod }_{i}\langle {P}_{i}\rangle \), where Pi are single-qubit Pauli operators. Therefore, we can show, using the Cauchy–Schwarz inequality, that \(\langle {S}_{i}\rangle +\langle {S}_{j}\rangle +\langle {S}_{i}{S}_{j}\rangle \le 1\) and that \({{\mathcal{W}}}_{i,j}\ge 0\) for separable states.

$$\langle {S}_{i}\rangle +\langle {S}_{j}\rangle +\langle {S}_{i}{S}_{j}\rangle =\langle {Z}_{j}\rangle \langle {X}_{i}\rangle \prod _{k\in N(i)\backslash j}\langle {Z}_{k}\rangle $$

(9)

$$+\langle {X}_{j}\rangle \langle {Z}_{i}\rangle \prod _{k\in N(j)\backslash i}\langle {Z}_{k}\rangle +\langle {Y}_{i}\rangle \langle {Y}_{j}\rangle \prod _{k\in M(i,j)}\langle {Z}_{k}\rangle $$

(10)

$$\le | \langle {Z}_{j}\rangle | | \langle {X}_{i}\rangle | +| \langle {X}_{j}\rangle | | \langle {Z}_{i}\rangle | +| \langle {Y}_{j}\rangle | | \langle {Y}_{i}\rangle | $$

(11)

$$\le \sqrt{{\langle {X}_{i}\rangle }^{2}+{\langle {Y}_{i}\rangle }^{2}+{\langle {Z}_{i}\rangle }^{2}}\sqrt{{\langle {X}_{j}\rangle }^{2}+{\langle {Y}_{j}\rangle }^{2}+{\langle {Z}_{j}\rangle }^{2}}$$

(12)

$$\le 1.$$

(13)

The step from equation (10) to equation (11) relies on ∏iai ≤ ∏i ∣ai∣ and that \({\prod }_{k}| \langle {Z}_{k}\rangle | \le 1\), where the product runs over nodes that do not contain i or j. The step from equation (11) to equation (12) is based on the Cauchy–Schwarz inequality. The final step relies on the fact that \({\langle {X}_{i}\rangle }^{2}+{\langle {Y}_{i}\rangle }^{2}+{\langle {Z}_{i}\rangle }^{2}\le 1\) with pure states equal to one. Therefore, the witness \({{\mathcal{W}}}_{i,j}\) will be negative if the state is not separable.

In the graph states presented in the main text, we execute a statistical test at a 99% confidence level to detect entanglement. As discussed in the Supplementary Information and shown in Fig. 2b, some witnesses may go below −1/2 because of readout error mitigation, the QPD and Switch ZNE. We, therefore, consider an edge to have the statistics of entanglement if the deviation from −1/2 is not statistically greater than ±1/2. Based on a one-tailed test, we consider that edge (i, j) is bi-partite entangled if

$$-\frac{1}{2}+\left|\langle {{\mathcal{W}}}_{i,j}\rangle +\frac{1}{2}\right|+{z}_{99 \% }{\sigma }_{{\mathcal{W}},i,j} < 0.$$

(14)

Similarly, we form a success criterion based on \({{\mathcal{W}}}_{i,j}^{{\prime} }\) as

$$-1+| \langle {{\mathcal{W}}}_{i,j}^{{\prime} }\rangle +1| +{z}_{99 \% }{\sigma }_{{{\mathcal{W}}}^{{\prime} },i,j} < 0.$$

(15)

This criterion penalizes any deviation from −1, that is, the most negative value that \({{\mathcal{W}}}_{i,\,j}^{{\prime} }\) can have. Here, z99% = 2.326 is the z-score of a Gaussian distribution at a 99% confidence level and \({\sigma }_{{\mathcal{W}},i,j}\) is the standard deviation of edge witness \({{\mathcal{W}}}_{i,j}\). These tests are conservative as they penalize any deviation from the ideal values. Moreover, these tests are most suitable for circuit cutting because the QPD may increase the variance \({\sigma }_{{{\mathcal{W}}}_{i,j}}\) of the measured witnesses. Therefore, the statistics of entanglement are detected only if the mean of a witness is sufficiently negative and its standard deviation is sufficiently small. An edge (i, j) ∈ E fails the criteria if equation (14) or equation (15) is not satisfied. All edges in E, including the cut edges, pass the test based on \({{\mathcal{W}}}_{i,j}\) when implemented with LO and LOCC (Extended Data Table 2). However, some edges fail the test based on \({{\mathcal{W}}}_{i,\,j}^{{\prime} }\) because of the lower noise robustness of \({{\mathcal{W}}}_{i,\,j}^{{\prime} }\) compared with \({{\mathcal{W}}}_{i,j}\).

Circuit count for stabilizer measurements

Obtaining the bipartite entanglement witnesses requires measuring the expectation values of ⟨Si⟩, ⟨Sj⟩ and ⟨SiSj⟩ of each edge (i, j) ∈ E. For the 103- and 134-node graphs presented in the main text, all 219- and 278-node and edge stabilizers, respectively, can be measured in NS = 7 groups of commuting observables. To mitigate final measurement readout errors, we use twirled readout error extinction (TREX) with NTREX samples[57]. When virtual gates are used with LO and LOCC, we require ILO and ILOCC more circuits, respectively. In this work, we fully enumerate the QPD. Furthermore, for LOCC, we mitigate the delay of the switch instruction with ZNE based on NZNE stretch factors. Therefore, the four types of experiments are executed with the following number of circuits.

- Swaps: NSNTREX

- Dropped edge: NSNTREX

- LO: NSNTREXILO

- LOCC: NSNTREXILOCCNZNE

In the experiments for the 103- and 134-node graph states, we use NTREX = 5 and 3 TREX samples, respectively. Therefore, measuring the stabilizers without a QPD requires NS × NTREX = 35 circuits for the 103-node graph. For LO and LOCC, measuring the stabilizers for the graphs in the main text requires 6^4 and 27^2 circuits, respectively. However, owing to the graph structure, each edge witness is only ever in the light cone of two cut gates at most. We may thus execute a total of ILO = 6^2 and ILOCC = 27 circuits for LO and LOCC, respectively, based on the light cone of the gates. For higher-weight observables, this corresponds to sampling the diagonal terms of a joint QPD. Therefore, measuring the stabilizers with LO requires NS × NTREX × ILO = 1,260 circuits. For LOCC, we further perform error mitigation of the switch with NZNE = 5 stretch factors. We, therefore, execute NS × NTREX × ILOCC × NZNE = 4,725 circuits to measure the error-mitigated stabilizers needed to compute \({{\mathcal{W}}}_{i,j}\). Each circuit is executed with a total of 1,024 shots.

To reconstruct the value of the measured observables, we first merge the shots from the TREX samples. To do this, we flip the classical bits in the measured bit strings corresponding to measurements for which TREX prepended an X gate. These processed bit strings are then aggregated in a count dictionary with 1,024 × NTREX counts. Next, to obtain the value of a stabilizer, we identify which of the NS measurement bases we need to use. The value of a stabilizer and its corresponding standard deviation are then obtained by resampling the corresponding 1,024 × NTREX counts. Here, we randomly select 10% of the shots to compute an expectation value. Ten such expectation values are averaged and reported as the measured stabilizer value. The standard deviation of these 10 measurements is reported as the standard deviation of the stabilizer, shown as error bars in Fig. 2b. Finally, if the stabilizer is in the light cone of a virtual gate implemented with LOCC, we linearly fit the value of the stabilizer obtained at the NZNE = 5 switch stretch factors. This fit, shown in Extended Data Fig. 2d, enables us to report the stabilizer at the extrapolated zero-delay switch.

Data availability

The code to analyse the counts, reproduce the plots in this paper and produce the circuits for the cut Bell pairs are available on GitHub (https://github.com/eggerdj/cut_graph_state_data). The raw counts are unavailable on GitHub because of size constraints but are available upon reasonable request.

References

1. Kim, Y. et al. Evidence for the utility of quantum computing before fault tolerance. Nature 618, 500–505 (2023).

2. Bravyi, S., Dial, O., Gambetta, J. M., Gil, D. & Nazario, Z. The future of quantum computing with superconducting qubits. J. Appl. Phys. 132, 160902 (2022).

3. Bravyi, S. et al. High-threshold and low-overhead fault-tolerant quantum memory. Nature 627, 778–782 (2024).

4. Akhtar, M. et al. A high-fidelity quantum matter-link between ion-trap microchip modules. Nat. Commun. 14, 531 (2023).

5. Bluvstein, D. et al. A quantum processor based on coherent transport of entangled atom arrays. Nature 604, 451–456 (2022).

6. Krantz, P. et al. A quantum engineer’s guide to superconducting qubits. Appl. Phys. Rev. 6, 021318 (2019).

7. Conner, C. R. et al. Superconducting qubits in a flip-chip architecture. Appl. Phys. Lett. 118, 232602 (2021).

8. Gold, A. et al. Entanglement across separate silicon dies in a modular superconducting qubit device. npj Quantum Inf. 7, 142 (2021).

9. Zhong, Y. et al. Violating Bell’s inequality with remotely connected superconducting qubits. Nat. Phys. 15, 741–744 (2019).

10. Zhong, Y. et al. Deterministic multi-qubit entanglement in a quantum network. Nature 590, 571–575 (2021).

11. Malekakhlagh, M. et al. Enhanced quantum state transfer and Bell-state generation over long-range multimode interconnects via superadiabatic transitionless driving. Phys. Rev. Appl. 22, 024006 (2024).

12. Ang, J. et al. ARQUIN: architectures for multinode superconducting quantum computers. ACM Trans. Quantum Comput. 5, 19 (2024).

13. Córcoles, A. D. et al. Exploiting dynamic quantum circuits in a quantum algorithm with superconducting qubits. Phys. Rev. Lett. 127, 100501 (2021).

14. Bäumer, E. et al. Efficient long-range entanglement using dynamic circuits. PRX Quantum 5, 030339 (2024).

15. Hofmann, H. F. How to simulate a universal quantum computer using negative probabilities. J. Phys. A Math. Theor. 42, 275304 (2009).

16. Mitarai, K. & Fujii, K. Constructing a virtual two-qubit gate by sampling single-qubit operations. New J. Phys. 23, 023021 (2021).

17. Piveteau, C. & Sutter, D. Circuit knitting with classical communication. IEEE Trans. Inf. Theor. 1, 2734–2745 (2023).

18. Singh, A. P. et al. Experimental demonstration of a high-fidelity virtual two-qubit gate. Phys. Rev. Res. 6, 013235 (2024).

19. Gottesman, D. & Chuang, I. L. Demonstrating the viability of universal quantum computation using teleportation and single-qubit operations. Nature 402, 390–393 (1999).

20. Wan, Y. et al. Quantum gate teleportation between separated qubits in a trapped-ion processor. Science 364, 875 (2019).

21. Viola, L., Knill, E. & Lloyd, S. Dynamical decoupling of open quantum systems. Phys. Rev. Lett. 82, 2417–2421 (1999).

22. Temme, K., Bravyi, S. & Gambetta, J. M. Error mitigation for short-depth quantum circuits. Phys. Rev. Lett. 119, 180509 (2017).

23. Reilly, D. J. Challenges in scaling-up the control interface of a quantum computer. Preprint at arxiv.org/abs/1912.05114 (2019).

24. Peng, T., Harrow, A. W., Ozols, M. & Wu, X. Simulating large quantum circuits on a small quantum computer. Phys. Rev. Lett. 125, 150504 (2020).

25. Brenner, L., Piveteau, C. & Sutter, D. Optimal wire cutting with classical communication. Preprint at arxiv.org/abs/2302.03366 (2023).

26. Pednault, E. An alternative approach to optimal wire cutting without ancilla qubits. Preprint at arxiv.org/abs/2303.08287 (2023).

27. Zander, R. & Becker, C. K.-U. Benchmarking multipartite entanglement generation with graph states. Preprint at arxiv.org/abs/2402.00766 (2024).

28. Mundada, P. S. et al. Experimental benchmarking of an automated deterministic error-suppression workflow for quantum algorithms. Phys. Rev. Appl. 20, 024034 (2023).

29. Gupta, R. S. et al. Encoding a magic state with beyond break-even fidelity. Nature 625, 259–263 (2024).

30. Ufrecht, C. et al. Optimal joint cutting of two-qubit rotation gates. Phys. Rev. A 109, 052440 (2024).

31. Schmitt, L., Piveteau, C. & Sutter, D. Cutting circuits with multiple two-qubit unitaries. Preprint at arxiv.org/abs/2312.11638 (2023).

32. Cirac, J. I., Zoller, P., Kimble, H. J. & Mabuchi, H. Quantum state transfer and entanglement distribution among distant nodes in a quantum network. Phys. Rev. Lett. 78, 3221–3224 (1997).

33. Duan, L.-M., Lukin, M. D., Cirac, J. I. & Zoller, P. Long-distance quantum communication with atomic ensembles and linear optics. Nature 414, 413–418 (2001).

34. Magnard, P. et al. Microwave quantum link between superconducting circuits housed in spatially separated cryogenic systems. Phys. Rev. Lett. 125, 260502 (2020).

35. Niu, J. et al. Low-loss interconnects for modular superconducting quantum processors. Nat. Electron. 6, 235–241 (2023).

36. Orcutt, J. et al. Engineering electro-optics in SiGe/Si waveguides for quantum transduction. Quantum Sci. Technol. 5, 034006 (2020).

37. Lauk, N. et al. Perspectives on quantum transduction. Quantum Sci. Technol. 5, 020501 (2020).

38. Krastanov, S. et al. Optically heralded entanglement of superconducting systems in quantum networks. Phys. Rev. Lett. 127, 040503 (2021).

39. Bechtold, M., Barzen, J., Leymann, F. & Mandl, A. Circuit cutting with non-maximally entangled states. Preprint at arxiv.org/abs/2306.12084 (2023).

40. Bennett, C. H. et al. Purification of noisy entanglement and faithful teleportation via noisy channels. Phys. Rev. Lett. 76, 722–725 (1996).

41. Tornow, C., Kanazawa, N., Shanks, W. E. & Egger, D. J. Minimum quantum run-time characterization and calibration via restless measurements with dynamic repetition rates. Phys. Rev. Appl. 17, 064061 (2022).

42. Harrow, A. W. & Lowe, A. Optimal quantum circuit cuts with application to clustered Hamiltonian simulation. Preprint at arxiv.org/abs/2403.01018 (2024).

43. Caleffi, M. et al. Distributed quantum computing: a survey. Preprint at arxiv.org/abs/2212.10609 (2022).

44. Cai, Z. et al. Quantum error mitigation. Rev. Mod. Phys. 95, 045005 (2023).

45. Endo, S., Benjamin, S. C. & Li, Y. Practical quantum error mitigation for near-future applications. Phys. Rev. X 8, 031027 (2018).

46. McKay, D. C., Wood, C. J., Sheldon, S., Chow, J. M. & Gambetta, J. M. Efficient Z gates for quantum computing. Phys. Rev. A 96, 022330 (2017).

47. Tran, M. C. et al. Hierarchy of linear light cones with long-range interactions. Phys. Rev. X 10, 031009 (2020).

48. Zettles, G., Willenborg, S., Johnson, B. R., Wack, A. & Allison, B. 26.2 Design considerations for superconducting quantum systems. In 2022 IEEE International Solid-State Circuits Conference (ISSCC), Vol. 65, pp. 1–3 (IEEE, 2022).

49. Cross, A. et al. OpenQASM 3: a broader and deeper quantum assembly language. ACM Trans. Quantum Comput. 3, 1–50 (2022).

50. Vidal, G. & Tarrach, R. Robustness of entanglement. Phys. Rev. A 59, 141–155 (1999).

51. Kraft, D. A Software Package for Sequential Quadratic Programming (DFVLR, 1988).

52. Gupta, R. S. et al. Probabilistic error cancellation for dynamic quantum circuits. Phys. Rev. A 109, 062617 (2024).

53. Briegel, H. J. & Raussendorf, R. Persistent entanglement in arrays of interacting particles. Phys. Rev. Lett. 86, 910–913 (2001).

54. Hein, M., Eisert, J. & Briegel, H. J. Multiparty entanglement in graph states. Phys. Rev. A 69, 062311 (2004).

55. Jungnitsch, B., Moroder, T. & Gühne, O. Entanglement witnesses for graph states: general theory and examples. Phys. Rev. A 84, 032310 (2011).

56. Tóth, G. & Gühne, O. Entanglement detection in the stabilizer formalism. Phys. Rev. A 72, 022340 (2005).

57. van den Berg, E., Minev, Z. K. & Temme, K. Model-free readout-error mitigation for quantum expectation values. Phys. Rev. A 105, 032620 (2022).

Acknowledgements

We acknowledge the use of IBM Quantum services for this work. The views expressed are those of the authors and do not reflect the official policy or position of IBM or the IBM Quantum team. We acknowledge B. Donovan, I. Hincks and K. Barton for circuit compilation and execution. We also thank D. Sutter, J. Orcutt, E. van den Berg, K. Temme, L. Bishop and P. Seidler for their discussions.

Author information

Authors and Affiliations

1. IBM Quantum, IBM Research Europe - Zurich, Rüschlikon, Switzerland

Almudena Carrera Vazquez, Caroline Tornow, Stefan Woerner & Daniel J. Egger

2. Institute for Theoretical Physics, ETH Zurich, Zurich, Switzerland

Caroline Tornow

3. IBM Quantum, IBM Research Cambridge, Cambridge, MA, USA

Diego Ristè

4. IBM Quantum, T. J. Watson Research Center, Yorktown Heights, NY, USA

Maika Takita

Contributions

A.C.V. gathered the LO data and studied the error mitigation of MCMs. C.T. studied the error mitigation of dynamic circuits and graph states. D.J.E. gathered the LOCC data. A.C.V., C.T. and D.J.E. analysed the circuit-cutting data. D.R. implemented the error suppression for dynamic circuits and optimized their execution on hardware. S.W. designed the cut Bell pairs. M.T. coordinated hardware resources and experiment execution. D.J.E. and M.T. designed the graph state experiments. D.J.E. designed the switch error mitigation. All authors discussed the results and improved and approved the paper.

Corresponding author

Correspondence to Daniel J. Egger.

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Competing interests

A patent (application no. 18/523211) was filed on 29 November 2023 with listed inventors D.J.E., C.T., D.R., A.C.V., S.W. and M.T. The authors declare no other competing financial or non-financial interests.

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Extended data figures and tables

Extended Data Fig. 1 LO decomposition of a CZ gate.

A single CZ gate can be simulated through local operations by sampling from the shown QPD and applying classical post-processing to the results. Each of the six circuits has a sampling probability of 1/(2γ) = 1/6. For the four circuits featuring mid-circuit measurements, the corresponding QPD coefficient is adjusted by a factor of + 1 for outcome 0 and a factor of − 1 for outcome 1. To optimize the execution, these six circuits are consolidated into three parametrized circuits to enable a parametric circuit execution. The green, red, and yellow circuits correspond to the three template circuits generated from cutting a single CZ gate with the LO protocol. Here, the presence or absence of a mid-circuit measurement changes the pulse-level payload which thus requires compilation.

Extended Data Fig. 2 Zero-noise extrapolation of a switch.

a, Implementation of a switch with DD. The conditional gates (not shown) are executed after the last DD X gate. The red delay of τ shows the duration in which no gates can be executed as the control electronics is busy, see Sec. VIC 1. The three additional delays of τ enable staggered DD. The four additional and variable delays of δ allow us to vary the duration of the switch for ZNE. b,c, The ZX and XZ correlators measured on ibm_peekskill as a function of the switch stretch factor c for a two-qubit graph state on G = ({0, 1}, {(0, 1)}). d, Example correlators of the 103 node graph extrapolated with ZNE.

Extended Data Fig. 3 Quantum circuit of three cut Bell pairs.

A sum over the right set of parameter vectors {θ} results in three cut Bell pairs between qubit pairs (q_0, q_3), (q_1, q_4), and (q_2, q_5). The gate U(θ, ϕ) corresponds to the gate sequence \(\sqrt{X}{R}_{z}(\theta )\sqrt{X}{R}_{z}(\phi )\). The blue and red shaded regions correspond to the two disjoint portions of the quantum circuit.

Extended Data Fig. 4 LOCC Bell pair benchmark.

a, Quantum circuit that creates an uncut Bell pair on qubits (1, 2) and consume it in a teleportation circuit to create a Bell state on qubits (0, 3). b, Cumulative distribution function of the MSE of ⟨ZX⟩ and ⟨XZ⟩ for all groups of four linearly connected qubits on each device. The stars correspond to the qubits used in the 103- and 134-node graph states presented in the main text. The numbers in brackets indicate the qubit numbers corresponding to (q_1, q_2) in panel (a).

Extended Data Table 1 Circuit structure and node error

Extended Data Table 2 Witness tests

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Carrera Vazquez, A., Tornow, C., Ristè, D. et al. Combining quantum processors with real-time classical communication. Nature 636, 75–79 (2024). https://doi.org/10.1038/s41586-024-08178-2

- Received: 21 March 2024

- Accepted: 08 October 2024

- Published: 20 November 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08178-2

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Observation of Hilbert space fragmentation and fractonic excitations in 2D

Daniel Adler,

David Wei,

Melissa Will,

Kritsana Srakaew,

Suchita Agrawal,

Pascal Weckesser,

Roderich Moessner,

Frank Pollmann,

Immanuel Bloch &

…

Johannes Zeiher

Nature volume 636, pages 80–85 (2024)

Abstract

The relaxation behaviour of isolated quantum systems taken out of equilibrium is among the most intriguing questions in many-body physics[1]. Quantum systems out of equilibrium typically relax to thermal equilibrium states by scrambling local information and building up entanglement entropy. However, kinetic constraints in the Hamiltonian can lead to a breakdown of this fundamental paradigm owing to a fragmentation of the underlying Hilbert space into dynamically decoupled subsectors in which thermalization can be strongly suppressed2,3,4,5. Here we experimentally observe Hilbert space fragmentation in a two-dimensional tilted Bose–Hubbard model. Using quantum gas microscopy, we engineer a wide variety of initial states and find a rich set of manifestations of Hilbert space fragmentation involving bulk states, interfaces and defects, that is, two-, one- and zero-dimensional objects. Specifically, uniform initial states with equal particle number and energy differ strikingly in their relaxation dynamics. Inserting controlled defects on top of a global, non-thermalizing chequerboard state, we observe highly anisotropic, subdimensional dynamics, an immediate signature of their fractonic nature6,7,8,9. An interface between localized and thermalizing states in turn shows dynamics depending on its orientation. Our results mark the observation of Hilbert space fragmentation beyond one dimension, as well as the concomitant direct observation of fractons, and pave the way for in-depth studies of microscopic transport phenomena in constrained systems.

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Main

The eigenstate thermalization hypothesis expresses the notion of thermalization in closed quantum systems by stating that eigenstates produce expectation values for local observables that are consistent with those of a thermal ensemble, and thus lose all memory of the initial states during relaxation10,11. Recently, several mechanisms have been delineated, where systems defy such thermalizing dynamics and the eigenstate thermalization hypothesis, such as integrability in one-dimensional systems12,13, many-body localization in models with quenched disorder14,15,16 or the emergence of many-body scars for specific initial settings5,17. Another mechanism is the emergence of kinetic constraints connected with Hilbert space fragmentation (HSF)2,3,4,5,18,19,20,21. In systems showing HSF, a hierarchy of conservation laws exists. First, the full Hilbert space can be divided into (polynomially many) subspaces characterized by global quantum numbers such as particle number or dipole moment. In the corresponding subspaces with constant quantum numbers, local kinetic constraints lead to a further fragmentation of the Hilbert space into exponentially many smaller subsectors, the so-called Krylov subsectors, which cannot be characterized by simple quantum numbers. All states in a single Krylov sector are, by definition, dynamically connected, that is, they can be reached through unitary time evolution with the Hamiltonian2,3. One striking consequence of HSF is the possible existence of fragments containing specific states that evade thermalization because of the underlying kinetic constraints and the small size of the associated Krylov sector.

Another particularly interesting consequence of constrained dynamics is the potential emergence of fractons that show restricted mobility6,7,9. Fractons can either be immobile under local Hamiltonian dynamics or show subdimensional dynamics, such as propagation in an effectively one-dimensional subspace of two-dimensional space6,7,8,9,22, as well as anomalous diffusion22,23,24. Previous theoretical studies2,3,6,7,21,25 have also related the emergence of fractons to gauge theories associated with local conservation laws and to topological defects in elasticity theory[26]. Relaxation of systems showing fractonic excitations is expected to be strongly impeded, leading to non-ergodic behaviour and strongly temperature-dependent transport dynamics7,24. Subdiffusive transport in the tilted Fermi–Hubbard model has recently been observed experimentally[27]. Related theoretical work has connected the emerging subdiffusive hydrodynamic behaviour with the presence of kinetic constraints22,28. The kinetic constraints underlying HSF have been experimentally probed directly in one-dimensional tilted Hubbard chains29,30. These systems show dipole-moment conservation for strong interactions, as a consequence of the interplay between interaction and tilt energy2,3,31,32,33. Recently, state-specific relaxation behaviour in systems with HSF was also observed in one-dimensional Rydberg arrays kinetically constrained in the facilitation regime[34] and quantum ladders realized with a superconducting quantum processor[35]. The evasion of thermalization tends to depend, frequently qualitatively, on the underlying dimensionality, particularly for both integrable or disorder-localized systems. Consequently, this naturally motivates the study of the hallmarks of non-ergodicity in higher-dimensional HSF.

Here we investigate this question for a two-dimensional tilted Bose–Hubbard model, where we study the non-equilibrium dynamics owing to HSF in bulk (2D), interface (1D) and point-like defect (0D) dynamics, and find a rich and interrelated phenomenology. Our experiments leverage the single-site control achievable in a quantum gas microscope to prepare specific initial product states in different Krylov sectors and measure their dynamics after a quantum quench. We find markedly different relaxation dynamics for a chequerboard state and a dimer state (Fig. 1c), which are characterized by the same quantum numbers but are part of different Krylov subsectors. Moreover, we prepare and dynamically track defects on top of the otherwise immobile chequerboard state. Our measurements reveal the fractonic nature of such defects, which manifests itself as strongly constrained, subdimensional motion along a one-dimensional manifold in the two-dimensional system. Finally, we prepare an interface between a chequerboard state and a dimer state and observe strongly asymmetric dynamics across the interface consistent with the fractonic nature of the excitations.

Fig. 1: HSF and schematic of the experiment.

a, The Hilbert space consists of sectors with fixed energy and particle number (conserved quantities), (E, N) (grey squares). Adding dynamical constraints to the system, these sectors fragment into decoupled Krylov subsectors (pink squares). b, Our system is described by a tilted Bose–Hubbard model with a diagonal tilt (realized using a magnetic field B) along the x + y direction tuned to resonance with the interactions, Δ = U. Tilt and interaction energy are much larger than the tunnel coupling J. c, Dimer (blue points, bottom left) and chequerboard state (orange points, top right) and first-order processes exemplifying the presence (absence) of density-assisted resonant couplings in the lattice for the dimer (chequerboard) state.

The tilted Bose–Hubbard model has been studied in a number of works theoretically31,33,36 and experimentally, focusing on the interesting ground-state phases32,37 or emerging long-range tunnelling dynamics[38]. The corresponding Hamiltonian is given by

$$\begin{array}{l}\widehat{H}\,=\,-J\sum _{\langle i,j\rangle }{\widehat{a}}_{i}^{\dagger }{\widehat{a}}_{j}+\frac{U}{2}\sum _{i,j}{\widehat{n}}_{i,j}({\widehat{n}}_{i,j}-1)\\ \,\,+\varDelta \sum _{i,j}(i+j){\widehat{n}}_{i,j},\end{array}$$

(1)

where \({\widehat{a}}_{i}^{\dagger },{\widehat{a}}_{i}\) are the raising and lowering operators and the sum over \(\langle i,j\rangle \) runs over all nearest-neighbour sites while \({\widehat{n}}_{i,j}\) is the number operator on site i,j. The tunnel coupling between two sites in the lattice is denoted as J, and the interaction energy of two bosons occupying the same site is U. Applying a strong tilt along the diagonal of the lattice with Δ ≫ J introduces dynamical constraints. A particularly interesting regime is reached in the limit U/J ≫ 1 with resonant tilt Δ = U. Here both particle number N and the sum of tilt and interaction energy, E = ∑iΔi + Ui, with Δi and Ui the local values of Δ and U on site i, are approximately conserved globally such that sectors with fixed quantum numbers (E, N) emerge[8] (Fig. 1a). In addition, atoms can only couple resonantly to already occupied sites and are thus subject to strong dynamical constraints. Retaining only terms up to and including second order in J/U, these constraints have been recently shown to result in HSF[8]. In particular, HSF can be observed in first order in this model, which experimentally allows access to longer timescales compared with other models showing HSF based on second-order processes2,3. Two states of a single sector with fixed (E, N) that are expected to show markedly different thermalization behaviour are the chequerboard state and the dimer state shown in Fig. 1c. In the chequerboard state, isolated atoms are not coupled to neighbouring sites, and they are expected to remain frozen and retain memory of the initial density pattern. This contrasts with the dimer state, which is characterized by neighbouring pairs and thus features resonances Δ = U that can facilitate dynamics and thus lead to a relaxation of the initial density pattern.

We start our experiments by preparing a near-unity-filled Mott insulator of about 200 bosonic 87Rb atoms in the \(| F=1,{m}_{F}=-\,1\rangle \) ground state (where F is the total angular momentum and mF is the Zeeman sublevel) in a single slice of a vertical optical lattice. In the two-dimensional plane, we set the Hubbard parameters by controlling the depth of a two-dimensional folded horizontal lattice[39]. We use a digital micromirror device to reduce the harmonic confinement induced by the optical lattice beams and realize approximately homogeneous trapping conditions[40]. We then exploit single-site addressing41,42 to prepare different initial states in sectors with fixed energy and particle number (E, N). Next, we adiabatically ramp up a potential gradient using a magnetic field to the resonance condition Δ ≈ U and then quench the lattice depth to U/J ≫ 1, initiating dynamics (Methods and Extended Data Figs. 1 and 2). After a variable evolution time, we rapidly ramp up the lattice to freeze the dynamics and then record a fluorescence image of the parity-projected occupation per lattice site[43].

In a first set of measurements, we aimed to directly show the emergence of HSF through the vastly different dynamics of different initial states in our model[8]. We prepare the chequerboard state, the dimer state and also the ‘squares’ state, a chequerboard-like arrangement where four atoms and four empty sites, respectively, form the building blocks of a larger chequerboard-like structure. For perfect initial-state preparation, all of these states have the same energy and particle number. The chequerboard state is part of a small fragment, dynamically disconnected from all other states, and thus frozen, whereas the dimer state is part of the largest fragment of the Hilbert space. The squares state is expected to lie in-between, that is, it is part of a larger but not the largest fragment. To probe the relaxation behaviour for each pattern, we evaluate the imbalance defined as

$${\mathcal{I}}=\frac{{N}_{{\rm{o}}}-{N}_{{\rm{u}}}}{{N}_{{\rm{o}}}+{N}_{{\rm{u}}}}$$

(2)

where No and Nu are the parity-projected, detected number of atoms on initially occupied and unoccupied sites, respectively. The imbalance captures the degree to which the system retains a memory of the initially prepared pattern. Tracking the evolution of the states in Fig. 2, we find that for the chequerboard, the imbalance is finite and large even for the longest evolution times up to t/τ = 80, where τ = ħ/J denotes the timescale associated with tunnelling in our experiment (where ℏ is Planck’s constant divided by 2π). We attribute the initial small decay of the chequerboard imbalance within a few τ to imperfect preparation of the initial state and higher-order processes[8]. By contrast, the dimer state initially decays much faster to a strikingly lower imbalance, which then only slowly decays towards zero for the longest evolution times. The imbalance of the squares pattern is found to lie approximately between the two extremal cases. Interestingly, analysing the density at the largest evolution times, we observe that the residual imbalance for the squares pattern is due to a larger-scale structure in the density formed in particular by sites that are inaccessible for the atoms owing to the presence of kinetic constraints. For details about the presented numerical simulations, see Methods and Extended Data Fig. 7.

Fig. 2: Relaxation of the imbalance for different initial states.

a, Short-time evolution of the imbalance. Imbalance of different initial states as a function of evolution time in units of the hopping timescale τ for the chequerboard (orange), squares (green) and dimer (blue) initial states with (circles) and without (desaturated circles) applied tilt. The imbalance in the case without tilt quickly decays to zero regardless of the initial state, whereas the decay strongly depends on the initial state once the tilt is applied, a clear signature of HSF. Insets: average densities n corresponding to the respective states at the indicated times in the 8 × 8 sites region of interest. The shaded, coloured areas denote the areas between theoretical calculations under imperfect (dashed lines) and perfect (solid lines) conditions. Theoretical data were obtained using TeNPy46,47 (for details, see Methods). b, Imbalance for longer evolution times. The grey shaded area highlights the data points shown in a. All error bars denote the standard error of the mean.

The relaxation of the dimer state occurs microscopically through a resonant three-site subsystem that is initially connected via first-order tunnelling, which effectively allows the dimers to flip their orientation. These processes are clearly visible in the time evolution of the density (Fig. 3a), where the initial dimer pattern evolves into a stripe-like pattern resembling a charge density wave (CDW) before evolving back into the dimer pattern. This characteristic relaxation behaviour also becomes apparent in a Fourier analysis of the density. Following a quick initial decay of the initial Fourier component (π, π/2) characterizing the dimer state, we observe the growth of the (0, π) component corresponding to a CDW along the y direction. Subsequently, we also observe a small revival of the initial dimer pattern during the relaxation dynamics, both in density and Fourier component (Fig. 3a and Extended Data Fig. 3). In stark contrast, for the chequerboard state, the (π, π) component remains the dominant Fourier component at all times and shows a fast initial decay followed by a slow decrease at long times. For the squares initial state, two Fourier components with orthogonal orientation are relevant. First, a fast relaxation occurs within the squares, whereas the coupling between different squares leads to a further slow decay of the (π/2, π/2) and (π/2, −π/2) components (as shown in Fig. 3c), consistent with the slow relaxation of the imbalance. Here the (π/2, −π/2) component shows a faster decay compared with the (π/2, π/2) component and becomes consistent with the ‘background’ of all other components at late times. By contrast, the (π/2, π/2) component is above the background level at all times. This is owing to the faster decay of the initial state along the direction of the equipotential lines, which corresponds to the (π/2, −π/2) component. Orthogonal to this direction, as described by the (π/2, π/2) component, the kinetic constraints inhibit this decay, as is also visible in the inset in Fig. 2b.

Fig. 3: Microscopic study of relaxation.

a, Fourier analysis of the average densities for the dimer state. The (π, π/2) Fourier component corresponding to the dimer state (dark blue) shows a fast decay, whereas the (0, π) Fourier component for the CDW along the vertical direction (light blue) increases before decreasing again, corresponding to the first hopping processes. b, Fourier analysis for the chequerboard state. The (π, π) component decays only slightly and remains the dominant component. c, Fourier analysis for the squares state. Both the (π/2, −π/2) and the (π/2, π/2) components decay quickly. The (π/2, −π/2) component, which describes decay in the direction of the equipotential lines, decays to a lower value and quickly becomes indistinguishable from the background, whereas the (π/2, π/2) component is above the background even at late times. For all initial states, all other components fall in-between the grey shaded areas describing the homogeneous background. Insets: the discrete two-dimensional Fourier transforms F(k) (orange colourmap) with Fourier modes k of the average densities (blue colourmap) for selected times. The coloured rectangles highlight the Fourier components shown in the plots. Error bars denote the standard error of the mean.

After establishing the strong dependence of the observed dynamics on the initially prepared state, we aimed to study the dynamics of excitations on top of the fragmented states. Owing to the kinetically constrained dynamics, defects prepared on top of the chequerboard state are expected to show fractonic behaviour[8]. To prepare ‘positive’ (‘negative’) defects, we displace one atom in the chequerboard state by one site such that its energy with respect to the tilt is increased (decreased). As shown in Fig. 4a for the positive defect, the displacement leads to the emergence of new resonant processes, rendering the defect mobile with a subsequent dynamical evolution. We make two striking observations when tracking the dynamics of positive and negative defects following a quench to finite tunnelling. First, both types of defect are confined to movements in a one-dimensional subspace along the equipotential lines on the lattice grid, as shown in Fig. 4c, left (see Methods and Extended Data Fig. 4 for the negative defect). Second, within this one-dimensional subspace, the positive defect propagates asymmetrically to only one side, which can be understood from the presence of the hole associated with the defect, which results in a blocked site in the direct vicinity of the prepared defect (Fig. 4c, right). Only for the positive defect, this hole is immobile to first order and, for the times studied here, remains at the site where it was originally created (Methods and Extended Data Fig. 5). The combination of blocked site and immobile hole observed in our experiment thus directly explains the asymmetric expansion. We can observe the (asymmetric) propagation in the one-dimensional subspace also directly in the average occupation as a reduction of the chequerboard contrast in the direction where the defect can move (Fig. 4b; see Methods and Extended Data Fig. 4 for the negative defect). The subdimensional propagation for both the positive and the negative defects and the unidirectional motion of the positive defect are strong indications that the prepared defects indeed show the expected fractonic properties. For details about the presented numerical simulations, see Methods and Extended Data Fig. 6.

Fig. 4: Dynamics of fractonic excitations (positive defect).

a, Schematic of the spreading of the defect atom (filled green circle) to first order, which can move by forming doublons (dark-green shaded area). On short timescales (to first order), the associated defect hole (dashed circle) is stuck. b, Anisotropic spreading of the defect. The spreading of the defect can be observed in the occupations (owing to parity projection) and more clearly in the difference plots when subtracting the time-evolved reference chequerboard (CHB) background. Theory calculations are shown for comparison (Methods). The motion of the defect atom is restricted to a narrow stripe, along which it can move in only one direction. c, Diagonal sums over the reference-subtracted occupations. Orthogonal to the equipotential lines (left), the defect atom cannot spread, whereas parallel to the equipotential lines (right), it shows asymmetric propagation on the subdimensional manifold. Grey lines are guides to the eye, based on the points (sampled every 0.25 t/τ) where the theory calculations (green solid lines) exceed a value larger than 0.05. The coloured arrows show the direction of the summation of the diagonals, as indicated by the insets in the bottom, rightmost plot in b. Error bars denote the standard error of the mean.

In a final set of measurements, we studied the relaxation of an interface between the mobile dimer state and the immobile chequerboard state. Such a measurement directly probes the impact of kinetic constraints for the underlying defects on transport characteristics in systems showing HSF. Interestingly, we observe strikingly different dynamics depending on the alignment of the interface along or orthogonal to the equipotential surfaces. If the interface is aligned with the equipotential surface, the chequerboard state remains stable, whereas the dimer rapidly decays. Importantly, the interface stays intact, which indicates that—consistent with the subdimensional character of the defects—no transport occurs across the boundary. Conversely, if the interface is oriented orthogonal to the equipotential lines, a chequerboard structure starts building up even on the sites initially prepared with a dimer pattern. These observations can be directly explained by the type and location of defects initially injected into the system, in combination with the strongly asymmetric fractonic character of single defects (Fig. 5a). In the vicinity of the interface, the mobile atom in each initially prepared dimer will propagate into the chequerboard region. The remaining atoms in these dimers form a chequerboard pattern, effectively increasing the overall area of the then immobile chequerboard. This is evidenced by the increase of the chequerboard imbalance evaluated in the half of the system initially prepared in the dimer state (Fig. 5c). Simultaneously, as shown in Fig. 5d, the chequerboard imbalance within the other half of the system drops significantly lower than for an independent reference chequerboard measurement without an interface or for the interface orientation parallel to the equipotential lines, owing to the influence of the mobile defects.

Fig. 5: Domain wall dynamics between the chequerboard and dimer states.

a, Depending on the interface orientation relative to the tilt (orthogonal, left; parallel, right), the dimers act as positive (green circles) or negative (red circles) defects in the chequerboard (grey area). At the interface, the upper atom in the dimers can propagate, whereas the lower atom stays at its original site. b, Mean density as a function of evolution time for the central 10 × 10 sites. We observe the emergence of a larger chequerboard area for the orthogonal orientation (top row), indicated by the black dashed line at t/τ = 40. For the parallel orientation, the chequerboard remains intact. c, Evaluating the data in the green and red triangular regions of interest (chosen such that there is an even, identical number of sites in both halves of the system; Methods) illustrated in b, we find that the overlap with the chequerboard (CHB) remains constant for the parallel orientation (red) whereas we observe an increase for the orthogonal orientation (green). d, For the orthogonal interface orientation (green), the imbalance in the chequerboard half of the system decays. For the parallel orientation (red), the imbalance stays consistent with the reference measurement of a pure chequerboard (grey). All error bars denote the standard error of the mean. ROI, region of interest.

In summary, we have demonstrated the emergence of HSF in a two-dimensional tilted Bose–Hubbard model as a consequence of strong kinetic constraints. Our measurements provide a comprehensive study of HSF in two-dimensional systems through their thermalization properties, which differ strikingly for different initial states. Our results immediately raise follow-up questions, such as whether the tilted Bose–Hubbard model is weakly or strongly fragmented. Such a study would require measurements for much larger system sizes and a finite-size scaling analysis of the final imbalance, or the preparation of a number of other initial states from one (E, N) subsector. It would also be interesting to study the predicted diffusive and subdiffusive behaviour of negative and positive defects[8], which would be possible in larger systems with longer coherence times. Furthermore, the detailed understanding of the complex dynamics emerging at the interface between states from different fragments remains open and is left for further work, stressing that numerical simulations are limited to evolution times much smaller than those accessible in our experiment. In future work, it may also be interesting to study HSF in open systems experimentally by adding controlled dissipation[44], or explore quantum HSF, where the Hilbert space fragments are characterized by entangled substates[45].

Methods

Experimental details

Here we briefly describe the initial-state preparation common to all measurements. Experiments were performed in a single plane of a vertical one-dimensional optical lattice. For the in-plane lattice, we used the folded lattice described in ref. [39]. As the in-plane lattice is subject to disorder and harmonic confinement, we used a digital micromirror device to shape the horizontal on-site potential, allowing us to achieve approximately homogeneous trapping depths and tunnelling energies throughout the system. Using a second digital micromirror device, we additionally projected a tapered, rectangular box in the centre of this corrected system, to achieve reliable loading and high filling in a central area of about 15 × 15 lattice sites.

Starting from these Mott insulators, to prepare the initial states of interest, we then performed local addressing over the entire area41,42, whereas the data analysis was performed in a smaller region of interest (ROI) of either 8 × 8 or 10 × 10 lattice sites at the centre of the system. In addition, working with larger systems than the size of the ROI minimizes the influence of finite-size and boundary effects. With this preparation sequence, we achieved a filling of 0.88(2) per site on the addressed sites and a filling of 0.04(2) on the non-addressed sites in the ROI. These values were averaged over all datasets and initial configurations.

Magnetic-field gradient calibration

The potential tilt in our experiments was realized by global magnetic fields, which allowed us to induce the most homogeneous gradients. We calibrated the magnitude and the orientation of the magnetic gradient using spatially resolved microwave spectroscopy on the magnetic-field-sensitive transition between the \(| F=1,{m}_{F}=-\,1\rangle \) and the \(| F=2,{m}_{F}=-\,2\rangle \) hyperfine ground states.

To this end, we prepared a large Mott insulator with all atoms in the \(| F=1,{m}_{F}=-\,1| \rangle \) state. We then adiabatically ramped up the magnetic field to its target configuration and performed narrow microwave sweeps at variable centre frequencies. As a consequence, atoms were addressed resonantly within a narrow stripe subjected to the same magnetic-field strength and flipped into the \(| F=2,{m}_{F}=-\,2\rangle \) state, which were then removed before imaging. We fitted a two-dimensional Gaussian to these stripes of missing atoms, which allowed us to map the field strength and gradient orientation versus their position (Extended Data Fig. 1a).

To be able to continuously vary the applied gradient strength, we used a combination of coils: a single vertical gradient coil and a pair of vertical offset coils in Helmholtz configuration with reversed field polarity to realize a quadrupole field near the plane of the atoms. For the initial calibration, we worked with a fixed gradient coil setpoint and tuned the vertical offset and additional in-plane offset fields such that the magnetic zero point was at the location of the atoms; subsequently, we shifted the zero point by a fixed amount using the in-plane offset fields, resulting in an in-plane gradient at the correct angle. We then proceeded to calibrate the gradient strength for various gradient coil setpoints as described above; for technical reasons, we tuned the gradient coil instead of the offset coils. We interpolated between the calibrated values by fitting them with the function

$$g(\Delta B)=\frac{{g}_{0}^{2}r}{\sqrt{{g}_{0}^{2}{r}^{2}+{(\Delta B+{B}_{0})}^{2}}},$$

(3)

where r is the displacement of the magnetic field zero to the atoms, g_0 is the maximal gradient strength, B_0 describes background fields and ΔB is the change of the setpoint of the gradient coil. As we changed ΔB by only a few per cent, we can assume g_0(B) = constant, which is also supported by the fact that the fit function describes the data well, as shown in Extended Data Fig. 1b.

On the basis of this curve, we can then rescale the x axis in Extended Data Fig. 2 and obtain an absolute value for the gradient strength.

Hubbard parameters

To extract the Hubbard parameters of our folded optical lattice[39], we made use of two methods. First, we performed amplitude modulation spectroscopy to calibrate the lattice depth. The results were then compared with a band-structure calculation to obtain the values for the on-site interaction U and the tunnelling energy J. Here we found U/J = 21(2) with U = h × 275(5) Hz and J = h × 13(1) Hz. The error bars arise from the uncertainty of the lattice-depth calibration itself as well as the slightly anisotropic hopping along the two lattice axes[39]. Second, we can independently calibrate the Hubbard parameters using the quench dynamics of isolated dimers (see Extended Data Fig. 3 and below). As a result, we extracted τ = ħ/J = 10.0(3) ms, equivalent to J = h × 16.0(5) Hz. Comparing again with our band-structure calculation, this corresponds to U/J = 17(1) with U = h × 260(5) Hz. We attribute the deviations between these two calibrations to day-to-day drifts of the lattice beam alignment over the entire data-taking period.

For data evaluation, we used τ = 11 ms for all datasets, motivated by the long data-taking period of several days for a given dataset. Theory calculations (see below) were performed for U/J = 18, which was chosen as an intermediate value between the two calibrations.

Tuning the gradient to resonance

For the presented studies, it is important that the applied gradient matches the on-site interaction, that is, Δ = U. We benchmarked the resonance location by measuring the dimer imbalance as a function of the gradient strength for various tunnelling times, as illustrated in Extended Data Fig. 2. Here we expected the strongest decay of the dimer imbalance, as defined in the main text, when the resonance condition is fulfilled. For smaller gradients, we expected a slower drop in imbalance, whereas for much stronger gradients, we expected all processes to be off-resonant and no dynamics to occur at all, leading to high imbalance even at later times.

Our experimental results match the described expectation qualitatively. To confirm that we were not accidentally probing at a time where the imbalance shows any Δ-dependent oscillations, we probed for multiple fixed evolution times (up to t/τ = 40), observing consistent behaviour for all of the chosen evolution times. The resonance width is inherently limited by the finite tunnelling bandwidth and residual potential disorder. Our chosen operation point was located at the centre of the resonance and showed the strongest decay, as marked by the vertical dashed line in Extended Data Fig. 2. On the basis of our gradient calibration presented above and in Extended Data Fig. 1b, this point corresponds to a value of Δ = h × 238(3) Hz.

Comparing with our independent band-structure calculation, we found a qualitative agreement within 15% to the value of U for both calibration methods of the Hubbard parameters described above. In particular, U changes only very slowly with the lattice depth and varies by less than J for our calibrations. As such, this gradient setpoint remains valid throughout all measurements.

Data analysis

All data, unless specified differently, were analysed as explained in the following: we calculated the quantity of interest (imbalance, Fourier components, diagonal sums) on the individual experimental shots, then averaged over these results to obtain the data shown in the figures. For the reference-subtracted defect occupations (Fig. 4b, middle, and Extended Data Fig. 4b), we subtracted the densities averaged over all shots. To calculate the imbalances in Fig. 5c,d, we chose the boundary of the respective ROIs such that atoms close to the interface boundary that could be part of either the chequerboard or the dimer were counted as belonging to the dimer part of the system. As such, we obtained the same number of atoms for both halves of the system and the imbalance can, in principle, reach its typical limits of ±1; this explains the perhaps unintuitive shape of the ROIs shown in Fig. 5b.

Fourier analysis

To analyse the Fourier components of the average densities, we calculated the discrete Fourier transform according to

$$\begin{array}{l}F({\bf{k}})\,=\,\mathop{\sum }\limits_{n=0}^{N-1}\mathop{\sum }\limits_{m=0}^{M-1}{a}_{n,m}\exp \,\left(-2{\rm{\pi }}i(\frac{nj}{N}+\frac{ml}{M})\right)\\ \,\,=\mathop{\sum }\limits_{n=0}^{N-1}\mathop{\sum }\limits_{m=0}^{M-1}{a}_{n,m}\exp (\,-\,{\rm{i}}{k}_{x}n-{\rm{i}}{k}_{y}m)\end{array}$$

(4)

with an,m the average densities at site n, m for an ROI of size N × M and \({k}_{x}=\frac{2{\rm{\pi }}j}{N},{k}_{y}=\frac{2{\rm{\pi }}l}{M}\). Here the index j runs from \(-\left(\frac{N-1}{2}\right),\ldots ,0,\ldots ,\frac{N-1}{2}\) for odd N and \(\lceil \frac{N-1}{2}\rceil ,\ldots ,0,\ldots ,\frac{N}{2}\) for even N and analogously for l. The value at (kx, ky) = (0, 0) is just the sum of the signal; it contains no additional relevant information and is thus neglected (white rectangles in the insets of Fig. 3).

It is noted that the discrete Fourier transform obeys point reflection symmetry, that is, F(k) = F(−k). Therefore, in the main text, we plot only the parts of the momentum space (kx, ky) that contain non-redundant information.

Isolated dimer dynamics

To further understand and investigate the decay of the dimer pattern on a microscopic level, we prepared isolated dimers and tracked their evolution after a sudden quench. We isolated the dimers by adding empty columns between the atom pairs, as illustrated in the inset of Extended Data Fig. 3. For this configuration, the dimers were, including only first-order processes, completely decoupled from one another, allowing us to study the formation of the horizontally oriented dimers described in Fig. 3a. The change in orientation can be understood intuitively. Starting from a dimer, the upper atom can tunnel onto the neighbouring site by forming a doublon, as illustrated in the middle inset of Extended Data Fig. 3. From there, the atoms can either rearrange into the original dimer or into the flipped dimer, which is energetically degenerate to the original dimer configuration.

Extended Data Fig. 3 shows the time evolution of the isolated dimers. Here we plot the populations of the three possible states: the vertical dimer, the doublon and the horizontal dimer. Although the dimer states can be detected unambiguously, we assigned the doublon if all three sites were empty. To correct, on average, for cases where no atoms were initially prepared, we subtracted the value obtained analogously from a reference measurement tracking the initial-state preparation. We observed a clear oscillation between the two cases of vertically and horizontally oriented dimers, which quickly dephases owing to residual potential disorder. We compared the measured data with a numerical simulation of a three-state model given by

$$\widehat{H}=\left(\begin{array}{ccc}{\delta }_{i} & \sqrt{2}J & 0\\ \sqrt{2}J & U-\varDelta +{\delta }_{j} & \sqrt{2}J\\ 0 & \sqrt{2}J & {\delta }_{k}\end{array}\right)$$

(5)

where δi, δj, δk describe the disorder strength between adjacent sites. For the calculation, we sampled δi, δj, δk from a normal distribution around zero and averaged over N = 100 such realizations. The additional factor of \(\sqrt{2}\) for the hopping has to be taken into account owing to the bosonic enhancement characteristic for indistinguishable bosons. We then fitted the calculations to the measured occupation of the vertical and horizontal dimers to generate the solid lines in Fig. 3b. Here we allowed for the disorder strength, the difference U − Δ, an overall amplitude (which respects normalization) as well as the timescale as free fit parameters. The initial time offset was kept fixed at zero. It is noted that the doublon occupation was not included in the fits, instead the solid line in Extended Data Fig. 3 is given by the model expectation using the fit values obtained from fitting the two other curves. We observed good agreement between the doublon occupation as obtained from our measured data and the numerical model using the fit parameters for the two other curves, validating our method of extracting the doublon occupation.

From the fit, we extracted the standard deviation of the disorder distribution σ = 1.2(1) × J, a deviation from resonance of U − Δ = 0.0(3) × J and a timescale of τ = 10.0(3) ms. The latter can serve as a secondary way to calibrate the Hubbard parameters of our system (see above).

Negative defect and additional analysis

Here we present our measurements on the negative defect and describe the data presented in Fig. 4 and Extended Data Fig. 4 in more detail. We also present an alternative way of evaluating the data for the positive defect and directly compare the spreading of the defect holes for both the negative and the positive defects.

The spreading of the defects can be observed directly in the average occupations (Fig. 4b and Extended Data Fig. 4b, leftmost column), through a reduced contrast of the (background) chequerboard on sites accessible to the defect atoms. This is owing to the following processes. First, the defect atoms can move to initially empty sites of the chequerboard, thereby increasing the average density on these sites. The defect atoms can also move onto initially occupied sites of the chequerboard, where we then observe a reduced average density owing to parity projection. Finally, nearby atoms from the background chequerboard can become mobile owing to the presence of the defect and move onto the site occupied by the defect atom, thus reducing the average density on their original sites as well as on the site of the defect atom owing to parity projection. For the negative defect in particular, the motion of the hole can be observed by an increase of the average occupation on its initial site (see also Extended Data Fig. 5b in the following), and a simultaneous decrease of the average occupation on the neighbouring sites on its equipotential line. By contrast, the hole site for the positive defect remains unoccupied. These effects are highlighted by subtracting the occupation of a chequerboard state without deterministically created defects. Initially empty sites accessible to the defect atoms will feature a positive, reference-subtracted value, whereas initially occupied sites accessible to the defect atoms will show negative values. The latter, as explained above, is due to either parity projection, atoms becoming mobile owing to the defect or, for the case of the negative defect, also the spreading of the defect hole. When comparing with theory, we observed good agreement, especially for the negative defect (Extended Data Fig. 4b,c). For the simulations, we did not include any experimental imperfections such as disorder and initial-state preparation fidelities. We further quantified the directional spreading of the defects by summing along the diagonals of the reference-subtracted occupations. When summing parallel to the equipotential lines, the occupation is only different from zero on the diagonals on which the defect atom and hole were initially placed. The growth by one additional diagonal for times t/τ > 0 can be explained by the above-mentioned processes, that is, the defect’s influence on the neighbouring atoms. As an additional characterization of the positive defect, we also studied the spreading on the zigzag-shaped equipotential line (Extended Data Fig. 5a, inset) instead of summing the reference-subtracted signal along the ROI diagonals. The result of this analysis is shown in Extended Data Fig. 5a. Here we again observe that the spread occurs along only one direction, as the immobile hole prevents the spread in the opposite direction. The latter is expected, as the hole can only move by second-order processes[8]. This is also evidenced by the density on the site of the hole remaining nearly unchanged.

Looking at this further, by comparing the increase of the densities on the sites initially occupied by the defect holes, we can also clearly observe the difference between the positive and the negative defects (Extended Data Fig. 5b). For the positive defect, the density increased only slightly, whereas for the negative defect we observed an immediate, fast increase, as here the hole is mobile in first order. Specifically, the hole of the negative defect can move in processes where neighbouring particles located on the equipotential line above the defect atom hop onto the defect atom and then to the site of the hole (Extended Data Fig. 4a, bottom right). The hole’s motion is restricted to its initial equipotential line. As for all other measurements on the spreading of defects on top of the chequerboard background, we attribute deviations from the theoretical expectations to disorder in the system and imperfect initial-state preparation, that is, the presence of additional, non-deterministic defects.

Numerical methods for defects

The underlying physics in the Bose–Hubbard model is described by an effective Hamiltonian derived in ref. [8], which features HSF. In Extended Data Fig. 6, we compare the time evolution of the positive defect under this effective Hamiltonian with the time evolution of the original Bose–Hubbard model. We show the parity-projected on-site occupation and have subtracted a perfect chequerboard state (at t/τ = 0, that is, without time evolution) to better highlight the differences. It is noted that in Fig. 4 and Extended Data Figs. 4 and 5a, we instead subtract the theory calculations with a time-evolved version of the chequerboard for better comparison with the experimental data. In contrast to the effective model, the background chequerboard state is not completely frozen under time evolution with the Bose–Hubbard model. Nevertheless, this additional dynamics of the background does not strongly influence the dynamics of the mobile defect compared with the effective model. Therefore, we conclude that the underlying physics of the Bose–Hubbard model in the chosen limits are well captured by the effective model featuring HSF.

Numerical methods for convergence

The data were obtained using tensor-network methods and exact diagonalization. All data were calculated by matrix-product-operator time evolution using the TeNPy package46,47, except for the time evolution with the effective model in Extended Data Fig. 6, which was performed with exact diagonalization. In Extended Data Fig. 7, convergence in the bond dimension and in the Trotter step is studied. In Extended Data Fig. 7a, the evolution of the imbalance for the chequerboard and dimer states shows perfect overlap for bond dimensions χ = 256 and χ = 300. For both curves, a Trotter step size of dt = 0.001 was used. In Extended Data Fig. 7b, the imbalance is compared for Trotter step sizes of dt = 0.001 and dt = 0.0005 at a fixed bond dimension of χ = 256.

Numerical methods for imbalance

In Extended Data Fig. 7c, we compare the imbalance of the perfect case to the time evolution under imperfect conditions, similar to those of the experiment. For the latter, we have included deviations of all relevant quantities away from optimum, fidelities for state preparation and an additional random on-site potential (see the caption of Extended Data Fig. 7 for details). Each time step is averaged over Nav,dimer ∈ [29, 100], Nav,squares ∈ [17, 100], Nav,CHB ∈ [10, 100] different preparations. We find that the effect of state-dependent dynamics is still clearly visible also for experimental conditions. For the dimer state, the impact of experimental conditions is the strongest, which we attribute to the highest sensitivity to imperfect state preparation. In the case of the dimer state, all atoms have only one nearest neighbour. Removing this neighbour directly leads to a decrease in mobility and can induce frozen particles. By contrast, the squares state does not suffer from this effect on the same level. Each atom has three nearest neighbours, and therefore one missing neighbour does not lead to frozen sites.

Data availability

The data shown in the main text and Extended Data figures are available from the Edmond repository of the Max Planck Society at https://doi.org/10.17617/3.FPKWEQ.

Code availability

The code underlying the numerical simulations is available from the Edmond repository of the Max Planck Society at https://doi.org/10.17617/3.FPKWEQ.

References

1. Polkovnikov, A., Sengupta, K., Silva, A. & Vengalattore, M. Nonequilibrium dynamics of closed interacting quantum systems. Rev. Mod. Phys. 83, 863 (2011).

2. Khemani, V., Hermele, M. & Nandkishore, R. Localization from Hilbert space shattering: from theory to physical realizations. Phys. Rev. B 101, 174204 (2020).

3. Sala, P., Rakovszky, T., Verresen, R., Knap, M. & Pollmann, F. Ergodicity breaking arising from Hilbert space fragmentation in dipole-conserving Hamiltonians. Phys. Rev. X 10, 011047 (2020).

4. Moudgalya, S., Prem, A., Nandkishore, R., Regnault, N. & Bernevig, B. A. in Memorial Volume for Shoucheng Zhang (eds Lian, B. et al.) 147–209 (World Scientific, 2020); https://doi.org/10.1142/9789811231711_0009

5. Moudgalya, S., Bernevig, B. A. & Regnault, N. Quantum many-body scars and Hilbert space fragmentation: a review of exact results. Rep. Prog. Phys. 85, 086501 (2022).

6. Nandkishore, R. M. & Hermele, M. Fractons. Annu. Rev. Condens. Matter Phys. 10, 295 (2019).

7. Pretko, M., Chen, X. & You, Y. Fracton phases of matter. Int. J. Mod. Phys. A 35, 2030003 (2020).

8. Will, M., Moessner, R. & Pollmann, F. Realization of Hilbert space fragmentation and fracton dynamics in 2D. Preprint at https://arxiv.org/abs/2311.05695 (2023).

9. Gromov, A. & Radzihovsky, L. Fracton matter. Rev. Mod. Phys. 96, 011001 (2024).

10. D’Alessio, L., Kafri, Y., Polkovnikov, A. & Rigol, M. From quantum chaos and eigenstate thermalization to statistical mechanics and thermodynamics. Adv. Phys. 65, 239 (2016).

11. Deutsch, J. M. Eigenstate thermalization hypothesis. Rep. Prog. Phys. 81, 082001 (2018).

12. Essler, F. H. L. & Fagotti, M. Quench dynamics and relaxation in isolated integrable quantum spin chains. J. Stat. Mech. 2016, 064002 (2016).

13. Bertini, B. et al. Finite-temperature transport in one-dimensional quantum lattice models. Rev. Mod. Phys. 93, 025003 (2021).

14. Nandkishore, R. & Huse, D. A. Many-body localization and thermalization in quantum statistical mechanics. Annu. Rev. Condens. Matter Phys. 6, 15 (2015).

15. Altman, E. Many-body localization and quantum thermalization. Nat. Phys. 14, 979 (2018).

16. Abanin, D. A., Altman, E., Bloch, I. & Serbyn, M. Many-body localization, thermalization, and entanglement. Rev. Mod. Phys. 91, 021001 (2019).

17. Serbyn, M., Abanin, D. A. & Papić, Z. Quantum many-body scars and weak breaking of ergodicity. Nat. Phys. 17, 675 (2021).

18. Garrahan, J. P. Aspects of non-equilibrium in classical and quantum systems: slow relaxation and glasses, dynamical large deviations, quantum non-ergodicity, and open quantum dynamics. Physica A 504, 130–154 (2018).

19. Lan, Z., van Horssen, M., Powell, S. & Garrahan, J. P. Quantum slow relaxation and metastability due to dynamical constraints. Phys. Rev. Lett. 121, 040603 (2018).

20. Khudorozhkov, A., Tiwari, A., Chamon, C. & Neupert, T. Hilbert space fragmentation in a 2D quantum spin system with subsystem symmetries. SciPost Phys. 13, 098 (2022).

21. Moudgalya, S. & Motrunich, O. I. Hilbert space fragmentation and commutant algebras. Phys. Rev. X 12, 011050 (2022).

22. Gromov, A., Lucas, A. & Nandkishore, R. M. Fracton hydrodynamics. Phys. Rev. Res. 2, 033124 (2020).

23. Feldmeier, J., Sala, P., De Tomasi, G., Pollmann, F. & Knap, M. Anomalous diffusion in dipole- and higher-moment-conserving systems. Phys. Rev. Lett. 125, 245303 (2020).

24. Morningstar, A., Khemani, V. & Huse, D. A. Kinetically constrained freezing transition in a dipole-conserving system. Phys. Rev. B 101, 214205 (2020).

25. Pai, S., Pretko, M. & Nandkishore, R. M. Localization in fractonic random circuits. Phys. Rev. X 9, 021003 (2019).

26. Pretko, M. & Radzihovsky, L. Fracton-elasticity duality. Phys. Rev. Lett. 120, 195301 (2018).

27. Guardado-Sanchez, E. et al. Subdiffusion and heat transport in a tilted two-dimensional Fermi–Hubbard system. Phys. Rev. X 10, 011042 (2020).

28. Zhang, P. Subdiffusion in strongly tilted lattice systems. Phys. Rev. Res. 2, 033129 (2020).

29. Scherg, S. et al. Observing non-ergodicity due to kinetic constraints in tilted Fermi–Hubbard chains. Nat. Commun. 12, 4490 (2021).

30. Kohlert, T. et al. Exploring the regime of fragmentation in strongly tilted Fermi–Hubbard chains. Phys. Rev. Lett. 130, 010201 (2023).

31. Sachdev, S., Sengupta, K. & Girvin, S. M. Mott insulators in strong electric fields. Phys. Rev. B 66, 075128 (2002).

32. Simon, J. et al. Quantum simulation of antiferromagnetic spin chains in an optical lattice. Nature 472, 307–312 (2011).

33. Pielawa, S., Kitagawa, T., Berg, E. & Sachdev, S. Correlated phases of bosons in tilted frustrated lattices. Phys. Rev. B 83, 205135 (2011).

34. Zhao, L., Datla, P. R., Tian, W., Aliyu, M. M. & Loh, H. Observation of quantum thermalization restricted to Hilbert space fragments Preprint at https://arxiv.org/abs/2403.09517 (2024).

35. Wang, Y.-Y. et al. Exploring Hilbert-space fragmentation on a superconducting processor. Preprint at https://arxiv.org/abs/2403.09095 (2024).

36. Lake, E., Hermele, M. & Senthil, T. Dipolar Bose–Hubbard model. Phys. Rev. B 106, 064511 (2022).

37. Kim, S. et al. Adiabatic state preparation in a quantum Ising spin chain. Preprint at https://arxiv.org/abs/2404.07481 (2024).

38. Meinert, F. et al. Observation of many-body dynamics in long-range tunneling after a quantum quench. Science 344, 1259 (2014).

39. Wei, D. et al. Observation of Brane parity order in programmable optical lattices. Phys. Rev. X 13, 021042 (2023).

40. Wei, D. et al. Quantum gas microscopy of Kardar–Parisi–Zhang superdiffusion. Science 376, 716 (2022).

41. Weitenberg, C. et al. Single-spin addressing in an atomic Mott insulator. Nature 471, 319 (2011).

42. Fukuhara, T. et al. Quantum dynamics of a mobile spin impurity. Nat. Phys. 9, 235 (2013).

43. Sherson, J. F. et al. Single-atom-resolved fluorescence imaging of an atomic Mott insulator. Nature 467, 68 (2010).

44. Li, Y., Sala, P. & Pollmann, F. Hilbert space fragmentation in open quantum systems. Phys. Rev. Res. 5, 043239 (2023).

45. Brighi, P., Ljubotina, M. & Serbyn, M. Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models. SciPost Phys. 15, 093 (2023).

46. Hauschild, J. & Pollmann, F. Efficient numerical simulations with tensor networks: Tensor Network Python (TeNPy). SciPost Phys. Lect. Notes https://doi.org/10.21468/SciPostPhysLectNotes.5 (2018).

47. TeNPy. GitHub https://github.com/tenpy/tenpy (2024).

Acknowledgements

We thank M. Aidelsburger, S. Gopalakrishnan and D. Huse for discussions. We acknowledge funding by the Max Planck Society (MPG) the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy–EXC-2111–390814868 and through the DFG Research Unit FOR 5522 (project-id 499180199), and by the cluster of excellence ct.qmat (EXC-2147–390858490). We also received funding under the Horizon Europe programme HORIZON-CL4-2022-QUANTUM-02-SGA via the project 101113690 (PASQuanS2.1). K.S. and S.A. acknowledge funding from the International Max Planck Research School (IMPRS) for Quantum Science and Technology. F.P. acknowledges support from European Union’s Horizon 2020 research and innovation programme under grant agreement number 771537, the DFG TRR 360 - 492547816 and the Munich Quantum Valley, which is supported by the Bavarian state government with funds from the Hightech Agenda Bayern Plus. P.W. acknowledges funding through the Walter Benjamin programme (DFG project 516136618).

Funding

Open access funding provided by Max Planck Society.

Author information

Authors and Affiliations

1. Max-Planck-Institut für Quantenoptik, Garching, Germany

Daniel Adler, David Wei, Kritsana Srakaew, Suchita Agrawal, Pascal Weckesser, Immanuel Bloch & Johannes Zeiher

2. Munich Center for Quantum Science and Technology (MCQST), Munich, Germany

Daniel Adler, David Wei, Melissa Will, Kritsana Srakaew, Suchita Agrawal, Pascal Weckesser, Frank Pollmann, Immanuel Bloch & Johannes Zeiher

3. Technische Universität München, TUM School of Natural Sciences, Garching, Germany

Melissa Will & Frank Pollmann

4. Max-Planck-Institut für Physik komplexer Systeme, Dresden, Germany

Roderich Moessner

5. Fakultät für Physik, Ludwig-Maximilians-Universität, Munich, Germany

Immanuel Bloch

Contributions

D.A. acquired the data underlying this study. D.A., D.W., K.S., S.A. and P.W. maintained the experimental set-up. D.A. and D.W. analysed the data. I.B. and J.Z. supervised the study. M.W. contributed the theoretical simulations, supervised by F.P. and R.M. All authors worked on the interpretation of the data and contributed to the final paper.

Corresponding author

Correspondence to Johannes Zeiher.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Magnetic field gradient calibration.

a Fitted center positions of the spatial profiles; gradient calibration (strength and orientation). Insets show exemplary single shots of the microwave (MW) resonances for different centre frequencies. These shots are averaged and fitted with a 2D Gaussian to extract the gradient strength and orientation. b Gradient strength from the analysis in Extended Data Fig. 1a at different gradient coil settings. The magnetic field is interpolated using Eq. (3). The inset shows that the angle does not change significantly even when tuning the gradient. The vertical, dashed lines indicate the operating point chosen based on the measurement in Extended Data Fig. 2.

Extended Data Fig. 2 Dimer imbalance for different gradient strengths.

Imbalance of the dimer at t/τ = 2 (dark blue circles) and t/τ = 5 (light blue circles) for different gradient strengths. The vertical line indicates the operating point chosen for all other measurements. Insets illustrate our expectations about the functional shape of the imbalance (see text) based on the possible processes. Error bars denote the standard error of the mean (s.e.m.).

Extended Data Fig. 3 Dynamics of isolated dimers.

Preparing an initial state of dimers with additional free sites, we can study the dynamics of isolated dimer pairs and clearly resolve the oscillatory behavior between the initially prepared vertical (dark blue) and the flipped, horizontal (light blue) dimers. The blue solid lines are fits to the data according to a three state model. Additionally, we can also resolve the occupation of the (grey) doublon state. The grey solid line is the expected doublon occupation for the ideal fit parameters and serves as a guide to the eye. All error bars denote the s.e.m.

Extended Data Fig. 4 Dynamics of fractonic excitations (negative defect).

a Schematic of the spreading of the defect atom (filled red circle) to first order, which can move by forming doublons (dark red shaded area). The defect hole (dashed circle) can also move, facilitated by the defect atom (light red shaded area). b Anisotropic spreading of the defect analyzed in analogy to Fig. 4. c Sums over the reference-subtracted occupations, analyzed and compared to theory (red solid lines) in analogy to Fig. 4. Compared with the positive defect, the negative defect exhibits symmetric propagation on the sub-dimensional manifold. All error bars denote the s.e.m.

Extended Data Fig. 5 Additional defect data.

a Positive defect on equipotential surface. Spreading of the defect on the zigzag-shaped equipotential line bounded by the black lines shown in the inset. The defect atom can move, but only in one direction, whereas the hole remains immobile. Grey lines are guides to the eye, based on the points (sampled every 0.25 t/τ) where the absolute value of the theory calculation (green solid lines) is larger than 0.03. Error bars are on the order of the marker sizes. b Density on the site initially occupied by the defect hole. For the negative defect (red), the hole can move in first order, whereas the hole of the positive defect (green) is stuck. Solid lines are theory calculations. Error bars denote the s.e.m.

Extended Data Fig. 6 Dynamics of the positive defect.

All figures show the parity projected number of particles over time subtracted by a perfect (at t/τ = 0) chequerboard state. a Time evolution with the effective Hamiltonian derived in ref. [8] using ED. The background chequerboard pattern is completely frozen, and only the defect is mobile along the diagonal. b MPO time-evolution with the full Bose-Hubbard model shows great agreement with the effective time-evolution. In contrast to the effective model, the background chequerboard is not completely frozen. Parameters used: dt = 0.0005, χ = 256, Nmax = 2.

Extended Data Fig. 7 Convergence analysis and experimental versus “clean” time evolution.

a Comparison of the time evolution for variable bond dimension χ for identical dt = 0.001. We find perfect agreement between χ = 256 and χ = 300, demonstrating the convergence of our code, assuming the experimental parameter regime J = 1, U = Δ = 18. b Similar analysis as in Extended Data Fig. 7a for χ = 256, yet with variable Trotter step size dt. We again observe excellent agreement. c Comparison of clean and imperfect system. Including experimental imperfections (circles) leads to a less pronounced decrease of the imbalance in comparison to the clean case (solid lines). Even with modified parameters, the effect of state-dependent dynamics is still clearly visible. Parameters used: Jx = 1, Jy = 1.1 × Jx, U = Δx = Δy = 18 × Jx, δ = [−2 × Jx, 2 × Jx], Nav,dimer ∈ [29, 100], Nav,squares ∈ [17, 100], Nav,CHB ∈ [10, 100], fidelity empty = 0.97, fidelity filled = 0.87. For all simulations, we have assumed a maximal filling of Nmax = 2 per site. The on-site potential δ is sampled from a uniform distribution of the given width. Note that this is the same data as shown in Fig. 2a in the main text.

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Adler, D., Wei, D., Will, M. et al. Observation of Hilbert space fragmentation and fractonic excitations in 2D. Nature 636, 80–85 (2024). https://doi.org/10.1038/s41586-024-08188-0

- Received: 23 April 2024

- Accepted: 09 October 2024

- Published: 13 November 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08188-0

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Mineralized collagen plywood contributes to bone autograft performance

Marc Robin,

Elodie Mouloungui,

Gabriel Castillo Dali,

Yan Wang,

Jean-Louis Saffar,

Graciela Pavon-Djavid,

Thibaut Divoux,

Sébastien Manneville,

Luc Behr,

Delphine Cardi,

Laurence Choudat,

Marie-Madeleine Giraud-Guille,

Anne Meddahi-Pellé,

Fannie Baudimont,

Marie-Laure Colombier &

…

Nadine Nassif

Nature volume 636, pages 100–107 (2024)

Abstract

Autologous bone (AB) is the gold standard for bone-replacement surgeries[1], despite its limited availability and the need for an extra surgical site. Traditionally, competitive biomaterials for bone repair have focused on mimicking the mineral aspect of bone, as evidenced by the widespread clinical use of bioactive ceramics[2]. However, AB also exhibits hierarchical organic structures that might substantially affect bone regeneration. Here, using a range of cell-free biomimetic-collagen-based materials in murine and ovine bone-defect models, we demonstrate that a hierarchical hybrid microstructure—specifically, the twisted plywood pattern of collagen and its association with poorly crystallized bioapatite—favourably influences bone regeneration. Our study shows that the most structurally biomimetic material has the potential to stimulate bone growth, highlighting the pivotal role of physicochemical properties in supporting bone formation and offering promising prospects as a competitive bone-graft material.

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Main

The osteoconductive and osteoinductive properties1,3 of AB are mainly attributed to its biological characteristics, that is, the presence of progenitor cells, osteoblasts and blood cells, fresh growth factors, pre-existing vascular network, extracellular matrix (ECM) composition including mineralized collagen and an inherent and total immunocompatibility[2]. Additionally, the physical properties of the substrate are now considered crucial for driving the physiological cells phenotype expression, opening a new paradigm for cell–matrix interactions and mechanobiology4,5,6,7,8,9. Such a structure–function relationship in bone is also fundamental10,11. Notably, the collagen–mineral composite structure determines the final properties of bone material, which does not solely depend on the mineral quantity, rather on the interactions between the organic and mineral components[12]. These cooperative mechanisms of deformation take place at different length scales, from the intrafibrillar to the tissue organizational level. Despite the recognized importance of microstructure hierarchy in fundamental research, its application in the development of competitive bone grafts tends to be overlooked, with the emphasis placed on achieving bone properties by means of mineral formulation (for example, bioceramics). The organic components in mineral-based scaffolds merely serve as a ‘simple’ coating to improve cell attachment[13]. However, demineralized bone is a self-standing material (Supplementary Video 1), whereas the mineral counterpart exists as a powder (Extended Data Fig. 1), highlighting the importance of the organic scaffold for bone mechanical properties. Another notable deviation is the degree of crystallization of these alternative materials (highly crystalline hydroxyapatite (HA) versus bioapatite). Various experimental methodologies are used to decipher both biological and mechanical aspects involved in this complex physiological process, increasingly supplemented by computational modelling[14]. However, synthetic bone materials still markedly differ from the native tissue, as they fail to integrate both the bone composition and its microstructure simultaneously.

Bone is characterized by a hierarchical organization, usually categorized into distinct structural levels10,15. It is a hybrid material, mainly composed of type I collagen fibrils and poorly crystalline apatite nanoparticles. In both compact (Fig. 1a) and trabecular bone tissues, the elongated orientation of the apatite platelets tends to align preferentially along the long axis of the collagen fibrils. A notable characteristic is the presence of a twisted plywood pattern made of mineralized collagen fibrils10,15,16 (Fig. 1b). Such a structural feature encompasses a high density of monodispersed collagen fibrils coupled with a high degree of structural order. The mineralized plywood was successfully reproduced in 3D scaffolds17,18 by using the lyotropic properties of type I collagen in vitro[19], the co-alignment[20] being performed with biomimetic carbonated apatite. These materials combine the three main components of bone (collagen, apatite and water) providing, to the best of our knowledge, the highest level of in vitro bone organization achieved so far (that is, 9 out of 12 levels[15]). Consequently, these constructs present an opportunity for investigation as bone models to explore the importance of such structural characteristics in autologous graft performance, potentially paving the way for the development of a new type of competitive bone biomaterial.

Fig. 1: Characterization of collagen suprafibrillar organization in bone and synthetic collagen-based materials.

a,b, SEM micrographs of decalcified human compact bone (n = 1 sample) at low and high magnification, respectively. Scale bars, 50 μm (a), 5 μm (b). c, SEM micrograph of synthetic collagen-based materials characterized by a 3D dense and organized biomimetic structure (Col100; n = 3 samples). Scale bar, 5 μm. The plywood organization is depicted in the associated schematic representation below b. d, SEM micrograph of a non-organized collagen fibrillar network with large (micrometric) interfibrillar spaces (Col40; n = 3 samples). Scale bar, 2 μm.

Here we propose that the microstructure of bone is just as crucial as its molecular and cell biological components in determining the healing efficacy of AB. Accordingly, we present findings on how the bone hierarchical microstructure, specifically the collagen plywood and the related bioapatite co-alignment, contributes to bone regeneration in rat and ewe bone defects[21] using a variety of tuned cell-free biomimetic scaffolds. Then, their capacity to enhance bone growth was compared with commercially available synthetic ceramics and the established AB gold standard.

Collagen plywood promotes rat bone healing

Although murine bone lacks the osteon units22,23 found in human bone, which consist of concentric layers of collagen fibrils with successive different orientations[16], it still showcases successive dense fibrillar layers with differing orientations, akin to those observed in human bone (Extended Data Fig. 2a).

We evaluated dense collagen matrices containing two variations (see Methods): one comprising twisted plywood of fibrils (100 mg ml−1; Col100) and another with randomly dispersed fibrils (40 mg ml−1; Col40), as shown by scanning electron microscopy (SEM) (Fig. 1c,d, respectively).

A rat calvaria critical-size defect (Fig. 2a) was created24,25 (Extended Data Fig. 2b–d). Briefly, a piece of cranial bone (Fig. 2b) was extracted and the defect either filled with the collagen-based matrices (Col40 (n = 10) and Col100 (n = 10)) (Fig. 2c and Extended Data Fig. 2b) or left empty to serve as a control (n = 8). Notably, the implants immediately adhered to bone and dura mater (Extended Data Fig. 2c). This contact is crucial for bone repair and is potentially facilitated by the well-known haemostatic properties of collagen. Microradiographs were used to estimate the area covered by newly formed mineralized tissue, expressed as a percentage of bone defect closure (Extended Data Fig. 3).

Fig. 2: Effect of collagen-based matrices in calvaria bone-defect model.

a, Schematic representation of rat calvaria showing the characteristics of the defect (localization, size and shape; dashed red circle). Pictures of the 8-mm-diameter removed cranial bone piece (b) and collagen matrix implant (Col40) (c). Representative microradiographies of the empty-site control (d1; n = 6 defects) and defect filled with Col40 (d2; n = 10 defects) and Col100 (d3; n = 9 defects). µCT scans of a representative control (e1) and defect filled with Col40 (e2) and Col100 (e3). Representative overview by toluidine-blue-stained histological sections of defect left empty (f1; n = 6 defects) or implanted with Col40 (f2; n = 10 defects) or Col100 (f3; n = 9 defects). Col40 is partly replaced by mineralized tissue surrounded by areas of unbound bone edges (stars). Conversely, bone formation in Col100 is more homogeneous and the bounding of bone edges is more frequent. Scale bars, 1 mm (f1, f2, f3). At higher magnification, both Col40 (g1) and Col100 (g2) matrices show a similar integration mechanism. Remnant matrix (yellow star) is observed infiltrated by vascular cells (isolated or structured in blood vessels, black and yellow arrows, respectively). Concomitantly, the remnant matrix is undergoing an in situ mineralization without structural changes (granular aspect and more intense staining, yellow dot). The remnant matrix areas are surrounded by more mature bone regions with areas of active osseous formation, including remodelling sites around medullar spaces (blue stars) and osteoid tissue deposition (red dots). For Col100, larger medullar structures are observed, leading to mature bone formation (yellow cross). h1, A newly formed lamellar bone containing osteocytes (yellow circle) that surrounds a less mature fraction of Col40 (yellow dot for mineralized areas and yellow star for remnant material). h2, For Col100, a more mature lamellar bone morphology (yellow cross) is observed, with noticeable marrow spaces (blue stars), containing vessels surrounded by osteoblasts forming osteoid matrix (red dots). Remnants of the collagen matrix are more sparsely distributed. Scale bars, 50 μm (g1, g2, h1, h2).

In the control group, bone-growth pattern was centripetal, processing inward from the defect rim (Fig. 2d1,e1) with a very small amount of newly formed bone after 10 weeks (15.2 ± 9.0%). However, in the cases in which defects were filled, there was a significant increase in the mean percentage of bone-defect reconstruction, reaching 69.2 ± 24.1% for Col40 (Fig. 2d2) and 87.9 ± 6.1% for Col100 (Fig. 2d3). Complete defect reconstruction was observed in 40% and 60% of animals with Col40 and Col100, respectively. Unlike the control group, microradiographs and micro-computed tomography (µCT) scans of explants from the implants groups revealed the formation of mineralized structures originating independently from the edges of the defect and growing from its core (Fig. 2d2,d3,e2,e3), as described earlier[26]. Despite the notable discrepancy in the average percentage of mineralized filling between Col40 and Col100, this difference lacks statistical significance, even if a trend emerges (P = 0.086). Notably, the biological response for Col40 shows considerable variability owing to some very low mineralized filling values observed for certain animals. By contrast, Col100 demonstrates improved reproducibility (with a s.d. of 6.1% compared with 24.1% for Col40) (Extended Data Fig. 3a). Notably, only 40% of the implant sites filled with Col40 show a binding between the defect edges and the host bone on at least half of the rim, whereas in animals implanted with the anisotropic Col100, it is found in approximately 70% of cases. Also, µCT observations of a defect implanted with Col40 show small and less radiopaque domains in the thickness of the new forming bone, indicating the presence of non-mineralized tissue (Fig. 2e2). By contrast, a more homogeneous and mature mineralized bone repair is observed for Col100 (Fig. 2e3).

The histological analysis (illustrated in Fig. 2f–h) confirms the formation of mineralized tissue, guided by the collagen matrices from the centre of the defect (Fig. 2f). No evidence of bone reconstruction is observed in the control (Fig. 2f1). However, for Col40, the observations show the presence of both mineralized domains and residual collagenic material (Fig. 2f2,g1,h1). This remnant material (Fig. 2g1,h1, yellow star) seems to undergo (1) ‘in situ mineralization’[27] without apparent remodelling or ossification (Fig. 2g1,h1, yellow dot) and (2) angiogenesis with infiltration by many cells, either isolated or structured in blood vessels (Fig. 2g1, yellow and black arrows). These remaining material areas are surrounded by dense mineralized collagen zones showing active remodelling with resorption lacunae and deposition of osteoid tissue (active osteoblasts) organized around large vascular structure (Fig. 2g1, blue stars), characteristic of mature bone production (red dot). Notably, a lamellar bone formation is observed on the external surface of the matrix (Fig. 2h1, yellow circle), mostly at the exocranial aspect, demonstrating the osteoconductive properties of Col40.

The integration process for Col100 seems qualitatively similar. A mineralization of the implanted matrix (Fig. 2g2, yellow star) is observed without structural modifications (yellow dot), a phenomenon correlated with the presence of Ca and P ions (Extended Data Fig. 4). Concomitantly, endothelial cells infiltrate the matrix with sprouting small vessels (Fig. 2g2, yellow and black arrows). They provide ‘on-site’ bone remodelling cells possibly involved in the resulting mature bone (Fig. 2g2,h2, yellow cross) formed through remodelling (red dots) around vascularized spaces (blue stars). Similar to Col40, a lamellar bone formation on the external part of the matrix is found with Col100 (Extended Data Fig. 5a).

The key difference between Col40 and Col100 lies in the overall implant-scale bone formation. Indeed, Col100 exhibits a more homogenous bone formation within the defect thickness (Fig. 2f3). with widely mineralized domains (Fig. 2h2, cross). Furthermore, a notable surface area of the implant includes zones featuring small medullar spaces containing vessels and cells (Fig. 2h2, blue stars), accompanied by rows of active osteoblasts depositing osteoid matrix along mineralized bone trabeculae (red dots). The presence of bone-marrow spaces is considered beneficial for sustained healing by osteogenesis, as it serves as a vital source of nutriments and progenitor cells for bone tissue[28]. Moreover, the remodelling seems more intense in the newly formed bone with Col100 compared with Col40, as evidenced by alkaline phosphatase (ALP) and tartrate-resistant acid phosphatase (TRAP) enzyme histochemistry (Extended Data Fig. 5b,c). Indeed, lower resorption and osteoblasts recruitment activities are observed for Col40 (with an average of 4 osteoclasts mm−1 versus 7 osteoclasts mm−1 for Col100), explaining both the heterogeneous reconstruction observed in the radiographic analysis and the slightly weaker percentage of mineralized tissue surface extracted from von Kossa sections for Col40 compared with Col100 (41.0 ± 9.1% versus 49.8 ± 13.9%; Extended Data Fig. 3b). These findings demonstrate that bone formation with Col40 is less pronounced than with Col100, indicating that the latter induces a more active bone reconstruction by comparison.

Several factors may contribute to the performance of the Col100 matrix, including: (1) enhanced mechanical properties associated with increased collagen concentration29,30, leading to a notable 100-fold increase in the Young’s modulus when compared with the Col40 as measured by microindentation, performed at the cell scale, that is, of about 10 μm (Supplementary Table 1 and Extended Data Fig. 6); (2) the new microenvironment set by the material composition, density and/or organization, probably favouring the migration of host cells and the correct expression of their phenotype4,6,9; and (3) a possible electrostatic environment created by the collagen fibrils density that is known to dictate mineralization in vitro[31]. Another important consideration is that bone healing begins with the formation of a collagenic scar tissue, gradually maturing with the production of dense collagen by osteoblasts originating from the surrounding bone and periosteum. The resulting tissue is then mineralized at a later stage[32]. The use of AB bypasses these steps by immediately providing an existing vascular network, potentially connecting to surrounding vessels as observed in pre-vascularized scaffolds[33]. Also, it provides the native microenvironment with respect to the quantity and quality of collagen fibrils, their organization and density, as well as other bone components, such as mineral, cells and growth factors. The bone-like twisted plywood in Col100 might similarly induce, to some extent, such a sequence of events.

Notably, Col100 is a bulk material with porosity at the nanometre scale, whereas Col40 exhibits larger interfibrillar spaces at the micrometre scale, averaging 10 μm in pore size[30] (Fig. 1c,d). Here, similar to AB graft and dense collagen matrix in vitro[34], the high fibrillar density and organization do not hinder cell migration. In fact, it seems to enhance cell migration compared with a matrix with lower collagen density. This suggests that the conventional notion in the literature favouring macroporous scaffold for cell colonization and subsequent bone healing might not be mandatory when mimicking the composition and microstructure of the ECM. Accordingly, local lamellar orientation and a continuous curvature have been shown to orient bone-tissue formation9,35.

In summary, our findings indicate that the quality of bone reconstruction improves when the biomaterial replicates the plywood structure of the host native bone. Also, our results confirm that physicochemical properties take part in bone healing by driving the biological response. Considering the differences in tissue architecture between murine and human bone[22], further experiments were conducted using a preclinical ewe model. Indeed, sheep shares substantial similarities with human in weight, bone and joint structure and in bone regeneration[21].

Mineralized plywood enhances ewe bone repair

We investigated the addition of apatite mineral—specifically, biomimetic carbonated HA (CHA)—whose arrangement in collagen (that is, co-alignment, marked as a star in transmission electron microscopy (TEM)[20]; Fig. 3a), composition, size, hydration[36] and structure[37] (wide-angle X-ray diffraction (WAXD); Fig. 3b) closely resembles that found in bone[38] (Fig. 3c). Comparative mechanical analysis against a non-mineralized matrix exhibiting identical collagen plywood organization (Col) revealed that this hybrid material (Col-CHA) shows much larger Young’s moduli at the micrometre scale. Furthermore, the local Young’s modulus shows a much broader distribution in Col-CHA, as previously reported[20] (Supplementary Table 1 and Extended Data Fig. 6).

Fig. 3: Characterization and implantation of bone-like material in ewe model.

a, TEM micrograph of Col-CHA (n = 3 samples), in which the collagen–apatite co-alignment is observed (star). Scale bar, 500 nm. b, 1D radial average of the WAXD pattern of Col-CHA and the X-ray diffraction pattern of synthetic HA from the database PDF-5+ (JCPDS #00-009-0432). c, TEM micrograph of partially decalcified human compact bone (as shown in a; the typical co-alignment is highlighted by a star) (n = 1 sample). Scale bar, 500 nm. d, Schematic representation of the ewe surgical procedure and metaphyseal implantation sites (spherical inset). Pictures of Col-CHA (about 8 mm in diameter) before (e) and during (f) implantation, its size and shape fitting the bone defect (arrowhead). Scale bar, 1 cm. a.u., arbitrary units.

Both Col and Col-CHA were surgically implanted into critical-size bone defects (cortico-cancellous sites) in ewes39,40 (Fig. 3d) (see Methods). As for the previous implantations, the biomaterials were shaped to fill the defect (Fig. 3e) and then implanted (Fig. 3f; n = 5 for each matrix). After 2 months, bone blocks were scanned computed tomography (Fig. 4a), radiographed (Fig. 4b) and prepared for histopathological analysis (Fig. 4c,d). The sites of bone defects were easily detected thanks to the rim of adjacent bone (Fig. 4a,b, arrowheads), which is a consequence of the surgical procedure and subsequent inflammation. The area covered by bone was measured on both radiographic images and slices extracted from the scanning and were expressed as a percentage of bone formation (Fig. 4e). For the histological images, only the internal part of the explants is shown, excluding the strong and non-specific surface reaction owing to the mechanical load applied on the external part[41].

Fig. 4: Comparison of bone materials in ewe model after 2 months of implantation using non-mineralized or mineralized twisted plywood scaffolds.

Scanner imaging sections of the control in which the defect was left empty (a1; n = 5 defects), ewe implanted with Col (a2; n = 5 defects) and Col-CHA (a3; n = 5 defects) matrices. Red arrowheads show the original defect identified by strong peripheral ossification as a result of the surgical procedure. Faxitron radiographic imaging of the empty-site control (b1; n = 5 defects), ewe implanted with Col (b2; n = 5 defects) and Col-CHA (b3; n = 5 defects) matrices. The strong peripheral ossification is also evidenced here (red arrowheads). Histological GT-stained thin sections of implantation sites for the empty-site control (c1,d1; n = 5 defects), Col (c2,d2; n = 5 defects) and Col-CHA (c3,d3; n = 5 defects) matrices. Dashed-dotted red circles represent the approximate initial defects. Higher-magnification observations allow the visualization of tissular components and organization: fat adipose tissue (bars), collagenic scar tissue (stars) and bone trabeculae with remodelling (black arrowheads). Sections are perpendicular to the long axis of the defect. Scale bars, 2 mm (c1,c2,c3), 50 μm (d1,d2,d3). e, The percentage of bone filling for each defect was blindly measured on radiographies and scanner imaging. Data were analysed from the total number of defects in each group, as indicated in the bars (n = 5), expressed as mean ± s.d. and compared using nonparametric Kruskal–Wallis with Mann–Whitney multiple comparison test (scanner imaging) or independent unpaired two-sided Mann–Whitney tests (radiographic imaging). *Significant difference with P < 0.05. **A difference/trend with P < 0.10.

When the defects were left empty (n = 5) as controls (Fig. 4a1,b1), the percentage of newly formed mineralized bone was found to be 80 ± 12% on radiography and 54 ± 7% on scanner. This difference is attributed to the techniques used. Specifically, the frontal X-ray radiographic imaging method inherently stacks all depth levels into a single image, occulting potential reconstruction flaws inside the defect and consequently resulting in higher filling percentages. Conversely, the lateral scanner imaging technique enables the differentiation between superficial and internal bone reconstruction, thereby leading to lower filling percentages. Moreover, apart from the evident strong peripheral ossification that shows newly formed bone close to the defect border (Fig. 4c1, within the dashed-dotted red circle), the centre of the unfilled defect exhibits fatty and collagenic scar tissues, as shown in the histopathological sections (Fig. 4d1, bar and star, respectively). These results indicate a minimal amount of newly formed bone, failing to recover both the native physiological and mechanical functions, as well as local morphology.

By contrast, the percentage of newly formed bone in the presence of Col exhibits substantial increase (89 ± 9% on radiography and 66 ± 7% scanner; Fig. 4a2,b2). Similar to the control group, all sites implanted with Col show a strong peripheral ossification (arrowheads), but not always evidenced in histopathological observations (Fig. 4c2, dashed-dotted red circle). However, as well as locally loose collagen aggregates in few areas (Fig. 4d2, star), the internal volume of the defect is mainly filled with newly formed lamellar bone composed of a bone trabeculae network and vascularized medullary spaces (Fig. 4d2, arrowhead and bar, respectively). The resulting network is connected with the surrounding trabecular system and reproduces the local architecture. This newly formed bone seems morphologically normal, with areas of physiologic remodelling (Fig. 4d2, red osteoid layer, arrowhead), albeit with fewer interconnections compared with the surrounding bone. The presence of deep-seated reconstruction within the defect distinguishes it markedly from the control, as observed in scanner imaging (Fig. 4a2).

Addition of biomimetic co-aligned CHA to Col results in a further increase of new bone formation (99 ± 1% on radiography and 78 ± 10% on scanner; very different compared with the control; Fig. 4a3,b3). The high degree of bone reconstruction inside the defect shows the limitation of the radiographic evaluation. Notably, the peripheral ossification is less visible and the reconstruction is localized within the whole defect volume. Furthermore, the bone trabeculae seem thicker, with increased interconnections compared with those observed with Col alone. This trend suggests a higher bone density, consistent with both radiographic imaging and histopathological observations (Fig. 4c3,d3). Despite the rapid bone reconstruction, the local architecture seems physiological, featuring vascularized medullary spaces between trabeculae and normal bone remodelling sites (Fig. 4d3, arrowhead).

These results confirm, in a more challenging bone-healing model, that the collagen plywood greatly enhances bone reconstruction compared with self-healing (control). Furthermore, the biomimetic mineralization of this hierarchical collagen structure improves the healing process. This outcome is in line with previous in vitro studies that illustrate the influence of collagen–apatite co-alignment on osteoblasts reaction and mobility[34]. It is well known that addition of calcium phosphate to collagen scaffold stimulates bone healing[42]. Furthermore, apatite deposition aligned along the long axis of the collagen fibrils has been linked to superior bone reconstruction compared with isotropic mineralization around the fibrils[43]. However, this effect has not previously been demonstrated for hierarchically organized co-aligned collagen–apatite, specifically depicting the twisted plywood geometry. Note that further studies may determine whether an optimum collagen–apatite composition exists.

Preclinical bone grafts evaluation

Extending into clinical considerations, we synthesized Col-CHA using clinical-grade collagen (ColCG-CHA) (see Methods and characterization in Extended Data Fig. 7). The ColCG-CHA matrix was compared with two commercial crystalline mineral-based substitutes available as macroporous block (Vitoss, VO) and as macroporous granules (Mastergraft, MG) and to the AB gold standard (Extended Data Fig. 8) following the aforementioned ewe implantation procedure (n = 12 for ColCG-CHA and n = 5 for AB, VO and MG).

The rationale behind including two different types of ceramic (blocks and granules) was to discern the bone-formation response associated with the shape of the material. Ceramics being radiodense, a clear X-ray evaluation of the fraction area of mineralized bone formation remains challenging. The comparison between radiographic images and macroscopic observations of the defect (identified by white materials) clearly indicates that ceramic materials remained within the defect (Fig. 5a and Extended Data Fig. 8d). This observation may account for the thicker appearance of peripheral ossification observed with ceramics, in contrast to both ColCG-CHA and AB. Therefore, a histomorphological analysis focusing on bone mineralization, the presence of collagenic scar tissue and osteoblastic activity was deemed more suitable for this evaluation.

Fig. 5: Comparison of bone formation in ewe model between bone-like material, ceramic biomaterials and AB after 2 months of implantation.

Faxitron radiographic imaging of ewe bone defects implanted with ColCG-CHA matrices (a1; n = 12 defects), MG ceramic (a2), VO ceramic (a3) and AB graft (a4) (n = 5 for MG, VO and AB). Histological GT-stained thin sections of ewe bone defects implanted with ColCG-CHA matrices (b1; n = 12 defects), MG ceramic (b2), VO ceramic (b3) and AB graft (b4) (n = 5 for MG, VO and AB). Dashed-dotted red circles represent the approximate initial defect. Scale bars, 2 mm. Higher-magnification observations (c1–c4, ColCG-CHA, VO, MG and AB, respectively) allow the visualization of tissular components and organization, such as residual materials (red stars in c1 and c2), active (black arrows in c1 and c4) or quiescent osteoblast layers (black arrows in c2 and c3), active osteoid deposition (c1 and c4 only, red staining) indicative of active bone formation and fibrous scar tissue often associated with medullary spaces (black stars). The dashed curve in c1 shows the limit between the residual cell-free collagenic material (red star in c1) and mature bone formation including entrapped osteocytes. Residual material for the ceramics implant exists as ceramic particles (red star in c2) or voids reflecting the presence of ceramic particles probably released during the cutting step of thin-section preparation (red star in c3). Scale bars, 50 μm.

Quantitative and qualitative histological evaluation of non-decalcified sections (Supplementary Table 2 and Extended Data Fig. 9) reveal bone-healing processes in all of the samples, both at the periphery and within the centre of the defect (Fig. 5b,c). Specifically, the fraction area of mineralized bone formation within the defect measured on sections stained with von Kossa stain ranged from 23.7 ± 12.6 to 32.0 ± 17.4% for the ceramics (MG and VO, respectively), 32.8 ± 9.8 for AB and 30.4 ± 7.6% for ColCG-CHA. These values are close to that found for physiological trabecular bone, as measured in the adjacent bone (Extended Data Fig. 10a–d). It is noteworthy that our focus was on assessing new bone formation, excluding mineral residues resulting from the non-degradation of ceramics. However, the use of von Kossa stain, which identifies divalent ions, prevents from distinctly differentiating between calcium from the matrix and that of the de novo mineralization[44].

In all four defects, mature and mineralized trabecular bone are observed, forming a regular network, as well as the presence of fibrous scarring tissue (Fig. 5c, black stars). However, the quantity of collagenic scar tissue is notably small (with a mean quantity of fibrous tissue scored as 1.2 for ColCG-CHA, 1.2 for AB, 0.8 for MG and 1.6 VO). This low score of fibrous reaction surrounding material suggests good biocompatibility of the implants, further supported by the remarkably low intensity of the peri-material inflammatory response (with a mean score 0.6 for ColCG-CHA, 0.4 for AB, 0.6 for MG and 1.4 VO).

An interesting discrepancy in bone-healing profiles is observed at the bone marrow–bone interface when comparing the composites (ColCG-CHA and AB; Fig. 5c1,c4) to the pure mineral-based materials (Fig. 5c2,c3). Both ColCG-CHA and AB exhibit regular osteoid layers (red staining) associated with active rows of osteoblasts (Fig. 5c1,c4, black arrows) along thick and multifocal connected bone trabeculae or along residual matrix plugs. By contrast, such osteoid layers are not observed within the implanted ceramics. Quantitatively, the extent of active bone formation (measured by the quantity of bone-forming cells—numerous clusters of osteoblasts—and osteoid deposition) is much higher for ColCG-CHA in comparison with the other biomaterials (osteoid activity scored 3.6 for ColCG-CHA versus 0.8–2 for ceramics and AB and measured by histomorphometry at 0.11% of the osteoid surface for ColCG-CHA-implanted defect versus 0.07% for AB and 0.09–0.11% for ceramics). The presence of nearby osteoclasts adjacent to the osteoid tissue strengthens the occurrence of an active bone-remodelling process in ColCG-CHA (Fig. 6a, blue arrows). Therefore, the simultaneous observation of osteoclastic resorption and multiple active osteoid layers (‘coupled remodelling’) in ColCG-CHA indicates the integration, colonization and continuing regeneration of the normal bone structure (for example, dashed curve in Fig. 5c1). This suggests that bone remodelling is more pronounced with delayed plasticity, potentially leading to more physiological reconstruction and better adaptation to mechanical stress, unlike the intense remodelling induced by growth factors[45]. Notably, the presence of active osteoid layers and the remodelling sequence bear resemblance to processes typically observed for AB. Further exploration could be highly valuable to understand the connection between the presence of such active osteoid layers and the osteoinductive properties of grafting materials.

Fig. 6: In-depth histopathological analysis of bone-like material implantation in ewe after 2 months.

a, GT-stained histological thin section of implantation site with ColCG-CHA (n = 12 defects) showing the presence of active osteoclasts (blue arrows). Scale bar, 100 μm. b–d, HE histological thin sections of various areas in defects implanted with ColCG-CHA (n = 12 defects). The remnant material (yellow stars in b and c) is being infiltrated by vascular cells (isolated or grouped, black and yellows arrows, respectively, in b and c), whereas other regions take a granular aspect reminiscent of the in situ mineralization observed in the rat study (yellow dots in c). The higher-magnification inset in b shows a grouped vascular cells infiltration from another section. Blue star in b points to a more structured vascular formation. The dashed oval in c indicates an area of the mineralized remnant material reflecting a maturation towards a woven bone aspect that can then be further remodelled into mature bone. Such intermediate area is abundant in d. Remodelling is observed, including active osteoclasts (blue arrow and d1), small resorption lacunae and larger vascularized medullary spaces (blue stars and d2). The medullary spaces are surrounded by mature lamellar bone (yellow cross) with osteocytes and quiescent or active osteoblasts rows (d3). Scale bars, 100 μm (b,c,d), 50 μm (b inset,d1–d3) e, Histological GT-stained section and schematic representation of the main processes induced by bone-like biomaterials: (1) implant (yellow star) infiltration by vascular cells (black and yellow arrows) concomitantly with (2) in situ collagen mineralization (yellow dot); (3) evolution towards an intermediate bone-like aspect reminiscent of the structure of woven bone (dashed oval); (4) resorption by osteoclasts (blue arrows); (5) osteoblasts layer formation (mononuclear cuboid cells in pink) producing collagen fibrils (newly formed osteoid tissue in light blue and red), that is, active remodelling and bone formation (red dot) around large vascularized medullary spaces (blue stars); (6) mature bone formation (yellow cross) with osteoblasts migration/embedment, which may differentiate into osteocytes. Scale bar, 100 μm.

Moreover, both ceramics show thin trabeculae with few connections within the trabecular network (Fig. 5c2,c3). The newly formed bone tissue in ceramics also exhibits much lower maturity compared with that observed in ColCG-CHA and AB (with an average maturity score ranging from 2.2 to 2.6 for ceramics versus 3.1 and 4.2 for ColCG-CHA and AB, respectively). These bone trabeculae are found at the interface with either a loosely non-mineralized fibrous tissue for MG or spherical voids in VO (black and red stars in Fig. 5c2,c3, respectively), indicating that ceramic particles were removed during the preparation of thin sections, that is, during the cutting step. Also, ceramic residues seen as grey/black granular deposits (Fig. 5c2, red stars and Extended Data Fig. 10e) are observed all over the samples, demonstrating incomplete degradation of the material according to the macroscopic observations and to X-ray images. Notably, bone reconstruction seems frozen in ceramics, with ‘passive’ mature trabeculae (quiescent osteoblasts with little to no osteoid deposits; Fig. 5c3, black arrow) surrounding the ceramic residues, suggesting an inability to further adapt to local mechanical constraints. Nevertheless, the results confirm that ceramics are osteointegrated and osteoconductive3,46.

These results have several implications. First, ceramic residues confirm that crystalline calcium phosphates are treated as a foreign material during bone reconstruction[47], highlighting the importance of using minerals with bone crystalline features and properties (for example, hydrophilicity)[36]. Second, open macroporosity in these materials is crucial for physiological fluid impregnation and host-cell colonization, ensuring osteoconductivity. However, bone and vascular cells possess all the biomolecules (for example, matrix metalloproteinases, non-collagenous proteins and collagen-binding integrins) to migrate through the tissue without requiring macropores[34]. This migration also occurs for ColCG-CHA, although this material is free of any host cells on implantation. Therefore, although macropores facilitate cell diffusion, they are not essential for collagen-based scaffold.

A comprehensive histopathological analysis of the ColCG-CHA integration mechanism was conducted using haematoxylin and eosin (HE) and Goldner trichrome (GT) staining (Fig. 6b–e). Observation of the remnant matrix (Fig. 6b,c,e, yellow star) reveals infiltration by numerous migrating cells, either isolated or in small groups, showing small branching lines (Fig. 6b,c,e, black and yellow arrows, respectively). Within these regions, ingrowth of capillaries filled with erythrocytes at various stages of development are observed (Fig. 6b,d,e, blue stars and inset), confirming that the dense collagen structure does not hinder cell migration, even without macropores, cells or growth factors supplementation. Notably, the absence of ischaemic lesions (for example, avascular necrosis/osteonecrosis) indicates proper vascularization. Direct evidence of successful vascularization includes the presence of morphologically mature and functional vascular structures, as evidenced by circulating erythrocytes in the lumen of the neovessels. This vascularization is required for biomaterials integration. Notably, some areas of the remnant material retain a more intense (basophilic) stain in HE staining and show a granular appearance without substantial structural changes (Fig. 6c,e, yellow dots). These areas are similar to the in situ mineralization observed in the rat study, as confirmed by von Kossa staining (Extended Data Fig. 10f). The area highlighted by the dashed oval in Fig. 6c reflects the conversion of such mineralized material into a bone-like matrix with a woven bone aspect (Fig. 6d), which is favourable for further remodelling and maturation.

In regions indicating later stages of bone maturity, the matrix shows increased degradation by resorption lacunae containing active osteoclasts (Fig. 6d,d1, blue arrows) and larger vascularized medullary spaces (Fig. 6d, blue stars and Fig. 6d2). Moreover, substantial portions of the implanted matrix have been replaced by lamellar mature bone (Fig. 6d, yellow cross and Fig. 6d3) encompassing osteocytes in their lacunae, covered by rows of quiescent or active osteoblasts and surrounded by bone marrow spaces. These mature-bone regions correspond to normal bone tissue and affix to the remnant matrix, indicating that the implant is undergoing remodelling towards mature bone. These observations are clearly highlighted by the various tinctorial affinities in HE staining, which discriminate areas with an ‘initial aspect’ (smooth appearance and homogeneous light pink) from those exhibiting mature bone (bright pink and mineralized). This integration mechanism closely resembles that observed in the rat study for the densest collagen matrix.

In line with general wound-healing principles, bone repair encompasses four main sequential steps: haematoma formation (haemostasis phase), followed by the fibrocartilaginous and subsequent bony callus formation (inflammation, angiogenesis and proliferative phases) and finally bony callus remodelling[48] (maturation phase). On the basis of histological observations, it is conceivable that our bone-like biomaterial may accelerate or bypass stages leading up to the remodelling. This hypothesis is supported by previous findings demonstrating that primary human osteoblasts seeded on similar materials exhibit migration and subsequent differentiation into osteocytes[34]. Building on these in vitro works and the present in vivo histological observations (Fig. 6e), we propose that our structurally designed bone-like biomaterial (yellow star) could enhance performance by: (1) supporting angiogenesis, cell attachment and migration (black and yellow arrows); (2) driving in situ mineralization[27] (yellow dot) and progressive conversion to a woven bone-like matrix (dashed oval); (3) enabling resorption by osteoclasts (blue arrows) and related enzymatic machinery supplied by angiogenesis (blue stars), allowing collagen digestion and remodelling; (4) facilitating the formation of active osteoid layers with osteoblasts (red dot) producing collagen fibrils; and (5) promoting osteoblasts migration, embedment and differentiation into osteocytes, leading to physiologically mature bone (yellow cross). The proposed mechanism for bone regeneration with our material is schematically outlined in Fig. 6e.

Taken together, these results provide the experimental evidence for the pivotal role of ECM microstructure in driving cellular behaviour[5] in bone.

Conclusion

This work differs from conventional regenerative medicine approaches that use cell-preloaded matrices[49], as our biomaterials are free of cells and any associated molecular machinery (such as non-collagenous proteins or growth factors). Using sophisticated bone biomaterials was crucial to demonstrate that the mineralized twisted plywood pattern stimulates bone formation and contributes to performance comparable with AB, with the structural aspect complementing the biological and mechanical ones[14]. This result reinforces the role played by physicochemical processes in bone physiology50,51 and promising potential for application in bone replacement while also offering innovative models to investigate the interaction between materials and living tissues.

Methods

Matrix preparation

Collagen extraction

Type I collagen monomers were extracted from rat tail tendons following a classical procedure. Fresh tendons were washed with phosphate-buffered saline solution and solubilized in 0.5 M acetic acid. The raw solution was centrifuged and the supernatant was selectively precipitated with 0.7 M NaCl. Precipitated type I collagen was solubilized in 0.5 M acetic acid and then desalted by dialysis against 0.5 M acetic acid. The concentrations of type I collagen solutions were assessed by hydroxyproline titration and adjusted to a final stock concentration of approximately 3 mg ml−1.

Matrix synthesis

All of the experiments were carried out at room temperature (19 ± 2 °C) to prevent collagen denaturation and sterility was kept throughout the procedure. The matrices were stored in distilled water at 4 °C before implantation.

Col40 (rat model)

The collagen solution was progressively concentrated up to 40 mg ml−1 by slow evaporation of the solvent in a safety cabinet[52]. Then, fibrillogenesis in vitro was performed under ammonia vapours for 24 h. This step allows the stabilization of the liquid-crystalline organization into dense fibrillar matrices[53]. Finally, matrices were punched with an 8-mm steel trepan and rinsed with sterile phosphate-buffered saline until neutralization of the pH.

Col100 (rat model)

Below the critical concentration of 40 mg ml−1, type I collagen molecules do not organize[52]. However, above this threshold concentration, they undergo a spontaneous transition into ordered liquid-crystalline phases owing to the lyotropic properties of acidic type I collagen solution in vitro[19]. After a sol–gel transition leading to collagen fibril formation, the geometries of fibrillary networks closely mimic those found in living tissues, depending on the targeted concentration (at least 80 mg ml−1 for cholesteric bone-like mesophase)53,54. The Col100 matrix was prepared using a procedure that combines injection and reverse-dialysis processes to increase the collagen concentration[17]. A 15 ml volume of about 1 mg ml−1 soluble acidic collagen solution (0.5 mM acetic acid) was continually injected (rate range 1 μl min−1 to 15 ml min−1) in a closed dialysis chamber (3 mm thickness and 10 mm width) for 8 days. The bottom of the chamber contained a dialysis membrane with a molecular weight cut-off of 12–14 kDa. The reverse-dialysis process was set against polyethylene glycol (35 kDa, Fluka) dissolved in 0.5 M acetic acid up to about 150 mg ml−1. The flow of the collagen solution was controlled to maintain the same pressure on each side of the dialysis membrane. After injection, dialysis was continued for 8 days, to obtain a homogeneous concentration in the samples. The pH was then increased to a range of 9–10 by ammonia gas diffusion for 4 days to induce collagen fibrillogenesis and stabilize the liquid-crystalline organization into dense fibrillar matrices[29]. The matrices were then removed from the dialysis chamber and washed several times in double-distilled water until neutralization. Finally, matrices were punched with an 8-mm steel trepan before implantation.

Col (ewe model)

The procedure matched the Col100 matrix except that: (1) the initial volume of the acid solution was 30 ml and (2) the mould dimensions were diameter 8 mm and depth 13 mm. Here the process to elaborate the materials ends with the removal of the stabilized fibrillar matrix directly from the mould that allows the shaping of the collagen construct.

Col-CHA (ewe model)

The Col-CHA matrix was prepared as follows: a volume of a 3 mg ml−1 soluble acidic collagen solution (0.5 mM acetic acid) was mixed with a CaCl_2/NaH_2PO_4/NaHCO_3 acidic solution (0.5 mM acetic acid). According to a previously described procedure (col/CHA in ref. [20]), the procedure is consistent with the synthesis of preferentially B-type CHA, which has a formula of Ca10−x(PO_4)6–x(CO_3)x(OH)2–x with 0 ≤ x ≤ 2. The pH was adjusted to 2.2. The final concentration of the collagen solution was about 1 mg ml−1 and the final ionic strength was 165.9 mM. The procedure matched the Col matrix except that the initial volume of the acid solution was 30 ml.

ColCG-CHA (ewe model)

The procedure matched the Col-CHA matrix except that commercial acidic clinical-grade collagen was used instead of collagen extracted from rat tail tendons to avoid inflammatory response. Bovine skin type I collagen diluted in acetic acid at a concentration of 5 mg ml−1 was purchased from Symatese (reference ACI070). The use of clinical-grade collagen was chosen to eliminate any potential immunologic responses arising from variations in collagen-purification processes[55], thereby enabling the evaluation of our samples as potential bone biomaterials. Although both collagen solutions exhibit lyotropic properties and the ability to form fibrils necessary to produce high-density twisted plywood collagen matrices, clinical-grade collagen demonstrates higher purity and greater thermal stability (see Extended Data Fig. 7).

Commercially available mineral-based substitutes

Vitoss 1.2 cc blocks (reference 2102-0013 Stryker) is composed of a porous (up to 90%) structure of β-tricalcium phosphate. The Vitoss block was shaped with a scalpel to fit the defect. Mastergraft 5cc Granules (reference 7600105, Medtronic) is composed of 80% porous resorbable ceramic granules (15% HA, 85% β-tricalcium phosphate).

Autologous bone

AB fragments were extracted from iliac crest. The animal’s own blood serum was kept and the fragments were subsequently used as bone filler.

Reference human bone sample for SEM and TEM

The bone samples observed by SEM in Fig. 1a,b and by TEM in Fig. 3c were prepared as part of a previous study[56].

Bone powder

The organic matrix (collagen and other proteins) was removed from bone sample by immersion in dilute NaClO aqueous solution treatment to extract the mineral particles (Extended Data Fig. 1), as previously described[36].

Matrix characterization

SEM and elemental characterizations

Collagen matrices were fixed in 2.5% glutaraldehyde in a cacodylate solution (0.05 M). After washing in a cacodylate/saccharose buffer solution (0.05 M/0.6 M, pH 7.4), dehydration in increasing ethanol baths, matrices were dried at the carbon dioxide critical point using a Leica EM CPD300. Samples coated with a 10 nm gold layer were observed in a Hitachi S-3400N at an accelerating voltage of 10 kV.

TEM

Matrices were fixed in glutaraldehyde, washed and dehydrated as described for SEM and embedded in araldite. The matrices prepared without mineral (Col40, Col100 and Col) were also post-fixed with 2% osmium tetroxide for 1 h at 4 °C before dehydration. Ultrathin sections (70 nm) were obtained, stained with uranyl acetate and observed in a FEI Tecnai G2 Spirit TWIN electron microscope operating at 120 kV.

WAXD (transmission mode)

Small parts of the mineral matrices were cut out of the bulk sample and inserted in X-ray cylindrical borosilicate capillary tubes. The tubes were flame-sealed to keep the samples hydrated and then placed directly in the vacuum chamber beam. X-ray diffraction experiments were performed with a S-MAX 3000 RIGAKU using a monochromatic CuKα radiation. The data were collected in the 5–60° range (2θ). The sample-to-detection distance was 0.059 m. The data were analysed using FIT2D (version 18, beta) software. The 2D WAXD patterns were collected with imaging plates and then scanned with 50 μm resolution. The diameter of the cylindrical beam dimension at the specimen was 400 μm and the sample thickness was approximately 1 mm.

Differential scanning calorimetry

Experiments were performed on a TA Q20 apparatus. The heating rate was set at 5 °C min−1 and the temperature ranged from 25 °C to 55 °C. About 20 mg of sample was weighed and put in an aluminium pan, the reference being an empty sealed aluminium pan. Collagen purity is determined on the basis of thermal stability. Collagen solutions typically exhibit an endothermic peak around 40 °C, indicating denaturation into gelatine that occurs through the irreversible unfolding of the triple helix[57].

Polarized light microscopy

The materials were placed, without any treatment, between a glass slide and a coverslip. Observations were made using a transmission Zeiss Axio Imager A2 POL. The microscope was equipped with the standard accessories for examination of birefringent samples under polarized light (that is, crossed polars) and an Axiocam CCD camera.

SDS-PAGE

Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) of type I collagen extracted from rat tail tendons and bovine dermis (clinical grade) was performed, using an acrylamide (10%)/bis-acrylamide (30%) gel in the presence of tris-HCl 1.5 M (pH 8.8), SDS, EDTA, glycerol and 2-mercaptoethanol. Migration was checked by adding bromophenol blue to the samples (0.05%). The separated protein bands were identified by comparison with a standard molecular mixture marker (Sigma-Aldrich) including the α chain of type I collagen (125.103 KDa).

Mechanical characterization

The local Young’s modulus E of the Col40, Col100 and Col-CHA matrices was estimated at room temperature (22 °C) through microindentation (Piuma Nanoindenter, Optics 11 Life). To this aim, the Col40 and Col100 matrices were used as is (see Fig. 2c), whereas the Col-CHA matrix shown in Fig. 3e was cut into a piece of thickness about 1 mm. These matrices were immersed in their conservation medium at the bottom of a small Petri dish and their upper surface was indented using a spherical glass probe of radius R attached to a cantilever of calibrated stiffness k, with R = 105 µm and k = 0.45 N m−1 for Col40 and R = 51 µm and k = 0.26 N m−1 for Col100 and Col-CHA. Depending on the sample, these indentation tests involved forces ranging from 0.15 µN to 1.2 µN and penetration depths of 0.5–12 µm at the end of the loading phase, and to contact radii of 5–30 µm between the probe and the matrix. For each sample, about 200 independent indentations spaced by 50–200 µm were performed and each force–depth curve was fitted by a Hertzian contact model, yielding the effective Young’s modulus Eeff at the locus of the indent. The local Young’s modulus E was then computed through E = Eeff(1 − ν^2), assuming a Poisson ratio ν of 0.5 for all samples. The statistical analysis of the Young’s modulus data was performed using MATLAB (release R2018b) software.

Implantations

Rat calvaria critical-size defect

The study was reviewed and approved by the Animal Care Committee of the University Paris Descartes (no. P2.JLS.174.10) The 30 8-week-old (220–240 g) male Wistar rats used in the study were housed in individual cages at stable conditions in the animal facility of the Laboratoire Pathologies, Imagerie et Biothérapies Orofaciales, Université Paris Cité. The surgical procedures were performed as described previously24,25,58. After anaesthesia with an intraperitoneal injection of 100 mg per kg body weight of ketamine and 10 mg per kg body weight of xylazine hydrochloride (Centravet Alfort), the cranial area was shaved, disinfected and the skin was incised in the sagittal direction. The periosteum was then incised in the same direction and elevated to expose the underlying calvaria. An 8-mm defect was created in the centre of the calvaria using a steel trephine mounted on a low-speed dental handpiece, under sterile saline irrigation. The defects were randomly filled with dense collagen matrices, either twisted plywood organized fibrils (100 mg ml−1; Col100, n = 10) or randomly dispersed fibrils (40 mg ml−1; Col40, n = 10) or left empty (controls n = 8). The skin flap was then replaced and secured with interrupted sutures. After 10 weeks, rats were anaesthetized as previously and euthanized using exsanguination. The skulls were removed and fixed in 70% ethanol. Three animals were excluded from further analysis: in the control group, one animal died and another had an incomplete defect created during the surgical procedure; in the Col100 group, one animal had its underlying dura mater and mid-sagittal sinus damaged, thus introducing a technical bias.

Radiomorphometric analysis (rat)

The skulls were radiographed (exposure 13 kV, 12 mA, 15 min) in a microradiography unit (model Sigma 2060, CGR) with X-ray film (Kodak Professional Films). Morphometry was performed at a constant magnification with a semi-automatic image analyser coupling the microscope to a video camera and a computer. The percentage of bone-defect closure, corresponding to the percentage of radiopacity in skull defect, was calculated as the following ratio: area of radiopaque formation in the defect/area of the defect created by trephination × 100 using ImageJ software (v1.52a).

µCT

µCT was performed in IMOSAR, Laboratoire de Biologie, Bioingénierie et Bioimagerie Ostéo-articulaires (B3OA), UMR CNRS 7052 INSERM U1271, Université de Paris. Representative samples were scanned using a desktop micro-X-ray computed tomography (Micro-CT SkyScan 1172, Bruker), source voltage 59 kV, source current 100 μA, image pixel size 17.77 μm. Each sample was rotated 180° with a rotation step of 0.7°, exposure time 90 ms. 3D reconstruction and analysis were made with NRecon SkyScan software 1.7.1.6 (Bruker).

Statistical analysis

Given the high variability of the in vivo response and the related small sample size, the Kruskal–Wallis test (α = 0.05, no correction, multiple comparison using the Mann–Whitney test) was used to assess overall differences between groups owing to its robustness in handling nonparametric data, whereas independent two-sided pairwise comparisons using the Mann–Whitney U test were performed to detect any potential trends or variations between specific groups. The results were discussed following two levels of significance. Differences were considered strictly significant if P < 0.05, whereas a P < 0.10 indicates a trend in the data. The statistical tests conducted in this study are indicated in the figure legends as follows: *P < 0.05 and **P < 0.10. Data are given as mean ± s.d.

Histomorphometric analysis (rat)

After dehydration, the explants were embedded without demineralization in methyl methacrylate (Merck). 4-μm-thick sections were cut in the frontal plane with a Polycut E microtome (LEICA) in the central part of the defect. Sections were stained with toluidine blue, von Kossa or processed for enzyme histochemistry (Extended Data). Morphometry was performed at a constant magnification with a semi-automatic image analyser coupling the microscope to a video camera and a computer. TRAP sections were used to count the number of osteoclasts by millimetre based on 1/10th of the section. The von Kossa staining was used to quantify the percentage of mineralized tissue in the defect, calculated as the following ratio: area of matrix-related mineralized tissue/area of the defect × 100.

Enzyme histochemistry (rat)

ALP activity was revealed with naphthol AS-TR phosphate (Sigma-Aldrich) and diazoted Fast Blue RR (Sigma-Aldrich). TRAP was detected using Fast Red TR Salt (Sigma-Aldrich) and naphthol AS-TR phosphate (Sigma-Aldrich).

SEM characterization of histological sections

Unstained bone histological thin sections were coated with 10 nm of carbon and imaged with a Hitachi S-3400N scanning electron microscope with an accelerating voltage of 10 kV. Energy-dispersive X-ray spectroscopy was performed using an Oxford Instruments X-Max detector (20 mm^2). Using SEM to characterize histological sections is useful to visualize the in situ mineralization in which the fibrillar collagen matrix disappears in favour of mineral aggregates59,60.

Ewe critical-size defect surgical procedure

Two studies were conducted on ewe. Both studies were reviewed and approved by the IMM Recherche’s Institutional Animal Care and Use Committee before the initiation of this study. The Animal Care and Use Committee of the IMM Recherche is registered at the CNREEA under the Ethics Committee no. 37. The animal research centre (IMM Recherche) received an agreement (no. 75-14-01) by the Direction Départementale de la Protection des Populations. The studies were also performed in compliance with the Principles of Laboratory Animal Care, formulated by the National Society for Medical Research, and the Guide for the Care and Use of Laboratory Animals, by the Institute for Laboratory Animal Resources (published by the National Academies Press), as amended by the Animal Welfare Act of 1970 (P.L. 91-579) and the 1976 amendments to the Animal Welfare were followed. The surgical procedures were performed as described in ref. [39]. This model for bone-defect reconstruction differs from the healing process of larger load-bearing defects, which often necessitate a further specific procedure, such as fixation plate[61]. A total of eight hole defects (diameter 8 mm, depth 13 mm) per animal (two animals for a total of 16 for ewe study no. 1 and six animals for a total of 48 for ewe study no. 2) were created with a drill into the distal and proximal metaphysis of the humerus and femur. After washing the bone cavity with saline solution, the holes were randomly left empty to heal or fill with the different materials (ewe study no. 1: Col and Col-CHA, n = 5 each; ewe study no. 2: ColCG-CHA n = 12, VO, MG and AB, n = 5 each). Then, the wound was covered with the adjacent tissues and the skin was stapled. Two months after surgery, the sheep were euthanized and both femoral and humeral bones were collected, freed from all overlying tissues and the drill holes were identified. Bone samples were cut perpendicular to the original drill hole.

Faxitron X-ray radiographic imaging

Bone samples were imaged with a cabinet X-ray system to screen, track and evaluate structural, bone density and bone distribution changes using a Faxitron system. Transversal slices of bones taken from the implant sites were submitted to X-ray analysis using Faxitron SR v1.5.

Scanner imaging

Bone samples were submitted to computed tomography scan analysis to investigate 3D reconstruction of the implants sites using a 256 Slice GE Revolution CT Scanner and observed with RadiAnt DICOM Viewer 2022.1.1.

Radiological image analysis

ImageJ software (v1.52a) was used to blindly analyse radiographic images from the rat experiment and Faxitron X-ray and scanner images from the first ewe experiment. First, each image was converted to greyscale and a grey-level threshold was set to discriminate radiopaque and non-radiopaque areas. Then, an area corresponding to the original defect size was set. Pixels with grey level above the threshold, corresponding to radiopaque bone, were counted and divided by the total amount of pixels in the selected area to obtain a bone-filling percentage. For scanner, as full depth acquisition was performed, several relevant images were analysed and the result was expressed as a mean to represent the bone filling of the entire depth of the defect.

Bone histological preparation (ewe)

Samples were fixed in 4% paraformaldehyde solution, dehydrated with increasing ethanol baths and immersed for a week in a solution of butyl methacrylate, methyl benzoate, polyethylene glycol and benzoyl peroxide resin. Polymerization was triggered by the addition of N,N-dimethyl-toluidine and the samples were placed at −20 °C for 48 h. Serial thin sections (4–8 μm) were cut using tungsten carbide knives. Thereafter, the resin was removed using 2-ethoxyethyl acetate and the sections were rehydrated. The sections were then stained for the identification of tissue components in light microscopy analysis, scanned and observed using NDP.view2.8.24.

Histochemical staining

GT (haematoxylin, fuchsine, light green)

The sections were immersed in haematoxylin Weigert solution. After washing, the sections were stained with Ponceau fuchsine. Non-specific staining areas were washed with a 1% phosphomolybdic acid solution. Subsequently, the sections were stained with a light-green solution. After a washing step, the sections were dehydrated with ethanol (increasing gradient) and xylene solutions and then mounted with a resin mounting liquid. Using GT, mineralized bone is stained in green, osteoid in red/orange, nuclei in blue and cytoplasm in light red.

von Kossa/Van Gieson staining

Sections were preincubated with 1% silver nitrate solution under ultraviolet light. Then, the non-specific staining patches were washed with 5% sodium thiosulfate solution. The sections were counterstained with Van Gieson picrofuscine. After a washing step, the sections were dehydrated with ethanol (increasing gradient) and xylene solutions and then mounted with resin mounting fluid. After von Kossa staining, calcium deposits are highlighted in brown-black, osteoid in red and other tissue in yellow.

Haematoxylin–eosin

Sections were immersed in haematoxylin solution. Then, they were rinsed and stained with a 1% eosin solution. After a washing step, the sections were dehydrated with ethanol (increasing gradient) and xylene solutions and then mounted with resin mounting fluid. After HE staining, nuclei are stained purple, cytoplasm pink and collagen pink-red.

Histomorphometry

On the basis of GT and von Kossa-stained histological sections, image analysis (ImageJ2 software v2.0.0) allowed measurement of (1) the amount of mineralized newly formed bone into the defect (mineralized bone surface/total surface) after exclusion of residual biomaterials and (2) the amount of osteoid tissue into the defect (osteoid surface/total surface).

Reporting summary

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

Data availability

Radiographies, scanner files, micro-computed tomography scans and histological raw imaging of all the samples included in this work are available on request to the corresponding author. All other data are available in the main text or in the Extended Data.

References

1. Pape, H. C., Evans, A. & Kobbe, P. Autologous bone graft: properties and techniques. J. Orthop. Trauma 24, S36–S40 (2010).

2. Giannoudis, P. V., Dinopoulos, H. & Tsiridis, E. Bone substitutes: an update. Injury 36, S20–S27 (2005).

3. Bauer, T. W. & Muschler, G. F. Bone graft materials. An overview of the basic science. Clin. Orthop. Relat. Res. 371, 10–27 (2000).

4. Discher, D. E., Mooney, D. J. & Zandstra, P. W. Growth factors, matrices, and forces combine and control stem cells. Science 324, 1673–1677 (2009).

5. Engler, A. J., Sen, S., Sweeney, H. L. & Discher, D. E. Matrix elasticity directs stem cell lineage specification. Cell 126, 677–689 (2006).

6. Discher, D. E., Janmey, P. & Wang, Y. Tissue cells feel and respond to the stiffness of their substrate. Science 310, 1139–1143 (2005).

7. Matsugaki, A. et al. Abnormal arrangement of a collagen/apatite extracellular matrix orthogonal to osteoblast alignment is constructed by a nanoscale periodic surface structure. Biomaterials 37, 134–143 (2015).

8. Park, Y. et al. Trabecular bone organoid model for studying the regulation of localized bone remodeling. Sci. Adv. 7, eabd6495 (2021).

9. Chen, S. et al. Biomaterials with structural hierarchy and controlled 3D nanotopography guide endogenous bone regeneration. Sci. Adv. 7, eabg3089 (2021).

10. Weiner, S. & Wagner, H. D. The material bone: structure-mechanical function relations. Annu. Rev. Mater. Sci. 28, 271–298 (1998).

11. Seeman, E. & Delmas, P. D. Bone quality — the material and structural basis of bone strength and fragility. N. Engl. J. Med. 354, 2250–2261 (2006).

12. Gupta, H. S. et al. Cooperative deformation of mineral and collagen in bone at the nanoscale. Proc. Natl Acad. Sci. 103, 17741–17746 (2006).

13. Walsh, W. R. et al. Critical size bone defect healing using collagen–calcium phosphate bone graft materials. PLoS One 12, e0168883 (2017).

14. Ghiasi, M. S., Chen, J., Vaziri, A., Rodriguez, E. K. & Nazarian, A. Bone fracture healing in mechanobiological modeling: a review of principles and methods. Bone Rep. 6, 87–100 (2017).

15. Reznikov, N., Bilton, M., Lari, L., Stevens, M. M. & Kröger, R. Fractal-like hierarchical organization of bone begins at the nanoscale. Science 360, eaa02189 (2018).

16. Giraud-Guille, M. M. Twisted plywood architecture of collagen fibrils in human compact bone osteons. Calcif. Tissue Int. 42, 167–180 (1988).

17. Wang, Y. et al. Controlled collagen assembly to build dense tissue-like materials for tissue engineering. Soft Matter 7, 9659–9664 (2011).

18. Lama, M. et al. Self‐assembled collagen microparticles by aerosol as a versatile platform for injectable anisotropic materials. Small 16, 1902224 (2020).

19. Giraud-Guille, M. M. Liquid crystallinity in condensed type I collagen solutions: a clue to the packing of collagen in extracellular matrices. J. Mol. Biol. 224, 861–873 (1992).

20. Wang, Y. et al. The predominant role of collagen in the nucleation, growth, structure and orientation of bone apatite. Nat. Mater. 11, 724–733 (2012).

21. Nunamaker, D. M. Experimental models of fracture repair. Clin. Orthop. Relat. Res. 355, S56–S65 (1998).

22. Moreno-Jiménez, I. et al. Human and mouse bones physiologically integrate in a humanized mouse model while maintaining species-specific ultrastructure. Sci. Adv. 6, eabb9265 (2020).

23. Kerschnitzki, M. et al. The organization of the osteocyte network mirrors the extracellular matrix orientation in bone. J. Struct. Biol. 173, 303–311 (2011).

24. Colombier, M. L. et al. A single low dose of RGTA, a new healing agent, hastens wound maturation and enhances bone deposition in rat craniotomy defects. Cells Tissues Organs 164, 131–140 (1999).

25. Blanquaert, F. et al. Heparan-like molecules induce the repair of skull defects. Bone 17, 499–506 (1995).

26. Bohner, M. & Miron, R. J. A proposed mechanism for material-induced heterotopic ossification. Mater. Today 22, 132–141 (2019).

27. van der Meijden, R. H. et al. A 3D cell‐free bone model shows collagen mineralization is driven and controlled by the matrix. Adv. Funct. Mater. 33, 2212339 (2023).

28. Manolagas, S. C. & Jilka, R. L. Bone marrow, cytokines, and bone remodeling — emerging insights into the pathophysiology of osteoporosis. N. Engl. J. Med. 332, 305–311 (1995).

29. Nassif, N. et al. Self-assembled collagen–apatite matrix with bone-like hierarchy. Chem. Mater. 22, 3307–3309 (2010).

30. Ramtani, S., Takahashi-Iniguez, Y., Helary, C., Geiger, D. & Guille, M. M. G. Mechanical behavior under unconfined compression loadings of dense fibrillar collagen matrices mimetic of living tissues. J. Mech. Med. Biol. 10, 35–55 (2010).

31. Marelli, B., Ghezzi, C. E., Barralet, J. E. & Nazhat, S. N. Collagen gel fibrillar density dictates the extent of mineralization in vitro. Soft Matter 7, 9898–9907 (2011).

32. Brighton, C. T. & Hunt, R. M. Histochemical localization of calcium in the fracture callus with potassium pyroantimonate. Possible role of chondrocyte mitochondrial calcium in callus calcification. J. Bone Joint Surg. 68, 703–715 (1986).

33. Tsigkou, O. et al. Engineered vascularised bone grafts. Proc. Natl Acad. Sci. 107, 3311–3316 (2010).

34. Robin, M. et al. Involvement of 3D osteoblast migration and bone apatite during in vitro early osteocytogenesis. Bone 88, 146–156 (2016).

35. Paris, M. et al. Scaffold curvature-mediated novel biomineralization process originates a continuous soft tissue-to-bone interface. Acta Biomater. 60, 64–80 (2017).

36. Wang, Y. et al. Water-mediated structuring of bone apatite. Nat. Mater. 12, 1144–1153 (2013).

37. Nassif, N. et al. In vivo inspired conditions to synthesize biomimetic hydroxyapatite. Chem. Mater. 22, 3653–3663 (2010).

38. Von Euw, S. et al. Bone mineral: new insights into its chemical composition. Sci. Rep. 9, 8456 (2019).

39. Nuss, K. M. R., Auer, J. A., Boos, A. & von Rechenberg, B. An animal model in sheep for biocompatibility testing of biomaterials in cancellous bones. BMC Musculoskelet. Disord. 7, 67 (2006).

40. Sparks, D. S. et al. A preclinical large-animal model for the assessment of critical-size load-bearing bone defect reconstruction. Nat. Protoc. 15, 877–924 (2020).

41. Glimcher, M. J. Bone: nature of the calcium phosphate crystals and cellular, structural, and physical chemical mechanisms in their formation. Rev. Mineral. Geochem. 64, 223–282 (2006).

42. Liu, S. et al. Bioinspired collagen-apatite nanocomposites for bone regeneration. J. Endod. 42, 1226–1232 (2016).

43. Liu, Y. et al. Hierarchically staggered nanostructure of mineralized collagen as a bone‐grafting scaffold. Adv. Mater. 28, 8740–8748 (2016).

44. Boskey, A. L. Matrix proteins and mineralization: an overview. Connect. Tissue Res. 35, 357–363 (1996).

45. Liu, T., Wu, G., Wismeijer, D., Gu, Z. & Liu, Y. Deproteinized bovine bone functionalized with the slow delivery of BMP-2 for the repair of critical-sized bone defects in sheep. Bone 56, 110–118 (2013).

46. Albrektsson, T. & Johansson, C. Osteoinduction, osteoconduction and osseointegration. Eur. Spine J. 10, S96–S101 (2001).

47. Li, X., Zou, Q., Chen, H. & Li, W. In vivo changes of nanoapatite crystals during bone reconstruction and the differences with native bone apatite. Sci. Adv. 5, eaay6484 (2019).

48. Sheen, J. R. & Garla, V. V. Fracture Healing Overview (StatPearls, 2022).

49. Park, H. et al. Acellular dense collagen-S53P4 bioactive glass hybrid gel scaffolds form more bone than stem cell delivered constructs. Mater. Sci. Eng. C 120, 111743 (2021).

50. Robin, M. et al. Acidic osteoid templates the plywood structure of bone tissue. Adv. Sci. 11, 2304454 (2024).

51. Chaudhuri, O., Cooper-White, J., Janmey, P. A., Mooney, D. J. & Shenoy, V. B. Effects of extracellular matrix viscoelasticity on cellular behaviour. Nature 584, 535–546 (2020).

52. Giraud-Guille, M. M. & Besseau, L. Banded patterns in liquid crystalline phases of type I collagen: relationship with crimp morphology in connective tissue architecture. Connect. Tissue Res. 37, 183–193 (1998).

53. Besseau, L. & Giraud-Guille, M. M. Stabilization of fluid cholesteric phases of collagen to ordered gelated matrices. J. Mol. Biol. 251, 197–202 (1995).

54. Giraud-Guille, M. M, Nassif, N. & Fernandes, F. M. in Materials Design Inspired by Nature: Function Through Inner Architecture (eds Fratzl, P., Dunlop, J. W. C. & Weinkamer, R.) 107–127 (RSC Publishing, 2013).

55. Salameh, C. et al. Origin of transparency in scattering biomimetic collagen materials. Proc. Natl Acad. Sci. 117, 11947–11953 (2020).

56. Giraud-Guille, M. M. Cholesteric twist of collagen in vivo and in vitro. Mol. Cryst. Liq. Cryst. 153, 15–30 (1987).

57. Miles, C. A., Burjanadze, T. V. & Bailey, A. J. The kinetics of the thermal denaturation of collagen in unrestrained rat tail tendon determined by differential scanning calorimetry. J. Mol. Biol. 245, 437–446 (1995).

58. Vigier, S. et al. Dense fibrillar collagen matrices sustain osteoblast phenotype in vitro and promote bone formation in rat calvaria defect. Tissue Eng. Part A 17, 889–898 (2011).

59. Mckee, M., Buss, D. J. & Reznikov, N. Mineral tessellation in bone and the stenciling principle for extracellular matrix mineralization. J. Struct. Biol. 214, 107823 (2022).

60. Shah, F. A., Ruscsák, K. & Palmquist, A. Transformation of bone mineral morphology: from discrete marquise-shaped motifs to a continuous interwoven mesh. Bone Rep. 13, 100283 (2020).

61. Reichert, J. C. et al. A tissue engineering solution for segmental defect regeneration in load-bearing long bones. Sci. Transl. Med. 4, 141ra93 (2012).

Acknowledgements

We thank the IMOSAR, Laboratoire de Biologie, Bioingénierie et Bioimagerie Ostéo-articulaires (B3OA), UMR CNRS 7052 INSERM U1271, Université de Paris for the micro-computed tomography imaging, C. Torrens and M. Selmane for technical help, L. Cailleau for the plywood illustration, A. P. Hammersley and the ESRF for the use of FIT2D and S. Von Euw and P. Zaslansky for comments after reading the manuscript. We also acknowledge the DRITT (UPMC), particularly E. Bastin, and I. Coquilleau, as well as SATT Lutech, for financial support. N.N. thanks the Fondation du Collège de France and Fondation EDF for their support, especially M. Cheron for her valuable insights and trust.

Author information

Author notes

1. Gabriel Castillo Dali

Present address: Instituto de Ciencia de Materiales de Sevilla (ICMS), Consejo Superior de Investigaciones Científicas, Seville, Spain

Authors and Affiliations

1. Laboratoire de Chimie de la Matière Condensée de Paris (LCMCP), Sorbonne Université, CNRS, Collège de France, Paris, France

Marc Robin, Elodie Mouloungui, Yan Wang, Marie-Madeleine Giraud-Guille & Nadine Nassif

2. URP2496, Laboratoire Pathologies, Imagerie et Biothérapies Orofaciales, UFR d’Odontologie, Université Paris Cité, Montrouge, France

Gabriel Castillo Dali, Jean-Louis Saffar & Marie-Laure Colombier

3. Laboratory for Vascular Translational Science, Cardiovascular Bioengineering, Université Sorbonne Paris Nord, Inserm U1148, Villetaneuse, France

Graciela Pavon-Djavid & Anne Meddahi-Pellé

4. Laboratoire de Physique, ENSL, CNRS, Lyon, France

Thibaut Divoux & Sébastien Manneville

5. Institut Universitaire de France (IUF), Paris, France

Sébastien Manneville

6. IMM Recherche, Paris, France

Luc Behr

7. SATT Lutech, Paris, France

Delphine Cardi

8. Département de Pathologie, Hôpital Bichat, Paris, France

Laurence Choudat

9. ABS Atlantic Bone Screen, Saint-Herblain, France

Fannie Baudimont

Contributions

Conceptualization: M.-M.G.-G., A.M.-P., M.-L.C., N.N. Methodology: M.R., J.-L.S., G.P.-D., L.B., L.C., A.M.-P., F.B., M.-L.C., N.N. Investigation: M.R., E.M., G.C.D., Y.W., G.P.-D., T.D., S.M., L.B., L.C., F.B., M.-L.C., N.N. Visualization: M.R., G.C.D., J.-L.S., G.P.-D., L.C., F.B., M.-L.C., N.N. Funding acquisition: D.C., M.-M.G.-G., A.M.-P., N.N. Supervision: N.N. Writing – original draft: M.R., F.B., M.-L.C., N.N. Writing – review and editing: M.R., E.M., T.D., S.M., L.B., M.-M.G.-G., F.B., M.-L.C., N.N.

Corresponding author

Correspondence to Nadine Nassif.

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Extended data figures and tables

Extended Data Fig. 1 Picture of bone powder.

Mineral extracted from porcine bone with NaClO aqueous solution.

Extended Data Fig. 2 Microstructure of collagen fibrils in bone extracted from rat calvaria and related rat calvaria critical-size defect surgical procedure.

a, TEM image of bone ultrathin section extracted from the rat calvaria (Fig. 2c, n = 1 sample) showing dense fibrillar layers of different orientations. Yellow dots and bars represent fibrils that are perpendicular and parallel to the observation plan, respectively. Picture of circular-shaped collagen matrix fitting the calvaria defect in rat (b), the matrix adhering to the surrounding bone after several minutes (c) and the skin sutured (d).

Extended Data Fig. 3 Bone filling for rat experiment.

Bone filling on the basis of microradiographies (a) and von Kossa-stained thin sections (b). The percentage of bone filling for each defect was blindly measured on individual images. Microradiograph data were analysed from the total number of animals included in each group, as indicated in the bars (n = 6, 10 and 9 for negative control, Col40 and Col100, respectively). von Kossa data were analysed from representative animals in each group, as indicated in the bars (n = 3, 5 and 6 for negative control, Col40 and Col100, respectively). Data were expressed as mean ± s.d. and compared using nonparametric Kruskal–Wallis with two-sided Mann–Whitney multiple comparison test. *Significant difference with P < 0.05. **A difference/trend with P < 0.10.

Extended Data Fig. 4 Characterization of the in situ mineralization in the collagen materials implanted in rat calvaria model.

SEM observations of a histological section from a defect implanted with Col100 (n = 1 sample) at low (a) and higher (b) magnifications. The interface in which in situ mineralization occurs (yellow dot, granular aspect) is clearly observed, the fibrillar aspect of the matrix (yellow star) disappearing in favour of mineral aggregates (refs. 59,60). c, This difference of texture is also seen at the TEM scale (n = 1 sample), in which some domains seem more mineralized with a higher contrast (yellow dot versus star), as the section is not stained. d, SEM coupled with energy-dispersive X-ray spectroscopy microanalysis (n = 1 sample). The elemental mapping of the area shows the presence of carbon (C in red), calcium (Ca in purple) and phosphorus (P in turquoise) and confirms that the Ca and P are more pronounced in the granular (yellow dot) part of the matrix than in the fibrillar part (yellow star).

Extended Data Fig. 5 Toluidine blue, ALP and TRAP stains for rat experiments.

a, Col100 histological thin sections stained with toluidine blue (n = 9 defects) show the formation of lamellar bone on the external surface of the matrix (yellow circle). b, ALP detection sections (n = 10 and 9 for Col40 and Col00, respectively). A thin layer of ALP-positive cells (osteoblasts) lines at the exocranial surface and numerous cells are observed in the mineralized formations for both Col40 (b1) and Col100 (b2). c, TRAP detection sections (n = 10 and 9 for Col40 and Col00, respectively). Few TRAP-positive cells (osteoclasts) are found in Col40 (c1; around 4 osteoclasts mm−1), whereas many osteoclasts (around 7 osteoclasts mm−1) are homogeneously distributed within the newly formed bone tissue in Col100 (c2).

Extended Data Fig. 6 Mechanical characterization of the Col40, Col100 and Col-CHA matrices.

Distributions of the local Young’s modulus E inferred from typically 200 independent indentation tests performed on the Col40 matrix (left, n = 265 independent microindentations), the Col100 matrix (centre, n = 230 independent microindentations) and the Col-CHA matrix (right, n = 183 independent microindentations). The minimum and maximum of the whiskers respectively correspond to cumulative probabilities of 0.02 and 0.98, whereas the bounds of the box indicate the first and third quartiles and the centre of the box shows the median. The probability density shown with a shaded area is that of the logarithm of the effective Young’s modulus. See also Supplementary Table 1.

Extended Data Fig. 7 Comparison between rat tail collagen and clinical-grade collagen.

a, SDS-PAGE of type I collagen extracted from rat tails tendons (1, n = 1 sample) and bovine dermis (commercialized by Symatese as clinical grade) (2, n = 1 sample), which confirms the higher purity of the commercial solution. The white double-headed arrow shows the band corresponding to the α chain of type I collagen (125.103 KDa). b, Differential scanning calorimetry of a collagen solution extracted from rat tail tendons (dashed line, 7 mg ml−1) and from bovine dermis (clinical grade) (solid line, 5 mg ml−1). The endothermic peak is seen at temperature around 40 °C and is typical of collagen denaturation into gelatine that occurs through the irreversible unfolding of the triple helix (ref. [57]). The purest collagen solution is the most stable, which may be because of a higher number of interactions between molecules. c, TEM observations of an ultrathin section of clinical-grade collagen fibrils showing the typical cross-striated pattern (n = 3 samples). d,d′, Polarized light microscopy of clinical-grade collagen solution (concentration approximately 80 mg ml−1) observed between crossed polars (at 0° (d) and at 45° (d′)) and showing the typical birefringence of cholesteric order (alternating bright and dark bands). e, Picture of a ColCG-CHA matrix. f, SEM micrograph of a ColCG-CHA matrix (n = 1 sample) showing a periodic stratification typical of the twisted plywood organization. g, TEM micrograph of a ColCG-CHA matrix (n = 1 sample) showing the co-alignment between apatite platelets and the main axis of collagen fibrils.

Extended Data Fig. 8 Pictures of implanted materials and macroscopic evaluation of the bone explants (ewe study no. 2).

a,b, Commercial ceramics biomaterials MG granules (a) and VO block (b). Granules are directly implanted into the defect, whereas ceramic blocks are first cut and then implanted to fit the defect. c, Bone fragments extracted from the iliac crest used as bone filler, that is, AB graft. d, ColCG-CHA biomimetic material, MG, VO and AB explants. The remaining white ceramic material is seen on the VO and MG ceramics-implanted explants.

Extended Data Fig. 9 Histopathological scoring for the four implanted materials (ewe study no. 2).

ColCG-CHA, MG, VO and AB. Blinded histopathological scoring data were analysed from the total number of animals included in each group, as indicated in the bars (n = 12 for ColCG-CHA and n = 5 MG, VO and AB, respectively, except for von Kossa, in which n = 5 defects for all groups, and for osteoid production score, in which n = 4 for VO and AB). Data were expressed as mean ± s.d. and compared using nonparametric Kruskal–Wallis with two-sided Mann–Whitney multiple comparison test. *Significant difference with P < 0.05.

Extended Data Fig. 10 Histological evaluation for no. 2 ewe experiment.

a–d, Histological thin sections stained with von Kossa for ewe bone defect implanted with ColCG-CHA (a), MG (b), VO (c) and AB (d) (n = 5 defects for all groups). The dashed yellow circle represents the approximate initial defect (8 mm diameter). e, Histological thin section stained with GT of MG (n = 5 defects) implantation site showing black precipitates residues of the ceramic biomaterial (stars), either free or phagocytized by macrophages. f, The von Kossa staining highlights the in situ mineralization of ColCG-CHA (highlighted within the dashed yellow line).

Supplementary information

Supplementary Tables

Supplementary Table 1 contains parameters and statistical analysis of indentation tests for Col40, Col100 and Col-CHA and Supplementary Table 2 contains histopathological evaluation data for ewe study no. 2.

Reporting Summary

Supplementary Video 1

Demineralized bone. EDTA/acid-demineralized porcine bone maintains its shape and exhibits self-standing and cohesive properties.

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Robin, M., Mouloungui, E., Castillo Dali, G. et al. Mineralized collagen plywood contributes to bone autograft performance. Nature 636, 100–107 (2024). https://doi.org/10.1038/s41586-024-08208-z

- Received: 07 February 2023

- Accepted: 11 October 2024

- Published: 20 November 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08208-z

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Coupling of unactivated alkyl electrophiles using frustrated ion pairs

Sven Roediger,

Emilien Le Saux,

Philip Boehm &

…

Bill Morandi

Nature volume 636, pages 108–114 (2024)

Abstract

Cross-electrophile coupling reactions have evolved into a major strategy for rapidly assembling important organic molecules[1]. Two readily accessible electrophiles are coupled to form new C–C bonds, providing a key advantage over traditional cross-coupling strategies that require the preformation of reactive organometallic species. Yet, the formation of C(sp^3)–C(sp^3) bonds that form the core of nearly all organic compounds remains highly challenging with current approaches, calling for the design of innovative new strategies. Here we report a distinct, transition-metal-free platform to form such bonds without the need for activating or stabilizing groups on the coupling partners. The reaction is enabled by an unusual single-electron transfer in a frustrated ion pair, and it can couple fragments containing functional groups that would be challenging in related transition-metal-catalysed processes. Moreover, we could further leverage this new mechanistic manifold in the design of other reactions, showing the broad potential of this type of reactivity. We anticipate that our results will provide a framework for further exploration of this reactivity pattern to tackle challenging problems in organic synthesis.

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Main

Carbon-based frameworks constitute the backbone of organic molecules that are used in applications ranging from pharmaceuticals to materials. Thus, the modular and efficient construction of C–C bonds is one of the ultimate goals in organic synthesis. Traditionally, cross-coupling reactions between an organometallic reagent and an electrophile have been used for this task, leading to the synthesis of numerous essential molecules2,3. However, one of the coupling partners usually needs to be pre-functionalized as an organometallic nucleophilic reagent, increasing the step count and requiring the handling of potentially sensitive intermediates. By contrast, recently discovered cross-electrophile coupling reactions (XECs) bypass the use of organometallic reagents by directly coupling two electrophilic coupling partners1,4,5. XECs are most often catalysed by transition metals and rely on an external stoichiometric reductant for catalytic turnover. Often, these reactions require at least one coupling partner with C(sp^2) hybridization at the reactive site to control chemoselectivity1,6. Forming C(sp^3)–C(sp^3) bonds by XECs is notably more difficult7,8,9,10,11, but highly desirable owing to their ubiquity in organic molecules. For example, the fraction of C(sp^3) atoms in molecules has been positively correlated with the clinical success of drug candidates, creating an even stronger demand for C(sp^3)–C(sp^3) bond-forming reactions12,13,14. A challenge in transition-metal-catalysed C(sp^3)–C(sp^3) XECs is the propensity to form homocoupling side products instead of the desired heterocoupling products[15] (Fig. 1a). This unwanted reactivity can be partially reduced by using a large excess of one of the coupling partners, leading to additional waste[16]. Combined with the negative economic and environmental impact of transition metals, the development of alternative, transition-metal-free strategies is highly desirable. This would not only avoid potential issues with metal contamination for subsequent applications but could also improve the scope of C(sp^3)–C(sp^3) XECs to include functional groups that are reactive under many metal-catalysed conditions or that could poison a metal catalyst. In this context, two seminal reports emerged recently that used either electrochemical or enzymatic platforms to enable this highly challenging reactivity17,18 (Fig. 1b). These approaches rely on the formation of radical or anionic intermediates from one of the substrates that can then be engaged in C–C bond formation with the second coupling partner. To avoid undesired side reactions of these highly reactive intermediates, stabilizing substituents (for example, amides or aromatics) are required, inherently restricting these approaches in their reaction scope and preventing the use of fully unactivated substrates. Therefore, a transition-metal-free XEC of two fully unactivated and unstabilized C(sp^3) coupling partners has so far remained elusive, clearly highlighting the limitations of current approaches. There is, thus, an urgent need for the design of strategically distinct mechanistic manifolds that can address these challenges and thereby unlock the immense potential of XECs for the assembly of C(sp^3)–C(sp^3) linkages ubiquitously found across the molecular sciences.

Fig. 1: Context of this work.

a, Limitations of transition-metal-catalysed C(sp^3)–C(sp^3) XECs. b, Current methods for transition-metal-free C(sp^3)–C(sp^3) XECs. c, Discovery of C(sp^3)–P bond activation in phosphonium salts. d, This work: transition-metal-free C(sp^3)–C(sp^3) coupling of unactivated substrates enabled by a frustrated ion pair.

Here, we report the discovery and investigation of an unprecedented coupling related to XEC in which two completely unactivated and non-organometallic C(sp^3) fragments are combined in a transition-metal-free protocol. Alkylphosphonium salts are reacted with alkyl halides in the presence of a sterically hindered base as the sole reagent to give rise to the coupled products (Fig. 1d). Mechanistic studies suggest that the reaction is enabled by an unusual single-electron transfer (SET) in a frustrated ion pair that is reminiscent of frustrated Lewis pair radical reactivity19,20,21,22,23,24,25,26.

Phosphonium salts are key reagents in organic synthesis. They are readily available and widely used, for example, in the Wittig reaction to construct alkenes[27]. Recently, their intriguing reactivity has also been harnessed in unconventional manners, including the selective functionalization of heteroarenes28,29. Moreover, the use of phosphonium salts as C(sp^2) electrophiles in transition-metal-catalysed cross-coupling reactions has been investigated30,31,32. Our group has a longstanding interest in diverting the reactivity of phosphonium salts towards unusual transformations, such as C–P metathesis and related transformations for late-stage phosphine functionalization33,34. During our recent studies[35], we discovered conditions under which the P–C(sp^3) bond of the phosphonium salt is activated in addition to the more reactive P–C(sp^2) bond if the phosphonium salt contained a tertiary alkyl group (Fig. 1c). This peculiar hydro-dephosphination sparked our curiosity as it occurred in the absence of any conventional reductant. We reasoned that this unexpected bond activation likely arose from a different mechanism than oxidative addition to the metal catalyst. Our hypothesis was confirmed by subsequent control experiments, which demonstrated that the reaction does not require a transition metal catalyst to yield alkane products and is mediated by a sterically hindered base. Although the activation of C–P bonds in phosphonium salts is well known to occur by basic hydrolysis, it is documented to favour the cleavage of aromatic substituents over alkyl substituents if the alkyl group is not activated[36]. Indeed, subjecting tert-alkyltriphenylphosphonium salts to known hydrolysis conditions[37] did not lead to the formation of the desired alkane products (Supplementary Information section 5.8), implying that the discovered hydro-dephosphination process occurred by a different reaction pathway with distinct selectivity. Although the nature of this bond activation was unclear at this stage, we aimed to leverage this new reactivity to design an innovative approach to challenging C(sp^3)–C(sp^3) bond formation. We hypothesized that an α-tertiary alkylphosphonium salt could be formed in situ by the alkylation of a phosphorus ylide, which can be generated through the deprotonation of a phosphonium salt (Fig. 1d). Subsequent base-mediated hydro-dephosphination would enable an overall coupling between these two C(sp^3) electrophiles that does not require the presence of a transition metal catalyst.

Initial experiments using phosphonium salts and alkyl iodides as the alkylating agent indeed confirmed our hypothesis, as the alkane product was obtained in moderate yields using sterically hindered bases. After careful optimization (Supplementary Tables 1–5), we developed a highly efficient transition-metal-free coupling between unactivated sec-alkylphosphonium salts and unactivated alkyl halides. This reaction is mediated by lithium hexamethyldisilazide (LiHMDS) as the only added reagent.

As the reaction does not contain an obvious reductant and the mode of C–P bond activation is unconventional, we became interested in understanding the origin of this unique reactivity. As a first finding, we noticed that triphenylphosphine (5), the expected byproduct from a traditional reductive C–P bond activation, was formed only in small amounts (9% nuclear magnetic resonance (NMR) yield), whereas dibenzophosphole 4 was the main byproduct of the reaction in 86% NMR yield. This suggested that the phosphonium moiety serves as a reductant because of the oxidation of two phenyl C–H bonds to a C–C bond in dibenzophosphole 4 (Fig. 2a). Reaction monitoring by NMR spectroscopy also showed that the phosphonium salt 1 is rapidly deprotonated to its ylide in the presence of LiHMDS (Supplementary Fig. 8). When the phosphonium salt 1, LiHMDS, and alkyl iodide 2 are reacted at room temperature, the tert-alkylphosphonium salt 3 is formed as the main species after a few minutes. The identity of 3 was confirmed by an independent synthesis (Supplementary Information, section 6.3). The C–C bond formation is, thus, fast and proceeds through the alkylation of a phosphorus ylide, as set out in our reaction design. These results also highlight that the unique reactivity of the phosphonium electrophile is crucial for the coupling reactivity as it enables the C–C bond formation through a deprotonation-mediated philicity switch to the nucleophilic ylide and also acts as an internal reductant for the unusual oxidation to the dibenzophosphole 4 (refs. 24,25). Subjecting the tert-alkylphosphonium salt 3 to different bases demonstrated that product formation is strongly dependent on the size of the base (Fig. 2b). Although LiHMDS provided a high yield of product, smaller amide bases gave much lower yields that decreased with the base size. This demonstrates that the size of the base is not only important for limiting undesired SN2 reactions of the base with the alkyl halide substrate but also has an important role in the cleavage of the C–P bond. To further study this key step, which is at the core of this new reaction, we used adamantyltriphenylphosphonium bromide (9), a phosphonium salt that already contains a tertiary alkyl group, as a model substrate. When 9 was reacted with LiHMDS, adamantane (8) was formed in a similar yield as the coupling products under the model reaction conditions (Supplementary Information, section 6.5). However, adding 2,2,6,6-tetramethylpiperidinyloxyl (TEMPO) as a radical trap to this reaction shut down the formation of adamantane (8). Instead, the TEMPO adduct 7 was isolated in 62% yield, suggesting the formation of tert-alkyl radicals in the reaction (Fig. 2c). Interestingly, dibenzophosphole 4 was formed in a similar yield compared with the standard reaction conditions, and its formation was, therefore, not influenced by the presence of TEMPO. As the formation of dibenzophosphole 4 from the PPh_3 moiety in the starting phosphonium salts requires the loss of two aryl hydrogen atoms, we next subjected phosphonium salt 9-d_15, containing perdeuterated phenyl groups, to the reaction conditions (Fig. 2d). NMR analysis indicated that the adamantane isolated from this reaction contained 72% deuterium at one of the tertiary positions (8-d).

Fig. 2: Mechanistic studies.

a, Investigation of the C–C bond formation and identification of the main byproduct. RT, room temperature. b, Effect of the base size. c, Reaction inhibition by TEMPO. d, Deuteration experiment. e, Proposed mechanism of the C–P cleavage. f, Computed properties of the frustrated ion pair B. Energies are in kcal mol–1. a^31P{1H} NMR yield using triphenyl phosphate as the internal standard. bGas chromatography yield using n-dodecane as the internal standard. cDetermined by 1H NMR spectroscopy of the isolated material. G, Gibbs free energy; %Vbur, percent buried volume.

With these results in hand, we propose a plausible reaction mechanism (Fig. 2e). The starting phosphonium salt is deprotonated by LiHMDS, and the resulting ylide is rapidly alkylated by the alkyl halide to yield the α-tertiary phosphonium salt A. Next, salt metathesis between A and LiHMDS takes place first, leading to a sterically encumbered ion pair B. Experiments using different alkali HMDS salts showed that the presence of lithium ions is important for the reaction, possibly because the formation of LiX (X = Br or I) serves as a driving force for the salt metathesis step. Similarly, the addition of 12-crown-4 to the reaction, which selectively binds lithium ions[38], shut down the reactivity (Supplementary Table 12). The key step enabling the coupling reaction is a SET from the HMDS anion to the phosphonium cation in the frustrated ion pair B. Such SET processes in phosphonium halide salts are well known to occur photochemically by ultraviolet irradiation or also by visible-light activation in the case of phosphonium salts containing strongly electron-withdrawing substituents39,40,41,42,43. By contrast, the thermal process has only been postulated for the reaction of tetraphenylphosphonium chloride with lithium amides based on preliminary data24,25. At the time this study was carried out, no applications or detailed studies of such a process existed. We however would like to point out a complementary report[44], using a SET step in a frustrated ion pair for C–H activation, that appeared shortly after an initial version of our work was deposited as a preprint.

In the lowest-energy isomer of the salt B, the HMDS anion forms a C–H⋯anion interaction with a phenyl group of the phosphonium ion and an additional π–anion interaction with a second phenyl group (Fig. 2f). Isomers with the direct interaction of the phosphonium P atom and the anion in a phosphorane-type geometry (isomer B′), which are typical for smaller nucleophiles (Supplementary Information section 7.3), are much higher in energy[28]. We performed distortion–interaction analyses on the different phosphonium HMDS ion pair geometries and a related smaller ion pair containing the N(i-Pr)2 anion (structure H) to understand the origin of the unusual isomer preference (Fig. 2f). The analysis showed that the phosphorane-like isomer B′ of the HMDS ion pair is destabilized because the interaction energy is substantially lower than in the case of the smaller N(i-Pr)2 ion pair H (−75.7 kcal mol–1 and −107.3 kcal mol–1, respectively). This is caused by the large steric bulk of the two ions, which leads to a longer P–N distance in the HMDS ion pair B′ (2.10 Å) than in the N(i-Pr)2 ion pair H (1.87 Å). Because of this steric frustration, the formation of the atypical phosphonium amide ion pair B containing interactions between the HMDS anion and the phosphonium phenyl groups becomes favoured. Analysis of the frontier orbitals of B shows that the highest occupied molecular orbital is located on the HMDS anion whereas the lowest unoccupied molecular orbital is delocalized on the phosphonium cation, in line with the direction of the proposed electron transfer. Calculations of the redox potentials of the ions in B show that the proposed SET is accessible under the experimental conditions (Supplementary Table 14). The SET step would be preceded by the formation of a charge-transfer complex45,46. Indeed, strong charge-transfer bands at around 380 nm and 515 nm, potentially arising from such an interaction, were visible by ultraviolet–visible spectroscopy when the phosphonium salt 9 was mixed with LiHMDS (Supplementary Fig. 9). A calculated ultraviolet–visible spectrum of B shows a charge-transfer band at 348 nm, closely matching one of the experimentally observed bands. By contrast, conformer B′ is predicted to have little charge-transfer character (272 nm). This suggests that B is more likely to be the active ion pair leading to SET, and its formation is enabled by the steric frustration in the phosphonium HMDS ion pair.

The SET step leads to the formation of a phosphoranyl radical C and an HMDS radical. The α-scission of C forms the tertiary alkyl radical D and triphenylphosphine (5). The highly reactive HMDS radical can abstract a hydrogen atom from triphenylphosphine (5), resulting in the aryl radical E, which can undergo cyclization to the cyclic radical F. The weak alkyl C–H bond in F can be abstracted by the previously generated alkyl radical D to aromatize the phosphorus-containing ring system to the experimentally observed byproduct 4 and leading to the alkane product G. The proposed pathway is supported by additional density functional theory calculations (Supplementary Information, section 7).

Having gained an increased understanding of the reaction mechanism, we explored the scope of the reaction (Fig. 3). Reacting phosphonium salts with an almost equimolar amount of alkyl halides in the presence of LiHMDS provided a large variety of C(sp^3)–C(sp^3)-coupled products without forming homocoupling side products that are problematic under transition-metal-catalysed conditions. Although the reaction works best using alkyl iodides as coupling partners, alkyl bromides provide the product in a similar yield (79% versus 81%, respectively, for 12a). Alkyl tosylates could also be engaged and gave the product in a fair yield (6, 62%), providing an opportunity to use alcohol starting materials in the reaction after facile derivatization to the corresponding tosylate. Alkyl chlorides participated in the reaction as well, although in lower yields (6, 32%). Ethers, silyl-protected alcohols, and acetal moieties were well tolerated (12b–d). Substrates containing amides or tert-butyloxycarbonyl (Boc)-protected amines provided the desired coupled products in moderate yields (12e and 12f). Groups that would be reactive under many transition-metal-catalysed conditions, namely, aryl halides (12g–i) and aryl boronate esters (12j), gave rise to the desired products. The reaction also tolerates the presence of heterocycles such as phenoxazines (12k), indoles (12l), and pyrimidines (12m). When the homobenzylic iodide 11n was engaged as a coupling partner, the spirocyclopropane 12n was isolated in 44% yield instead of the coupling product. We hypothesize that the corresponding styrene of 11n is formed by a fast E2 elimination to which the in situ formed phosphorus ylide can be added. This would give rise to a benzylic carbanion that can undergo 1,3-elimination with the phosphonium moiety to form the cyclopropane ring[47]. In contrast to 11n, the benzylic bromide 11o formed the desired coupling product 12o in a moderate yield, demonstrating that the reaction can be used for the coupling of electron-rich activated alkyl halides in addition to the unactivated ones. The reaction can furthermore be carried out in the presence of acidic moieties such as alcohols (12p and 12s), carboxylic acids (12q), and unprotected azaindoles (12r) when additional LiHMDS is used in the reaction to account for the deprotonation of the acidic groups. Moreover, substrates containing the hormone oestradiol (11s) and the drug molecules nevirapine and metaxalone (11t and 11u) were successfully engaged in the coupling reaction. Phosphonium salts containing differently sized cyclic alkyl groups afforded the alkane products (12v–z). Smaller-ring phosphonium salts provided higher yields (12v and 12w) than larger-ring ones (12x–z). We noticed increased levels of alkene side products in these reactions that might arise from a Hofmann-type elimination of the alkylated phosphonium salt intermediate. A range of acyclic phosphonium salts containing functional groups such as aryl halides or heterocycles provided the corresponding products (12aa–12ag). Notably, the phosphonium salt 11ad was also engaged with iodomethane to afford the methylated product 12ae in the same yield as product 12ad, which contained a longer-chain alkyl group.

Fig. 3: Scope of the coupling reaction.

Yields refer to isolated compounds if not stated otherwise. aGas chromatography yield using n-dodecane as the internal standard. bIsolated after hydroboration–oxidation treatment of the crude reaction mixture. c4.0 equiv. LiHMDS. dThe product contains a small impurity. The yield has been adjusted accordingly. ePhosphonium iodide as the starting material. fReaction conducted at 120 °C.

To further evaluate the functional group tolerance of the reaction, we carried out a compatibility screen with different additives[48] (Supplementary Fig. 7). Several functional groups that are known to be reactive or inhibitory under many transition-metal-catalysed reactions, such as terminal alkenes, tertiary amines, pyridines, and epoxides, did not interfere with the reaction. Limitations of the reaction include nitro compounds and most carbonyl groups such as ketones, which can undergo Wittig-type side reactions. The addition of an enol ether, however, did not noticeably inhibit the reaction, indicating that protected ketones may be viable functionalities in substrates.

Using the insights gained from the mechanistic study, we were able to leverage this new reactivity beyond the coupling reaction in a series of preliminary results (Fig. 4). On the basis of the knowledge that the C–C bond is formed by the alkylation of a phosphorus ylide, we also devised a formal [1+n] annulation of n-alkylphosphonium salt 13 and 1,ω-dibromoalkane 14 (Fig. 4a). In this reaction, NaH-mediated phosphorus ylide alkylation occurs twice[49], leading to the same tert-alkylphosphonium salt intermediate 3 as in the standard reaction that can then undergo C–P fragmentation in the presence of LiHMDS. Besides this two-electron process leveraging the phosphorus ylide reactivity, we also used the one-electron reactivity of the alkyl radical intermediate for further functionalization. First, we were able to intercept it with styrene 16 to construct a quaternary centre in 15, forming two new C(sp^3)–C(sp^3) bonds in a single step (Fig. 4b). Similarly, the addition of heterocycle 17 to the standard conditions led to the formation of an additional C(sp^3)–C(sp^2) bond in product 18. Leveraging the hydrogen-atom transfer step of the tert-alkyl radical with the phosphonium phenyl groups, the coupling of a phosphonium salt containing perdeuterated phenyl groups (1-d_15) led to the formation of the monodeuterated product 12m-d. The deuteration is regioselective for the position at which the phosphorus was bound in the starting material over multiple other sites that would be prone to deuteration reactions using conventional approaches[50]. In the described reactions, the radical arising from the key frustrated ion pair ultimately engages in product formation. We envisioned that this radical could also be used to mediate the formation of other open-shell intermediates through atom transfer processes, thereby extending the applicability of this chemistry. When the alkyl iodide 19 and alkene 20 were reacted in the presence of tetraphenylphosphonium bromide and LiHMDS, C–C bond formation occurred and product 21 was formed. We believe that this reaction could proceed through the formation of a frustrated ion pair between the PPh_4 cation and the HMDS anion, which leads to a phenyl radical that can react in a halogen-atom transfer step with alkyl iodide 19.

Fig. 4: Mechanistically informed extensions of the reaction scope.

a, Annulation reaction through a double ylide alkylation. b, Difunctionalization and deuteration reactions by interception of the alkyl radical intermediate. c, Frustrated ion pairs as halogen-atom transfer reagents. aGas chromatography yield using n-dodecane as the internal standard. bIsolated after hydroboration–oxidation treatment of the crude reaction mixture. Boc, tert-butyloxycarbonyl.

Data availability

All experimental, spectroscopic and computational data are available in the Supplementary Information. Crystallographic data are available from the Cambridge Crystallographic Data Centre (CCDC) with the following codes: CCDC 2254109 (12f) and CCDC 2254108 (12m).

References

1. Everson, D. A. & Weix, D. J. Cross-electrophile coupling: principles of reactivity and selectivity. J. Org. Chem. 79, 4793–4798 (2014).

2. Campeau, L.-C. & Hazari, N. Cross-coupling and related reactions: connecting past success to the development of new reactions for the future. Organometallics 38, 3–35 (2019).

3. Magano, J. & Dunetz, J. R. Large-scale applications of transition metal-catalyzed couplings for the synthesis of pharmaceuticals. Chem. Rev. 111, 2177–2250 (2011).

4. Goldfogel, M. J., Huang, L. & Weix, D. J. Cross-electrophile coupling: principles and new reactions. In Nickel Catalysis in Organic Synthesis (ed. Ogoshi, S.) 183–222 (Wiley-VCH, 2020).

5. Kranthikumar, R. Recent advances in C(sp^3)–C(sp^3) cross-coupling chemistry: a dominant performance of nickel catalysts. Organometallics 41, 667–679 (2022).

6. Diccianni, J. B., Katigbak, J., Hu, C. & Diao, T. Mechanistic characterization of (Xantphos)Ni(i)-mediated alkyl bromide activation: oxidative addition, electron transfer, or halogen-atom abstraction. J. Am. Chem. Soc. 141, 1788–1796 (2019).

7. Smith, R. T. et al. Metallaphotoredox-catalyzed cross-electrophile Csp^3–Csp^3 coupling of aliphatic bromides. J. Am. Chem. Soc. 140, 17433–17438 (2018).

8. Sanford, A. B. et al. Nickel-catalyzed alkyl–alkyl cross-electrophile coupling reaction of 1,3-dimesylates for the synthesis of alkylcyclopropanes. J. Am. Chem. Soc. 142, 5017–5023 (2020).

9. Kang, K. & Weix, D. J. Nickel-catalyzed C(sp^3)–C(sp^3) cross-electrophile coupling of in situ generated NHP esters with unactivated alkyl bromides. Org. Lett. 24, 2853–2857 (2022).

10. Xu, Y., Zhang, M. & Oestreich, M. Photochemical, nickel-catalyzed C(sp^3)–C(sp^3) reductive cross-coupling of α-silylated alkyl electrophiles and allylic sulfones. ACS Catal. 12, 10546–10550 (2022).

11. Xu, H., Zhao, C., Qian, Q., Deng, W. & Gong, H. Nickel-catalyzed cross-coupling of unactivated alkyl halides using bis(pinacolato)diboron as reductant. Chem. Sci. 4, 4022–4029 (2013).

12. Lovering, F., Bikker, J. & Humblet, C. Escape from flatland: increasing saturation as an approach to improving clinical success. J. Med. Chem. 52, 6752–6756 (2009).

13. Hioki, Y. et al. Overcoming the limitations of Kolbe coupling with waveform-controlled electrosynthesis. Science 380, 81–87 (2023).

14. Seong, C. M., Ansel, A. Q. & Roberts, C. C. Redox inversion: a radical analogue of umpolung reactivity for base- and metal-free catalytic C(sp^3)–C(sp^3) coupling. J. Org. Chem. 88, 3935–3940 (2023).

15. Yu, X., Yang, T., Wang, S., Xu, H. & Gong, H. Nickel-catalyzed reductive cross-coupling of unactivated alkyl halides. Org. Lett. 13, 2138–2141 (2011).

16. Gu, J., Wang, X., Xue, W. & Gong, H. Nickel-catalyzed reductive coupling of alkyl halides with other electrophiles: concept and mechanistic considerations. Org. Chem. Front. 2, 1411–1421 (2015).

17. Fu, H. et al. An asymmetric sp^3–sp^3 cross-electrophile coupling using ‘ene’-reductases. Nature 610, 302–307 (2022).

18. Zhang, W. et al. Electrochemically driven cross-electrophile coupling of alkyl halides. Nature 604, 292–297 (2022).

19. Liu, L. L., Cao, L. L., Shao, Y., Ménard, G. & Stephan, D. W. A radical mechanism for frustrated Lewis pair reactivity. Chem 3, 259–267 (2017).

20. Liu, L. L. & Stephan, D. W. Radicals derived from Lewis acid/base pairs. Chem. Soc. Rev. 48, 3454–3463 (2019).

21. Holtrop, F. et al. Photoinduced and thermal single‐electron transfer to generate radicals from frustrated Lewis pairs. Chem. Eur. J. 26, 9005–9011 (2020).

22. Holtrop, F. et al. Single‐electron transfer in frustrated Lewis pair chemistry. Angew. Chem. Int. Ed. 59, 22210–22216 (2020).

23. van der Zee, L. J. C., Pahar, S., Richards, E., Melen, R. L. & Slootweg, J. C. Insights into single-electron-transfer processes in frustrated Lewis pair chemistry and related donor–acceptor systems in main group chemistry. Chem. Rev. 123, 9653–9675 (2023).

24. Rebrova, O. A., Nesmeyanov, N. A., Mikul’shina, V. V. & Reutov, O. A. Reaction of tetraphenylphosphonium chloride with lithium amides. Russ. Chem. Bull. 24, 326–329 (1975).

25. Nesmeyanov, N. A. et al. Interaction of lithium alkylamides or lithium dialkylamides with tetraphenylphosphonium chloride reaction course as a function of reaction conditions and dialkylamide structures. J. Organomet. Chem. 110, 49–57 (1976).

26. Mukherji, A., Addanki, R. B., Halder, S. & Kancharla, P. K. Sterically strained Brønsted pair catalysis by bulky pyridinium salts: direct stereoselective synthesis of 2-deoxy and 2,6-dideoxy-β-thioglycosides from glycals. J. Org. Chem. 86, 17226–17243 (2021).

27. Wittig, G. & Schöllkopf, U. Über Triphenyl-phosphin-methylene als olefinbildende Reagenzien (I. Mitteil.). Chem. Ber. 87, 1318–1330 (1954).

28. Anderson, R. G., Jett, B. M. & McNally, A. A unified approach to couple aromatic heteronucleophiles to azines and pharmaceuticals. Angew. Chem. Int. Ed. 57, 12514–12518 (2018).

29. Zhang, X. et al. Phosphorus-mediated sp^2–sp^3 couplings for C–H fluoroalkylation of azines. Nature 594, 217–222 (2021).

30. Hwang, L. K., Na, Y., Lee, J., Do, Y. & Chang, S. Tetraarylphosphonium halides as arylating reagents in Pd-catalyzed Heck and cross-coupling reactions. Angew. Chem. Int. Ed. 44, 6166–6169 (2005).

31. Zhang, X. & McNally, A. Phosphonium salts as pseudohalides: regioselective nickel-catalyzed cross-coupling of complex pyridines and diazines. Angew. Chem. Int. Ed. 56, 9833–9836 (2017).

32. Lee, Y. H. & Morandi, B. Transition metal-mediated metathesis between P–C and M–C bonds: beyond a side reaction. Coord. Chem. Rev. 386, 96–118 (2019).

33. Lian, Z., Bhawal, B. N., Yu, P. & Morandi, B. Palladium-catalyzed carbon-sulfur or carbon-phosphorus bond metathesis by reversible arylation. Science 356, 1059–1063 (2017).

34. Lee, Y. H. & Morandi, B. Metathesis-active ligands enable a catalytic functional group metathesis between aroyl chlorides and aryl iodides. Nat. Chem. 10, 1016–1022 (2018).

35. Roediger, S., Leutenegger, S. U. & Morandi, B. Nickel-catalysed diversification of phosphine ligands by formal substitution at phosphorus. Chem. Sci. 13, 7914–7919 (2022).

36. Fenton, G. W. & Ingold, C. K. CCCVII.—Influence of poles and polar linkings on the course pursued by elimination reactions. Part V. The mechanism of thermal decomposition of quaternary phosphonium hydroxides. J. Chem. Soc. https://doi.org/10.1039/JR9290002342 (1929).

37. Tran, U. P. N., Hock, K. J., Gordon, C. P., Koenigs, R. M. & Nguyen, T. V. Efficient phosphine-mediated formal C(sp^3)–C(sp^3) coupling reactions of alkyl halides in batch and flow. Chem. Commun. 53, 4950–4953 (2017).

38. Liotta, C. L. & Berkner, J. 12-Crown-4. In Encyclopedia of Reagents for Organic Synthesis (John Wiley & Sons, 2001).

39. Griffin, C. E. & Kaufman, M. L. Photolysis of benzyltriphenylphosphonium and tetraphenylphosphonium chlorides. Tetrahedron Lett. 6, 773–775 (1965).

40. Lin, Q.-Y., Xu, X.-H., Zhang, K. & Qing, F.-L. Visible-light-induced hydrodifluoromethylation of alkenes with a bromodifluoromethylphosphonium bromide. Angew. Chem. Int. Ed. 55, 1479–1483 (2016).

41. Liu, Q. et al. Visible‐light‐induced selective photolysis of phosphonium iodide salts for monofluoromethylations. Angew. Chem. Int. Ed. 60, 25477–25484 (2021).

42. Liu, Q. et al. Pnictogen bonding enabled photosynthesis of chiral selenium-containing pyridines from pyridylphosphonium salts. Fundam. Res. https://doi.org/10.1016/j.fmre.2023.03.013 (2023).

43. Maddigan‐Wyatt, J. & Hooper, J. F. Phosphorus compounds as precursors and catalysts for radical C−C bond‐forming reactions. Adv. Synth. Catal. 363, 924–936 (2021).

44. Lu, Z. et al. Regioselective aliphatic C–H functionalization using frustrated radical pairs. Nature 619, 514–520 (2023).

45. Hilinski, E. F., Masnovi, J. M., Amatore, C., Kochi, J. K. & Rentzepis, P. M. Charge-transfer excitation of electron donor-acceptor complexes. Direct observation of ion pairs by time-resolved (picosecond) spectroscopy. J. Am. Chem. Soc. 105, 6167–6168 (1983).

46. Siu, J. C. et al. Electrochemical azidooxygenation of alkenes mediated by a TEMPO–N_3 charge-transfer complex. J. Am. Chem. Soc. 140, 12511–12520 (2018).

47. Johnson, J. D., Teeples, C. R., Akkawi, N. R. & Wilkerson-Hill, S. M. Efficient synthesis of orphaned cyclopropanes using sulfones as carbene equivalents. J. Am. Chem. Soc. 144, 14471–14476 (2022).

48. Collins, K. D. & Glorius, F. A robustness screen for the rapid assessment of chemical reactions. Nat. Chem. 5, 597–601 (2013).

49. Bestmann, H. J. & Kranz, E. Reaktionen mit Phosphinalkylenen, XXII. Eine neue Ringschlußreaktion. Chem. Ber. 102, 1802–1815 (1969).

50. Prakash, G., Paul, N., Oliver, G. A., Werz, D. B. & Maiti, D. C–H deuteration of organic compounds and potential drug candidates. Chem. Soc. Rev. 51, 3123–3163 (2022).

Acknowledgements

We thank the NMR service, X-ray service (SMoCC), and the Molecular and Biomolecular Analysis Service (MoBiAS) of ETH Zürich for technical assistance and our group for discussions and critical proofreading of the manuscript. The European Research Council (Shuttle Cat, Project ID 757608), ETH Zürich, the FCI (scholarships to S.R. and P.B.) and the SNSF (scholarship to E.L.S.) are acknowledged for funding of this work.

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1. Laboratorium für Organische Chemie, ETH Zürich, Zürich, Switzerland

Sven Roediger, Emilien Le Saux, Philip Boehm & Bill Morandi

Contributions

S.R. discovered the reactivity and designed the project. S.R., E.L.S., and P.B. performed the experimental studies. S.R. performed the density functional theory studies. B.M. supervised the research. S.R. wrote the original draft of the manuscript, which was edited and approved by all authors.

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Correspondence to Bill Morandi.

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Supplementary Information Supplementary Information sections 1–8, Figs. 1–17, Tables 1–16 and references.

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Roediger, S., Le Saux, E., Boehm, P. et al. Coupling of unactivated alkyl electrophiles using frustrated ion pairs. Nature 636, 108–114 (2024). https://doi.org/10.1038/s41586-024-08195-1

- Received: 25 August 2023

- Accepted: 10 October 2024

- Published: 20 November 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08195-1

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Article Open access Published: 04 December 2024

New particle formation from isoprene under upper-tropospheric conditions

Jiali Shen,

Douglas M. Russell,

Jenna DeVivo,

Felix Kunkler,

Rima Baalbaki,

Bernhard Mentler,

Wiebke Scholz,

Wenjuan Yu,

Lucía Caudillo-Plath,

Eva Sommer,

Emelda Ahongshangbam,

Dina Alfaouri,

João Almeida,

Antonio Amorim,

Lisa J. Beck,

Hannah Beckmann,

Moritz Berntheusel,

Nirvan Bhattacharyya,

Manjula R. Canagaratna,

Anouck Chassaing,

Romulo Cruz-Simbron,

Lubna Dada,

Jonathan Duplissy,

Hamish Gordon,

Manuel Granzin,

Lena Große Schute,

Martin Heinritzi,

Siddharth Iyer,

Hannah Klebach,

Timm Krüger,

Andreas Kürten,

Markus Lampimäki,

Lu Liu,

Brandon Lopez,

Monica Martinez,

Aleksandra Morawiec,

Antti Onnela,

Maija Peltola,

Pedro Rato,

Mago Reza,

Sarah Richter,

Birte Rörup,

Milin Kaniyodical Sebastian,

Mario Simon,

Mihnea Surdu,

Kalju Tamme,

Roseline C. Thakur,

António Tomé,

Yandong Tong,

Jens Top,

Nsikanabasi Silas Umo,

Gabriela Unfer,

Lejish Vettikkat,

Jakob Weissbacher,

Christos Xenofontos,

Boxing Yang,

Marcel Zauner-Wieczorek,

Jiangyi Zhang,

Zhensen Zheng,

Urs Baltensperger,

Theodoros Christoudias,

Richard C. Flagan,

Imad El Haddad,

Heikki Junninen,

Ottmar Möhler,

Ilona Riipinen,

Urs Rohner,

Siegfried Schobesberger,

Rainer Volkamer,

Paul M. Winkler,

Armin Hansel,

Katrianne Lehtipalo,

Neil M. Donahue,

Jos Lelieveld,

Hartwig Harder,

Markku Kulmala,

Doug R. Worsnop,

Jasper Kirkby,

Joachim Curtius &

…

Xu-Cheng He

Nature volume 636, pages 115–123 (2024)

Abstract

Aircraft observations have revealed ubiquitous new particle formation in the tropical upper troposphere over the Amazon1,2 and the Atlantic and Pacific oceans3,4. Although the vapours involved remain unknown, recent satellite observations have revealed surprisingly high night-time isoprene mixing ratios of up to 1 part per billion by volume (ppbv) in the tropical upper troposphere[5]. Here, in experiments performed with the CERN CLOUD (Cosmics Leaving Outdoor Droplets) chamber, we report new particle formation initiated by the reaction of hydroxyl radicals with isoprene at upper-tropospheric temperatures of −30 °C and −50 °C. We find that isoprene-oxygenated organic molecules (IP-OOM) nucleate at concentrations found in the upper troposphere, without requiring any more vapours. Moreover, the nucleation rates are enhanced 100-fold by extremely low concentrations of sulfuric acid or iodine oxoacids above 10^5 cm−3, reaching rates around 30 cm−3 s−1 at acid concentrations of 10^6 cm−3. Our measurements show that nucleation involves sequential addition of IP-OOM, together with zero or one acid molecule in the embryonic molecular clusters. IP-OOM also drive rapid particle growth at 3–60 nm h−1. We find that rapid nucleation and growth rates persist in the presence of NOx at upper-tropospheric concentrations from lightning. Our laboratory measurements show that isoprene emitted by rainforests may drive rapid new particle formation in extensive regions of the tropical upper troposphere1,2, resulting in tens of thousands of particles per cubic centimetre.

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Main

Aerosol particles are important for climate because they scatter and absorb incoming solar radiation and seed cloud droplets by acting as cloud condensation nuclei (CCN). More CCN make clouds more reflective and may increase their extent and lifetime. Around half of CCN globally, and almost all in the upper troposphere[6], arise from new particle formation, which involves the spontaneous condensation of low-volatility vapours in the atmosphere to form liquid or solid particles (particle nucleation). The initial stable molecular clusters form at diameters slightly above 1 nm. To become CCN, the new particles should not be scavenged by pre-existing aerosol but grow by further vapour condensation to a size of around 50 nm and larger (particle growth). Although new particle formation has been extensively studied at ground-based sites[7], little is known about the precursor vapours responsible for new particles in the remote upper troposphere and in marine regions. In particular, high concentrations of freshly formed particles are observed in the upper free troposphere over the Amazon1,2 and the tropical Atlantic and Pacific oceans3,4. Chemical-transport models indicate that new particle formation persists across the tropical upper troposphere over a latitude band covering about 40% of Earth’s surface[3] and provides a global supply of CCN for low-altitude clouds in the sub-tropics and tropics3,8. However, the source of these particles has remained a puzzle for the past 20 years.

Early studies proposed that convective clouds could transport vapours from the boundary layer and form new particles in cold cloud outflows at high altitudes4,9. In the absence of an established mechanism, it was suggested that the oxidation products of isoprene (C_5H_8) could contribute to the high mass concentrations of freshly formed particles observed in the upper troposphere over the Amazon[10]. Recent modelling studies have speculated that pure biogenic new particle formation from monoterpenes is the source of these particles11,12, but insufficient monoterpene concentrations have been found to account for the high particle-number concentrations[13].

On the other hand, recent satellite observations have revealed high concentrations of isoprene in the upper troposphere over tropical South America, Central Africa and Southeast Asia, reaching up to around 1 ppbv during night-time[5] (1 ppbv is equivalent to around 0.6 × 10^10 molecules cm−3 at 10 km altitude and 2.5 × 10^10 molecules cm−3 at 0 km). The isoprene concentrations fall during daytime owing to high hydroxyl radical (OH) concentrations, which result in an isoprene lifetime of around 1–2 h. Furthermore, observations at 5,240 m altitude in the Bolivian Andes have found isoprene oxidation products in both gas and particle phases in air masses originating from the Amazon free troposphere[14]. Isoprene is emitted by vegetation, especially deciduous and broad-leaved evergreen trees, and is the most abundant hydrocarbon emitted into the atmosphere, after methane[15]. Median isoprene mixing ratios in the Amazon rainforest vary between 0.5 and 2.0 ppbv at night and 2.0–6.0 ppbv during the day[16]. Modelling studies show that isoprene is efficiently transported from the tropical boundary layer to the upper troposphere in deep convective clouds[17].

Isoprene influences the oxidation capacity of the atmosphere18,19 and contributes to the formation of secondary organic aerosol20,21,22,23,24 particle mass, which—in turn—affects the climate25,26. The contribution of isoprene to secondary organic aerosol particle mass is at present believed to be dominated by the reactive uptake of isoprene dihydroxy epoxide (IEPOX)21,27 and other IP-OOM[28]. Lower contributions are thought to arise from the condensation of low-volatility compounds formed during oxidation of the first-generation product, isoprene hydroxy hydroperoxide (ISOPOOH)29,30,31. IEPOX and ISOPOOH are isomers of C_5H_10O_3 and cannot be separated by normal chemical-ionization mass spectrometry. The ability of isoprene to form new particles is considered negligible[24] and, moreover, isoprene inhibits new particle formation from monoterpenes under boundary-layer conditions32,33,34. However, it has so far remained unknown whether IP-OOM[35] can form new particles in the upper troposphere, where it is extremely cold and scavenging losses are small, and—if so—what are the associated nucleation and growth rates.

CLOUD experiment

Here we report experiments performed in the CERN CLOUD chamber[36] to study new particle formation from the reaction of OH with isoprene at upper-tropospheric concentrations and temperatures of −30 °C and −50 °C. The experiments were performed during the CLOUD15 and CLOUD16 campaigns, September–November 2022 and 2023, respectively. Before injection into the chamber, the isoprene vapour was passed through a cryo-trap at −53 °C to eliminate low-volatility contaminants (as confirmed by mass-spectrometer measurements). Further details of the CLOUD facility and its analysing instruments are provided in Methods.

The range of experimental parameters is summarized in Extended Data Table 1, together with the ambient upper-tropospheric conditions over the Amazon measured by Curtius et al.[13] during research flight (RF) 19. To maximize the IP-OOM detection efficiency, we combined the measurements of three mass spectrometers that use ammonium, nitrate and bromide chemical-ionization (see Methods for further details), whereas the research flight uses nitrate ionization alone. In general, there is good overlap of the CLOUD experiments with the ambient conditions for this single flight (see Methods for further discussion). We note that, although the CLOUD chamber operates at atmospheric pressure, this does not affect the simulation of particle-nucleation dynamics at upper-tropospheric conditions. We performed experiments with nitrogen oxide (NO) concentrations varied between zero and around 7 × 10^9 cm−3, characteristic of the outflow from electrified deep convective clouds. Trace amounts of sulfuric acid (H_2SO_4), methanesulfonic acid (CH_3SO_3H) and iodine oxoacids[37] (HIOx, x = 2, 3) also exist in the upper troposphere from the oxidation of vapours such as sulfur dioxide (SO_2), dimethyl sulfide (DMS; CH_3SCH_3) and iodine (I_2), respectively. For some experiments, therefore, we introduced upper-tropospheric concentrations of sulfuric acid or iodine oxoacids to explore their interactions with IP-OOM.

IP-OOM

Hydroxyl radicals preferentially attack one of the terminal carbon atoms of isoprene, forming isoprene peroxy radicals (ISOPOO, C_5H_8(OH)(OO)). In the absence of NO, ISOPOO reacts with hydroperoxy radicals (HO_2) to form ISOPOOH (C_5H_8(OH)(OOH))35,38. In the presence of NO, ISOPOO also reacts to form isoprene hydroxy nitrate (IHN, C_5H_8(OH)(ONO_2)) and further products[35]. Further reaction of IHN with OH forms isoprene dihydroxy dinitrate (C_5H_8(OH)2(ONO_2)2) and other second-generation products containing one or two nitrogen atoms. Further reactions of ISOPOOH with OH include the formation of its isomer, IEPOX[21], as well as second-generation products containing zero or one nitrogen atom.

Oxidation by hydroxyl radicals of ISOPOOH29,30, IEPOX21,39,40 and IHN are the main channels that feed the second-generation IP-OOM, IP0-2N. Because the volatility of IP-OOM largely depends on their oxygen number[41], we define IP0-2N as CiHjOkNl with the requirements that i, j ≥ 4, l = 0–2 and with a minimum oxygen number that takes the nitrogen content into account. As a nitrate group (-O-NO_2) is considered to decrease the volatility of an organic compound by about the same factor as a hydroxyl group (-OH)[41], we require (k − 2l) ≥ 4. We define IP0N, IP1N and IP2N as IP-OOM containing 0, 1 or 2 nitrogen atoms, respectively. In our study, therefore, IP0-2N (= IP0N + IP1N + IP2N) excludes ISOPOOH and IEPOX but may include some first-generation IP-OOM from ISOPOO[35]. The IP0-2N include IP-OOM monomers with up to five carbon atoms (C_5) and dimers with up to ten carbons (C_10), formed by reactions between two isoprene peroxy radicals (RO_2), which produce covalently bound molecules.

As with other condensable vapours, both ISOPOOH and IEPOX deposit irreversibly on the CLOUD chamber walls at −50 °C (ref. [42]) and also at −30 °C. The latter is confirmed by our measurements of a wall-loss lifetime for C_5H_10O_3 at −30 °C, which indicates irreversible loss on impact (Extended Data Fig. 1a). Although we cannot distinguish ISOPOOH from IEPOX, chemical model calculations35,43 indicate that more than 80% of our C_5H_10O_3 signal is ISOPOOH at 5.5 × 10^6 cm−3 OH (Methods and Extended Data Fig. 1b). Assuming this ISOPOOH fraction, we measure the total molar yield of IP0N from the reaction of hydroxyl radicals with C_5H_10O_3 in the absence of nitrogen oxides (NOx) to be 46% at −30 °C and 55% at −50 °C, with a systematic uncertainty of a factor of two (Extended Data Fig. 2). In the presence of NOx, the IP0N yield falls to around 38% owing to RO_2 termination by NO or by further reactions that produce IP1N and IP2N (Extended Data Fig. 2). At upper-tropospheric NO concentrations of up to 7 × 10^9 cm−3, we measure the nitrate IP-OOM fraction to be IP1-2N/IP0-2N = 23–88%, in which IP1-2N = IP1N + IP2N.

Example experiments

A typical run sequence in the absence of NOx is shown in Fig. 1. The experiment started at stage 1 by setting the internal mixing fans from 100% (high wall-loss rate) to 12% (standard operation) and switching on the ultraviolet (UV) light, which photolysed ozone to produce 2 × 10^6 cm−3 OH and 6 × 10^7 cm−3 HO_2 radicals. Stages 1, 2 and 3 were, respectively, under different ionization conditions: neutral (all ions swept from the chamber by high-voltage electrodes); natural ionization from galactic cosmic rays (high voltage switched off); and pion beam (upper-tropospheric ion concentrations). The nucleation rate, J1.7, is the measured flux of particles passing a 1.7-nm threshold size. Because J1.7 varied little between these stages (Fig. 1b), it indicates relatively little sensitivity to ion concentrations. Before stage 5, the nucleation rate was 0.48–0.73 cm−3 s−1, whereas IP0N were around 1.6 × 10^7 cm−3 and sulfuric acid and iodine oxoacids were both below 10^5 cm−3. These nucleation rates far exceed those expected for HIOx and H_2SO_4, conservatively assuming 1 × 10^8 cm−3 contaminant ammonia (NH_3) (refs. 37,44,45). As no other condensable vapours were present, this experiment shows that IP-OOM form new particles under upper-tropospheric conditions at −50 °C.

Fig. 1: Example new particle formation experiment from IP-OOM at −50 °C, without NOx.

a–c, Evolution of vapour concentrations (a), particle-nucleation rates at 1.7 nm, J1.7 (b), and naturally charged negative particle number size distribution (dN/dlogDp) and growth rates measured between 3.2 and 8.0 nm, GR3.2-8 (nm h−1), for total (naturally charged + neutral) particles (c). The black lines in c depict the linear fits of 50% appearance time of particles between 3.2 and 8.0 nm. The vertical dashed lines and labels indicate the start of a new stage, at which the experimental conditions were adjusted. Trace sulfuric and iodic acid contaminants are present at the start of the run at concentrations of 1–5 × 10^4 cm−3. Sulfur dioxide is injected and progressively increased during stages 5–8, which produces steps in H_2SO_4. The dotted black curve in b shows the expected H_2SO_4–NH_3 nucleation rate, conservatively assuming that NH_3 is present at the 4 pptv limit of detection. Stages 1 and 3 are, respectively, under neutral (ion-free) and beam (ion-enhanced) conditions, whereas all of the other stages are under galactic cosmic ray (GCR; natural ion concentrations) conditions. The experimental conditions are: isoprene = 0.20–0.41 ppbv (6.2–13.0 × 10^9 cm−3), O_3 = 84–96 ppbv (2.6–3.0 × 10^12 cm−3), I_2 = 3.7–23.0 × 10^5 cm−3, SO_2 = 0–4 × 10^9 cm−3, OH = 1–3 × 10^6 cm−3, HO_2 = 4.7–6.0 × 10^7 cm−3, HO_2/OH ratio = 18–37, RH = 62%, NO less than limit of detection (7 pptv) and temperature = −49 °C.

During stages 5–8, SO_2 was introduced into the chamber in steps, while keeping all other experimental conditions fixed (Fig. 1a). This increased the H_2SO_4 concentration from 1.5 × 10^4 cm−3 to between 2.8 × 10^5 and 1.3 × 10^6 cm−3. Despite these extremely low acid concentrations, which are equivalent to between 0.05 and 0.2 parts per trillion by volume (pptv) at 10 km altitude, the nucleation rate increased from 6.2 to 37 cm−3 s−1 between stages 5 and 8, respectively (Fig. 1b). Once again, these nucleation rates far exceed those expected for H_2SO_4–NH_3. The particle growth rates between 3.2 and 8.0 nm increased from 2.6 to 4.1 nm h−1 between stages 5 and 8 (Fig. 1c). Because the expected particle growth rate for 1.3 × 10^6 cm−3 acid is around 0.15 nm h−1 (ref. [46]), these high growth rates must largely result from condensation of IP0N.

In other experiments, we introduced small concentrations of HIOx in the presence of negligible H_2SO_4. Extended Data Fig. 3 shows an example at −50 °C. Here IP0N were held constant at 1.6 × 10^7 cm−3 s−1 and contaminant H_2SO_4 was less than 5.0 × 10^4 cm−3. The HIOx concentration was increased in steps from 1.2 × 10^5 to 8.6 × 10^5 cm−3 and the nucleation rate increased from 3.3 to 18 cm−3 s−1. These nucleation rates closely match those measured for the IP0N–H_2SO_4 system, suggesting that either inorganic acid plays a similar role in nucleation by stabilizing the embryonic molecular clusters.

In Extended Data Fig. 4, we show two further examples at −50 °C, without (left panels) and with (right panels) NOx. Both experiments started with acid concentrations below the limit of detection (2 × 10^4 cm−3). Extended Data Fig. 4c shows a threefold enhancement in J1.7 with the transition from neutral to galactic cosmic ray conditions, reaching 0.7 cm−3 s−1 at 2.3 × 10^7 cm−3 non-nitrate IP-OOM (IP0N) but showing very little further increase at higher (beam) ionization rates. This indicates that ions can enhance the stability of IP-OOM molecular clusters at especially low concentrations of acids or IP-OOM. When NOx is present and under galactic cosmic ray conditions (Extended Data Fig. 4d), the initial nucleation rate is 1.7 cm−3 s−1 at 3.5 × 10^7 cm−3 IP0N plus 1.3 × 10^8 cm−3 IP1-2N. This comparison suggests that nitrate IP-OOM are less effective for nucleation than non-nitrate IP-OOM. In Extended Data Fig. 4b, sulfuric acid was increased in steps from 2 × 10^4 to 3 × 10^6 cm−3 and J1.7 increased from 1.7 to 43 cm−3 s−1, demonstrating that the synergy between IP-OOM and sulfuric acid also occurs in the presence of NOx.

New particle formation rates

We show in Extended Data Fig. 5 our measurements of J1.7 versus IP0N, IP1-2N and IP0-2N at −50 °C, in the absence of acids (near or below the limit of detection). Extended Data Fig. 5a shows that the nucleation rates increase from around 0.006 cm−3 s−1 to 48 cm−3 s−1 by increasing IP0N from 6 × 10^6 to 9 × 10^7 cm−3. By contrast, there is a relatively weak dependence of the nucleation rate on IP1-2N (Extended Data Fig. 5b,c, which cover a larger IP1-2N range from 2.4 × 10^6 to 2.5 × 10^8 cm−3). Nevertheless, careful inspection of the data in Extended Data Fig. 5b at the lowest IP0N concentrations does show some dependence of J1.7 on nitrate IP-OOM. The combined measurements suggest that IP0N are more effective for nucleation than IP1-2N, or—equivalently—that nitrate IP-OOM have higher volatilities than non-nitrate IP-OOM. However, at colder temperatures in the upper troposphere, the volatilities of all IP-OOM will decrease and nitrate IP-OOM will contribute more strongly to nucleation.

In Fig. 2, we present our measurements of J1.7 versus vapour concentrations at −30 °C and −50 °C. Here we consider only the IP0N component of IP0-2N, following the discussion above. In our experiments, the range of acid concentrations (mostly below 3 × 10^6 cm−3) is representative of the upper free troposphere47,48 and the isoprene concentrations (0.14–4.2 × 10^10 cm−3) correspond to those measured in the upper troposphere over tropical rainforests[5], as do the oxidant concentrations[18] (OH = 0.11–6.9 × 10^7 cm−3, HO_2 = 0.6–17 × 10^8 cm−3 and HO_2/OH ratio = 11–118). These vapour concentrations are sufficient to drive rapid nucleation rates between 0.006 and 48 cm−3 s−1, which greatly exceed HIOx and H_2SO_4–NH_3 nucleation under these conditions37,44,45. Such fast nucleation rates can readily account for the high particle-number concentrations of up to 20,000 cm−3 observed between 8 and 14 km over the Amazon[2].

Fig. 2: Particle-nucleation rates from IP-OOM at −30 °C and −50 °C, with variable NOx.

a–d, Nucleation rates at 1.7 nm, J1.7, versus IP0N without NOx present (a), IP0N with NOx (b), HIOx + H_2SO_4 with and without NOx (c) and the product (HIOx + H_2SO_4) × IP0N with and without NOx (d). Measurements without NOx are indicated by diamonds (without acids) and circles (with acids). Measurements with NOx are indicated by triangles (without acids) and squares (with acids). Hollow symbols indicate −30 °C and solid symbols indicate −50 °C. In c, nucleation rates measured at contaminant acid concentrations are assigned the acid limit of detection (around 2 × 10^4 cm−3). The solid lines in c show the nucleation rates expected for H_2SO_4 with 4 pptv NH_3 at −30 °C (red) and −50 °C (blue), both at 60% RH (ref. [44]). The dashed and solid lines in d represent fits to the equation 10a×log10(x)+b, in which, for the dashed line, a = 1.241 and b = −15.065, and for the solid line, a = 1.505 and b = −19.948. Panels a–c show that both IP0N and total acid (HIOx + H_2SO_4) contribute to the nucleation rate. Panel d indicates that it is the product of IP0N and total acid (that is, the dimer formation rate) that best describes the nucleation rate, J1.7, as the data points cluster into two groups primarily characterized by temperature alone. IP1-2N also contribute to particle nucleation but they are less effective than IP0N (Extended Data Fig. 5). The experimental conditions are: isoprene = 0.04–1.50 ppbv (0.1–4.2 × 10^10 cm−3), O_3 = 1–590 ppbv (3.7 × 10^10 to 1.8 × 10^13 cm−3), I_2 = 0–7.5 × 10^7 cm−3, SO_2 = 0–4.6 × 10^9 cm−3, OH = 0.11–6.90 × 10^7 cm−3, HO_2 = 0.6–17.0 × 10^8 cm−3, HO_2/OH ratio = 11–118, NO = 0–0.22 ppbv, NO_2 = 0–0.77 ppbv, RH = 29–70% and temperature = −30 °C and −50 °C. The error bars represent the standard deviation of the measurement at steady state. All measurements are made under galactic cosmic ray conditions (natural ionization amounts).

The nucleation rates depend on the concentrations of both IP0N (Fig. 2a,b) and total acid (HIOx + H_2SO_4; Fig. 2c). Figure  2d suggests that the nucleation rates are linearly dependent on the product of IP0N and total acid concentration, indicating that the critical step is dimer formation of an inorganic acid with a single IP0N. We note that the J1.7 measurements in Fig. 2d at −50 °C with acids and NOx (filled square symbols) are systematically around a factor of 2–3 lower than the measurements without NOx (filled circles). This is an artefact resulting from a systematic uncertainty in those measurements (filled square symbols), as it is absent when J2.5 is measured for the same events but with a different particle counter (Extended Data Fig. 6). We measure a 20-fold increase of J1.7 between −30 °C and −50 °C (Fig. 2d) owing to decreasing IP-OOM volatilities.

Molecular content of nucleating clusters

We have confirmed the nucleation mechanism inferred from Fig. 2d by direct molecular measurements with an Atmospheric Pressure Interface Time-of-Flight (APi-TOF) mass spectrometer during nucleation events without acids at −50 °C (Fig. 3a,c). Negatively charged (ion-induced) nucleation involves sequential accretion of IP-OOM monomers (C_5 band) or dimers (C_10 band) to an initial CiHjOx− or NO_3− ion. When NOx is present, a sharp reduction can be seen in the concentration of the C_15 clusters compared with C_10 (Fig. 3c). By contrast, the no-NOx data (Fig. 3a) show a smooth sequential growth of the clusters out to the detection limit. This indicates that the C_15 clusters with NOx are relatively unstable and have a high evaporation rate back to C_10 clusters, hence the high concentration of the latter. We infer that the IP1-2N have higher volatility than IP0N and are less effective for nucleation. Nevertheless, C_10 and C_15 molecular clusters are seen in Fig. 3c with more than 3N (including NO_3− core ion), which must include contributions from IP1-2N as well as IP0N. This confirms that IP1-2N do indeed participate in ion-induced nucleation, but they are less effective than IP0N, which leads to a rate-limiting step from C_10 to C_15. This is consistent with the previous conclusions drawn from measurements of nucleation rate.

Fig. 3: Molecular composition of charged and neutral clusters during IP-OOM nucleation without acids at −50 °C.

a–d, Mass defect (difference from integer mass) versus m/z during IP-OOM nucleation events without acid and without added NOx (a,b) or with NOx (c,d). The data points are coloured by the number of carbon (a,b) or nitrogen (c,d) atoms. The symbol area in a and c is proportional to the normalized signal intensity by total signal, whereas that in b and d is proportional to IP-OOM concentrations. The charger ions (Br−, NO_3− and NH_4+) are removed from the molecular formula in b and d. The data show that IP-OOM—which are around C_5 for the monomer or C_10 for the dimer—nucleate at −50 °C. The experimental conditions in a–d are: isoprene = 0.19, 0.07, 0.28 and 0.07 ppbv, O_3 = 196.0, 129.0, 1.9 and 1.8 ppbv, OH = 0.3, 0.3, 1.6 and 4.6 × 10^7 cm−3, HO_2 = 1.8, 2.2, 3.4 and 6.8 × 10^8 cm−3, NO = 0, 0, 0.07 and 0.18 ppbv, NO_2 = 0, 0, 0.10 and 0.54 ppbv, RH = 31, 57, 66 and 29% and temperature = −49, −49, −48 and −48 °C, respectively. The concentrations of IP0N were unmeasured (instrument was not available), 3.4 × 10^7, unmeasured and 2.5 × 10^7 cm−3 and the concentrations of IP1-2N were unmeasured, 0, unmeasured and 1.5 × 10^8 cm−3, respectively. The bromide chemical-ionization mass spectrometer was converted to measure charged clusters for the experiments shown in a and c, under ground-level and upper-tropospheric ion concentrations, respectively.

The composition of neutral clusters and molecules during IP-OOM nucleation without and with NOx is shown in Fig. 3b,d, respectively. Here no signal is detected above the C_10 band owing to the relatively low charging efficiency of the chemical-ionization mass spectrometers, compared with unit efficiency for ions and ion-induced (charged) clusters in the APi-TOF mass spectrometer. Nevertheless, the neutral data reveal a clear shift towards (higher-mass) IP1N and IP2N compounds after the addition of NOx. Furthermore, comparison with the corresponding charged clusters (Fig. 3a,c) shows that nucleation favours the more highly oxygenated compounds with lower volatility.

In Extended Data Fig. 7, we show the molecular composition of charged clusters in the presence of trace amounts of sulfuric acid (Extended Data Fig. 7a,c,e) and iodic acid (Extended Data Fig. 7b,d) during IP-OOM nucleation at −50 °C. The data are without NOx (Extended Data Fig. 7a–e) and with NOx (Extended Data Fig. 7f). These measurements confirm that the same nucleation mechanism occurs after addition of acids as seen previously without acids, that is, sequential addition of IP-OOM monomers (C_5) or dimers (C_10) to a core acid ion. Here the negatively charged core ions comprise the pure monomer, dimer or trimer of sulfuric acid (Extended Data Fig. 7a,c) with HSO_4−, or iodic acid with IO_3− (Extended Data Fig. 7b,d), whereas the positively charged core ions (Extended Data Fig. 7e,f) comprise an IP-OOM+. The core acid anion serves only to stabilize the initial molecular cluster with a single IP-OOM; there is no evidence for accretion of further acid molecules as the clusters grow, as expected at these very low acid concentrations (3–7 × 10^5 cm−3).

Particle growth rates

Our measurements indicate that IP0N (and, less effectively, IP1-2N) are rapidly nucleating together with H_2SO_4 and HIOx at −30 °C and −50 °C (Fig. 2). We therefore expect that IP-OOM will also drive rapid particle growth at larger sizes as the Kelvin (curvature) barrier falls and progressively higher-volatility compounds are able to condense onto the particles—as previously seen for new particle formation from α-pinene oxidation products[49]. We show in Fig. 4 our measurements of particle growth rates between 3.2 and 8.0 nm versus IP0N (Fig. 4a) and IP0-2N (Fig. 4b) for experiments with and without NOx at −30 °C and −50 °C. Figure 4a shows that IP1-2N are strongly contributing to early growth of particles; the measured growth rates cannot be explained by IP0N alone. This is confirmed in Fig. 4b, in which all data (with or without NOx and at either temperature) are consistent with having the same dependency of growth rate on IP0-2N.

Fig. 4: Particle growth rates and particle composition.

a,b, Particle growth rates from 3.2 to 8.0 nm, GR3.2-8, versus IP0N (a) and IP0-2N (b). The solid lines show the predicted GR3.2-8 at the kinetic limit assuming monomer condensation and without considering any dipole enhancement. The prediction assumes that IP-OOM have a general formula of C_5H_12O_6 and a density of 1.34 g cm−3 (ref. [50]). The dashed lines are linear fits to the experimental data of the form GR3.2-8 = 2.04 × 10−7 × IP0N (a) and GR3.2-8 = 1.23 × 10−7 × IP0-2N (b). c,d, Mass-defect plots showing the molecular composition of particles measured with the FIGAERO at −30 °C (Br-FIGAERO) and −50 °C (I-FIGAERO), with particle geometric mean sizes ranging from 6 to 20 nm. The colour legends indicate the number of atoms of carbon (c) and nitrogen (d). The symbol area is proportional to the normalized signal by the sum of measured signals. The charger ions are removed from the molecular formula. The annotations show the molecular formula of the particle-phase compounds. Our measurements show that both IP0N and IP1-2N drive rapid early particle growth and that C_5H_12O5-6 are the main condensing vapours in IP0N. The experimental conditions in a and b are: isoprene = 0.05–1.50 ppbv (0.15–4.40 × 10^10 cm−3), O_3 = 1–592 ppbv (3.6 × 10^10 to 1.8 × 10^13 cm−3), I_2 = 0–1.1 × 10^8 cm−3, SO_2 = 0–4.9 × 10^9 cm−3, OH = 0.09–7.40 × 10^7 cm−3, HO_2 = 0.6–19.0 × 10^8 cm−3, NO = 0–0.26 ppbv, NO_2 = 0–0.80 ppbv, RH = 29–72% and temperature = −30 °C and −50 °C. The experimental conditions for in c are: isoprene = 1.1 ppbv, O_3 = 184 ppbv, I_2 = 2.9 × 10^7 cm−3, SO_2 = 2.7 × 10^7 cm−3, OH = 2.3 × 10^6 cm−3, HO_2 = 0.8 × 10^8 cm−3, NO = 0 ppbv, NO_2 = 0 ppbv, RH = 72%, temperature = −30 °C and DMS = 0 ppbv and those for in d are: isoprene = 0.2 ppbv, O_3 = 1 ppbv, I_2 = 0, SO_2 = 1.2 × 10^8 cm−3, OH = 5.2 × 10^7 cm−3, HO_2 = 1.2 × 10^9 cm−3, NO = 0.11 ppbv, NO_2 = 0.65 ppbv, RH = 38%, temperature = −48 °C and DMS = 0.16 ppbv. The vertical error bars represent the statistical uncertainty in the appearance-time growth-rate measurements derived from the 95% confidence interval on the growth-rate fit. The horizontal error bars represent the standard deviation of measured IP0N or IP0-2N during the growth period.

We find that isoprene at upper-tropospheric concentrations will drive particle growth rates between 3 and 56 nm h−1. These rapid growth rates imply that new IP-OOM particles can reach several tens of nanometres in size within a few hours, which will help to prevent them from evaporation when descending to lower altitudes and warmer temperatures. Within measurement uncertainties, particle growth rates at all temperatures below −30 °C are linearly dependent on the IP0-2N concentration, and they reach the kinetic limit. As expected from their low concentrations, the growth rates show no correlation with H_2SO_4 and HIOx (not shown).

We have verified these observations by direct particle-phase measurements made with the Filter Inlet for Gases and AEROsols (FIGAERO; Fig. 4c,d). The main IP0-2N compounds in the particles are two second-generation oxidation products (C_5H_12O_5 and C_5H_12O_6) formed from reactions of ISOPOOH with OH, which together constitute 30% of the total signal. The same two compounds have been previously identified in the particle phase by isoprene experiments at room temperature[30]. The remaining particle-phase compounds are largely C_10 IP-OOM dimers, in agreement with their measured low volatilities. The non-nitrate C_10 dimers are suppressed by the presence of NOx, as seen by comparing Fig. 4c with Fig. 4d. We note that the nitrate IP-OOM in Fig. 4d are probably under-represented because we measured that they have lower evaporation temperatures and so may escape thermal-desorption measurement in the FIGAERO.

Upper-tropospheric particle formation

In summary, we find that IP-OOM rapidly form new particles at upper-tropospheric concentrations and temperatures below −30 °C. Moreover, the nucleation rates are up to 100 times faster in the presence of extremely low concentrations of sulfuric acid or iodine oxoacids, reaching rates around 30 cm−3 s−1 at −50 °C and acid concentrations of 10^6 cm−3. In the presence of NOx, a large fraction of IP-OOM—around 23–88%—are found to contain either one or two nitrogen atoms. We find that nitrate IP-OOM contribute relatively weakly to particle nucleation compared with non-nitrate IP-OOM at the same concentration. However, cooler temperatures will favour nucleation from nitrate isoprene products[13]. Both non-nitrate and nitrate IP-OOM are equally effective at driving rapid particle growth at several tens of nm h−1, at all temperatures below −30 °C. We present in Extended Data Fig. 8 four nucleation rate measurements that schematically encapsulate the effect of acids and NOx on IP-OOM nucleation.

Our measured nucleation and growth rates provide the mechanistic missing link connecting the presence of abundant isoprene in the tropical upper troposphere[5] with the high particle-number concentrations found at high altitudes over the Amazon[2]. Our findings reveal a new mechanism (Fig. 5) that switches on rapid particle nucleation in extensive regions of the upper troposphere. Isoprene emitted by tropical rainforests is efficiently transported by deep convective clouds and released at cloud outflows in the upper free troposphere10,17. During night-time, high isoprene concentrations build up in the upper troposphere[5] as a result of relatively slow oxidation by ozone and nitrate radicals[35]. During daytime, the isoprene is rapidly oxidized by hydroxyl radicals and mixed with NOx from lightning to produce IP-OOM. These mix with trace ambient acids to drive rapid particle nucleation and growth at cold temperatures below around −30 °C. Peak IP-OOM concentrations—and therefore the fastest new particle formation rates—will occur shortly after sunrise when the isoprene accumulated during the night is oxidized during the first 1–2 h of daylight[5]. However, later in the day, the increase of OH and HO_2 will accelerate reactions that form acids and favour production of non-nitrate IP-OOM from daytime-convected isoprene, which may lead to further particle nucleation. The newly formed particles grow over periods of several days by further condensation of low-volatility vapours, including acids. Model studies show that particles nucleated in the upper free troposphere over the Amazon are gradually transported downwards on horizontal scales much larger than 1,000 km (ref. [8]).

Fig. 5: Schematic of new particle formation from isoprene in the upper troposphere.

Isoprene from forests is efficiently transported at night by deep convective clouds into the upper troposphere. During daylight, the isoprene accumulated overnight, together with daytime-convected isoprene, reacts with hydroxyl radicals and NOx from lightning to produce IP-OOM. The IP-OOM combine with trace ambient acids to produce high particle-number concentrations at cold temperatures below −30 °C. The newly formed particles grow rapidly over several hours to days while following the descending air masses. This mechanism may provide an extensive source of CCN for shallow continental and marine clouds, which strongly influence Earth’s radiative balance.

Isoprene is the most abundant non-methane hydrocarbon emitted into the atmosphere, but its ability to nucleate particles in the boundary layer is considered negligible. Our findings show, however, that isoprene emitted by forests can drive rapid particle nucleation and growth in the upper troposphere. After further growth and descent to lower altitudes, these particles may represent a globally important source of CCN for shallow continental and marine clouds, and so influence Earth’s radiative balance. Isoprene from forests may therefore provide a major source of biogenic particles in both the present-day and pre-industrial atmospheres that are at present unaccounted in atmospheric chemistry and climate models.

Methods

The CLOUD experiments

The CERN CLOUD chamber[36] was used to conduct the experiments presented in this study. CLOUD is an electropolished, stainless-steel, 26.1-m^3 chamber designed to study new particle formation under the full range of tropospheric and lower-stratospheric conditions. The thermal housing around the chamber is able to control the temperature from 208 to 373 K with high precision (±0.1 K)[51]. CLOUD was operated at a pressure of approximately 965 ± 5 mbar in this study. To avoid cross-contamination between different experimental programmes and to achieve extremely low NH_3 concentrations, the chamber is cleaned by rinsing the chamber walls with ultrapure water and heating to 373 K for more than 24 h. To maintain cleanliness and ensure minimal contamination, ultrapure synthetic air—derived from mixing cryogenic liquids (21% oxygen and 79% nitrogen)—is continuously injected into the chamber. The chamber is characterized by a low loss rate, with condensation sink values comparable with those observed in pristine environments.

Various light sources are positioned in the CLOUD chamber to selectively drive photochemistry. OH production is initiated by illuminating O_3 with a UV fibre-optic system, a combination of four 200-W Hamamatsu Hg-Xe lamps with wavelengths spanning 250 and 450 nm, a krypton fluoride (KrF) excimer UV laser at 248 nm and a 52-W low-pressure mercury lamp centred at 254 nm. As well as O_3 photolysis, OH radicals are also produced by photochemical production from nitrous acid (HONO) and hydrogen peroxide (H_2O_2). Both the HONO and H_2O_2 generators were designed specifically for CLOUD experiments. Following the same principle as an earlier study[52], a gas–liquid mixture of HONO is synthesized from continuous mixing of H_2SO_4 (Sigma Aldrich, 99%) with sodium nitrite (NaNO_2, Sigma Aldrich, 99%) in a stainless-steel reactor[53]. HONO is transferred from liquid phase to gas phase by flowing nitrogen gas (1–2 l min−1) through the reactor. HONO is then introduced into the CLOUD chamber and photolysed by a UV light source centred at 385 nm to produce OH radicals and NO. The HONO reactor is continuously cooled to 5 °C and a cryo-trap is placed between the reactor and the chamber to remove excess water vapour and avoid ice blockage of the chamber input pipe. Gaseous H_2O_2 is produced from bubbling N_2 gas through a H_2O_2 solution. The H_2O_2 solution is stored in a glass beaker contained in a stainless-steel container at a constant temperature of 5 °C. A different combination of UV sources is used to photolyse H_2O_2 to produce different amounts of OH radicals.

A green light sabre centred at 528 nm is used to photolyse molecular iodine (I_2). All light systems are continuously monitored by a spectrometer and an array of photodiodes at the bottom of the chamber. Dedicated actinometry experiments allow quantitative determination of actinic fluxes of the light system at different intensities.

Particle formation under different ionization regimes is simulated by combining a strong electric field (±30 kV) and the pion beam produced by the CERN Proton Synchrotron. The electric field eliminates natural ions in less than 1 s, thus creating ion-free conditions (neutral experiments). The pion beam produced by the CERN Proton Synchrotron enhances ion production on top of the galactic cosmic rays. Two magnetically coupled stainless-steel fans mounted at the top and bottom of the chamber enable uniform spatial mixing of particles and vapours within a few minutes. Iodine is injected into the chamber from a temperature-controlled evaporator containing crystalline iodine (I_2, Sigma-Aldrich, 99.999% purity) at the bottom of the chamber. The SO_2 (Carbagas, 100 parts per million by volume (ppmv) in N_2) and isoprene (PanGas, 1,000 ppmv in N_2) are injected into the chamber from pressurized gas cylinders and the O_3 is introduced to the chamber by passing O_2 through an ozone generator.

The data presented in this study were collected in two consecutive CLOUD campaigns (CLOUD15 and CLOUD16). The CLOUD15 and CLOUD16 campaigns were carried out from September to November in 2022 and 2023, respectively. Because the experiments reported in this study were carried out at extremely low temperatures (−30 °C and −50 °C), heat-insulation systems (CLOUD15) and active cooling systems (CLOUD16) were used to reduce measurement systematic error. The heat-insulation systems were primarily made with thermal insulation foam to isolate the instrument inlet system from ambient air. The active cooling systems involved circulating the air inside the chamber thermal housing, at the same temperature as the chamber, around the inlet systems of different instruments. The active cooling systems were also wrapped with thermal insulation foam to allow for more effective inlet cooling. These cooling systems were applied to all mass spectrometers and particle counters, except a butanol condensation particle chamber (CPC; TSI 3776), a nano-scanning mobility particle sizer (nano-SMPS, TSI 3938) and a long-SMPS (TSI 3082), which used a heat-insulation system in both campaigns to act as a standard to avoid systematic errors resulting from changing from the heat-insulation system to the active cooling system.

Measurement of chemical composition

Ozone (O_3)

O_3 was monitored using a gas monitor (Thermo Environmental Instruments, TEI 49C).

Hydroxyl radicals (OH)

The OH radical was measured by HORUS[54] (HydrOxyl Radical measurement Unit based on fluorescence Spectroscopy).

Hydroperoxyl radical (HO_2)

The HO_2 radical was primarily measured using the bromide chemical-ionization mass spectrometer coupled with a multi-scheme chemical-ionization inlet-2 (Br-MION2-CIMS)[55] and HORUS in both CLOUD15 and CLOUD16 campaigns. HORUS measures HO_2 by chemically converting it to OH by NO. However, the RO_2 radical (organic peroxy radicals) produced from isoprene oxidation may also contribute to the HO_2 signal measured by HORUS, as the reaction between RO_2 + NO can also produce OH radicals. By contrast, the HO_2 measurement by Br-MION2-CIMS is less ambiguous, as it is defined by the peak HO_2Br− (ref. [55]). However, the measurement of HO_2 by Br-MION2-CIMS is severely affected by air–water content[55], making offline calibration difficult. Therefore, the HO_2 measurement by Br-MION2-CIMS was calibrated by HORUS under RO_2 radical-free conditions. The online calibration was carried out for every absolute humidity condition reported in this manuscript. During a small section in which the primary ions of Br-MION2-CIMS were saturated by either HONO or H_2O_2, either the low-pressure bromide chemical-ionization mass spectrometer or HORUS was used to complement the HO_2 measurement after intercomparing the data with Br-MION2-CIMS and HORUS during experiments without HONO and H_2O_2. The precision of OH and HO_2 data acquired by the HORUS instrument is quantified at 13% and 7%, respectively, with uncertainties calculated at 1σ over a 10-min averaging period. Furthermore, the systematic error of the measurement is calculated to be 12% for OH and 30% for HO_2.

Nitrogen oxide (NO) and dioxide (NO_2)

NO was measured by detecting the chemiluminescence of NO and O_3 using a chemiluminescence detector (ECO PHYSICS, CLD 780TR). This instrument was calibrated by a second NO monitor (ECO PHYSICS, CLD 780TR), which—in turn—was calibrated using the CMK5 Touch dilution system (Umwelttechnik MCZ GmbH) with a NO bottle (Praxair, 1.00 ppmv in N_2) and synthetic air (Nippon Gases, hydrocarbon-free). The first detector, which provides data for this study, was found to contain background values that have been subtracted in this study. NO_2 was measured by a cavity-attenuated phase-shift nitrogen dioxide monitor (CAPS NO_2, Aerodyne Research Inc.). Hourly, the instrument undergoes a 5-min background measurement of pure N_2 gas. During the 5-min background measurements, data have been interpolated to give a continuous time series. The NO_2 monitor was calibrated using a custom-made cavity-enhanced differential optical absorption spectroscopy instrument[56]. After the subtraction of an average instrument background concentration, the final NO_2 concentration was obtained.

Nitrous acid (HONO) and hydrogen peroxide (H_2O_2)

Both HONO and H_2O_2 were detected using bromide chemical-ionization mass spectrometry[55], as they exhibit reasonable affinity with the bromide anion. Direct calibrations of these two species were not carried out on-site and the current estimation assumes that they share the same detection sensitivity as H_2SO_4 (a low-limit estimation). Because these species serve as the precursors of OH and NO radicals, which were reliably traced, the concentrations of HONO and H_2O_2 are not crucial to the reported results and are therefore omitted from this study.

Two bromide chemical-ionization systems were used to detect HONO and H_2O_2. The first system, Br-MION2-CIMS, offers sensitive detection of both species at concentrations below about 10^10 cm−3, with a detection limit of around 6 × 10^6 cm−3 (H_2O_2) and 1.6 × 10^5 cm−3 (HONO). However, in some experiments, the estimated HONO and H_2O_2 concentrations exceeded 10^10 cm−3. The second system, Br-AIM-CIMS, uses bromide chemical-ionization at low pressure in combination with an active water feedback loop to control the Br-hydration in the ion molecule reactor and avoids saturation. Br-AIM-CIMS was used to measure concentrations from above the detection limit of 4.8 × 10^7 cm−3 (HONO) and 3.3 × 10^7 cm−3 (H_2O_2), based on a calibration factor of 3 × 10^12 for HONO and H_2O_2.

Sulfur dioxide (SO_2)

To measure the concentration of SO_2, a gas monitor (Thermo Fisher Scientific Model 42i-TLE) was used. However, as the SO_2 concentrations in our experiments were usually below 5 × 10^9 cm−3 (150 pptv), we also used the Br-MION2-CIMS to measure SO_2 (ref. [55]) in both CLOUD15 and CLOUD16 campaigns. The measurement of SO_2 by Br-MION2-CIMS is substantially affected by air–water content, so we conducted online SO_2 calibration using the SO_2 monitor at both −30 °C and −50 °C. The derived calibration factors are 1.7 × 10^13 at −30 °C and 1.5 × 10^11 at −50 °C for CLOUD15 and 3.1 × 10^11 at −50 °C for CLOUD16. During the experiments, when the primary ions of Br-MION2-CIMS were saturated by either HONO or H_2O_2, the Br-AIM-CIMS was used to complement the SO_2 measurement. With an active water sensitivity control, Br-AIM-CIMS measures SO_2 concentrations from above the detection limit of 3 × 10^7 cm−3 with a constant calibration factor of 20 × 10^12 at −30 °C and −50 °C.

Sulfuric acid (H_2SO_4)

To ensure the quality of the reported data, we monitored H_2SO_4 concentrations using two chemical-ionization mass spectrometers: the nitrate chemical-ionization mass spectrometer (NO_3-CIMS) and the MION2-CIMS operating in bromide chemical-ionization mode (Br-MION2-CIMS[55]). Furthermore, isotopically labelled H^15NO_3 was used during the CLOUD16 campaign to distinguish the nitrogen atom originating from the analyte with the reagent ion. The H_2SO_4 calibration was carried out by two independent calibration systems. The first set-up used the original calibration box designed by Kürten et al.[57] along with their in-house calibration scripts. The second set-up is similar to the original version but with different physical dimensions. Also, the recently developed open-source MARFORCE model is used to simulate H_2SO_4 production in both calibration set-ups[55].

In total, we conducted seven calibration experiments at different stages of the CLOUD15 campaign, and each CIMS instrument was calibrated using both calibration set-ups. Two calibrations were performed for the Br-MION2-CIMS, resulting in equivalent H_2SO_4 calibration factors of 157% and 149%. For the NO_3-CIMS, five calibrations were carried out, resulting in equivalent calibration factors of 88%, 100%, 95%, 154% and 164%. Given that the NO_3-CIMS provided most of the H_2SO_4 concentration in this study, we use the calibration carried out immediately after the experiments for this study. This results in a calibration factor of 6.2 × 10^9 cm−3 for the NO_3-CIMS and an equivalent calibration factor of 9.0 × 10^9 cm−3 for the Br-MION2-CIMS. We use the minimum and maximum of the seven calibrations, ranging from 88% to 164%, as the systematic error of the H_2SO_4 detection for CLOUD15. It is important to note that we had to change the optimal inlet flow rates of the Br-MION2-CIMS at −30 °C and −50 °C. The varying temperatures and flow rates result in different inlet loss rates, all of which have been accounted for in this dataset.

As well as the normal H_2SO_4 calibration, we conducted a set of iodine oxoacid nucleation experiments at −10 °C, similar to those presented in ref. [37]. The nucleation rates in these experiments are comparable with all of our earlier experiments, further enhancing our confidence in the reported acid concentrations.

In the CLOUD16 campaign, a total of seven calibration experiments were carried out. Two calibration experiments were conducted for the Br-MION2-CIMS, before and after the presented experiments. The results yield equivalent H_2SO_4 calibration factors of 120% and 118%. For the labelled NO_3-CIMS, six calibrations were performed in total, three before the isoprene experiments, resulting in equivalent calibration factors of 100%, 99% and 88%. It is important to note that, during the last few days of the isoprene experiments, the NO_3-CIMS suffered from a pump failure that may have caused a shift (by up to 20%) in the calibration factor owing to a slight change in the sample flow. This potentially affects only two experiments in this study. To correct for this, we have assumed a linear correlation between the sample flow and calibration factor. The failing pumps were then replaced and the data from the rest of the experiments were calibrated after the presented experiments, with two calibrations that yielded equivalent calibration factors of 190% and 185%. This yields a calibration factor of 1 × 10^10 cm−3 for the labelled NO_3-CIMS and an equivalent calibration factor of 1.9 × 10^10 cm−3 for the Br-MION2-CIMS. By considering all of the calibration experiments, the systematic error of H_2SO_4 detection for CLOUD16 is estimated to range from 88% to 120%. Furthermore, using these two instruments, after applying their respective calibration factors, we compared the measured methanesulfonic acid concentrations from the CLOUD chamber at −50 °C. This comparison demonstrated a good agreement, confirming the accuracy of the calibrations.

Iodine species

We measured iodic acid (HIO_3) and iodous acid (HIO_2) using both the NO_3-CIMS and Br-MION2-CIMS and we use the same calibration factor as H_2SO_4 in the data analysis, similar to our earlier studies37,45,55,58,59. We used Br-MION2-CIMS to measure I_2, which is detected at the collision limit, shown by our recent studies55,60.

Isoprene

Isoprene was measured by a proton transfer reaction mass spectrometer using the hydronium chemical-ionization method[61] (H_3O-PTR-MS). This particular instrument used in this study is an adapted version, which is explained in greater detail previously[62].

ISOPOOH and IEPOX detection and separation

Measuring and distinguishing between ISOPOOH and IEPOX can be experimentally challenging owing to their identical molecular formula (C_5H_10O_3). As a result, mass-spectrometric methods often detect them together at the same exact mass in the same peak[35]. To address this issue, techniques such as tandem mass spectrometry have been used to separate ISOPOOH and IEPOX from each other[29].

In this study, these two isomeric compounds were measured both by the Br-MION2-CIMS and the proton transfer reaction mass spectrometer 3 (ref. [63]) operating in ammonium chemical-ionization mode (NH_4-PTR3-CIMS[64]). NH_4-PTR3-CIMS measured ISOPOOH and IEPOX primarily as clusters with ammonium cation, as the proton affinity (see the ‘Quantum-chemical calculations’ section) of NH_3 (204.25 kcal mol−1) is higher than that of 1,2-ISOPOOH (198.31 kcal mol−1), 4,3-ISOPOOH (195.51 kcal mol−1) and cis-β-IEPOX (204.11 kcal mol−1). In this study, we also aim to investigate the capability of the Br-MION2-CIMS in detecting ISOPOOH and IEPOX. We calculate the formation free enthalpies of 1,2-ISOPOOH (−27.5 kcal mol−1), 4,3-ISOPOOH (−26.9 kcal mol−1) and cis-β-IEPOX (−28.0 kcal mol−1) with the bromide anion, respectively. We find that the formation free enthalpies are almost equal to the value of hypoiodous acid (HOI) clustered with the bromide anion (26.9 kcal mol−1), as presented in ref. [55]. Because the instrument used in ref. [55] and in this study is the same and the instrument tuning is identical, the fragmentation of these bromide anion cluster ions should be comparable. He et al.[55] calibrated both the H_2SO_4 and the HOI, and the calibration factor of HOI was approximately two times larger than that of H_2SO_4. Therefore, the calibration factor used for C_5H_10O_3 is two times the calibration factor for H_2SO_4 in this study.

As neither the NH_4-PTR3-CIMS nor the Br-MION2-CIMS are able to distinguish between ISOPOOH and IEPOX, the reported C_5H_10O_3 in this study is the sum of ISOPOOH and IEPOX. Earlier studies have shown that ISOPOOH is effectively lost to metal surfaces by converting it to methyl vinyl ketone (MVK) and methacrolein (MACR)42,65,66, whereas IEPOX is not affected by metal surfaces[67]. However, as the experiments in this study focus on extremely low temperatures (−30 °C and −50 °C), the chamber wall itself may also serve as a cryo-trap[68] for both ISOPOOH and IEPOX. Therefore, it prevents us from using wall-loss-rate perturbation experiments to separate these two species at these temperatures.

To understand the distribution of ISOPOOH and IEPOX in C_5H_10O_3, we carry out a kinetic simulation using the reduced isoprene oxidation mechanism provided in ref. [35]. The results are presented in Extended Data Fig. 1b. The simulation is carried out by the F0AM model[43]. The model requires input parameters such as isoprene, OH, HO_2 and O_3 concentrations measured by our instruments.

Another important parameter is the wall-loss rate of IP-OOM. We present an experiment in which we manipulate the loss rate of IP-OOM by turning off the light source and increasing the mixing fan spinning rate from 12% to 100% from the equilibrium conditions in Extended Data Fig. 1a. By turning off the light source, the production of IP-OOM stops. Furthermore, by increasing the fan speed, we increase the maximum wall-loss rate from approximately 1.6 × 10−3 s−1 to 8.5 × 10−3 s−1. The decay rates of C_5H_10O_3 and C_5H_12O_6, with lifetimes of 137 s and 112 s, respectively, are similar to the decay rate of HIO_3 (129 s) and also, from previous measurements, H_2SO_4. Because HIO_3 has an accommodation coefficient of unity to the chamber wall, we conclude that C_5H_10O_3 and other species with lower volatilities have similar wall-loss rates. In this study, we apply a general wall-loss rate for these species of 1.6 × 10−3 s−1. This wall-loss rate is calculated from the measured H_2SO_4 wall-loss rate by correcting the diffusivity of C_5H_12O_6 at −30 °C using the method described by our earlier study[58].

We further conduct simulations for all of our experiments using the same procedure, and the ratio of IEPOX in C_5H_10O_3 versus OH concentration is presented in Extended Data Fig. 1b. As anticipated, the IEPOX ratio is positively correlated with OH concentrations. For further analysis, a fit with an expression of ratio of \({10}^{(0.58\times {\log }_{10}([{\rm{OH}}])-4.6)}\) is plotted.

Gas-phase oxidized isoprene products

The gas-phase measurement of IP-OOM was achieved by using a combination of NO_3-CIMS, Br-MION2-CIMS and NH_4-PTR3-CIMS. As defined in this study, only the species with carbon and oxygen numbers equal to or larger than 4 are considered in the IP0-2N, which are primarily produced from OH oxidation of ISOPOOH and IEPOX with and without involving nitrogen oxides. Furthermore, the particle-phase IP0-2N were monitored by a FIGAERO[69], which operates with the bromide chemical-ionization method[60] in CLOUD15 (Br-FIGAERO-CIMS) and with the iodide chemical-ionization method in CLOUD16 (I-FIGAERO-CIMS). These chemical-ionization methods exhibit varying preferences for analytes. For example, the NO_3-CIMS is renowned for detecting highly oxygenated organic molecules[70] that contain more than 5 oxygen atoms. The H_3O-PTR-MS is the only one that can detect isoprene, whereas both the NH_4-PTR3-CIMS and the Br-MION2-CIMS are capable of detecting semi-volatile organic compounds. Consequently, the combination of these CIMS methods enables the measurement of IP-OOM at different oxidation states.

It is worth mentioning our specialized approach to measuring IP-OOM using Br-MION2-CIMS during experiments involving excess HONO and/or H_2O_2, as described previously. In these experiments, the primary ions (Br− and H_2OBr−) were substantially transformed into product ions such as HONOBr−, H_2O_2Br− and (H_2O_2)2Br−. Consequently, the measurement of IP-OOM could be compromised if HONO and H_2O_2 strongly bind with Br−, thereby impeding the ligand exchange with IP-OOM. Therefore, we extensively compared the Br-MION2-CIMS measurements with those of NO_3-CIMS and NH_4-PTR3-CIMS during experiments with and without such primary ion saturation to ensure reliable measurements. We found that the Br-MION2-CIMS measurement remained uncompromised when we included HONOBr−, H_2O_2Br− and (H_2O_2)2Br− as the primary ions. This is probably because of the relatively weak bonding of HONO and H_2O_2 with Br−, which enables effective charging of IP-OOM by allowing ligand exchange reaction. Quantum-chemical calculations further suggest that the formation free enthalpies of HONOBr− and H_2O_2Br− are −23.6 and −21.2 kcal mol−1, respectively. These numbers are sufficiently lower than other molecules that are detected at the collision limit by Br-MION2-CIMS[55].

To produce IP0-2N, we conducted a set of experiments in which we varied the concentrations of isoprene (ranging from 1.4 × 10^9 to 4.2 × 10^10 cm−3) and OH (ranging from 0.1 to 6.9 × 10^7 cm−3) to alter the distribution of oxidation products[35]. To analyse the results of these experiments, we present a generic algorithm to calculate the total sum of gaseous IP0-2N produced, with a focus on those with carbon and oxygen numbers greater than 3:

1. 1.
IP-OOM are independently identified by each of the CIMS instruments. Their responses to the isoprene oxidation in the chamber are observed to distinguish them from any background contaminations originating from either the chamber or the ion sources. If an individual IP0-2N is affected by contaminants of the same molecular formula, its background, derived from the nearest cleaning stage, is subtracted from its concentrations.

2. 2.
If an IP0-2N is detected by only one of the three CIMS, it is added to the total sum directly.

3. 3.
If several CIMS detect species with the same molecular formula, their measured signals are compared in pairs to derive a correlation coefficient. A pair is considered to measure identical molecules if the correlation coefficient is greater than 0.5. However, owing to the transfer of the H_2SO_4 calibration factors to the measured IP0-2N (NO_3-CIMS and Br-MION2-CIMS), the concentration of any molecule with a lower detection efficiency than H_2SO_4 may be underestimated. The extent of this underestimation depends on the chemical-ionization method used, as the binding enthalpies of the analyte-Br−, analyte-NO_3− and analyte-NH_4+ may differ. To address this, we add the highest measured concentration of the three CIMS to the IP0-2N and discard the rest, as the highest concentration is probably the closest to the actual concentration.

4. 4.
If the correlation coefficient is less than 0.5, we consider that this pair represents two different molecular structures, that is, two isomers or conformers. In this case, both will be added to the IP0-2N.

However, maintaining all three instruments to be operational throughout all experiments presents a challenge, for instance, the Br-MION2-CIMS operated in the APi-TOF mode to measure charged clusters. Therefore, we excluded data collected during periods in which any one of the instruments was not available.

Charged clusters

Naturally charged clusters were measured with two APi-TOF mass spectrometers (Aerodyne Research Inc.) operating at negative and positive ion mode[71]. The first instrument was equipped with a MION2 operating in the APi-TOF mode (MION2-APi-TOF)55,72 by deactivating the inlet voltages responsible for directing charged reagent ions into the sample flow. The second device was coupled with an ion-molecule reaction chamber (APi-TOF). Overall, the APi-TOF was less sensitive than the MION2-APi-TOF. The charged clusters reported in Fig. 3 were measured with the MION2-APi-TOF, which was validated by the APi-TOF. Because the MION2 inlet was operated in bromide chemical-ionization mode in some experiments, part of the data reported in Extended Data Fig. 7 was measured by the APi-TOF.

Particle-phase measurements

We measured the chemical composition of small particles using a FIGAERO coupled to a chemical-ionization mass spectrometer[69]. Particles were sampled from the CLOUD chamber onto a 5-µm-pore polytetrafluoroethylene (PTFE) filter (MilliporeSigma). Filter mass loading is dependent on particle distribution in the chamber, collection flow rate (typically 7–8 l min−1) and total collection time (1–2 h in this study). After particle collection, the filter was automatically moved to in front of the ion molecule reactor. The filter aligned with a sealed port that constantly flushes pure N_2. In CLOUD15, the flow rate during chemical measurement was 3 l min−1 and it was increased to 5 l min−1 in CLOUD16 for more efficient heat transfer in a longer port. Pure N_2 was heated from room temperature up to 180 °C using programmed thermal desorption controlled by eyeon software v2.1.4.5. As the filter temperature increased, we detected lower-volatility molecules partitioning back into the gas phase. For the particle filter loadings in this study, we observed that all signals decreased back to the baseline by the end of the heating cycle, indicating no notable remaining mass.

Typically, FIGAERO-CIMS is operated using I− chemical-ionization in a reduced-pressure ion molecule reactor (about 120–150 mbar). Pure N_2 is flowed around a CH_3I permeation tube (Vici) and through a 210Po ionizer (NRD LLC) to produce iodide ions. These polarizable ions effectively form adducts with oxygenated organic compounds, with a small fraction of interactions leading to charge transfer between the ion and neutral compound. In CLOUD15, we used Br− chemical-ionization to distinguish between our chemical-ionization reagent and iodine species inside the CLOUD chamber. The set-up is the same as iodide ionization mode except we exchange a CH_2Br_2 permeation tube and heat it to 40 °C to increase permeation rates. These chemical-ionization techniques are both sensitive to oxygenated organic compounds, organics with nitrate and sulfate functional groups and inorganic acids60,69. Compounds chemically transformed through deprotonation or thermal decomposition have been excluded, as their parent molecule is unknown.

Particle number size distribution

The Neutral cluster and Air Ion Spectrometer73,74 (NAIS) was used to measure the naturally charged particle number size distribution from 0.8 to 41 nm and the particle number size distribution (naturally charged + neutral) from 2 to 42 nm in both negative and positive polarities. The nano-condensation nucleus counter was used to measure the particle number size distribution between 1 and 3 nm. It consists of a particle size magnifier[75] (PSM, Airmodus Oy). The PSM, which is an aerosol pre-conditioner, uses diethylene glycol to grow aerosol particles as small as 1 nm to sizes that can be easily detected by a CPC[75]. Furthermore, a butanol CPC (TSI 3776) was used to measure the total number concentration of particles with diameters greater than 2.5 nm. A nano-scanning mobility particle sizer (TSI 3938)[76] coupled to a butanol CPC (TSI 3776), was used to measure the particle-size distribution within the range 6–65 nm, whereas particles larger than 65 nm were measured using a commercially available long-SMPS (TSI 3082) coupled to a water butanol CPC (TSI 3775).

Yield of IP0N from ISOPOOH

As shown in a previous section, the IP0N in this study is defined as species with C,O ≥ 4. Therefore, ISOPOOH and IEPOX are not included in the IP0N. ISOPOOH and IEPOX are treated as the direct precursors of IP0N, which in turn contribute to isoprene new particle formation. It is worth noting that both ISOPOOH and IEPOX undergo oxidation, producing compounds with C,O ≥ 4. However, the reaction rate of ISOPOOH is approximately ten times larger than IEPOX[35]. To account for the difference in reaction-rate coefficients, we predict the ratio of IEPOX in C_5H_10O_3 using the data shown in Extended Data Fig. 1b based on the OH concentrations. Assuming that the concentration of IP0N is at equilibrium, the primary mechanism for IP0N loss is wall deposition, which is approximately equal to the production of IP0N from ISOPOOH and IEPOX. Therefore,

$$\begin{array}{l}[{{\rm{IP}}}_{0{\rm{N}}}]\times {k}_{{\rm{wall}}}\,=\,R\times ({k}_{{\rm{OH}} \mbox{-} {\rm{ISOPOOH}}}\times [{\rm{OH}}]\times [{\rm{ISOPOOH}}]\\ \,\,\,\,\,\,+{k}_{{\rm{OH}} \mbox{-} {\rm{IEPOX}}}\times [{\rm{OH}}]\times [{\rm{IEPOX}}])\end{array}$$

in which kOH-ISOPOOH and kOH-IEPOX are the reaction-rate coefficients of ISOPOOH (10−10 cm^3 s−1) and IEPOX (10−11 cm^3 s−1) with OH (ref. [35]), respectively; [IP0N], [OH], [ISOPOOH] and [IEPOX] show concentrations and kwall is the wall-loss rate of C_5H_12O_6; R represents the yield of IP0N from C_5H_10O_3.

We then define the reacted C_5H_10O_3 (cm−3) as:

$${\rm{Reacted}}\,{{\rm{C}}}_{5}{{\rm{H}}}_{10}{{\rm{O}}}_{3}=\frac{{k}_{\text{OH-ISOPOOH}}\times [{\rm{OH}}]\times [{\rm{ISOPOOH}}]+{k}_{\text{OH-IEPOX}}\times [{\rm{OH}}]\times [{\rm{IEPOX}}]}{{k}_{{\rm{wall}}}}$$

The yield of IP0N from reacted C_5H_10O_3 is depicted in Extended Data Fig. 2. We find that the yields of IP0N are approximately 46% at −30 °C and 55% at −50 °C. However, it is essential to note that the detection of C_5H_10O_3, IP0N and OH has various uncertainties. We estimate that the derived yield has an uncertainty of at least a factor of two, with the quantification of IP0N being the main source of uncertainty.

One further source of error in determining the yield is the contribution of highly oxygenated molecule production from the first-generation isoprene hydroxy peroxy radical (ISOPOO, C_5H_9O_3) through auto-oxidation or dimer formation. For example, the reaction between two ISOPOO radicals can generate C_10H_18O_4, and intramolecular H-shift followed by HO_2 termination of ISOPOO produces C_5H_10O_5. Although these two molecules only contribute to a small fraction of IP0N in this study, other similar channels may contribute to a greater extent to IP0N, thereby reducing the yield of IP0N from C_5H_10O_3. As disentangling first-generation and second-generation highly oxygenated molecules from isoprene oxidation is not the objective of this study, future research is necessary to investigate this direction.

Quantum-chemical calculations

Quantum-chemical methods are used to compute cluster formation free enthalpies and proton affinities. Initially, the Spartan’18 program is used for the conformational sampling with the MMFF method. Subsequently, density function theory (DFT) methods are used to optimize the molecules first at the B3LYP/6-31+G(d) level of theory, followed by optimization and frequency calculations at the ωB97X-D/aug-cc-pVTZ-PP level of theory77,78 on conformers within 2 kcal mol−1 in relative electronic energies. Bromine pseudopotential definitions are obtained from the Environmental Molecular Sciences Laboratory (EMSL) basis set library79,80. The DFT calculations are carried out using the Gaussian 16 program[81]. To refine the DFT-calculated enthalpies, an extra coupled-cluster single-point energy correction is performed at the DLPNO-CCSD(T)/def2-QZVPP level of theory on the lowest-energy conformers. This coupled-cluster calculation is conducted using the ORCA program version 5.0.3 (ref. [82]).

Calculation of the nucleation and growth rates

The nucleation rate, J1.7, is calculated on the basis of PSM measurement of particles at a mobility diameter of 1.7 nm (1.4 nm in physical diameter[83]), which are generally considered to be larger than their critical cluster sizes and thus stable.

To determine the nucleation rates, the time evolution of the particle concentration is analysed, taking into account various loss processes that also affect the concentration. However, because loss processes in a chamber setting differ from those in the atmosphere, the calculation method must be adjusted for chamber experiments[84]. Specifically, the nucleation rate (J1.7) is calculated by factoring in losses specific to the CLOUD chamber, such as dilution, wall and coagulation losses. We calculated Jdp as follows:

$${J}_{1.7}=\frac{{\rm{d}}N}{{\rm{d}}t}+{S}_{{\rm{dil}}}+{S}_{{\rm{wall}}}+{S}_{{\rm{coag}}}$$

in which dN/dt is the time derivative of the total particle concentration above a certain particle size (here >1.7 nm for J1.7) and Sdil, Swall and Scoag are the particle losses owing to dilution, wall and coagulation. The details can be found in ref. [84]. To calculate the coagulation sink, we used the combined particle-size distribution from three instruments (NAIS, nano-SMPS and long-SMPS).

Furthermore, the nucleation rate at 2.5 nm, J2.5, derived from the butanol CPC and corrected by the same method described above, is calculated. The results are presented in Extended Data Fig. 6 in the same format as in Fig. 2. Because the CPC was not affected by the systematic upgrade in the cooling system between CLOUD15 and CLOUD16, it serves to distinguish subtle changes in our data. For example, the nucleation rates from experiments with NOx (filled squares in Fig. 2) seem to be similar to the experiments without NOx (filled circles in Fig. 2). This is probably a result of systematic errors introduced by changing either the cooling system or the instrument-calibration experiments. On the other hand, Extended Data Fig. 6 shows that experiments with NOx have nucleation rates higher than the experiments without NOx, therefore, isoprene nitrates (IP1-2N) do contribute, despite to a lesser extent compared with IP0N, to particle nucleation.

To calculate particle growth rates, we use the 50% appearance-time method, as outlined in previous studies58,84,85. It is worth noting that the appearance-time method can overestimate growth rates when the impacts of coagulation (coagulation sink, coagulation source and particle coagulation growth) are non-negligible compared with the condensation growth, but the coagulation impact is rather small in the CLOUD experiments. For a deeper understanding of the molecular-level theory behind the method, we refer to the theoretical derivation presented in ref. [58]. The particle number size distribution data used to calculate growth rates between 3.2 and 8.0 nm are measured by the NAIS. During previous experiments with α-pinene and sulfuric acid, we have confirmed that the growth rates measured with the NAIS in total mode are similar to those measured with the DMA-train[86].

Comparison of experimental and ambient conditions

To compare the CLOUD experimental conditions with ambient conditions in the tropical upper troposphere, we summarize in Extended Data Table 1 the key chemical and physical parameters of the CLOUD experiments and the CAFE-Brazil (CB) flight campaign[13]. The CLOUD statistics are summarized from all experiments presented in this study, separated into two temperature conditions at −30 °C and −50 °C, respectively. Statistics of the CB flight campaign are derived from a single research flight, RF 19, samples T4 and T9, shown in Fig. 1 of ref. [13]. All vapour concentrations are presented in units of molecules per cm−3—the quantities as measured—for both CLOUD and CB. The values from the CB campaign are not corrected to their values at standard temperature and pressure, to allow for a direct comparison of the chemical and aerosol formation kinetics between the CLOUD experiments and the flight measurements. We elaborate below on three aspects of this comparison: (1) isoprene non-nitrates (IP0N) and nitrates (IP1-2N); (2) atmospheric acids; and (3) impact of atmospheric pressure on particle nucleation.

Distribution of IP0N and IP1-2N

In general, the CLOUD experiments were designed to mimic ambient conditions as closely as possible. Key parameters such as temperature, relative humidity (RH), isoprene, O_3, NO and HO_2/OH ratios are directly comparable between the CLOUD experiments and the CB measurements. The largest differences between CLOUD and CB are higher atmospheric pressure in CLOUD and higher OH/HO_2 concentrations, resulting in a higher HO_2/NO ratio in CLOUD. The higher OH concentration in the CLOUD chamber is required to reproduce ambient IP-OOM concentrations at a chamber-wall-loss rate of approximately 2 × 10−3 s−1. In the upper troposphere, the condensation sink for low-volatility gaseous species could be several times or even up to one order of magnitude lower than the chamber-wall-loss rate.

Because this study aims to investigate the contribution of IP0N and IP1-2N to particle nucleation and growth, these parameters are critical for CLOUD to reproduce at atmospheric concentrations. A consequence of the relatively higher HO_2/NO ratio in CLOUD is that the IP0N to IP1-2N ratio is elevated compared with CB. However, because the higher operating pressure in CLOUD favours the formation of IP1-2N by accelerating the reaction of organic peroxy radical (RO_2) with NO as well as enhancing the organic nitrate formation branching ratio[35] (see Extended Data Table 1), this effect largely compensates for the higher HO_2/NO ratio in CLOUD. Nevertheless, regardless of the chemical details, which will be presented by follow-up studies, CLOUD successfully reproduces isoprene oxidation products IP0N and IP1-2N, in terms of both absolute values and relative ratios (Extended Data Table 1). The wide range of IP0N and IP1-2N concentrations and IP0N/IP1-2N ratios covered by CLOUD experiments enables CLOUD to reasonably simulate particle nucleation and growth dynamics, consistent with CB measurements during dawn hours (T4 period in Extended Data Table 1) and morning (T9 period in Extended Data Table 1). As the daylight hours proceed beyond the period measured by CB, both OH and HO_2 concentrations will increase, whereas NOx concentrations decrease, favouring the formation of IP0N over IP1-2N. Thus, the importance of IP0N may be further enhanced after noon. It is also noteworthy that chemical distribution and nucleation dynamics might differ in other seasons and locations from those covered by CB measurements, such as the cases presented in Fig. S11 of ref. [5]. Therefore, we believe that the wide range of conditions explored by CLOUD provides valuable data to enable global models to evaluate the impact of isoprene on new particle formation in other upper-tropospheric environments in which NOx concentrations may differ from those measured by CB.

Impact of sulfuric acid and iodine oxoacids

In this study, we observe a large enhancement of IP-OOM nucleation rates from trace amounts of atmospheric acids (specifically, H_2SO_4 and HIOx). The enhancement starts at acid concentrations of 10^5 cm−3 and, at an acid concentration of 2 × 10^6 cm−3, the particle-nucleation rate is approximately 100-fold faster than without added acids.

The CB measurements are unable to comment on this synergistic role of acids for IP-OOM particle nucleation owing to their H_2SO_4 detection limit of several times 10^6 cm−3. Nevertheless, acid enhancement of IP-OOM nucleation can be expected to occur in the atmosphere. Aircraft measurements indicate that approximately 10 pptv SO_2 is ubiquitous in the global atmosphere between the marine boundary layer and the upper troposphere87,88. Global simulations also suggest that H_2SO_4 concentrations greater than 10^5 cm−3 are widespread throughout the troposphere[44]. The global distribution of HIOx is less well known and global simulations and aircraft measurements are needed to quantify its concentrations in the upper troposphere. However, recent measurements of iodine oxide and particle-phase iodine in the upper troposphere[89] suggest that HIOx may also play a role in enhancing IP-OOM particle nucleation.

Impact of atmospheric pressure on particle nucleation

Cluster-forming interactions (such as nucleation) are of the form:

$$\begin{array}{ll}{\rm{A}}+{\rm{B}}\to {{\rm{C}}}^{* } & ({\rm{R}}1\,;\,{\rm{condensation}}),\\ {{\rm{C}}}^{* }\to {\rm{A}}+{\rm{B}} & ({\rm{R}}2\,;\,{\rm{evaporation}})\,{\rm{and}}\\ {{\rm{C}}}^{* }+{\rm{M}}\to {\rm{C}}+{\rm{M}} & ({\rm{R}}3\,;\,{\rm{thermalization}}),\end{array}$$

in which A and B are two molecules (or small clusters) forming the cluster C and C* is a vibrationally excited state of the cluster containing the cluster energy EAB as part of its internal vibrational energy. The excited cluster C* will lose energy to the bath gas, M, with a concentration given by pressure through the ideal gas law. Cluster-forming interactions can therefore depend on ambient pressure[90]. When pressure is relatively ‘low’, reaction R3 will be the rate-limiting step for cluster formation and the overall rate will be of the third order (in A, B and M). However, when pressure is relatively high, reaction R1 will be the rate-limiting step and the rate will be of the second order (in A and B) and be independent of pressure, that is, at the so-called ‘high-pressure limit’. The critical pressure occurs when the rates of R2 and R3 are equal and therefore depends on the lifetime (evaporation rate) of C* through reaction R2, as well as the ambient pressure through reaction R3.

In practice, only systems with very few heavy atoms (four or fewer heavy atoms, in which ‘heavy’ excludes hydrogen) would show any meaningful pressure dependence in the atmosphere[91], as quantified below. Because IP-OOM nucleation typically involves more than 15 heavy atoms, measurements in the CLOUD chamber at near 1 bar are directly applicable to the upper troposphere near 0.2 bar, provided the results are interpreted in terms of number concentrations (thus accounting for the dilution effect of reduced pressure) and not mixing ratios.

The (microcanonical) decomposition rates (inverse lifetimes) of the cluster are given by Rice–Ramsperger–Kassel–Marcus theory[90]. It depends strongly on the number of internal vibrational modes in C*. As a rule, it can be estimated (by Rice, Ramsperger and Kassel theory)[90] roughly as the ‘fractional excess free energy’, \(\upsilon {\left(\frac{e-{e}_{0}}{e}\right)}^{s}\), in which υ is a typical frequency, 100 THz or so, e is the cluster energy above the ground vibrational state and e_0 is the critical energy for decomposition (the cluster energy EAB) and s is an effective number of vibrational modes in the cluster. Approximately, e − e_0 will be on the order kT (200 cm−1), whereas for the systems nucleating under atmospheric conditions, e_0 will be on the order 800 cm−1 or more. Thus, e will be on the order 1,000 cm−1 and (e − e_0)/e will be on the order 0.2. Again, approximately and conservatively, s is 3N − 7, in which N is the number of heavy (non-H) atoms in C*. The ‘7’ excludes external modes as well as the reaction coordinate. A system with five heavy atoms would have a decay coefficient of roughly 2.6 × 10^8 s−1, whereas the collision frequency at 1 atm is near 10^10 s−1. Such a system would (barely) show some pressure dependence. By contrast, for isoprene oxidation products and H_2SO_4, N is probably 15, so s = 3N − 7 = 38. Given this, the microcanonical decay coefficients for these clusters will be on the order 3 × 10−13 s−1. This is extremely slow. In practice, it means that the energy distribution in cluster C will be entirely thermal, that is, given by a Boltzmann term at the ambient temperature, and the rates of cluster formation (and decomposition or evaporation) will be unaffected by pressure.

Data availability

The full dataset shown in the figures and tables is available open access at https://doi.org/10.5281/zenodo.13736557 (ref.[92]).

Code availability

Codes conducting the analysis presented here can be obtained on reasonable request to the corresponding authors.

References

1. Krejci, R. Evolution of aerosol properties over the rain forest in Surinam, South America, observed from aircraft during the LBA-CLAIRE 98 experiment. J. Geophys. Res. Atmos. 108, 4561 (2003).

2. Andreae, M. O. et al. Aerosol characteristics and particle production in the upper troposphere over the Amazon Basin. Atmos. Chem. Phys. 18, 921–961 (2018).

3. Williamson, C. J. et al. A large source of cloud condensation nuclei from new particle formation in the tropics. Nature 574, 399–403 (2019).

4. Clarke, A. D. et al. Particle production in the remote marine atmosphere: cloud outflow and subsidence during ACE 1. J. Geophys. Res. Atmos. 103, 16397–16409 (1998).

5. Palmer, P. I., Marvin, M. R., Siddans, R., Kerridge, B. J. & Moore, D. P. Nocturnal survival of isoprene linked to formation of upper tropospheric organic aerosol. Science 375, 562–566 (2022).

6. Gordon, H. et al. Causes and importance of new particle formation in the present-day and preindustrial atmospheres. J. Geophys. Res. Atmos. 122, 8739–8760 (2017).

7. Kerminen, V.-M. et al. Atmospheric new particle formation and growth: review of field observations. Environ. Res. Lett. 13, 103003 (2018).

8. Wang, X., Gordon, H., Grosvenor, D. P., Andreae, M. O. & Carslaw, K. S. Contribution of regional aerosol nucleation to low-level CCN in an Amazonian deep convective environment: results from a regionally nested global model. Atmos. Chem. Phys. 23, 4431–4461 (2023).

9. Kulmala, M. et al. Deep convective clouds as aerosol production engines: role of insoluble organics. J. Geophys. Res. Atmos. 111, 2005JD006963 (2006).

10. Ekman, A. M. L. et al. Do organics contribute to small particle formation in the Amazonian upper troposphere? Geophys. Res. Lett. 35, 2008GL034970 (2008).

11. Zhu, J. et al. Decrease in radiative forcing by organic aerosol nucleation, climate, and land use change. Nat. Commun. 10, 423 (2019).

12. Zhao, B. et al. Global variability in atmospheric new particle formation mechanisms. Nature 631, 98–105 (2024).

13. Curtius, J. et al. Isoprene nitrates drive new particle formation in Amazon’s upper troposphere. Nature https://doi.org/10.1038/s41586-024-08192-4 (2004).

14. Zha, Q. et al. Oxidized organic molecules in the tropical free troposphere over Amazonia. Natl Sci. Rev. 11, nwad138 (2023).

15. Guenther, A. et al. A global model of natural volatile organic compound emissions. J. Geophys. Res. Atmos. 100, 8873–8892 (1995).

16. Andreae, M. O. et al. The Amazon Tall Tower Observatory (ATTO): overview of pilot measurements on ecosystem ecology, meteorology, trace gases, and aerosols. Atmos. Chem. Phys. 15, 10723–10776 (2015).

17. Bardakov, R., Thornton, J. A., Riipinen, I., Krejci, R. & Ekman, A. M. L. Transport and chemistry of isoprene and its oxidation products in deep convective clouds. Tellus B Chem. Phys. Meteorol. 73, 1–21 (2021).

18. Lelieveld, J. et al. Atmospheric oxidation capacity sustained by a tropical forest. Nature 452, 737–740 (2008).

19. Squire, O. J. et al. Influence of isoprene chemical mechanism on modelled changes in tropospheric ozone due to climate and land use over the 21st century. Atmos. Chem. Phys. 15, 5123–5143 (2015).

20. Claeys, M. Formation of secondary organic aerosols through photooxidation of isoprene. Science 303, 1173–1176 (2004).

21. Paulot, F. et al. Unexpected epoxide formation in the gas-phase photooxidation of isoprene. Science 325, 730–733 (2009).

22. Kroll, J. H., Ng, N. L., Murphy, S. M., Flagan, R. C. & Seinfeld, J. H. Secondary organic aerosol formation from isoprene photooxidation. Environ. Sci. Technol. 40, 1869–1877 (2006).

23. Massoli, P. et al. Ambient measurements of highly oxidized gas-phase molecules during the Southern Oxidant and Aerosol Study (SOAS) 2013. ACS Earth Space Chem. 2, 653–672 (2018).

24. Pandis, S. N., Paulson, S. E., Seinfeld, J. H. & Flagan, R. C. Aerosol formation in the photooxidation of isoprene and β-pinene. Atmos. Environ. Part A Gen. Top. 25, 997–1008 (1991).

25. Weber, J. et al. Chemistry-driven changes strongly influence climate forcing from vegetation emissions. Nat. Commun. 13, 7202 (2022).

26. Kanakidou, M. et al. Organic aerosol and global climate modelling: a review. Atmos. Chem. Phys. 5, 1053–1123 (2005).

27. Budisulistiorini, S. H. et al. Real-time continuous characterization of secondary organic aerosol derived from isoprene epoxydiols in downtown Atlanta, Georgia, using the Aerodyne Aerosol Chemical Speciation Monitor. Environ. Sci. Technol. 47, 5686–5694 (2013).

28. Lamkaddam, H. et al. Large contribution to secondary organic aerosol from isoprene cloud chemistry. Sci. Adv. 7, eabe2952 (2021).

29. St. Clair, J. M. et al. Kinetics and products of the reaction of the first-generation isoprene hydroxy hydroperoxide (ISOPOOH) with OH. J. Phys. Chem. A 120, 1441–1451 (2016).

30. Krechmer, J. E. et al. Formation of low volatility organic compounds and secondary organic aerosol from isoprene hydroxyhydroperoxide low-NO oxidation. Environ. Sci. Technol. 49, 10330–10339 (2015).

31. Liu, J. et al. Efficient isoprene secondary organic aerosol formation from a non-IEPOX pathway. Environ. Sci. Technol. 50, 9872–9880 (2016).

32. Kiendler-Scharr, A. et al. New particle formation in forests inhibited by isoprene emissions. Nature 461, 381–384 (2009).

33. Heinritzi, M. et al. Molecular understanding of the suppression of new-particle formation by isoprene. Atmos. Chem. Phys. 20, 11809–11821 (2020).

34. McFiggans, G. et al. Secondary organic aerosol reduced by mixture of atmospheric vapours. Nature 565, 587–593 (2019).

35. Wennberg, P. O. et al. Gas-phase reactions of isoprene and its major oxidation products. Chem. Rev. 118, 3337–3390 (2018).

36. Kirkby, J. et al. Role of sulphuric acid, ammonia and galactic cosmic rays in atmospheric aerosol nucleation. Nature 476, 429–433 (2011).

37. He, X.-C. et al. Role of iodine oxoacids in atmospheric aerosol nucleation. Science 371, 589–595 (2021).

38. Crutzen, P. J. et al. High spatial and temporal resolution measurements of primary organics and their oxidation products over the tropical forests of Surinam. Atmos. Environ. 34, 1161–1165 (2000).

39. Bates, K. H. et al. Gas phase production and loss of isoprene epoxydiols. J. Phys. Chem. A 118, 1237–1246 (2014).

40. Jacobs, M. I., Darer, A. I. & Elrod, M. J. Rate constants and products of the OH reaction with isoprene-derived epoxides. Environ. Sci. Technol. 47, 12868–12876 (2013).

41. Kroll, J. H. & Seinfeld, J. H. Chemistry of secondary organic aerosol: formation and evolution of low-volatility organics in the atmosphere. Atmos. Environ. 42, 3593–3624 (2008).

42. Liu, Y. J., Herdlinger-Blatt, I., McKinney, K. A. & Martin, S. T. Production of methyl vinyl ketone and methacrolein via the hydroperoxyl pathway of isoprene oxidation. Atmos. Chem. Phys. 13, 5715–5730 (2013).

43. Wolfe, G. M., Marvin, M. R., Roberts, S. J., Travis, K. R. & Liao, J. The Framework for 0-D Atmospheric Modeling (F0AM) v3.1. Geosci. Model Dev. 9, 3309–3319 (2016).

44. Dunne, E. M. et al. Global atmospheric particle formation from CERN CLOUD measurements. Science 354, 1119–1124 (2016).

45. He, X.-C. et al. Iodine oxoacids enhance nucleation of sulfuric acid particles in the atmosphere. Science 382, 1308–1314 (2023).

46. Stolzenburg, D. et al. Enhanced growth rate of atmospheric particles from sulfuric acid. Atmos. Chem. Phys. 20, 7359–7372 (2020).

47. Lucas, D. D. & Prinn, R. G. Tropospheric distributions of sulfuric acid-water vapor aerosol nucleation rates from dimethylsulfide oxidation. Geophys. Res. Lett. 30, 2136 (2003).

48. Zauner-Wieczorek, M. et al. Mass spectrometric measurements of ambient ions and estimation of gaseous sulfuric acid in the free troposphere and lowermost stratosphere during the CAFE-EU/BLUESKY campaign. Atmos. Chem. Phys. 22, 11781–11794 (2022).

49. Stolzenburg, D. et al. Rapid growth of organic aerosol nanoparticles over a wide tropospheric temperature range. Proc. Natl Acad. Sci. 115, 9122–9127 (2018).

50. Clark, C. H. et al. Temperature effects on secondary organic aerosol (SOA) from the dark ozonolysis and photo-oxidation of isoprene. Environ. Sci. Technol. 50, 5564–5571 (2016).

51. Dias, A. et al. Temperature uniformity in the CERN CLOUD chamber. Atmos. Meas. Tech. 10, 5075–5088 (2017).

52. Taira, M. & Kanda, Y. Continuous generation system for low-concentration gaseous nitrous acid. Anal. Chem. 62, 630–633 (1990).

53. Xiao, M. et al. The driving factors of new particle formation and growth in the polluted boundary layer. Atmos. Chem. Phys. 21, 14275–14291 (2021).

54. Martinez, M. et al. Hydroxyl radicals in the tropical troposphere over the Suriname rainforest: airborne measurements. Atmos. Chem. Phys. 10, 3759–3773 (2010).

55. He, X.-C. et al. Characterisation of gaseous iodine species detection using the multi-scheme chemical ionisation inlet 2 with bromide and nitrate chemical ionisation methods. Atmos. Meas. Tech. 16, 4461–4487 (2023).

56. Thalman, R. & Volkamer, R. Inherent calibration of a blue LED-CE-DOAS instrument to measure iodine oxide, glyoxal, methyl glyoxal, nitrogen dioxide, water vapour and aerosol extinction in open cavity mode. Atmos. Meas. Tech. 3, 1797–1814 (2010).

57. Kürten, A., Rondo, L., Ehrhart, S. & Curtius, J. Calibration of a chemical ionization mass spectrometer for the measurement of gaseous sulfuric acid. J. Phys. Chem. A 116, 6375–6386 (2012).

58. He, X.-C. et al. Determination of the collision rate coefficient between charged iodic acid clusters and iodic acid using the appearance time method. Aerosol Sci. Technol. 55, 231–242 (2021).

59. Finkenzeller, H. et al. The gas-phase formation mechanism of iodic acid as an atmospheric aerosol source. Nat. Chem. 15, 129–135 (2023).

60. Wang, M. et al. Measurement of iodine species and sulfuric acid using bromide chemical ionization mass spectrometers. Atmos. Meas. Tech. 14, 4187–4202 (2021).

61. Graus, M., Müller, M. & Hansel, A. High resolution PTR-TOF: quantification and formula confirmation of VOC in real time. J. Am. Soc. Mass. Spectrom. 21, 1037–1044 (2010).

62. Canaval, E., Hyttinen, N., Schmidbauer, B., Fischer, L. & Hansel, A. NH_4+ association and proton transfer reactions with a series of organic molecules. Front. Chem. 7, 191 (2019).

63. Breitenlechner, M. et al. PTR3: an instrument for studying the lifecycle of reactive organic carbon in the atmosphere. Anal. Chem. 89, 5824–5831 (2017).

64. Hansel, A., Scholz, W., Mentler, B., Fischer, L. & Berndt, T. Detection of RO_2 radicals and other products from cyclohexene ozonolysis with NH_4+ and acetate chemical ionization mass spectrometry. Atmos. Environ. 186, 248–255 (2018).

65. Bernhammer, A.-K., Breitenlechner, M., Keutsch, F. N. & Hansel, A. Technical note: Conversion of isoprene hydroxy hydroperoxides (ISOPOOHs) on metal environmental simulation chamber walls. Atmos. Chem. Phys. 17, 4053–4062 (2017).

66. Rivera‐Rios, J. C. et al. Conversion of hydroperoxides to carbonyls in field and laboratory instrumentation: observational bias in diagnosing pristine versus anthropogenically controlled atmospheric chemistry. Geophys. Res. Lett. 41, 8645–8651 (2014).

67. Mentler, B. Measuring Isoprene Hydroxy Hydroperoxides and Isoprene Epoxydiols Using a Novel Funnel SRI-TOF-MS. Thesis, Univ. Innsbruck (2017).

68. Liu, Y. et al. Isoprene photochemistry over the Amazon rainforest. Proc. Natl Acad. Sci. 113, 6125–6130 (2016).

69. Lopez-Hilfiker, F. D. et al. A novel method for online analysis of gas and particle composition: description and evaluation of a Filter Inlet for Gases and AEROsols (FIGAERO). Atmos. Meas. Tech. 7, 983–1001 (2014).

70. Ehn, M. et al. A large source of low-volatility secondary organic aerosol. Nature 506, 476–479 (2014).

71. Junninen, H. et al. A high-resolution mass spectrometer to measure atmospheric ion composition. Atmos. Meas. Tech. 3, 1039–1053 (2010).

72. Rissanen, M. P., Mikkilä, J., Iyer, S. & Hakala, J. Multi-scheme chemical ionization inlet (MION) for fast switching of reagent ion chemistry in atmospheric pressure chemical ionization mass spectrometry (CIMS) applications. Atmos. Meas. Tech. 12, 6635–6646 (2019).

73. Manninen, H. E., Mirme, S., Mirme, A., Petäjä, T. & Kulmala, M. How to reliably detect molecular clusters and nucleation mode particles with Neutral cluster and Air Ion Spectrometer (NAIS). Atmos. Meas. Tech. 9, 3577–3605 (2016).

74. Mirme, S. & Mirme, A. The mathematical principles and design of the NAIS – a spectrometer for the measurement of cluster ion and nanometer aerosol size distributions. Atmos. Meas. Tech. 6, 1061–1071 (2013).

75. Vanhanen, J. et al. Particle size magnifier for nano-CN detection. Aerosol Sci. Technol. 45, 533–542 (2011).

76. Tröstl, J. et al. Fast and precise measurement in the sub-20 nm size range using a Scanning Mobility Particle Sizer. J. Aerosol Sci. 87, 75–87 (2015).

77. Chai, J.-D. & Head-Gordon, M. Long-range corrected hybrid density functionals with damped atom–atom dispersion corrections. Phys. Chem. Chem. Phys. 10, 6615 (2008).

78. Kendall, R. A., Dunning, T. H. & Harrison, R. J. Electron affinities of the first‐row atoms revisited. Systematic basis sets and wave functions. J. Chem. Phys. 96, 6796–6806 (1992).

79. Feller, D. The role of databases in support of computational chemistry calculations. J. Comput. Chem. 17, 1571–1586 (1996).

80. Peterson, K. A., Figgen, D., Goll, E., Stoll, H. & Dolg, M. Systematically convergent basis sets with relativistic pseudopotentials. II. Small-core pseudopotentials and correlation consistent basis sets for the post-d group 16–18 elements. J. Chem. Phys. 119, 11113–11123 (2003).

81. Frisch, M. J. et al. Gaussian 16, Revision C.01 (Gaussian, Inc., 2016).

82. Neese, F. The ORCA program system: the ORCA program system. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2, 73–78 (2012).

83. Ku, B. K. & de la Mora, J. F. Relation between electrical mobility, mass, and size for nanodrops 1–6.5 nm in diameter in air. Aerosol Sci. Technol. 43, 241–249 (2009).

84. Dada, L. et al. Formation and growth of sub-3-nm aerosol particles in experimental chambers. Nat. Protoc. 15, 1013–1040 (2020).

85. Lehtipalo, K. et al. Methods for determining particle size distribution and growth rates between 1 and 3 nm using the Particle Size Magnifier. Boreal Environ. Res. 19, 215–236 (2014).

86. Stolzenburg, D., Steiner, G. & Winkler, P. M. A DMA-train for precision measurement of sub-10 nm aerosol dynamics. Atmos. Meas. Tech. 10, 1639–1651 (2017).

87. Thompson, C. R. et al. The NASA Atmospheric Tomography (ATom) mission: imaging the chemistry of the global atmosphere. Bull. Am. Meteorol. Soc. 103, E761–E790 (2022).

88. Ranjithkumar, A. et al. Constraints on global aerosol number concentration, SO_2 and condensation sink in UKESM1 using ATom measurements. Atmos. Chem. Phys. 21, 4979–5014 (2021).

89. Koenig, T. K. et al. Quantitative detection of iodine in the stratosphere. Proc. Natl Acad. Sci. 117, 1860–1866 (2020).

90. Marcus, R. A. Unimolecular dissociations and free radical recombination reactions. J. Chem. Phys. 20, 359–364 (1952).

91. Atkinson, R. et al. Evaluated kinetic and photochemical data for atmospheric chemistry: supplement III. IUPAC Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. J. Phys. Chem. Ref. Data 18, 881–1097 (1989).

92. Shen, J. et al. New particle formation from isoprene under upper- tropospheric conditions: data sources. Zenodo https://doi.org/10.5281/zenodo.13736557 (2024).

93. Pfeifer, J. et al. Measurement of ammonia, amines and iodine compounds using protonated water cluster chemical ionization mass spectrometry. Atmos. Meas. Tech. 13, 2501–2522 (2020).

Acknowledgements

We thank CERN for supporting CLOUD with important technical and financial resources and for providing a particle beam from the CERN Proton Synchrotron. Funding: this work was supported by Research Council of Finland ACCC Flagship projects 337550, 337549 and 337552; Research Council of Finland professorship (302958); Research Council of Finland’s Centre of Excellence 346371; Research Council of Finland projects 349659, 346371, 345982, 1325656, 311932, 334792, 316114, 325647, 325681, 339489, 347782 and 355966; the Strategic Research Council (SRC) at the Research Council of Finland (352431); European Research Council project ATM-GTP (742206); European Union through Non-CO_2 Forcers and their Climate, Weather, Air Quality and Health Impacts (FOCI); European Research Council Consolidator Grant INTEGRATE grant 865799; EU MSCA Doctoral Network CLOUD-DOC 101073026; German Federal Ministry of Education and Research project CLOUD-22 01LK2201A; US National Science Foundation grants AGS-2132089, AGS-2215489, CHE-2336463, AGS-1602086, AGS-1801329, AGS-1951514, AGS-1801280, AGS-2027252 and AGS-2215489; NASA ROSES programme grant 80NSSC19K0949; Swiss National Science Foundation grants 200021_213071 and 216181; Knut and Alice Wallenberg Foundation projects 2021.0169 and 2021.0298; Federal Ministry of Education and Research (BMBF) financed project CLOUD-22 (01LK2201B); Untersuchung von Aerosolnukleation, Aerosolwachstum und Wolkenaktivierung an der CLOUD-Kammer am CERN zur Erforschung des Einflusses auf das Klima; Estonian Research Council project RVTT3 – ‘CERN Science Consortium of Estonia’ and project PRG714; Horizon 2020 research and innovation programme under grant 856612. We thank L. Yang for her contribution to Fig. 5. We thank the Jane and Aatos Erkko Foundation and the ‘Gigacity’ project financed by Wihuri foundation. A.M. and P.M.W. acknowledge financial support by the Vienna Doctoral School in Physics (VDSP).

Author information

Author notes

1. These authors contributed equally: Jiali Shen, Douglas M. Russell

Authors and Affiliations

1. Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland

Jiali Shen, Rima Baalbaki, Wenjuan Yu, Emelda Ahongshangbam, Dina Alfaouri, Jonathan Duplissy, Markus Lampimäki, Maija Peltola, Birte Rörup, Roseline C. Thakur, Jiangyi Zhang, Katrianne Lehtipalo, Markku Kulmala, Doug R. Worsnop & Xu-Cheng He

2. Helsinki Institute of Physics, University of Helsinki, Helsinki, Finland

Jiali Shen, Jonathan Duplissy & Markku Kulmala

3. Institute for Atmospheric and Environmental Sciences, Goethe University Frankfurt, Frankfurt am Main, Germany

Douglas M. Russell, Lucía Caudillo-Plath, Lisa J. Beck, Moritz Berntheusel, Manuel Granzin, Lena Große Schute, Martin Heinritzi, Hannah Klebach, Timm Krüger, Andreas Kürten, Pedro Rato, Sarah Richter, Mario Simon, Marcel Zauner-Wieczorek, Jasper Kirkby & Joachim Curtius

4. Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, USA

Jenna DeVivo, Nirvan Bhattacharyya, Hamish Gordon & Neil M. Donahue

5. Atmospheric Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany

Felix Kunkler, Monica Martinez, Jos Lelieveld & Hartwig Harder

6. Institute for Ion Physics and Applied Physics, University of Innsbruck, Innsbruck, Austria

Bernhard Mentler, Wiebke Scholz, Hannah Beckmann, Jakob Weissbacher, Zhensen Zheng & Armin Hansel

7. CERN, the European Organization for Nuclear Research, Geneva, Switzerland

Eva Sommer, João Almeida, Antti Onnela, Pedro Rato & Jasper Kirkby

8. Faculty of Physics, University of Vienna, Wien, Austria

Eva Sommer, Aleksandra Morawiec & Paul M. Winkler

9. Department of Chemistry, University of Helsinki, Helsinki, Finland

Emelda Ahongshangbam

10. CENTRA and Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal

João Almeida & Antonio Amorim

11. Department of Environmental Physics, University of Tartu, Tartu, Estonia

Hannah Beckmann, Kalju Tamme & Heikki Junninen

12. Aerodyne Research Inc., Billerica, MA, USA

Manjula R. Canagaratna & Doug R. Worsnop

13. Department of Environmental Science, Stockholm University, Stockholm, Sweden

Anouck Chassaing & Ilona Riipinen

14. Department of Chemistry, University of Colorado Boulder, Boulder, CO, USA

Romulo Cruz-Simbron, Mago Reza, Yandong Tong & Rainer Volkamer

15. Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA

Romulo Cruz-Simbron, Mago Reza, Yandong Tong & Rainer Volkamer

16. Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen, Switzerland

Lubna Dada, Lu Liu, Mihnea Surdu, Jens Top, Boxing Yang, Urs Baltensperger & Imad El Haddad

17. Aerosol Physics Laboratory, Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland

Siddharth Iyer

18. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

Brandon Lopez & Neil M. Donahue

19. Institute of Meteorology and Climate Research, Atmospheric Aerosol Research, Karlsruhe Institute of Technology, Karlsruhe, Germany

Milin Kaniyodical Sebastian, Nsikanabasi Silas Umo & Ottmar Möhler

20. Instituto Dom Luiz (IDL), Universidade da Beira Interior, Covilhã, Portugal

António Tomé

21. Atmospheric Microphysics Department, Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany

Gabriela Unfer

22. Department of Technical Physics, University of Eastern Finland, Kuopio, Finland

Lejish Vettikkat & Siegfried Schobesberger

23. Climate and Atmosphere Research Centre (CARE-C), The Cyprus Institute, Nicosia, Cyprus

Christos Xenofontos, Theodoros Christoudias & Jos Lelieveld

24. IONICON Analytik GmbH, Innsbruck, Austria

Zhensen Zheng & Armin Hansel

25. Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA

Richard C. Flagan

26. TOFWERK, Thun, Switzerland

Urs Rohner

27. Finnish Meteorological Institute, Helsinki, Finland

Katrianne Lehtipalo & Xu-Cheng He

28. Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA

Neil M. Donahue

29. Department of Chemistry, Carnegie Mellon University, Pittsburgh, PA, USA

Neil M. Donahue

30. Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, China

Markku Kulmala

31. Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, China

Markku Kulmala

32. Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK

Xu-Cheng He

Contributions

X.-C.H., J.S., D.M.R., J.K., J.C. and M.H. planned the experiments. J.S., D.M.R., J.De., F.K., R.B., B.M., W.S., W.Y., L.C.-P., E.S., E.A., D.A., J.A., A.A., L.J.B., H.B., M.B., N.B., M.R.C., A.C., L.D., J.D., M.G., L.G.S., M.H., H.K., T.K., A.K., L.L., M.M., A.M., A.O., M.P., P.R., M.R., B.R., M.K.S., M.Si., M.Su., K.T., R.C.T., A.T., Y.T., J.T., N.S.U., J.W., B.Y., M.Z.-W., J.Z., Z.Z., T.C., I.E.H., R.C.F., H.J., U.R., S.S., R.V., P.M.W., A.H., K.L., N.M.D., J.L., H.H., M.K., D.R.W., J.K., J.C. and X.-C.H. prepared the CLOUD facility or measuring instruments. J.S., D.M.R., J.De., F.K., R.B., B.M., W.S., W.Y., L.C.-P., E.S., E.A., J.A., A.A., L.J.B., H.B., N.B., A.C., R.C.-S., J.D., M.G., L.G.S., M.H., H.K., T.K., M.L., L.L., B.L., M.M., A.M., M.P., P.R., M.R., S.R., B.R., M.Si., M.Su., K.T., R.C.T., A.T., Y.T., J.T., G.U., L.V., J.W., C.X., B.Y., M.Z.-W., J.Z., Z.Z., I.E.H., O.M., R.V., P.M.W., A.H., K.L., H.H., J.K., J.C. and X.-C.H. collected the data. S.I. carried out quantum-chemical calculations. J.S., X.-C.H., D.M.R., J.De., F.K., R.B., B.M., W.S., W.Y., L.C.-P., E.S., H.K., T.K., L.L., P.R., M.Si., M.Su., Y.T., J.W., B.Y., M.Z.-W., J.Z., Z.Z., H.H. and J.K. analysed the data. J.K. and X.-C.H. wrote the manuscript, with contributions from J.S., D.M.R., J.De. and N.M.D. Finally, J.S., D.M.R., J.De., F.K., R.B., B.M., W.S., E.S., R.C.-S., L.D., H.G., B.R., M.Si., Y.T., L.V., C.X., B.Y., U.B., I.E.H., R.C.F., I.R., S.S., R.V., A.H., N.M.D., J.L., M.K., D.R.W., J.K., J.C. and X.-C.H. commented on and edited the manuscript.

Corresponding authors

Correspondence to Jasper Kirkby, Joachim Curtius or Xu-Cheng He.

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Competing interests

The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Wall-loss rates of C[5]H[10]O[3], C[5]H[12]O[6] and HIO[3] at −30 °C and the IEPOX fraction of C[5]H[10]O[3] at −30 °C and −50 °C.

a, Wall-loss lifetimes (1/(loss rate)) of C_5H_10O_3, C_5H_12O_6 and HIO_3 at 100% mixing fan speed (increased loss rate). At stage 1, the mixing fan speed is set to 12% (standard operation). At stage 2, the mixing fan speed is set to 100% to increase the wall-loss rate. All three vapours have wall-loss rates consistent with that measured for H_2SO_4 (not shown), indicating irreversible loss on wall impact. b, Simulation of the fraction of IEPOX in C_5H_10O_3 using the reduced Wennberg et al. mechanism[35]. The dashed grey line is a fit to the data of the form \({\rm{IEPOX}}\,{\rm{fraction}}={10}^{(0.58\times {\log }_{10}([{\rm{OH}}])-4.6)}\). Our simulation predicts that roughly 30% of the measured C_5H_10O_3 signal is IEPOX under the conditions of panel a and that both the ISOPOOH and IEPOX (summed as C_5H_10O_3) are lost to the chamber wall on impact, at −30 °C and at −50 °C. The experimental conditions for panel a are: isoprene = 0.13 ppbv (3.7 × 10^9 cm−3), O_3 = 30 ppbv (8.6 × 10^11 cm−3), I_2 = 3.1 × 10^7 cm−3, SO_2 = 3 × 10^8 cm−3, OH = 8.9 × 10^6 cm−3, HO_2 = 1.8 × 10^8 cm−3, RH = 57% and temperature = −30 °C. The experimental conditions for panel b are: isoprene = 0.04–1.50 ppbv (0.1–4.2 × 10^10 cm−3), O_3 = 1–590 ppbv (3.7 × 10^10 to 1.8 × 10^13 cm−3), I_2 = 0–7.5 × 10^7 cm−3, SO_2 = 0–4.6 × 10^9 cm−3, OH = 0.11–6.90 × 10^7 cm−3, HO_2 = 0.6–17.0 × 10^8 cm−3, HO_2/OH ratio = 11–118, NO = 0–0.22 ppbv, NO_2 = 0–0.77 ppbv, RH = 29–70% and temperature = −30 °C and −50 °C.

Extended Data Fig. 2 IP0N yield from OH reaction with C[5]H[10]O[3].

Measured IP0N concentration versus reacted C_5H_10O_3 (see Methods for details). The dashed lines show the predictions for several IP0N molar yields; a yield of 100% implies that each OH reaction with C_5H_10O_3 produces one IP0N molecule. The data indicate that the yield is between 20% and 50%, with an overall systematic uncertainty of a factor of two. When NOx is added to the system, the yield of IP0N is reduced, owing to NO terminating isoprene peroxy radicals. The ratios of IEPOX and ISOPOOH in C_5H_10O_3 are determined from the measured OH concentrations and the fit to the simulation shown in Extended Data Fig. 1b. For simplification, we apply a general reaction-rate coefficient of 10−10 and 10−11 cm^3 s−1 for the ISOPOOH + OH and IEPOX + OH reactions, respectively[35]. The experimental conditions are: isoprene = 0.04–1.50 ppbv (0.1–4.2 × 10^10 cm−3), O_3 = 1–590 ppbv (3.7 × 10^10 to 1.8 × 10^13 cm−3), I_2 = 0–7.5 × 10^7 cm−3, SO_2 = 0–4.6 × 10^9 cm−3, OH = 0.11–6.90 × 10^7 cm−3, HO_2 = 0.6–18.0 × 10^8 cm−3, HO_2/OH ratio = 11–117, NO = 0–0.22 ppbv, NO_2 = 0–0.77 ppbv, RH = 29–70% and temperature = −30 °C and −50 °C.

Extended Data Fig. 3 Example new particle formation experiment at −50 °C with added HIO[x].

Evolution of vapour concentrations (a), particle-nucleation rates at 1.7 nm, J1.7 (b), and naturally charged particle number size distribution (dN/dlogDp) and growth rate measured between 3.2 and 8.0 nm, GR3.2-8 (nm h−1) (c). The black line in panel c depicts the linear fit of 50% appearance time of particles between 3.2 and 8.0 nm. The expected nucleation rates for HIOx at the kinetic limit are less than 2 × 10−4 cm−3 s−1. The results show that HIOx enhances IP0N nucleation in a similar way as H_2SO_4. Discontinuous lines show missing data. The experimental conditions are: isoprene = 0.07–0.16 ppbv (2.1–4.9 × 10^9 cm−3), O_3 = 95 ppbv (3 × 10^12 cm−3), I_2 = 0.4–6.7 × 10^7 cm−3, OH = 3.2 × 10^6 cm−3, HO_2 = 7.4 × 10^7 cm−3, RH = 63% and temperature = −49 °C.

Extended Data Fig. 4 Aerosol formation from pure IP0N products and mixture of IP0N, IP1-2N and H[2]SO[4].

Evolution of vapour concentrations (a,b), particle-nucleation rates at 1.7 nm, J1.7 (c,d), and naturally charged particle number size distribution (dN/dlogDp) and growth rates measured between 3.2 and 8.0 nm, GR3.2-8 (nm h−1) (e,f). The black lines in panel f depict the linear fits of 50% appearance time of particles between 3.2 and 8.0 nm. The results show that pure IP0N aerosol formation is feasible and atmospheric ions can enhance the nucleation rate by a few times. The addition of low concentrations of H_2SO_4 further enhances IP0-2N nucleation by up to two orders of magnitude. The vertical dashed lines and labels indicate the start of a new stage in which the experimental conditions were adjusted. The dotted black curve in panel d shows the expected H_2SO_4–NH_3 nucleation rate, conservatively assuming that NH_3 is present at the 4 pptv limit of detection44,93. Stages 1 and 3 are, respectively, under neutral (ion-free) and beam (ion-enhanced) conditions, whereas all of the other stages are under galactic cosmic ray (GCR; natural ion concentrations) conditions. The SO_2 concentration is increased in steps during stages 5–12. The experimental conditions for the right panels are: isoprene = 0.08–0.63 ppbv (2.4–20.0 × 10^9 cm−3), O_3 = 189 ppbv (6 × 10^12 cm−3), OH = 2.4 × 10^6 cm−3, HO_2 = 1.8 × 10^8 cm−3, RH = 32% and temperature = −49 °C. The experimental conditions for the left panels are: isoprene = 0.05–0.50 ppbv (1.6–16.0 × 10^9 cm−3), O_3 = 1.2 ppbv (3.8 × 10^10 cm−3), SO_2 = 0–1.9 × 10^8 cm−3, OH = 6 × 10^7 cm−3, HO_2 = 1 × 10^9 cm−3, NO = 0.12–0.81 ppbv, NO_2 = 0.57–0.84 ppbv, RH = 52% and temperature = −48 °C.

Extended Data Fig. 5 Particle-nucleation rates from IP-OOM at −50 °C, in the absence of acids.

Nucleation rates at 1.7 nm, J1.7, versus IP-OOM without nitrogen, IP0N (a), with nitrogen, IP1N + IP2N (b) and the sum, IP0-2N (c). Panel a indicates a mild enhancement of the nucleation rates with increased IP1N + IP2N. Panels b and c confirm that the nucleation rates are only weakly dependent on IP1N + IP2N. These data show that non-nitrate IP-OOM are primarily responsible for particle nucleation, with a smaller contribution from nitrate-containing isoprene products. The experimental conditions are: isoprene = 0.04–0.50 ppbv (0.14–1.7 × 10^10 cm−3), O_3 = 1.2–590 ppbv (4 × 10^10 to 1.8 × 10^13 cm−3), OH = 0.14–6.40 × 10^7 cm−3, HO_2 = 1.6–17.0 × 10^8 cm−3, HO_2/OH ratio = 11–118, NO = 0–0.18 ppbv, NO_2 = 0–0.74 ppbv, RH = 29–61% and temperature = −50 °C. All experiments are carried out under galactic cosmic ray ionization (ambient-boundary-layer conditions). The error bars represent the standard deviation of the measurement at steady state.

Extended Data Fig. 6 Particle-nucleation rates at 2.5 nm versus vapour concentrations.

Nucleation rates at 2.5 nm, J2.5, versus IP0N (a,b), HIOx + H_2SO_4 (c) and the product (HIOx + H_2SO_4) × IP0N (d). Panel a and b show experiments without and with added NOx. Panel c shows all experiments and panel d shows experiment with acids, both with and without NOx. IP0N excludes nitrogen-containing isoprene-oxygenated molecules. The solid lines in panel c show the nucleation rates at 1.7 nm expected for H_2SO_4 with 4 pptv NH_3 at 60% RH (ref. [44]). The dashed and solid lines in panel d represent fits to the equation 10a×log10(x)+b, in which, for the dashed line, a = 1.167 and b = −14.743, and for the solid line, a = 1.669 and b = −22.707. Panels a and b show that both total acid (HIOx + H_2SO_4) and IP0N contribute to the nucleation rate. The experimental conditions are: isoprene = 0.04–1.50 ppbv (0.1–4.2 × 10^10 cm−3), O_3 = 1–590 ppbv (3.7 × 10^10 to 1.8 × 10^13 cm−3), I_2 = 0–7.5 × 10^7 cm−3, SO_2 = 0–4.6 × 10^9 cm−3, OH = 0.11–6.90 × 10^7 cm−3, HO_2 = 0.6–18.0 × 10^8 cm−3, HO_2/OH ratio = 11–118, NO = 0–0.22 ppbv, NO_2 = 0–0.77 ppbv, RH = 29–70% and temperature = −30 °C and −50 °C. All experiments are carried out under galactic cosmic ray ionization (ambient conditions). The error bars represent the standard deviation of the measurement at steady state.

Extended Data Fig. 7 Molecular composition of charged clusters with sulfuric acid, iodic acid and no acid during IP-OOM nucleation at −50 °C.

Mass defect (difference from integer mass) versus m/z (mass-to-charge ratio) during nucleation events for ions and negatively charged (a–d) and positively charged (e,f) molecular clusters measured with two APi-TOF instruments (see Methods), coloured by the number of carbon (a,b,e), sulfur (c), iodine (d) and nitrogen (f) atoms. The data are without NOx (a–e) and with NOx (f). In panel d, the (purple) clusters with no iodine contain a NO_3− ion. The symbol area is proportional to the ion signal rate (cps, counts s−1). It should be noted that the APi-TOF used in this experiment is less sensitive than the MION2-APi-TOF and thus the absolute ion signals and cluster population should not be compared between the instruments. Together with Fig. 3, our results show that ion-induced nucleation occurs both with pure IP-OOM and with further H_2SO_4 and HIOx. The experimental conditions for a,c,e are: isoprene = 0.19 ppbv (5.9 × 10^9 cm−3), O_3 = 93 ppbv (2.9 × 10^12 cm−3), I_2 = unmeasured, SO_2 = unmeasured, OH = 3.0 × 10^6 cm−3, HO_2 = unmeasured, RH = 63% and temperature = −49 °C. The concentrations of IP0N = unmeasured, H_2SO_4 = 3.0 × 10^5 cm−3 and HIOx = 2.8 × 10^4 cm−3. The experimental conditions for b,d are: isoprene = 0.09 ppbv (2.8 × 10^9 cm−3), O_3 = 97 ppbv (3 × 10^12 cm−3), I_2 = 5.1 × 10^7 cm−3, SO_2 = 0, OH = 3.4 × 10^6 cm−3, HO_2 = 7.6 × 10^7 cm−3, RH = 63% and temperature = −49 °C. The concentrations of low-volatility vapours are IP0N = 1.3 × 10^7 cm−3, H_2SO_4 = 8.3 × 10^3 cm−3 and HIOx = 6.7 × 10^5 cm−3. The experimental conditions for f are: isoprene = 0.12 ppbv (3.7 × 10^9 cm−3), O_3 = 1.2 ppbv (3.7 × 10^10 cm−3), SO_2 = 1.2 × 10^8 cm−3, OH = 6.2 × 10^7 cm−3, HO_2 = 1 × 10^9 cm−3, RH = 42%, NO = 0.25 ppbv, NO_2 = 0.78 ppbv and temperature = −48 °C. The concentrations of low-volatility vapours are IP0N = 1.2 × 10^7 cm−3, H_2SO_4 = 2.4 × 10^5 cm−3 and HIOx = 0 cm−3.

Extended Data Fig. 8 Four experiments that demonstrate the influence of acids and NO[x] on IP-OOM nucleation at −50 °C.

Four experiments showing nucleation rates at 1.7 nm, J1.7, measured at similar IP0N concentrations, 1.2–3.3 × 10^7 cm−3, but with added acids, NOx and acids + NOx, respectively. The marker area is proportional to the IP0N concentration. The experimental conditions are shown next to the data points. Circles correspond to absence of acids and squares include acids. The data show that extremely low H_2SO_4 concentrations, 1.3–2.8 × 10^6 cm−3, strongly enhance the nucleation rates regardless of the absence or presence of NOx. On the other hand, additions of high nitrate IP-OOM concentrations around 10^8 cm−3 only produce relatively small increases in nucleation rates.

Extended Data Table 1 Comparison of CLOUD experimental conditions with the ambient conditions during CAFE-Brazil research flight RF 19, periods T4 and T9 (Curtius et al.[13])

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Shen, J., Russell, D.M., DeVivo, J. et al. New particle formation from isoprene under upper-tropospheric conditions. Nature 636, 115–123 (2024). https://doi.org/10.1038/s41586-024-08196-0

- Received: 05 April 2024

- Accepted: 10 October 2024

- Published: 04 December 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08196-0

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Isoprene nitrates drive new particle formation in Amazon’s upper troposphere

Joachim Curtius,

Martin Heinritzi,

Lisa J. Beck,

Mira L. Pöhlker,

Nidhi Tripathi,

Bianca E. Krumm,

Philip Holzbeck,

Clara M. Nussbaumer,

Lianet Hernández Pardo,

Thomas Klimach,

Konstantinos Barmpounis,

Simone T. Andersen,

Roman Bardakov,

Birger Bohn,

Micael A. Cecchini,

Jean-Pierre Chaboureau,

Thibaut Dauhut,

Dirk Dienhart,

Raphael Dörich,

Achim Edtbauer,

Andreas Giez,

Antonia Hartmann,

Bruna A. Holanda,

Philipp Joppe,

Katharina Kaiser,

Timo Keber,

Hannah Klebach,

Ovid O. Krüger,

Andreas Kürten,

Christian Mallaun,

Daniel Marno,

Monica Martinez,

Carolina Monteiro,

Carolina Nelson,

Linda Ort,

Subha S. Raj,

Sarah Richter,

Akima Ringsdorf,

Fabio Rocha,

Mario Simon,

Sreedev Sreekumar,

Anywhere Tsokankunku,

Gabriela R. Unfer,

Isabella D. Valenti,

Nijing Wang,

Andreas Zahn,

Marcel Zauner-Wieczorek,

Rachel I. Albrecht,

Meinrat O. Andreae,

Paulo Artaxo,

John N. Crowley,

Horst Fischer,

Hartwig Harder,

Dirceu L. Herdies,

Luiz A. T. Machado,

Christopher Pöhlker,

Ulrich Pöschl,

Anna Possner,

Andrea Pozzer,

Johannes Schneider,

Jonathan Williams &

…

Jos Lelieveld

Nature volume 636, pages 124–130 (2024)

Abstract

New particle formation (NPF) in the tropical upper troposphere is a globally important source of atmospheric aerosols1,2,3,4. It is known to occur over the Amazon basin, but the nucleation mechanism and chemical precursors have yet to be identified[2]. Here we present comprehensive in situ aircraft measurements showing that extremely low-volatile oxidation products of isoprene, particularly certain organonitrates, drive NPF in the Amazonian upper troposphere. The organonitrates originate from OH-initiated oxidation of isoprene from forest emissions in the presence of nitrogen oxides from lightning. Nucleation bursts start about 2 h after sunrise in the outflow of nocturnal deep convection, producing high aerosol concentrations of more than 50,000 particles cm−3. We report measurements of characteristic diurnal cycles of precursor gases and particles. Our observations show that the interplay between biogenic isoprene, deep tropical convection with associated lightning, oxidation photochemistry and the low ambient temperature uniquely promotes NPF. The particles grow over time, undergo long-range transport and descend through subsidence to the lower troposphere, in which they can serve as cloud condensation nuclei (CCN) that influence the Earth’s hydrological cycle, radiation budget and climate1,4,5,6,7,8.

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Main

Isoprene is emitted in large quantities by vegetation such as broad-leafed trees and it has the largest source strength of all biogenic volatile organic compounds (VOCs). Globally, emissions amount to about 500–600 Tg a−1, representing more than half of the total biogenic VOC emissions, with tropical South America contributing an estimated 163 Tg a−1 (refs. 9,10). Isoprene is primarily emitted into the atmosphere during daylight hours and is typically converted within 1–2 h into oxygenated VOCs, mostly through reaction with hydroxyl radicals (OH)11,12. Isoprene mixing ratios in the continental boundary layer of the Amazon basin range between 1 and 20 ppbv, with a diurnal cycle peaking in the afternoon[13]. Although isoprene-derived oxygenated organic molecules (IP-OOMs) are known to contribute substantially to the production of secondary organic aerosol mass by means of condensation onto pre-existing particles14,15, they are unable to form new particles in the boundary layer and are even thought to inhibit NPF from monoterpenes16,17.

Deep convection can transport isoprene from the boundary layer to the upper troposphere within 1–2 h (refs. 18,19). Palmer et al.[20] demonstrated that nocturnal deep convection delivers isoprene to the tropical upper troposphere, leading to secondary organic aerosol production[20]. Deep convective clouds are also associated with lightning activity[21] and thus represent a main source of nitrogen oxides (NOx)[22] that play a central role in OH formation (recycling) in the upper troposphere[23]. Furthermore, pre-existing aerosol particles are efficiently scavenged from boundary-layer air that ascends in precipitating deep convective clouds2,24. As a result, the outflow of nocturnal deep convection contains increased levels of isoprene and NOx, with a weak condensation sink (CS) for condensation of vapours onto pre-existing aerosols. The low temperatures of the upper troposphere and intense solar (actinic) radiation during the daytime create favourable conditions for NPF, that is, aerosol nucleation and initial growth up to detectable particle size in the convective outflow1,2,7,25.

Although high number concentrations of freshly formed particles have frequently been observed in the upper troposphere1,2,3,7,25,26,27,28, the conditions and chemical processes underlying NPF have remained elusive. Kulmala et al.[29] proposed that insoluble organics are transported to the upper troposphere by deep convection to produce new particles and Ekman et al.[30] suggested that isoprene itself could be the precursor for upper tropospheric NPF over the Amazon. A study of the aerosol composition in the upper troposphere over the Amazon, focusing on larger particles (dp > 50 nm), found isoprene-derived secondary organic aerosols and organic nitrates to be the main components[31]. Recent measurements at the Chacaltaya mountain site in Bolivia (5,240 m a.m.s.l.) revealed isoprene oxidation products, including organonitrates in the gas and aerosol phases in air masses from the free troposphere over the Amazon basin[32]. However, the precursors that drive particle nucleation and early growth were not identified. Although NPF supplies about half the CCN globally4,33, in the continental and marine boundary layer in the tropics it is rare[34] and typical NPF events have not been observed over the Amazon rainforest, mostly because vapours that are condensable at cold conditions remain volatile at high temperatures35,36,37,38. However, several studies have demonstrated that particles form in the cold upper troposphere and are subsequently transported to lower altitudes, at which they act as CCN1,2,4,6,7,39,40.

Observation of NPF

The Chemistry of the Atmosphere: Field Experiment in Brazil (CAFE-Brazil) took place from December 2022 to January 2023 (Extended Data Fig. 1). It studied NPF by conducting several measurement flights in the upper troposphere up to 13.8 km altitude over the Amazon basin (Methods). High concentrations of freshly formed aerosol particles (dp < 5 nm) were frequently detected at altitudes above 8 km. Several condensation nuclei (CN)-counter channels were operated to measure the total particle number concentrations N_2, N_3, N_4 and N_5 larger than the lower detection cut-off diameters of 2, 3, 4 and 5 nm, respectively, and the differences, for example, N2–5, denote the concentration of freshly formed particles in the range 2–5 nm (Methods). In situ observations of the chemical transformation of precursor gases into extremely low-volatility organic compounds (ELVOCs) and the onset of rapid and strong NPF in air masses influenced by recent outflow from a nocturnal mesoscale convective system are shown in Fig. 1. By repeatedly examining the same air masses while flying a holding pattern centred on the outflow, we followed the photochemical development during the night–day transition (Extended Data Fig. 2). Measurements before sunrise were used to characterize air masses at an altitude of 13 km. Higher isoprene mixing ratios of 400–850 pptv (equivalent to pmol mol−1) were found up to 3 h after cloud outflow. Trajectory calculations indicate that the air had been in contact with an electrically active mesoscale convective system that had developed south of the flight path during night-time (Fig. 2), which was sampled frequently (T1–T9 in Figs. 1 and 2). The outflow also comprised high levels of NOx. Photochemical production of OH radicals was observed to start at sunrise and increased with solar elevation, reaching concentrations of about 6 × 10^5 cm−3 (corresponding to 0.1 pptv) during T4. During this period, radiation reflected from cirrus layers below the sampled air masses resulted in an increase in actinic flux and enhanced rates of photochemical processing, with 300–500 pptv of NO formed from NO_2 photolysis after sunrise (Fig. 1 and Extended Data Fig. 2). The reaction of isoprene with OH initiates the formation of isoprene hydroxy peroxy radicals (ISOPOO).

Fig. 1: Time series of radicals, trace gases and aerosol particles.

Measurements were performed on 23 January 2023 over the Amazon basin north of Manaus (RF 19). a, OH and HO_2. b, Isoprene, sum of the first-generation isoprene oxidation products methyl vinyl ketone (MVK), methacrolein (MACR) and isoprene hydroxy hydroperoxide (ISOPOOH) and NO. c, Isoprene-derived organonitrates C_5H_11O_4(ONO_2) and C_5H_10O_2(ONO_2)2. d, Particle concentrations N2–4 and N4–5, as well as pressure altitude. The light-red bar (T9) indicates the period between 10:05 and 10:10 local time, during which the strongest NPF was detected. Grey bars (T1–T8) represent previous sampling periods of the air mass examined at T9, traced by backward trajectories originating from the position of the aircraft during that period. The ambient temperature was about −58 °C at a flight altitude of 12.3 km during T1–T9. During the 4–5 min of the NPF event encounters, the aircraft travels more than 50 km.

Source Data

Fig. 2: Flight path and air-mass back trajectories.

a, Flight path for RF 19 (grey). The diamond-shaped pattern ABCD was flown repeatedly and the NPF air mass was encountered several times (thick-coloured sections T4–T9) also before the NPF event started (T1–T3). Coloured sections T1–T8 are each slightly shifted to the right to prevent overlap. Thin yellow lines with 1-h markers indicate 3 h of back trajectories from the NPF period T9 used to calculate and identify periods T1–T8. b, Back trajectories from the locations of the aircraft between 08:05 and 08:15 local time (black and red markers) to 140 min before (yellow-centred markers) and infrared satellite image (GOES-16, band 13: 10.3 µm; https://ftp.cptec.inpe.br/goes/goes16/retangular/ch13/2023/01/) indicating the approximate cloud-top temperatures at 05:50 local time. Temperatures below −50 °C are coloured. The back-trajectory calculations used the horizontal wind speed and direction measured by the research aircraft. The air parcels in which NPF was detected (red circles) trace back to the convective cloud and were in contact with convection more recently (relative to the sampling time) than those in which NPF was not detected (black circles). Satellite picture data in a obtained from https://wvs.earthdata.nasa.gov, NASA Worldview Snapshots.

Source Data

Further reactions lead to forming first-generation and second-generation products, including various organonitrates. High concentrations of highly oxidized isoprene alkyl nitrates were observed for the first time at 08:10–08:14 local time (T4), coincident with bursts of N2–5 particles (Fig. 1 and Extended Data Figs. 2–5). The chemical ionization atmospheric pressure interface ime-of-flight (CI-APi-TOF) mass spectrometer can detect highly oxidized ELVOCs as well as inorganic species such as sulfuric acid41,42,43, which cause NPF (Methods). The CI-APi-TOF data were strongly dominated by the mononitrate C_5H_11O_4(ONO_2) and the dinitrate C_5H_10O_2(ONO_2)2 of isoprene (Fig. 3), causing nucleation and the early growth of the freshly formed particles (Methods and Extended Data Fig. 3). These molecular properties have been confirmed by laboratory observations of isoprene nitrates in charged molecular clusters and neutral particles at upper-troposphere conditions[44]. The CI-APi-TOF measurements represent a combination of gas phase and nanoaerosol composition owing to ram-pressure-induced adiabatic heating and, thus, partial evaporation of molecular clusters and nanoparticles in the inlet system (Methods).

Fig. 3: Molecular composition detected with the CI-APi-TOF mass spectrometer during the strongest NPF event of RF 19 (T9, 10:06–10:10 local time).

a, Mass defect versus mass-to-charge ratio with labelling of the molecular composition for prominent peaks. For clarity, the nitrate reagent ion (NO_3−, m/z = 62) has been omitted from all labels. Circle size indicates peak intensity and colour indicates isoprene oxidation products without a nitrate group (IP0N, green), mononitrates (IP1N, magenta), dinitrates (IP2N, blue) and MSA signals (orange). b, Saturation vapour pressure C* of isoprene and its oxidation products as calculated with the SIMPOL model[49]. The compounds C_5H_12O_6, C_5H_11O_4(ONO_2) and C_5H_10O_2(ONO_2)2 are ELVOCs at −58 °C. c, Reaction scheme leading to the formation of C_5H_12O_6, C_5H_11O_4(ONO_2) and C_5H_10O_2(ONO_2)2. The molecular structures shown are exemplary. IVOCs, intermediate-volatility organic compounds; LVOCs, low-volatility organic compounds; SVOCs, semivolatile organic compounds.

Source Data

The air mass in which the first NPF burst was observed at 08:10–08:14 local time (T4) initially contained a low number of pre-existing particles (N_2 < 3,000 cm−3) and hence a low CS, estimated at CS < 1 × 10−3 s−1 (T2 and T3; Methods and Extended Data Fig. 3). Although no N2–5 particles were detected in T2, several hundred more particles were detected in the 2-nm CN-counter channel during period T3, indicating the onset of NPF (Extended Data Fig. 3). Within the 20 min between T3 and T4, more than 25,000 cm−3 N2–5 particles were formed, which corresponds to a formation rate J_2 for particles >2 nm of about 20 cm−3 s−1. For this first NPF burst, particle concentrations measured in all CN-counter channels increased by at least an order of magnitude, except for the largest channel (>5 nm), which only increased slightly over previous background values. This implies a growth rate of about 9 nm h−1 from an initial cluster size of around 1 nm up to 4 nm. At the subsequent encounter of the air mass, another 20 min later (T5), the 5-nm channel shows an increase by one order of magnitude, indicating growth of the 2–5-nm particles to sizes beyond 5 nm. The growth rate represents an estimate, limited by the uncertainties of cut-off diameters of the CN-counter channels and air-mass inhomogeneities. Later in the day, N2–5 particles were ubiquitous throughout the upper troposphere, indicating that NPF events are widespread, and nucleation plumes disperse and mix with background air. At that time, the numbers of N2–5 particles did not reach the same levels as during the early morning, indicating reduced availability of precursors and particle abundance owing to coagulation and mixing. During the later flight sections, we could not repeat quasi-Lagrangian air-mass encounters, thus formation and growth rates cannot be estimated. Nevertheless, the CN-counter measurements confirm the presence of N2–5 particles.

Formation of organonitrates

The highest concentrations of nitrates and N2–5 particles were detected at 10:06–10:10 local time (T9), for which CI-APi-TOF data are shown in Fig. 3. Isoprene oxygenated organic molecules comprise about 97% of the detected reaction products, with two compounds dominating, C_5H_11O_4(ONO_2) and C_5H_10O_2(ONO_2)2. We categorized all isoprene oxidation products detected by the CI-APi-TOF mass spectrometer into non-nitrogen-containing, mononitrates and dinitrates (IP0-2N = IP0N + IP1N + IP2N). The CI-APi-TOF mass spectrometer only detects oxidized isoprene products with number of oxygen atoms ≥ 4 and with increasing sensitivity with the number of oxygen atoms and functional groups[41] (Methods). Therefore, these measurements represent lower limits of the ambient concentrations of compounds that contain 4–9 oxygen atoms and, although species such as C_5H_8O_4 are detected, they may be underestimated.

The two main compounds C_5H_11O_4(ONO_2) and C_5H_10O_2(ONO_2)2 are the likely products of second-generation OH oxidation in which the intermediate RO_2 radicals react twice (sequentially) with NO to form the dinitrate C_5H_10O_2(ONO_2)2 or with HO_2 and NO to form the mononitrate C_5H_11O_4(ONO_2), respectively (Fig. 3c). The corresponding first-generation intermediate closed-shell products are isoprene hydroxyl peroxide (ISOPOOH, C_5H_10O_3), formed when ISOPOO reacts with HO_2, and isoprene hydroxy nitrate (IHN, C_5H_9O(ONO_2)) in the case of NO reaction. The isomeric structures of the first-generation and second-generation products shown in Fig. 3c are exemplary; other formation pathways may exist and further reaction pathways (for example, forming peroxy nitrates RO_2NO_2 from reaction with NO_2) complement the scheme. Although isoprene oxidation chemistry has been investigated in detail, including under high-NOx conditions11,45,46,47,48, studies at low-temperature and low-pressure conditions in the upper troposphere are lacking. The reaction of NO with the RO_2 peroxy radical can produce either nitrates or alkoxy radicals and NO_2. The branching ratio for this reaction is an intricate function of temperature, pressure, molecular size and structure. Still, at the low-temperature conditions in the upper troposphere, the branching ratio probably shifts towards the termolecular channel, which produces organic nitrates[11].

Further isoprene nitrate compounds include, for example, C_5H_11NO_8 and C_5H_10N_2O_9, which are probably peroxynitrates formed from the reaction of the RO_2 radicals with NO_2. These compounds were also detected, but at 10–40 times lower abundance compared with the respective alkyl nitrates (Fig. 3). The lower abundance is explained by the much lower concentration of NO_2 (as derived from the photostationary state approximation) compared with NO (Extended Data Fig. 2).

We used the SIMPOL model[49] to estimate the volatility and the supersaturation ratio for C_5H_12O_6, C_5H_11O_4(ONO_2) and C_5H_10O_2(ONO_2)2 (Methods). At −60 °C in the upper troposphere, all three compounds have a saturation vapour pressure, C*, of about 3 × 10−7 µg m−3, which is in the volatility class of ELVOCs[50]. At T9, a total supersaturation ratio of 0.5–1.0 × 10^5 is calculated. For these conditions, C_5H_11O_4(ONO_2) and C_5H_10O_2(ONO_2)2 are expected to participate in particle nucleation and early growth. It can be expected that the nucleation is boosted by the involvement of IP0N, as well as small amounts of inorganic species, such as sulfuric acid[44]. The influence of H_2SO_4 on the nucleation cannot be directly inferred from our observations as the sulfuric acid concentration was low, below the detection limit of about 2.0 × 10^6 molecules cm−3 (Methods).

Role of monoterpenes and ions

Highly oxidized organic molecules (HOMs) generated from the oxidation of monoterpenes such as α-pinene have been suggested to cause NPF in the tropical upper troposphere owing to their extremely low or ultralow volatility42,43. Our observations at high altitudes show no evidence of monoterpene-derived HOMs (neither nitrates nor non-nitrates) and therefore do not support this hypothesis. We only observed typical monoterpene-derived HOMs in the Amazon boundary layer. Even in fresh convective outflow, the monoterpene mixing ratio in the upper troposphere was typically well below 60 pptv (Extended Data Fig. 2), providing insufficient precursor concentrations to explain our NPF data. Furthermore, HOM formation in the upper troposphere is too slow[43] for monoterpenes to play an important role in NPF or early particle growth, as the HOM yield decreases with decreasing temperature.

Ion-induced nucleation51,52 could play a notable role in NPF, in which ions produced from galactic cosmic rays may promote IP-OOMs nucleation[44]. This nucleation pathway is limited by the ion pair production rate in the tropical upper troposphere, which may reach 40 cm−3 s−1 (ref. [51]). A NPF rate of about 20 cm−3 s−1, as determined by our observations, is thus within this range. Nevertheless, our observations show that the limiting factor for NPF is not the presence of ions (which are ubiquitous) but, rather, is determined by the photochemical generation of ELVOCs from isoprene.

Diel cycles of NPF

Our aircraft measurements, aggregated from 11 flights, show a pronounced diel cycle for the concentration of N2–5 particles, NPF events and isoprene oxidation products in the tropical upper troposphere (Fig. 4 and Extended Data Figs. 6 and 7). Here NPF events are defined conservatively from the differences of the 2-nm and 5-nm CN-counter channels by 0.7N_2 − 1.3N_5 > 0 (Methods). The concentration of N2–5 particles at night and in the early morning was generally small and no NPF events were detected before 08:00 local time. About 2 h after sunrise, we frequently detected high concentrations of N2–5 particles, dominating the total particle number concentration. Although N2–5 particle concentrations were negligible before the events, N2–5 was frequently greater than 10,000 scm−3 (converted to standard conditions according to the International Union of Pure and Applied Chemistry (IUPAC): 273.15 K and 1,000 hPa) during the events. NPF events were similar to those observed near the Earth’s surface in other environments in extratropical regions[34], that is, in terms of abrupt occurrence of particles, strong initial growth, the role of photochemical production of the nucleating and condensable species and the large spatial extent of the events[34]. During a dedicated flight, the high N2–5 particle and organonitrate concentrations were observed in an area extending more than 300 km across and the two vertical layers in which the NPF was detected each extended over more than 0.5 km (Extended Data Fig. 8). During all flights, NPF events were associated with the occurrence of isoprene nitrates IP1N and IP2N (Fig. 4 and Extended Data Fig. 4). Shen et al.[44] studied isoprene-driven NPF for upper-troposphere conditions around −30 and −50 °C with and without NOx at the CLOUD chamber facility. For isoprene + NOx conditions, both the gas-phase IP-OOMs spectrum as well as the resulting nucleation rates are in good agreement with our field study (see discussion in Methods, Extended Data Fig. 9 and Extended Data Table 1). Furthermore, Shen et al.[44] show with APi-TOF measurements that isoprene organonitrates participate directly in the initial cluster formation. Although they found that, without NOx, IP-OOMs nucleate even stronger than in the presence of NOx, nitrate-dominated IP-OOMs still lead to substantial nucleation rates, which are in agreement with our findings. During CAFE-Brazil, for all observed NPF events, the CI-APi-TOF spectra were dominated by nitrate IP-OOMs, IP1N and IP2N, as the presence of isoprene in the tropical upper troposphere is intrinsically coupled with high NOx conditions through the NOx production from lightning during the deep convective transport. We found strong NPF events during all five morning flights in the upper troposphere. Therefore, it seems likely that, at least at the beginning of the wet season (December to January), such events occur every morning over a large part of the Amazon, affected by the outflow of night-time deep convection accompanied by lightning.

Fig. 4: Diurnal cycle and altitude dependence of NPF events, N2–5 particles and precursor gases.

Pixels are calculated as bins of 1 km altitude and 30 min time and combine data from 11 research flights over the Amazon basin. a, Frequency of occurrence of NPF events, defined as the ratio of NPF events to the total number of measurements for each pixel. b, Concentrations of N2–5 particles. c–e, Isoprene organonitrates IP0N (c), mononitrates IP1N (d) and dinitrates IP2N (e). Panels b–e show the 95th percentile of the concentration, that is, 5% of the data contributing to a pixel are even higher than the value indicated by the colour. In the upper troposphere, high concentrations of organonitrates and N2–5 particles occurred frequently, whereas concentrations were low in the lower and middle tropospheres and during the night. Data influenced by the Manaus plume and by biomass burning have been removed. For direct comparability, all concentration data are normalized to standard conditions (273.15 K, 1,000 hPa).

Source Data

The aggregated concentration measurements of the IP0N, IP1N and IP2N compounds show a similar diel cycle and altitude dependence as the N2–5 particles (Fig. 4). To be representative of pristine conditions, data that were measured during two encounters with biomass-burning plumes and data influenced by pollution emissions from the city of Manaus have been removed. Note that all data of Fig. 4 (as opposed to Figs. 1–3) were converted to standard temperature and pressure conditions for altitude-independent comparability and comparability with previous studies1,2. Overall, we observed NPF events during nearly 30% of measurements conducted at altitudes between 8 and 14 km and between 08:00 and 18:00 local time (Extended Data Fig. 7). At altitudes <8 km at any time and at >8 km during the night and in the early morning before 08:00 local time, NPF events represented less than 1%. Notable amounts of IP0N, IP1N and IP2N are also observed at altitudes <8 km, particularly in the boundary layer at midday, but the compounds are too volatile at these temperatures for NPF to occur (Figs. 3 and 4). We note that our measurements do not represent random averages, as the measurement flights had specific objectives, including the identification of NPF events in the upper troposphere.

Atmospheric implications

The newly formed particles in the upper troposphere can be transported over large distances well beyond the Amazon region[27]. During transport, the aerosol size distribution changes owing to coagulation and continued condensation of low-volatile species. A fraction of the newly formed particles is expected to be transported downwards38,40, affecting the abundance of CCN at low altitudes in the tropics1,6. The modelling study in ref. [4] suggests that 35% of the low-altitude CCN were initially formed by nucleation in the free and upper troposphere[4]. Williamson et al.[1] demonstrated that descending dry air in subsidence over the oceans contains more CCN-sized particles than moist air at low altitudes, indicating that NPF in tropical convective regions can increase the CCN concentrations of the lower troposphere. Because higher CCN concentrations can substantially influence the cloud microphysical and optical properties[8], the process probably affects the atmospheric radiation budget and the climate[5].

To put this into perspective, more than 7,000 mesoscale convective systems occur over the Amazon each year, covering a surface area of more than 40,000 km^2 per system, typically lasting for 4 h (refs. 53,54) and with high lightning activity21,55. Therefore, NPF from the oxidation of biogenic isoprene combined with lightning NOx may represent a notable mechanism of particle production in the tropical upper troposphere. It seems likely that this also occurs in the upper troposphere over other tropical forests, that is, in Central Africa, Southeast Asia and Northern Australia, that are also characterized by strong isoprene emissions9,10 and ubiquitous deep convection associated with lightning.

Methods

The CAFE-Brazil mission took place in Brazil between 30 November 2022 and 29 January 2023, which covers the end of the dry-to-wet season transition and the beginning of the wet season in the Amazon. The German High Altitude and Long Range Research Aircraft (HALO) was stationed at Manaus International Airport, conducting a total of 16 local research flights (143 flight hours), as well as the transfer flights between Germany and Brazil (26 flight hours). An overview of the flight tracks is shown in Extended Data Fig. 1a. The research flights, at altitudes between 0.3 and 13.8 km, covered the region from 11° 33′ S to 4° 40′ N and from 72° 33′ W to 33° 50′ W. The aircraft was equipped with instrumentation to measure the in situ concentration of trace gases, radicals, aerosol number concentration and several aerosol physical and chemical properties. Descriptions of the methods used for the detection of the gas compounds and aerosol properties are given here.

CI-APi-TOF mass spectrometer

The CI-APi-TOF mass spectrometer56,57 measures gaseous molecules that can contribute to NPF (that is, aerosol nucleation and initial growth up to detectable particle size). Gases such as isoprene oxidation products (IP0,1,2N), methanesulfonic acid (MSA) and sulfuric acid are detected. The instrument was specifically designed and certified for aircraft use and it is optimized for low inlet losses, a constant ionization pressure and minimal internal gas consumption.

The CI-API-TOF mass spectrometer uses a trace-gas inlet initially developed to measure OH radicals. It is almost identical to the inlet used for the HydrOxyl Radical measurement Unit based on fluorescence Spectroscopy (HORUS) instrument. The sampling location is placed at a sufficient distance from the fuselage to avoid the influence of the aircraft’s boundary layer. The inlet uses a set of shrouds and a flow restrictor to decelerate the air by a factor of 10 to around 25 m s−1 before sampling into the inlet line to reduce turbulence[58]. The flow restrictor, however, also causes a ram-pressure effect and thus adiabatic heating of the sample air depending on ambient pressure and air velocity, which will be discussed below in more detail.

The sampling line consists of a 20.5-mm inner diameter 1.8-m-long stainless-steel tube with an 8-mm orifice at the beginning (located in the centre part of the inlet). The sampling line has two bends with radii of about 120 and 500 mm, respectively, to enable installation close to the cabin wall. The inlet line is thermally insulated and has a temperature sensor 300 mm downstream of the sampling position inside the inlet. The sample flow is kept constant at 25.0 slpm for all altitudes to reduce wall losses, while still being in a laminar flow regime.

At the end of the sampling tube, the air reaches the SCORPION (Switchable Corona Powered Ion Source), which consists of a pressure-control and an ionization stage. The pressure-control stage comprises two sequentially placed and conically shaped orifices with 1.4 mm inner diameter each. Between these two orifices, a PID-controlled solenoid regulation valve allows for variable pumping, which provides a constant pressure of 200 hPa in the ionization region. The ionization region is located directly after the second orifice. It consists of a 20.5-mm inner diameter stainless-steel tube and two orthogonally attached ion source units, of which only one is used at any given time. The ion source uses a corona discharge to produce (HNO_3)0,1,2NO_3− reagent ions from gaseous HNO_3. Because a corona discharge also produces OH radicals, which could alter the chemical composition of the sample air, we implemented a counter-flow regime, such that the nitrate reagent ions are pushed towards the sample air by means of an electric field, whereas the gas flow, which also carries OH radicals, is directed away from the sample air towards the exhaust.

The nitrate reagent ions are mixed with the sample air and travel along the main drift tube (130 mm length) with a reaction time between 180 and 350 ms, depending on altitude. Although the ionization pressure is kept constant at 200 hPa, the flow along the drift region varies between 2.7 slpm at ground level and 1.4 slpm at 12 km altitude, causing different reaction times. The higher flow at lower altitudes is needed to maintain the ionization pressure at 200 hPa with the given orifice diameters. Although the reaction times are around 3–5 times longer than in an Eisele–Tanner-type ion source[59] (about 50 ms), the ionization pressure is also lower by a factor of 5, leading to a roughly comparable number of collisions between reagent ions and sample gas. However, because of the extra orifices in the pressure stage and a relatively long inlet line, the overall sensitivity of SCORPION is lower by about one order of magnitude compared with a nitrate reagent ion long time-of-flight (LTOF) mass spectrometer instrument as deployed, for example, at the CLOUD chamber42,43. The detection limit for SCORPION is between 5 × 10^5 and 5 × 10^6 cm−3.

At the end of the SCORPION drift tube, the ions enter the Tofwerk time-of-flight mass spectrometer by means of a 350-µm inner diameter orifice. We use an HTOF with a resolution of around 4,000 at the m/z range 250–300 amu. The data are recorded at 1 Hz; however, it is averaged to 10 s resolution before post-processing and high-resolution peak fitting in Tofware (version 3.2.5, Aerodyne).

To derive the ambient concentrations of IP0,1,2N, several pressure-dependent and temperature-dependent correction factors have to be applied to the fitted peak intensities as follows:

$$\begin{array}{l}[{{\rm{IP}}}_{{\rm{0,1,2N}}}]=A({p}_{{\rm{a}}},{p}_{{\rm{i}}}{,T}_{{\rm{a}}}{,T}_{{\rm{i}}})\times I({p}_{{\rm{i}}},{T}_{{\rm{i}}},{F}_{{\rm{i}}})\\ \,\,\,\,\times \left(C({p}_{{\rm{i}}})\times {\rm{ln}}\left(1+\frac{{({{\rm{IP}}}_{{\rm{0,1,2N}}})}_{{\rm{cps}}}}{{\rm{Tr}}\left(\frac{m}{z}\right)\sum {{{\rm{NO}}}_{3}}^{-}{({{\rm{HNO}}}_{3})}_{i=0,1,2}}\right)-{\rm{BG}}\right)\end{array}$$

Here (IP0,1,2N)cps represents the fitted peak intensities in counts per second. Tr(m/z) is the m/z-dependent correction factor for the relative change in instrument transmission efficiency[60]. For the IP0,1,2N m/z range, this correction is about 10–20%, depending on the exact m/z ratio.

A pressure-dependent calibration factor is applied to the normalized signal (at 12.2 km altitude, C230hPa = 6.5 × 10^10 cm−3, whereas at ground level, C1000hPa = 2.1 × 10^11 cm−3). This calibration factor was experimentally estimated for gaseous sulfuric acid by generating a known amount of gaseous sulfuric acid by means of ultraviolet (UV)-induced OH production (from photolysis of H_2O) and subsequent oxidation of sulfur dioxide[61] to sulfuric acid[62]. We constructed a dedicated calibration rack for the CI-APi-TOF mass spectrometer, similar to that described in ref. [62]. The calibration unit can be operated at pressures between 200 and 1,000 hPa.

After applying the calibration factor, a background correction of the signal is performed (BG). Background measurements were performed in flight by overflowing SCORPION with synthetic air from the internal gas bottle. This can, however, only be done above 9 km altitude, as at lower altitudes more gas would be needed to overflow the ion source than provided by the internal gas bottle. Typically, 2–3 background measurements are performed per flight, each lasting 10 min.

The factor I(pi, Ti, Fi) represents the correction for losses to the wall in the 1.8-m-long sampling line and depends on inlet pressure pi, inlet temperature Ti and inlet flow Fi. It is based on the parametrization for straight tube losses and thus ignores the two curves in our inlet tube. The inlet loss estimation uses the experimentally determined diffusion coefficient for gaseous sulfuric acid and its pressure-dependent and temperature-dependent parametrizations[61]. The inlet correction factor is about 1.65 for a typical high-altitude flight scenario (12 km altitude).

The correction factor A(pa, pi, Ta, Ti) scales the concentration levels from inlet temperature Ti and pressure pi conditions to ambient temperature Ta and pressure pa conditions. This scaling is necessary as the ram pressure caused by the flow restrictor increases the inlet pressure compared with ambient conditions (at 12.2 km altitude, at which most of research flight (RF) 19 was flown from 188 hPa ambient pressure to 268 hPa inlet pressure). This pressure increase also induces an adiabatic heating of the sampling air. Further heating comes from the limited thermal insulation of the inlet line. At 12.2 km altitude, we measure a temperature increase from −58 °C (ambient) to −13 °C (inlet), so ΔT = 45 °C. Although the inlet residence time at these conditions is rather short (0.38 s), this temperature increase could lead to the evaporation of molecules from the aerosol to the gas phase, which could enhance our measured gas-phase signals. At −58 °C, the main IP0,1,2N products are ELVOCs, whereas they shift to the low-volatility organic compounds range at −13 °C (Fig. 3). Especially for freshly nucleated particles with diameters in the size range in which the Kelvin effect has a role, this could cause evaporation. Therefore, the measurements are considered to be a combination of pure gas-phase concentration and potentially re-evaporated aerosol-phase molecules. However, even if a large fraction of our signal would originate from evaporation of freshly nucleated particles, this still highlights the crucial role of isoprene-derived oxidation products for the NPF process in the upper troposphere over the Amazon.

The measurement of sulfuric acid was affected by a sulfur contamination inside the stainless-steel vessel containing the liquid HNO_3 supply of the ion source. This led to an increased instrumental background for sulfuric acid of about 2 × 10^6 cm−3 for our measurements at altitudes above 8 km.

The overall uncertainty of the CI-APi-TOF measurements is ±62%. This consists mainly of the uncertainty of the sulfuric acid calibration factor of 55%, as well as the uncertainty of the mass-dependent transmission correction (20%) and the inlet loss correction (20%). No calibration standards are available for the measurement of ELVOCs from isoprene or monoterpenes. Therefore, we use the same calibration factor as determined for the sulfuric acid measurements. The sensitivity of the CI-APi-TOF instrument is high for the detection of highly oxidized organics with many functional groups, for example, HOMs from monoterpenes. For those, this approach is well established[42], but the CI-APi-TOF instrument is expected to be less sensitive for the smaller IP-OOMs, especially when the molecules contain few oxygen atoms. This general dependence is confirmed by the comparison of nitrate reagent ions with, for example, bromide or iodide reagent ions[41]. The concentrations of the isoprene oxidation products detected by the CI-APi-TOF mass spectrometer should therefore be considered as lower limits. Compared with the IP0N, IP1N and IP2N reported in ref. [44], the CI-APi-TOF instrument will detect smaller fractions of these compound classes, as more mass spectrometers using other reagent ions are used in ref. [44] to complement the IP0,1,2N measurements. Nevertheless, the relative changes of individual compounds over time, as shown, for example, in Extended Data Fig. 5, are not affected by this effect.

We also apply the calibration factor estimated for sulfuric acid to derive the concentration of IP0-2N, as a direct calibration for IP0-2N is not possible. It was shown that HOMs with sufficient oxygen content, that is, a sufficient number of functional groups to cluster with NO_3−, are charged with nitrate reagent ions at the kinetic limit, similar to sulfuric acid41,63. Riva et al.[41] conducted a detailed comparison of various reagent ions and their respective sensitivities towards pure organic as well as nitrate HOMs derived from monoterpene oxidation. They found that both nitrate and non-nitrate HOMs can be charged by NO_3− reagent ions at the kinetic limit as long as HOMs contain more than six oxygen atoms in the non-nitrate case and more than seven oxygen atoms in the nitrate case. Assuming that these results are transferable to IP0-2N, this means that some of our reported IP0-2N could be charged below the unit charging efficiency of the kinetic limit. This leads to an increase in calibration factor for these species and our reported concentrations represent a lower limit estimation, especially for low-oxygen-content IP0-2N.

Aerosol number concentration and nucleation-mode particles

The Fast Aerosol Size Distribution (FASD) instrument is a compact multichannel system to detect newly formed particles. It was developed specifically for operation aboard the HALO aircraft. The system combines the concept of well-established commercial ultrafine condensation particle counters[64] (CPCs) with central temperature management, central butanol vapour supply, central pressure control and a new type of flow system. The prototype, designed and built at the Max Planck Institute for Chemistry (MPIC), simultaneously measures aerosol concentration in ten channels every second. The ten-channel device is about the size of two commercial ultrafine CPCs. Similar aircraft-based instruments use several CPCs65,66,67.

For each CPC channel, the sample air is fed by means of a short (6 mm) capillary into a heated mixing chamber, surrounded by particle-free butanol-rich sheath air and fed into the cold condensation region. The supersaturated butanol condenses on the aerosol particles, causing them to grow sufficiently to be detected in a downstream optical particle counter. To minimize diffusional losses, all channels share a central sample line with a distance of only about 38 mm between adjacent inlets and the inlet capillaries protrude into the core sample flow.

The FASD instrument gradually reduces the butanol content of each sheath flow in a dilution chamber between the saturator and the channels. The exhaust air from each channel is collected and cleaned of particles by a HEPA filter and returned to the saturator and dilution stage by a central pump. There is a total of four pressure stages in the sheath flow loop, with the highest pressure in the saturator and the butanol dry air upstream of the dilution. Two valves control the saturated and dry air flow ratio into the dilution chamber. Although the saturated air is introduced at a single point, the dry air flow is split to replenish the air flowing to each channel. The mixing, condensation and detection areas of each channel are close to the ambient pressure level in the inlet and outlet lines. Finally, the lowest pressure is upstream of the sheath flow pump and also drives the removal of butanol that condenses on the walls of each condenser. This butanol is collected in a reservoir and can be reused as normally no water vapour condenses.

A valve controls the total sample flow. This valve allows some air to leave the sheath flow loop from the dilution stage to the exhaust line. In total, an equal amount of air enters through the short inlet capillary of each channel. Another valve sets the desired flow through the dilution stage and allows some air to flow directly from the end of the dilution stage to the sheath flow pump. Flow uniformity is achieved by carefully selecting the flow resistances, which results in pressure differentials between stages in the range 10–20 hPa. The condenser sections are cooled to a target temperature of 10 °C by a water cooling system. The waste heat is transferred from the cold water cycle to the hot water cycle through Peltier elements, in which the heat is eventually released to the ambient air through an external radiator. To avoid unwanted butanol condensation, it is essential that the dilution chamber and the individual sheath flow transfer lines and mixing chambers of each channel maintain temperatures above the saturator temperature. This is achieved by heating the saturator indirectly, while keeping the other parts actively warm, resulting in a permanent temperature gradient that prevents unwanted condensation.

The particle activation is determined by the temperature difference between condensation and saturation, the pressure differentials and the dilution factor. By measuring the activation curves under given conditions and comparing them with theoretical calculations, the activation behaviour can be derived for fluctuating measurement conditions[65]. Initial measurements of selected particle sizes in the range 2–30 nm show that the performance of the first channel of the prototype is comparable with a commercial ultrafine CPC, whereas the measured values of the other channels fit to an effective dilution to around 75% per channel.

Although diffusional losses between the channels are not apparent, the final calibration requires further measurements65,67. Therefore, for this study, the theoretical cut-off diameters are obtained using Kelvin’s equation[68] with a dilution of 75% per stage. For each measurement time step, the corresponding particle activation diameters can be calculated from the actual temperatures and pressure differences and are typically in the range 2–6 nm. Measurement periods without NPF events can be used to determine systematic deviations of sample flows owing to slight variations in flow resistances. To keep the measurement conditions of the channels stable, regardless of changes in ambient pressure, the FASD instrument is operated at a constant pressure of typically 200 hPa. The pressure control is achieved by continuously calculating the net flows in and out of the FASD instrument based on flow controller and pressure sensor data. The calculation results are used to drive two PID controllers that regulate the outflow by using a mass flow controller and the inflow by using a custom-made size-changing orifice[69].

Proton transfer reaction time-of-flight mass spectrometry

A proton transfer reaction time-of-flight mass spectrometer46,70,71 (PTR-TOF-MS 8000, Ionicon Analytik) was used for the fast high-mass-resolution airborne measurements of VOCs (m/z < 500 amu). Isoprene, its oxidation products including methyl vinyl ketone, methacrolein and isoprene hydroxyhydroperoxide, and total monoterpenes reported in this study were measured at m/z = 69.069, 71.049 and 137.132 amu, respectively. In this technique, hydronium ions (H_3O+) are used as a reagent to ionize molecules in air that have a higher proton affinity than water (693 kJ mol−1). The instrument was operated with a drift pressure of 2.2 mbar and an E/N of 137 Td. Air was drawn from outside the aircraft to the instrument through a fuselage-mounted inlet housing, in a heated 2-m-long, 0.64-cm outer diameter Teflon inlet line. Quantification of the detected compounds was performed by frequent in-flight background determinations with zero air and several ground-based calibrations using a gravimetrically prepared gas standard containing isoprene, methyl vinyl ketone and α-pinene (Apel-Riemer Environmental). At a time resolution of 1 min, the detection limit (3σ) was calculated to be 100, 41 and 25 pptv for isoprene, its oxidation products and total monoterpenes, respectively. An ozone correction for isoprene was applied on the basis of laboratory experiments and comparison with the gas chromatography–mass spectrometry data. The total uncertainty of measurement was usually below 25%. Further detailed information about the proton transfer reaction time-of-flight mass spectrometry used is given ref. [72], its response to atmospheric ozone in ref. [73] and the configuration in the aircraft is described in ref. [74].

Gas chromatography–mass spectrometry

VOCs were measured in situ using a customized gas chromatograph coupled to a commercial quadrupole mass spectrometer (Agilent Technologies 5973 MSD). The system has been described in detail previously[75] and its configuration in the aircraft payload for the CAFE-Brazil campaign is given elsewhere[74]. In brief, ambient air is drawn at 200 scm^3 through the Trace Gas Inlet (TGI, Enviscope) to the instrument by means of a heated 2-m-long Teflon line (0.6 cm outer diameter) equipped with a sodium thiosulfate ozone scrubber[73]. Within a series of traps in a liquid-nitrogen-cooled cryo-concentrator, the sampled air is dried (−10 °C), enriched for VOCs (−160 °C) for 1 min and then concentrated into a small volume (−160 °C) before being rapidly heated to inject the sample into the gas chromatograph. The compounds are separated by a DB-624 UI, 10 m, 0.25 mm, 1.4 µm capillary column (Agilent Technologies). The temperature programme of the custom-built gas chromatograph oven is as follows: 30 °C for 50 s, then 30 °C to 200 °C at 1.8 °C s−1 and constant 200 °C for the rest of the chromatogram. After separation, the compounds are electronically ionized (70 eV) and detected by the mass spectrometer in the selected ion mode. In the configuration used for the CAFE-Brazil campaign, more than 35 compounds could be resolved and quantified in a 2.4-min chromatogram, the overall measurement frequency being 3 min. Calibration was achieved using a gravimetrically prepared multicomponent pressurized standard (Apel-Riemer Environmental), with a stated accuracy of 5%, with calibrations being performed before, during and after each flight. Isoprene was detected at m/z = 67 amu and a retention time of 0.7 min with a detection limit of 5 pptv and an uncertainty of about 10%.

NOx measurements

Nitrogen oxides were measured through photolysis chemiluminescence with the two-channel instrument Nitrogen Oxides Analyzer for HALO (NOAH). In one channel, nitric oxide (NO) is converted to excited-state nitrogen dioxide (NO_2*) by reaction with excess amounts of ozone (O_3). A photon is emitted during de-excitation of NO_2*, which is detected by a photomultiplier tube. The signal is converted to ambient NO mixing ratios using normal on-ground calibrations between the flights. The second channel is identical except for using a photolytic converter, in which NO_2 is photolysed to NO at a wavelength of around 395 nm before the addition of O_3. The conversion efficiency of the converter (the fractional conversion of NO_2 to NO) was 29% during the CAFE-Brazil campaign. Owing to enhanced temperatures in the instrument and the photolytic converter, NO_2 reservoir species, mostly methyl peroxy nitrate, can release NO_2. Therefore, the NO_2 measurement represents a sum of NO_2 and thermally labile nitrates and represents an upper limit of the NO_2 mixing ratios at night-time. For the day-time data, we use NO_2 derived from the photostationary state (inferred from NO, O_3 and j(NO_2)) in this study instead (Extended Data Fig. 2). The 1 Hz detection limit for NO is 8 pptv and the overall measurement uncertainty is 5%. A detailed description of the instrument is presented in refs. [76],[77]. Details on the thermal decomposition of NO_2 reservoir species in photolytic converters and resulting interferences can be found, for example, in refs. 77,78,79.

HOx measurements

The airborne HORUS instrument is based on the Fluorescence Assay by Gas Expansion–Laser-Induced Fluorescence of OH (FAGE-LIF) instrument as described in detail in ref. [80]. It was developed specifically for operation on the HALO research aircraft and combines an external inlet shroud with an in-flight calibration system, OH and HO_2 detection axes, a laser system and a vacuum system. The OH is drawn into the detection axis through a critical orifice at a pressure range of 300–1,300 Pa, depending on ambient pressure. It is selectively excited on the Q1(2) transition line (A2Σ +−X2Π, ν′ = 0, ν″ = 0) by a 3-kHz pulsed UV laser light around 308 nm. The UV laser emission wavelength is periodically tuned on and off resonance of the OH Q1(2) transition to quantify the fluorescence background. An inlet pre-injector (IPI) system is installed to remove atmospheric OH to measure the chemical OH background signal. The airborne IPI system has been redesigned to fit within the inlet shroud system, while maintaining similar operational features as the on-ground IPI installation[81].

HO_2 is measured indirectly through the quantitative conversion of atmospheric HO_2 to OH by injection of NO within HORUS.

$${{\rm{HO}}}_{2}+{\rm{NO}}\to {{\rm{NO}}}_{2}+{\rm{OH}}$$

(1)

With excess amounts of NO used in the conversion of HO_2 to OH, subsequent HONO formation has to be taken into account.

$${\rm{OH}}+{\rm{NO}}+{\rm{M}}\to {\rm{HONO}}+{\rm{M}}$$

(2)

The losses resulting from internal HONO formation are dependent on pressure and amount to less than 1% above 10 km and less than 2% at ground level.

We account for the reaction of RO_2 with NO leading to HO_2 formation, which then generates OH in the presence of NO. We reduce the NO addition to limit the contribution of RO_2 to the detected OH levels. The instrument operates in two modes during flights: one with a low NO addition, creating an internal NO concentration of 1.5 ± 0.1 × 10^13 cm−3 and achieving a conversion rate of 20–40% depending on altitude, and another with a high NO addition of 7 ± 0.1 × 10^13 cm−3, reaching a conversion rate of more than 95%. Furthermore, NO titrations are conducted at different altitudes to ensure accurate measurement of the HO_2 contribution. The HORUS instrument 1σ accuracy is ±22.6% for OH and ±22.1% for HO_2. Precision depends on ambient pressure and instrument performance.

Aerosol mass spectrometer

The composition of non-refractory aerosol particles in the diameter range approximately 50–800 nm was measured using a compact time-of-flight aerosol mass spectrometer (C-ToF-AMS)82,83. The instrument samples the aerosol particles by means of a constant pressure inlet and an aerodynamic lens into the vacuum system. The particles are flash vaporized on a 600 °C surface and the resultant gas-phase molecules are ionized by electron ionization. The ions are analysed by a time-of-flight mass spectrometer. The C-ToF-AMS has been operated on HALO since the ACRIDICON-CHUVA campaign in 2014, which also took place over the Amazon rainforest[31].

Other measurements

Carbon monoxide was measured with the quantum cascade laser absorption spectrometer TRISTAR with a mean total measurement uncertainty of 3.5% (refs. 84,85).

Upward and downward spectral actinic flux densities in the range 280–650 nm are measured by combinations of two CCD spectroradiometers86,87.

The Fast AIRborne Ozone (FAIRO) instrument measures ozone with high temporal resolution (10 Hz). It combines two independent techniques, UV photometry and chemiluminescence detection[88].

Data from the Basic HALO Measurement and Sensor System (BAHAMAS) are used for determination of the aircraft position, wind velocity and direction, humidity, temperature and pressure[89].

Trajectory calculations

Quasi-Lagrangian sampling periods during HALO flights were identified in Fig. 1 (marked by grey shading) using the flight measurements in combination with backward trajectories as described in the following.

The sampling period with the highest number of N2–5 particles (light-red shading in Fig. 1 (T9)) was chosen as the reference period, with its limits defined as 10:05:46 and 10:10:30 local time. During this sampling period, HALO covered a distance of 62.2 km, which becomes relevant during the final computational step determining the quasi-Lagrangian sampling periods. To identify time intervals earlier during the flight in which approximately the same air mass as during the reference period was examined, backward trajectories were calculated for a set of ten parcels. These were initialized at HALO’s instantaneous location every 30 s (at 0 s and 30 s times) during the reference period. We used a simple Euler scheme with a 30-s time step to calculate the trajectories. We further assumed a constant wind velocity for each trajectory. The wind speed and direction were based on HALO measurements at the parcel initialization time. Vertical movements were not considered. These computations result in an m × 10 matrix containing the ten position vectors of the parcels initialized at HALO’s location during the reference period (p_1, p_2,…, p_10) at each historical time (ti, ti − Δt,…, ti − (m − 1)Δt). We then calculated the Euclidean distance from each parcel to HALO’s location (in km), at each location along the trajectories. This distance is always computed with respect to HALO’s instantaneous position at the equivalent time. Finally, the quasi-Lagrangian intervals highlighted by the grey shading in Fig. 1 (T1–T8) were determined by selecting time periods along the backward trajectories for which the mean distance from the parcels to HALO was less than the sampling distance of the reference period (that is, 62.2 km).

The trajectories shown in Fig. 2 were computed using the above described method, but instead initializing 1-min-spaced parcels between 08:05 and 08:15 local time.

It should be noted that the calculated trajectories involve uncertainties, mainly because of the assumption of constant wind speed and the neglect of vertical velocity. However, tests, in which further backward trajectories were computed from each semi-Lagrangian time interval itself (not shown), indicate that the time-dependent changes in wind speed from one phase of the flight pattern loop to another did not substantially affect the estimates of the air-mass location. This indicates that we may use the approximation of constant horizontal wind speed on the spatio-temporal scales analysed for this specific flight. Also, convective clouds typically induce atmospheric gravity waves in their surroundings. Consequently, air parcels expelled by convective outflows may oscillate in the vertical dimension during horizontal displacements, remaining close to the outflow level for a few hours. Thus, vertical errors in parcel location are expected to be within a range smaller than the spatial extent of the air mass in which NPF was identified (about 62 km). We thus argue that neglecting the vertical velocity component serves as a reasonable proxy for the air-mass locations before the measurements in the near outflow proximity, in the absence of sufficiently accurate time-dependent vertical wind-speed data. This assumption also immediately breaks down when the backward trajectories come in contact with active convection.

Given the absence of a true value to evaluate parcel trajectories at this scale (note that atmospheric models provide time-dependent three-dimensional wind velocities, but these are highly sensitive to the representation of the location and structure of convective storms, making them unsuitable for this type of high-resolution analysis), the final validation should come from the tracer measurements themselves. The strong agreement between this trajectory analysis and the in situ measurements is reassuring and provides some validation of the conclusions presented here. Note that, during T8, only small amounts of N2–5 particles and IP0-2N were found, indicating that, at this time, the centre of the NPF air mass had probably moved north of section BC of the flight track (Fig. 2).

Identification of NPF

To identify a NPF event, we conservatively assumed a measurement uncertainty of 30% for each channel of the FASD instrument (including statistical uncertainty, drifts in the flows and other systematic uncertainties). The difference between N_2 and N_5 is classified as a NPF event if 0.7N_2 − 1.3N_5 > 0 cm−3 (refs. 28,66).

Condensation sink

The condensation of a vapour to aerosol particles is described by the CS (ref. [90]). For vapours of ultralow or extremely low volatility, the condensation to pre-existing large particles competes with the NPF process. The size distribution of the aerosol is crucial for determining the CS. An Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) was generally used in combination with the FASD CPC measurements to determine the aerosol size distribution in the size range 60–1,000 nm, but for RF 19, the UHSAS was not operational. Therefore, we performed a rough estimation of the range of the upper limit of the CS by assuming that all aerosol particles measured by the 5-nm FASD channel N_5 have a size of 20, 50 or 100 nm. These three estimates are given in Extended Data Figs. 2 and 3. Before and outside the NPF events, the 50-nm or 100-nm assumptions give a reasonable range (compared with the other research flights when the UHSAS was operational), whereas during the NPF events, the <20-nm assumption is more likely to be correct.

Saturation vapour pressure

The SIMPOL model is used to obtain an estimate of the temperature-dependent saturation vapour pressures Ci* for the isoprene oxidation products[49] (Fig. 3). SIMPOL is based on the group-contribution method, in which the number of functional groups of an organic molecule determines its saturation vapour pressure. For the SIMPOL-derived saturation vapour pressures, an uncertainty of one order of magnitude in the volatility distribution is assumed[43]. The saturation ratio Si* of a compound can be determined by calculating Si* = [ci]mi/(NACi*), with [ci] denoting the concentration of compound ci and mi its molecular mass. The saturation ratio, Si*, of a compound can then be compared with estimates of the Kelvin diameters beyond which condensation is favoured over evaporation (Extended Data Fig. 3).

Comparison of CAFE-Brazil results to the CLOUD laboratory measurements of Shen et al.

Shen et al.[44] report laboratory measurements from the CLOUD chamber that investigate the role of isoprene for NPF at cold upper troposphere conditions (around −30 and −50 °C) with and without NOx. For isoprene + NOx conditions at −48 °C, they report an IP-OOMs distribution (Fig. 3d in ref. [44]) that is similar to the one we report for our atmospheric measurements (Fig. 3a and Extended Data Fig. 9). Note that the figure in ref. [44] shows not only IP-OOMS data from a NO_3− reagent ion CIMS similar to the CI-APi-TOF used in our study, but, in addition, data from IP-OOMs measured by further mass spectrometers using NH_4+ and Br− reagent ions. This extends the range of detected compounds towards higher volatility compounds with lower oxygen content41,44. These extra compounds are not expected to drive nucleation on their own but may contribute to the growth of newly formed particles after they have reached the respective Kelvin diameter. Comparing only the NO_3− reagent ion data, the CLOUD data match our mass-defect plot (Extended Data Fig. 9). Nitrates, especially dinitrates, dominate the range in both cases. In ref. [44], the NO_3−-CIMS measurement shows an even stronger dominance of nitrate IP-OOMs than our study. The highest peak in our study (C_5H_10N_2O_8) is the second highest peak in ref. [44], whereas the highest peak found by Shen et al. is the closely related C_5H_10N_2O_9, which can be formed by low-temperature RO_2 reaction with NO_2 instead of NO (ref. [44]). This is more likely in CLOUD as it was operating at a higher NO_2/NO ratio owing to lower NO_2 photolysis rates in CLOUD compared with the upper troposphere over the Amazon during daytime (CLOUD: NO_2/NO ≈ 3.1; CAFE-Brazil, RF 19, T9, NO_2/NO ≈ 1.1; Extended Data Fig. 9 and Extended Data Table 1). Nevertheless, there is good overall agreement between the spectra recorded in CLOUD and our study.

Shen et al.[44] report that non-nitrate IP-OOMs are more effective for nucleation than nitrate IP-OOMs, which can be seen from lower nucleation rates at comparable concentration (extended data figure 5 in ref. [44]). Even with the lower nucleation efficiency for isoprene nitrates, the authors still measured notable nucleation rates at −48 °C in a nitrate-IP-OOMs-dominated experiment (J1.7 ≈ 4 cm−3 s−1 for 2 × 10^8 cm−3 of IP1-2N) with a gas-phase IP-OOMs spectrum similar to the one we report, as discussed above. Furthermore, figure 3c in ref. [44] confirms that nitrate-IP-OOMs do participate in initial cluster formation. The authors also state that the role of nitrates for nucleation could increase at colder temperatures, as encountered during our flights (−58 °C for RF 19). They report a more than 100-fold increase in nucleation rate for isoprene + NOx conditions when the temperature is reduced from about −30 to −50 °C. Given the observation that, at −50 °C, nitrate-IP-OOMs are weaker nucleators than non-nitrate-IP-OOMs, which means that they do not nucleate at the kinetic limit at −50 °C, it is plausible that the nitrate-IP-OOMs-driven nucleation rate increases when the temperature is reduced from −50 °C to −58 °C. Although the CLOUD experiment could only measure as cold as −50 °C, the expected increase in nucleation rate at colder temperatures leads to a good agreement with the NPF rate of 20 cm−3 s−1 for a concentration of 2.7 × 10^8 cm−3 of IP1-2N reported here. We note that, even if nitrates dominate the IP-OOMs spectra, we do not rule out an important contribution of non-nitrates to the initial steps of cluster formation and nucleation.

Overall, the results of the Shen et al.[44] laboratory study agree with the nitrate-dominated IP-OOMs spectra that were measured in the upper troposphere over the Amazon. Taking into account the lower temperatures during our flights, the formation rates measured in the laboratory for isoprene + NOx conditions are comparable with those estimated in our study.

The probable reason for why nitrate IP-OOMs are much more prevalent than non-nitrate-IP-OOMs is that the presence of isoprene and NOx in the upper troposphere over the Amazon region is intrinsically coupled through deep convection, as isoprene is transported rapidly from the boundary layer to the upper troposphere, and NOx is produced by lightning. Both isoprene and NOx accumulate during the night. After sunrise, photolysis leads to the production of OH and NO (with NO presence enhancing OH recycling as well). OH and NO then trigger isoprene oxidation and the corresponding RO_2 termination reactions that lead to the reported nitrate-dominated IP-OOMs spectra and NPF events.

Data availability

The full dataset shown in the figures is publicly available at https://doi.org/10.5281/zenodo.12527358 (ref. [91]). Source data are provided with this paper.

Code availability

Data-analysis routines used to process data and generate plots are available from the corresponding author on reasonable request.

References

1. Williamson, C. J. et al. A large source of cloud condensation nuclei from new particle formation in the tropics. Nature 574, 399–403 (2019).

2. Andreae, M. O. et al. Aerosol characteristics and particle production in the upper troposphere over the Amazon Basin. Atmos. Chem. Phys. 18, 921–961 (2018).

3. Brock, C. A., Hamill, P., Wilson, J. C., Jonsson, H. H. & Chan, K. R. Particle formation in the upper tropical troposphere: a source of nuclei for the stratospheric aerosol. Science 270, 1650–1653 (1995).

4. Merikanto, J., Spracklen, D. V., Mann, G. W., Pickering, S. J. & Carslaw, K. S. Impact of nucleation on global CCN. Atmos. Chem. Phys. 9, 8601–8616 (2009).

5. Bellouin, N. et al. Bounding global aerosol radiative forcing of climate change. Rev. Geophys. 58, e2019RG000660 (2020).

6. Wang, X., Gordon, H., Grosvenor, D. P., Andreae, M. O. & Carslaw, K. S. Contribution of regional aerosol nucleation to low-level CCN in an Amazonian deep convective environment: results from a regionally nested global model. Atmos. Chem. Phys. 23, 4431–4461 (2023).

7. Clarke, A. D. et al. Particle production in the remote marine atmosphere: cloud outflow and subsidence during ACE 1. J. Geophys. Res. Atmos. 103, 16397–16409 (1998).

8. Carslaw, K. S. et al. Large contribution of natural aerosols to uncertainty in indirect forcing. Nature 503, 67–71 (2013).

9. Sindelarova, K. et al. Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30 years. Atmos. Chem. Phys. 14, 9317–9341 (2014).

10. Guenther, A. B. et al. The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions. Geosci. Model Dev. 5, 1471–1492 (2012).

11. Wennberg, P. O. et al. Gas-phase reactions of isoprene and its major oxidation products. Chem. Rev. 118, 3337–3390 (2018).

12. Lelieveld, J. et al. Atmospheric oxidation capacity sustained by a tropical forest. Nature 452, 737–740 (2008).

13. Yáñez-Serrano, A. M. et al. Diel and seasonal changes of biogenic volatile organic compounds within and above an Amazonian rainforest. Atmos. Chem. Phys. 15, 3359–3378 (2015).

14. Claeys, M. et al. Formation of secondary organic aerosols through photooxidation of isoprene. Science 303, 1173–1176 (2004).

15. Claeys, M. & Maenhaut, W. Secondary organic aerosol formation from isoprene: selected research, historic account and state of the art. Atmosphere 12, 728 (2021).

16. Kiendler-Scharr, A. et al. New particle formation in forests inhibited by isoprene emissions. Nature 461, 381–384 (2009).

17. Heinritzi, M. et al. Molecular understanding of the suppression of new-particle formation by isoprene. Atmos. Chem. Phys. 20, 11809–11821 (2020).

18. Bardakov, R., Krejci, R., Riipinen, I. & Ekman, A. M. L. The role of convective up- and downdrafts in the transport of trace gases in the Amazon. J. Geophys. Res. Atmos. 127, e2022JD037265 (2022).

19. Wang, D., Giangrande, S. E., Feng, Z., Hardin, J. C. & Prein, A. F. Updraft and downdraft core size and intensity as revealed by radar wind profilers: MCS observations and idealized model comparisons. J. Geophys. Res. Atmos. 125, e2019JD031774 (2020).

20. Palmer, P. I., Marvin, M. R., Siddans, R., Kerridge, B. J. & Moore, D. P. Nocturnal survival of isoprene linked to formation of upper tropospheric organic aerosol. Science 375, 562–566 (2022).

21. Albrecht, R. I., Morales, C. A. & Silva Dia, M. A. F. Electrification of precipitating systems over the Amazon: physical processes of thunderstorm development. J. Geophys. Res. Atmos. 116, D08209 (2011).

22. Schumann, U. & Huntrieser, H. The global lightning-induced nitrogen oxides source. Atmos. Chem. Phys. 7, 3823–3907 (2007).

23. Lelieveld, J. et al. The South Asian monsoon—pollution pump and purifier. Science 361, 270–273 (2018).

24. Yu, P. et al. Efficient in-cloud removal of aerosols by deep convection. Geophys. Res. Lett. 46, 1061–1069 (2019).

25. Clarke, A. D. & Kapustin, V. N. A Pacific aerosol survey. Part I: a decade of data on particle production, transport, evolution, and mixing in the troposphere. J. Atmos. Sci. 59, 363–382 (2002).

26. Clarke, A. D. Atmospheric nuclei in the remote free-troposphere. J. Atmos. Chem. 14, 479–488 (1992).

27. Krejci, R. et al. Evolution of aerosol properties over the rain forest in Surinam, South America, observed from aircraft during the LBA-CLAIRE 98 experiment. J. Geophys. Res. Atmos. 108, D18 (2003).

28. Weigel, R. et al. In situ observations of new particle formation in the tropical upper troposphere: the role of clouds and the nucleation mechanism. Atmos. Chem. Phys. 11, 9983–10010 (2011).

29. Kulmala, M. et al. Deep convective clouds as aerosol production engines: role of insoluble organics. J. Geophys. Res. Atmos. 111, D17202 (2006).

30. Ekman, A. M. L. et al. Do organics contribute to small particle formation in the Amazonian upper troposphere?. Geophys. Res. Lett. 35, L17810 (2008).

31. Schulz, C. et al. Aircraft-based observations of isoprene-epoxydiol-derived secondary organic aerosol (IEPOX-SOA) in the tropical upper troposphere over the Amazon region. Atmos. Chem. Phys. 18, 14979–15001 (2018).

32. Zha, Q. et al. Oxidized organic molecules in the tropical free troposphere over Amazonia. Natl Sci. Rev. 11, nwad138 (2023).

33. Gordon, H. et al. Causes and importance of new particle formation in the present-day and preindustrial atmospheres. J. Geophys. Res. Atmos. 122, 8739–8760 (2017).

34. Kerminen, V.-M. et al. Atmospheric new particle formation and growth: review of field observations. Environ. Res. Lett. 13, 103003 (2018).

35. Wimmer, D. et al. Ground-based observation of clusters and nucleation-mode particles in the Amazon. Atmos. Chem. Phys. 18, 13245–13264 (2018).

36. Varanda Rizzo, L. et al. Multi-year statistical and modeling analysis of submicrometer aerosol number size distributions at a rain forest site in Amazonia. Atmos. Chem. Phys. 18, 10255–10274 (2018).

37. Martin, S. T. et al. Sources and properties of Amazonian aerosol particles. Rev. Geophys. 48, RG2002 (2010).

38. Franco, M. A. et al. Occurrence and growth of sub-50 nm aerosol particles in the Amazonian boundary layer. Atmos. Chem. Phys. 22, 3469–3492 (2022).

39. Wang, J. et al. Amazon boundary layer aerosol concentration sustained by vertical transport during rainfall. Nature 539, 416–419 (2016).

40. Zhao, B. et al. High concentration of ultrafine particles in the Amazon free troposphere produced by organic new particle formation. Proc. Natl Acad. Sci. 117, 25344–25351 (2020).

41. Riva, M. et al. Evaluating the performance of five different chemical ionization techniques for detecting gaseous oxygenated organic species. Atmos. Meas. Tech. 12, 2403–2421 (2019).

42. Kirkby, J. et al. Ion-induced nucleation of pure biogenic particles. Nature 533, 521–526 (2016).

43. Simon, M. et al. Molecular understanding of new-particle formation from α-pinene between −50 and +25 °C. Atmos. Chem. Phys. 20, 9183–9207 (2020).

44. Shen, J. et al. New particle formation from isoprene under upper-tropospheric conditions. Nature https://doi.org/10.1038/s41586-024-08196-0 (2024).

45. D’Ambro, E. L. et al. Molecular composition and volatility of isoprene photochemical oxidation secondary organic aerosol under low- and high-NOx conditions. Atmos. Chem. Phys. 17, 159–174 (2017).

46. Liu, Y. et al. Isoprene photochemistry over the Amazon rainforest. Proc. Natl Acad. Sci. 113, 6125–6130 (2016).

47. Thornton, J. A. et al. A near-explicit mechanistic evaluation of isoprene photochemical secondary organic aerosol formation and evolution: simulations of multiple chamber experiments with and without added NOx. ACS Earth Space Chem. 4, 1161–1184 (2020).

48. Massoli, P. et al. Ambient measurements of highly oxidized gas-phase molecules during the Southern Oxidant and Aerosol Study (SOAS) 2013. ACS Earth Space Chem. 2, 653–672 (2018).

49. Pankow, J. F. & Asher, W. E. SIMPOL.1: a simple group contribution method for predicting vapor pressures and enthalpies of vaporization of multifunctional organic compounds. Atmos. Chem. Phys. 8, 2773–2796 (2008).

50. Schervish, M. & Donahue, N. M. Peroxy radical chemistry and the volatility basis set. Atmos. Chem. Phys. 20, 1183–1199 (2020).

51. Kazil, J., Lovejoy, E. R., Barth, M. C. & O’Brien, K. Aerosol nucleation over oceans and the role of galactic cosmic rays. Atmos. Chem. Phys. 6, 4905–4924 (2006).

52. Yu, F. Q. & Turco, R. P. Ultrafine aerosol formation via ion-mediated nucleation. Geophys. Res. Lett. 27, 883–886 (2000).

53. Rehbein, A., Ambrizzi, T. & Mechoso, C. Mesoscale convective systems over the Amazon basin. Part I: climatological aspects. Int. J. Climatol. 38, 215–229 (2018).

54. Anselmo, E. M., Machado, L. A. T., Schumacher, C. & Kiladis, G. N. Amazonian mesoscale convective systems: life cycle and propagation characteristics. Int. J. Climatol. 41, 3968–3981 (2021).

55. Albrecht, R. I., Goodman, S. J., Buechler, D. E., Blakeslee, R. J. & Christian, H. J. Where are the lightning hotspots on Earth? Bull. Am. Meteorol. Soc. 97, 2051–2068 (2016).

56. Jokinen, T. et al. Atmospheric sulphuric acid and neutral cluster measurements using CI-APi-TOF. Atmos. Chem. Phys. 12, 4117–4125 (2012).

57. Kürten, A. et al. Neutral molecular cluster formation of sulfuric acid–dimethylamine observed in real time under atmospheric conditions. Proc. Natl Acad. Sci. 111, 15019–15024 (2014).

58. Broch, S. Ein neues LIF-Instrument für flugzeug- und bodengebundene Messungen von OH- und HO_2-Radikalen in der Troposphäre. Dissertation, Bergische Universität Wuppertal (2011).

59. Tanner, D. J., Jefferson, A. & Eisele, F. L. Selected ion chemical ionization mass spectrometric measurement of OH. J. Geophys. Res. Atmos. 102, 6415–6425 (1997).

60. Heinritzi, M. et al. Characterization of the mass-dependent transmission efficiency of a CIMS. Atmos. Meas. Tech. 9, 1449–1460 (2016).

61. Hanson, D. R. & Eisele, F. Diffusion of H_2SO_4 in humidified nitrogen: hydrated H_2SO_4. J. Phys. Chem. A 104, 1715–1719 (2000).

62. Kurten, A., Rondo, L., Ehrhart, S. & Curtius, J. Calibration of a chemical ionization mass spectrometer for the measurement of gaseous sulfuric acid. J. Phys. Chem. A 116, 6375–6386 (2012).

63. Ehn, M. et al. A large source of low-volatility secondary organic aerosol. Nature 506, 476–479 (2014).

64. Kuang, C. & Ashish, S. Ultrafine Condensation Particle Counter Instrument Handbook https://doi.org/10.2172/1245986 (US Department of Energy, 2024).

65. Brock, C. A. et al. Ultrafine particle size distributions measured in aircraft exhaust plumes. J. Geophys. Res. Atmos. 105, 26555–26567 (2000).

66. Weigel, R. et al. Experimental characterization of the COndensation PArticle counting System for high altitude aircraft-borne application. Atmos. Meas. Tech. 2, 243–258 (2009).

67. Williamson, C. et al. Fast time response measurements of particle size distributions in the 3–60 nm size range with the nucleation mode aerosol size spectrometer. Atmos. Meas. Tech. 11, 3491–3509 (2018).

68. Thomson, W. LX. On the equilibrium of vapour at a curved surface of liquid. London Edinburgh Dublin Philos. Mag. J. Sci. 42, 448–452 (1871).

69. Molleker, S. et al. Application of an O-ring pinch device as a constant-pressure inlet (CPI) for airborne sampling. Atmos. Meas. Tech. 13, 3651–3660 (2020).

70. Jordan, A. et al. A high resolution and high sensitivity proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF-MS). Int. J. Mass Spectrom. 286, 122–128 (2009).

71. Rivera-Rios, J. C. et al. Conversion of hydroperoxides to carbonyls in field and laboratory instrumentation: observational bias in diagnosing pristine versus anthropogenically controlled atmospheric chemistry. Geophys. Res. Lett. 41, 8645–8651 (2014).

72. Wang, N. et al. Measurements of carbonyl compounds around the Arabian Peninsula: overview and model comparison. Atmos. Chem. Phys. 20, 10807–10829 (2020).

73. Ernle, L., Ringsdorf, M. A. & Williams, J. Influence of ozone and humidity on PTR-MS and GC-MS VOC measurements with and without Na_2S_2O_3 ozone scrubber. Atmos. Meas. Tech. 16, 1179–1194 (2023).

74. Voigt, C. et al. Cleaner skies during the COVID-19 lockdown. Bull. Am. Meteorol. Soc. 103, E1796–E1827 (2022).

75. Bourtsoukidis, E. et al. An aircraft gas chromatograph–mass spectrometer System for Organic Fast Identification Analysis (SOFIA): design, performance and a case study of Asian monsoon pollution outflow. Atmos. Meas. Tech. 10, 5089–5105 (2017).

76. Tadic, I. et al. Net ozone production and its relationship to nitrogen oxides and volatile organic compounds in the marine boundary layer around the Arabian Peninsula. Atmos. Chem. Phys. 20, 6769–6787 (2020).

77. Nussbaumer, C. M. et al. Modification of a conventional photolytic converter for improving aircraft measurements of NO_2 via chemiluminescence. Atmos. Meas. Tech. 14, 6759–6776 (2021).

78. Reed, C., Evans, M. J., Di Carlo, P., Lee, J. D. & Carpenter, L. J. Interferences in photolytic NO_2 measurements: explanation for an apparent missing oxidant? Atmos. Chem. Phys. 16, 4707–4724 (2016).

79. Jordan, N. et al. Potential interferences in photolytic nitrogen dioxide converters for ambient air monitoring: evaluation of a prototype. J. Air Waste Manage. Assoc. 70, 753–764 (2020).

80. Marno, D. et al. Calibration of an airborne HOx instrument using the All Pressure Altitude-based Calibrator for HOx Experimentation (APACHE). Atmos. Meas. Tech. 13, 2711–2731 (2020).

81. Novelli, A. et al. Characterisation of an inlet pre-injector laser-induced fluorescence instrument for the measurement of atmospheric hydroxyl radicals. Atmos. Meas. Tech. 7, 3413–3430 (2014).

82. Drewnick, F. et al. A new time-of-flight aerosol mass spectrometer (TOF-AMS)—instrument description and first field deployment. Aerosol Sci. Technol. 39, 637–658 (2005).

83. Schmale, J. et al. Aerosol layers from the 2008 eruptions of Mount Okmok and Mount Kasatochi: in situ upper troposphere and lower stratosphere measurements of sulfate and organics over Europe. J. Geophys. Res. Atmos. 115, D00L07 (2010).

84. Röder, L. L., Ort, L. M., Lelieveld, J. & Fischer, H. Quantitative analysis of temporal stability and instrument performance during field experiments of an airborne QCLAS via Allan-Werle-plots. Appl. Phys. B 130, 118 (2024).

85. Tadic, I., Parchatka, U., Königstedt, R. & Fischer, H. In-flight stability of quantum cascade laser-based infrared absorption spectroscopy measurements of atmospheric carbon monoxide. Appl. Phys. B 123, 146 (2017).

86. Bohn, B. & Lohse, I. Calibration and evaluation of CCD spectroradiometers for ground-based and airborne measurements of spectral actinic flux densities. Atmos. Meas. Tech. 10, 3151–3174 (2017).

87. Bohn, B. & Lohse, I. Optical receiver characterizations and corrections for ground-based and airborne measurements of spectral actinic flux densities. Atmos. Meas. Tech. 16, 209–233 (2023).

88. Zahn, A. et al. A fast and precise chemiluminescence ozone detector for eddy flux and airborne application. Atmos. Meas. Tech. 5, 363–375 (2012).

89. Giez, A. et al. Determination of the Measurement Errors for the HALO Basic Data System BAHAMAS by Means of Error Propagation DLR-Forschungsbericht, DLR-FB-2022-27; https://doi.org/10.57676/5rdc-q708 (2023).

90. Tuovinen, S., Kontkanen, J., Cai, R. & Kulmala, M. Condensation sink of atmospheric vapors: the effect of vapor properties and the resulting uncertainties. Environ. Sci. Atmos. 1, 543–557 (2021).

91. Curtius, J. et al. Isoprene nitrates drive new particle formationin Amazon’s upper troposphere. Zenodo https://doi.org/10.5281/zenodo.12527358 (2024).

Acknowledgements

This work was conducted under authorization 002785/2019-51 and 1.043/2022 of the Brazilian National Council for Scientific and Technological Development (CNPq). We thank the German Aerospace Center Flight Experiments (DLR-FX) facility for operating HALO, specifically, the pilots, engineers, technicians and the operations team. We thank the CLOUD team for providing the intercomparison data and discussion. We acknowledge the use of imagery from the Worldview Snapshots application (https://wvs.earthdata.nasa.gov), part of the Earth Observing System Data and Information System (EOSDIS). This research has received support from the Max Planck Society and the German Research Foundation (DFG) within the SPP 1294 HALO (316646266, 442647232, 461448963, 461450470, 461450583), the TRR 301 TP-Change (428312742, projects A03, A04 and C06), the São Paulo Research Foundation (FAPESP) (grants 2017-17047-0 and 2023-04358-9), the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (438638/2018-2, 313355/2021-5 and 440171/2022-9) and the Dr. Hans Messer Foundation. N.T. received support from the Alexander von Humboldt Foundation. M.Z.-W. received support from the Heinrich Böll Foundation. G.R.U. was supported by FAPESP grants 2021/03547-7 and 2022/01780-9. M.A.C. was supported by FAPESP grant 2022/13257-9 and by the Serrapilheira Institute (Serra – 2211-41823). R.B. received financing from the Knut and Alice Wallenberg Foundation (project 2021.0169).

Author information

Author notes

1. These authors contributed equally: Joachim Curtius, Martin Heinritzi

Authors and Affiliations

1. Institute for Atmospheric and Environmental Sciences, Goethe University Frankfurt, Frankfurt am Main, Germany

Joachim Curtius, Martin Heinritzi, Lisa J. Beck, Lianet Hernández Pardo, Timo Keber, Hannah Klebach, Andreas Kürten, Sarah Richter, Mario Simon, Marcel Zauner-Wieczorek & Anna Possner

2. Atmospheric Microphysics Department, Leibniz Institute for Tropospheric Research, Leipzig, Germany

Mira L. Pöhlker, Konstantinos Barmpounis & Gabriela R. Unfer

3. Faculty of Physics and Earth Sciences, Leipzig Institute for Meteorology, Leipzig University, Leipzig, Germany

Mira L. Pöhlker

4. Max Planck Institute for Chemistry, Mainz, Germany

Nidhi Tripathi, Bianca E. Krumm, Philip Holzbeck, Clara M. Nussbaumer, Thomas Klimach, Konstantinos Barmpounis, Simone T. Andersen, Dirk Dienhart, Raphael Dörich, Achim Edtbauer, Antonia Hartmann, Bruna A. Holanda, Philipp Joppe, Katharina Kaiser, Ovid O. Krüger, Daniel Marno, Monica Martinez, Carolina Monteiro, Carolina Nelson, Linda Ort, Subha S. Raj, Akima Ringsdorf, Sreedev Sreekumar, Anywhere Tsokankunku, Gabriela R. Unfer, Nijing Wang, Meinrat O. Andreae, John N. Crowley, Horst Fischer, Hartwig Harder, Luiz A. T. Machado, Christopher Pöhlker, Ulrich Pöschl, Andrea Pozzer, Johannes Schneider, Jonathan Williams & Jos Lelieveld

5. Lemon Labs Ltd., Nicosia, Cyprus

Konstantinos Barmpounis

6. Department of Environmental Science, Stockholm University, Stockholm, Sweden

Roman Bardakov

7. Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden

Roman Bardakov

8. Institute of Climate and Energy Systems (ICE-3), Forschungszentrum Jülich GmbH, Jülich, Germany

Birger Bohn

9. Institute of Astronomy, Geophysics and Atmospheric Sciences, University of São Paulo, São Paulo, Brazil

Micael A. Cecchini, Isabella D. Valenti & Rachel I. Albrecht

10. Laboratoire d’Aérologie, Université de Toulouse, CNRS, UT3, IRD, Toulouse, France

Jean-Pierre Chaboureau & Thibaut Dauhut

11. Flight Experiments, German Aerospace Center (DLR), Weßling, Germany

Andreas Giez & Christian Mallaun

12. Institute for Atmospheric Physics, Johannes Gutenberg-University, Mainz, Germany

Philipp Joppe

13. National Institute for Space Research, Cachoeira Paulista, Brazil

Fabio Rocha, Gabriela R. Unfer & Dirceu L. Herdies

14. National Institute of Amazonian Research, Manaus, Brazil

Isabella D. Valenti

15. Institute of Meteorology and Climate Research (IMK), Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany

Andreas Zahn

16. Department of Geology and Geophysics, King Saud University, Riyadh, Saudi Arabia

Meinrat O. Andreae

17. Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA

Meinrat O. Andreae

18. Center for Sustainable Amazon Studies (CEAS), University of São Paulo, São Paulo, Brazil

Paulo Artaxo

19. Instituto de Física, University of São Paulo, São Paulo, Brazil

Luiz A. T. Machado

20. Climate and Atmosphere Research Center, The Cyprus Institute, Nicosia, Cyprus

Andrea Pozzer, Jonathan Williams & Jos Lelieveld

Contributions

J.C., M.H., L.J.B., M.L.P., N.T., B.E.K., P.H., C.M.N., L.H.P., T.Kl., K.B., A.E., A.G., A.R., A.T., A.Z., B.B., B.A.H., C.Ma., C.Mo., C.N., D.D., D.M., F.R., G.R.U., I.D.V., J.-P.C., K.K., L.O., M.A.C., M.M., M.S., M.Z.-W., N.W., O.O.K., P.J., R.D., S.R., S.S.R., S.S., T.D., T.Ke., R.I.A., M.O.A., P.A., H.F., H.H., D.L.H., L.A.T.M., C.P., U.P., J.S., J.W. and J.L. prepared the measurement instruments or participated in scientific planning and forecasting. M.H., L.J.B., M.L.P., N.T., B.E.K., P.H., C.M.N., T.Kl., K.B., S.T.A., A.E., A.G., A.H., A.R., A.T., B.B., C.Ma., C.Mo., C.N., D.D., G.R.U., K.K., L.O., M.M., M.Z.-W., N.W., O.O.K., P.J., R.D., S.R., S.S.R., S.S., R.I.A., P.A., H.H., D.L.H. and C.P. collected the data. J.C., M.H., L.J.B., M.L.P., N.T., B.E.K., P.H., C.M.N., L.H.P., T.Kl., K.B., A.E., A.G., A.Z., B.B., C.N., H.K., K.K., M.Z.-W., N.W., S.R., R.I.A., H.H., C.P., U.P., J.S. and J.L. analysed the data. J.C., M.H., L.J.B., M.L.P., N.T., B.E.K., L.H.P., K.B., S.T.A., A.K., D.M., F.R., M.A.C., M.M., M.S., M.Z.-W., R.B., S.R., R.I.A., M.O.A., P.A., J.N.C., H.H., D.L.H., L.A.T.M., C.P., U.P., A.Pos., A.Poz., J.S., J.W. and J.L. contributed to the scientific discussion. J.C., M.H., L.J.B., L.H.P., T.Kl., K.B., F.R., R.I.A., M.O.A., P.A., H.H., U.P., A.Pos., A.Poz., J.W. and J.L. contributed to writing the manuscript.

Corresponding author

Correspondence to Joachim Curtius.

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Extended data figures and tables

Extended Data Fig. 1 Overview of flight tracks, NPF events and schematic of the isoprene-driven NPF.

a, HALO aircraft flight tracks during CAFE-Brazil. RF 19, described in detail in this study, is highlighted in orange. b, NPF events over the Amazon basin and adjacent areas. Flight path segments are shown as black lines for flight altitudes >8 km for all research flights RF 05 to RF 20 in Brazil (except RF 16, and for RF 17, channel N_2 is replaced by channel N_3). NPF data points are coloured according to the local time of measurement. c, Transport and chemistry of isoprene-driven NPF. Emission of isoprene by vegetation, transport to the upper troposphere by nocturnal deep convection, photochemical formation of first-generation and second-generation oxidation products owing to reactions involving OH and NO at upper tropospheric temperatures, NPF from isoprene-derived organonitrates, zonal and downward transport, aerosol growth to CCN size and activation of CCN into cloud droplets. Satellite picture data in a and b from https://wvs.earthdata.nasa.gov.

Source Data

Extended Data Fig. 2 Extended dataset for RF 19.

a, Actinic flux j(O^1D) resulting from total spectral actinic flux densities (downward + upward). Note that the high reflectivity from a cirrus cloud deck below the aircraft increased the total flux considerably. b, OH and HO_2. c, Ozone and CO. d, isoprene, sum of methyl vinyl ketone (MVK), methacrolein (MACR) and isoprene hydroxyhydroperoxide (ISOPOOH), monoterpenes. e, NO, NO_2 (upper limit) and NO_2 calculated from photostationary state using NO, O_3 and j(NO_2). f, Organonitrates C_5H_11O_4(ONO_2) and C_5H_10O_2(ONO_2)2. g, Gaseous sulfuric acid and MSA (upper limit). Note that a fraction of the signals (especially for methane sulfonic acid) is probably because of evaporation of pre-existing particles in the inlet line. h, Particle number concentrations N_2, N_3, N_4 and N_5. i, Aerosol compounds sulfate, nitrate and organics for particles >50 nm (AMS measurements). Note the high correlation between organics and nitrate also for the particles >50 nm. j, Particle concentration for particle sizes 0.25–1 µm and 1–40 µm (OPC measurements) and estimated CS for different assumptions of particle size. k, Water vapour and flight altitude. l, Latitude and longitude of aircraft position. m, Ambient temperature and pressure. n, Wind velocity and direction. For details on uncertainties and detection limits, see Methods.

Source Data

Extended Data Fig. 3 Details of NPF event during RF 19.

a, IP0N, IP1N and IP2N measured with the CI-APi-TOF mass spectrometer as well as IP0-2N = IP0N + IP1N + IP2N and MSA. b, Calculated saturation ratio for IP0N, IP1N and IP2N, as well as estimated saturation ratio necessary to overcome the Kelvin barrier for particle diameters of 1.0, 1.1 and 1.2 nm, respectively (Methods). c, Particle concentrations N_2, N_3, N_4 and N_5 as measured by four channels of the FASD instrument. d, Estimated maximum of the CS when assuming all particles N_5 to have maximum sizes of 20, 50 or 100 nm. During T3, when NPF is first observed by the initial increase of N_2, the CS is less than 2 × 10−4 s−1.

Source Data

Extended Data Fig. 4 Correlation of N2–5 particle concentration with trace gases.

Correlation of N2–5 with IP0N (a), IP1N (b), IP2N (c), MSA (d), sulfuric acid (e) and relative humidity (f). Coloured data represent research flights RF 09 to RF 20, excluding RF 16, and for RF 17, channel N_2 is replaced by N_3 for technical reasons. Only data during NPF events measured between 08:00 and 18:00 local time (LT) and for altitudes >8 km are shown (see Fig. 4 and Extended Data Fig. 8). Data points with black circles represent RF 19. Correlation coefficients represent the entire dataset.

Source Data

Extended Data Fig. 5 Time series and mass-defect plots for CI-APi-TOF data before and during NPF events of RF 19.

a, Time series of the most prominent mononitrates and dinitrates C_5H_11O_4(ONO_2) and C_5H_10O_2(ONO_2)2, representative IP0N compound C_5H_12O_6 and MSA. b, Signals strongly shift between an air parcel that is not influenced by the fresh convection (which contains larger amounts of pre-existing particles that probably contribute to the MSA signal owing to particle evaporation in the inlet) and signals during the first (c) and second (d) encounters of the strong NPF event (T4 and T5). High concentrations of the mononitrates and dinitrates are rapidly forming.

Source Data

Extended Data Fig. 6 Altitude profiles for N2–5 particles and isoprene oxidation products IP0,1,2N measured by the CI-APi-TOF mass spectrometer.

All data were converted to standard conditions (273.15 K, 1,000 hPa) for altitude-independent comparability and comparability with previous studies1,2. Converted to standard conditions, maximum particle concentrations N2–5 for upper tropospheric NPF events were frequently larger than 100,000 scm−3. Data for flights RF 09 to RF 20, excluding RF 16 and using N3–5 for RF 17. Data influenced by biomass burning and by the Manaus plume have been removed. Grey lines denote average concentrations and shaded areas give 5th to 95th percentiles.

Source Data

Extended Data Fig. 7 Frequency of NPF events detected during RF 09 to RF 20.

a, Data from Fig. 4 are binned vertically into boundary layer (0–2 km), lower and middle free troposphere (2–8 km) and the upper troposphere (>8 km). Time bins with respect to day-time when NPF is observed (08:00–18:00 LT) as well as night-time and early morning (before 08:00 LT and after 18:00 LT). b, The number of measurements per pixel for the data shown in Fig. 4. The number in each pixel denotes the number of contributing aircraft flights. Numbers in red denote pixels with at least three flights and 20 data points per flight. The colours denote the total number of data points obtained, with each data point representing a 1-min average value during a flight. Data from RF 16 are excluded; N2–5 is replaced by N3–5 for RF 17.

Source Data

Extended Data Fig. 8 Spatial extent of the NPF event observed during RF 17, measured in the morning of 18 January 2023.

a, Continuously high concentrations of N3–5 particles are detected in an area with an extent of more than 300 km in two layers between 10.6–11.5 and 12.0–12.6 km. A cirrus cloud deck was present at altitudes below 10.8 km. b, High concentrations of dinitrates IP2N were also detected. c, Vertical profile of the number concentration of N3–5 along the latitudinal cross-section, indicated by colour. d, Vertical profile of the number concentration of IP2N along the latitudinal cross-section, indicated by colour. Satellite picture data in a and b obtained from https://wvs.earthdata.nasa.gov.

Source Data

Extended Data Fig. 9 Comparison of isoprene-driven NPF of CAFE-Brazil RF 19 with the CLOUD isoprene + NO[x] experiment.

Mass-defect plots for NO_3−-CIMS spectra from CAFE-Brazil, RF 19, T9, at −58 °C (same data as in Fig. 3a) (a) and Shen et al.[44], adopted from their Fig. 3d, measured at the CLOUD chamber at −48 °C (b). Note that the original Fig. 3d in Shen et al. also contains the signals from NH_4+-CIMS and Br−-CIMS instruments, which extends the range towards lower-oxygen-content molecules. Note also that their Fig. 3d shows the signals with the mass and mass defect of the reagent ion removed. We have added the effect of the NO_3− clustering here to make the data directly comparable with Fig. 3a of our study. Marker size is linearly scaled to concentration. The CLOUD NO_3−-CIMS has a lower limit of detection (2 × 10^4 cm−3 NO_3−-CIMS at CLOUD versus 1 × 10^6 cm−3 for the NO_3−-CI-APi-TOF aircraft instrument at CAFE-Brazil) and thus can detect many smaller peaks. During RF 19, T9, the overall signal intensity of IP0-2N was higher than in the CLOUD experiment, so the marker size in the lower panel has been scaled down by a factor of 5 for comparability. The red edge indicates C_5H_10O_2(ONO_2)2 clustered to a NO_3− reagent ion, which is the highest peak in RF 19, T9. Panels c–j show the conditions during time intervals T1–T9 from RF 19 (this study) and from the CLOUD isoprene + NOx experiment[44]. c, Concentrations of IP0N, IP1N and IP2N. d, Relative fractions of IP0N, IP1N and IP2N. e, Ratio of NO to HO_2. f, Mixing ratios of isoprene, NO and NO_2. g, Rate coefficients for RO_2 + HO_2 and for RO_2 + NO reactions. h, Rate coefficients kRO2+HO2 and kRO2+NO. i, Organonitrate yield α. j, Ratio of (kNO+RO2*[NO]*α)/(kHO2+RO2*[HO_2]).

Source Data

Extended Data Table 1 Comparison of NPF conditions during CAFE-Brazil RF 19 with the CLOUD experiment

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Curtius, J., Heinritzi, M., Beck, L.J. et al. Isoprene nitrates drive new particle formation in Amazon’s upper troposphere. Nature 636, 124–130 (2024). https://doi.org/10.1038/s41586-024-08192-4

- Received: 05 April 2024

- Accepted: 09 October 2024

- Published: 04 December 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08192-4

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Global potential for natural regeneration in deforested tropical regions

Brooke A. Williams,

Hawthorne L. Beyer,

Matthew E. Fagan,

Robin L. Chazdon,

Marina Schmoeller,

Starry Sprenkle-Hyppolite,

Bronson W. Griscom,

James E. M. Watson,

Anazélia M. Tedesco,

Mariano Gonzalez-Roglich,

Gabriel A. Daldegan,

Blaise Bodin,

Danielle Celentano,

Sarah Jane Wilson,

Jonathan R. Rhodes,

Nikola S. Alexandre,

Do-Hyung Kim,

Diego Bastos &

…

Renato Crouzeilles

Nature volume 636, pages 131–137 (2024)

An Author Correction to this article was published on 06 December 2024

Abstract

Extensive forest restoration is a key strategy to meet nature-based sustainable development goals and provide multiple social and environmental benefits[1]. Yet achieving forest restoration at scale requires cost-effective methods[2]. Tree planting in degraded landscapes is a popular but costly forest restoration method that often results in less biodiverse forests when compared to natural regeneration techniques under similar conditions[3]. Here we assess the current spatial distribution of pantropical natural forest (from 2000 to 2016) and use this to present a model of the potential for natural regeneration across tropical forested countries and biomes at a spatial resolution of 30 m. We estimate that an area of 215 million hectares—an area greater than the entire country of Mexico—has potential for natural forest regeneration, representing an above-ground carbon sequestration potential of 23.4 Gt C (range, 21.1–25.7 Gt) over 30 years. Five countries (Brazil, Indonesia, China, Mexico and Colombia) account for 52% of this estimated potential, showcasing the need for targeting restoration initiatives that leverage natural regeneration potential. Our results facilitate broader equitable decision-making processes that capitalize on the widespread opportunity for natural regeneration to help achieve national and global environmental agendas.

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Main

Extensive forest restoration is critical for mitigating climate change[4]. Natural climate solutions remain the most under-invested climate mitigation opportunity among all climate mitigation sectors[5] and, specifically, forest restoration is one of the five largest cost-effective climate mitigation options across all sectors[6]. Global climate mitigation policy platforms have set ambitious forest restoration targets to substantially increase the area of natural ecosystems by 2050, such as the Bonn Challenge, which aims to restore 350 million hectares (Mha) by 2030[7], and target 2 of the recently adopted Global Biodiversity Framework, which calls for 30% of the area of degraded ecosystems to be brought under restoration by 2030[8]. Forest restoration at this scale could sequester hundreds of gigatonnes of carbon and reduce the risk of extinction for thousands of species, particularly if strategic planning is used to prioritize areas for restoration[4]. Tropical forested regions are particularly important owing to their unparalleled biodiversity[9], the magnitude of economic, cultural and recreational services that they provide to people and their rapid growth rates relative to other forest types[10], and because large areas have already been cleared and degraded[11]. The salience of reforestation is recognized internationally, with 51 out of 55 nations’ nationally determined contributions to the Paris Agreement containing targets related to forests as a nature-based solution to climate change mitigation and adaptation[12].

Identifying areas where forests can recover effectively with minimal intervention is critical for achieving forest restoration at scale. Tree planting and extensive site preparation are popular strategies and can be effective, particularly when using locally adapted native tree species in mixtures[13]. However, implementing tree planting at large scales is prohibitively expensive, especially for developing nations, and only sometimes effectively helps native biodiversity to recover[14]. In areas where ecological conditions are such that forests can grow back on their own or with low-cost assistance, natural regeneration methods are less costly (for example, US$12–3,880 per ha compared with US$105–25,830 for forest restoration projects in the tropics and subtropics[15]) and often are more effective than full tree planting in terms of their long-term success rates and biodiversity outcomes[3]. Yet natural regeneration has been underused as a restoration strategy, in part because planners and implementers lack knowledge of where the process can occur and the time it will take to deliver socioeconomic and environmental benefits[3].

The restoration community currently lacks an effective tool for predicting where natural regeneration is most likely to occur and therefore offer multiple benefits of native forest recovery with higher certainty. This gap is a key reason why approaches based on natural regeneration are not applied at larger scales[16]. Previous approaches to mapping forest restoration potential17,18 have relied on coarse-scale data, expert opinion and assumptions about potential forest cover19,20,21, rather than actual data on natural forest regrowth after deforestation, all of which leads to underestimates of natural regeneration potential. By using robust pantropical data on where forests have grown back to quantify the potential for natural regeneration at a high spatial resolution, our study provides essential guidance to enable forest restoration planning and policy in countries with tropical forest.

Tropical natural regeneration potential

We build on the methods used for a recently published pantropical remote sensing analysis[22] that identified 31.6 ± 11.9 Mha of natural regrowth in 4.78 million patches globally between 2000 and 2012 that persisted to 2016 (hereafter, 2000–2016), with an average patch area of 1.2 ha, to assess the scale and locations where natural regeneration has potential in tropical forest biomes (±25° latitude). The analysis[22] distinguished areas of natural regrowth from plantations on the basis of extensive training data and ground-truth information; patches of natural regrowth were defined as at least 0.45 ha in area with vegetation taller than 5 m in height.

Based on a sample of 5.4 Mha of natural regrowth detected previously[22], we used machine learning methods to distinguish areas in which natural regeneration did or did not occur across the global tropics as a function of a suite of geospatial, biophysical and socioeconomic variables. To predict potential tropical forest regeneration across Earth, we selected predictor variables spanning local (site) and landscape scales (from 30 m to country level) that are known to influence the potential for tropical forest regrowth. These biophysical and socioeconomic variables include distance to nearest tree cover; local forest density in a 1 km^2 area; land cover; 12 soil metrics reflecting conditions in the top 30 cm of soil; 19 bioclimatic variables reduced to 5 principal component axes; slope; net primary productivity; monthly average of burned area due to wildfires over the period of 2001–2017; distance to water; population density; gross domestic product; human development index; road density; distance to urban areas; and protected area status (Supplementary Table 2). Spatially explicit values of predictor variables were then used to model the relative potential for natural regeneration in the present (2015) and in the near future (2030), assuming that overall conditions from 2000 to 2016 apply to future scenarios (Fig. 1). For reporting purposes, we translate the resulting continuous potential for natural regeneration value (0–1) to area-based values by multiplying the relative potential for natural regeneration by the area of each pixel, resulting in a weighted-area value. We provide a sensitivity analysis to this calculation where area is regarded as cells that have greater than 50% potential (Supplementary Table 4). The continuous potential for natural regeneration can be interpreted as the probability of natural regeneration per pixel; the weighted-area value is therefore the expected area of natural regeneration per pixel.

Fig. 1: The potential for natural regeneration.

a–f, The potential for natural regeneration across the global tropics and subtropics, with examples across Cuba (a), Colombia (b), Thailand (c), Brazil (d), the Democratic Republic of Congo (e) and Indonesia (f). The areas in grey were excluded from this analysis and were not considered to be available for forest regeneration. Tree cover is represented according to ref. [11] for the year 2018.

We estimate that biophysical conditions can support natural regeneration in tropical forests over 215 Mha (confidence interval (CI) = 214.78–215.22 Mha) globally (Fig. 2; neotropics: 98 Mha (CI = 97.80–98.20 Mha); Indomalayan tropics: 90 Mha (CI = 89.82–90.18 Mha); and Afrotropics: 25.5 Mha (CI = 25.47–25.53 Mha)) until 2030. This is a smaller estimate, unsurprisingly given our smaller study region (the pan tropics), compared with other studies such as ref. [10], in which it was estimated that natural forest regrowth could be biophysically possible across 349 Mha (constrained by existing restoration commitments) and 678 Mha (maximum potential) globally. Five countries—Brazil, Indonesia, China, Mexico and Colombia—account for 52% of the global potential for natural regeneration (Fig. 2; 20.3, 13.6, 7.2, 5.6 and 5.2%, respectively). This result is unsurprising given the large areas of land that these countries cover (24.3% of the study region), but presents important implications for restoration action. We find that 29 other countries have natural regeneration potential areas of over 1 Mha each (Supplementary Table 3). Although some of this area could regenerate without direct human intervention, assisted natural regeneration methods are likely to improve the rate of regeneration and carbon and biodiversity outcomes[2]. This means that some management actions to remove obstacles to forest regeneration (for example, excluding grazers, fire protection, removal of invasive species) would be required2,13. Importantly, our analysis does not specify the type of policy approach or management action, if any, that may be required to facilitate or enhance natural regeneration. Such management decisions should be made locally on the basis of local conditions and restoration objectives defined by local stakeholders.

Fig. 2: Land area that can support natural regeneration.

Summary of the area of land that can support natural forest regeneration (has potential for natural regeneration) shown in green, with the total land area available for forest restoration shown in yellow (in Mha) for countries located at least in part within humid tropical and subtropical forest biomes (tropical and subtropical dry broadleaf forests, tropical and subtropical moist broadleaf forests, and tropical and subtropical coniferous forests). The full table is provided in Supplementary Table 3.

The accuracy of the predictions as assessed through validation was 87.9% based on autocorrelation effects (further details are provided in the Methods and Supplementary Information 1). Spatial variation in accuracy was considerable, with the lowest accuracies occurring in portions of Southeast Asia. By comparison, the out-of-bag accuracy estimated during model-fitting was 87.8%, indicating good model fit and minimal overfitting. At the biome level, validation accuracies were similarly high, with 87.9%, 87.9% and 87.8% in tropical and subtropical moist broadleaf forests, tropical and subtropical dry broadleaf forests, and tropical and subtropical coniferous forests, respectively. The validation accuracy estimate of 87.9% was based on an independent set of 4.87 million random points (equally stratified with respect to the two levels of the dependent variable; further details are provided in Supplementary Information 1 and Extended Data Fig. 4). We contrasted machine learning models of the presence or absence of natural regeneration that included both socioeconomic and biophysical variables to models that only included biophysical variables. As both models showed similar accuracy (0.886 and 0.880, respectively), we based the spatial predictions of natural regeneration potential on the model that contained only biophysical variables, as these factors are less subject to change and have a higher spatial resolution than socioeconomic variables (extended rationale is provided in Supplementary Information 1).

The potential for natural regeneration was positively associated with local forest density within a 1 km radius and negatively associated with distance to existing forest (Fig. 3a,b). For example, out of a random sample of 62,493 grid cells (of all 30 × 30 m grid cells) across the study region, 98.1% of cells with a potential of >0.5 (for illustrative purposes) occur within 300 m of a forest edge. We find that this association is especially pronounced in the neotropics (Fig. 3c,d), probably owing to the higher density of persisting intact forest[23]. Our pantropical remote-sensing-based finding is consistent with extensive previous field-based studies that have shown that nearby local forests are essential for forest regeneration as they moderate climate, serve as seed sources and provide core habitat to seed-dispersing species[24]. Similarly, soil organic carbon content was a positive predictor of the potential for natural regeneration, perhaps because soil organic carbon content is higher within and near forests[25], and low levels of soil carbon are indicators of higher intensification of land use because of tillage and cultivation practices that destroy soil aggregates[26] and remove rootstocks that can promote woody regeneration through resprouts[27].

Fig. 3: The relationship between forest density, distance to forest and potential for natural regeneration.

a–d, Partial plots showing the relationship between forest density (frequency of forest within a 1 km circular buffer) (a) and distance to forest (b) and the potential for natural regeneration, and the frequency at which training points with a potential for natural regeneration value of over 0.5 (n = 3,533,732) fell within each forest density value (c) and distance to forest value (d). The distance to nearest forest partial plot (b) is plotted over a limited x axis range to improve legibility. The y axis of the partial plots (a and b) shows the predicted values (probability scores) associated with the forest density and distance to forest values (the x axes). The y axes of partial dependence plots do not typically represent the true value of the prediction because they visualize the marginal effect of a single feature on model predictions, while keeping other features fixed at certain values. The y axes represent the model’s predicted values (not the actual true values) for the target variable.

On the basis of recent spatially explicit assessments of above-ground carbon accumulation potential from natural regeneration[10], we estimate that forest regeneration over this 215 Mha could sequester 23.4 Gt of C (range, 21.1–25.7) in aboveground biomass alone over a 30 year period if these potential regrowing forests have the opportunity to establish and persist (neotropics: 11.1 Gt (CI = 10.0–12.2 Gt); Indomalayan tropics: 5.42 Gt (CI = 4.87–5.96 Gt); Afrotropics: 3.1 Gt (CI = 2.83–3.37 Gt)). This is more than three years’ worth of gross carbon removals by primary and secondary tropical and subtropical forests globally at current C sequestration rates (7.01 Gt of C per year), considering that the values from our model are additional (occurring on currently deforested lands) to natural regeneration removal factors accounted for in this global estimate[28]. Our estimates do not account for any existing above-ground biomass that may be overtaken by regenerating forests, although such losses are expected to be negligible on converted lands such as pasture or croplands with relatively low initial aboveground biomass[29]. Furthermore, these estimates do not account for belowground biomass, which is difficult to estimate spatially but could account for around an additional 22–28%[30] of the aboveground biomass, implying total carbon sequestration of 28.6–30.0 Gt of C, and up to 1 Gt of potential C removals per year. New carbon sinks in naturally regenerating forests could increase current global carbon sequestration potential in primary and secondary tropical and subtropical forests by 14.3% per year[28].

Discussion

Assisted natural regeneration of forests after deforestation and land use has been successfully applied in many local contexts and has the potential to contribute to considerable carbon sinks and biodiversity conservation areas if upscaled. However, forest restoration is still largely based on tree planting approaches[2]. Here we show that there are substantial opportunities for tropical forests to regenerate with little or moderate assistance (including protection from reclearance and fires), particularly in the Neotropics and Indomalayan tropics. Moreover, if assisted natural regeneration is undertaken over the 30-year period, we show that these efforts will sequester substantial amounts of carbon, mitigating current pantropical forest carbon losses by approximately 90.5% per year[31] or accounting for 26.9% of total potential carbon across global deforested areas[32]. Our results also demonstrate fine-scale spatial variation in the potential for natural tropical forest regeneration (for example, near existing forests), highlighting the importance of using spatially explicit methods to target priority regions and set ambitious and feasible restoration targets. Five nations—Brazil, Indonesia, China, Mexico and Colombia—stand out, accounting for 52% of this potential for natural regeneration (20.3, 13.6, 7.2, 5.6 and 5.2%, respectively) showcasing the need for targeted forest restoration initiatives such as payment for ecosystem service schemes[33] and improving current structures for compensation for environmental damage[34]. However, beyond national opportunities, our maps could facilitate accounting for natural regeneration explicitly in multiobjective planning analyses and decision support tools that are increasingly being used by nations[4]. Our analysis can also inform priority locations for restoration to support incentives for nature-based solutions and to identify locations for carbon offsets where additionality and permanence may be greatest, therefore improving the effectiveness of climate mitigation strategies. We suggest using our predictive model as a first level of assessment, as local site factors and indicators of long-term land use also strongly determine the success and quality of natural regeneration[35].

Our analysis may underestimate regeneration potential in two ways. First, natural regeneration is a positive-feedback process (Fig. 3). As forests regrow better closer to existing forest cover, areas that regenerate naturally will extend the area that supports natural regeneration in the future by serving as seed sources, habitat for seed dispersers, making conditions such as soil quality and microclimates amenable to tree regeneration[36] and potentially enhancing local and regional climate conditions[37]. Our area estimates are based on contemporary conditions, but natural regeneration may be feasible over even larger areas over the long term. Second, our analysis estimates natural regeneration potential on the basis of observed occurrences of recent historic natural regeneration only in areas that support dense natural forest ecosystems, excluding non-forested or sparsely forested ecosystems such as savannas[20]. These two factors reinforce our conclusion that there is substantial and widespread but overlooked potential for natural regeneration across the global tropics. Note that the effects of climate change, such as increased drought and fire risk, increased precipitation and CO_2 fertilization, may alter the successional trajectories of tropical forests, impeding or encouraging natural regeneration processes[38].

The benefits and the benefactors of forest restoration depend heavily on the approaches used[3] and the locations and socioeconomic contexts in which restoration occurs[4]. Tree planting enables implementers to influence the precise species composition of restored forest, which could include planting commercially or culturally valuable species. However, in contexts in which forests can regenerate naturally or with some assistance, reforestation based on tree planting can actually reduce biodiversity conservation benefits if selected tree species or management practices do not provide suitable habitat for species of conservation concern or for keystone species[39]. This is especially true when planting and managing non-native tree species as monocultures[40]. Conversely, initial stages of natural regeneration may be delayed due to limited seed dispersal or poor germination and survival of native tree species[2]. This delay could translate to reduced rates of carbon sequestration and biodiversity value that could be mitigated by assisted natural regeneration interventions[41]. These limitations to establishment are offset by the potential to implement natural regeneration over much larger areas because of lower establishment costs[16].

Forests undergoing post-agricultural natural regeneration are highly vulnerable to reclearance[40]. Young forests fail to persist for many reasons, including market shifts, perverse policies, vulnerability to fire and a lack of awareness of the benefits they provide[42]. Where forests are recovering on unused agricultural land, global forces, such as market shifts in the prices of forest-risk commodities (for example, cattle, coffee, cacao or palm oil) can push landholders to reclear land if commodities become more valuable. In many countries, national policies aimed at protecting forests by imposing use or clearing restrictions on forests of a certain age, height or area can create perverse incentives to prevent forests from regrowing if landholders seek to retain control of their land[43]. At local levels, young regenerating forests are often perceived as unused or wasteland, and may be recleared to claim or retain control of land[44]. In all cases, taking appropriate measures to protect young regenerating forest (for example, firebreaks, fencing and/or patrolling at the local level, along with adequate incentives or compensation, appropriate legislation and effective resources for enforcement) is important for them to persist. Changing perceptions at the local and national levels around the value of these forests through education and/or market opportunities is a key intervention in many places[45]. Despite the ephemeral nature of naturally regenerating areas, they create additionality when actively protected, and could be used to leverage funds from carbon markets and other environmental service payment programs.

A number of pathways exist for achieving large-scale forest regeneration, but each comes with specific challenges that have limited implementation up to now. Compensating farmers and communities for engaging in natural regeneration, for example, through payments for ecosystem services or offset markets, is a possible solution, but should be combined with integrating longer-term benefits from forests to help increase persistence. In particular, carbon offsetting projects through the voluntary carbon market could be a large source of funding for natural regeneration activities (as it is for REDD+ (reducing emissions from deforestation and forest degradation in developing countries) methods and reforestation), but suitable methodologies to certify natural regeneration projects are lacking. Naturally regenerating forests do not fit into many current categories for carbon offsetting[46]. For example, currently, REDD+ methods require project areas to be forested for 10 years before a project starts, whereas afforestation, reforestation and revegetation methods require project areas to be unforested for 10 years before a project starts, which would invalidate recently cleared areas or areas already under regeneration46,47. Improved forest management project areas typically have to be within a logging concession. Robust, natural regeneration certification schemes may also be needed—without them, issuing carbon credits will be challenging, limiting the ability of projects to be financially viable and to provide benefits and incentives to landholders and communities. A major challenge for certification will be accurately quantifying the additionality of natural regenerating forests used as offsets against carefully constructed counterfactuals to avoid overestimating carbon sequestration46,48.

At regional and local scales, providing local people with training to harvest and market products from naturally regenerating forests could be another pathway to finance and provide an incentive to keep young naturally regenerating forests standing[16]. However, additional interventions (and costs) would be required to ensure that the appropriate species return (for example, through enrichment planting[49]), and ongoing support to access and build market connections would be needed to reduce risks to farmers. Implementers could also provide economic or technical support for engaging in alternative, less-land-extensive livelihoods (such as micro-lending schemes, capacity building) that would free up land for forest recovery, provided that setting aside this ‘extra’ land for forest recovery is made explicit. Devolving control of forests and securing land rights to local communities through community forest arrangements is another way to practice restoration that meets local needs[50], provided that appropriate technical support and monitoring are provided. Creating multifunctional landscapes with local peoples and communities involved in the planning, implementation and monitoring phases[16] can also help to ensure that local needs and values are met. At national scales, legislation that prescribes forest management for landowners, such as Brazil’s Forest Code, may be effective at scaling up restoration efforts but can be politically and socially unpopular and vulnerable to political change[51].

Previous studies10,52,53 used general potential areas for reforestation (that is, Forest Landscape Restoration Opportunities[54]) and assumed that natural regeneration was possible and would occur across the entire potential areas (excluding a small fraction (7%) set aside for plantation forestry52,53). A previous study[52] estimated the maximum additional global reforestation mitigation potential to be 1.03 Gt C per year over 215 Mha (scaled to our area with potential for natural regeneration based on supplementary table 9 of ref. [52]). To determine this area, Griscom et al.[52] assumed that natural regeneration would occur on 93% of potential reforestation areas, with the remainder going to plantations to maintain the status quo fraction of plantations to forest (7%). The Griscom et al.[53] update provided an estimated cost-effective potential of reforestation for tropical regions of 0.28 Gt C yr−1 (based on values from ref. [55]). Similar to refs. 52,53, our spatially explicit estimates equate to 0.8 Gt C per year. However, we note that each of these analyses is quite distinct, and key differences in our analyses make direct comparison to previously published estimates convoluted. For example, cost-effective reforestation from ref. [53] summarized country-level mitigation potential values from ref. [55], which was based on an economic modelling process to simulate the aggregate response of abatement suppliers to a variable carbon price. To our knowledge, there are no studies that exist for the global tropics similar enough to ours (that is, informed by data on actual forest regeneration) to provide directly comparable estimates of natural regeneration or C sequestration potential. Given that our model (and therefore estimates of C sequestration potential) accounts for the heterogeneity of potential for natural regeneration across nations, our estimates are an advance compared with total estimates derived from flat sequestration rates.

Importantly, our analysis does not define where restoration activities should or should not occur, nor does it take into account opportunities such as feasibility, socioeconomic constraints or benefits (beyond potential C sequestration) such as specific biodiversity conservation objectives, as other studies do[56]. Our high-resolution maps of natural regeneration (and therefore climate change mitigation) potential are intended to be used as one input into broader decision-making processes for conservation and land-use planning. Our analysis focuses on tropical forest natural regeneration, specifically in response to the demand for cost-effective restoration solutions for nations that are under-resourced (compared with those at temperate latitudes) and where natural regeneration has a proven potential for positive outcomes. A recent assessment of a prioritization analysis sought to identify places where restoration of agricultural land might provide the greatest biodiversity and carbon sequestration benefits at the lowest cost. The findings showed that restoration priorities tended to be concentrated in areas that have emerging economies, and are more populated and more economically unequal, have less food security and employ more people in agriculture[57]. We echo calls to ensure that all tropical forest restoration planning and implementation activities should be undertaken with procedural, distributional, recognitional and contextual equity taken into account[57]. The biophysical potential for natural regeneration is only one of many factors to be considered in restoration planning decisions and target setting.

Note that, although our spatially explicit model is produced at a 30-m resolution, and our subsequent analyses are carried out using this produced dataset, the input covariate and predictor datasets used were a mix of resolutions—subject to the best data available for the necessary time periods (a list of data and sources used in the analysis is provided in Supplementary Table 2). Thus, users should be aware that, in some places, predictions of the potential for natural regeneration are driven by data at resolutions coarser than 30 m (visually evident in Fig. 1). This emphasizes the need for our product to be improved over time, and for implementation activities that take into account the local context.

The total pantropical potential carbon sequestration values that we present here reflect the maximum potential of natural regeneration if it takes place over the entire 215 Mha area for 30 years. The realized potential is likely to be much lower, as reforestation may include commercial or exotic species, not persist or cause leakage. Leakage is sometimes considered at the project level (although not necessarily mitigated against), but rarely at broad scales, and is likely to lead to reduced overall additionality of restoration projects[58]. In the context of restoration, leakage occurs when a restoration intervention has an effect outside the accounting boundary used to track mitigation effects (for example, an action causing emissions reductions in one place may also cause increases elsewhere) and frequently results from restoration activities[58]. That said, regional forest expansions are commonly observed across the tropics when deforestation pressures decline[59], indicating that societal and policy changes to increase reforestation are possible.

Validation of our modelling approach indicates reasonable predictive performance of our model (approximately 87.9% accuracy), similar to the 74–79% accuracy reported in an analysis of the potential for natural regeneration within Brazil’s Atlantic Forest[60]. The high level of accuracy and the 30-m mapping resolution of the predictions make our model predictions useful for informing a range of forest restoration planning activities at international to subnational scales. For example, high-resolution maps of natural regeneration potential could be used to identify regions for testing national forest restoration incentive programs, or to estimate implementation costs in global-scale restoration planning activities[4], thereby improving the estimates of return-on-investment that such approaches attempt to maximize through formal optimization. Our analysis provides a tool to highlight and leverage natural regeneration potential as an integral component of local, national and global restoration planning and implementation.

Conclusion

Natural forest regeneration presents an opportunity to achieve cost-effective forest restoration at scale, delivering major climate mitigation benefits through carbon sequestration and contributing to net-zero emission targets by mid-century. It can also produce critical co-benefits including conserving biodiversity, regulating water resources, reducing erosion and increasing resilience; thus, the maps that we present here can inform multiple and interlinked environmental agendas at national and international scales. As regenerating forests can promote further natural regeneration in the future through a positive-feedback loop, there should be a strong incentive to begin supporting these processes immediately. Our pantropical analysis clarifies the extent of this immediately available opportunity, showing where costs of tree planting can be avoided or minimized while launching ambitious global forest restoration objectives for this decade. Recognizing the massive regeneration capacity of tropical forests is key to stabilizing climate change and reducing biodiversity loss alongside protecting intact forests, achieving regenerative land use and reducing deforestation, emphasizing the need to enhance recovery of degraded forests and target conservation efforts and land-use planning in human-modified landscapes where natural regeneration is most likely to succeed.

Methods

We model the potential for natural regeneration using a binary dependent variable representing point locations where natural regeneration occurred (1) or did not occur (0) between 2000 and 2016 and a suite of biophysical and socioeconomic independent variables. Locations where natural regeneration occurred from 2000 to 2016 were identified by Fagan et al.[22], who delineated patches of at least 0.45 ha of gain of vegetation taller than 5 m height from 2000 to 2016 using the 30-m-resolution Global Forest Watch time-series of tree cover[11]. Natural regeneration was differentiated from planted tree crop monocultures using a combination of geospatial datasets and a machine learning algorithm that was trained and validated using a large sample of known natural regeneration and planted patches[22]. The overall model accuracy for the three-class classification (plantation, open, natural regrowth) of regrowth patches is 90.6% (±0.7), and the mean global model accuracy estimate of the natural regrowth class is 88.9% (±1.2)[22]. Like the product it is derived from[11], the final predicted map omitted the majority of natural regrowth patches in the humid biome. The class producer’s accuracy for natural regrowth patches in the humid biome was 78.8 (±5.6) but dropped to 18.7 (±5.4) when based on estimated area (Extended Data Fig. 1). The low producer’s accuracy when based on estimated area is in part due to Fagan et al.[22] conservatively erring on the side of omission errors, and also because natural regeneration occupies a proportionately small area compared to the class it is separated from (all other classes), meaning that any natural regeneration patch erroneously mapped as other classes had a large influence on estimated regrowth area. Although we refer to both the mapped and estimated area, we emphasize the higher (mapped) estimate, given that this is a common and known issue with reporting on producer’s accuracy61,62. Furthermore, regrowth patches were accurately classified when detected (class user’s accuracy of 85.1 ± 5.6), permitting robust estimates of the overall area and distribution of regrowth (Supplementary Information 1 and Extended Data Figs. 1−4).

Study region

Our study area comprises all tropical and subtropical dry broadleaf forests, tropical and subtropical moist broadleaf forests, and tropical and subtropical coniferous forests[63] within ±25° latitude. The domain-defining areas where natural regeneration could have occurred but did not in the interval 2000–2016 were defined as areas within the aforementioned biomes excluding areas associated with: (1) land cover classes that precluded forest regeneration (European Space Agency (ESA) CCI 2000 land cover classes[64]: water bodies, bare areas, urban areas and sparse vegetation, codes 210, 200, 190 and 150, respectively); (2) areas that were already classed as tree cover in the Global Forest Watch dataset for the year 2000[11]; (3) areas that were deemed to have regenerated naturally between 2000 and 2016; and (4) areas associated with forestry activity[22]. We adopted this liberal definition to allow the subsequent modelling to identify empirically areas associated with low probabilities of natural regeneration, rather than imposing subjective assumptions in the form of more stringent criteria a priori. There are three reasons why we do not adopt stricter criteria for defining the domain of areas available for regeneration. First, there is substantial scope for the introductions of error when adopting constraints on a 30-m-resolution analysis with data mapped at coarser resolutions. For example, 51.8% of the random points generated within the regeneration patches delineated at a 30-m resolution fall within the forest-related ESA CCI land cover categories mapped at a 300-m resolution. This is consistent with our findings of the importance of distance to existing forest and local forest density being important predictors of the potential for natural regeneration, but highlights the problems with using coarser-resolution data to impose strict constraints on an analysis at a finer resolution. Conversely, only 0.3% of the random points generated within the regeneration patches fall within the excluded ESA CCI land cover categories (such as bare ground or urban areas), which we suggest is an acceptable rate of potential misidentification for those areas. Second, land cover classes are estimated with error implying that even classes that we would expect to be associated with a low probability of regeneration are likely to include some regeneration as a result of this misidentification. This could explain, for example, why a small rate of regeneration appears to occur in the excluded land cover classes. Finally, decisions regarding areas within which regeneration are deemed to be possible are subjective. Here, we prefer to limit such assumptions in the definition of the sampling domain and allow the model to identify empirical relationships about the probability of regeneration.

Modelling approach

The statistical modelling and validation is based on a set of six million random points generated within the regeneration and non-regeneration spatial domains defined above, stratified so that each level of the dependent variable was equally represented but with no other form of spatial stratification. For each of these random points, we used geographical information systems to extract values for a range of biophysical and socioeconomic variables (see the ‘Predictor variables’ section). We based the spatial predictions on the model containing biophysical variables only (the rationale is provided in Supplementary Information 1).

We used randomForests[65] to fit models of natural regeneration occurrence and estimate spatially explicit predictions of the potential for natural regeneration in the near future (2030), assuming that overall conditions from 2000 to 2016 apply to future scenarios. We adopted a variable-selection procedure to improve parsimony and reduce the high computational burden of making predictions at 30-m resolution over large scales. RandomForests involves stochastic fitting processes, so two models fit to the same data may differ. We therefore fit ten models with all biophysical covariates by randomly selecting 500,000 training records (balanced with respect to the levels of the dependent variable) from a pool of six million records. The variables were ordered by the mean decrease in accuracy resulting from their omission[65], and the overall ordering of variable importance was based on summing the rank position of each variable among each of these ten models. Following a previous study[60], we then iteratively fit new models adding one additional variable in order of importance each time to identify the point at which the addition of new variables does not improve model accuracy. The final model was fit using only those variables with one million records. Model validation was based on an independent set of random points that was balanced with respect to the dependent variable. A concern with this approach to validation is that it may overestimate model performance, as it does not account for autocorrelation[66]. We evaluated this effect by quantifying the distance between the validation and training locations and assessing model performance in 1-km distance increments from the training locations.

Predictions of the potential for natural regeneration were based on this model with three covariates updated to reflect contemporary conditions rather than conditions in 2000. The revised forest density and distance to forest covariates were derived from the Global Forest Watch 2018 tree cover dataset[11] at a 30-m resolution, and the revised land user/cover dataset was derived from the combination of the 2015 ESA CCI dataset with the aforementioned tree cover data. Locations associated with open water, urban areas and rock or bare ground were deemed to be unavailable for forest regeneration and coded as NoData in the predictions. Further details on the modelling approach are provided in Supplementary Information 1. We used GADM (database of global administrative areas[67]) for all country-based summaries in this analysis.

Predictor variables

ESA CCI land cover (cropland density and distance to urban area)

Land cover is an important consideration for predicting the potential for natural regeneration for various reasons. For example, land with higher cropland density is less likely to regenerate, and distance to urban areas is positively associated with natural regeneration (regenerating patches far from urban areas are less disturbed and less likely to be deforested)[68]. The Climate Change Initiative from the ESA makes available several products on land cover with global coverage. We obtained the 300-m-resolution land-cover rasters, available annually from 1992 to 2015[64]. The typology used by ESA/CCI follows the Land Cover Classification System (LCCS) developed by the United Nations Food and Agriculture Organization, being globally consistent, but also presenting a more detailed classification at regional scales.

The land-cover classification scheme was simplified from 31 to 11 classes based on ecological and land use similarity as follows (semi-colon delimited lists of the ESA CCI land cover classes):

1. (1)
Cropland, rainfed; cropland, irrigated or post-flooding.

2. (2)
Herbaceous cover.

3. (3)
Mosaic cropland (>50%)/natural vegetation (tree, shrub, herbaceous cover) (<50%); mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%)/cropland (<50%).

4. (4)
Tree cover, broadleaved, evergreen, closed to open (>15%); shrubland evergreen.

5. (5)
Tree cover, broadleaved, deciduous, closed to open (>15%); tree cover, broadleaved, deciduous, closed (>40%); tree cover, broadleaved, deciduous, open (15–40%).

6. (6)
Tree cover, needle leaved, evergreen, closed to open (>15%); tree cover, needle leaved, deciduous, closed to open (>15%); tree cover, mixed leaf type (broadleaved and needle leaved).

7. (7)
Mosaic tree and shrub (>50%)/herbaceous cover (<50%); mosaic herbaceous cover (>50%)/tree and shrub (<50%).

8. (8)
Shrubland; shrubland deciduous.

9. (9)
Grassland.

10. (10)
Sparse vegetation (tree, shrub, herbaceous cover) (<15%); sparse tree (<15%); sparse shrub (<15%); sparse herbaceous cover (<15%); urban areas; bare areas; consolidated bare areas; unconsolidated bare areas; water bodies.

11. (11)
Tree cover, flooded, fresh or brackish water; tree cover, flooded, saline water; shrub or herbaceous cover, flooded, fresh/saline/brackish water.

We derived a cropland density covariate from the ESA CCI dataset representing the mean cropland area within a 5-km circular buffer for each pixel within the study region, using the focal statistics tool in ArcMap (v.10.8)[69]. Some of the CCI classes represent composites of several land cover classes. When estimating density, we adopted the following reclassification for determining the proportion of each cell that was considered to represent cropland: 1: cropland; 1: cropland (irrigated or post-flooding); 0.75: mosaic cropland (>50%)/natural vegetation (tree); 0.25: mosaic natural vegetation, herbaceous cover (>50%)/cropland (<50%); 0: all other classes.

We also derived a distance to urban area covariate representing the Euclidean distance from each cell to the nearest cell classed as urban.

Tree cover (forest density and distance to forest patch)

Previous studies have shown that distance to forest patches is negatively associated with natural regeneration potential (lower seedling density and species richness as the distance from the forest edge increases)[68]. The Global Forest Watch provides data on tree cover, gain and loss for the interval from 2000 to 2018 at a global scale, at 30-m resolution[11]. This dataset was used in three ways in our modelling processes. First, it was used to define the areas that were already forested in 2000, thereby precluding the possibility of natural regeneration within those areas. Random points for the non-regeneration (0) level of the dependent variable were excluded from these locations. Second, the 2000 tree cover data were used to estimate mean forest area within a 1-km circular buffer for each pixel within the study region using the focal statistics tool in ArcMap (v.10.8)[69], thereby resulting in two new derived 30-m-resolution datasets that were covariates in the models. Previous work[60] has demonstrated that forest density and distance to forest are strong predictors of the potential for natural regeneration. Finally, when generating the spatial predictions of the potential for natural regeneration, the 2018 tree cover data were used to define the areas that were already forested (and could therefore not regenerate naturally), and to update the forest density and distance to forest covariates.

We caution users that forest-based predictor (2000–2012 dataset) and explanatory variables (2011–2018 dataset) from the Global Forest Watch[11] used to generate the layers used in our analysis may differ in accuracy given that, for the 2011–2018 dataset, Landsat 8 OLI data were used, loss data were reprocessed, improved training data for calibrating the loss model were used, improved per sensor quality assessment models to filter input data were used, and improved input spectral features for building and applying the loss model were applied. The variety of mapping methods and attribution approaches means that the input datasets may locally lack coherency (for example, gain may occur in pixels not mapped as forest cover in a later revision). However, this is the most comprehensive and reliable dataset currently available, especially at broader scales. More information on the differences between the datasets is available online (https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.6.html).

Protected areas

Natural regeneration potential has been shown to be higher when deforested areas neighbour or are newly incorporated into protected areas[70]. To represent protected areas, we used the World Database on Protected Areas[71], which we downloaded and rasterized according to the methods of a previous study[72]. We incorporated into the August 2020 version of WDPA 768 protected areas (1.43 million km^2) in China (sites that were available in the June 2017 version of WDPA, but not publicly available thereafter). Following the WDPA best practice guidelines (www.protectedplanet.net/c/calculating-protected-area-coverage) and similar global studies, we included in our analysis only protected areas from the WDPA database that have a status of ‘designated’, ‘inscribed’ or ‘established’, and removed all points and polygons with a status of ‘proposed’ or ‘not reported’. We buffered the point feature class in accordance with the area of the points as stated in the ‘REP AREA’ field, and merged the buffered points with polygons to create one polygon layer. We then dissolved this layer by IUCN protection category.

Biomes

We include biome[63] as a categorical variable in our models, as biomes represent broad ecological systems within which the processes governing natural forest regeneration may vary. The analysis is restricted to the three tropical and subtropical forest biomes (tropical and subtropical dry broadleaf forests, tropical and subtropical moist broadleaf forests, and tropical and subtropical coniferous forests).

Global Soil Map (International Soil Reference and Information Centre)

Soil properties can influence forest regeneration potential as they may affect seedlings’ establishment, growth and survival[73]. Thus, 12 soil properties mapped at a 250-m resolution[74] were evaluated for use in the modelling, including: (1) total profile depth (cm); (2) plant exploitable (effective) soil depth (cm); (3) organic carbon (g per kg); (4) pH (×10); (5) sand (g per kg); (6) silt (g per kg); (7) clay (g per kg); (8) gravel (m^3); (9) ECEC (cmolc kg−1); (10) bulk density of the fine earth (< 2 mm) fraction (excludes gravel) (t m−3); (11) bulk density of the whole soil in situ (includes gravel) (t m−3); and (12) available water capacity (mm) were included. These are mapped at six depth intervals (0–5 cm; 5–15 cm; 15–30 cm; 30–60 cm; 60–100 cm; 100–200 cm). We calculated the mean of each of the 12 properties within the top 30 cm of soil (weighted by the depth of each layer).

Slope (derived from SRTM elevation)

Slope can be positively associated with natural regeneration potential, especially for areas dominated by mechanized crop and pasture land uses[75], where slope areas are frequently abandoned to regenerate. The mean elevation above sea level was obtained from the Shuttle Radar Topography Mission (SRTM)[76]. The data are made available by NASA in raster format at a 1 arc-second (approximately 30 m) resolution, based on Digital Elevation Models and other open source data to fill the voids in that dataset using Google Earth Engine[77]. Slope was derived from this elevation dataset at 30-m resolution.

Net primary productivity (annual 2000–2015, global; MOD17A3)

Natural regeneration potential is positively associated with net primary productivity. We used the mean net primary productivity estimate from the MODIS-based estimates of net primary productivity (gC m−2 yr−1) at 1-km^2 resolution[78].

WorldClim v.2.1 climate data for 1970–2000

Climatic variables may influence natural regeneration potential, as some biophysical conditions, such as high annual precipitation, can promote tree regeneration and reduce fire frequency[79]. We used the WorldClim dataset[80] which includes 19 bioclimatic variables derived from the monthly temperature and rainfall values. They are 30-arc-second-resolution (approximately 1 km) rasters and include: (1) annual mean temperature; (2) mean diurnal range; (3) isothermality; (4) temperature seasonality; (5) max temperature of the warmest month; (6) min temperature of the coldest month; (7) temperature annual range; (8) mean temperature of the wettest quarter; (9) mean temperature of the driest quarter; (10) mean temperature of the warmest quarter; (11) mean temperature of the coldest quarter; (12) annual precipitation; (13) precipitation of the wettest month; (14) precipitation of the driest month; (15) precipitation seasonality (coefficient of variation); (16) precipitation of the wettest quarter; (17) precipitation of the driest quarter; (18) precipitation of the warmest quarter; and (19) precipitation of the coldest quarter.

Because several of the bioclimatic variables are correlated, we used principal component analysis (PCA) to reduce the set of 19 bioclimatic variables to a set of 5 principal components that capture 99.4% (Supplementary Table 1) of the variation among the original 19 variables but that are orthogonal (uncorrelated) with each other. This was achieved by generating a random sample of 1 million points within land areas (the bioclimatic datasets have NoData values for marine areas, which were excluded) and extracting the cell values associated with those locations from each of the 19 bioclimatic variables into a matrix. Each raster contained over 309 million data values; it was therefore necessary to base the PCA on a sample of the values rather than all values. The ‘prcomp’ function[81] in R was used to calculate the principal components based on this matrix. This PCA model was then applied to all pixels in the rasters (using the ‘predict.prcomp’ function[81] in R) and the first five components were written as new raster datasets that were used in subsequent modelling.

Global Human Settlement dataset (GHS-POP)

Human population density may negatively influence natural regeneration potential, as deforested areas may be subjected to further disturbances due to human populations[82]. The global human settlement dataset depicts the distribution of population, expressed as the number of people per cell[83]. Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4.10 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch.

Gross domestic product and human development index

Natural regeneration potential is positively associated with gross domestic product[82] and human development index[84]. Kummu et al.[85] provide gridded global datasets for gross domestic product and human development index over 1990–2015.

Road density

Natural regeneration potential may be higher in areas of low road density, where agricultural land is more likely to be abandoned[86]. The Global Roads Inventory Project[87] gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. From this dataset, we obtained the value of road density in m per km^2.

Fire: burned area

While, under favourable ecological conditions, some recently burned areas may have a higher natural regeneration rate[88], humid tropical forests are often poorly adapted to fire, and therefore less likely to naturally regenerate after a fire event[89]. The monthly average of burned area due to wildfires over the period of 2001–2017 (January–December for all years) was obtained from ref. [90].

Distance to water

Natural regeneration potential can be influenced by distance to watercourses and waterbodies as riparian areas have higher water availability, higher fauna movement, and usually present remnant trees and forests supplying seeds for regeneration[75]. We used a layer which represents the Euclidean distance (metres) of each raster cell to the closest freshwater feature (lake or river). Detailed methodology on how the dataset was produced is given in ref. [91].

A summary of all data used in this analysis is available in Supplementary Table 2.

Areal calculations

Area values reported in this study were calculated as the area of the 30 m × 30 m grid cell multiplied by its relative potential for natural regeneration value. We provide a sensitivity analysis to this calculation where area is regarded as cells that have >50% potential for natural regeneration, which expands areal estimates slightly (globally from 215 million ha to 263 million ha; Supplementary Table 4). All calculations were carried out using the Mollweide projection.

Carbon accumulation potential calculations

We calculate carbon accumulation potential using the Cook-Patton et al.[10] dataset, which provides carbon accumulation in t of C yr−1 over 30 years of forest regrowth. We converted this layer to gross accumulation potential over the entire 30 year period by multiplying values by 30. Original values were in t per ha at a 1 km by 1 km resolution. To downscale to a 30 m by 30 m cell resolution, we first resampled the layer and multiplied it by 0.09, because there are 0.09 hectares in the 30 m by 30 m cell. This gave us a total value per cell, which we then multiplied by the potential for natural regeneration, and finally summarized (sum) at the global, and bioregional levels.

Reporting summary

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

Data availability

All input datasets are available from the references cited. The raster datasets arising from this work are available link below and include (1) the approximately 1-km-resolution overview raster representing the proportion of the area of each pixel that has the potential for natural regeneration (‘prop pnv v1 1 km.tif’; 1.1 GB) for display purposes only (not used in this analysis); (2) the 30-m-resolution binary predictions (where a value of >0.5 is allocated a value of 1, acknowledging that users may prefer to use a different threshold for their context) of areas suitable for natural regeneration (files named with the prefix ‘pnv bin 30 m’; 2.3 GB); and (3) the 30-m continuous probability predictions of the potential for natural regeneration, stored as integers representing percentages to minimize file sizes (files named with the prefix ‘pnv pct 30 m’; 9.3 GB). The 30-m-resolution datasets have been tiled into 10-degree latitude/longitude tiles. The dataset is available at Zenodo[92] (https://zenodo.org/record/7428804).

Code availability

R and python codes developed for and used in this analysis are available on request from hawthornebeyer@gmail.com.

References

1. Holl, K. D. Restoring tropical forests from the bottom up. Science 355, 455–456 (2017).

2. Chazdon, R. L. & Guariguata, M. R. Natural regeneration as a tool for large‐scale forest restoration in the tropics: prospects and challenges. Biotropica 48, 716–730 (2016).

3. Crouzeilles, R. et al. Ecological restoration success is higher for natural regeneration than for active restoration in tropical forests. Sci. Adv. 3, e1701345 (2017).

4. Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).

5. Wolosin, M. et al. Exponential Roadmap for Natural Climate Solutions (Conservation International, 2022).

6. IPCC Working Group. Climate Change 2022 Mitigation of Climate Change Working Group III Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2014).

7. The Bonn Challenge www.bonnchallenge.org/about (IUCN, 2020).

8. First Draft of the Post-2020 Global Biodiversity Framework (CBD, 2021).

9. Pillay, R. et al. Tropical forests are home to over half of the world’s vertebrate species. Front. Ecol. Environ. 20, 10–15 (2022).

10. Cook-Patton, S. C. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020).

11. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

12. NDC’s—a Force for Nature? (WWF, 2021).

13. Brancalion, P. H. S. et al. What makes ecosystem restoration expensive? A systematic cost assessment of projects in Brazil. Biol. Conserv. 240, 108274 (2019).

14. Hua, F. et al. The biodiversity and ecosystem service contributions and trade-offs of forest restoration approaches. Science 376, 839–844 (2022).

15. Bodin, B. et al. A standard framework for assessing the costs and benefits of restoration: introducing The Economics of Ecosystem Restoration. Restor. Ecol. 30, e13515 (2022).

16. Chazdon, R. L. et al. Fostering natural forest regeneration on former agricultural land through economic and policy interventions. Environ. Res. Lett. 15, 043002 (2020).

17. Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).

18. Laestadius, L. et al. Mapping Opportunities for Forest Landscape Restoration (FAO, 2011).

19. Veldman, J. W. et al. Comment on “The global tree restoration potential”. Science 366, eaay7976 (2019).

20. Fagan, M. E. A lesson unlearned? Underestimating tree cover in drylands biases global restoration maps. Glob. Change Biol. 26, 4679–4690 (2020).

21. Ling, P.-Y. et al. Mapping global forest regeneration—an untapped potential to mitigate climate change and biodiversity loss. Environ. Res. Lett. 18, 054025 (2023).

22. Fagan, M. E. et al. The expansion of tree plantations across tropical biomes. Nat. Sustain. 5, 681–688 (2022).

23. Grantham, H. S. et al. Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity. Nat. Commun. 11, 5978 (2020).

24. Sloan, S., Goosem, M. & Laurance, S. G. Tropical forest regeneration following land abandonment is driven by primary rainforest distribution in an old pastoral region. Landsc. Ecol. 31, 601–618 (2016).

25. Cerri, C. C., Volkoff, B. & Andreaux, F. Nature and behaviour of organic matter in soils under natural forest, and after deforestation, burning and cultivation, near Manaus. For. Ecol. Manage. 38, 247–257 (1991).

26. Damian, J. M. et al. Deforestation and land use change mediate soil carbon changes in the eastern Brazilian Amazon. Reg. Environ. Change 21, 64 (2021).

27. Shoo, L. P. & Catterall, C. P. Stimulating natural regeneration of tropical forest on degraded land: approaches, outcomes, and information gaps. Restor. Ecol. 21, 670–677 (2013).

28. Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Change 11, 234–240 (2021).

29. Chazdon, R. L. et al. Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci. Adv. 2, e1501639 (2016).

30. Annex 3A.1 Biomass Default Tables for Section 3.2 Forest Land (ICPP, 2003).

31. Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).

32. Mo, L. et al. Integrated global assessment of the natural forest carbon potential. Nature https://doi.org/10.1038/s41586-023-06723-z (2023).

33. López-Cubillos, S. et al. Spatial prioritization to achieve the triple bottom line in payment for ecosystem services design. Ecosyst. Serv. 55, 101424 (2022).

34. Bustamante, M. M. C. et al. Ecological restoration as a strategy for mitigating and adapting to climate change: lessons and challenges from Brazil. Mitig. Adapt. Strateg. Glob. Change 24, 1249–1270 (2019).

35. César, R. G. et al. It is not just about time: agricultural practices and surrounding forest cover affect secondary forest recovery in agricultural landscapes. Biotropica https://doi.org/10.1111/btp.12893 (2021).

36. Chazdon, R. L. Second Growth: The Promise of Tropical Forest Regeneration in an Age of Deforestation (Univ. Chicago Press, 2014).

37. Lawrence, D., Coe, M., Walker, W., Verchot, L. & Vandecar, K. The unseen effects of deforestation: biophysical effects on climate. Front. For. Glob. Change 5, 756115 (2022).

38. Uriarte, M. et al. Impacts of climate variability on tree demography in second growth tropical forests: the importance of regional context for predicting successional trajectories. Biotropica 48, 780–797 (2016).

39. Chazdon, R. L. et al. The potential for species conservation in tropical secondary forests. Conserv. Biol. 23, 1406–1417 (2009).

40. Brancalion, P. H. S. & Holl, K. D. Guidance for successful tree planting initiatives. J. Appl. Ecol. 57, 2349–2361 (2020).

41. Shono, K., Chazdon, R., Bodin, B., Wilson, S. J. & Durst, P. Assisted natural regeneration: harnessing nature for restoration. Unasylva 252, 71–81 (2020).

42. Holl, K. D., Loik, M. E., Lin, E. H. V. & Samuels, I. A. Tropical Montane forest restoration in Costa Rica: overcoming barriers to dispersal and establishment. Restor. Ecol. 8, 339–349 (2000).

43. Chazdon, R. L. et al. When is a forest a forest? Forest concepts and definitions in the era of forest and landscape restoration. Ambio 45, 538–550 (2016).

44. Zahawi, R. A., Reid, J. L. & Holl, K. D. Hidden costs of passive restoration. Restor. Ecol. 22, 284–287 (2014).

45. Reid, J. L. et al. How long do restored ecosystems persist? Ann. Mo. Bot. Gard. 102, 258–265 (2017).

46. Brancalion, P. H. S. et al. A call to develop carbon credits for second-growth forests. Nat. Ecol. Evol. 8, 179–180 (2024).

47. Warsaw Framework for REDD+ (UNFCCC, 2023).

48. West, T. A. P., Börner, J., Sills, E. O. & Kontoleon, A. Overstated carbon emission reductions from voluntary REDD+ projects in the Brazilian Amazon. Proc. Natl Acad. Sci. USA 117, 24188–24194 (2020).

49. Yeong, K. L. et al. Enrichment planting to improve habitat quality and conservation value of tropical rainforest fragments. Biodivers. Conserv. 25, 957–973 (2016).

50. Wilson, S. J. Communal management as a strategy for restoring cloud forest landscapes in Andean Ecuador. World Dev. Perspect. 3, 47–49 (2016).

51. Soares-Filho, B. et al. Land use. Cracking Brazil’s forest code. Science 344, 363–364 (2014).

52. Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).

53. Griscom, B. W. et al. National mitigation potential from natural climate solutions in the tropics. Philos. Trans. R. Soc. Lond. B 375, 20190126 (2020).

54. Potapov, P., Laestadius, L. & Minnemeyer, S. Global Map of Potential Forest Cover www.wri.org/resources/maps/atlas-forest-and-landscape-restoration-opportunities/data-info (2011).

55. Busch, J. et al. Potential for low-cost carbon dioxide removal through tropical reforestation. Nat. Clim. Change 9, 463–466 (2019).

56. Brancalion, P. H. S. et al. Global restoration opportunities in tropical rainforest landscapes. Sci. Adv. 5, eaav3223 (2019).

57. Schultz, B. et al. Recognizing the equity implications of restoration priority maps. Environ. Res. Lett. 17, 114019 (2022).

58. Streck, C. REDD+ and leakage: debunking myths and promoting integrated solutions. Clim. Policy 21, 843–852 (2021).

59. Meyfroidt, P. & Lambin, E. F. Global forest transition: prospects for an end to deforestation. Annu. Rev. Environ. Resour. 36, 343–371 (2011).

60. Crouzeilles, R. et al. Achieving cost‐effective landscape‐scale forest restoration through targeted natural regeneration. Conserv. Lett. 13, e12709 (2020).

61. Wang, Y. et al. High-resolution maps show that rubber causes substantial deforestation. Nature 623, 340–346 (2023).

62. Olofsson, P. et al. Mitigating the effects of omission errors on area and area change estimates. Remote Sens. Environ. 236, 111492 (2020).

63. Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).

64. Land Cover CCI Product User Guide Version 2 Technical Report (ESA, 2017).

65. Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).

66. Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11, 4540 (2020).

67. Database of Global Administrative Areas (GADM, 2022).

68. Cubina, A. & Aide, T. M. The effect of distance from forest edge on seed rain and soil seed bank in a tropical Pasture1. Biotropica 33, 260–267 (2001).

69. ArcGIS (GIS software) v.10.8. (ESRI, 2022).

70. Algeet-Abarquero, N., Sánchez-Azofeifa, A., Bonatti, J. & Marchamalo, M. Land cover dynamics in Osa Region, Costa Rica: secondary forest is here to stay. Reg. Environ. Change 15, 1461–1472 (2015).

71. Protected Planet: The World Database on Protected Areas (WDPA) v.1.6 www.protectedplanet.net/en (UNEP-WCMC, IUCN, 2020).

72. Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).

73. Heneghan, L. et al. Integrating soil ecological knowledge into restoration management. Restor. Ecol. 16, 608–617 (2008).

74. Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).

75. Molin, P. G., Chazdon, R., Frosini de Barros Ferraz, S. & Brancalion, P. H. S. A landscape approach for cost‐effective large‐scale forest restoration. J. Appl. Ecol. 55, 2767–2778 (2018).

76. Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).

77. Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

78. Zhao, M., Heinsch, F. A., Nemani, R. R. & Running, S. W. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ. 95, 164–176 (2005).

79. Yackulic, C. B. et al. Biophysical and socioeconomic factors associated with forest transitions at multiple spatial and temporal scales. Ecol. Soc. 16, 15 (2011).

80. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1 km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

81. R Core Team. R: a Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).

82. Piffer, P. R., Rosa, M. R., Tambosi, L. R., Metzger, J. P. & Uriarte, M. Turnover rates of regenerated forests challenge restoration efforts in the Brazilian Atlantic forest. Environ. Res. Lett. 17, 045009 (2022).

83. Schiavina, M., Freire, S. & MacManus, K. GHS Population Grid Multitemporal (1975, 1990, 2000, 2015) R2019A (European Commission JRC, 2019).

84. Redo, D. J., Grau, H. R., Aide, T. M. & Clark, M. L. Asymmetric forest transition driven by the interaction of socioeconomic development and environmental heterogeneity in Central America. Proc. Natl Acad. Sci. USA 109, 8839–8844 (2012).

85. Kummu, M., Taka, M. & Guillaume, J. H. A. Data from: Gridded global datasets for gross domestic product and human development index over 1990–2015, v2. Dryad https://doi.org/10.5061/dryad.dk1j0 (2020).

86. Thomlinson, J. R. et al. Land-use dynamics in a post-agricultural Puerto rican landscape (1936-1988). Biotropica 28, 525 (1996).

87. Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 064006 (2018).

88. Maillard, O. Post-fire natural regeneration trends in Bolivia: 2001–2021. Fire 6, 18 (2023).

89. Scheper, A. C., Verweij, P. A. & van Kuijk, M. Post-fire forest restoration in the humid tropics: a synthesis of available strategies and knowledge gaps for effective restoration. Sci. Total Environ. 771, 144647 (2021).

90. Artés, T. et al. A global wildfire dataset for the analysis of fire regimes and fire behaviour. Sci. Data 6, 296 (2019).

91. Kummu, M., de Moel, H., Ward, P. J. & Varis, O. How close do we live to water? A global analysis of population distance to freshwater bodies. PLoS ONE 6, e20578 (2011).

92. Williams, B. A. et al. Data for ‘Global potential for natural regeneration in deforested tropical regions’. Zenodo https://doi.org/10.5281/zenodo.7428803 (2024).

Acknowledgements

We acknowledge financial support from Conservation International, and the Walton Family Foundation (B.W.G.). The mapping work from Fagan and colleagues 2022, the basis of this analysis, was supported by NASA grant 80NSSC21K0297. J.R.R. was supported by an ARC Future Fellowship (FT200100096).

Author information

Author notes

1. These authors contributed equally: Brooke A. Williams, Hawthorne L. Beyer

Authors and Affiliations

1. Institute for Capacity Exchange in Environmental Decisions, Canberra, Australian Capital Territory, Australia

Brooke A. Williams, Hawthorne L. Beyer, Robin L. Chazdon, Marina Schmoeller, Anazélia M. Tedesco, Diego Bastos & Renato Crouzeilles

2. School of Biology and Environmental Science, Queensland University of Technology, Brisbane, Queensland, Australia

Brooke A. Williams & Jonathan R. Rhodes

3. School of the Environment, University of Queensland, St Lucia, Queensland, Australia

Brooke A. Williams, James E. M. Watson, Anazélia M. Tedesco & Jonathan R. Rhodes

4. Centre for Biodiversity and Conservation Science, University of Queensland, St Lucia, Queensland, Australia

Brooke A. Williams, James E. M. Watson, Anazélia M. Tedesco & Jonathan R. Rhodes

5. Mombak, São Paulo, Brazil

Hawthorne L. Beyer & Renato Crouzeilles

6. Department of Geography and Environmental Systems, University of Maryland Baltimore County, Baltimore, MD, USA

Matthew E. Fagan

7. Forest Research Institute, University of the Sunshine Coast, Sippy Downs, Queensland, Australia

Robin L. Chazdon

8. Center for Natural Climate Solutions, Conservation International, Arlington, VA, USA

Starry Sprenkle-Hyppolite, Bronson W. Griscom & Nikola S. Alexandre

9. Moore Center for Science, Conservation International, Arlington, VA, USA

Mariano Gonzalez-Roglich & Gabriel A. Daldegan

10. Wildlife Conservation Society, Buenos Aires, Argentina

Mariano Gonzalez-Roglich

11. Amazon Conservation Association, Washington, DC, USA

Blaise Bodin

12. Instituto Socioambiental, São Paulo, Brazil

Danielle Celentano

13. School of Environmental Studies, University of Victoria, Victoria, British Columbia, Canada

Sarah Jane Wilson

14. United Nations Children’s Fund, New York, NY, USA

Nikola S. Alexandre & Do-Hyung Kim

Contributions

B.A.W. and H.L.B. contributed equally. H.L.B. conceived the study with the support of R. Crouzeilles and N.S.A. H.L.B. coordinated the initial development of the modelling approach with the support of B.A.W., R. Crouzeilles, M.E.F., R. Chazdon, M.S., S.S.-H., B.W.G. and M.G.-R. and led the analysis with the support of B.A.W. H.L.B. led the first draft of the paper, and B.A.W. led the writing, synthesis, figure development and coordination of all subsequent versions. B.A.W. led the additional analyses and synthesis conducted during the review process with the support of M.E.F. All of the authors provided input into subsequent versions of the manuscript.

Corresponding author

Correspondence to Brooke A. Williams.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Edward Mitchard, Charlotte Wheeler 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 The change in model accuracy (y axis) among a set of increasingly complex models (x axis).

Covariates were added sequentially in order of importance (mean decrease in accuracy metric) to identify the point at which the addition of any additional variables no longer improved model accuracy (here, ten variables).

Extended Data Fig. 2 Variable importance plots for the model with ten biophysical variables.

Mean decrease in accuracy (a) reflects the loss of accuracy that arises from the omission of a variable, whereas the Gini coefficient (b) reflects the contribution of a variable to the purity of the classification (how cleanly the levels of the dependent variable can be separated). Variables are: bioclim1, 2, 3, and 4 = bioclim principle component axes 1, 2, 3, and 4, biome = biome, distfor = distance to forest, focalfor = forest density, landuse = land use, soc = soil organic carbon content, soilph = soil pH.

Extended Data Fig. 3 Plots indicating how each covariate (x axis) is related to the predicted probability of natural regeneration (y axis).

Red lines are the mean predicted probability value calculated over a range of 100 intervals of the covariate. Given that the predicted probabilities of the random sample are skewed towards lower and higher probability scores (a bimodal distribution) and the mean considers all values equally, the mean results in a value that doesn’t correspond to any of the probability scores. Open circles are a random sample of 1000 records from the training dataset to provide an indication of the distribution and variation in the data. The blue rug marks along the bottom of each plot represent the deciles of the distribution of values of that covariate. The covariate include: forest density in a 1 km^2 area, distance to nearest forest, soil pH (PHIHOX), soil organic carbon content (OCDENS), and four bioclimatic variables representing the first four axes of a principal components analysis based on 19 original bioclimatic variables (see Data Sources for details). The distance to nearest forest graph is plotted over a limited x axis range to improve legibility.

Extended Data Fig. 4 Model accuracy based on a random sample of 4.87 M points with equal number of regeneration and non-regeneration observations was 87.9%.

To evaluate evidence of autocorrelation effects in this estimate accuracy was estimated in 16 0.5-km intervals between the training and validation points. (a) Accuracy was highest in the shortest distance interval, declined to 81.4% in the 2.0-2.5 km interval, and then increased. This pattern was driven by the combined effect of different accuracies for each outcomes (regeneration vs. non-regeneration) and the change in the relative frequency of each outcome as the distance interval increases (b). Accuracies were lower when forcing balanced samples in each interval and using a bootstrapping approach with 50 replicates of sampling with replacement. Sample sizes varied among intervals (c - see legend) because of the relative rarity of some outcomes at some distance-intervals (b). Vertical lines represent the range of accuracies among all 50 replicates.

Supplementary information

Supplementary Information 1

Extended methodology and rationale for the modelling approach, including Supplementary Tables A1.1–A1.5.

Reporting Summary

Supplementary Table 1

Characteristics of the first 5 components (PC1–5) of a PCA of the 19 bioclimatic variables.

Supplementary Table 2

Table of data used in the analysis.

Supplementary Table 3

Table of the potential for natural regeneration across all countries. Area values were calculated by multiplying the relative potential for natural regeneration by the area of each pixel, resulting in a weighted-area value, for countries located at least in part within humid tropical and subtropical forest biomes (tropical and subtropical dry broadleaf forests, tropical and subtropical moist broadleaf forests, and tropical and subtropical coniferous forests).

Supplementary Table 4

Area values were calculated as the area of cells that have >50% potential for natural regeneration. These were calculated by counting the number of binary pixels within a given area and multiplying them by the 0.09 ha area of the 30 m by 30 m pixels. Values are for countries located at least in part within humid tropical and subtropical forest biomes (tropical and subtropical dry broadleaf forests, tropical and subtropical moist broadleaf forests, and tropical and subtropical coniferous forests).

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Williams, B.A., Beyer, H.L., Fagan, M.E. et al. Global potential for natural regeneration in deforested tropical regions. Nature 636, 131–137 (2024). https://doi.org/10.1038/s41586-024-08106-4

- Received: 04 August 2023

- Accepted: 24 September 2024

- Published: 30 October 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08106-4

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Exaptation of ancestral cell-identity networks enables C_4 photosynthesis

Joseph Swift,

Leonie H. Luginbuehl,

Lei Hua,

Tina B. Schreier,

Ruth M. Donald,

Susan Stanley,

Na Wang,

Travis A. Lee,

Joseph R. Nery,

Joseph R. Ecker &

…

Julian M. Hibberd

Nature volume 636, pages 143–150 (2024)

Abstract

C_4 photosynthesis is used by the most productive plants on the planet, and compared with the ancestral C_3 pathway, it confers a 50% increase in efficiency[1]. In more than 60 C_4 lineages, CO_2 fixation is compartmentalized between tissues, and bundle-sheath cells become photosynthetically activated[2]. How the bundle sheath acquires this alternate identity that allows efficient photosynthesis is unclear. Here we show that changes to bundle-sheath gene expression in C_4 leaves are associated with the gain of a pre-existing cis-code found in the C_3 leaf. From single-nucleus gene-expression and chromatin-accessibility atlases, we uncover DNA binding with one finger (DOF) motifs that define bundle-sheath identity in the major crops C_3 rice and C_4 sorghum. Photosynthesis genes that are rewired to be strongly expressed in the bundle-sheath cells of C_4 sorghum acquire cis-elements that are recognized by DOFs. Our findings are consistent with a simple model in which C_4 photosynthesis is based on the recruitment of an ancestral cis-code associated with bundle-sheath identity. Gain of such elements harnessed a stable patterning of transcription factors between cell types that are found in both C_3 and C_4 leaves to activate photosynthesis in the bundle sheath. Our findings provide molecular insights into the evolution of the complex C_4 pathway, and might also guide the rational engineering of C_4 photosynthesis in C_3 crops to improve crop productivity and resilience3,4.

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Main

In multicellular systems, changes to the patterning of gene expression drive modifications in cell function and trait evolution. One notable example is found in more than 60 plant lineages, in which compartmentalization of photosynthesis between cell types allowed the evolution of the efficient C_4 pathway from the ancestral C_3 state5,6. In most land plants, CO_2 fixation occurs in mesophyll cells and is dependent on ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). Because the first fixation product of RuBisCO is a three-carbon metabolite, this pathway has been termed C_3 photosynthesis[7]. Although most land plants use the C_3 pathway, RuBisCO is not able to completely discriminate between CO_2 and O_2. In addition to the loss of carbon fixation, when RuBisCO carries out oxygenation reactions it generates a toxic intermediate, phosphoglycolate, which must rapidly be metabolized through the energy-intensive photorespiratory cycle[8]. In multiple plant lineages, including staple crops such as maize and sorghum, evolution has reconfigured the functions of mesophyll and bundle-sheath cells such that CO_2 fixation by RuBisCO is repressed in the mesophyll and activated in the bundle sheath (Fig. 1a). These species are known as C_4 plants because the pathway’s first step produces C_4 acids in mesophyll cells, which then diffuse into bundle-sheath cells before decarboxylation in close proximity to RuBisCO[9]. This process leads to a tenfold increase in CO_2 concentration in bundle-sheath chloroplasts[10], thereby reducing oxygenation reactions so that photosynthetic as well as water and nitrogen use efficiencies are markedly increased[1]. As a result, C_4 plants grow particularly well in hot and dry climates and constitute some of the most productive crop species in the world11,12.

Fig. 1: Gene expression and chromatin accessibility of single nuclei from rice and sorghum undergoing de-etiolation.

a, Photosynthesis in mesophyll and bundle-sheath cells of C_3 and C_4 plants. CA, carbonic anhydrase; ME, malic enzyme; PEPC, phosphoenolpyruvate carboxylase. b, Schematic of de-etiolation time course. Plants were grown in the dark for 5 days (0-h time point) before exposure to light. c, Scanning electron micrographs (SEMs) of etioplasts and chloroplasts in the mesophyll and bundle-sheath cells of rice and sorghum. Thylakoid stacks (black arrows) were present in rice mesophyll and bundle-sheath chloroplasts as well as sorghum mesophyll chloroplasts (SEMs consistent across three biological replicates). Scale bars, 1 μm. d,e, UMAP of transcript profiles of rice (d) and sorghum (e) nuclei from all time points assayed. Distinct colours indicate different cell types. f,g, In total, 2,948 accessible chromatin peaks were cell-type specific in rice promoters (f), and 1,820 peaks were cell-type specific in sorghum promoters (g).

In both C_3 and C_4 plants, photosynthetic efficiency is dependent on mechanisms that pattern differential gene expression between each cell type of the leaf. However, so far, only a small number of cis-elements that control the cell-specific expression of C_4 genes have been identified13,14,15,16,17,18, and it is not clear how strict partitioning of photosynthesis between cell types is established and maintained, or how these patterns change to allow the evolution of C_4 photosynthesis from the ancestral C_3 pathway.

To define the transcriptional identity of each cell type and uncover how the patterning of photosynthesis gene expression is established, we generated gene-expression and chromatin-accessibility atlases in rice and sorghum, which use the C_3 and the C_4 pathways, respectively. We sampled nuclei after the transfer of seedlings from dark to light—a stimulus that induces photomorphogenesis and thus activates the expression of photosynthesis genes. Both rice and sorghum are models and diploid crops of global importance, representing distinct clades in the monocotyledons that diverged approximately 81 million years ago[19]. Karyotype reconstruction indicates a shared genome structure in both sorghum and rice, as well as their grass ancestor[20]. Thus, molecular signatures of each cell type shared by rice and sorghum may also be found in the approximately 11,000 species derived from their last common ancestor.

In both species, we found that gene expression was rapidly induced by light in all cell types. However, before the perception of light, the expression and chromatin accessibility of many photosynthesis genes was conditioned by cell identity. Although transcriptional cell identities changed across species, we found that cis-elements defining cell identity were conserved. Genes that rewire their expression to become bundle sheath specific in C_4 sorghum acquired ancestral cis-elements that direct expression in the C_3 bundle sheath. The simplest explanation from these findings is that the evolution of photosynthesis involves C_4 genes acquiring cis-elements associated with bundle-sheath identity, which then harness a stable patterning of transcription factors between cell types of C_3 and C_4 leaves.

Rice and sorghum single-nucleus atlases

To understand how different cell types in rice and sorghum shoots respond to light, we grew seedlings of each species in the dark for five days and then exposed them to a light–dark photoperiod for 48 h (Fig. 1b). As expected, shoot tissue underwent photomorphogenesis during this time. Leaves emerged and chlorophyll accumulated within the first 12 h of de-etiolation (Fig. 1b and Extended Data Fig. 1). Scanning electron microscopy (SEM) showed that etioplasts in both mesophyll and bundle-sheath cells contained prolamellar bodies before light exposure (Fig. 1c and Extended Data Fig. 1). Within 12 h of light, etioplasts had converted into mature chloroplasts with assembled thylakoid membranes. Compared with rice, chloroplast development was more pronounced in the bundle sheath of sorghum, and clear differences in thylakoid stacking in chloroplasts from sorghum mesophyll and bundle-sheath cells were evident (Fig. 1c and Extended Data Fig. 1).

Underlying this cellular remodelling and activation of the photosynthetic apparatus are changes in gene regulation. However, so far, these have been described in bulk tissue samples, so how each cell type responds is not known21,22,23,24,25. We generated single-nucleus atlases of transcript abundance for both rice and sorghum shoots as they undergo photomorphogenesis. Shoot tissue at nine time points during de-etiolation was collected and the nuclei were sequenced[26] (Fig. 1b and Extended Data Fig. 1), resulting in gene-expression atlases derived from 190,569 and 265,701 nuclei from rice and sorghum, respectively. We also assayed cell-specific changes in chromatin accessibility at 0 h and 12 h after light exposure by sequencing 22,154 and 20,169 nuclei from rice and sorghum, respectively.

These outputs were visualized using uniform manifold approximation projection (UMAP), and 19 distinct clusters were identified for each species (Fig. 1d,e, Extended Data Fig. 2 and Supplementary Table 1). Using the expression of previously described marker genes and their orthologues, cell types were assigned to each main cluster (Extended Data Fig. 2). This included mesophyll, guard, epidermal, xylem parenchyma and phloem cells (Fig. 1d,e). Gene Ontology (GO) terms derived from cluster-specific genes reflected previously documented functions for each cell type (Supplementary Table 1). For example, mesophyll nuclei showed high expression of genes involved in photosynthesis, and clusters containing nuclei from epidermis cells were enriched in genes involved in lipid biosynthesis and export, consistent with the role of this tissue in cutin production[27].

We identified nuclei of the sorghum bundle sheath through the expression of C_4 genes such as NADP-malic enzyme (NADP-ME) and glycine decarboxylase (GDC) (Extended Data Fig. 2). However, to our knowledge there are no such markers for the bundle sheath in rice undergoing photomorphogenesis. We therefore generated a stable reporter line in which bundle-sheath nuclei were labelled with an mTurquoise2 reporter under the control of the PHOSPHOENOLCARBOXYKINASE promoter from the C_4 plant Zoysia japonica (Extended Data Fig. 3). This promoter drives bundle-sheath expression in rice[27]. Transcriptome sequencing of a rice leaf nuclei population enriched with bundle-sheath nuclei identified 14 clusters, with the expression of mTurquoise2 identifying the rice bundle sheath (Extended Data Fig. 3). In the same cluster, we also detected the expression of genes such as PLASMA MEMBRANE INTRINSIC PROTEIN (PIP1.1) and SULFITE REDUCTASE (SIR), which have previously been shown to be expressed in the bundle sheath of mature rice leaves[28] (Supplementary Table 2). Using marker genes from this cluster, it was then possible to annotate nuclei with bundle-sheath identity in our de-etiolation dataset (Extended Data Fig. 3).

Complementing this atlas describing cell-type gene expression, the multiome assay (RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin with sequencing (ATAC-seq)) allowed changes in chromatin accessibility during photomorphogenesis to be detected. After cross-validation with single-nucleus transcriptional atlases, multiome atlases identified six cell types from each species (Extended Data Fig. 4) and revealed accessible peaks in promoter regions of genes specific to each cell type (Fig. 1f,g). As expected, genes proximal to these peaks held enriched GO terms associated with known cell functions (Supplementary Table 3). Peaks were detected upstream of canonical marker genes for each cell type. For example, the promoters of the RuBisCO small subunit (OsRBCS4) from rice and NADP-ME from sorghum were most accessible in mesophyll and in bundle-sheath cells, respectively (Extended Data Fig. 4). In summary, these data provide cell-level insights into changes in gene expression and chromatin accessibility associated with the induction of photosynthesis in cereal crops.

Repurposing the bundle sheath

C_4 evolution has repeatedly repurposed the bundle sheath to perform photosynthesis29,30. However, the extent to which this cell type has been altered transcriptionally is not known. To understand how the transcriptional identities of each cell type from rice and sorghum differ, we generated a pan-transcriptome atlas of photosynthetic tissue sampled at 48 h after light exposure. This was achieved by identifying sorghum and rice orthologues and clustering nuclei from both species together. Despite the evolutionary distance between rice and sorghum, most cell types from these species co-clustered (Fig. 2a). The most notable exception were nuclei from bundle-sheath cells, with those from the sorghum bundle sheath clustering distinctly from those from the rice bundle sheath (Fig. 2a and Extended Data Fig. 5). Supporting this observation, GO enrichment analysis indicated that C_3 and C_4 bundle-sheath cells have distinct functions—whereas genes expressed in the bundle sheath of rice were predominantly associated with transport and localization, those of sorghum were associated with organic acid metabolism and the generation of precursor metabolites and energy (Supplementary Table 1).

Fig. 2: Rice and sorghum bundle-sheath cells show low conservation in transcript partitioning.

a, Pan-transcriptome of rice and sorghum nuclei based on orthologues. UMAPs indicate rice (top) and sorghum (bottom) nuclei. Areas indicated with black circles indicate nuclei from the sorghum bundle sheath that do not co-cluster with rice nuclei. b, Transcript abundance for sorghum NADP-ME and its rice orthologue displayed in UMAP format from a. c, Sankey plot of changes in the cell-type partitioning of marker genes. Markers for sorghum mesophyll and bundle-sheath cell types are highlighted in green and blue, respectively. BS, bundle sheath; E, epidermis; GC, guard cell; M, mesophyll; P, phloem; X, xylem. d, Differentially expressed orthologue pairs in rice and sorghum mesophyll and bundle-sheath cells. Genes fall into two categories: those consistently partitioned (more highly expressed in the same cell type in both rice and sorghum); and those differentially partitioned (swapping expression from one cell type to the other). GO terms associated with each category are shown on the right.

More than 180 genes specific to the bundle-sheath cells of sorghum had orthologues that were either poorly expressed or not specific to any cell type in rice (Supplementary Table 4). For example, the canonical C_4 gene NADP-ME was strongly and specifically expressed in the sorghum bundle sheath but was poorly expressed and not cell specific in rice (Fig. 2b). Similar patterns of high and localized expression were evident in the sorghum bundle sheath for other genes involved in photosynthesis, photorespiration and chloroplast functions (Extended Data Fig. 5 and Supplementary Table 4). The bundle sheath of C_4 sorghum also lost the expression of genes associated with this cell type in rice (Extended Data Fig. 5 and Supplementary Table 4). Notably, this included genes involved in hormone signalling and biosynthesis, including the gibberellic acid, ethylene and auxin pathways, as well as genes that encode sugar and water transporters.

Next, we investigated how conserved cell-type-specific patterns of gene expression were across species (Supplementary Table 5). Although most cell types showed conserved patterns of expression between rice and sorghum, this was not the case for the bundle sheath (Fig. 2c and Extended Data Fig. 5). In fact, transcripts from only 31 orthologues (out of 229 rice bundle-sheath markers), including genes involved in sulfur metabolism and transport, were specific to the bundle sheath of both species (Fig. 2c and Supplementary Table 5). The C_4 bundle sheath of sorghum had also obtained patterns of gene expression from other cell types (Fig. 2c). Indeed, the bundle-sheath cells of sorghum were transcriptionally more similar to the mesophyll and guard cells of rice, whereas the bundle sheath of rice was most similar to the phloem of sorghum (Fig. 2c and Extended Data Fig. 5). Similarities between sorghum bundle sheath and rice mesophyll or guard cells were driven mainly by changes in the expression of genes involved in the Calvin–Benson–Bassham cycle and starch metabolism (Supplementary Table 5).

Because the differential expression of photosynthesis genes between bundle-sheath and mesophyll cells (hereafter, partitioning) is considered crucial for the evolution of C_4 photosynthesis[29] we examined this phenomenon. A pairwise comparison of gene expression in response to light revealed that in each species, transcripts from more than 1,000 genes were partitioned between mesophyll and bundle-sheath cells, and this included 225 orthologues (Extended Data Fig. 5 and Supplementary Table 6). Of these, 126 were partitioned identically between the same cell types in both rice and sorghum (Fig. 2d). Of note, an additional 99 orthologues showed opposing patterns in the two species; that is, they were ‘differentially’ partitioned (Fig. 2d). Forty-three orthologues that had swapped from strong expression in the mesophyll of rice to strong expression in the bundle sheath of sorghum included genes encoding proteins of the Calvin–Benson–Bassham cycle, as well as organic acid and nitrogen metabolism. Fifty-six genes that swapped from strong expression in the bundle sheath of rice to the mesophyll of sorghum were associated with the transport of metabolites and solutes (Fig. 2d and Supplementary Table 6). We note that 12% of orthogroups had a more complex pattern of expression (Extended Data Fig. 5).

To investigate how conserved partitioning was between all cell types, we assessed the degree of cross-species overlap between each pair of cell types. This revealed that the mesophyll and bundle sheath had the smallest set of partitioned genes across species and the weakest statistical overlap (Extended Data Fig. 6 and Supplementary Table 7). In addition to mesophyll and bundle-sheath cells of rice showing the lowest conservation in terms of transcript partitioning, it was also noticeable that a large proportion of genes that were partitioned between these cells had swapped cell types (Extended Data Fig. 6). This suggests that swapping of functions or ‘identity’ between other cell types is a rare event genome-wide but occurs relatively frequently between mesophyll and bundle sheath.

Light regulation of the C_4 bundle sheath

Because light induces photomorphogenesis, we investigated how individual nuclei from each cell type responded to this stimulus. Rice mesophyll and sorghum bundle-sheath nuclei clustered by time of sampling, indicating that light was a dominant driver of transcriptional state (Fig. 3a,b). Canonical marker genes showed the expected induction. For example, RBCS and NADP-ME were activated by light in the mesophyll cells of rice and in the bundle-sheath cells of sorghum, respectively (Fig. 3a,b). Similarly, transcript abundances of light signalling transcription factors such as ELONGATED HYPOCOTYL (HY5) (ref. [31]) and PHYTOCHROME INTERACTING FACTORS 3–5 (PIF3, PIF4 and PIF5) (refs. 32,33) were dynamic during de-etiolation (Extended Data Fig. 7). We detected global cell-type-specific differential gene-expression responses by fitting statistical models to pseudo-bulked transcriptional profiles. In rice, each of the six cell types showed a distinct and cell-type-specific response (Extended Data Fig. 7 and Supplementary Table 8). Apart from the bundle-sheath and epidermal cells in rice, hundreds of cell-type-specific light-responsive genes were detected (Extended Data Fig. 7). In both species, mesophyll- and bundle-sheath-specific genes were involved in photosynthesis and chloroplast-related functions, consistent with the rapid greening of shoots and conversion of etioplasts into chloroplasts (Supplementary Table 8). Bundle-sheath cells from rice and sorghum showed the greatest difference in their response to light, with only 35 light-responsive bundle-sheath-specific genes detected in rice, but more than 1,000 genes induced by light in the bundle sheath of sorghum (Extended Data Fig. 7)

Fig. 3: Light changes cell-type-specific transcript abundance and chromatin accessibility.

a, Sub-clustering of rice mesophyll nuclei undergoing de-etiolation. The transcript abundance of RBCS1A is shown on the right. b, Sub-clustering of sorghum bundle-sheath nuclei undergoing de-etiolation. The transcript abundance of NADP-ME is shown on the right. c,d, Heat map of photosynthesis gene expression in different cell types of rice (c) and sorghum (d) during the first 12 h of light. Genes encoding proteins involved in C_4 photosynthesis, the Calvin–Benson–Bassham cycle and light reactions are shown in red, purple and yellow, respectively. e,f, Differences in the chromatin accessibility of photosynthesis genes in different cell types of rice (e) and sorghum (f) measured at 0 h (dark) and 12 h (light). Welch’s t-test indicated.

Light-induced partitioning of canonical photosynthesis genes between the mesophyll and bundle sheath was apparent (Fig. 3c,d and Supplementary Table 9). In rice, photosynthesis genes were most strongly induced in the mesophyll, although a similar but weaker response was also seen in the bundle sheath and other cell types (Fig. 3c). SEM confirmed that in the dark, etioplasts were present in vascular and epidermal cells, and after exposure to light, thylakoid-like membranes were evident (Extended Data Fig. 8). This supports the observation that photosynthesis can be weakly induced in these cell types. In sorghum, light strongly induced photosynthesis genes in both mesophyll and bundle-sheath cell types, and these included genes that are important for the light-dependent reactions of photosynthesis as well as the Calvin–Benson–Bassham and C_4 cycles (Fig. 3d). Agreeing with these data, chromatin of photosynthesis genes was more accessible in mesophyll cells than in other cell types (P = 9.7 × 10−16, Welch’s t-test) (Fig. 3e,f), although the difference in accessibility in response to light was only marginally significant in the rice mesophyll (P = 0.053). By contrast, in sorghum, the accessibility of photosynthesis genes increased in response to light in both mesophyll and bundle-sheath cells (P < 4.69 × 10−10) (Fig. 3f). These data indicate that a pervasive gain of light regulation by photosynthesis genes in the bundle sheath of sorghum is likely to be facilitated by increased chromatin accessibility.

Cell identity conditions gene expression

In both rice and sorghum, differences in the expression of photosynthesis genes between mesophyll and bundle-sheath cells increased over time (Fig. 3c,d). This is exemplified by the GLYCOLATE OXIDASE (GLO) and RBCS genes, the transcripts of which showed greater partitioning to mesophyll cells of rice and bundle-sheath cells of sorghum in response to light (Fig. 4a and Extended Data Fig. 9). After 12 h of light, 72 photosynthesis genes in rice and 77 in sorghum were partitioned between mesophyll and bundle-sheath cells (Extended Data Fig. 9). However, for some photosynthesis genes, differences in expression between cells were already evident in the dark, suggesting that cell identity conditions light responses. Specifically, in the dark, 29% and 58% of photosynthesis transcripts in rice and sorghum, respectively, were significantly partitioned between mesophyll and bundle-sheath cells (Fig. 4b). This finding is consistent with the observation that promoters of photosynthesis genes contained regions of open chromatin in the etiolated state (Fig. 3e,f). In fact, in the etiolated state, many photosynthesis genes showed differences in chromatin accessibility between cell types, as exemplified by GLO and RBCS (Fig. 4c and Extended Data Fig. 9). And, in many instances, light exposure then increased chromatin accessibility, suggesting that light signalling enhances but does not establish chromatin accessibility within the promoters of photosynthesis genes (Extended Data Fig. 9). We conclude that intrinsic differences in cell identity contribute to the partitioning of photosynthesis gene expression between cells in both C_3 rice and C_4 sorghum, and that differential partitioning is not driven exclusively by light signalling.

Fig. 4: Both cell identity and light drive the partitioning of photosynthesis genes between mesophyll and bundle-sheath cells.

a, Transcript abundance of the photorespiration gene GLO during de-etiolation in the mesophyll and bundle-sheath cells of rice and sorghum. Points indicate mean expression, line fit using locally estimated scatter plot smoothing. b, Volcano plots of genes significantly partitioned to either mesophyll (M) or bundle sheath (BS) under etiolated conditions (0-h time point, adjusted P < 0.05, likelihood-ratio test). Genes encoding proteins involved in C_4 photosynthesis, the Calvin–Benson–Bassham cycle and light reactions are shown in red, purple and yellow, respectively. c, Chromatin accessibility of GLO at 0 h (dark) and 12 h (light) in mesophyll and bundle-sheath nuclei. d, Overlap of cis-elements associated with accessible chromatin in each cell type of rice and sorghum (Fisher’s exact test adjusted P indicated). The consensus motif for the most over-represented cis-element within each overlap is shown on the right (additional over-represented motifs in Supplementary Table 10). e, Overlap of cis-elements associated with accessible chromatin in each cell type in response to light in rice and sorghum (Fisher’s exact test adjusted P indicated). The consensus motif for the most over-represented cis-element in all overlaps is shown on the right (additional over-represented motifs in Supplementary Table 12).

C_4 co-opts an ancestral cis-code

Cis-elements have a key role in driving the patterning of gene expression34,35. Therefore, we next searched for cis-elements that underlie the observed cell-identity- and light-dependent patterns of gene expression. When regions of open chromatin specific to each cell type were assessed for over-represented transcription-factor-binding sites, we found dozens of enriched cis-regulatory elements for each cell type (Supplementary Table 10). We compared the 25 most significantly enriched cis-regulatory motifs for each cell type across species. This identified a significant overlap of enriched motifs in the same cell types of both rice and sorghum (Fig. 4d and Supplementary Table 10). Thus, both species share a conserved cell-type-specific cis-regulatory code. For example, motifs bound by the myeloblastosis (Myb)-related, NAM, ATAF1, ATAF2 and CUC2 (NAC) transcription factors defined accessible chromatin regions in mesophyll nuclei from both rice and sorghum, whereas the DOF motif was enriched in bundle-sheath- and phloem-specific peaks of both species (Fig. 4d and Extended Data Fig. 10). We also detected DOF motif enrichment in promoter regions of homologues of rice and sorghum bundle-sheath partitioned genes in several other C_3 Poaceae species, including Chasmanthium laxum, Hordeum vulgare and Brachypodium distachyon (Extended Data Fig. 10 and Supplementary Table 11). By contrast, when we examined motifs in chromatin that were differentially accessible in response to light, we found that the same motifs were enriched regardless of cell type. These comprised the light-responsive circadian-clock-related basic leucine zipper (bZIP) and CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) motifs (Fig. 4e, Extended Data Fig. 11 and Supplementary Table 12). These findings suggest that cell-type-specific patterning of gene expression is defined by cell-identity cis-elements, whereas light-responsive gene expression is regulated by similar cis-elements shared by all cell types.

Next, we investigated whether the cis-code associated with each cell type regulates genes that are differentially partitioned between rice and sorghum. To this end, we examined genes that were preferentially expressed in the rice mesophyll, but whose orthologues were partitioned to the sorghum bundle sheath (Fig. 5a). Among the 40 orthologues in this category were the Calvin–Benson–Bassham cycle genes FRUCTOSE BISPHOSPHATE ALDOLASE and GLYCERALDEHYDE 3-PHOSPHATE DEHYDROGENASE (GAPDH), photorespiration genes such as GLO and light reaction genes including the LHCII subunit (Fig. 5a and Supplementary Table 13). Notably, among these differentially partitioned genes, we found that associated chromatin was enriched in cell-type-specific Myb-related, high-mobility group (HMG), REVEILLE 5 (RVE5) and DF1 binding sites in mesophyll-specific genes in rice (Fig. 5a), but that it was enriched in cell-type-specific DOF and JACKDAW (JKD) or indeterminate domain (IDD) binding sites in bundle-sheath-specific orthologues in sorghum (Fig. 5a, Extended Data Fig. 12 and Supplementary Table 13). This indicates that these orthologues swapped their partitioning from mesophyll to bundle sheath by changing identity-associated cis-regulatory motifs. Specifically, our data indicate that DOF motifs were acquired by genes that swap expression from the mesophyll of C_3 rice to the bundle sheath of C_4 sorghum.

Fig. 5: A cell-type-specific cistrome in C_3 rice and C_4 sorghum drives the partitioning of photosynthesis between mesophyll and bundle-sheath cells.

a, Gene-expression heat maps (left) of differentially partitioned orthologues in rice and sorghum, and the four most enriched cis-elements (right) in the accessible chromatin of corresponding genes. Additional over-represented motifs are shown in Supplementary Table 13. b, DOF transcription factor expression in mesophyll and bundle sheath in each species. Sorghum gene names based on orthology with rice genes. c, Transactivation of sorghum GAPDH promoter and rice minimal SIR promoter by DOF transcription factors from rice (orange) and sorghum (blue) (one-sided Welch’s t-test P indicated; n = 4 biological replicates; boxes indicate 25th, median and 75th quartiles; whiskers extend to the outermost value within 1.5× interquartile range; assay repeated three times independently with similar results). d, Activity of GUS reporter driven by minimal SIR promoter (containing two DOF motifs) in transgenic rice determined using the fluorometric 4-methylumbelliferyl-β-d-glucuronide assay (left). DOF motifs were mutated to replace G in AAAG with C (one-sided Welch’s t-test P indicated; n = 29 independent transformants for minimal SIR promoter; n = 23 independent transformants for mutated DOF motifs). Representative cross-sections of GUS-stained transgenic leaves are shown on the right. Bundle-sheath cells are outlined with a dotted line. Scale bars, 50 µm. e, By acquiring DOF cis-elements, C_4 genes co-opt and amplify the ancestral bundle-sheath cell-identity network that is common between both species.

The consensus sequence common to all DOF-binding motifs is AAAG (ref. [36]). Thus, we next examined the frequency of this core binding motif in open chromatin within 1,500 nucleotides of the transcription start site of differentially partitioned genes. Among these genes, there were more DOF-binding sites in accessible chromatin in sorghum compared with orthologues in rice (binomial P = 5.5 × 10−3; Extended Data Fig. 12). By contrast, we did not find such enrichment among consistently partitioned genes (P = 0.48; Extended Data Fig. 12). These trends were also evident in canonical photosynthesis genes expressed in the sorghum bundle sheath. For example, the accessible chromatin of sorghum GAPDH contained more than twice as many DOF motifs as the rice orthologue did, and a similar enrichment of DOF motifs was seen in the accessible chromatin of sorghum C_4 photosynthesis genes such as NADP-ME (Extended Data Fig. 12).

Furthermore, genes encoding DOF family transcription factors were typically more strongly expressed in the bundle-sheath cells of both rice and sorghum (Fig. 5b), suggesting that the cell-type patterning of these transcription factors has not changed during the transition from C_3 to C4. To test whether DOF transcription factors are sufficient to regulate the expression of bundle-sheath-specific genes, we performed effector assays in rice protoplasts. Rice OsDOF8 and OsDOF27, as well as sorghum SbDOF17, SbDOF8 and SbDOF11, activated the expression of a LUCIFERASE reporter gene from the sorghum GAPDH promoter, whereas OsDOF2, OsDOF23 and SbDOF2 did not (Fig. 5c). Similarly, several rice and sorghum DOF transcription factors were able to activate expression from the sorghum NADP-ME promoter (Extended Data Fig. 12), as well as from the minimal OsSIR promoter, which drives bundle-sheath expression in rice[37] (Fig. 5c). Mutating two DOF-binding sites present in this minimal OsSIR promoter reduced GUS activity in stably transformed rice plants 2.8-fold (Fig. 5d). These data indicate that specific members of the DOF family are sufficient, and their cognate motifs are necessary for strong expression in the rice bundle sheath.

From our analysis we propose a model that explains the rewiring of cell-type-specific regulation of photosynthesis genes in C_4 leaves (Fig. 5e). The model suggests that (i) the same mesophyll- and bundle-sheath-specific cis-elements are active in rice and sorghum; (ii) patterning of transcription factors between the two species is relatively stable; and (iii) photosynthesis genes expressed in the sorghum bundle sheath have acquired DOF cis-elements associated with bundle-sheath cells in rice to amplify expression in this cell type.

Discussion

Our findings indicate that the expression of C_4 genes in bundle-sheath cells is underpinned by the integration of two characteristics found in the ancestral C_3 state: first, a conserved cell-type-specific transcription factor network; and second, acquisition of ancestral cell identity cis-elements. In both C_3 rice and C_4 sorghum, DOF transcription factors were preferential to the bundle sheath, a finding strongly supporting the stability of the trans-network across C_3 and C_4 species. The acquisition of DOF-binding sites in the promoters of C_4 genes then allowed these genes to decode the pre-existing patterning of transcription factors to amplify expression in the bundle sheath. Together, this allowed C_4 evolution through the broadening of cell-identity networks.

It is noteworthy that DOF motifs were also enriched in genes expressed in the bundle sheath of C_4 maize[38], and transient assays led to the proposal that DOF transcription factors control bundle-sheath-specific genes such as maize NADP-ME[18]. Thus, a DOF-mediated mechanism for the regulation of bundle-sheath gene expression seems to be present in several C_4 species. Moreover, DOF motifs were not only enriched in bundle-sheath-specific genes from C_3 rice, but also in orthologues from C. laxum, a C_3 species from the PACMAD clade otherwise made up of C_4 grasses39,40, as well as H. vulgare and B. distachyon, two C_3 grasses from the Poaceae[39]. Together, these results support a model in which DOF regulation of bundle-sheath expression is ancestral and has been co-opted during C_4 evolution to strengthen the expression of C_4 genes in this cell type.

Compared with previous analyses, the model we propose supports a distinct evolutionary trajectory leading to the C_4 state. For example, in C_4 dicotyledons, bundle-sheath- and mesophyll-specific gene expression has been reported to be mediated by existing cis-elements allowing changes in the trans-network to be decoded14,15,16. In contrast to previous work with monocotyledons, we were able to provide insight into cis-elements associated with bundle-sheath identity in a C_3 as well as a C_4 grass, and this allowed distinct insight into the evolutionary rewiring of cell-specific gene expression. Because we took an unbiased genome-wide approach, this reconfiguration in cis is likely to constitute one of the more notable changes in cis-code during C_4 evolution. Combined with previous analyses, which indicated that the acquisition of pre-existing cis-elements associated with light-responsive elements allows enhanced C_4 gene expression in C_4 compared with C_3 leaves of dicotyledons[25], our data argue for evolution repeatedly making use of an existing trans-code through pervasive rewiring in cis.

These findings have key implications for ongoing efforts to engineer C_3 plants with C_4 photosynthetic characteristics[41]. For example, considerable efforts have been made to install the biochemistry of C_4 photosynthesis in rice3,4,42. However, a major obstacle has so far been the robust expression of C_4 acid decarboxylase genes in the rice bundle sheath3,41. The data reported here indicate that rewiring such genes to recognize an ancient bundle-sheath-identity network based on DOF transcription factors could be a useful way forward.

Methods

Plant growth

For the de-etiolation time course, seeds of Oryza sativa spp. japonica cultivar Kitaake and Sorghum bicolor BTx623 were incubated in sterile water for two days and one day, respectively, at 29 °C in the dark. Germinated seedlings were transferred in a dark room equipped with green light to a 1:1 mixture of topsoil and sand supplemented with fertilizer granules and grown for five days in the dark by wrapping the tray and lid several times with aluminium foil. Plants were placed in a controlled environment room with 60% humidity and temperatures of 28 °C and 20 °C during the day and night, respectively. Plants were exposed to light at the beginning of a photoperiod of 12 h light and 12 h dark and shoots were harvested at different time points during de-etiolation by flash-freezing tissue in liquid nitrogen. For the 0-h time point, seedlings were harvested in a dark room equipped with green light and flash-frozen immediately.

For microscopy analysis and enrichment of bundle-sheath nuclei using fluorescence-activated nuclei sorting, O. sativa spp. japonica cultivar Kitaake single-copy homozygous T2 seeds were de-husked and sterilized in 10% (v/v) bleach for 30 min. After washing several times with sterile water, seeds were incubated for two days in sterile water at 29 °C in the dark. Germinated seedlings were transferred to half-strength Murashige and Skoog medium with 0.8% agar in Magentas and grown for five days in the light in a growth chamber at temperatures of 28 °C and 20 °C during the day and night, respectively, and a photoperiod of 12 h light and 12 h dark.

Construct design and cloning

To generate constructs for the rice bundle-sheath marker line, the coding sequence for mTurquoise2 was obtained from a previous report[43], and the promoter sequence from Zoysia japonica PHOSPHOENOLPYRUVATE CARBOXYKINASE in combination with the dTALE STAP4 system was obtained from a previous report[44]. The coding sequence of Arabidopsis thaliana H2B (At5g22880) was used as an N-terminal signal for targeting mTurquoise2 to the nucleus. All sequences were domesticated for Golden Gate cloning45,46. Level 1 and Level 2 constructs were assembled using the Golden Gate cloning strategy to create a binary vector for the expression of STAP4-mTurquoise2-H2B driven by PCK-dTALE.

For the transactivation assay in rice protoplasts, transcription factor coding sequences were amplified using rice leaf cDNA or synthesized using GeneArt after domesticating the sequences for Golden Gate cloning41,42 (OsDOF2, LOC_Os01g15900, OsDOF8, LOC_Os02g45200, OsDOF23, LOC_Os07g32510, OsDOF27, LOC_Os10g26620, SbDOF2, Sobic.003G121400, SbDOF8, Sobic.004G284400, SbDOF11, Sobic.001G489900 and SbDOF17, Sobic.006G182300). The coding sequences were assembled into a Level 1 module with a Zea mays UBI promoter and Tnos terminator module as described previously[37]. For the minimal SIR promoter, nucleotides –980 to –829, as well as the endogenous core promoter (nucleotides –250 to +42), were fused with the LUCIFERASE reporter to measure transcription activity[37].

To generate GUS reporter rice lines, the minimal SIR promoter was assembled into a Level 1 module with the coding sequence for kzGUS (an intronless version of the GUS reporter gene) and the Tnos terminator as described previously[37]. The DOF motifs in the minimal SIR promoter were mutated using PCR amplification.

Rice transformation

Oryza sativa spp. japonica cultivar Kitaake was transformed using Agrobacterium tumefaciens as described previously[47], with several modifications. Seeds were de-husked and sterilized with 10% (v/v) bleach for 15 min before placing them on nutrient broth (NB) callus induction medium containing 2 mg l−1 2,4-dichlorophenoxyacetic acid for four weeks at 28 °C in the dark. Growing calli were co-incubated with A. tumefaciens strain LBA4404 carrying the expression plasmid of interest in NB inoculation medium containing 40 μg ml−1 acetosyringone for three days at 22 °C in the dark. Calli were transferred to NB recovery medium containing 300 mg −1 timentin for one week at 28 °C in the dark. They were then transferred to NB selection medium containing 35 mg l−1 hygromycin B for four weeks at 28 °C in the dark. Proliferating calli were subsequently transferred to NB regeneration medium containing 100 mg l−1 myo-inositol, 2 mg l−1 kinetin, 0.2 mg l−1 1-naphthaleneacetic acid and 0.8 mg l−1 6-benzylaminopurine for four weeks at 28 °C in the light. Plantlets were transferred to NB rooting medium containing 0.1 mg l−1 1-naphthaleneacetic acid and incubated in Magenta pots for two weeks at 28 °C in the light. Finally, plants were transferred to a 1:1 mixture of topsoil and sand and grown in a controlled environment room with 60% humidity, temperatures of 28 °C and 20 °C during the day and night, respectively, and a photoperiod of 12 h light and 12 h dark.

Transactivation assay

Rice leaf protoplast isolation was performed as described previously37,48. Protoplasts were transformed using Golden Gate Level 1 modules designed for constitutive expression of transcription factors, alongside the LUC reporter and the ZmUBIpro::GUS-Tnos transformation control, which were prepared with the ZymoPURE II Plasmid Midiprep Kit. The transformation mixture contained 2 µg of control plasmids, 5 µg of reporter plasmids and 5 µg of transcription factor plasmids, which were transformed into 180 µl of protoplasts. After incubating protoplasts for 20 h in the light, proteins were extracted using passive lysis buffer (Promega), and GUS activity was measured with 20 µl of the protein extract. A fluorometric MUG (4-methylumbelliferyl-β-d-glucuronide) assay was used for quantifying GUS activity[49] in a reaction mixture of 200 µl containing 50 mM phosphate buffer (pH 7.0), 10 mM EDTA-Na_2, 0.1% (v/v) Triton X-100, 0.1% (w/v) N-lauroylsarcosine sodium, 10 mM DTT and 2 mM MUG. The assay was performed at 37 °C, and 4-methylumbelliferone (4-MU) fluorescence was recorded every 2 min for 20 cycles at 360 nm excitation and 450 nm emission using a CLARIOstar plate reader. In addition, LUC activity was determined using 20 µl of protein sample and 100 µl of LUC assay reagent from Promega. Transcription activity was quantified as LUC luminescence relative to the rate of MU accumulation per second.

GUS staining

GUS staining was performed as described previously[49], with minor modifications. Leaf tissue was fixed in 90% (v/v) acetone for 12 h at 4 °C. After washing with 100 mM phosphate buffer (pH 7.0), samples were transferred into 1 mg ml−1 5-bromo-4-chloro-3-indolyl glucuronide (X-Gluc) GUS staining solution and vacuum was applied five times for 2 min each. The samples were incubated at 37 °C for 48 h. To clear chlorophyll, samples were incubated in 90% (v/v) ethanol at room temperature. Cross-sections were prepared with a razor blade and images were taken with an Olympus BX41 light microscope.

To quantify GUS activity, a fluorometric MUG assay was used[49] as described above, using 200 mg of mature leaf tissue. A standard curve of ten 4-MU standards was used to determine the 4-MU concentration in each sample.

Confocal microscopy

To test the bundle-sheath-specific expression of mTurquoise2-H2B, recently expanded leaf 3 of seven-day-old seedlings was prepared for confocal microscopy by scraping the adaxial side of the leaf blade two to three times with a sharp razor blade, transferring to water to avoid drying out and then mounting on a microscope slide with the scraped surface facing upwards. Confocal imaging was performed on a Leica TCS SP8 X using a 10× air objective (HC PL APO CS2 10×0.4 Dry) with optical zoom, and hybrid detectors for fluorescent protein and chlorophyll autofluorescence detection. The following excitation (Ex) and emission (Em) wavelengths were used for imaging: mTurquoise2 (Ex = 442, Em = 471–481), chlorophyll autofluorescence (Ex = 488, Em = 672–692).

SEM

For the de-etiolation experiment of rice and sorghum, samples from four to six individual seedlings for each time point (0 h, 6 h, 12 h and 48 h) were collected for electron microscopy. Leaf segments (around 2 mm^2) were excised with a razor blade and immediately fixed in 2% (v/v) glutaraldehyde and 2% (w/v) formaldehyde in 0.05–0.1 M sodium cacodylate (NaCac) buffer (pH 7.4) containing 2 mM calcium chloride. Samples were vacuum infiltrated overnight, washed five times in 0.05–0.1 M NaCac buffer and post-fixed in 1% (v/v) aqueous osmium tetroxide, 1.5% (w/v) potassium ferricyanide in 0.05 M NaCac buffer for three days at 4 °C. After osmication, samples were washed five times in deionized water and post-fixed in 0.1% (w/v) thiocarbohydrazide for 20 min at room temperature in the dark. Samples were then washed five times in deionized water and osmicated for a second time for 1 h in 2% (v/v) aqueous osmium tetroxide at room temperature. Samples were washed five times in deionized water and subsequently stained in 2% (w/v) uranyl acetate in 0.05 M maleate buffer (pH 5.5) for three days at 4 °C and washed five times afterwards in deionized water. Samples were then dehydrated in an ethanol series, and transferred to acetone and then to acetonitrile. Leaf samples were embedded in Quetol 651 resin mix (TAAB Laboratories Equipment) and cured at 60 °C for two days. Ultra-thin sections of embedded leaf samples were prepared and placed on Melinex (TAAB Laboratories Equipment) plastic coverslips mounted on aluminium SEM stubs using conductive carbon tabs (TAAB Laboratories Equipment), sputter-coated with a thin layer of carbon (around 30 nm) to avoid charging, and imaged in a Verios 460 scanning electron microscope at a 4 keV accelerating voltage and 0.2 nA probe current using the concentric backscatter detector in field-free (low-magnification) or immersion (high-magnification) mode (working distance 3.5–4 mm, dwell time 3 µs, 1,536 × 1,024 pixel resolution). For overserving plastid ultrastructure, SEM stitched maps were acquired at 10,000× magnification using the FEI MAPS automated acquisition software. Greyscale contrast of the images was inverted to allow easier visualization.

Enrichment of bundle-sheath nuclei using fluorescence-activated cell sorting

To purify the nuclei population from whole leaves, recently expanded leaves 3 from five seven-day-old wild-type rice seedlings were chopped on ice in nuclei buffer (10 mM Tris-HCl, pH 7.4, 10 mM NaCl, 3 mM MgCl_2, 0.5 mM spermidine, 0.2 mM spermine, 0.01% Triton X, 1× Roche complete protease inhibitors, 1% BSA and Protector RNase inhibitor) with a sharp razor blade. The suspension was filtered through a 70-mm filter and subsequently through a 35-mm filter. Nuclei were stained with Hoechst and purified by fluorescence-activated cell sorting (FACS) on an AriaIII instrument, using a 70-mm nozzle. Nuclei were collected in an Eppendorf tube containing BSA and Protector RNase inhibitor. Using the same approach, nuclei from the bundle-sheath marker line expressing mTurquoise2-H2B were isolated. Nuclei were sorted on the basis of the mTurquoise2 fluorescent signal. Nuclei were collected in minimal nuclei buffer (10 mM Tris-HCl, pH 7.4, 10 mM NaCl, 3 mM MgCl_2, RNase inhibitor and 0.05% BSA). After collection, nuclei were spun down in a swinging bucket centrifuge at 405g for 5 min, with reduced acceleration and deceleration. Nuclei were resuspended in minimal nuclei buffer and mixed with the unspun whole leaf nuclei population to achieve a proportion of approximately 25% mTurquoise2-positive nuclei. The bundle-sheath enriched nuclei population was sequenced using the 10X Genomics Gene Expression platform with v.3.1 chemistry, and sequenced on the Illumina NovaSeq 6000 with 150-bp paired-end chemistry.

Chlorophyll quantification

Seedlings were harvested at specified time points during de-etiolation and immediately flash-frozen in liquid nitrogen. Frozen tissue was ground into fine powder and the weight was measured before suspending the tissue in 1 ml of 80% (v/v) acetone. After vortexing, the tissue was incubated on ice for 15 min with occasional mixing of the suspension. The tissue was spun down at 15,700g at 4 °C and the supernatant was removed. The extraction was repeated, and supernatants were pooled before measuring the absorbance at 663.6 nm and 646.6 nm in a spectrophotometer. The total chlorophyll content was determined as described previously[50].

Nuclei extraction and single-nucleus RNA-seq (10X RNA-seq)

Frozen tissue from each time point (one biological replicate per time point, eight time points) was crushed using a bead bashing approach, and nuclei were released from homogenate by resuspending in nuclei buffer (10 mM Tris-HCl, pH 7.4, 10 mM NaCl and 3 mM MgCl_2). The resulting suspension was passed through a 30-μm filter. To enrich the filtered solution for nuclei, an Optiprep (Sigma) gradient was used. Enriched nuclei were then stained with Hoechst, before being FACS purified (BD Influx Software v.1.2.0.142). Purified nuclei were run on the 10X Gene Expression platform with v.3.0 chemistry, and sequenced on the Illumina NovaSeq 6000 with 150-bp paired-end chemistry. Single-cell libraries were made following the manufacturers protocol. Libraries were sequenced to an average saturation of 63% (14% s.d.) and aligned either to the rice (O. sativa, subspecies Nipponbare; MSU annotation)[51] or sorghum (S. bicolor v.3.0.1; JGI annotation)[52] genome. Chloroplast and mitochondrial reads were removed. For each time point, an average of 12,524 nuclei were sequenced (6,405 s.d.), with an average median unique molecular identifier (UMI) of 1,152 (420 s.d.) across both species. Doublets were removed using doubletFinder[53].

Nuclei extraction and single-nucleus RNA-seq (sci-RNA-seq3)

Each individual frozen seedling (10–12 individual seedlings per time point) was crushed using a bead bashing approach in a 96-well plate, after which homogenate was resuspended in nuclei buffer. Resulting suspensions were passed through a 30-μm filter. Washed nuclei were then reverse-transcribed with a well-specific primer. After this step, remaining pool and split steps for sci-RNA-seq3 were followed as outlined previously[26]. We note the same approach was used to sequence the 48-h time point; however, a population of six plants was used instead of individual seedlings. Libraries were sequenced to an average saturation of 80% (5% s.d.), and sequenced on the Illumina NovaSeq 6000 with 150-bp paired-end chemistry. Reads were aligned to either the rice or the sorghum genome, as described above. Chloroplast and mitochondrial reads were removed. For 0–12-h time points, an average of 6,527 nuclei were sequenced (5,039 s.d.), with an average median UMI of 423 (41 s.d.) across both species. For the 48-h time point, 77,208 and 82,748 nuclei were sequenced with a median UMI of 757 and 740 for rice and sorghum, respectively.

Nuclei extraction and single-nucleus RNA-seq (10X Multiome)

Fresh seedling tissue was collected after 0 or 12 h light treatment (two biological replicates per species, each with two to four technical replicates per time point; n = 11). Fresh tissue was chopped finely on ice in green room conditions in nuclei buffer. The resulting homogenate was filtered using a 30-μm filter. Nuclei were enriched using Optiprep gradient. No FACS was performed. Nuclei were run on the 10X Multiome platform with v.1.0 chemistry. Single-cell libraries were made following the manufacturer’s protocol, and sequenced on the Illumina NovaSeq 6000 with 150-bp paired-end chemistry. Reads were aligned to either the rice or the sorghum genome, as described above. Chloroplast and mitochondrial reads were removed. For each sample, an average of 1,923 nuclei were sequenced (1,334 s.d.), with an average median UMI of 1,644 (646 s.d.) and median ATAC fragments 10,251 (7,001 s.d.) across both species.

Nuclei clustering

Transcriptional atlases were generated separately for each species using Seurat[54]. Nuclei were first aggregated across various time points (ranging from 0 to 48 h) and methods (10X and sci-RNA-seq3). The integrated dataset was subjected to clustering, using the top 2,000 variable features that were shared across all datasets. Each cluster contained nuclei sampled from all time points, indicating that clustering was driven predominantly by cell type rather than by time after exposure to light (Extended Data Fig. 2). Subsequent UMAP projections were constructed using the first 30 principal components. UMAP projections of mesophyll and bundle-sheath sub-clusters in rice and sorghum, respectively, were achieved using genes found to be significantly differentially expressed in response to light as variable features. To analyse the rice bundle-sheath-specific mTurquoise line, we integrated two treatment replicates into a unified dataset. For this dataset, we clustered using the first 30 principal components. Cluster-specific markers were identified using the FindMarkers() command (adjusted P value < 0.01). To determine the correspondence between the mTurquoise-positive cluster and clusters within the rice-RNA atlas, we compared the lists of cluster-specific markers (adjusted P value 0.01, specificity > 2) to those obtained from the rice atlas. For the 10X-multiome (RNA + ATAC) clustering we used Signac[55]. Biological and technical replicates for each species were integrated, and clustering was conducted using the first 50 principal components derived from expression data. After the initial peak calling using Cell Ranger (10X Genomics), peaks were subsequently re-called using MACS2 (ref. [56]). Differentially accessible peaks between cell types were identified using the FindMarkers() command (adjusted P value < 0.05, per cent threshold > 0.3), before being associated with the nearest gene (±2,000 bp from transcription start site)

Orthology analyses

We determined gene orthologues between rice and sorghum using OrthoFinder[57]. We constructed pan-transcriptome atlases by selecting expressed rice and sorghum genes that had cross-species orthologues. To construct the pan-transcriptome atlas, orthologue conversions were performed in a one-to-one manner, meaning that if multiple orthologues for a gene were found across species, only one was retained. We integrated these datasets with Seurat using the clustering approaches described above. To assign cell identities, we drew on cell-type labels that were previously assigned to each species separately and mapped them onto the pan-transcriptome clusters. To assess specific transcriptional differences in gene expression between the bundle-sheath clusters of sorghum and rice within this dataset, we used the FindMarkers() command (adjusted P value < 0.05). Sorghum DOF transcription factor orthologue names kept the same numerical identifier as their rice orthologues.

To examine the overlap of cell-type-specific gene-expression markers between the two species, we identified cell-type markers from our main transcriptional dataset using FindMarkers() (adjusted P value < 0.05, min.pct > 0.1). We note that some genes were found to be significant across multiple cell types. To assess the significance of the overlap between cell types across species, we converted genes to orthogroups and conducted a Fisher’s exact test, with the total number of orthogroups in the dataset as the background. The proportion of conserved marker genes for each cell type across species ranged from 43% for mesophyll (184 out of 426 rice marker genes conserved in sorghum) to 13% for bundle sheath (31 out of 229 rice marker genes conserved in sorghum). We note that by relying on orthogroups, we included higher-order orthology relationships beyond a one-to-one manner.

Next, we assessed consistent and differential partitioning of gene-expression patterns among each cell-type pair (15 pairs total). To do this, we first calculated differentially expressed genes for each cell-type pair by pseudo-bulking transcriptomes of individual cell types across 0–12-h time points. Next, we identified partitioned expression patterns between cell types using an ANCOVA model implemented in DESeq2 (adjusted P < 0.05). To perform cross-species comparisons of cell-type pairs, we first converted differentially expressed genes to their orthogroup. We then overlapped each cell-type pair across species, using orthogroup membership, and evaluated the significance of these overlaps using the Fisher’s exact test, with the total number of orthogroups as background. Finally, to distinguish whether a gene displayed consistent or differential partitioning in a particular cell type, we examined whether its fold change expression was higher or lower compared with its counterpart in the corresponding cell type of the other species.

Differential expression and accessibility responses to light

We discovered cell-type-specific differentially expressed genes during the first 12 h of light by pseudo-bulking transcriptional profiles. To create pseudo-bulk profiles for each cell type, we first refined our nuclei clusters through re-clustering mesophyll, epidermal and vasculature cell classes separately, before selecting sub-clusters that most strongly expressed known cell-type marker genes. For each cell type, we calculated the first and second principal component of these bulked profiles and found differentially expressed genes through fitting linear models to each of these principal components, as well as those that responded linearly with time using DESeq2 (adjusted P < 0.05). We treated the assay with which the nuclei were sequenced (10X or sci-RNA-seq3) as a covariate. In this list of differentially expressed genes, we also included genes that were differentially expressed between time points 0 h and 12 h in a pairwise test (adjusted P < 0.05). Next, to uncover the different trends of gene expression among differentially expressed genes, we clustered genes using hierarchical clustering, choosing clustering cut-offs that resulted in 10 rice and 18 sorghum clusters that contained at least 10 genes. To visualize the expression of these clusters, we scaled the expression and fitted a non-linear model to capture the dominant expression trend. Accessible chromatin within canonical photosynthesis genes was found through pseudo-bulking accessible chromatin by cell type. Accessible peaks needed to be within 2,000 bp of the gene body. Only one peak per gene was retained for subsequent analyses, and extreme outliers were removed (around 5% of called peaks). To compare peak accessibility across species, reads per peak were re-normalized between 0 and 1. Significant differences in accessibility between cell types of this group of genes were assessed using a Student’s t-test (one-sided).

GO analyses

To identify GO terms associated with cell-type-specific genes and genes that swap expression patterns in rice and sorghum leaves, we performed singular enrichment analysis using the web-based tool AgriGO v.2.0 (ref. [58]). Oryza sativa or S. bicolor gene identifiers were used for the input sample list, and the whole genome of the respective plant species was used as background.

Cis-element analyses

We detected cell-type-specific accessible motifs within each cell type using the chromVAR function[59] implemented in Signac. In brief, this approach detected over-represented cis-regulatory elements within the JASPAR2020 plant taxon group[60] among peaks that are differentially accessible across cell-type clusters. GC enrichment and genomic backgrounds used for statistical tests were derived from BSGenome assembled genomes[61]. The same approach was also used to detect light-responsive cis-elements, using light- and dark-treated nuclei within each cell type. We overlapped enriched cis-regulatory elements identified across species by selecting the top 25 most significantly over-represented motifs (adjusted P < 0.05), before computing a Fisher’s exact test using all computed motifs as background, and then clustered the resulting motifs using TOBIAS[62].

To find consistently and differentially partitioned orthologous genes within our multiome gene-expression dataset, we found mesophyll and bundle-sheath-specific genes in rice and sorghum, respectively, using the FindMarkers() command, with a P value threshold cut-off of 0.01 and an expression specificity above 1.25. To find over-represented motifs within differentially partitioned genes, we correlated peak accessibility with gene expression using the LinkPeaks() command and kept only those peaks which were significantly associated with gene expression. We identified enriched cis-elements within these peaks using the FindMotifs() command; ranking by significance (adjusted P < 0.05). Because the resulting significance depends on the subset of the genome chosen as background, we iterated the FindMotifs() command over 100 permutations to rank motifs that were consistently reported as enriched. We then averaged each motif’s respective rank across the 100 permutations to create a final ranked value (Supplementary Table 13).

To quantify the occurrence of DOF-binding sites, we extracted the genomic sequence of peaks that were proximal to the transcription start site (±1,500 bp). If a peak was proximal to two transcription start sites, it was assigned to the closer one. We then implemented Find Individual Motif Occurrences (FIMO) to quantify the number of DOF consensus sites within these chromatin regions (P value threshold = 0.005). We chose the DOF2 (MA0020.1) motif as representative of the core DOF consensus sequence AAAG.

We implemented analysis of motif enrichment (AME) to detect DOF transcription factor motifs enriched within C. laxum (http://phytozome-next.jgi.doe.gov/info/Claxum_v1_1), H. vulgare (Hvulgare_r1)[63] or B. distachyon (Bdistachyon_314_v3.0)[64] homologues of genes consistently partitioned to the rice and sorghum bundle sheath. To identify homologues, the NCBI BLASTN tool v.2.15.0 was used by comparing coding sequences, and the top identified homologue for each gene was selected for cis-element enrichment analyses. We used 1,000 bp upstream of the transcription start site for each homologous gene and tested against reported plant motifs present within the JASPAR database.

Reporting summary

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

Data availability

Raw and processed data, including assembled atlases, have been deposited at the Gene Expression Omnibus and are publicly available at GSE248919. Raw data for chlorophyll measurement, transactivation assays and GUS quantification have been deposited at Mendeley (https://doi.org/10.17632/6xmsdg9xcr.1). Microscopy data reported in this paper will be shared by the J.M.H. upon request. Any additional information required to reanalyse the data reported in this paper is available from J.M.H. upon request.

Code availability

The code for bioinformatic analyses presented in this manuscript is available on GitHub at https://github.com/joey1463/C3-C4.git.

References

1. Ghannoum, O., Evans, J. R. & von Caemmerer, S. in C4 Photosynthesis and Related CO_2 Concentrating Mechanisms (eds Raghavendra, A. & Sage, R.) 129–146 (Springer, 2011).

2. Sage, R. F., Christin, P. A. & Edwards, E. J. The C_4 plant lineages of planet Earth. J. Exp. Bot. 62, 3155–3169 (2011).

3. Ermakova, M., Danila, F. R., Furbank, R. T. & Von Caemmerer, S. On the road to C_4 rice: advances and perspectives. Plant J. 101, 940–950 (2020).

4. Hibberd, J. M., Sheehy, J. E. & Langdale, J. A. Using C_4 photosynthesis to increase the yield of rice-rationale and feasibility. Curr. Opin. Plant Biol. 11, 228–231 (2008).

5. Sage, R. F. The evolution of C_4 photosynthesis. New Phytol. 161, 341–370 (2004).

6. Sage, R. F., Sage, T. L. & Kocacinar, F. Photorespiration and the evolution of C_4 photosynthesis. Annu. Rev. Plant Biol. 63, 19–47 (2012).

7. Bassham, J. A., Benson, A. A. & Calvin, M. The path of carbon in photosynthesis. J. Biol. Chem. 185, 781–787 (1950).

8. Bowes, G., Ogren, W. L. & Hageman, R. H. Phosphoglycolate production by ribulose diphosphate carboxylase. Biochem. Biophys. Res. Commun. 45, 716–722 (1971).

9. Hatch, M. D. & Slack, C. R. Photosynthesis by sugar-cane leaves. A new carboxylation reaction and the pathway of sugar formation. Biochem. J. 101, 103–111 (1966).

10. Furbank, R. T. Evolution of the C_4 photosynthetic mechanism: Are there really three C_4 acid decarboxylation types? J. Exp. Bot. 62, 3103–3108 (2011).

11. Jordan, D. B. & Ogren, W. L. The CO_2/O_2 specificity of ribulose 1,5-bisphosphate carboxylase/oxygenase: dependence on ribulosebisphosphate concentration, pH and temperature. Planta 161, 308–313 (1984).

12. Sage, R. F. & Zhu, X. G. Exploiting the engine of C_4 photosynthesis. J. Exp. Bot. 62, 2989–3000 (2011).

13. Gowik, U. et al. Cis-regulatory elements for mesophyll-specific gene expression in the C_4 plant Flaveria trinervia, the promoter of the C_4 phosphoenolpyruvate carboxylase gene. Plant Cell 16, 1077–1090 (2004).

14. Brown, N. J. et al. Independent and parallel recruitment of preexisting mechanisms underlying C_4 photosynthesis. Science 331, 1436–1439 (2011).

15. Williams, B. P. et al. An untranslated cis-element regulates the accumulation of multiple C_4 enzymes in Gynandropsis gynandra mesophyll cells. Plant Cell 28, 454–465 (2016).

16. Reyna-Llorens, I. et al. Ancient duons may underpin spatial patterning of gene expression in C_4 leaves. Proc. Natl Acad. Sci. USA 115, 1931–1936 (2018).

17. Burgess, S. J. et al. Genome-wide transcription factor binding in leaves from C_3 and C_4 grasses. Plant Cell 31, 2297–2314 (2019).

18. Borba, A. R. et al. Compartmentation of photosynthesis gene expression in C_4 maize depends on time of day. Plant Physiol. 193, 2306–2320 (2023).

19. Huang, W. et al. A well-supported nuclear phylogeny of Poaceae and implications for the evolution of C_4 photosynthesis. Mol. Plant 15, 755–777 (2022).

20. Murat, F. et al. Ancestral grass karyotype reconstruction unravels new mechanisms of genome shuffling as a source of plant evolution. Genome Res. 20, 1545–1557 (2010).

21. Boffey, S. A., Sellden, G. & Leech, R. M. Influence of cell age on chlorophyll formation in light-grown and etiolated wheat seedlings. Plant Physiol. 65, 680–684 (1980).

22. Sullivan, A. M. et al. Mapping and dynamics of regulatory DNA and transcription factor networks in A. thaliana. Cell Rep. 8, 2015–2030 (2014).

23. Armarego-Marriott, T. et al. Highly resolved systems biology to dissect the etioplast-to-chloroplast transition in tobacco leaves. Plant Physiol. 180, 654–681 (2019).

24. Armarego-Marriott, T., Sandoval-Ibañez, O. & Kowalewska, Ł. Beyond the darkness: recent lessons from etiolation and de-etiolation studies. J. Exp. Bot. 71, 1215–1225 (2020).

25. Singh, P. et al. C_4 gene induction during de-etiolation evolved through changes in cis to allow integration with ancestral C_3 gene regulatory networks. Sci. Adv. 9, eade9756 (2023).

26. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

27. Suh, M. C. et al. Cuticular lipid composition, surface structure, and gene expression in Arabidopsis stem epidermis. Plant Physiol. 139, 1649–1665 (2005).

28. Hua, L. et al. The bundle sheath of rice is conditioned to play an active role in water transport as well as sulfur assimilation and jasmonic acid synthesis. Plant J. 107, 268–286 (2021).

29. Hibberd, J. M. & Covshoff, S. The regulation of gene expression required for C_4 photosynthesis. Annu. Rev. Plant Biol. 61, 181–207 (2010).

30. Langdale, J. A. C_4 cycles: past, present, and future research on C_4 photosynthesis. Plant Cell 23, 3879–3892 (2011).

31. Lee, J. et al. Analysis of transcription factor HY5 genomic binding sites revealed its hierarchical role in light regulation of development. Plant Cell 19, 731–749 (2007).

32. Moon, J., Zhu, L., Shen, H. & Huq, E. PIF1 directly and indirectly regulates chlorophyll biosynthesis to optimize the greening process in Arabidopsis. Proc. Natl Acad. Sci. USA 105, 9433–9438 (2008).

33. Gommers, C. M. M. & Monte, E. Seedling establishment: a dimmer switch-regulated process between dark and light signaling. Plant Physiol. 176, 1061–1074 (2018).

34. Marand, A. P., Zhang, T., Zhu, B. & Jiang, J. Towards genome-wide prediction and characterization of enhancers in plants. Biochim. Biophys. Acta Gene Regul. Mech. 1860, 131–139 (2017).

35. Kaufmann, K., Pajoro, A. & Angenent, G. C. Regulation of transcription in plants: mechanisms controlling developmental switches. Nat. Rev. Genet. 11, 830–842 (2010).

36. Yanagisawa, S. & Schmidt, R. J. Diversity and similarity among recognition sequences of Dof transcription factors. Plant J. 17, 209–214 (1999).

37. Hua, L. et al. A transcription factor quartet orchestrating bundle sheath expression in rice. Preprint at bioRxiv https://doi.org/10.1101/2024.06.17.599020 (2024).

38. Dai, X. et al. Chromatin and regulatory differentiation between bundle sheath and mesophyll cells in maize. Plant J. 109, 675–692 (2022).

39. Aliscioni, S. et al. New grass phylogeny resolves deep evolutionary relationships and discovers C_4 origins. New Phytol. 193, 304–312 (2012).

40. Carvalho, D. S., Nishimwe, A. V. & Schnable, J. C. IsoSeq transcriptome assembly of C_3 panicoid grasses provides tools to study evolutionary change in the Panicoideae. Plant Direct 4, e00203 (2020).

41. Ermakova, M. et al. Installation of C_4 photosynthetic pathway enzymes in rice using a single construct. Plant Biotechnol. J. 19, 575–588 (2021).

42. Kajala, K. et al. Strategies for engineering a two-celled C_4 photosynthetic pathway into rice. J. Exp. Bot. 62, 3001–3010 (2011).

43. Luginbuehl, L. H., El-Sharnouby, S., Wang, N. & Hibberd, J. M. Fluorescent reporters for functional analysis in rice leaves. Plant Direct 4, e00188 (2020).

44. Danila, F. et al. A single promoter-TALE system for tissue-specific and tuneable expression of multiple genes in rice. Plant Biotechnol. J. 20, 1786–1806 (2022).

45. Weber, E., Engler, C., Gruetzner, R., Werner, S. & Marillonnet, S. A modular cloning system for standardized assembly of multigene constructs. PLoS ONE 6, e16765 (2011).

46. Engler, C., Gruetzner, R., Kandzia, R. & Marillonnet, S. Golden gate shuffling: a one-pot DNA shuffling method based on type IIs restriction enzymes. PLoS ONE 4, e5553 (2009).

47. Hiei, Y. & Komari, T. Agrobacterium-mediated transformation of rice using immature embryos or calli induced from mature seed. Nat. Protoc. 3, 824–834 (2008).

48. Page, M. T., Parry, M. A. J. & Carmo-Silva, E. A high-throughput transient expression system for rice. Plant Cell Environ. 42, 2057–2064 (2019).

49. Jefferson, R. A., Kavanagh, T. A. & Bevan, M. W. GUS fusions: beta-glucuronidase as a sensitive and versatile gene fusion marker in higher plants. EMBO J. 6, 3901–3907 (1987).

50. Porra, R. J., Thompson, W. A. & Kriedemann, P. E. Determination of accurate extinction coefficients and simultaneous equations for assaying chlorophylls a and b extracted with four different solvents: verification of the concentration of chlorophyll standards by atomic absorption spectroscopy. Biochim. Biophys. Acta Bioenerg. 975, 384–394 (1989).

51. Kawahara, Y. et al. Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice 6, 4 (2013).

52. McCormick, R. F. et al. The Sorghum bicolor reference genome: improved assembly, gene annotations, a transcriptome atlas, and signatures of genome organization. Plant J. 93, 338–354 (2018).

53. McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337 (2019).

54. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

55. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

56. Zhang, Y. et al. Model-based analysis of ChIP–seq (MACS). Genome Biol. 9, R137 (2008).

57. Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).

58. Tian, T. et al. agriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update. Nucleic Acids Res. 45, W122–W129 (2017).

59. Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. ChromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

60. Castro-Mondragon, J. A. et al. JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 50, D165–D173 (2022).

61. Pagès, H. BSgenome: Software infrastructure for efficient representation of full genomes and their SNPs.R package version 1.72.0 https://doi.org/10.18129/B9.bioc.BSgenome (2024).

62. Bentsen, M. et al. ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation. Nat. Commun. 11, 4267 (2020).

63. Beier, S. et al. Construction of a map-based reference genome sequence for barley, Hordeum vulgare L.Sci. Data 4, 170044 (2017).

64. Vogel, J. P. et al. Genome sequencing and analysis of the model grass Brachypodium distachyon. Nature 463, 763–768 (2010).

65. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

Acknowledgements

J.S. is an awardee of the Life Sciences Research Foundation (funded by Open Philanthropy) and a recipient of the American–Australian Association Fellowship (funded by Pratt Industries). L.H.L. is a recipient of the Herchel Smith Postdoctoral Research Fellowship and was supported by BBSRC grant BBP0031171 to J.M.H. L.H., N.W. and S.S. were supported by a Bill and Melinda Gates Foundation C_4 Rice grant awarded to the University of Oxford (2019-2024 INV-002970). T.B.S. was supported by a Swiss National Science Foundation (SNSF) Postdoc Mobility Fellowship (P500PB_203128) and an EMBO Long-Term Fellowship (ALTF 531-2019). J.R.E. is an Investigator of the Howard Hughes Medical Institute. We acknowledge the flow cytometry facility from the School of the Biological Sciences (University of Cambridge) for their support and assistance in this work. We thank K. H. Müller, G. E. Lindop and M. J. Drignon from the Cambridge Advanced Imaging Centre for the electron microscopy sample preparation and for support during image acquisition. The sequence data for C. laxum were produced by the Department of Energy’s Joint Genome Institute.

Author information

Author notes

1. Tina B. Schreier

Present address: Department of Biology, University of Oxford, Oxford, UK

2. These authors contributed equally: Joseph Swift, Leonie H. Luginbuehl

Authors and Affiliations

1. Plant Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA

Joseph Swift, Travis A. Lee & Joseph R. Ecker

2. Department of Plant Sciences, University of Cambridge, Cambridge, UK

Leonie H. Luginbuehl, Lei Hua, Tina B. Schreier, Ruth M. Donald, Susan Stanley, Na Wang & Julian M. Hibberd

3. Genomic Analysis Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA

Joseph R. Nery & Joseph R. Ecker

4. Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, CA, USA

Joseph R. Ecker

Contributions

L.H.L., J.S., J.R.E. and J.M.H. designed the experimental plan. J.S. and L.H.L. performed laboratory experiments and genomic analyses. L.H. and N.W. performed transactivation assays and GUS expression analyses of stable rice lines. T.B.S. performed SEM imaging. R.M.D. and S.S. performed stable rice transformation. T.A.L. optimized nuclei isolation. J.R.N. performed Illumina sequencing. L.H.L., J.S., J.M.H. and J.R.E. wrote the manuscript, with input from all authors.

Corresponding authors

Correspondence to Leonie H. Luginbuehl, Joseph R. Ecker or Julian M. Hibberd.

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Competing interests

The authors declare no competing interests.

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Nature thanks Dominique Bergmann, Elizabeth Cooper, Klaas Vandepoele 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-nucleus sequencing of rice and sorghum shoots during de-etiolation.

a, Summary of 10X Genomics platform used for RNA and ATAC-sequencing of single nuclei extracted from a population of plants (adapted from Zheng et al.[65]), and sci-RNA-seq3 used for sequencing single nuclei from individual plants to provide increased biological replication. b, Scanning electron micrographs (SEMs) of etioplasts and chloroplasts of rice and sorghum mesophyll and bundle-sheath cells after 0 h, 6 h, 12 h and 48 h of light exposure (SEMs consistent across 3 biological replicates). c, SEMs of rice and sorghum leaf cross-sections showing leaf maturation from 0 h to 48 h after light exposure. (SEMs consistent across 3 biological replicates). d, Total chlorophyll (chlorophyll a + chlorophyll b) measured at different time points during de-etiolation in rice and sorghum. Each data point represents the mean of 3 biological replicates, +/− standard deviation from the mean. The experiment was repeated 3 times independently with similar results.

Extended Data Fig. 2 Single-nucleus atlases for gene expression in rice and sorghum shoots during de-etiolation.

a,b, UMAP of transcript profiles from single nuclei across rice (a) and sorghum (b), across all time points tested. c,d, Each cluster in rice (c) and sorghum (d) contained nuclei sequenced either by 10X or by sci-RNA-seq3 methods. e,f, Similarly, each cluster in rice (e) and sorghum (f) contained nuclei sequenced from each time point assayed. g,h, Transcript abundance from marker genes in cell types of rice (g) and sorghum (h).

Extended Data Fig. 3 Bundle-sheath transgenic marker line identifies the bundle-sheath cluster in the rice transcriptional atlas.

a, Confocal laser scanning microscopy image of a rice bundle-sheath marker line expressing nuclear-localized mTurquoise2 driven by the bundle-sheath-specific ZjPCK promoter. Fluorescent signal from chlorophyll is indicated in magenta. The outline of bundle-sheath cells is indicated with a dotted line (scale = 20 µm). Similar expression patterns were observed for mTurquoise2 across 3 independent transgenic lines. b, Clustered single nuclei transcript profiles from the rice line expressing mTurquoise2 driven by the bundle-sheath-specific ZjPCK promoter. c, mTurquoise2 expression in the transgenic line. d, Cluster of the rice transcriptional atlas (de-etiolation data). e, Per cent overlap of top cluster markers shared between the bundle-sheath transgenic line (c) and the rice transcriptional atlas (d).

Extended Data Fig. 4 Identifying cell types in rice and sorghum 10X Multiome (RNA + ATAC) datasets.

a,b, UMAP clustering of 22,154 rice nuclei (a) and 20,169 sorghum nuclei (b) sequenced using the 10X Multiome workflow. c,d, Cell transcriptional identities assigned to each cluster in rice (c) and sorghum (d). e, Accessibility for the promoter of OsRBCS4 in each rice cell type. f, Accessibility of promoter region for NADP-ME in each sorghum cell type. g,h, Overlap of significant marker genes from cell-types identified within the 10X Multiome dataset with those identified within the transcriptome atlas’ dataset for rice (g) and sorghum (h).

Extended Data Fig. 5 Comparison of transcriptional cell identities in rice and sorghum shoots.

a,b, UMAP clustering visualizing the single nuclei pan-transcriptomes of rice (a) and sorghum (b) nuclei 48 h after light exposure. c, Fold enrichment of cluster membership found in sorghum relative to that of rice. d, Heat map of transcript abundance for bundle-sheath marker genes in rice and sorghum in each cell type 48 h after exposure to light. e, Overlap of cell type specific marker genes of rice and sorghum, significance of overlap indicated (log-normalized Fisher’s exact test adjusted P indicated). f, Differentially expressed orthologous gene pairs within mesophyll and bundle-sheath cells of rice and sorghum. Genes fall into two categories, those consistently partitioned (more highly expressed in the same cell type in both rice and sorghum), and those that are differentially partitioned (swap expression from one cell type to the other). Overall, 152 orthogroups were identified, for which the frequency of higher-order orthology relationships (>1:1 orthology) are indicated.

Extended Data Fig. 6 Quantifying instances of consistent and differential partitioning across all cell-type pairs.

a, Overlap of orthologous genes found partitioned between mesophyll and bundle-sheath cell types in rice and sorghum. b, Quantifying the overlap of orthologous genes partitioned between each possible cell type pair in rice and sorghum. c, Percentage of partitioned genes in b that are either differentially or consistently partitioned.

Extended Data Fig. 7 Light induces changes in cell-type-specific transcript abundance.

a,b, Activation of transcript abundance of light-responsive genes during the first 12 h of light exposure. Each panel shows z-score normalized gene-expression trends unique to each cell type for rice (a) and sorghum (b). c,d, Repression of transcript abundance from light-responsive genes in the first 12 h of exposure to light. Each cluster shows patterns of gene-expression induction unique to each cell type for rice (c) and sorghum (d). e,f, HY5, PIF, GLK1 (GOLDEN2-LIKE1), GLK2 and GNC (GATA NITRATE-INDUCOBLE CARBON-METABOLISM-INVOLVED) expression in rice (e) and sorghum (f) during the first 12 h of light exposure across six cell types.

Extended Data Fig. 8 Scanning electron micrographs of etioplasts and chloroplasts at 0 h and 12 h after exposure to light in different cell types of rice and sorghum shoots.

Etioplasts are indicated with a red arrowhead. ‘S’ indicates starch granules. Unlike rice, sorghum does not have a mestome sheath. SEMs were consistent across 3 biological replicates.

Extended Data Fig. 9 Both light exposure and transcriptional cell identity partition photosynthesis gene expression.

a, Transcript abundance of RBCS2 in rice and RBCS1A in sorghum during de-etiolation in mesophyll and bundle-sheath cells of rice and sorghum. b, Volcano plots of genes significantly partitioned to either mesophyll or bundle-sheath cell types under light conditions (12-h time point, adjusted p < 0.05, likelihood-ratio test). c, Differences in fold change gene expression in rice or sorghum genes in the etiolated state (t = 0) vs their expression after 12 h of light exposure between mesophyll and bundle sheath. d, Chromatin accessibility in the mesophyll and bundle-sheath promoters of RBCS2 in rice and RBCS1A in sorghum under etiolated and light conditions. e, Chromatin-accessibility differences adjacent (+−2,000 bp) to photosynthesis genes at 0 h (dark) and 12 h (light) after light exposure. Genes encoding proteins involved in C_4 photosynthesis, the Calvin–Benson–Bassham cycle and the light reactions are shown in red, purple, and yellow, respectively.

Extended Data Fig. 10 Discovering conserved cell-type-specific cis-elements across species.

a,b, 10X Multiome UMAPs of rice (a) and sorghum (b). c,d, Dof3 motif prevalence within accessible chromatin is restricted to bundle-sheath and phloem clusters in rice (c) and sorghum (d). e, For each species, the top 25 most significantly enriched motifs within the bundle sheath were overlapped, and clustered using TOBIAS. A threshold of 0.4 was used to find consensus motifs (indicated on left). f, Number and log-normalized adjusted p-values of enriched cis-elements in rice (Oryza sativa) and sorghum (Sorghum bicolor) within genes that are consistently partitioned to the bundle-sheath cell type in both species (Rank sum test). In addition, enriched cis-elements in homologues of bundle-sheath-specific rice genes in the C_3 grasses Chasmanthium laxum, Hordeum vulgare, and Brachypodium distachyon are indicated.

Extended Data Fig. 11 Discovering conserved light-responsive cis-elements across species.

a,b, 10X Multiome UMAPs of rice (a) and sorghum (b). c–f, RVE5 motif prevalence within accessible chromatin under etiolated conditions in rice (c) and sorghum (d), and light conditions in rice (e) and sorghum (f). g, For each species, the top 25 most significantly enriched motifs were overlapped, and clustered using TOBIAS. A threshold of 0.4 was used to find consensus motifs.

Extended Data Fig. 12 Discovering cis-elements underlying differentially partitioned genes.

a, Gene-expression heat maps of differentially partitioned genes in rice and sorghum 10X Multiome data. b, Accessible chromatin associated with these expression patterns. c, Enriched motifs among accessible chromatin, clustered using TOBIAS to find consensus motifs (indicated on left). d, Number of DOF motifs within accessible chromatin of differentially partitioned orthologues, two-sided binomial p indicated. e, Number of DOF motifs within accessible chromatin of consistently bundle-sheath partitioned orthologues, two-sided binomial p indicated. f, Chromatin accessibility in mesophyll (green) and bundle sheath (blue) for GAPDH. Sequences within accessible chromatin were analysed for DOF motifs. g, Chromatin accessibility in mesophyll (green) and bundle sheath (blue) for NADP-ME. DOF motifs shown in red. h, Transactivation of sorghum NADP-ME promoter by DOF transcription factors from rice (orange) and sorghum (blue; one-sided Welch’s t-test p indicated, n = 4 biological replicates, boxes indicate 25th, median and 75th quartiles, whiskers extend to the outermost value within 1.5× of interquartile range, assay repeated 3 times independently with similar results).

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Swift, J., Luginbuehl, L.H., Hua, L. et al. Exaptation of ancestral cell-identity networks enables C_4 photosynthesis. Nature 636, 143–150 (2024). https://doi.org/10.1038/s41586-024-08204-3

- Received: 26 January 2024

- Accepted: 11 October 2024

- Published: 20 November 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08204-3

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Abstract

The brain processes an array of stimuli, enabling the selection of appropriate behavioural responses, but the neural pathways linking interoceptive inputs to outputs for feeding are poorly understood1,2,3. Here we delineate a subcortical circuit in which brain-derived neurotrophic factor (BDNF)-expressing neurons in the ventromedial hypothalamus (VMH) directly connect interoceptive inputs to motor centres, controlling food consumption and jaw movements. VMHBDNF neuron inhibition increases food intake by gating motor sequences of feeding through projections to premotor areas of the jaw. When food is unavailable, VMHBDNF inhibition elicits consummatory behaviours directed at inanimate objects such as wooden blocks, and inhibition of perimesencephalic trigeminal area (pMe5) projections evokes rhythmic jaw movements. The activity of these neurons is decreased during food consumption and increases when food is in proximity but not consumed. Activity is also increased in obese animals and after leptin treatment. VMHBDNF neurons receive monosynaptic inputs from both agouti-related peptide (AgRP) and proopiomelanocortin neurons in the arcuate nucleus (Arc), and constitutive VMHBDNF activation blocks the orexigenic effect of AgRP activation. These data indicate an Arc → VMHBDNF → pMe5 circuit that senses the energy state of an animal and regulates consummatory behaviours in a state-dependent manner.

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Main

The principles of how innate behaviours are generated have long been subject to intense theoretical and experimental inquiry, but the precise neural mechanisms underlying behaviour selection remain largely unknown1,2,3. Feeding is a typical innate behaviour in which an array of sensory and interoceptive inputs are integrated to generate an adaptive behavioural response. However, because these many inputs typically change the probability of feeding, in contrast to a reflex, behaviour initiation is context dependent[4]. Although many brain areas and neuronal types that modulate feeding behaviours have been identified, mapping a complete circuit linking relevant sensory and interoceptive inputs to consummatory behaviours is important for understanding how feeding is regulated. We thus sought to identify a neural pathway that would directly connect interoceptive signalling to premotor centres that control consumption. Identifying the essential components of such a circuit would be informative in its own right and potentially provide a framework for investigating how top-down signals regulate innate behaviours[1]. Here we report the components of a simple circuit regulating feeding that directly connects leptin signalling in the arcuate nucleus (Arc) to the jaw premotor area of the perimesencephalic trigeminal area (pMe5) that controls food consumption.

These elements emerged from studies that aimed to identify the circuit of a population of brain-derived neurotrophic factor (BDNF) neurons that regulate food intake and body weight. A role for BDNF neurons in feeding has been shown in studies of mice and humans carrying mutations in the genes encoding BDNF or its receptor tyrosine kinase B (TrkB)5,6,7,8,9,10. Heterozygous BDNF or TrkB mutations cause overconsumption and massive obesity in animals and humans. In addition, genome-wide association studies11,12 have suggested a role for BDNF in the development of obesity. However, the neural circuits responsible for the obesity and hyperphagia seen with defects in BDNF signalling are unknown. As BDNF mutations cause obesity, we initially set out to identify which BDNF neurons normally restrict overfeeding in animals fed a HPD. We found that a discrete subpopulation of BDNF neurons in the ventromedial hypothalamus (VMH) but not elsewhere are activated in diet-induced obese (DIO) mice fed a high-fat diet (HFD). We then aimed to define the functions of these neurons, including identification of their inputs and outputs. Here we report that VMHBDNF neurons act as key nodes regulating food consumption that directly link neurons in the Arc that receive interoceptive inputs to premotor sites in the brainstem that regulate jaw movements and consummatory behaviour.

VMHBDNF neurons cause obesity

Mutations of the genes encoding BDNF or TrkB, its receptor, cause extreme obesity in mice and humans, indicating that neurons that express these genes normally restrict food intake and weight gain. However, the identity of the BDNF neurons and the underlying neuronal mechanism that regulates feeding in response to body weight changes was unclear. We reasoned that the key subpopulation(s) of BDNF-expressing neurons would be activated when animals were placed on a HFD and gained body weight. This was investigated by identifying BDNF neurons that showed increased expression of c-Fos, an activity marker, in response to a HFD in BDNF–Cre mice mated to Rosa26fsTRAP reporter mice expressing a Cre-dependent GFP. Brain sections from HFD-fed and chow-fed mice were costained for both GFP (BDNF) and c-Fos. This revealed a 50-fold increase in the number of BDNF neurons that expressed c-Fos in the central and dorsomedial regions of the VMH (Fig. 1a,b). Indeed, there was a nearly complete overlap between GFP and c-Fos in VMH, with 98% of the neurons from DIO mice that expressed c-Fos also expressing BDNF (Fig. 1c). The VMHBDNF neurons showed limited overlap with steroidogenic factor 1 (SF1), a canonical marker for VMH, which, as shown below, has different functional effects13,14,15. Furthermore, in situ hybridization of SF1 and BDNF revealed that approximately half of the BDNF neurons in the dorsomedial VMH did not express SF1 (Extended Data Fig. 1a–c), consistent with previous studies using RNA sequencing[16] and immunohistochemistry[17] that showed BDNF-expressing neurons to be a distinct subpopulation of the neurons in this nucleus. The data thus suggested that VMHBDNF neurons represent a distinct subpopulation of VMH neurons that are specifically activated in animals fed a HFD. Further experiments (Extended Data Fig. 1d,e) revealed that 99% of the VMHBDNF neurons colocalized with vesicular glutamate transporter 2 (Vglut2) and were thus glutamatergic. To directly test the possibility that these neurons normally restrict food intake and body weight, we analysed the effects of selectively ablating VMHBDNF neurons in mice fed chow or a HFD.

Fig. 1: VMHBDNF neurons suppress food intake and body weight.

a, Schematic of a coronal brain slice containing the VMH and representative example of immunolabelling for c-Fos (red) and BDNF–GFP (green) after 16 weeks of HFD of n = 3 mice. b, Quantification of c-Fos-positive and BDNF–GFP cells in the VMH of n = 3 HFD-fed and n = 3 chow-fed mice. c, Quantification of c-Fos-positive cells expressing BDNF–GFP after HFD (n = 3 mice). d, Schematic of bilateral DtA injection and timeline for mice fed chow ad lib. e,f, Weekly time course of body weights (e) and calorie intake (f) of chow-fed DtA (n = 8) and control (n = 8) mice. g, Comparison of percentage body fat between DtA (n = 8) and control (n = 8) mice. h, Timeline for mice fed HPD ad lib. i,j, Weekly time course of body weights (i) and calorie intake (j) of HPD-fed DtA (n = 8) and control (n = 8) mice. k, Comparison of percentage body fat between DtA (n = 8) and control (n = 8) mice on HPD. l,m, Body weight comparison between chow-fed DtA mice (n = 8) and control mice (n = 8) before DtA ablation and 16 weeks postsurgery (l) and HPD-fed mice (m) (n = 8 mice per group). n,o, Comparison of average daily calorie intake between chow-fed DtA and control mice at 1 and 16 weeks postsurgery (n) and HPD-fed mice (o) (n = 8 mice per group). In b, t-tests were performed using the Holm–Sidak method. Unpaired t-tests with Welch’s correction were used in g and k. Two-way repeated-measures analysis of variance (RM ANOVA) with Šídák’s multiple comparisons test was used in l–n. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Error bars indicate s.e.m. Scale bar, 200 μm. Elements (mice) in a, d and h were created using BioRender (https://biorender.com). d is adapted from ref. [56], Elsevier.

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VMHBDNF neurons were ablated by injecting an adeno-associated virus (AAV) expressing a floxed diphtheria toxin A subunit (DtA) construct into the VMH of BDNF–Cre mice (Fig. 1d–o). Chow-fed mice with DtA ablation (Fig. 1d–g) ate significantly more than controls (Fig. 1n), resulting in significantly increased body weight (Fig. 1l) and body fat content (Fig. 1g), consistent with previous results[10]. We next ablated VMHBDNF neurons in animals fed a highly palatable diet (HPD) (Fig. 1h–k). Initially, mice with ablation of VMHBDNF neurons showed overfeeding similar to that of control mice (Fig. 1j,o). However, whereas food intake in control animals fed a HPD gradually decreased over time as weight increased, animals with VMHBDNF neuron ablation continued to consume significantly more food for an extended period. When mice with a VMHBDNF ablation were fed a HFD, their weight ultimately stabilized at a significantly higher level than that of control mice (Fig. 1i,m). Mice with VMHBDNF ablation also showed significantly higher levels of adiposity (Fig. 1k). These results show that VMHBDNF neurons normally restrict overfeeding and obesity in animals fed a HPD or chow diet.

VMHBDNF neurons regulate food intake

To investigate whether VMHBDNF neuron activity regulated food intake, we used optogenetics to either inhibit or activate these neurons. AAV strains encoding channelrhodopsin (ChR) (Fig. 2a–d) or Guillardia theta anion ChR (GtACR) (Fig. 2e–h) were injected into the VMH of BDNF–Cre mice, and food intake was measured in response to light. To investigate whether activation of VMHBDNF neurons suppressed feeding, we fasted mice overnight and then presented them with a chow pellet (Fig. 2c). Without light activation, control and ChR mice consumed similar amounts of food during the first 30 min. However, VMHBDNF neuron stimulation at 2 Hz inhibited food intake by 99.8%, and intake returned to control levels when light stimulation ceased. Thus, VMHBDNF neuron activation can entirely suppress the hunger-induced drive to eat. To test whether VMHBDNF neuron activity was also sufficient to suppress hedonic feeding, we presented sated chow-fed mice with a highly palatable pellet with high sugar and high fat content (Fig. 2d). Control and ChR mice consumed similar amounts at baseline, but optogenetic activation of VMHBDNF neurons at 2 Hz significantly decreased intake of the highly palatable pellets by 76%. We also found that in the period after light activation ceased, the mice in which VMHBDNF neurons had been activated consumed even more HPD than controls. These data show that VMHBDNF neural activation significantly diminishes both homeostatic and hedonic feeding.

Fig. 2: VMHBDNF neuron activity bidirectionally controls food consumption.

a, Schematic of ChR expression and optic fibre placement. b, Example of ChR expression in n = 5 mice with experimental timeline. c,d, Food intake of fasted ChR (n = 5) and control (n = 5) mice tested with acute chow (c) and mice fed ad lib with acute HPD (d). e, Schematic of GtACR expression and optic fibre placement. f, Example of GtACR expression in n = 6 mice. g,h, Food intake of GtACR mice fed chow ad lib (n = 6) and control mice (n = 6) tested with acute chow (g) and with acute HPD (h). i, Schematic of open loop optogenetic inhibition and experimental timeline. j, Quantification of food intake of GtACR (n = 6) and control mice (n = 6). k, Schematic of closed loop optogenetic inhibition and timeline. l, Food intake of GtACR (n = 6) and control (n = 6) mice. m, Latency of GtACR mice approaching the chow pellet in open- and closed loop inhibition (n = 6). n, Schematic of open loop optogenetic inhibition with bedding present and timeline. o, Food intake of GtACR (n = 6) and control (n = 6) mice. p, Comparison of food intake during optogenetic inhibition of six GtACR mice in the open loop, closed loop, and open loop with bedding set-up. q, Schematic of closed loop optogenetic inhibition with a wood block and experimental timeline. r, Example of consummatory biting behaviour displayed upon VMHBDNF neuron inhibition. s, Quantification of percentage of time spent biting the wood block for GtACR (n = 6) and control (n = 6) mice. Two-way RM ANOVA with Šídák’s multiple comparisons test was used in c, d, g, h, j, l, m, o and s. A mixed-effects model (restricted maximum likelihood) with Dunnett’s multiple comparison was used in p. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Error bars indicate s.e.m. Scale bars, 200 µm. Elements in i, k, n and q were created using BioRender (https://biorender.com). a and e are adapted from ref. [56], Elsevier.

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We next tested the effects of VMHBDNF neuron inhibition by measuring food intake in fed BDNF–Cre animals expressing GtACR in VMH neurons. Whereas the baseline intake of the control and GtACR mice was the same, optogenetic inhibition in the GtACR mice significantly increased food intake (by 1183%) during the 30 min testing period (Fig. 2g). To test whether inhibiting VMHBDNF neuron activity would also increase the intake of HPD, we measured food intake after presenting mice with a HPD pellet. Initial intake before optogenetic inhibition was similar between control and GtACR mice, and light-induced neural inhibition again increased consumption, by 133% (Fig. 2h).

We next evaluated our hypothesis that VMHBDNF neurons would be activated by obesity to suppress further weight gain (see data in Fig. 1a–c) by measuring food intake during VMHBDNF inhibition in DIO mice that had been fed a HPD for 4 weeks. Consistent with the studies of VMHBDNF neuron inhibition in lean mice, optogenetic inhibition of these neurons using GtACR in the obese mice increased consumption of both chow (706%) and HPD (1279%) pellets during the epochs of light exposure (Extended Data Fig. 2a–d).

In these studies, we tested the effects of VMHBDNF inhibition in animals before and after they were fed a HPD. We noted that at baseline, before optogenetic inhibition, when the DIO mice were later presented with chow, they consumed smaller amounts then they had before they were fed the HPD (Extended Data Fig. 2e). One potential explanation for this finding is that the chow was devalued in the DIO animals fed the HPD18,19. However, we found that at baseline before VMHBDNF inhibition, the DIO animals also consumed less of the HPD (Extended Data Fig. 2f). We thus considered that this reduced consumption of the HPD and chow after obesity had developed might have been caused by hyperactive VMHBDNF neurons (as suggested by the increased c-Fos expression). If this hypothesis were true, optogenetic inhibition of VMHBDNF neurons would be expected to have a greater quantitative effect in DIO versus lean animals. Consistent with this, we found that optogenetic inhibition of VMHBDNF neurons using GtACR resulted in a greater quantitative increase in food intake in DIO compared with lean animals (Extended Data Fig. 2g). We interpreted this to mean that VMHBDNF neurons become more active once animals develop diet-induced obesity, at which point they then restrict overeating. (We also imaged these neurons and indeed found increased Ca2+ signals in VMHBDNF neurons from DIO mice; see below.) These studies show that VMHBDNF neurons normally restrict intake of both chow and palatable food in DIO animals.

We then assessed whether BDNF itself contributes to these effects by inhibiting BDNF signalling in DIO mice, using a TrkBF616A knock-in mouse carrying a point mutation that renders the receptor sensitive to an allele-specific kinase inhibitor (1-NM PP). Homozygous TrkBF616A mice were fed a HFD, after which they were treated with 1-NM PP1. Mice treated with 1-NM PP1 showed significant increases in body weight (of 17%; Extended Data Fig. 2h) and food intake (of 27%; Extended Data Fig. 2i,j). These data show that BDNF signalling is also required to suppress food intake and weight gain in HPD-fed mice after obesity has developed.

To further explore the mechanism by which VMHBDNF neurons control feeding, we tested whether VMHBDNF neuron activity altered valence, which would suggest a possible role in motivation. However, in an operant conditioning task, VMHBDNF neuron activation failed to entrain a preference for self-inhibition (Extended Data Fig. 2k–m) and also failed to entrain a preference in a flavour conditioning assay (Extended Data Fig. 2n–p). These studies suggest that the effect of VMHBDNF neurons on feeding is not mediated by a general effect on valence. An alternative possibility is that VMHBDNF neurons regulate the consummatory rather than the appetitive phase of feeding. We tested this by analysing whether VMHBDNF neuron inhibition influenced food approach versus food consumption after optogenetic inhibition of VMHBDNF neurons, using two different paradigms: (1) constant photoinhibition for 5 min in an open loop system (Fig. 2i,j); and (2) inhibition in a closed loop system, in which light was delivered only when the head of the mouse was within 3 cm of a chow pellet (Fig. 2k,l). We found that the closed loop configuration led to an approximately three times greater food intake compared with open loop inhibition (Fig. 2l). Consistent with this, the latency of the approach to the pellet was also increased five-fold in the open loop paradigm, showing that VMHBDNF neuron inhibition increased the time taken for mice to reach the food (Fig. 2m). We also noted that open loop VMHBDNF neuron photoinhibition often triggered consummatory behaviour targeted at objects in immediate proximity to the mouse, including the wall and bare floor of the cage. Thus, VMHBDNF neuron photoinhibition not only reduced food approach but seemed to trigger motor behaviours associated with consumption. To characterize this further, we studied the effect of VMHBDNF neuron photoinhibition in the open loop system in the presence of corn cob bedding (Fig. 2n,o). We observed that animals would often chew the bedding during VMHBDNF neuron photoinhibition, and that the presence of bedding further decreased food intake compared with open loop stimulation in a bare cage (Fig. 2p). These data show that VMHBDNF neuron inhibition drives feeding most effectively when food is in proximity to the animal and suggests the possibility that VMHBDNF neurons regulate motor programs required for consumption, independent of caloric value. To further evaluate this, we repeated the closed loop study but provided the mice with a wooden block instead of a chow pellet (Fig. 2q–s). VMHBDNF neuron inhibition in the closed loop led mice to spend around 9% of the trial duration engaged in biting the wooden block (Supplementary Video S1). Taken together, these results indicate that inhibition of VMHBDNF neuron activity may increase food intake by triggering consumption of whatever objects are in proximity (such as wood blocks or corn cob bedding) rather than by altering valence or food-seeking. This finding is analogous to that reported after activation of central amygdala (CeA) projections to the reticular formation, the motor pattern generator for killing bites[20]. Moreover, it raises the possibility that VMHBDNF neurons represent a distal node in the neural circuit regulating feeding, downstream of hedonic and homeostatic drives, that controls the motor components of consumption including biting and chewing. Although we found that a population of BDNF-expressing neurons in the dorsomedial VMH regulates feeding, other studies have identified a separate population of lateral VMH neurons expressing oestrogen receptors and VGlut2 that trigger aggressive biting by males targeted at females21,22. We thus tested whether inhibition of VMHBDNF neurons stimulated aggression, using the same assay. However, we did not observe any effect of VMHBDNF neurons on aggressive behaviours (Extended Data Fig. 2q,r). Consistent with the possibility that distinct populations control these behaviours, previous studies have indicated that the VMHER neurons target the periaqueductal gray as the main output for this bite-based behaviour and have found no convergence with VMHBDNF projections for feeding[23].

VMHBDNF neural activity during feeding

The potent effect by which VMHBDNF neurons reduced feeding suggested that the activity of these neurons might be decreased as the animals consumed food and thus become permissive for feeding. We measured the in vivo activity of VMHBDNF neurons using fibre photometry to monitor intracellular calcium levels when animals consumed or rejected food. BDNF–Cre mice received injections of a Cre-dependent AAV encoding GCamp6s into the VMH (Fig. 3a), and calcium signals were measured during bouts of feeding. Time-locked recordings showed a significant (4%) decrease in VMHBDNF neuron activity coinciding with consumption of a chow pellet (Fig. 3b,c and Supplementary Video S2). This was consistent with the optogenetic results and suggests that VMHBDNF neuron activity gates feeding behaviour and needs to be inhibited for consumption to commence. To test whether the activity of VMHBDNF neurons was influenced by food palatability, we recorded their response during consumption of sucrose treat pellets; this revealed a decrease of similar magnitude to that seen after chow consumption (Fig. 3b,c).

Fig. 3: VMHBDNF neuron activity is decreased during food consumption and tuned to energy state.

a, Schematic of a coronal brain slice with GCaMP expression and fibre placement and (below) recording timeline for energy state manipulations. b, Average photometry trace ± s.e.m. (n = 6 mice) aligned to a consumption bout of a treat pellet in mice fed chow ad lib (left) or a chow pellet (right). c, Comparison of the minimum signal between chow and treat pellet consumption (n = 6 mice). d, Heatmap of average photometry recordings per mouse (n = 6 mice) aligned to a bout of treat pellet consumption. e, Average overall photometry trace ± s.e.m. (n = 6 mice) aligned to a bout of treat pellet consumption in fasted (purple) and 4 weeks HPD-fed (beige) mice. f, Comparison of the minimum signal during treat consumption in n = 6 mice. g, Heatmap of average photometry recordings per mouse (n = 6 mice) aligned to food approach without consumption. h, Overall average overall photometry (n = 6 mice) aligned food approach without consumption in fasted (purple) and 4 weeks HPD-fed (beige) mice. i, Comparison of the maximum signal. Paired t-test was used in c, and a mixed-effects model (restricted maximum likelihood) with Holm–Šídák’s multiple comparisons test was used in f and i. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Error bars indicate s.e.m. Elements in b, e and h were created using BioRender (https://biorender.com). a is adapted from ref. [56], Elsevier. Photographs of food pellets in b,e,h are from Bio-Serv.

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Having established that VMHBDNF neuron activity was reduced during the consumption of chow and sucrose pellets, we investigated the effects of alterations in energy balance. Neural activity was analysed in three sets of conditions: after an overnight fast and in mice fed a chow or a HPD for 4 weeks (Fig. 3a). The baseline activity of these neurons in an empty home cage showed no difference among these three conditions (Extended Data Fig. 3a,b). However, recordings of the activity of VMHBDNF neurons during bouts of feeding revealed an approximately 60% higher level of activity of VMHBDNF neurons in the DIO mice versus chow-fed animals (Fig. 3d–f). In addition, the activity in VMHBDNF neurons was around 30% lower in fasted versus chow-fed animals. We also observed that when chow-fed animals approached a chow pellet within a radius of 2 cm but did not consume it, the calcium signal in VMHBDNF neurons significantly increased (Fig. 3g–i and Supplementary Video S3). There was also an increase in VMHBDNF activity when fasted mice approached but did not consume the food. However, in this case, the increased activity was only half that observed when fed mice approached but did not consume the pellet, and only 30% of that when DIO mice did not consume it (Fig. 3i). In aggregate, these data show that VMHBDNF neural activity is inversely correlated with food consumption, and that activity responses are tuned to the energy state of an animal.

VMHBDNF neurons are downstream of the Arc

Our finding that VMHBDNF neural dynamics were altered by energy state—that is, in fed, fasted and DIO animals—raised the possibility that they might respond to adipocyte-derived hormone leptin24,25. This was tested using fibre photometry recordings after leptin versus saline injections into fasted animals (Fig. 4a–d). Leptin increased VMHBDNF neural activity by around 80% when animals approached but did not consume a chow pellet (Fig. 4d) and also increased it by around 25% when animals consumed a treat pellet in comparison with saline-injected mice (Extended Data Fig. 4a,b). To confirm that these neurons contributed to the effect of leptin on food intake, we treated animals with leptin (3 mg kg−1, intraperitoneally) after VMHBDNF neural ablation with DtA (Extended Data Fig. 4c,d). The reduction in food intake caused by leptin was significantly decreased in animals in which VMHBDNF neurons were ablated. To test whether VMHBDNF neurons could respond to signals besides leptin, we crossed BDNF–Cre mice to leptin-deficient ob/ob mice and injected DtA into the VMH (Extended Data Fig. 3e–g). The ob/ob mice with VMHBDNF ablation showed significantly increased food intake and became more obese. As ob/ob mice lack leptin, these data indicate that VMHBDNF neurons sense satiety signals in addition to leptin.

Fig. 4: VMHBDNF neurons are anatomically and functionally downstream of AgRP and POMC neurons.

a, Schematic of experimental design. b,c, Heatmap of average photometry recordings of six mice (b) and overall average photometry trace (±s.e.m.) (n = 6 mice) aligned to food approach without consumption after injection with saline (purple) or leptin (green) (c). d, Comparison of the peak photometry signal. e, Schematic of retrograde viral tracing approach. f, Representative example (n = 3 mice) of a coronal Arc section with in situ hybridization for Rabies (white), AgRP (red) and POMC (green) and magnification examples of boxed areas 1 and 2. g, Quantification of average overlap of Rabies-labelled cells with AgRP and POMC in the Arc (n = 3 mice). h, Schematic of retrograde ChR expression in Arc and fibre placement in VMH. i, Food intake of ad lib fed ChR (n = 4) and control mice (n = 4) tested with chow. j, Schematic of closed loop optogenetic inhibition with a wood block and timeline. k, Quantification of percentage of time spent biting the wood block for ChR (n = 4) and control mice (n = 4). l, Schematic of bilateral expression of Cre-dependent NaChBac in Arc and change in neural activity. m,n, Weekly time course of body weight (m) and calorie intake (n) of AgRP–Cre (n = 6 mice), BDNF–Cre (n = 6 mice), AgRP/BDNF–Cre mice (n = 6 mice) and control mice (n = 7 mice) after NaChBac injection. o, Schematic of bilateral expression of Dre-dependent NaChBac in Arc and Cre-dependent TelC in VMH and respective changes in neuronal activity. p,q, Weekly time course of body weight (p) and calorie intake (q) of POMC–Dre (n = 6 mice), BDNF–Cre (n = 7 mice), POMC–Dre/BDNF–Cre (n = 6 mice) and control (n = 8 mice) mice after AAV injection. Paired t-test was used in d, and two-way RM ANOVA with Šídák’s multiple comparisons test was used in i, k, m, n, p and q. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Error bars indicate s.e.m. Scale bars, 50 µm (f, left), 30 µm (f, right). Elements in a, b and j were created using BioRender (https://biorender.com). e, h, l and o are adapted from ref. [56], Elsevier. Photograph of food pellets in b is from Bio-Serv.

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Previous anatomic[26] and single-cell RNA sequencing studies[16] have indicated that VMHBDNF neurons do not express leptin receptors, suggesting that the effects of leptin on these neurons are indirect. Therefore, we next determined whether VMHBDNF neurons received inputs from the Arc (Fig. 4e), a key site of action for multiple interoceptive signals including leptin. The inputs to VMHBDNF neurons were labelled by monosynaptic tracing with pseudotyped Rabies. We injected helper AAVs and G-deleted Rabies-mCherry viruses into the VMH of BDNF–Cre mice and observed prominent inputs from the Arc, where leptin-responsive proopiomelanocortin (POMC) and AgRP neurons are located (Fig. 4f,g). We next performed multichannel fluorescence in situ hybridization (FISH) using probes for Rabies, POMC and AgRP; this revealed that a large proportion of Arc inputs were from either POMC (42.28 ± 7.95%) or AgRP (43.25 ± 3.53%) neurons. In addition, FISH with a LepR probe revealed that many of the Rabies-labelled AgRP and POMC neurons also expressed the leptin receptor (Extended Data Fig. 4h–l). Consistent with previous studies27,28, in situ hybridization also showed that 93 ± 2.29% VMHBDNF neurons coexpressed both neuropeptide Y receptor 5 (NPY5R) and melanocortin receptor 4 (MC4R), suggesting that VMHBDNF neurons are a point of convergence for ArcPOMC and ArcAgRP outputs (Extended Data Fig. 5a,b).

We next investigated whether the AgRP projections to the VMH could drive feeding, similar to the effect of somatic activation of AgRP neurons in the Arc[29]. A Cre-dependent retrograde AAV encoding ChR was injected into the VMH of AgRP–Cre mice, and optogenetic fibres were implanted above the VMH (Fig. 4h). AgRP neurons are GABAergic, and optogenetic activation of the AgRP projections to the VMH increased food intake in chow-fed mice (Fig. 4i) but did not cause any decrease in food approach (Extended Data Fig. 5c–e). Moreover, closed loop activation targeted at a wood block did not elicit motor sequences of consummatory behaviour (Fig. 4j,k), suggesting that although projection-specific activation regulates food intake, activation of these inhibitory inputs by themselves is not of sufficient intensity to activate motor programs of food consumption. This is in contrast to the effect observed when inhibiting the soma of VMHBDNF neurons. AgRP projections to the paraventricular hypothalamus (PVH) have also been reported to increase food intake through MC4R-expressing neurons30,31, and retrograde tracing from VMHBDNF neurons also showed inputs from the PVH. In addition, in situ hybridization of PVH neurons for MC4R after retrograde tracing revealed that VMHBDNF neurons also receive monosynaptic inputs from PVHMC4R neurons (Extended Data Fig. 5f,g). Thus, melanocortin pathways involving neurons from multiple sites converge on VMHBDNF neurons.

To assess whether VMHBDNF neurons were functionally downstream of interoceptive neurons in the Arc, we simultaneously activated both ArcAgRP and VMHBDNF neurons by expressing NaChBac in one or both populations (Fig. 4l–n). NaChBac is a bacterial sodium channel that leads to constitutive neural activation. AgRP–Cre mice were crossed to BDNF–Cre mice, and an AAV expressing a floxed NaChBac was stereotactically injected into the Arc and VMH (Fig. 4l). Consistent with previous results[32], activation of ArcAgRP neurons alone led to marked hyperphagia and obesity. Constitutive activation of VMHBDNF neurons alone significantly reduced food intake and body weight. However, simultaneous activation of both populations in double-positive AgRP–Cre/BDNF–Cre mice fully recapitulated the effect seen with BDNF activation alone, with these animals showing significantly decreased food intake and weight. These data show that VMHBDNF neurons are functionally downstream of ArcAgRP neurons and thus represent an important output site.

The functional relationship between ArcPOMC and VMHBDNF neurons was tested by crossing POMC–Dre mice to BDNF–Cre mice and injecting a Dre-dependent NaChBac into the Arc and a Cre-dependent TelC virus into the VMH (Fig. 4o). TelC prevents vesicular release and thus silences neurons. Consistent with the effect of DtA ablation of VMHBDNF neurons, BDNF–Cre mice expressing TelC alone showed significantly increased food intake and body weight (Fig. 4p,q). Constitutive activation of ArcPOMC neurons led to slight decreases in food intake and body weight, consistent with previous studies[33]. Here, again, the phenotype of the POMC–Dre/BDNF–Cre double-positive mice recapitulated the phenotype of mice with constitutive inhibition of VMHBDNF neurons alone. These data show that VMHBDNF neurons are also functionally downstream of ArcPOMC neurons. Overall, this set of experiments showed that VMHBDNF neurons receive inputs from interoceptive neurons in the Arc and are functionally downstream of the melanocortin system, as previously suggested[28]. We next determined where they projected to and further assessed their effects on consummatory behaviours.

VMHBDNF neurons project to brainstem

We mapped the projection sites of VMHBDNF neurons by injecting an AAV with a Cre-dependent mGFP–synaptophysin–Ruby gene into the VMH of BDNF–Cre mice (Fig. 5a–c). Fluorescence imaging of neurons expressing mGFP revealed dense projections to several known premotor sites20,34,35,36,37 in the brainstem that have previously been shown to send projections on to motor neurons of the jaw muscle and tongue; these included the mesencephalic nucleus (Me5), lateral paragigantocellular (LPGi) and gigantocellular reticular nucleus alpha part (GiA), with only weak projections to the parvocellular reticular formation (PCRT). We next assessed the function of each of these VMHBDNF neuron-to-premotor projections by optogenetically activating VMHBDNF nerve terminals there. Optical fibres were implanted above the peri-Me5 (pMe5), LPGi and GiA, and PCRT in animals expressing ChR in VMHBDNF neurons (Fig. 5d–g), and feeding of a chow pellet was tested after an overnight fast. Projections to the LPGi and GiA were of interest, as the LPGi–motor connection develops during weaning and might therefore be involved in solid food consumption. However, activation had only a modest effect on feeding, reducing it by 37%, whereas projections to the PCRT, which has been reported to stimulate the killing bite in crickets, failed to suppress feeding at all. By contrast, projections to pMe5 significantly reduced feeding (by 80%), similar to the effect we observed when stimulating VMHBDNF soma. In addition, activation of pMe5 projections suppressed consumption of HPD feeding (Fig. 5h) and was specific to solid foods, as it did not affect liquid diet licking (Extended Data Fig. 6c,d). Moreover, pMe5 projections specifically control food consumption and not approach, as inhibition of the terminals did not shorten the latency of the approach to a chow pellet (Extended Data Fig. 6a,b).

Fig. 5: Activation of VMHBDNF neuron projections to premotor areas.

a, Left, schematic of a sagittal section with mGFP–synaptophysin–Ruby expression. Right, representative image (n = 3 mice) of injection site. b, Brain atlas with square indicating the section on the right: representative (n = 3 mice) coronal section of mGFP expression and DAPI (blue) of Me5. scp, superior cerebellar peduncles. c, Brain atlas with dotted square indicating the imaged section on the right: representative (n = 3 mice) coronal section of mGFP expression and DAPI (blue). IRT, intermediate reticular nucleus; 7N, cranial nerve 7; RMg, raphe magnus nucleus. d, Schematic of a sagittal section with ChR expression. e–g, Chow intake of overnight fasted mice with implants above pMe5 (n = 7 mice each group; e), PCRT (n = 4 mice per group; f), or LPGi + GiA (n = 5 mice each group; g). h, Ad lib fed mice tested with HPD ChR (n = 7) and control mice (n = 7). i, Representative (n = 3 mice) coronal image of Me5 after mGFP–synaptophysin–Ruby injection in VMH (see a) with projections (green), synaptophysin (red) and immunofluorescence staining for advillin (white). j, Same image as in i but with advillin (white) and synaptophysin (red) only. k, Enlargement of the square in j. l, Schematic of a sagittal section with retrograde tracer injection into Mo5. m, Representative coronal image (n = 4 mice) of Me5 with Rabies-labelled nuclei (red) and overlay with advillin immunofluorescent staining (white) (below). n, Quantification of Rabies-labelled neurons (n = 4 mice) colabelled with advillin. o, Schematic of a sagittal section with retro-tracer injection into pMe5. p, Representative image (n = 3 mice) of VMH with mCherry-labelled BDNF neurons (red) and DAPI. q, Quantification of mCherry-labelled neurons in the dorsomedial, central and ventrolateral VMH. Two-way RM ANOVA with Šídák’s multiple comparisons test was used in e–h. Mixed-effects analysis with Holm–Šídák’s multiple comparisons test was used in q. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Error bars indicate s.e.m. Scale bars, 200 µm (a), 100 µm (b,m), 400 µm (c), 50 µm (i), 20 µm (j), 200 µm (p). Elements in a, d, l and o were created using BioRender (https://biorender.com). b and c are adapted from ref. [56], Elsevier.

Source Data

We next characterized the inputs and outputs from Me5 more fully. Me5 is a small nucleus; to further confirm that VMHBDNF neurons project there, we performed immunofluorescence for advillin, a marker of Me5 neurons[38], together with Ruby-tagged synaptophysin introduced into VMHBDNF neurons as above (Fig. 5i–k). Dense synapses were found in Me5 and on advillin-expressing cells, as well as cells in Me5 that did not express advillin. Monosynaptic retrograde Rabies tracing from Chat–Cre-expressing neurons in the trigeminal motor nucleus (Mo5) confirmed that in Me5 both advillin and non-advillin neurons project to motor neurons in Mo5 (Fig. 5l–n).

Next, we performed retrograde tracing from pMe5 by injecting a retro-AAV expressing a Cre-dependent mCherry into the pMe5 of BDNF–Cre mice. In line with the c-Fos data (Fig. 1a), we found that most BDNF neurons projecting to pMe5 were located in the ventromedial and central parts of the VMH project (Fig. 5o–q). We also investigated whether the other non-BDNF VMH populations projected to pMe5 using a ‘Cre-off’ strategy (Extended Data Fig. 6e–g). In this experiment, we used an AAV in which Cre turns off a eYFP cassette such that it will be constitutively expressed in neurons that do not express Cre. A ‘Cre-off’ AAV encoding eYFP-ChR was injected into the VMH of BDNF–Cre mice, and the projection sites were ascertained. The non-BDNF neurons in VMH showed only minimal projections in the Barrington nucleus and locus coeruleus and failed to show projections to pMe5.

To investigate potential inputs to pMe5 from extrahypothalamic areas with known premotor effects on jaw movements, we tested for projections from the CeA. CeA neurons control the killing bite of crickets during prey-hunting and have been reported to project to the PCRT[20]. However, we did not find any further inputs to pMe5 from the CeA, using two anterograde tracing AAV1 viruses encoding either Flp or Cre. In this study, the Flp-expressing AAV1 was injected into the CeA and the Cre AAV1 was injected into the VMH of mice carrying both a Cre-dependent GFP and Flp-dependent tdTomato reporter (Extended Data Fig. 6h–j). With this approach, CeA projections appear red and VMH projection neurons appear green. As before, the VMH projection neurons were localized primarily in pMe5, whereas the CeA projections were seen in PCRT neurons, consistent with a previous report[20], and in the medial and lateral parabrachial nucleus next to pMe5 (Extended Data Fig. 6i,j). Our finding that the targets of the VMHBDNF neurons are different from CeA is consistent with the finding[20] that the CeA → PCRT circuit does not alter food consumption.

VMHBDNF → pMe5 regulates jaw movements

We next investigated whether inhibition of the VMHBDNF projections to pMe5 recapitulated the effects seen during inhibition of VMHBDNF soma (Fig. 2e–h). A Cre-dependent AAV encoding eOPN3, an inhibitory mosquito-derived rhodopsin, was injected into the VMH of BDNF–Cre mice, and implants were placed above the pMe5 projection sites (Fig. 6a,b). Inhibition of the VMHBDNF projections to pMe5 increased food intake of both chow (863%) and HPD (170%), with a similar magnitude to that seen after inhibition of the soma (Fig. 6c,d). Of note, eOPN3 is a GPCR and thus does not elicit the instantaneous inhibition seen after photoinhibition using GtACR, precluding studies of inhibition in a closed loop paradigm. However, constant inhibition (light) in an open loop configuration decreased food approach (329% latency; Extended Data Fig. 6k,l) and increased chewing of spaghetti and non-nutritious wooden sticks (wood: 642%, spaghetti: 394%; Fig. 6e–h and Supplementary Videos 4–6). This suggested that inhibition of VMHBDNF projections to pMe5 might directly regulate consummatory actions, including movement of the jaw.

Fig. 6: Inhibition of VMHBDNF projections to Me5.

a, Schematic of a sagittal brain with eOPN3 expression in the VMH and fibre placement above pMe5. b, Representative image (n = 5 mice) of eOPN3–Ruby expression and fibre placement above Me5. c,d, Food intake of ad lib fed mice tested with chow (c), and ad lib fed eOPN3 (n = 5) and control mice (n = 5) tested with HPD (d). e, Schematic of constant optogenetic inhibition during spaghetti feeding and experimental timeline. f, Quantification of time spent biting spaghetti of ad lib fed mice (n = 4). g, Schematic of constant optogenetic inhibition during wood-stick biting and experimental timeline. h, Quantification of time spent biting wood sticks of ad lib fed mice (n = 4). i, Schematic of high-speed jaw-tracking in head-fixed mice with concurrent optogenetic inhibition and subsequent pose estimation. j, Representative example (n = 4 mice) of jaw movements in two dimensions over time of an eOPN3-expressing mouse with the laser-on time indicated in green (60–120 s). k, Representative example (n = 4 mice) of jaw movements in two dimensions over time of a control mouse with the time of laser stimulation indicated in green (60–120 s). l, Quantification of absolute jaw movements during laser on and off periods in four control and four eOPN3 mice. m, Comparison of jaw movement between control and eOPN3 mice after normalization to laser-off period. Two-way RM ANOVA with Šídák’s multiple comparisons test in c, d, f, h, l. Unpaired t-test in m. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Error bars indicate s.e.m. Scale bar, 1,000 µm. Elements in a, e, g and i were created using BioRender (https://biorender.com).

Source Data

We directly assessed jaw movements in the absence of food or other stimuli in head-fixed mice using a high-speed side camera during optogenetic inhibition of the VMHBDNF projections to Me5. Pose estimation of the jaw position was performed with DeepLabCut (Fig. 6i). Photoinhibition of VMHBDNF terminals in pMe5 using eOPN3 reproducibly evoked rhythmic jaw movements, with a similar time course to that reported in studies of food intake (Fig. 6j,k and Supplementary Video 7). By contrast, control mice without eOPN3 expression showed infrequent jaw movements (Supplementary Video 8). Photoinhibition of VMHBDNF terminals in pMe5 led to an increase of approximately 160% in jaw movements during laser inhibition compared with controls (Fig. 6l,m).

Discussion

A key function of the brain is to generate adaptive behaviours in response to an array of interoceptive and sensory inputs. A full understanding of how behaviours are controlled in higher organisms thus requires the elucidation of neural circuits linking these inputs to motor outputs. In this work, we show that BDNF neurons in the VMH convey inputs from an interoceptive node to motor outputs associated with feeding. These neurons receive direct monosynaptic inputs from AgRP and POMC neurons in the Arc, two key interoceptive populations. In vivo imaging studies show that the activity of VMHBDNF neurons is inversely correlated with feeding, tuned to energy state and modulated by leptin. Optogenetic activation of VMHBDNF neurons reduces feeding, whereas inhibiting them increases food intake. In the absence of food, these actions are directed at inedible objects such as wood blocks or sticks, suggesting that they drive motor sequences associated with feeding. Inhibition of VMHBDNF projections to pMe5, a known premotor site, elicits rhythmic activation of the jaw muscles even in the absence of food. In aggregate, these data show that VMHBDNF neurons directly connect neurons receiving interoceptive inputs in the Arc to a premotor site that controls jaw movements, thus defining a simple subcortical circuit that regulates food consumption (Extended Data Fig. 6m).

Complex behaviours such as feeding, which are context dependent and thus variable, are considered to be mechanistically distinct from reflexes in which a defined stimulus generally results in an invariant response[4]. However, as suggested by Sherrington in 1906[1], the ‘transition from reflex action to volitional control is not abrupt and sharp’. Sherrington pointed out that reflexes are also under volitional control, noting that the cough reflex (and others) can be ‘checked, released, or modified in its reaction with such variety and seeming independence of external stimuli by the existence of a spontaneous internal process expressed as will’. The identification of an Arc → VMHBDNF → pMe5 circuit reported here is consistent with Sherrington’s hypothesis. Moreover, this line of reasoning suggests the possibility that even among mammals, feeding and other complex behaviours might be controlled in part by simple, reflex-like circuits that are in turn modulated by descending inputs from higher centres, as has also been suggested by Crick and Koch[39]. By identifying pMe5 as a premotor node regulating consummatory behaviours, this circuit potentially provides a framework for establishing a hierarchy among the many previously identified nodes that have been shown to control food intake[40]. Although the identification of this circuit does not preclude the possibility that other premotor sites may also regulate feeding, our finding that VMHBDNF projections to pMe5 regulate consummatory behaviours is consistent with studies of mice with Me5 lesions as a result of Drg11 knockout. The Drg11-knockout mice starved to death at weaning owing to an inability to consume solid food but survived if provided with liquid nutrient[41]. Our finding of a premotor site regulating consummatory behaviours thus raises the possibility that other innate behaviours are controlled by premotor sites elsewhere. Tinbergen suggested that key nodes regulating motor patterns are released from inhibition in response to sensory and interoceptive inputs[42], and our identification of a node regulating food consumption may therefore have general implications for how specific behaviours are selected to the exclusion of other competing behaviours[2].

The data reported here also show that VMHBDNF neurons are downstream of AgRP neurons, which play an essential part in driving food intake when an animal’s energy state is low. AgRP levels are suppressed by leptin[43], and our findings thus suggest that the obesity seen with mutations of the genes encoding BDNF, TrkB, leptin, the LepR receptor and melanocortin is caused by altered function of the same expanded feeding circuit. Whereas AgRP neuron activation by itself normally drives feeding, concurrent VMHBDNF neuron activation entirely blocks this. AgRP neurons act as interoceptive sensors of changes in energy state and drive all aspects of hunger29,44, including both appetitive behaviours such as food-seeking and consummatory behaviours. However, we found that AgRP inputs to VMHBDNF neurons only increased the consummatory phase of feeding and did not alter food approach, suggesting that different projection sites of these neurons regulate the motivational component. This is consistent with a hierarchical architecture for innate behaviours, as proposed by Craig[45] and Lorenz[3]—and especially Tinbergen[2], who suggested that appetitive and consummatory behaviours are controlled by different sites, and that the initiation of the consummatory phase only begins when the appetitive phase is completed and food is in proximity. Moreover, AgRP neuron projections to the VMH did not drive the consummatory motor sequences by themselves in the absence of food, suggesting that further signals are necessary to ‘release’ the motor programs necessary for mastication. As suggested by Tinbergen, such inhibition could result from sensory information derived from potential food sources, signalling that food is in proximity. The possibility that AgRP inputs to VMHBDNF neurons are not sufficient to activate consumption is analogous to Tinbergen’s experiment in which honey bees only landed on colourful artificial flowers when these were paired with appropriate odours[42].

The data also show that VMHBDNF neurons represent a critical node downstream of leptin signalling, as demonstrated by our findings that leptin increases the gain of these neurons, according to fibre photometry, and that ablating VMHBDNF neurons increases the weight of leptin-deficient ob/ob mice. Retrograde viral tracing from VMHBDNF neurons identified inputs from AgRP and POMC in the Arc, the site of a circumventricular organ; these convey the leptin signal to VMHBDNF neurons, which themselves do not express leptin receptors. Constitutive alterations in VMHBDNF neural activity entirely blocked the effects of both POMC or AgRP neural activation on feeding, confirming that they are key functional target of these neurons. This finding is in line with those of prior studies reporting the presence of POMC and AgRP fibres and terminals in the VMH28,46 and with findings that VMHBDNF neurons coexpress MC4R and NPY5R and integrate melanocortin and NPY feeding signals[28]. This is also consistent with previous studies[28] by Xu et al. who reported that BDNF expression in the VMH was increased by melanocortin agonists and that intracerebroventricular BDNF injections ameliorated the hyperphagic phenotype of AY mice. Although leptin increases both melanocortin signalling and VMHBDNF activity, other leptin-independent signals are also likely to contribute, because ablating these neurons in leptin-sensitive (ob/ob) and leptin-resistant (DIO) mice increased weight. The data presented here are thus consistent with the possibility that both leptin-dependent and leptin-independent signals regulate the activity of VMHBDNF neurons to establish a set point for weight in lean and obese mice. A leptin-independent effect is also consistent with a previous report showing that ob/ob mice receiving a 6 month infusion of leptin while on a HFD ended up at a similar weight to wild-type animals fed the same diet[47]. The fact that leptin levels were fixed at a constant low level suggests that another signal besides leptin was limiting the weight of this group. Another study also suggested the existence of signals in addition to leptin[48]. It will now be important to identify the putative leptin-independent signals.

Also consistent with previous reports[10], our findings suggest that ablation of VMHBDNF neurons accounts for the massive obesity associated with lesions of the VMH in rodents[49] and humans50,51. This effect is not recapitulated by knockout of SF1, a canonical marker expressed in most VMH neurons, or by knockout of BDNF in SF1 neurons13,52, suggesting that the VMHBDNF neurons are distinct from SF1-expressing neurons there. We found only limited overlap between BDNF and SF1 neurons, which marked nearly all of the other neurons in this nucleus (around 6% of SF1 neurons also express BDNF according to single-cell RNA sequencing)[16]. Thus, our data and those of others[10] suggest that the subpopulation of BDNF neurons in VMH accounts for the hyperphagia and massive obesity that follows a VMH lesion. In aggregate, these findings and those of others strongly suggest that VMHBDNF neurons contribute significantly to the obesity associated with BDNF and TrkB mutations. Further support for this conclusion was provided by a recent study that found an interaction of BDNF and astrocytes in the VMH that was essential for the suppression of weight[53]. However, these results do not exclude the possibility that other sites might also contribute to the obesity associated with a BDNF or TrkB mutation. For example, although they do not regulate feeding, BDNF-expressing neurons in the paraventricular nucleus have been shown to regulate energy expenditure, with effects on thermogenesis and adipose tissue innervation54,55.

In summary, these data show that VMHBDNF neurons are a key element of a simple circuit that regulates food intake and body weight by directly connecting interoceptive neurons to motor outputs controlling consummatory behaviours and jaw movements. These results provide a framework for studying the role of this Arc → VMHBDNF → pMe5 circuit and factors that modulate it to control feeding.

Methods

Mice

All animal care and experimental procedures were ethically performed and approved by the Institutional Animal Care and Use Committee at Rockefeller University. Male mice were single-housed with a 12 h light/12 h dark cycle and ad libitum access to regular chow and water, except in fasting and DIO studies, where either a HFD with 45 kcal%fat (4.7 kcal g−1) or a HPD with 42 kcal%fat and high sucrose content (4.5 kcal g−1) (TD.88137, Envigo) was provided. We used male ob/ob (B6.Cg-Lepob/J; 000632, Jackson Laboratory; or bred in-house), Rosa26fsTRAP (B6.129S4-Gt(ROSA)26Sortm1(CAG-EGFP/Rpl10a,-birA)Wtp/J; 022367, Jackson Laboratory), AgRP–Cre (AgRPtm1(cre)Lowl/J; 012899, Jackson Laboratory) and Flp reporter mice (RCF-tdTomato, B6.Cg-Gt(ROSA)26Sortm65.2(CAG-tdTomato)Hze/J; 032864, Jackson Laboratory) crossed to Cre reporter mice (Rosa26fsTRAP). BDNF–IRES–Cre mice[57] were provided by W. Shen (Shanghai Institute of Technology). POMC Dre mice were provided by J. Bruning (Max Planck Institute for Metabolism Research). For retrograde tracing from motor neurons in Mo5, Chat–Cre mice (B6.129S-Chattm1(cre)Lowl/MwarJ; 031661, Jackson Laboratory) were crossed to Helper RabV mice (B6;129P2-Gt(ROSA)26Sortm1(CAG-RABVgp4,-TVA)Arenk/J; 024708, Jackson Laboratory) and TrkBF616A knock-in mice carrying a point mutation that renders the receptor sensitive to an allele-specific kinase inhibitor (1-NM PP)[58]. All mouse lines were in a WT (C57BL/6 J) background. For brain surgeries, male mice of at least 8 weeks of age were anesthetized with isoflurane and placed in a stereotaxic frame (David Kopf Instruments), a craniotomy was performed, and a borosilicate glass pipette was used to inject viral vectors. For VMH injections: three injections (each 50 nl) were made into each hemisphere (bregma, −1.36 mm; midline, ±0.35 mm; from brain surface, 5.70 mm, 5.60 mm and 5.50 mm). For injections into the Arc, 50 nl was injected as follows: bregma, −1.45 mm; midline, ±0.45 mm; from brain surface, 5.70 mm, 5.60 mm and 5.50 mm. For injections into Mo5, 75 nl was injected as follows: bregma, −5.20 mm, midline, ±1.5 mm; from brain surface, 4.60 mm and 4.50 mm. For Me5 injections: from bregma, −5.4 mm; midline, ±0.9 mm; from brain surface, 4.5 mm, 4.0 mm, 3.5 mm.

Reagents

Leptin was diluted in sterile saline (3 mg kg−1) and injected intraperitoneally. For photometry recording, mice were fasted overnight and injected with either saline or leptin 2 h before recordings. All mice received saline and leptin injections in alternating order. For acute food intake experiments, leptin or saline was injected 2 h before onset of the dark period, and all mice received saline and leptin injections in a cross-over design.

Viruses

Cre-dependent neuronal ablation was performed by injection of AAV1-mCherry-flex-dtA (UNC Vector Core)[59]. To target expression of calcium activity indicator GCaMP6s to VMHBDNF neurons, we used an AAV vector carrying a double-floxed GCaMP6s construct (AAV5-Syn-Flex-GCaMP6s-WPRE-SV40, Addgene)[60]. For optogenetic manipulations, a somatic targeting GtACR (AAV5-hSyn1-SIO-stGtACR1-FusionRed)[61] or ChR (AAV5-EF1a-double-floxed-hChR2(H134R)-EYFP-WPRE-HGHpA)[62] was used (both Addgene). For long-term silencing, a Cre-dependent TelC AAV (AAV5-hSyn-FLEX-TeLC-P2A-dTomato, Addgene) was used, and for long-term activation a Cre-dependent NaChBac (AAV-Syn-DIO-NaChBac-dTomato) and a Dre-dependent NaChBac[63] (AAV5-hSyn-roxSTOProx-NaChBac-dTomato, HHMI-Janelia Research Campus) were used. For retrograde tracing, a combination of two helper AAVs (AAV1-TREtight-mTagBFP2-B19G and AAV1-syn-FLEX-splitTVA-EGFP-tTA, both Addgene) and pseudotyped Rabies (EnvA G-Deleted Rabies-mCherry, Salk Institute)64,65 were injected; for retrograde labelling from Mo5, a G-deleted Rabies-H2B-mCherry (Salk viral core) was used; and for anatomical tracing from Me5, a retrograde mCherry construct (pAAV-hSyn-DIO-hM4D(Gi)-mCherry, Addgene) was used. For projection activation, we used an AAV encoding eOPN3 (AAV-hSyn1-SIO-eOPN3-mScarlet-WPRE, Addgene)[61], and for labelling projections with ChR we used a retro-AAV (AAV-EF1a-double-floxed-hChR2(H134R)-mCherry-WPRE-HGHpA). Anterograde labelling was done with AAV1 (ref. [66]) encoding Cre (AAV-hSyn-Cre-WPRE-hGH, Addgene) and Flp (AAV-EF1a-Flpo, Addgene). For ‘Cre-out’ experiments, AAV-Ef1a-DO-ChETA-EYFP-WPRE-pA (Addgene) was used.

Immunofluorescence

For c-Fos staining after DIO, BDNF–Cre mice were crossed to Rosa26fsTRAP to express eGFP in a Cre-dependent manner in BDNF neurons. Mice were fed a HFD, while littermate control mice were fed chow. After 16 weeks, mice were transcardially perfused with 4% paraformaldehyde, and their brains were postfixed for 1 day in 4% paraformaldehyde. Brains were then placed in 30% sucrose in phosphate-buffered saline (PBS) until precipitation and frozen and coated in OCT for cryosectioning. Cryosections (50 μm) were cut using a Leica cryostat (CM1950). Brain sections were washed in PBS with 0.1% Triton X-100 (PBST, pH 7.4) and blocked in 3% normal goat/donkey serum (Jackson ImmunoResearch Laboratories) and 2% BSA (Sigma) in PBST for 2 h. Slides were then incubated overnight at room temperature with primary antibody. After being washed in PBST, sections were incubated with fluorescein-conjugated goat IgG. The primary antibodies used and their dilutions were as follows: rabbit anti-FOS (1:1,000; mAb 2250S, Cell Signaling), chicken anti-GFP (1:1,000, ab13970, Abcam). Secondary antibodies conjugated with Alexa-594 and Alexa-488 were purchased from Invitrogen. Brain sections were mounted on to SuperFrost (Fisher Scientific 22-034- 980) slides and then visualized with an inverted Zeiss LSM 780 laser scanning confocal microscope with a ×10 or ×20 lens. Images were imported to Fiji for further analysis and to count cells. To quantify numbers of stained cells, brain slides were imaged under a ×20 objective. For advillin staining, the procedure was the same as above but with rabbit anti-advillin (1:500, NBP2-92263, Novus Biologicals) as the primary antibody and Alexa 647 donkey anti-rabbit (1:500, ab150075, Abcam) as the secondary antibody. For anterograde tracing, brains were processed as described above; tdtomato/Ruby and GFP were amplified with rabbit anti-RFP (1:1000, 600-401-379, Rockland) and chicken anti-GFP (1:1,000, ab13970, Abcam) as primary antibodies, and secondary antibodies conjugated with Alexa-594 and Alexa-488 were purchased from Invitrogen.

In situ hybridization

Mice were briefly transcardially perfused with RNase-free PBS to remove blood. Brains were then quickly collected, frozen in OCT and stored at −80 °C until they were sectioned by cryostat (15 μm sections) and attached on Superfrost Plus Adhesion Slides (Thermo Fisher). RNAscope Fluorescent Multiplex assay (Advanced Cell Diagnostics Bio) was then performed using the RNAscope system as per the manufacturer’s protocol. Probes for the following mRNAs were used (all from ACDBio): mm-BDNF (catalogue no. 424821) and eGFP (catalogue no. 400281), VGlut2 (catalogue no. 319171), RabV (catalogue no. 456781), AgRP (catalogue no. 400711), POMC (catalogue no. 314081), MC4R (catalogue no. 319181-C2) and NPY5R (catalogue no. 589811), LepR (catalogue no. 402731). Briefly, a hydrophobic barrier was created using Immedge Hydrophobic Barrier Pen (Vector Laboratories). Slides were pretreated by serial submersion in 1× PBS, 50% EtOH, 70% EtOH and twice 100% EtOH for 2 min each, at room temperature. Probe hybridization was achieved by incubation of 35 μl mRNA target probes for 2 h at 40 °C using a HyBez oven. The signal was amplified by subsequent incubation of Amp-1, Amp-2, Amp-3 and Amp-4, one drop each, for 30, 15, 30 and 15 min, respectively, at 40 °C using a HyBez oven. Each incubation step was followed by two 2 min washes with RNAscope washing buffer. Nucleic acids were stained using DAPI Fluoromount-G (SouthernBiotech) mounting medium before coverslipping. Slides were visualized with an inverted Zeiss LSM 780 laser scanning confocal microscope using a ×20 or ×40 lens. Images were imported to Fiji for further analysis.

Long-term body weight and food intake measures

Single-housed mice were measured weekly to assess body weight and food intake. Whole-body composition was measured using nuclear magnetic resonance relaxometry (EchoMRI) at the end of the 16 week period.

Optogenetics

After injection of AAVs encoding either ChR or GtACR, we bilaterally implanted 200 μm fibre optic cannulas (Thorlabs) in BDNF–Cre mice and control mice (Cre-negative littermates). For VMH targeting, implants were angled at 15° and placed at the following positions: bregma, −1.36 mm; midline, ±1.85 mm; from brain surface: 5.25 mm. For brainstem targeting, implants were angled at 15° and placed at the following positions: bregma, −5.4 mm; midline, ±1.75 mm; from brain surface, 2.6 mm (for Me5); bregma, −6.3 mm; midline, ±2.15 mm; from brain surface, 5.5 mm (for LPGi); and bregma, −5.7 mm; midline, ±2.6 mm; from brain surface, 4.7 mm (for PCRT). Implants were subsequently fixed with dental cement (C&B Metabond). After a minimum of 3 weeks expression time, mice were handled and habituated to tethering with optical fibres. A constant 473 nm laser (OEM Lasers/OptoEngine) was used for optogenetic inhibition with GtACR and pulsed at 2 Hz (5 ms) for optoactivation with ChR. For inhibition with OPN, a 532 nm laser at 10 Hz was used. Lasers were connected to bifurcated optical fibres (Thorlabs) with an output of approximately 2–5 mW at the implant. For AgRP projection stimulation, the laser power was reduced to 1–2 mW. Food intake studies were done in home cage-like arenas during the light phase without bedding unless otherwise stated.

Acute food intake experiments

With optogenetic activation or inhibition, mice were habituated to the arena for 10 min without food present. Then, consumption of a single food pellet was measured every 30 min for five repetitions, with only the second repetition being paired with optogenetic activation or inhibition. For open loop and closed loop feeding experiments, a single chow pellet was fixed to the middle of a home cage-style arena with fun-tak (Loctite). Food intake was assessed every 5 min in three repetitions, with only the second repetition being paired with optogenetic inhibition. Inhibition was either 5 min constant laser (open loop) or triggered (closed loop) by real-time video tracking (Noldus, Ethovision) whenever the head of the mouse was within a 3 cm radius of the pellet. For modification with bedding present, the same open loop set-up was used but with corn cob bedding covering the floor. For wood block trials, the chow pellet was replaced by a wood block that was fixed with fun-tak. Time spent biting the wood block was manually assessed and quantified by scoring of video recordings.

Liquid diet experiments

Ensure Vanilla (20 µl) was pipetted on to the bare floor of a cage in three repetitions without light activation, followed by three repetitions with light activation and another three repetitions without light activation. For quantification purposes, experiments were video recorded, and latencies from Ensure delivery to full consumption were scored and averaged over the three repetitions.

Operant conditioning

Trials were performed in a home cage-style arena with two capacitive touch plates mounted on opposite sides. Both touch plates were connected to an Arduino to register numbers of touches, and one randomly assigned side would trigger cessation of a constant 2 Hz laser (for ChR) for 3 s or activation of a constant laser for 3 s (for GtACR). Trials lasted for 1 h.

Conditioned flavour preference assays

Assays were performed as previously described[44]. Briefly, mice were habituated overnight to orange- and strawberry-flavoured sugar-free Juicy Gels (Hunt’s). Initial preference was assessed in a 30 min session without any light application. The preferred flavour was then paired with light exposure for ChR mice, or the less preferred flavour paired with light exposure for GtACR mice. Conditioning was repeated daily for 3 days and consisted of one light exposure session in which light exposure started after 5 min and lasted for 25 min while the paired gel was presented and a 30 min session with the non-paired gel without any light exposure. A 15 min test session in which both gels were available was performed on the day after conditioning ended.

Spaghetti and wooden stick experiments

Five spaghetti sticks or wooden sticks of similar length were distributed equally in an empty home cage. Control and eOPN3 mice were given 5 min baseline exploration time, 5 min with laser inhibition and 5 min without laser with the spaghetti or sticks present. A side and overhead camera were used to quantify the time spent chewing.

Head-fixed jaw-tracking

Mice for head-fixed experiments had a small metal bar fixed to their skull with dental cements during implant surgery. After a minimum of 3 weeks recovery, mice were habituated to being head-fixed in a custom head-fixation set-up. This set-up consisted of a side camera (Basler a2A1920-160umPRO -ace 2) and a laser source controlled and synchronized by Bonsai[67]. Frames were acquired at 100 Hz at 722 × 878 pixel size. Optogenetic inhibition trials consisted of 1 min with laser, 1 min on and 1 min off. Jaw pose was subsequently estimated with DeepLabCut[68].

Fibre photometry

After injection of an AAV encoding Cre-dependent GCaMP6s into the VMH of male BDNF–Cre mice, a unilateral 400 μm fibre optic cannula was implanted as described for optogenetics. After a minimum of 4 weeks expression time, mice were habituated to tethering and a home cage-style arena.

Data were collected with a Fiber Photometry system by Tucker-Davis Technologies (RZ5P, Synapse), and Doric components and recordings were synched to video recordings in Ethovision by TTL triggering. A 465 nm and isosbestic 405 nm LED (Doric) were reflected into a dual fluorescence Mini Cube (Doric) before entering the recording fibre that connects to the implant. Recording fibres were photobleached overnight before recordings to minimize autofluorescence. GCaMP6s fluorescence was collected as a calcium-dependent signal (525 nm) and isosbestic control (430 nm) with a femtowatt photoreceiver (Newport, 2151) and a lock-in amplifier using the RZ5P at a 1 kHz sampling rate.

Mice were allowed to habituate for 30 min at the start of each recording session before any items were introduced into the arena. Feeding bouts were manually assessed and scored from video recordings when mice were given single pellets of chow or 20 mg sucrose treat pellets (Bio-Serv). Instances of food interaction without consumption were defined as approach within a 2 cm radius around a chow pellet without subsequent consumption. To measure the effects of different energy states, the same mice underwent the same standardized recording procedure in the following states: lean ad lib chow-fed, overnight fasted injected with saline, and overnight fasted injected with leptin and 4 weeks DIO. The order of the lean, fasted saline and fasted leptin states was randomized to avoid any order effects.

A script written in MATLAB based on a previously published method and code was used for analysis[69]. Bleaching and movement artefacts were removed by applying a polynomial least-squares fit to the 405 nm signal, adjusting it to the 465 nm trace (405fitted) to then calculate the GCaMP signal as %ΔF/F = (465signal − 405fitted)/405fitted. Traces were filtered with a moving average filter and downsampled by a factor of 20. Three trials per mouse were averaged to derive data for peri-event plots and analysis of maximum and minimum signals.

Quantification and statistics

Sample sizes were chosen on the basis of similar studies previously published and kept to a minimum to reduce unnecessary use of animals. Experimenters were blinded to group allocation as much as possible, but small groups sizes and concurrent recordings of control and treatment animals meant it was sometimes not possible. Group allocation was done at random, unless genetic backgrounds dictated group assignment. Microscopy images were analysed and quantified in ImageJ/Fiji. Photometry recordings were processed and analysed with MATLAB (MathWorks). Statistical analyses were performed in GraphPad Prism. All tests were two-sided, and results are displayed as mean ± s.e.m. Statistical details are provided in the figure legends and source data, including definitions of n and significance. Significance was defined as P < 0.05. Mice were randomized into control or treatment groups. Control mice were age-matched littermate controls where possible. Graphs were produced using GraphPad Prism and Adobe Illustrator, and schematic illustrations were prepared in BioRender.

Reporting summary

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

Data availability

All data generated or analysed during this study are included in the article and its Supplementary Information files. Source data are provided with this paper.

Code availability

The code for photometry analysis, Arduino code for operant conditioning, and Bonsai code for head-fixed behaviour and analysis are available from GitHub (https://github.com/ckosse/Jaw_2024).

Change history

- 08 November 2024

In the version of the article initially published, in the key to Fig. 1i, the red “VMHBDNF DtA” and black “Control” labels were switched and have now been amended in the HTML and PDF versions of the article.

References

1. Sherrington, C. S. The Integrative Action of the Nervous System (Yale Univ. Press, 1906).

2. Tinbergen, N. The hierarchical organization of nervous mechanisms underlying instinctive behaviour. Symp. Soc. Exp. Biol. 4, 305–312 (1950).

3. Lorenz, K. The Foundations of Ethology (Springer Nature, 1981).

4. Burnett, C. J. et al. Hunger-driven motivational state competition. Neuron 92, 187–201 (2016).

5. Gray, J. et al. Hyperphagia, severe obesity, impaired cognitive function, and hyperactivity associated with functional loss of one copy of the brain-derived neurotrophic factor (BDNF) gene. Diabetes 55, 3366–3371 (2006).

6. Kernie, S. G., Liebl, D. J. & Parada, L. F. BDNF regulates eating behavior and locomotor activity in mice. EMBO J. 19, 1290–1300 (2000).

7. Lyons, W. E. et al. Brain-derived neurotrophic factor-deficient mice develop aggressiveness and hyperphagia in conjunction with brain serotonergic abnormalities. Proc. Natl Acad. Sci. USA 96, 15239–15244 (1999).

8. Rios, M. et al. Conditional deletion of brain-derived neurotrophic factor in the postnatal brain leads to obesity and hyperactivity. Mol. Endocrinol. 15, 1748–1757 (2001).

9. Yeo, G. S. H. et al. A de novo mutation affecting human TrkB associated with severe obesity and developmental delay. Nat. Neurosci. 7, 1187–1189 (2004).

10. Unger, T. J., Calderon, G. A., Bradley, L. C., Sena-Esteves, M. & Rios, M. Selective deletion of Bdnf in the ventromedial and dorsomedial hypothalamus of adult mice results in hyperphagic behavior and obesity. J. Neurosci. 27, 14265–14274 (2007).

11. Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

12. Thorleifsson, G. et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat. Genet. 41, 18–24 (2009).

13. Kamitakahara, A., Xu, B. & Simerly, R. Ventromedial hypothalamic expression of Bdnf is required to establish normal patterns of afferent GABAergic connectivity and responses to hypoglycemia. Mol. Metab. 5, 91–101 (2015).

14. Kennedy, A. et al. Stimulus-specific hypothalamic encoding of a persistent defensive state. Nature 586, 730–734 (2020).

15. Kunwar, P. S. et al. Ventromedial hypothalamic neurons control a defensive emotion state. eLife 4, e06633 (2015).

16. Chen, R., Wu, X., Jiang, L. & Zhang, Y. Single-cell RNA-seq reveals hypothalamic cell diversity. Cell Rep. 18, 3227–3241 (2017).

17. Tran, P. V. et al. Diminished hypothalamic bdnf expression and impaired VMH function are associated with reduced SF-1 gene dosage. J. Comp. Neurol. 498, 637–648 (2006).

18. Beutler, L. R. et al. Obesity causes selective and long-lasting desensitization of AgRP neurons to dietary fat. eLife 9, e55909 (2020).

19. Mazzone, C. M. et al. High-fat food biases hypothalamic and mesolimbic expression of consummatory drives. Nat. Neurosci. 23, 1253–1266 (2020).

20. Han, W. et al. Integrated control of predatory hunting by the central nucleus of the amygdala. Cell 168, 311–324.e18 (2017).

21. Lee, H. et al. Scalable control of mounting and attack by Esr1+ neurons in the ventromedial hypothalamus. Nature 509, 627–632 (2014).

22. Falkner, A. L. et al. Hierarchical representations of aggression in a hypothalamic-midbrain circuit. Neuron 106, 637–648.e6 (2020).

23. Lo, L. et al. Connectional architecture of a mouse hypothalamic circuit node controlling social behavior. Proc. Natl Acad. Sci. USA 116, 7503–7512 (2019).

24. Zhang, Y. et al. Positional cloning of the mouse obese gene and its human homologue. Nature 372, 425–432 (1994).

25. Campfield, L. A., Smith, F. J., Guisez, Y., Devos, R. & Burn, P. Recombinant mouse OB protein: evidence for a peripheral signal linking adiposity and central neural networks. Science 269, 546–549 (1995).

26. Liao, G.-Y. et al. Dendritically targeted Bdnf mRNA is essential for energy balance and response to leptin. Nat. Med. 18, 564–571 (2012).

27. Huszar, D. et al. Targeted disruption of the melanocortin-4 receptor results in obesity in mice. Cell 88, 131–141 (1997).

28. Xu, B. et al. Brain-derived neurotrophic factor regulates energy balance downstream of melanocortin-4 receptor. Nat. Neurosci. 6, 736–742 (2003).

29. Aponte, Y., Atasoy, D. & Sternson, S. M. AGRP neurons are sufficient to orchestrate feeding behavior rapidly and without training. Nat. Neurosci. 14, 351–355 (2011).

30. Shah, B. P. et al. MC4R-expressing glutamatergic neurons in the paraventricular hypothalamus regulate feeding and are synaptically connected to the parabrachial nucleus. Proc. Natl Acad. Sci. USA 111, 13193–13198 (2014).

31. Li, M. M. et al. The paraventricular hypothalamus regulates satiety and prevents obesity via two genetically distinct circuits. Neuron 102, 653–667.e6 (2019).

32. Krashes, M. J. et al. Rapid, reversible activation of AgRP neurons drives feeding behavior in mice. J. Clin. Invest. 121, 1424–1428 (2011).

33. Jiang, J., Morgan, D. A., Cui, H. & Rahmouni, K. Activation of hypothalamic AgRP and POMC neurons evokes disparate sympathetic and cardiovascular responses. Am. J. Physiol. Heart Circ. Physiol. 319, H1069–H1077 (2020).

34. Moriyama, Y. Rhythmical jaw movements and lateral ponto-medullary reticular neurons in rats. Comp. Biochem. Physiol. A Comp. Physiol. 86, 7–14 (1987).

35. Sotgiu, M. L. & Marini, G. Projections from lateral reticular nucleus neurons to trigeminal motor nucleus revealed by antidromic activation in rabbits. Exp. Neurol. 98, 26–33 (1987).

36. Stanek, E., Cheng, S., Takatoh, J., Han, B.-X. & Wang, F. Monosynaptic premotor circuit tracing reveals neural substrates for oro-motor coordination. eLife 3, e02511 (2014).

37. Guo, H. et al. Whole-brain monosynaptic inputs to hypoglossal motor neurons in mice. Neurosci. Bull. 36, 585–597 (2020).

38. Hunter, D. V. et al. Advillin is expressed in all adult neural crest-derived neurons. eNeuro 5, ENEURO.0077-18.2018 (2018).

39. Crick, F. & Koch, C. A framework for consciousness. Nat. Neurosci. 6, 119–126 (2003).

40. Andermann, M. L. & Lowell, B. B. Toward a wiring diagram understanding of appetite control. Neuron 95, 757–778 (2017).

41. Wang, C.-Z. et al. Development of the mesencephalic trigeminal nucleus requires a paired homeodomain transcription factor, Drg11. Mol. Cell. Neurosci. 35, 368–376 (2007).

42. Tinbergen, N. The Study of Instinct (Clarendon/Oxford Univ. Press, 1951).

43. Baver, S. B. et al. Leptin modulates the intrinsic excitability of AgRP/NPY neurons in the arcuate nucleus of the hypothalamus. J. Neurosci. 34, 5486–5496 (2014).

44. Betley, J. N. et al. Neurons for hunger and thirst transmit a negative-valence teaching signal. Nature 521, 180–185 (2015).

45. Craig, W. Appetites and aversions as constituents of instincts. Proc. Natl Acad. Sci. USA 3, 685–688 (1917).

46. Wang, D. et al. Whole-brain mapping of the direct inputs and axonal projections of POMC and AgRP neurons. Front. Neuroanat. 9, 40 (2015).

47. Knight, Z. A., Hannan, K. S., Greenberg, M. L. & Friedman, J. M. Hyperleptinemia is required for the development of leptin resistance. PLoS ONE 5, e11376 (2010).

48. Ravussin, Y. et al. Evidence for a non-leptin system that defends against weight gain in overfeeding. Cell Metab. 28, 289–299.e5 (2018).

49. Hetherington, A. W. & Ranson, S. W. Hypothalamic lesions and adiposity in the rat. Anat. Rec. 78, 149–172 (1940).

50. Bray, G. A. & Gallagher, T. F. Jr. Manifestations of hypothalamic obesity in man: a comprehensive investigation of eight patients and a review of the literature. Medicine 54, 301–330 (1975).

51. Pinkney, J., Wilding, J., Williams, G. & MacFarlane, I. Hypothalamic obesity in humans: what do we know and what can be done? Obes. Rev. 3, 27–34 (2002).

52. Kim, K. W. et al. Steroidogenic factor 1 directs programs regulating diet-induced thermogenesis and leptin action in the ventral medial hypothalamic nucleus. Proc. Natl Acad. Sci. USA 108, 10673–10678 (2011).

53. Ameroso, D. et al. Astrocytic BDNF signaling within the ventromedial hypothalamus regulates energy homeostasis. Nat. Metab. 4, 627–643 (2022).

54. Wang, P. et al. A leptin–BDNF pathway regulating sympathetic innervation of adipose tissue. Nature 583, 839–844 (2020).

55. Wu, S.-W. & Xu, B. Rapid and lasting effects of activating BDNF-expressing PVH neurons on energy balance. eNeuro https://doi.org/10.1523/ENEURO.0009-22.2022 (2022).

56. Paxinos, G., & Franklin, K. B. J. The Mouse Brain in Stereotaxic Coordinates (Elsevier, 2007).

57. Luo, F. et al. Whole-brain patterns of the presynaptic inputs and axonal projections of BDNF neurons in the paraventricular nucleus. J. Genet. Genomics 46, 31–40 (2019).

58. Chen, X. et al. A Chemical-genetic approach to studying neurotrophin signaling. Neuron 46, 13–21 (2005).

59. Wu, Z., Autry, A. E., Bergan, J. F., Watabe-Uchida, M. & Dulac, C. G. Galanin neurons in the medial preoptic area govern parental behaviour. Nature 509, 325–330 (2014).

60. Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

61. Mahn, M. et al. High-efficiency optogenetic silencing with soma-targeted anion-conducting channelrhodopsins. Nat. Commun. 9, 4125 (2018).

62. Boyden, E. S., Zhang, F., Bamberg, E., Nagel, G. & Deisseroth, K. Millisecond-timescale, genetically targeted optical control of neural activity. Nat. Neurosci. 8, 1263–1268 (2005).

63. Xue, M., Atallah, B. V. & Scanziani, M. Equalizing excitation–inhibition ratios across visual cortical neurons. Nature 511, 596–600 (2014).

64. Lavin, T. K., Jin, L., Lea, N. E. & Wickersham, I. R. Monosynaptic tracing success depends critically on helper virus concentrations. Front. Synaptic Neurosci. 12, 6 (2020).

65. Liu, K. et al. Lhx6-positive GABA-releasing neurons of the zona incerta promote sleep. Nature 548, 582–587 (2017).

66. Zingg, B. et al. AAV-mediated anterograde transsynaptic tagging: mapping corticocollicular input-defined neural pathways for defense behaviors. Neuron 93, 33–47 (2017).

67. Lopes, G. et al. Bonsai: an event-based framework for processing and controlling data streams. Front. Neuroinform. 9, 7 (2015).

68. Lauer, J. et al. Multi-animal pose estimation, identification and tracking with DeepLabCut. Nat. Methods 19, 496–504 (2022).

69. Lerner, T. N. et al. Intact-brain analyses reveal distinct information carried by SNc dopamine subcircuits. Cell 162, 635–647 (2015).

Acknowledgements

We thank all members of the Friedman laboratory for discussions and comments on the project, especially A. Ilanges; K. Hedbacker for breeding ob/ob mice; Rockefeller Bio-Imaging Resource Center for microscopy; and Rockefeller Precision Instrumentation Technologies for fabrication of head bars and help with the head-fixed set-up. We thank J. Bruning (Max Planck Institute) for provision of POMC Dre mice, and R. Smith for assistance with Arduino and DeepLabCut implementation. We thank Bio-Serv for the use of their pictures of chow and HFD pellets in Figs. 3b,e,h, 4b and Extended Data Figs. 2a,c,d, 4b,d, and Elsevier for reuse of figure elements from The Mouse Brain in Stereotaxic Coordinates[56] in Figs. 1d, 2a,e, 3a, 4e,h,l,o, 5b,c. Elements in Figs. 1a,d,h, 2i,k,n,p, 3e,h, 4a,b,j, 5a,d,l,o, 6a,e,g,i and Extended Data Fig. 1a were created with BioRender (https://biorender.com). J.M.F. acknowledges support from the JBP Foundation and the Howard Hughes Medical Institute. C.K. was supported by a fellowship from the Leon-Levy Foundation and an NIH K99 BRAIN Pathway to Independence award.

Author information

Authors and Affiliations

1. Laboratory of Molecular Genetics, Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA

Christin Kosse, Jessica Ivanov, Kyle Pellegrino & Jeffrey Friedman

2. Department of Physiology, University of California, San Francisco, San Francisco, CA, USA

Zachary Knight

Contributions

C.K. designed and performed all experiments. J.F. and C.K. wrote the manuscript. Z.K. designed and performed TrkB mutant experiments. J.I. and K.P. assisted C.K. with experiments.

Corresponding author

Correspondence to Jeffrey Friedman.

Ethics declarations

Competing interests

J.F. receives royalty payments for the sale of leptin through the Rockefeller University as part of its policy for distributing the proceeds for inventions to the inventors. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature thanks the anonymous reviewers 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 Overlap of VMHBDNF neurons with Vglut2 and Sf1 and effect of TrkB inhibition.

a, Representative example of coronal sections (n = 3 mice) through the VMH with in situ hybridization for BDNF (magenta) and Sf1 (white) and DAPI (blue). b, Quantification of overlap with SF1 and BDNF in dorsomedial and central VMH of n = 3 mice. c, High magnification examples of boxed areas from a with white arrows indicating BDNF only, yellow arrows Sf1 only, and green arrows BDNF and SF1 expression. d, Representative example of coronal sections (n = 3 mice) through the VMH with in situ hybridization for BDNF (magenta) and Vglut2 (green) and DAPI (blue). e, High magnification examples of overlap (indicated by white arrows) of boxed areas from d. Right, quantification of Glut2 expression in VMHBDNF neurons of 3 mice. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Error bars ± s.e.m.

Source Data

Extended Data Fig. 2 Inhibition of VMHBDNF neurons in the DIO state and VMHBDNF neuron effects on valence and social interaction.

a, Schematic of a HPD induced obese state. b, Changes in body weight after DIO in (n = 6 mice each group) control and GtACR mice. Food intake with optoinhibition in GtACR (n = 6) and control mice (n = 6) both given chow (c) and (d) HPD after DIO with two-way RM ANOVA with Šídák’s multiple comparisons test. e, Cumulative chow intake of all (n = 12) mice before and after DIO during laser off (paired t-test), and f, HPD intake (paired t-test). g, Optoinhibition evoked feeding of the same mice, two-way RM ANOVA with Šídák’s multiple comparisons test. Time course of body weight (h) and food intake of HFD (i) with 1-NM PP1 treatments in control (n = 4 each) and TrkB mutant mice and j, average food intake before and during 1-NM PP1 treatment (two-way RM ANOVA with Šídák’s multiple comparisons test). k, Schematic of touch activated self-inhibition with an active site controlling the laser. Number of touches at active and inactive site for (l) ChR (n = 6) and control mice (n = 6) during self-inhibition and (m) for positive reinforcement with GtACR (n = 6) and control mice (n = 6) (two-way RM ANOVA with Šídák’s multiple comparisons test). n, Schematic of conditioned flavour preference assay. o, Preference at the initial test (pre) and conditioned preference test (post) in ChR (n = 6) and control mice (n = 6) (two-way RM ANOVA with Šídák’s multiple comparisons test) and right, change in preference (Welch’s t test). p, Left, flavour preference at the initial test (Pre) and conditioned preference test (post) of in GtACR (n = 6) and control mice (n = 6) (two-way RM ANOVA with Šídák’s multiple comparisons test) and right, change in preference (Welch’s t test). q, Schematic of social interaction assay. r, Time spent in social interactions for GtACR (n = 6) and control mice (n = 6). Two-way RM ANOVA with Šídák’s multiple comparisons test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Error bars ± s.e.m.

Source Data

Extended Data Fig. 3 Detector unit counts of photometry mice in fed, fasted and DIO state.

a, 200 s long example traces in detector units (not normalized) during empty cage exploration and b, comparison of the median thereof. Repeated measures one-way ANOVA. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Error bars ± s.e.m.

Source Data

Extended Data Fig. 4 Leptin engages VMHBDNF neurons to suppress feeding.

a, Schematic of experimental design, below, comparison of the average photometry signal during food consumption between 6 mice treated with leptin and saline (paired t-test). b, Average photometry trace ( ± sem) of mice injected with saline (purple) or leptin (green) (n = 6 mice) aligned to a bout of treat pellet consumption. c, Schematic of experimental design and below food intake in 24 h after a leptin or saline ip injection in Dta and control mice (n = 8 each group) (DtA: p = 0.0031, Ctrl: p < 0.0001, two-way ANOVA with Holm-Šídák’s multiple comparisons test). d, Quantification of the feeding suppressive effect of leptin by normalizing to the saline injected food intake (p = 0.0187, Welch’s t-test). e, Weekly time course of body weight (at week 12: p = 0.0248, Welch’s t-test) and f, calorie intake of chow fed ob/ob mice crossed to BDNF cre (n = 7) and ob/ob control mice (n = 7) injected with DtA with quantification of average food intake in g before and after DtA ablation (P = 0.036, two-way RM ANOVA with Šídák’s multiple comparisons test). h, Schematic of G-deleted pseudotyped rabies and helper AAV expression. i, Quantification of average co-expression of rabies labelled POMC neurons with LepRb of n = 3 mice. j, Representative example (n = 3 mice) of a coronal Arc section with in situ hybridization for POMC (white), rabies (red) and LepRb (green), right, high magnification examples of dotted square with arrows indicating triple labelled neurons, scale bar = 10 µm. k, Quantification of average co-expression of rabies labelled AgRP neurons with LepRb of n = 3 mice. l, Representative example of a coronal Arc section with in situ hybridization for AgRP (white), rabies (red) and LepRb (green); right, high magnification examples of dotted square with arrows indicating triple labelled neurons, scale bar = 10 µm. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Error bars ± s.e.m.

Source Data

Extended Data Fig. 5 VMHBDNF neurons are downstream of melanocortin signalling.

a, Representative example (n = 3 mice) of a coronal VMH section with in situ hybridization for BDNF (white), MC4R (red) and NPY5R (green), right, high magnification examples of overlap. b, Quantification of average co-expression of VMHBDNF neurons (of n=3 mice) with MC4R and NPY5R. c, Schematic of retrograde ChR expression in Arc and optic fibre placement in VMH and d, schematic of open loop optostimulation for food approach assessment. e, Quantification of latency to reach a food pellet of AgRP cre mice injected with retro ChR into the VMH (n = 4) and control mice (n = 4). Two-way RM ANOVA with Šídák’s multiple comparisons test. f, Schematic of G-deleted pseudotyped rabies and helper AAV injection. g, Representative example of a coronal Arc section with in situ hybridization for MC4R (white) and rabies (red); right, high magnification examples of overlap and quantification of n = 3 mice. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Error bars ± s.e.m.

Source Data

Extended Data Fig. 6 VMHBDNF neuron projections to brainstem drive solid food intake.

a, Schematic of open loop optogenetic activation with ChR and experimental timeline. b, Latency to approach the chow pellet in ChR and Ctrl mice (n = 7 mice each group). Two-way RM ANOVA with Šídák’s multiple comparisons test. c, Schematic of open loop optogentic activation and experimental timeline for licking measurements. d, Quantification of time spent licking of ChR (n = 7) and control mice (n = 6) given 50 µl liquid diet in an open loop inhibition setup. ChR: 43.71 ± 10.84 s, two-way RM ANOVA with Šídák’s multiple comparisons test. e, Schematic of a ‘cre out’ Cheta YFP virus injection into VMH of BDNF cre mice, labelling non-BDNF neurons. f, Representative image (n = 4 mice) of a coronal section of the injection site and g, image of projections to Barrington nucleus and surrounding areas and high magnification of area in dotted square. Me5 was marked by autofluorescence of neurons in this nucleus. 4 V = fourth ventricle, Bar = Barrington’s nucleus, LDTg = Laterodorsal tegmental nucleus. h, Schematic of virus injection into the CeA and VMH of Cre and Flp reporter expressing mice. i, Representative example of n = 3 mice of brainstem premotor areas (mesencephalic) and j, high magnification of AAV1 labelled neurons. k, Schematic of open loop optostimulation for food approach assessment. l, Quantification of latency to reach a food pellet of BDNF cre mice injected with eOPN3 into VMH and fibres implanted above pMe5 (n = 5) and control mice (n = 5). Two-way RM ANOVA with Šídák’s multiple comparisons test. m, Schematic of a proposed circuit for energy state driven food consumption behaviour. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Error bars ± s.e.m.

Source Data

Supplementary information

Reporting Summary

Supplementary Video 1

Inhibition of VMHBDNF neurons causes eating-like behaviour targeted at a wood block: Mice biting a wood block in response to optogenetic inhibiting of VMHBDNF neurons when a mouse is near a wood block.

Supplementary Video 2

Photometry recording of VMHBDNF neurons during food consumption: photometry recording of a mouse approaching and consuming a treat pellet.

Supplementary Video 3

Photometry recording of VMHBDNF neurons during food rejection: photometry recording of a mouse approaching and not consuming a chow pellet.

Supplementary Video 4

Mouse eating spaghetti without laser: representative recording of OPN mouse interacting with spaghetti without laser (4× speed).

Supplementary Video 5

Inhibition of VMHBDNF to pMe5 projections causes spaghetti biting: representative recording of OPN mouse interacting with spaghetti during VMHBDNF to Me5 inhibition (4× speed).

Supplementary Video 6

Inhibition of VMHBDNF to pMe5 projections causes wood-stick biting: representative recording of OPN mice interacting with wooden sticks during VMHBDNF to Me5 inhibition (4× speed).

Supplementary Video 7

Jaw movements and tracking during inhibition of VMHBDNF neuron-to-Me5 projections (5× speed): synchronized video of jaw movements before, during and after laser silencing of VMHBDNF neuron projections to pMe5 with position of jaw in x-y space.

Supplementary Video 8

Jaw movements and tracking in a control mouse (5× speed): synchronized video of jaw movements before, during and after laser-on of a control mouse with position of jaw in x-y space.

Source data

Source Data Figs. 1–6

Source Data Extended Data Figs. 1–6

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Kosse, C., Ivanov, J., Knight, Z. et al. A subcortical feeding circuit linking an interoceptive node to jaw movement. Nature 636, 151–161 (2024). https://doi.org/10.1038/s41586-024-08098-1

- Received: 24 October 2022

- Accepted: 23 September 2024

- Published: 23 October 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08098-1

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Long-term lineage commitment in haematopoietic stem cell gene therapy

Andrea Calabria,

Giulio Spinozzi,

Daniela Cesana,

Elena Buscaroli,

Fabrizio Benedicenti,

Giulia Pais,

Francesco Gazzo,

Serena Scala,

Maria Rosa Lidonnici,

Samantha Scaramuzza,

Alessandra Albertini,

Simona Esposito,

Francesca Tucci,

Daniele Canarutto,

Maryam Omrani,

Fabiola De Mattia,

Francesca Dionisio,

Stefania Giannelli,

Sarah Marktel,

Francesca Fumagalli,

Valeria Calbi,

Sabina Cenciarelli,

Francesca Ferrua,

Bernhard Gentner,

Giulio Caravagna,

Fabio Ciceri,

Luigi Naldini,

Giuliana Ferrari,

Alessandro Aiuti &

…

Eugenio Montini

Nature volume 636, pages 162–171 (2024)

Abstract

Haematopoietic stem cell (HSC) gene therapy (GT) may provide lifelong reconstitution of the haematopoietic system with gene-corrected cells[1]. However, the effects of underlying genetic diseases, replication stress and ageing on haematopoietic reconstitution and lineage specification remain unclear. In this study, we analysed haematopoietic reconstitution in 53 patients treated with lentiviral-HSC-GT for diverse conditions such as metachromatic leukodystrophy2,3 (MLD), Wiskott–Aldrich syndrome4,5 (WAS) and β-thalassaemia[6] (β-Thal) over a follow-up period of up to 8 years, using vector integration sites as markers of clonal identity. We found that long-term haematopoietic reconstitution was supported by 770 to 35,000 active HSCs. Whereas 50% of transplanted clones demonstrated multi-lineage potential across all conditions, the remaining clones showed a disease-specific preferential lineage output and long-term commitment: myeloid for MLD, lymphoid for WAS and erythroid for β-Thal, particularly in adult patients. Our results indicate that HSC clonogenic activity, lineage output, long-term lineage commitment and rates of somatic mutations are influenced by the underlying disease, patient age at the time of therapy, the extent of genetic defect correction and the haematopoietic stress imposed by the inherited disease. This suggests that HSCs adapt to the pathological condition during haematopoietic reconstitution.

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Main

In haematopoietic stem cell (HSC) gene therapy (GT) (HSC-GT) integrating viral vectors are used to deliver therapeutic genetic material into haematopoietic stem and progenitor cells (HSPCs) from patients affected by inherited disorders and restore the defective function. Autologous gene-corrected HSPCs engraft, self-renew and give rise to all cells of the blood and the immune system providing long-term therapeutic benefits to patients. HSC-GT is an effective treatment option for several monogenic diseases including blood disorders, such as primary immune deficiencies and hemoglobinopathies, in which the administered HSPCs restore the functionality of the defective lineages, and non-haematopoietic diseases such as lysosomal storage disorders, in which the transduced HSPC progeny such as monocytes and macrophages perform the clearance of the stored material and deliver the therapeutic enzyme to cross-correct other haematopoietic and non-haematopoietic cells in tissues1,7,8. To assess the safety and effectiveness of the treatment and to shed light on the biology of haematopoiesis in various disease conditions, it is important to understand how haematopoietic reconstitution occurs in terms of lineage output and commitment, kinetics, clonal composition and succession.

In patients receiving GT, the process of haematopoietic reconstitution has been analysed by tracking viral vector integration sites (ISs) retrieved from whole peripheral blood (PB) and bone marrow (BM) and purified cell lineages gathered at sequential time points after reinfusion2,4,6,9,10,11,12. Indeed, because vector integrations occur semi-randomly in the host cells, each IS represents a distinct marker of clonal identity that is stably maintained over time and inherited by all its progeny. High-throughput sequencing and mapping of the junctions between the integrated vector and the host cellular genome13,14 allowed retrieval and identification of hundreds of thousands of ISs that are used to monitor the clonal composition during the haematopoietic reconstitution, the multi-lineage potential of HSPC subsets and the hierarchical relationships among haematopoietic lineages.

Previous clonal tracking studies on patients receiving HSC-GT affected by primary immune deficiencies, including adenosine-deaminase severe combined immune-deficiency (SCID), X-linked SCID type 1 (XSCID) and Wiskott–Aldrich syndrome (WAS), in which B and T cells are profoundly impaired, have clearly shown a selective advantage of genetically modified lymphoid cells after transplantation indicating that the disease background influences the proportion and the type of lineage-committed cells over long periods of time4,10,11,15,16,17,18,19. On the other hand, in other diseases such as chronic granulomatous disease, a progressive phagocytic disorder or leukodystrophies and lysosomal storage disorders2,3,9,20,21,22 no selective advantage in growth or survival was observed in any cell lineage. Comparative analyses of HSPC lineage commitment were performed in a few studies with a limited number of patients, mainly in primary immune deficiencies such as WAS and hemoglobinopathies (β-thalassaemia (β-Thal) and sickle cell disease)15,16.

It is also relevant to understand how the replicative stress imposed by haematopoietic reconstitution would affect HSPCs’ multi-lineage potential, dynamics of lineage commitment and, ultimately, long-term safety, and how this may vary in different disease conditions. Indeed, patients with sickle cell disease have an increased risk of developing haematologic malignancies, and an increased frequency of haematopoietic clones with driver mutations associated with myeloid cancer or clonal haematopoiesis, as observed in untreated and treated patients receiving GT23,24,25,26.

A comprehensive comparison of the haematopoietic reconstitution in different disease backgrounds with large cohorts of patients, including both haematopoietic and non-haematopoietic disorders and with or without selective advantage and different amounts of haematopoietic stress, is still missing.

Here we present a comparative analysis of the haematopoietic reconstitution at the clonal level in 53 patients enrolled in three distinct lentiviral vector-based HSC-GT for metachromatic leukodystrophy (MLD, a neurodegenerative lysosomal storage disorder), WAS and β-Thal carried out by our institution2,3,4,5,6, and, to further extend and validate some findings, of ten patients with XSCID treated with lentiviral vector-based HSC-GT in another institution and previously published[19].

Our results uncover that the long-term output, lineage commitment of HSPCs, and accumulation of somatic mutations, are strongly modulated by the patients’ genetic backgrounds, conceivably to better compensate for the demands posed by the specific clinical condition.

Clonal dynamics of vector-marked cells

We studied the clonal reconstitution and multi-lineage potential over time (up to 8 years of follow-up) in 53 patients receiving HSC-GT affected by three different diseases: 29 with MLD2,3,22, 15 with WAS4,5,15,17 and 9 with β-Thal[6] (Supplementary Table 1). The lentiviral vector constructs used in these clinical programmes have the same backbone but different promoters and transgenes: MLD vector contains the ubiquitous human phosphoglycerate kinase promoter driving the expression of the human arylsulfatase A complementary DNA (cDNA)[2]; WAS vector contains a portion of the human WAS gene promoter driving the expression of WASP cDNA[4] and β-Thal vector contains in addition to the human β-globin promoter two hypersensitive sites from the β-globin locus control region driving the expression of β-globin gene. In each of the three clinical programmes, different conditioning regimens have been adopted (Methods). Most of the patients analysed in this study were paediatric (age range from 6 months to 15 years) at the time of treatment, except for three adult patients with β-Thal(age range 31–35 years).

Clonal tracking was performed on total BM and PB cells, as well as on purified myeloid cells (CD13+, CD14+, CD15+), B cells (CD19+), T cells (CD3+, CD4+, CD8+), erythroid cells and precursor (GpA+ for MLD and WAS and GpA+, CD36+ for β-Thal) and CD34+ progenitor cells (Fig. 1a, Methods and Supplementary Table 2), with purity levels in line with the previous reports. Samples were collected at 1, 3, 6, 9 and 12 months posttreatment during the first year, and once a year thereafter. By this strategy, we were able to analyse more than 6,700 samples overall, processed by PCR methods13,27 and sequenced with Illumina paired-end reads (Methods). After all the quality controls, we obtained 1,516,818 unique ISs for MLD, 1,647,351 ISs for WAS and 1,180,274 ISs for β-Thal, totalling more than 4.3 million univocally mapped ISs. ISAnalytics[28] was used also to streamline clonal analyses such as clonal abundance, clonal population diversity, population size estimate of active stem cells, common insertion site identification and the analyses of shared ISs across lineages and time points (Methods).

Fig. 1: HSPC clonal complexity, size and source time point of long-lasting clones.

a, Clonal tracking through ISs begins with a patient’s autologous transplantation of vector-marked cells. Periodic sampling and DNA processing isolate markers for distinct lineages (myeloid, erythroid, B and T cells). ISs are retrieved by means of custom PCRs and deep sequencing, allowing clonal population diversity, abundance, lineage tracking and vector integration frequency to be assessed for treatment safety and efficacy. b, The cumulative number of ISs retrieved for each patient by disease (MLD, WAS and β-Thal) increases over time after gene therapy, with a log-based regression curve showing the progression (confidence interval (CI) 0.75). c, Clonal population diversity index (Shannon index, y axis) by clinical trial, tissue (BM and PB) and lineage (different colours), over time (months, x axis); spline CI 0.75. d, The analytical process of HSPC estimate, starting from ISs derived by short-lived myeloid cells in PB, filtering ISs by low sequencing reads (removing ISs with n < 3), and estimating HSPCs over time by triplets of consecutive time points using the Chao1 model. e, Results of estimated HSPCs (y axis) over time (x axis) for each clinical trial, normalized by in vivo VCN for samples with VCN > 1. f, Percentage of the estimated active HSPCs on the total number of infused CD34+ BM cells, stratified by clinical trial; statistical results obtained with Fisher’s exact test. g, Long-term clones are identified by tracking ISs back to their first observed time points, categorized into early (1–6 months), mid (12–18 months) and steady (24–30 months) phases. Statistical comparisons were made using the Kruskal–Wallis test corrected by FDR. h, Percentage of long-lasting clones, for each lineage (in colours) and clinical study, backtracked by the first observed time point grouped by the haematopoietic reconstitution phase (early, mid and steady). RBC, red blood cell; MK, megakaryocyte; NK, natural killer cell; DCs, dendritic cells; Mφ, macrophage. Graphics in a were created using BioRender (https://biorender.com).

Source Data

The analysis of ISs, which serve as a marker for clonal identity, showed that more than 75% of ISs were captured within the first 12 months posttreatment in most patients, with fewer new ISs appearing over time (Fig. 1b). This indicates that most active clones contributing to haematopoiesis were identified early and remained stable over nearly a decade. ISs tended to integrate into gene-dense regions (Extended Data Fig. 1a,b), forming hotspots and gene ontology (GO) analysis confirmed a preference for genes involved in chromatin and histone modification, as already observed in previous studies2,4 (Extended Data Fig. 1c), with comparable targeting specific gene classes among the three clinical trials (correlation greater than 0.96, Extended Data Fig. 1d), and well-known hotspots of lentiviral vector integration (targeting genes such as KDM2A, PACS1, HLA, TNRC6C, SETD2; Extended Data Fig. 1e) without significant differences in gene targeting frequencies (Supplementary Table 3) across studies. Clonal abundance analysis (Methods) did not find any persisting dominant clone (Extended Data Fig. 1f). Moreover, specific ISs were identified at several time points and across different lineages, highlighting the persistence of long-term repopulating clones and multi-lineage marking.

The diversity of clones in each patient, measured by the Shannon diversity index (H) and corrected by a Bayesian multivariate linear regression algorithm to model remove biases induced by different technical confounding factors as well as their interactions (Methods), showed that all patients had a polyclonal repertoire (Fig. 1c), with some lineages showing higher complexity depending on the underlying disease. Specifically, to compare the diversity index of the lineages against all the other lineages within the same clinical programme and the same lineage across the different clinical programmes, we first selected the interval of 24–60 months after therapy (at stability) and normalized the H index of each lineage by Z-score (Extended Data Fig. 2a), and then compared by the Kruskal–Wallis non-parametric test (Extended Data Fig. 2b,c). Patients with MLD had more complex myeloid lineages, patients with WAS had more complex B and T lymphocyte lineages and patients with β-Thal had more complex erythroid lineages. These differences suggest that the underlying disease biology significantly affects haematopoietic reconstitution.

HSPC population size

We compared the estimated number of active HSPCs and the contribution of CD34+ cells towards myeloid, B, T and erythroid precursor cells over time in the three disease conditions. Using the Chao1 model[29] and short-living cells as surrogates of stemness (Methods), active HSPC populations were estimated during early haematopoietic reconstitution and found to decrease significantly after 24 months posttreatment (Fig. 1d). Active HSPCs were on average in patients with MLD 8,984 (range 3,583–19,498), in WAS 55,495 (range 5,378–363,649) and in β-Thal 54,297 (range 6,470–196,773) (Fig. 1e). After 24 months from treatment, the number of active HSPCs decreased significantly to an average of 6,497 (range 1,865–16,302, roughly 1.3-fold) in MLD, 6,151 (range 1,882–23,774, roughly ninefold) in WAS and 10,446 (range 3,475–18,480, roughly 5.2-fold) in β-Thal (Extended Data Fig. 3a). The decrease was more rapid in patients with β-Thal due to faster recovery from mobilized CD34+ cells (cut off to 12 and 24 months, Extended Data Fig. 3a). These results are compatible with published models, showing early phases of haematopoietic reconstitution sustained by many progenitors and short-term HSCs that were exhausted by 24 months and progressively replaced by a smaller, yet substantial number of long-term HSCs that stably sustained steady-state haematopoiesis2,4,15,16. The fraction of engrafted transduced HSCs actively contributing to the haematopoiesis at steady state, estimated as the recaptured population size divided by the total CD34+ cell number infused, was similar across the three trials, regardless as to whether HSPCs were collected from the BM or mobilized PB (MPB): MLD 0.007% (range 0.001–0.027%), WAS 0.003% (range 0.001–0.005%) and β-Thal 0.003% (range 0.001–0.004%) (Fig. 1f and Supplementary Table 4). These percentages corresponded to 15–270 active long-term HSCs per million infused CD34+ cells in MLD, 10–48 in WAS and 9–44 in β-Thal, and from two- to tenfold more short-term engrafting progenitors.

We then investigated when long-term clones originated during the haematopoietic reconstitution. To this aim, we selected the ISs retrieved from 36 months after infusion until the latest time point (long-term ISs, long-term clones) and tracked each long-term clone backward to the time point when it appeared for the first time, and we determined the percentage of long-term clones collected within the following three time intervals: from 1 to 6 months (‘early’ phase of haematopoietic reconstitution), from 9 to 18 months (‘mid’) and from 24 to 30 months (‘steady’) (Fig. 1g). We found that the long-term clones were poorly retrieved (between 0.5 and 4.5%) during the early time points but increased during the mid and steady phases between 1.3- and 18-fold for the early phase for all lineages and disease conditions (Fig. 1h and Extended Data Fig. 3b,c). We calculated the percentage of ISs detected at long-term (more than 24 months after therapy) for the three patients that showed the greatest number of ISs. In the patient with WAS with roughly 450,000 ISs (Pt17) the proportion of ISs detected long term was 4.22%, whereas in the two patients with β-Thal with roughly 432,000 and 220,000 ISs (respectively, Pt36 and Pt41), the proportions were roughly 3.5 and 1.74%, respectively. These data further confirmed that the early phase of the haematopoietic reconstitution is sustained by short-lived progenitors and probably, at least in part, by HSPCs clones that have undergone exhaustion.

Given the observed inter-patient variability in clonality and HSPC size among patients and clinical programmes, we investigated whether the number of estimated active HSPCs and the clonal complexity (diversity index) calculated in patients at steady state would correlate with treatment variables such as vector copy number (VCN) (average VCN in PB-derived myeloid cells), transduction efficiency in vitro in the clonogenic outgrowth of the infused cell product, age at treatment and the dose of infused CD34+ cells per kg (Extended Data Fig. 4a). Our results showed that the patient’s age at treatment had a negative correlation with transduction efficiency (Pearson correlation −0.51 with P = 0.01, meaning that younger patients had a higher transduction efficiency) and a positive correlation with active HSPC size (Pearson correlation 0.53, P = 0.006). Active HSPC size also negatively correlated with transduction efficiency (Pearson correlation −0.56, P = 0.003), but positively correlated with cell dose (Pearson correlation 0.45, P = 0.046). The Shannon population diversity index correlated with VCN in vivo (Pearson correlation 0.53, P = 0.006) and with cell dose (Pearson correlation 0.46, P = 0.035). Multivariate analyses with principal component analysis (PCA) confirmed pair-wise correlations and combined the variables by reducing the results in two main dimensions (with a percentage of explained data greater than 74%). Moreover, clustering results showed that patients treated with MPB CD34+ cells were well separated from all the remaining patients transplanted with BM CD34+ cells or a mixed BM-MPB dose (Extended Data Fig. 4b).

Lineage output of CD34+ cells

We then analysed the HSPC contribution to each lineage over time in the different trials. To this end, we calculated the percentage of ISs retrieved from CD34+ cells shared with lineage-specific cells (sharing ratio,  Methods) from PB and BM (Fig. 2a). We selected ISs represented by three or more sequencing reads and excluded those with fewer than 10% of the sequence reads to eliminate confounding effects. To address dataset size differences and prevent biases, we used the Good–Turing model and Bayesian multivariate linear regression (Methods) (Fig. 2b).

Fig. 2: Lineage output of CD34+ cells.

a, Strategy for the analysis of CD34+ cells and lineage output: for each time point, we computed the proportion of the shared ISs retrieved for each lineage (among myeloid, erythroid, B and T cells) and CD34+ ISs (sharing ratio). b, Dynamics of the sharing ratio (y axis) as lineage output (with different colours) of CD34+ cells over time (x axis) for the three clinical trials. Spline curves with CI 0.75. c, Box plots of sharing ratio (bar indicates median, whisker 25–75 percentiles), normalized by Z-score (y axis) isolated in all patients from 24 months and averaged, in BM and PB tissues (circle or triangular dot shapes), grouping cell markers (colours) by cell lineages. Statistical tests are performed between pairs of clinical studies (Kruskal–Wallis test). The bars represent the median, the whiskers extend to 1.5 times the interquartile range (IQR) and the P value threshold is set at 0.05. d, Similar to b, CD34+ BM lineage output over time stratified by age groups (0–2, 2–15 and older than 30 years old).

Source Data

In patients with MLD, the ISs shared between CD34+ cells and myeloid lineages increased from 10 to 12% initially to 20% at 24 months, stabilizing thereafter (Fig. 2b). B, T and erythroid lineage sharing was initially minimal but increased to around 10% for B and T cells and 5% for erythroid cells after 24 months. In patients with WAS, T cells had the highest contribution from CD34+ cells, reaching 20% within 30 months, whereas B cells reached roughly 12.5% at 18 months and slowly increased to roughly 20% at 60 months. Myeloid and erythroid lineages were slower, reaching roughly 14 and 8%, respectively. In patients with β-Thal, sharing between CD34+ cells and myeloid, B and T lineages plateaued at 12 months at around 4, 3 and 2%, respectively, but the contribution to erythroid lineage increased to 40% at the latest time points.

We compared the contribution of CD34+ cells to each lineage in different disease conditions using Z-score transformation (Extended Data Fig. 5a) and the non-parametric Kruskal–Wallis test (Methods). Patients with MLD showed higher sharing with myeloid cells and lower with lymphoid cells compared to patients with WAS, who had higher T cell lineage sharing (in line with the selective advantage of gene-corrected T cell precursors). In patients with β-Thal, the erythroid lineage showed the highest sharing (Fig. 2c).

We stratified the patients by treatment age into three groups: from 0 to 2 years, represented by 18 patients with MLD and five patients with WAS, from more than 2 to 15 years, represented by 11 MLD, ten WAS and six patients with β-Thal, and adults with more than 30 years of age represented by only three patients with β-Thal, and performed the analyses of the output of CD34+ cells into mature lineages (Fig. 2d). In the adult patients with β-Thal (above 30 years), the lineage sharing of CD34+ cells with the erythroid lineage were roughly 5% until 24 months and then progressively increased over time and reached up to roughly 40% at 60 months after therapy. The erythroid output in the six paediatric (4–13 years) patients with β-Thal was 5% until 12 months, increasing progressively to roughly 15% at 36 months. The output towards B cell and especially the T cell lineages was delayed in adult and paediatric patients with β-Thal compared to patients with WAS. The contribution towards B and T cell linages was superior in the paediatric patients with β-Thal (15%) compared to adults (5% for B cells and roughly 2% for T cells), as expected by the higher number of BM B-lymphoid committed progenitors in children versus adults[30] and the specific myeloablative conditioning that results in a delayed T cell reconstitution similar to that observed in patients with MLD.

The contribution of CD34+ cells to mature lineages across different diseases and age groups was analysed using Z-score transformation and the Kruskal–Wallis test (Methods and Extended Data Fig. 5b). In patients with MLD aged 0–2 years, CD34+ cells showed significantly higher sharing with myeloid cells, with T cells having the lowest sharing. Similar patterns were observed in the 2–15 years age group, except T cell sharing was lower than B cells. Patients with WAS aged 0–2 and 2–15 years showed predominant T cell sharing, with B cell sharing being lower, reflecting a less pronounced selective advantage5,31. Both paediatric and adult patients with β-Thal showed higher sharing with erythroid cells than myeloid, T and B cells (Fig. 2e). The only significant age-related difference was in patients with β-Thal over the age of 30, who had higher erythroid lineage sharing compared to younger patients (Fig. 2f).

To confirm that the increased lineage output for the lymphoid B and T cell lineages in patients with WAS was not specific to the disease but a general characteristic of the lymphoid impairment observed also in other immunodeficiencies, we analysed previously published[19] IS datasets of ten patients receiving XSCID treated with lentiviral vector-HSC-GT. The output of CD34+ cells towards the T and B cell lineages was initially low (less than 2%) but increased progressively up to 25% at 60 months, the last time point of the analysis (Extended Data Fig. 5c). Therefore, our data show that the preferential output towards T and B cells is common between these two lymphoid immunodeficiencies. B cell output was similar to or faster than T cell output in patients receiving XSCID, contrary to expectations.

Longitudinal analysis of HSPC commitment

We then further investigated whether the disease condition could also affect the multi-lineage potential of HSPC by promoting long-term commitment to a specific lineage. We defined clones with multi-lineage potential when the ISs, represented by 3 or more sequencing reads, were shared by at least two mature lineages (among myeloid, B, T or erythroid cells) at any time point. We defined lineage-committed clones when their ISs were retrieved consistently in a single lineage at least during two distinct time points, whereas clones observed in one time point only were defined as singletons (Fig. 3a). We addressed biases arising from confounding variables with a Bayesian multivariate linear regression model and biases from the comparison of datasets with varying sizes using the Good–Turing model (Methods); then we calculated the contribution of each multi- and uni-lineage-committed class over time (Fig. 3b). We compared multi-lineage and lineage-committed clones across different diseases using Z-score scaling and the Kruskal–Wallis test (Methods and Extended Data Fig. 6a). Multi-lineage clones in BM and PB reached up to 75% at early time points (less than 12 months) and stabilized at 50–60% in all clinical programmes, with MLD showing fewer multi-lineage clones compared to patients with WAS and β-Thal. Myeloid-committed clones were higher in MLD than in WAS but similar to β-Thal. T-committed clones were higher in WAS, whereas erythroid-committed clones were highest in β-Thal. Overall, our results showed that the disease background affected haematopoietic reconstitution, determining a preferential and specific lineage commitment of HSPC clones (Fig. 3c). Multi-lineage clones amounted to roughly 90–75% at the earliest time points and decreased progressively to 50–60% in all clinical programmes. But age affected lineage commitment, with older patients with MLD and β-Thal showing fewer multi-lineage clones (Extended Data Fig. 6b–d). In patients with MLD, myeloid-committed clones increased over time, particularly in the 2–15 years age group. In patients over the age of 30 with β-Thal, myeloid commitment was higher but did not increase over time as in MLD. Erythroid commitment was low in older patients with β-Thal, peaking at 12.5% before decreasing. By contrast, erythroid clones remained below 1% in patients with MLD and WAS across all age groups.

Fig. 3: HSC lineage commitment.

a, The workflow for analysing HSC commitment involved computing IS sharing among myeloid, erythroid, B and T cell markers at each time point. Shared ISs were categorized as multi-lineage (if found in several lineages, ‘Multi’) or uni-lineage (if found in one lineage, ‘Uni-myeloid’ for uni-lineage myeloid and ‘Uni-LyT’ or ‘Uni-LyB’ for uni-lineage lymphoid B or T cell, respectively). We then calculated the percentage of each lineage and analysed the profiles over time. b, HSC lineage commitment was tracked for different diseases and tissues, showing the relative percentage of shared ISs over time for multi-lineage and mature uni-lineage clones, using spline regression with a 0.75 CI. c,The box plot compares normalized HSC lineage commitment (Z-score) across clinical trials, tissues and lineages, with statistical significance indicated by Kruskal–Wallis test P values. The bars represent the median, the whiskers extend to 1.5 times the IQR, and the P value threshold is set at 0.05. d–g, Box plots represent lineage commitment (myeloid (d), T cell (e), B cell (f), erythroid (g)) during early and late phases of haematopoietic reconstitution in BM and PB, focusing on multi-lineage clones transitioning to uni-lineage and those remaining committed. Statistical comparisons used one-way analysis of variance (ANOVA). The central line represents the median, and the whiskers indicate the range, showing the minimum and maximum values. h, Box plots for multi-lineage clones remaining multi-lineage in BM and PB, with statistical analysis as above. The bar represents the median and the whiskers the range.

Source Data

To analyse lineage commitment dynamics over time, clones were classified as transitioning from multi- to uni-lineage or remaining consistently committed between early and late phases of haematopoietic reconstitution (less than 24 months and more than 24 months, respectively). This single clone level analysis allowed us to compare if and how the different disease conditions affected the rate of lineage commitment over time as well as the relative contribution of long-lived clones already committed since the early phases of haematopoietic reconstitution. Patients with MLD showed higher myeloid commitment (10–12%) compared to patients with WAS and β-Thal (less than 5%) (Fig. 3d). Persistently myeloid-committed clones (uni-myeloid) were more prevalent in patients with MLD and β-Thal (20–30%) than in patients with WAS (less than 5%). T cell commitment was higher in WAS (Fig. 3e,f), whereas erythroid commitment was notably higher in patients with β-Thal (Fig. 3g). In agreement with our previous findings, multi-lineage clones remained abundant across all programmes, exceeding 50% (Fig. 3h). Stratifying by age, older patients with MLD and β-Thal had more uni-myeloid clones, whereas younger patients with MLD had more T cell commitment. In patients with β-Thal over the age of 30, persisting multi-lineage clones decreased significantly compared to younger cohorts (Extended Data Fig. 7a–e). Age did not significantly affect other lineages within the studied age ranges.

A recent study in nonhuman primates suggested that clonal abundance might bias vector integration studies[32], encompassing lineage output and commitment. To investigate this, we compared clonal abundances between uni-lineage (erythroid, B, T, myeloid) and multi-lineage clones during early and late phases (Extended Data Fig. 8a). The abundance distributions for multi-lineage, uni-lineage and transitioning clones were similar, with no significant differences found between early and late datasets (Extended Data Fig. 8b,c). A bootstrap approach added a confidence interval to each IS generated through using incremental percentage of reads’ sampling (50, 70, 80, 90%) and ten randomizations (Methods) and further confirmed the robustness of our findings, showing high accuracy (more than 0.9 from 80% subsampling) across lineage classes (Extended Data Fig. 9). These results indicate that clonal abundance did not significantly affect lineage output or commitment in our dataset.

Clones captured at only one time point (singletons), specifically in the early phase of haematopoietic reconstitution (less than 9 months), were used as surrogates of progenitors and low-abundant HSPCs. Analysis of these early-phase singletons revealed that myeloid cells were dominant across all clinical studies, with patients with β-Thal also showing erythroid lineage singletons, suggesting erythroid precursor expansion (Extended Data Fig. 10a). This indicates early haematopoietic reconstitution is driven by myeloid-committed clones regardless of the disease.

Lineage commitment analysis, performed for the ten patients receiving XSCID described above, showed that the T cell committed clones were already 25% at the early time points and increased up to 50% at 60 months (Extended Data Fig. 10b). Multi-lineage clones initially rose but later declined, whereas B cell clones started high (more than 50%) but decreased to roughly 25%. Myeloid commitment was roughly 6%, similar to patients with WAS, indicating a strong uni-lineage T cell commitment in XSCID, with a lesser extent for B cells.

TPO and EPO in patients with WAS and β-Thal

Given that haematopoietic cytokines influence HSPC lineage fate and are regulated by disease states, we examined whether elevated thrombopoietin (TPO) concentrations in patients with WAS indicated haematopoietic recovery by promoting lymphoid and platelet maturation, and if elevated erythropoietin (EPO) concentrations in patients with β-Thal—the classic hallmark of anaemic patients with β-thalassaemia and patients receiving GT33,34,35—correlate with increased erythroid output.

In patients with WAS the TPO concentrations before transplant were all near or within the normal TPO concentrations ranging around 99 pg ml−1. Elevated TPO concentrations were noted at 1 year post-GT, especially in those treated at 0–2 years, but decreased to near-normal concentrations over time. The highest TPO concentrations were significantly higher in younger patients (0–2 years) compared to others (Fig. 4a).

Fig. 4: TPO and EPO concentrations in patients with WAS and β-Thal, and somatic mutations in patients with MLD and β-Thal.

a, TPO concentration s in patients with WAS before GT (Pre-GT), at 1 year follow-up (1 yr FU) and 3–4 years post-GT (3–4 yr FU), stratified by age at treatment: 0–2 years (0–2 yr) and 2–15 years (2–15 yr). b, EPO concentrations in patients with β-Thal at 1 year (1 yr FU), 2 year (2 yr FU) and 3 year (3 yr FU) follow-ups, stratified by age at treatment: 2–15 years (2–15 yr) and more than 30 years (>30 yr). One-way ANOVA. c,d, Somatic mutations in patients with β-Thal (n = 9) (c) and MLD (n = 16) (d) over time (T0, before infusion; TP1, 2 years posttransplant; TP2, max 5–7 years posttransplant). No significant differences by Friedman’s test. e, Comparison of somatic mutation frequencies between patients with β-Thal and MLD by age group. One-way ANOVA. f, Percentage of VAF over time for somatic mutations found in several time points in patients with MLD and β-Thal, with mutated genes and clinical programmes indicated. In all box plots, the central line represents the median and the whiskers indicate the range, showing the minimum and maximum values.

Source Data

In patients with β-Thal, EPO concentrations were significantly elevated 1 year post-GT, particularly in adults (older than 30 years) where concentrations were 17-fold higher than normal (reported at 24 mU ml−1). Although EPO concentrations slightly decreased over the following years, they remained elevated, with adult patients showing 3.5-fold higher concentrations than younger patients (Fig. 4b).

Somatic mutation in MLD and β-Thal

We conducted a comprehensive analysis of somatic mutations in 40 genes linked to clonal haematopoiesis and myeloid cancer using the Illumina AmpliSeq Myeloid Panel. We examined peripheral blood mononuclear cells before treatment and at roughly 2 years posttreatment and the last available time point (2.5 to 7.5 years) in nine patients with β-Thal and 23 with MLD. From 20 ng of genomic DNA per time point, we obtained more than 100 million high-quality sequence reads, with an average depth of 4,400 reads per base for β-Thal and 4,300 for patients with MLD (Methods and Supplementary Table 5). Variant analysis, with custom filters to remove false positives, revealed 96 somatic mutations (85 with a variant allele frequency (VAF) less than 2%). The most abundant mutation discovered in a patient with MLD, involving the p53 gene with an average VAF of 15%, was annotated as benign (Supplementary Table 6).

In patients with β-Thal, we found 68 somatic mutations (67 single nucleotide variants and one single nucleotide deletion), from 2 to 24 mutations per patient. The average number of mutations in patients with β-Thal remained consistent across all time points, showing no statistically significant variations (P > 0.9 by Friedman test) (Fig. 4c). Considering that the sequenced genomic interval corresponds to 76,715 bp and that we analysed a total of 8,100 equivalent genomes per patient, the resulting mutation rate in patients with β-Thal was 1.21 × 10−8 mutations per bp. In patients with MLD, we identified 26 somatic mutations (25 single nucleotide variants and 1 deletion), from one to three mutations per patient resulting in a mutation rate of 2.6 × 10−9 mutations per bp. The average number of mutations in patients with MLD was similar at all time points without any statistically significant variations (P > 0.9 by Friedman test) (Fig. 4d). Adult patients with β-Thal had a significantly higher mutation rate than paediatric patients, and both groups had higher rates than patients with MLD (P < 0.05) (Fig. 4e). Five out of the 96 mutations (four in β-Thal and one in MLD) were detected at several time points, none showing a progressive increase in abundance, indicating no selective advantage (Fig. 4f).

Discussion

In this work, we carried out a detailed analysis of lentiviral vector integrations in terminally differentiated myeloid, lymphoid and erythroid cell lineages, as well as CD34+ HSPCs purified from BM or collected in MPB of patients subjected to HSC-GT for MLD, WAS and β-Thal. Thanks to the large number of patients and clones tracked over extended periods of time (up to 8 years from therapy), our work constitutes a large and highly detailed dataset addressing the safety and graft dynamics and, consequently, the overall safety outlook of lentiviral vector-HSC-GT. Exploiting ISs as unique clonal markers we confirm and provide further evidence of long-term efficacy and safety of GT in patients with MLD, WAS and β-Thal and provide biological insights into haematopoietic reconstitution and lineage commitment in the different disease contexts, further modulated by the specific conditioning protocols and the age of the patients at the time of treatment (see Supplementary Discussion for IS sensitivity).

Studies by us[15] and others36,37,38 have highlighted different cell division rates and latency characteristics of distinct HSPC subsets, explaining the plateau we observed in most patients with the switch from short- to long-term HSCs during the 12 to 24 months’ time window. One patient with WAS (Pt17) and two with β-Thal (Pt36 and Pt41) showed continuous retrieval of new ISs beyond 24 months and until the latest follow-up, reaching up to 450,000 ISs. Of note, these increases in new ISs may not raise per se safety concerns, contrary to the increasing abundance of a single IS. The biological reasons behind these exceptions remain to be explained but suggest a particularly high number of genetically engineered HSCs that were engrafted and continue to be recruited for blood cell production. Potential explanations include higher baseline HSC frequency within the transduced CD34+ cells, higher permissiveness of these HSCs to the ex vivo engineering process or more efficient engraftment, for example, by prolonged in vivo persistence after infusion and/or availability of extra niche space in these patients.

In this study, the number of active HSPCs during steady-state haematopoiesis in patients ranged from 1,800 to 74,000, representing roughly 0.0007 to 0.03% of the total infused CD34+ cells. A positive correlation was observed between the number of estimated HSCs and the total infused CD34+ cells[16], with no evidence of saturation even at high doses. Patients with β-Thal, who received the highest CD34+ cell doses, showed significantly higher HSC estimates than patients with MLD and WAS. However, when normalized to the total number of infused CD34+ cells, HSC frequency was similar across the three diseases, regardless of whether HSPCs were collected from BM or MPB. This suggests that higher CD34+ cell doses are beneficial for gene therapy, as they do not saturate stem cell niches and may protect against clonal haematopoiesis and malignancy[39]. This study also highlights the advantages of using MPB-derived[40] CD34+ cells in gene therapy, as they yield more transplantable cells and may allow faster blood cell recovery. Furthermore, the higher number of active HSPCs in patients with β-Thal may be due to more efficient engraftment from intrabone infusion compared to intravenous infusion40,41, although the superiority of this method in long-term engraftment remains debated42,43,44.

The estimated number of HSPCs and their frequency in the infused product align with previous findings in patients with MLD, WAS and β-Thal, and patients receiving sickle cell anaemia GT2,15,16. However, studies of native haematopoiesis in healthy donors45,46,47 suggest higher HSPC numbers, ranging from 20,000 to 200,000: two to ten times more than in patients receiving HSC-GT. The lower HSPC estimates in patients receiving HSC-GT may be due to underestimation in young patients, competition for engraftment in damaged niches or the detrimental effects of ex vivo culture and lentiviral vector transduction on stemness. Furthermore, the number of persisting clones after haematopoietic reconstitution exceeded initial HSPC estimates, suggesting that capture–recapture methods may underestimate true HSC numbers.

We found that for all the diseases analysed, the output of HSPCs with multi-lineage potential represented only 50% of the clones, whereas the remaining 50% was constituted by clones committed towards a specific lineage and with specific differences across the clinical programmes. In patients with MLD, WAS and β-Thal, the haematopoiesis showed a preferential output and lineage commitment towards myeloid, lymphoid and erythroid lineages respectively, even after years from treatment. It is important to note that we classify HSPC clones as multi-lineage if they are detected in at least two mature lineages, rather than in all four lineages analysed. Our choice is driven by the high polyclonality of our patients, which reduces of recapture in all four linages, whereas barcoding tracking studies in animal models showed higher sensitivity[48].

These persistent biases might be influenced by the disease type, the age at which treatment occurs and incomplete correction of the disease posttherapy. In patients with MLD, a significant number of long-lived myeloid clones were present early after transplant, in agreement with previous reports on mouse models49,50, with older patients (aged 2–15 years) showing higher amounts of these myeloid cells compared to younger patients (0–2 years). This difference may be attributed to the accumulation of disease burden with age, causing chronic inflammation that primes HSPCs towards myeloid differentiation. Despite this, the overall balance between multi- and uni-lineage clones remains stable over time, indicating a long-term homeostatic equilibrium. By contrast, patients with WAS and those receiving XSCID, treated with reduced-intensity conditioning, showed rapid lymphoid repopulation, particularly in T cells, due to the selective advantage of gene-corrected cells and the effects of lymphodepleting agents4,15,16. Patients from the β-Thal study showed the most complex scenario in which lineage output and commitment are influenced by factors such as age at treatment and therapeutic outcomes. Adult patients with β-Thal, who remained transfusion dependent, showed a significant increase in erythroid lineage output over time, whereas younger patients (aged 4–13 years) had a lower but stable erythroid output, suggesting a correlation with the residual presence of ineffective erythropoiesis[6]. Despite normal B cell lineage output, patients with β-Thal showed higher amounts of long-lived B cell committed clones, in agreement with other reports on animal models[51], indicating that the disease affects both erythroid and B cell lineages. Furthermore, older patients with β-Thal demonstrated increased uni-myeloid commitment and reduced multi-lineage contributions compared to younger patients, suggesting that age at treatment or therapeutic outcomes play a role in lineage commitment. The stable yet altered HSPC output and commitment observed in both patients with WAS and β-Thal over time indicates that the BM microenvironment and specific cytokines may drive long-term commitment to the most needed lineages, reflecting incomplete normalization of haematopoiesis posttherapy52,53,54,55,56,57. In agreement with these hypotheses, in patients with WAS, TPO concentrations were initially high after GT but decreased over time, suggesting partial therapeutic success. In patients with β-Thal, EPO concentrations varied, being normal in some transfusion-independent paediatric patients but abnormally high in others, particularly in adults and transfusion-dependent individuals. This indicates a partial correlation between EPO concentrations and therapeutic outcomes, with other factors probably influencing HSPC behaviour in β-Thal58,59,60. Environmental signals, such as mitogens and cytokines, together with intrinsic or epigenetic factors, may lead to a biased but not exclusive production of deficient mature lineages. The increased abundance of these lineages suggests higher detection rates, whereas contributions to other lineages might fall below the detection threshold. Future experiments aimed at analysing the expression signatures and epigenetic states of HSPCs subsets in bulk and at the single cell level will allow us to better explain the biological mechanisms underlying the biological differences in each disease condition.

No harmful somatic mutations were found from GT in patients with β-Thal and MLD, but patients with β-Thal had significantly more somatic mutations than those with MLD, probably due to haematopoietic stress from oxidative damage related to iron overload, which could result in increased amounts of genomic and mitochondrial DNA damage and telomere erosion, previously reported only in leukocytes, and the altered BM niche25,60,61,62,63,64.

Despite these disease-specific differences, lentiviral vector-based genetic modification of HSPCs and their transplantation were shown to preserve the cells’ ability to respond to disease demands and environmental stressors. An important aspect of this study is that despite the observed differences in these disease conditions, lentiviral vector-based genetic modification of HSPCs and their transplantation do not harm their remarkable capability to respond to the demands imposed by genetic diseases and the ability to respond to infections and environmental damage. Indeed, genetically modified HSPCs with multi-lineage potential once reached stability, contributed to about 50% of the haematopoiesis while the remaining clones were committed. The concept that LT-HSCs are the sole contributors to haematopoiesis has already been challenged[65] and our observations reinforce the hypothesis that at late time points after transplantation haematopoiesis is maintained by many lineage-committed HSPCs able to persist for several years with no evidence of clonal dominance after several years from GT.

Methods

Characteristics of patients in the study

Patients receiving GT who were included in the study were treated in the context of clinical trials or early access programmes approved by ethical committee and competent regulatory authorities. The treatment was administered at the Bone Marrow Transplantation Unit at the San Raffaele Scientific Institute in Milan, Italy. We have complied with all the ethical regulations for retrieving biological materials from patients receiving GT. Parents signed informed consent for research protocols approved by the San Raffaele Scientific Institute’s Ethics Committee (TIGET06 and TIGET09). All patients received autologous HSPC transduced with transgene encoding lentiviral vectors under the same transduction protocol, as previously described2,4,5,6. Patients with MLD received full myeloablative conditioning2,3,22 with busulfan at doses ranging from 10 to 14 mg kg−1. Patients with WAS received a reduced-intensity conditioning regimen consisting of the combined administration of a monoclonal antibody against CD20, busulfan (7.6 to 10.1 mg kg−1) and fludarabine (60 mg m−2)4,5,15,17. In patients with β-Thal, the conditioning consisted in thiotepa (6–8 mg kg) and dose- adjusted treosulfan (14 g m−2) administration[6]. Patients with MLD were enrolled in a phase 1 or 2 clinical trial (NCT01560182) or treated with a hospital exemption programme or compassionate use programme. A total of 29 patients had been treated with the HSPC gene therapy clinical protocol for arylsulfatase A deficiency). Sixteen of the treated patients (Pt16, Pt32, Pt47, Pt02, Pt20, Pt34, Pt31, Pt03, Pt37, Pt33, Pt08, Pt53, Pt23, Pt40, Pt25, Pt28) were affected by late infantile MLD in a presymptomatic stage and have been identified by molecular and biochemical tests in the presence of at least an affected older sibling, whereas 13 patients (Pt38, Pt01, Pt10, Pt43, Pt42, Pt04, Pt30, Pt51, Pt44, Pt18, Pt50, Pt14, Pt07) were affected by early juvenile MLD in a pre- or early-symptomatic stage. Patients were treated with a myeloablative busulfan conditioning regimen administered before reinfusion of the engineered HSPCs3,22. Fourteen male patients with WAS for whom no human leukocyte antigen–identical sibling donor or suitable matched unrelated donor was available underwent lentiviral GT after a reduced conditioning regimen protocol. Patients Pt52, Pt21, Pt48, Pt13, Pt29, Pt11, Pt39 and Pt17 were enrolled in an open-label, non-randomized, phase 1 or 2 clinical study[5] registered with ClinicalTrial.gov (number NCT01515462) and EudraCT (number 2009-017346-32). The other patients with WAS were treated under an early access programme, compassionate use programme or hospital exemption. Among patients with β-Thal, three adults and six children with β0 or severe β+ mutations were enrolled in a phase 1 or 2 trial (NCT02453477) for intrabone administration of GLOBE lentiviral vector–modified HSPCs after myeloablative conditioning with treosulfan–thiotepa[6].

Sample collection

PB and BM samples were obtained from patients before and after gene therapy at different times. From each patient, 12 ml of blood was collected at time points of 30 days, 60 days (only for PB sample), 90 days, 6 months during the first year and once every year and half year thereafter. BM cells were isolated by aspirate of 12 ml and specific cell lineages purified as previously described2,4,6. Briefly, PB and BM mononuclear cells from patients receiving GT were isolated using Ficoll-Hypaque gradient separation (Lymphoprep, Fresenius). Mature PB lineages and BM progenitors were purified using positive selection with immunomagnetic beads according to the manufacturer’s specifications (Miltenyi Biotec) and genomic DNA was extracted with the QIAamp DNA Blood Mini or Micro Kit (Qiagen). CD34+ cells were isolated using the CD34+ MicroBead Kit UltraPure, human (lyophilized), MILTENYI BIOTEC CD15+ were isolated from granulocytes cells using CD15 Micro Beads Human, MILTENYI BIOTEC. CD13+ (CD13 Antibody, Antihuman, Biotin and Antibiotin Micro Beads UltraPure), CD3+ (CD3 Micro Beads Human, MILTENYI BIOTEC), CD56+ (CD56 Micro Beads Human, MILTENYI BIOTEC), GLYA+ (Glycophorin A CD235a Micro Beads, human, MILTENYI BIOTEC S.R.L), CD19+ (CD19 Micro Beads Human, MILTENYI BIOTEC) were isolated in sequence from mononuclear cells after CD34+ purification. PB mononuclear cells were isolated by aspirate of 12 ml and specific cell lineages were purified. From granulocytes, CD15+ were isolated using CD15 Micro Beads Human, MILTENYI BIOTEC). From peripheral mononuclear cells CD14+ (CD14 Micro Beads Human, MILTENYI BIOTEC), CD19+ (CD19 Micro Beads Human, MILTENYI BIOTEC), CD3+ (CD3 Micro Beads Human, MILTENYI BIOTEC) and CD56+ (CD56 Micro Beads Human, MILTENYI BIOTEC) were isolated in sequence.

Genomic DNA was extracted using the QIAamp DNA blood mini kit (Qiagen) or QIAamp DNA Micro Kit (Qiagen), based on a cell pellet. The QIAamp DNA Micro Kit was used with fewer than 1 × 10^6 cells, whereas the QIAamp DNA Mini Kit was used with a dry pellet from 0.5 × 10^6 to 5 × 10^6 cells.

Retrieval of vector ISs

The genomic DNA from the patients’ cells was extracted and split in three technical replicates that were subjected to custom PCR amplification to retrieve vector ISs, initially the linear amplification-mediated (LAM)-PCR[27] and in more recent samples the sonication linker-mediated (SLiM)-PCR[13]. Briefly, the SLiM-PCR procedure consists of the following steps: (1) fragmentation by sonication of the DNA, (2) ligation of the fragments to a linker cassette and (3) two consecutive rounds of PCR, to specifically amplify vector to cellular-genome junctions, by using primers annealing to the vector genome end (long terminal repeats) and the linker cassette. The list of primers and sequences used for SLiM-PCR procedure is reported in Supplementary Tables 7–9. Primers contain DNA barcodes, which allow univocal barcoding of all the SLiM-PCR replicates, and sequencing adaptors that allow multiplexed sequencing on Illumina sequencers. The list of samples with the details on the applied PCR procedure and the number of reads retrieved is reported in Supplementary Table 2.

The resulting PCR products were sequenced using Illumina platforms (Mi-seq, Hi-seq, Next-seq and Nova-seq), for a total amount of 194 sequencing pools for more than 17,000 PCR reactions and more than 14.5 × 10^9 raw reads.

Identification of vector ISs

We used VISPA2 (ref. [66]) to identify ISs from PCR samples sequenced with Illumina paired-end reads. Briefly, for each Illumina sequencing library, paired-end reads are filtered for quality standards, barcodes are identified for sample demultiplexing, vector sequences are trimmed from each read and the remaining cellular genomic sequence is mapped on the reference Human Genome (Human Genome_GRCh37/hg19 February 2019). For the quantification of the number of genomes representing each clone, we adopted an estimation method based on the number of distinct fragments for each IS, SonicLength[67], such that each IS abundance will be proportional to the initial number of contributing cells, allowing to estimate the clonal abundance in the starting sample. The final list of unique ISs is composed of univocally mapped loci annotated with the nearest RefSeq gene.

We then used a new R package, ISAnalytics[28] to integrate the output files of VISPA2 and perform downstream analyses of ISs, from quality controls to the analyses of shared ISs among samples. A detailed report and code of ISAnalytics is available in the GitHub repository (https://github.com/calabrialab/Code_HSPCdynamics). We first removed the same IS present in different independent samples, named collisions, using the same approach previously described[2]. Briefly, given two patients and the list of shared ISs between the two patients, we assign to one patient the IS if it was observed and retrieved for the first time in that patient or if the relative abundance of that read was at least ten times higher than the other patient. The analysis and removal of collisions in independent samples was realized using ISAnalytics. We then performed data quality analysis of sequencing samples by analysing the number of reads of each PCR sample in each sequencing pool, and we removed the samples containing a number of raw reads highly under-represented (threefold less) than the average number of reads of the other samples in the pool. The quality control analyses included (1) the removal of sequencing samples with a number of raw reads threefold less than the average number of reads per sample in the pool, (2) the removal of contaminations identified as identical ISs retrieved in independent samples as previously described[2]. Through these filtering approaches, 99 samples (0.7%) were removed because they had a number of raw reads below the acceptance threshold (less than threefold difference on raw reads average per pool), whereas the amount of removed contaminant ISs was less than 1%. For common insertion site analysis, we used Grubbs test for outliers[68] (implemented in ISAnalytics). Briefly, for each patient, we computed the targeting frequency of each gene using the number of ISs landing in the gene body ±100 kbp and then normalized by the gene length. After the log_2 transformation of the gene distribution frequency, we computed the Grubbs test for outliers to identify genes with a targeting frequency significantly higher than the average observed frequency. The analysis of the cumulative ISs over time allowed us to quantify how many new ISs we observed at each collection or sample compared to all previous samplings. To this aim, we used all ISs observed at each time point. We computed the cumulative number of ISs using ISAnalytics with function ‘cumulative_is’.

Clonal population diversity

An ecological system is maintained stable if the populating species are in equilibrium, suggesting a healthy environment. Population dynamics can be characterized in terms of species diversity, using for example a quantitative measure such as the Shannon diversity index (H index). The H index accounts for the number of distinct species (richness) and their relative abundance with the following formula:

$${H}^{{\prime} }=-\mathop{\sum }\limits_{i=1}^{R}{p}_{i}{\rm{ln}}{p}_{i}$$

where i is a clone (an IS), pi is the clonal abundance and R is the set of clones.

Several clonal studies already approached the analysis of heterogeneity and complexity of the vector-marked cells over time, tissues and differentiated lineages, using IS as surrogate of different species and the IS size as species abundance, and then measuring richness and evenness over time to quantify long-term efficacy (when the H index is maintained at high values) or observe malignant occurrences (when the H index drastically decreases over time). The diversity index has been computed using the R package Vegan and is integrated in ISAnalytics. Z-score normalization has been performed on the H index by patient and time point using the R function ‘scale’.

As different PCR methods (LAM-PCR and SLiM-PCR) were used to retrieve ISs, which have different retrieval efficiencies, we performed a comparative analysis of clonal diversity only on samples in which the same technology within the same clinical programme was used. For MLD and β-Thal, we selected only the samples processed by SLiM-PCR whereas for patients with WAS only LAM-PCR samples were available. We used the Bayesian multivariate linear regression algorithm to model and correct biases induced by different technical confounding factors as well as their interactions. These factors included PCR method, amount of DNA used, dose of CD34+ infused per kg, VCN, sequencing depth, patient gender and age.

Estimate of active repopulating HSPCs

Similar to what was done in our previous studies2,4, we used short-living cells as the readout of HSC output. Specifically, from our data, we selected myeloid-derived cells from PB samples (cell markers CD14+ and CD15+) from each time point and we then filtered ISs from these cell lineages by sequence count sum less than three across time. When estimating HSPC from several samples, we used mark-recapture statistics that leverage on the number of recaptured ISs across the different observations to estimate the size of the source population. Population size estimate has been performed in R using the package Rcapture with Chao1 (ref. [69]) model considering a closed population. For the estimate of HSPCs over time, we used triplets of consecutive time points using a sliding window from the first month in vivo to the last follow-up month. For the estimate of the size of HSPCs per patient, we selected used all stable time points (from months 9 or 12 after gene therapy). Then, we corrected the number of estimated HSPCs by the VCN in in vivo cells if VCN > 1. Moreover, when we correlated the HSPC size to the cell dose, we corrected the dose by the engraftment using the in vitro VCN (if VCN < 1) as reported[16]. HSPC estimate has been implemented in ISAnalytics, here run with the following prototype function ‘HSC_population_size_estimate’.

Clonal composition through IS sharing

To analyse the composition of long-lasting clones by tracking the originating time points (that is, the first observed time point of each specific IS) for each patient, sample and tissue, we processed the matrix of ISs to label each clone with the first observed time point (among the previous ones) such that if the clones were recaptured in a subsequent time point we could backtrack from which month it has been initially found. We then computed the percentage of the recaptured clones on the total observed clones at each time point. For this purpose, we used the function ‘iss_source’ in ISAnalytics.

To quantify the composition of long-lasting clones, we isolated ISs retrieved from 36 months (both PB and BM) and averaged the percentages for each source time point. Source time points were then grouped by haematopoietic phases: 1–6 months as early, 12–18 months as mid and 24–30 months as steady. Once collected data from each patient and calculated the percentage in each lineage, we then performed the non-parametric Kruskal–Wallis statistical test on the means between pairs of groups (early versus mid, early versus steady, mid versus steady) using the R function compare_means and returned P value after false discovery rate (FDR) correction.

Sharing ratio and Z-score

A single HSPC clone can proliferate and differentiate in distinct mature lineages. Using ISs, we can account for how many lineages have been retrieved for every single clone, thus studying the output of HSPCs. Specifically, given two distinct lineages (namely A and B), we can calculate the number of shared ISs or not shared ISs (as exclusive of A or B) on the overall number of clones per set (A or B). This ratio (or relative percentage) is called the ‘sharing ratio’.

The sharing ratio for CD34+ BM cells is calculated as the number of shared IS retrieved in both CD34+ BM cells and each mature cell marker on the total number of CD34+ BM IS. From each patient’s matrix of IS, we applied the filter for intra-marker contamination (as reported in previous works2,4, here using ISAnalytics with the function named ‘purity_filter’ with parameters impurity_threshold = 10, min_value = 3) and we then computed the sharing ratio. The parameter ‘input_threshold’ is the fold difference, set to 10 as already configured in past analyses, whether ‘min_value’ is the minimum number of reads to accept and IS as true.

Profiles of the sharing ratio over time were generated using the log-spline function in R. The Z-score was computed within each patient, time point and tissue by the R function ‘scale’. The statistical test to compare means was the non-parametric Kruskal–Wallis test with FDR correction. For data selection for time course analysis, repeated measurements were obtained as the average of ISs between 24 and 60 months, corresponding to the overlapping time points among the three clinical studies, with a minimum number of three observations per patient.

The sharing ratio for whole populations (in BM and PB separately) is calculated as the number of shared IS retrieved in both whole populations and each mature cell marker on the total number of IS retrieved in the whole population. Once computed the sharing ratio, we then compared these values among the lineages to observe any skewing or imbalance within each clinical trial and performed the non-parametric Kruskal–Wallis test (with FDR correction) on the means to quantify the differences. When we compared the different clinical trials, we needed to apply Z-score statistics and transform the data before data comparison.

HSC lineage commitment

If we considered pairs of sets in the sharing ratio, allowing a clone to belong to different mature lineages, to study lineage commitment, we required a method that accounted for sharing across all lineages together, assigning each IS unambiguously to a specific intersection (Fig. 3a). In our study, we defined the following sets to intersect for each patient and time point: CD34+, erythroid, myeloid, B and T cells. A single IS (with a minimum of three reads) exclusively belongs to either a single lineage or to an intersection of several lineages. For each time point, we classified an IS as ‘multi-lineage’ if it was shared among at least two mature lineages (excluding CD34+ cells). Otherwise, it was labelled as ‘uni-lineage’ if the IS was found exclusively within one mature lineage (or shared with CD34+ cells).

Computationally, we addressed this by generating a binary table that included all possible combinations of intersections for each time point (see Supplementary Methods for more details).

If a clone has been found in several time points, then it is considered ‘recurrent’, otherwise it is a ‘singleton’. The union of recurrent and singletons sum to all patient IS. For the analysis of lineage commitment in a percentage, the sharing ratio was then computed for each time point placing at the numerator the number of IS observed in a specific group (among multi-lineage of one of the uni-lineage) and at the denominator the number of clones captured at that time point (belonging to one of the groups, recurrent or singleton, if the ratio was within the group). For single clone lineage commitment, we tracked the classification dynamics of each clone over time and then we selected recurrent clones in early and late phases (less than 24 months and more than 24 months, respectively). For the analyses of the abundance of committed clones over time, we then joined each IS with the relative abundance (quantified with the number of genomes) and averaged the clonal abundance within each assemblage.

Gene Ontology and feature annotation

Gene Ontology has been realized using R packages for Gene Ontology clusterProfiler, the annotation DB org.Hs.eg.db and msigdbr. Semantic similarity has been done with the R package GOSemSim[70]. Feature annotations have been realized with the R packages ChIPseeker and database TxDb.Hsapiens.UCSC.hg19.knownGene (up-set plot) and the closest genes have been annotated with RefGene table (UCSC database hg19). The circos plot was generated by the R package ‘circlize’.

Statistical analysis

For pair-wise correlation and PCA analysis we used the R packages PerformanceAnalytics and factoextra, respectively. Kruskal–Wallis test performed in the R package ggpubr and stat_compare_means function. The R library ‘stats’ has been used for all P value corrections. The following libraries and software version have been used for IS analysis and statistics: Prism (v.10); R v.4.0.3 (package dependencies linked to this version include ISAnalytics, scales, dplyr, rstatix for wilcox_test, splines, Rcapture, vegan, psych, cluster, DescTools, fpc, pca, factorextra, ggplot). The following packages have been used for Good–Turing and Bayesian regression: R v.4.2.2 (2022-10-31), plyr_1.8.9, tools_4.2.2, jsonlite_1.8.8, grid_4.2.2, tidyselect_1.2.0; Python v.3.8.15, packaged by conda-forge, sklearn v.0.2, joblib v.1.2.0, numpy v.1.24.1, scipy v.1.10.1 and threadpoolctl v.3.1.0.

Multivariate Bayesian linear regression

Accurate determination of species counts and their respective abundances may face challenges due to technical or patient-specific confounders, potentially hindering the identification of true variability within the data. To overcome this, in our analyses focusing on clonal diversity, the assessment of long-lasting clones and HSC lineage commitment, we included these factors as covariates in a multivariate Bayesian linear regression model. The set of covariates includes positive real values and categorical values: time after gene therapy (positive real value), amount of sequenced DNA (positive real value), next-generation sequencing technology (categorical value), PCR methodology (categorical value), average VCN (positive real value), dose of cells (CD34 pro per kg) (positive real value) and patient gender and age (positive real value). Further details are reported in Supplementary Methods.

To model the nonlinearity among the response and the predictors, a second-order spline transformation on the input data X is performed before fitting the model and categorical predictors are included in the model by means of one-hot encoding. The preprocessing has been carried out through the functions SplineTransformer() and OneHotEncoder(), whereas for the model fitting, the ARDRegression() function was used; all functions are implemented in the scikit-learn Python package. We carried out a Bayesian linear regression to predict the Shannon diversity index (H index), the Sharing ratio (S ratio) and the CD34 output, taking into account confounding covariates to reduce their effect on the observed quantity.

Good–Turing estimator

The Good–Turing model has been used to estimate the number of undetected species in an assemblage. This estimation can be extended to evaluate the number of shared species between two assemblages and it is particularly useful in situations in which rare species, often not detected, make up a significant portion of the total. This method has been used in ecology to correct the bias between the observed and true number of species in a given area. By treating each IS as a species and cell markers as distinct assemblages, we can apply this approach to better estimate the richness of ISs within specific cell markers and the number of shared ISs between CD34 and each marker, accounting for undetected species. Two assemblage models exist and have been used: single assemblage and two assemblages. In the single assemblage model, the true number of species includes both observed and undetected species. The undetected species count is estimated based on the rarest observed species by using singletons and doubletons. This model is extended to two assemblages when estimating shared species between two sets. The true count of shared species accounts for those undetected in one or both assemblages. Estimations are based on observed species frequencies and population sizes, with specific formulas for undetected species in each case. A detailed formulation is reported in Supplementary Methods.

We applied the Good–Turing estimators to account for undetected ISs in lineage analyses. In single assemblage cases, we calculated the adjusted richness of CD34+ cells. For two assemblages, we estimated shared ISs between CD34+ cells and mature cell markers. The adjusted sharing ratio was then used in a multivariate Bayesian linear regression model to analyse lineage commitment and patient heterogeneity. We categorized each IS as multi- or uni-lineage and calculated sharing ratios, correcting them for confounding factors using the Good–Turing method and Bayesian analysis.

IS confidence interval by bootstrap

Each IS at each time point has been classified as either multi- or uni-lineage if shared across distinct cell lineages or a single cell lineage, respectively. To test whether the potential subsampling bias for poorly abundant clones could result in misclassification of an IS as uni-lineage instead of multi-lineage, we further evaluated the robustness of the commitment results by performing a parametric bootstrap with incremental subsampling percentages of the number of reads per IS and replicating this procedure ten times (ten is the number of randomizations). By this approach, we can evaluate the robustness and stability of the assigned label class (multi- or uni-lineage) for each IS during early, less than 24 months, versus late, greater than or equal to 24 months, phases of haematopoietic reconstitution by assessing how many times each single IS maintains the same commitment or multi-lineage state (in percent) generating the confidence interval. The standard error of the confidence interval is then computed across all IS.

In more detail, for each experiment i, let Xi be the observed read counts matrix. In this matrix, the rows represent the distinct ISs and the columns represent the distinct time points and cell markers, with the column-wise sums yielding the total number of observed reads for the given sample. We assumed to collect a subsample of the total number of reads, testing the robustness using 50, 70, 80 and 90% of the observed read counts. For each subsampling percentage p, we generated a matrix X(p)i. To generate several stochastic bootstrap replicates, we then sampled each matrix entry from a negative binomial distribution, using as mean and overdispersion the subsampled number of reads X(p)i. For each replicate, we performed the classification of the ISs as multi- or uni-lineage. We then compared each IS’s classifications with those obtained from the observed matrix (without subsampling, 100%) using a measure of accuracy, defined as accuracy = (number of correct predictions)/(number of total predictions).

Somatic mutations with the myeloid panel

DNA was extracted from PB mononuclear cells of 71 MLD (23 patients) and 27 β-Thal samples (nine patients) using the QIAamp DNA Blood Mini Kit (Qiagen). Twenty nanograms of genomic DNA were subjected to the AmpliSeq for Illumina Myeloid Panel (Illumina) procedure, following the manufacturer’s instructions. Special control DNA samples to assess the performance of next-generation sequencing assays for the detection of somatic mutations (Horizon Discovery HD701 and HD729) were processed simultaneously. PCR products were barcoded with AmpliSeq for Illumina CD Indexes Set A and B and pooled in one library that was paired-end sequenced on 1 Nova-Seq SP500 flow cells.

Sequences were aligned to the human reference genome (hg19/GRChg37) using BWA-MEM. Pileup files were generated using Samtools (options -B -q 1), followed by variant calling with VarScan2 (options --min-coverage 100, --min-var-freq 0.01). To filter out false positives, we removed mutations that: (1) appeared in several samples, (2) were in low-covered amplicons (fewer than 200 reads), (3) were germline (49 < VAF < 51 or VAF > 99), (4) occurred in the last 3 bp of reads or (5) were in poly-T or poly-A regions. Most somatic mutations had a VAF of less than 2%. Detected mutations were annotated using gnomAD, dbSNP and ClinVar databases. Further details are reported in Supplementary Methods.

TPO and EPO assays

To measure TPO in patients with WAS, we used the Quantikine ELISA—Human Thrombopoietin Immunoassay kit on the frozen plasma of patients according to the manufacturer’s instructions.

EPO was dosed from each patient’s serum by chemiluminescence with the Immulite 2000Xpi. Normal values are 3–24 mU ml−1.

Reporting summary

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

Data availability

Sequencing data of mutations have been deposited into the Sequencing Read Archive (NCBI SRA) with the BioProject PRJNA1150995. IS data have been deposited in GitHub at the following project archive: https://github.com/calabrialab/Code_HSPCdynamics. Source data are provided with this paper.

Code availability

We released our source code in GitHub (https://github.com/calabrialab/Code_HSPCdynamics).

References

1. Ferrari, G., Thrasher, A. J. & Aiuti, A. Gene therapy using haematopoietic stem and progenitor cells. Nat. Rev. Genet. 22, 216–234 (2021).

2. Biffi, A. et al. Lentiviral hematopoietic stem cell gene therapy benefits metachromatic leukodystrophy. Science 341, 1233158–1233158 (2013).

3. Sessa, M. et al. Lentiviral haemopoietic stem-cell gene therapy in early-onset metachromatic leukodystrophy: an ad-hoc analysis of a non-randomised, open-label, phase 1/2 trial. Lancet https://doi.org/10.1016/s0140-6736(16)30374-9 (2016).

4. Aiuti, A. et al. Lentiviral hematopoietic stem cell gene therapy in patients with Wiskott-Aldrich syndrome. Science 341, 1233151 (2013).

5. Ferrua, F. et al. Lentiviral haemopoietic stem/progenitor cell gene therapy for treatment of Wiskott-Aldrich syndrome: interim results of a non-randomised, open-label, phase 1/2 clinical study. Lancet Haematol. 6, e239–e253 (2019).

6. Marktel, S. et al. Intrabone hematopoietic stem cell gene therapy for adult and pediatric patients affected by transfusion-dependent ss-thalassemia. Nat. Med. 25, 234–241 (2019).

7. Tucci, F. et al. Bone marrow harvesting from paediatric patients undergoing haematopoietic stem cell gene therapy. Bone Marrow Transplant 54, 1995–2003 (2019).

8. Tucci, F., Galimberti, S., Naldini, L., Valsecchi, M. G. & Aiuti, A. A systematic review and meta-analysis of gene therapy with hematopoietic stem and progenitor cells for monogenic disorders. Nat. Commun. 13, 1315 (2022).

9. Eichler, F. et al. Hematopoietic stem-cell gene therapy for cerebral adrenoleukodystrophy. N. Engl. J. Med. 377, 1630–1638 (2017).

10. Hacein-Bey Abina, S. et al. Outcomes following gene therapy in patients with severe Wiskott-Aldrich syndrome. JAMA 313, 1550–1563 (2015).

11. Hacein-Bey-Abina, S. et al. A modified γ-retrovirus vector for X-linked severe combined immunodeficiency. New Engl. J. Med. 371, 1407–1417 (2014).

12. Kohn, D. B. et al. Lentiviral gene therapy for X-linked chronic granulomatous disease. Nat. Med. https://doi.org/10.1038/s41591-019-0735-5 (2020).

13. Cesana, D. et al. Retrieval of vector integration sites from cell-free DNA. Nat. Med. https://doi.org/10.1038/s41591-021-01389-4 (2021).

14. Firouzi, S. et al. Development and validation of a new high-throughput method to investigate the clonality of HTLV-1-infected cells based on provirus integration sites. Genome Med. 6, 46–46 (2014).

15. Scala, S. et al. Dynamics of genetically engineered hematopoietic stem and progenitor cells after autologous transplantation in humans. Nat. Med. 24, 1683–1690 (2018).

16. Six, E. et al. Clonal tracking in gene therapy patients reveals a diversity of human hematopoietic differentiation programs. Blood 135, 1219–1231 (2020).

17. Biasco, L. et al. In vivo tracking of human hematopoiesis reveals patterns of clonal dynamics during early and steady-state reconstitution phases. Cell Stem Cell 19, 107–119 (2016).

18. Aiuti, A. et al. Multilineage hematopoietic reconstitution without clonal selection in ADA-SCID patients treated with stem cell gene therapy. J. Clin. Invest. 117, 2233–2240 (2007).

19. De Ravin, S. S. et al. Lentivector cryptic splicing mediates increase in CD34+ clones expressing truncated HMGA2 in human X-linked severe combined immunodeficiency. Nat. Commun. 13, 3710 (2022).

20. Cartier, N. et al. Hematopoietic stem cell gene therapy with a lentiviral vector in X-linked adrenoleukodystrophy. Science 326, 818–823 (2009).

21. Gentner, B. et al. Hematopoietic stem- and progenitor-cell gene therapy for Hurler syndrome. N. Engl. J. Med. 385, 1929–1940 (2021).

22. Fumagalli, F. et al. Lentiviral haematopoietic stem-cell gene therapy for early-onset metachromatic leukodystrophy: long-term results from a non-randomised, open-label, phase 1/2 trial and expanded access. Lancet 399, 372–383 (2022).

23. Goyal, S. et al. Acute myeloid leukemia case after gene therapy for sickle cell disease. N. Engl. J. Med. 386, 138–147 (2022).

24. Jones, R. J. & DeBaun, M. R. Leukemia after gene therapy for sickle cell disease: insertional mutagenesis, busulfan, both, or neither. Blood 138, 942–947 (2021).

25. Spencer Chapman, M. et al. Clonal selection of hematopoietic stem cells after gene therapy for sickle cell disease. Nat. Med. https://doi.org/10.1038/s41591-023-02636-6 (2023).

26. Kanter, J. et al. Lovo-cel gene therapy for sickle cell disease: treatment process evolution and outcomes in the initial groups of the HGB-206 study. Am. J. Hematol. 98, 11–22 (2023).

27. Schmidt, M. et al. High-resolution insertion-site analysis by linear amplification-mediated PCR (LAM-PCR). Nat. Methods 4, 1051–1057 (2007).

28. Pais, G. et al. ISAnalytics enables longitudinal and high-throughput clonal tracking studies in hematopoietic stem cell gene therapy applications. Brief Bioinform. 24, bbac551 (2023).

29. Chao, A., Chiu, C. H. & Jost, L. Phylogenetic diversity measures based on Hill numbers. Phil. Trans. R. Soc. B: Biol. Sci. 365, 3599–3609 (2010).

30. van Lochem, E. G. et al. Immunophenotypic differentiation patterns of normal hematopoiesis in human bone marrow: reference patterns for age-related changes and disease-induced shifts. Cytometry B Clin. Cytom. 60, 1–13 (2004).

31. Magnani, A. et al. Long-term safety and efficacy of lentiviral hematopoietic stem/progenitor cell gene therapy for Wiskott-Aldrich syndrome. Nat. Med. 28, 71–80 (2022).

32. Radtke, S. et al. Stochastic fate decisions of HSCs after transplantation: early contribution, symmetric expansion, and pool formation. Blood 142, 33–43 (2023).

33. Locatelli, F. et al. Betibeglogene autotemcel gene therapy for non-beta(0)/beta(0) genotype beta-thalassemia. N. Engl. J. Med. 386, 415–427 (2022).

34. Thompson, A. A. et al. Gene therapy in patients with transfusion-dependent beta-thalassemia. N. Engl. J. Med. 378, 1479–1493 (2018).

35. Cazzola, M. et al. Red blood cell precursor mass as an independent determinant of serum erythropoietin level. Blood 91, 2139–2145 (1998).

36. Laurenti, E. et al. CDK6 levels regulate quiescence exit in human hematopoietic stem cells. Cell Stem Cell 16, 302–313 (2015).

37. Kaufmann, K. B. et al. A latent subset of human hematopoietic stem cells resists regenerative stress to preserve stemness. Nat. Immunol. 22, 723–734 (2021).

38. Bernitz, J. M., Kim, H. S., MacArthur, B., Sieburg, H. & Moore, K. Hematopoietic stem cells count and remember self-renewal divisions. Cell 167, 1296–1309 e1210 (2016).

39. Glait-Santar, C. et al. Functional niche competition between normal hematopoietic stem and progenitor cells and myeloid leukemia cells. Stem Cells 33, 3635–3642 (2015).

40. Lidonnici, M. R. et al. Plerixafor and G-CSF combination mobilizes hematopoietic stem and progenitors cells with a distinct transcriptional profile and a reduced in vivo homing capacity compared to plerixafor alone. Haematologica 102, e120–e124 (2017).

41. Larochelle, A. et al. AMD3100 mobilizes hematopoietic stem cells with long-term repopulating capacity in nonhuman primates. Blood 107, 3772–3778 (2006).

42. Felker, S. et al. Differential CXCR4 expression on hematopoietic progenitor cells versus stem cells directs homing and engraftment. JCI Insight 7, e151847 (2022).

43. Feng, Q. et al. Nonhuman primate allogeneic hematopoietic stem cell transplantation by intraosseus vs intravenous injection: engraftment, donor cell distribution, and mechanistic basis. Exp. Hematol. 36, 1556–1566 (2008).

44. Yahata, T. et al. A highly sensitive strategy for SCID-repopulating cell assay by direct injection of primitive human hematopoietic cells into NOD/SCID mice bone marrow. Blood 101, 2905–2913 (2003).

45. Lee-Six, H. et al. Population dynamics of normal human blood inferred from somatic mutations. Nature 561, 473–478 (2018).

46. Werner, B. et al. Reconstructing the in vivo dynamics of hematopoietic stem cells from telomere length distributions. eLife 4, e08687 (2015).

47. Mitchell, E. et al. Clonal dynamics of haematopoiesis across the human lifespan. Nature 606, 343–350 (2022).

48. Adair, J. E. et al. DNA barcoding in nonhuman primates reveals important limitations in retrovirus integration site analysis. Mol. Ther. Methods Clin. Dev. 17, 796–809 (2020).

49. Pei, W. et al. Resolving fates and single-cell transcriptomes of hematopoietic stem cell clones by PolyloxExpress barcoding. Cell Stem Cell 27, 383–395 e388 (2020).

50. Pietras, E. M. et al. Functionally distinct subsets of lineage-biased multipotent progenitors control blood production in normal and regenerative conditions. Cell Stem Cell 17, 35–46 (2015).

51. Eisele, A. S. et al. Erythropoietin directly remodels the clonal composition of murine hematopoietic multipotent progenitor cells. eLife 11, e66922 (2022).

52. Mossadegh-Keller, N. et al. M-CSF instructs myeloid lineage fate in single haematopoietic stem cells. Nature 497, 239–243 (2013).

53. Grover, A. et al. Erythropoietin guides multipotent hematopoietic progenitor cells toward an erythroid fate. J. Exp. Med. 211, 181–188 (2014).

54. Endele, M. et al. CSF-1-induced Src signaling can instruct monocytic lineage choice. Blood 129, 1691–1701 (2017).

55. Brown, G. The social norm of hematopoietic stem cells and dysregulation in leukemia. Int. J. Mol. Sci. 23, 5063 (2022).

56. Ding, Y., Liu, Z. & Liu, F. Transcriptional and epigenetic control of hematopoietic stem cell fate decisions in vertebrates. Dev. Biol. 475, 156–164 (2021).

57. Meng, Y. et al. Epigenetic programming defines haematopoietic stem cell fate restriction. Nat. Cell Biol. 25, 812–822 (2023).

58. Takizawa, H., Boettcher, S. & Manz, M. G. Demand-adapted regulation of early hematopoiesis in infection and inflammation. Blood 119, 2991–3002 (2012).

59. Batsivari, A. et al. Dynamic responses of the haematopoietic stem cell niche to diverse stresses. Nat. Cell Biol. 22, 7–17 (2020).

60. Aprile, A. et al. Hematopoietic stem cell function in beta-thalassemia is impaired and is rescued by targeting the bone marrow niche. Blood 136, 610–622 (2020).

61. Nanthatanti, N. et al. Leukocyte telomere length in patients with transfusion-dependent thalassemia. BMC Med. Genomics 13, 73 (2020).

62. Aprile, A. et al. Inhibition of FGF23 is a therapeutic strategy to target hematopoietic stem cell niche defects in beta-thalassemia. Sci. Transl. Med. 15, eabq3679 (2023).

63. Crippa, S. et al. Bone marrow stromal cells from beta-thalassemia patients have impaired hematopoietic supportive capacity. J. Clin. Invest. 129, 1566–1580 (2019).

64. Gorur, V., Kranc, K. R., Ganuza, M. & Telfer, P. Haematopoietic stem cell health in sickle cell disease and its implications for stem cell therapies and secondary haematological disorders. Blood Rev. 63, 101137 (2024).

65. Cavazzana-Calvo, M. et al. Is normal hematopoiesis maintained solely by long-term multipotent stem cells. Blood 117, 4420–4424 (2011).

66. Spinozzi, G. et al. VISPA2: a scalable pipeline for high-throughput identification and annotation of vector integration sites. BMC Bioinf. 18, 520 (2017).

67. Berry, C. C. et al. Estimating abundances of retroviral insertion sites from DNA fragment length data. Bioinformatics https://doi.org/10.1093/bioinformatics/bts004 (2012).

68. Biffi, A. et al. Lentiviral vector common integration sites in preclinical models and a clinical trial reflect a benign integration bias and not oncogenic selection. Blood 117, 5332–5339 (2011).

69. Chao, A. et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).

70. Yu, G. Gene ontology semantic similarity analysis using GOSemSim. Methods Mol. Biol. 2117, 207–215 (2020).

Acknowledgements

We acknowledge the physicians and nursing team of the Pediatric Immunohematology Unit, Stem Cell Transplant Program of the IRCCS San Raffaele Scientific Institute, for their professional care of patients during hospitalization and visits; Fondazione Telethon ‘Just Like Home’ Program, S. Zancan and all in the SR-TIGET Clinical Trial Office, M. Soncini and all at the SR-TIGET clinical laboratory for their support; the team of AGC Biologics (formerly MolMed) for manufacturing the vector and medicinal product. We thank E. Zonari for the support with Fig. 1, C. Sartirana for technical support on TPO measurements and C. Cipriani for his support with R. We thank A. Biffi as former PI of the MLD GT study. This work has been supported by grants to A.C. and D. Cesana from Italian Ministry of Health (grant no. Giovani Ricercatori GR-2016-02363681) and to E.M. from the Telethon Foundation (grant nos. TGT11D1, TGT16B01 and TGT16B03) and from the European Leukodystrophies Association (ELA, grant no. 2021-019I1).

Author information

Authors and Affiliations

1. San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy

Andrea Calabria, Giulio Spinozzi, Daniela Cesana, Fabrizio Benedicenti, Giulia Pais, Francesco Gazzo, Serena Scala, Maria Rosa Lidonnici, Samantha Scaramuzza, Alessandra Albertini, Simona Esposito, Francesca Tucci, Daniele Canarutto, Maryam Omrani, Fabiola De Mattia, Francesca Dionisio, Stefania Giannelli, Sarah Marktel, Francesca Fumagalli, Valeria Calbi, Sabina Cenciarelli, Francesca Ferrua, Bernhard Gentner, Luigi Naldini, Giuliana Ferrari, Alessandro Aiuti & Eugenio Montini

2. Department of Mathematics, Informatics and Geosciences, University of Trieste, Triste, Italy

Elena Buscaroli & Giulio Caravagna

3. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy

Francesco Gazzo

4. Pediatric Immunohematology and BMT, San Raffaele Hospital, Milan, Italy

Francesca Tucci, Daniele Canarutto, Sarah Marktel, Francesca Fumagalli, Valeria Calbi, Sabina Cenciarelli, Francesca Ferrua, Fabio Ciceri & Alessandro Aiuti

5. Vita Salute San Raffaele University, Milan, Italy

Daniele Canarutto, Luigi Naldini, Giuliana Ferrari & Alessandro Aiuti

Contributions

A.C. performed most of the research, interpreted data, designed the study, developed the computational biology analyses and wrote the manuscript. G.S., G.P., F.G., E.B. and M.O. performed bioinformatics analyses under the supervision of A.C., G.C. and E.M. D. Cesana supported the design and the interpretation of lineage commitment analyses. B.G., S. Scala, M.R.L. and S. Scaramuzza critically interpreted the results of the analyses and supported the revision of the manuscript. F.B., A. Albertini and S.E. performed most of the experiments. F.T. and D. Canarutto contributed to patient follow-up and data collection. F.D.M., F.D., S.C. and S.G. managed clinical samples. S.M., V.C., F. Fumagalli and F. Ferrua performed clinical follow-up to patients receiving G.T. G.C. supervised the Good–Turing and Bayesian data corrections. F.C., L.N., G.F. and A.A. provided interpretation of all data and critically reviewed the manuscript. E.M. contributed to the study design, data collection and interpretation of all data, manuscript writing and provided overall supervision.

Corresponding authors

Correspondence to Andrea Calabria or Eugenio Montini.

Ethics declarations

Competing interests

The San Raffaele Telethon Institute for GT (SR-Tiget) is a joint venture between the Telethon Foundation and Ospedale San Raffaele. Lentiviral vector-based gene therapy for metachromatic leukodystrophy (MLD), developed at SR-Tiget, was licensed to Orchard Therapeutics in 2018. Lentiviral vector-based gene therapy for Wiskott–Aldrich (WAS) syndrome was developed by Fondazione Telethon. Lentiviral vector-based gene therapy for β-thalassemia was developed at SR-Tiget. Gene therapy for MLD is approved in the EU (Libmeldy) and in the US (Lenmeldy). A.A. was the principal investigator of the pilot and pivotal SR-Tiget clinical trial of GT for MLD, WAS and β-Thal. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Vijay Sankaran, Stefan Cordes and Mark Walters 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 Genome wide distribution of IS.

(A) Circular representation of the genomic distribution of the ISs for the three clinical trials (MLD in the blue track, WAS in the green track, β-Thal in the red track). (B) UpSet plot displaying IS distribution around targeted genes for a sample patient in each trial (Pt16 for MLD, Pt52 for WAS, Pt45 for β-Thal). The rows list gene features, with combinations represented by connecting lines and histograms showing element counts. (C) Gene ontology analysis for the three trials, showing the results for biological processes (BP), first 15 classes. (D) GO similarity plot comparing ontological enrichments among the trials. Pairwise similarity scores (“MLD-WAS,” “MLD-β-Thal,” “WAS-β-Thal”) are presented on the y-axis, covering all GO categories cellular components (CC), biological processes (BP), and molecular functions (MF). (E) CIS results displayed as volcano plots for each trial. Each gene is represented as a dot, with integration frequency on the x-axis (log2 scale) and the associated p-value on the y-axis (−log10). The orange dashed line indicates the alpha value of 0.05. Genes are colored gray if never significant or violet if significant in at least one patient. (F) Stacked bar plot tracking top clones (≥ 1% in MLD/WAS, ≥0.1% in β-Thal) over time for the first patient in each trial (MLD Pt42, WAS Pt52, β-Thal Pt45). Each colored bar represents a clone, with height proportional to abundance. Colored ribbons connect neighboring time points for recaptured clones. BM-derived clones are on the left, PB-derived IS on the right, with the number of observed IS reported above each bar.

Source Data

Extended Data Fig. 2 Clonal complexity and diversity.

(A) Population diversity index (Shannon entropy, H) over time, normalized with Z-score by patient and time point, divided by disease condition and tissue. Colors refer to lineages (CD34+ cells, myeloid, erythroid, B and T); spline with CI 0.75. (B) Statistical comparisons of population diversity index among the different diseases grouping values by lineages and tissues (Kruskal–Wallis test). The bars represent the median, the whiskers extend to 1.5 times the IQR, and the p-value threshold is set at 0.05. (C) Similar to (B), statistical comparisons of the population diversity index among the different lineages, grouping values by disease and tissues.

Source Data

Extended Data Fig. 3 HSPC population size.

(A) Estimated number of HSPCs (y-axis) by disease comparing early time points (<24 months post GT) and late time points (>24 months post GT) using Wilcoxon matched pairs signed rank test. β-Thal patients showed not statistically significant decrease when comparing <24 Vs. >24 months, while showed a significant decrease when comparing <12 Vs. >24 months. (B) Statistical analysis of the composition of long-lasting clones analyzed by the time of origin (first observed time point). For each lineage (in columns) and clinical trial (in rows), we plot the percentages of shared ISs (in dots, distributions with violin plots) within each time point grouped by the three phases (“early” from 1 to 6 months after GT, “mid” from 12 to 18 months, and “steady” from 24 to 30 months). P-values obtained by Kruskal Wallis test. (C) Percentages of the composition of long-lasting clones analyzed by the time of origin expressed as fold change (y-axis) of the “mid” and “steady” phase on the “early” phase (x-axis) for the three diseases (MLD in blue, WAS in green, β-Thal in red), separated by lineage.

Source Data

Extended Data Fig. 4 Integrated and unsupervised analysis of patients’ variables, combining clinical and molecular data.

(A) Pair-wise correlation results among all variables (represented in the diagonal). The distribution of each variable is shown within each box in the diagonal. On the lower triangular below the diagonal, the bivariate scatter plots with a fitted line. On the upper triangular side of the matrix above the diagonal, the value of the correlation plus the significance level (p-values, adjusted Benjamini). (B) PCA analysis with biplot representation showing on the axes the first 2 dimensions explaining >74% of the data; dots are all patients labeled with patient ID; blue arrows are the eigenvectors of the PCA; in colors the three GT types (BM = bone marrow, MPB = mobilized peripheral blood) with centroids of clustering as elliptic shapes.

Source Data

Extended Data Fig. 5 CD34+ cell output towards mature lineages.

(A) Dynamics of the lineage output over time (x-axis) calculated with the sharing ratio (y-axis) between CD34+ cells and each lineage (different colors) expressed as percentage normalized with Z-score by patient and time point, reported by clinical study. Spline curves with CI 0.75. (B) Similar to (A), dynamics of the lineage output (y-axis) over time (x-axis) stratified by patient age (0–2 years, 2–15 years, >30 years). (C) Lineage output for XSCID patients (N = 10) under LV-HSPC gene therapy treatment (De Ravin S., et al. Nature Communications 2022). (D) Box-plots of sharing ratio (bar indicates median, whisker 25–75 percentiles), normalized by Z-score (y-axis) isolated in all patients from 24 months and averaged, in BM and PB tissues (circle or triangular dot shapes), grouping cell markers (colors) by cell lineages, and stratified by patient’s age within each clinical trial. Statistical tests are performed comparing the different lineages (Kruskal Wallis test). The bars represent the median, the whiskers extend to 1.5 times the IQR, and the p-value threshold is set at 0.05. (E) Similar to (D), box-plot representation of normalized sharing ratio (y-axis) stratified by lineage within each clinical trial, comparing the different groups of treatment age (Kruskal Wallis test).

Source Data

Extended Data Fig. 6 HSC lineage commitment.

(A) Z-score normalization of the HSC lineage commitment over time expressed as percentage on the recaptured clones. The plot represents the scaled relative percentage of the shared IS (y-axis) over time (x-axis) for recurrent clones (IS observed in at least two time points for each patient) on the overall number of cells observed in multilineage (green line) or mature uni-lineages (erythroid in red, myeloid in yellow, B in blue, T in light blue). Lines are spline regression curves using a log curve with 0.75 CI. (B) HSC lineage commitment as the percentage of shared ISs of recurrent clones (y-axis) over time (x-axis) for PB samples stratified by clinical trials (in columns) and patient’s age (in rows the three classes of age: 0–2 years, 2–15 years, and >30 years). Colors are associated with multilineage clones (green line) or mature uni-lineages (erythroid in red, myeloid in yellow, B in blue, T in light blue). (C) HSC lineage commitment in BM, similar to (B). (D) Similar to (A), Z-score normalization of the HSC lineage commitment over time expressed as percentage on the recaptured clones and stratified by age. (E) Boxplot representation of the HSC lineage commitment normalized (Z-score) and averaged to compare the different clinical studies (in columns) for multi-lineage or mature committed lineages and tissues (rows); patients are grouped by their age of treatment (groups “0–2” years, “2–15” years, and “>30” years). Statistical results are expressed with lines between pairs of age groups (Kruskal Wallis test). The bars represent the median, the whiskers extend to 1.5 times the IQR, and the p-value threshold is set at 0.05.

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Extended Data Fig. 7 Unilineage or multilineage commitment.

(A) The first six panels show the percentage of multilineage clones (identified <24 months post-transplant) in BM and PB that transition into unimyeloid clones in the late phase (>24 months). The remaining six panels show the percentage of unimyeloid clones that remain committed in the late phase. Each panel compares lineage commitment across clinical programs by age at treatment: 0–2 years and 2–15 years for MLD and WAS, and 2–15 and >30 years for β-Thal. Statistical comparisons used Student’s T-test. (B-E) Box plots representing T-cell, B-cell, erythroid, and multilineage commitment during early and late hematopoietic reconstitution. Panel order and statistical analysis are consistent with (A).

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Extended Data Fig. 8 Transitioning clones in HSPC commitment.

(A) Boxplot representation of single IS clonal abundances per class (multi-lineage or specific uni-linage) in early time points (<24 months) or late time points (>24 months). In all boxplots, the bars represent the median, the whiskers extend to 1.5 times the IQR. (B) Lineage commitment per clone comparing clonal abundances over time (early versus late, <24 months and >24 months respectively) in BM and PB across the different classes (multi-lineage or uni-lineage) and transitions (multi-multi, multi-uni, or uni-uni). Statistical results are expressed with lines between pairs of age groups (Kruskal Wallis test). Patients in each clinical program are represented with dots and colored in blue, green, or red if enrolled in MLD, WAS, or β-Thal study. (C) Similar to (A), we compared lineage commitment clonal abundances per class (multi-lineage or uni-linage) within early time points (<24 months) or late time points (>24 months).

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Extended Data Fig. 9 Reliability of HSPC commitment through IS analysis.

(A) The bootstrap strategy. At each timepoint (T = 1 and T = 60 in this case), each IS is observed in different lineages (B, T, and Myeloid here), with a specific number of reads. An IS is given a label (Multi-lineage “Multi” or unilineage “UniT”/”My”) based on the lineages and the number of reads observed. During the bootstrap procedure, each IS undergoes read subsampling, which reduces the number of reads and may affect its labeling. For example, due to low observed abundances, IS1 might not be detected in B cells, causing its final label at the last time point to change to “UniMy”. After 10 randomizations, we evaluate each IS by assessing the accuracy of classifications in early and late phases across bootstrap samples. ISs with fluctuating labels, like IS1, will show low accuracy, whereas ISs with consistent results and a 100% label, like IS2 and IS3, will yield more robust classifications. (B) Mean accuracy with standard error of all IS Confidence Intervals (CI), y-axis, across randomizations, by sampling percentage (x-axis), per class (multi-lineage or uni-linage), for each clinical study. The bars represent the median, the whiskers while the whiskers indicate the range showing the minimum and maximum values.

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Extended Data Fig. 10 Singleton clones, exhausting HSPCs, Lineage commitment in XSCID patients.

(A) HSC commitment analysis of ISs observed only at one time point (singletons), representing committed clones that exhaust after GT. Results were stratified by clinical trials and patient age, with trends shown for multilineage and mature committed lineages in BM samples, using log spline curves with a 0.75 CI. (B) HSC lineage commitment over time expressed as percentage on the recaptured clones in XSCID patients. The plot represents the scaled relative percentage of the shared IS (y-axis) over time (x-axis) for recurrent clones (IS observed in at least two time points for each patient) on the overall number of cells observed in multilineage (green line) or mature uni-lineages (erythroid in red, myeloid in yellow, B in blue, T in light blue). Lines are spline regression curves using a log curve with 0.75 CI.

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Supplementary information

Supplementary Information

This file contains Supplementary Methods, legends for Tables 1–9, Discussion and References.

Reporting Summary

Supplementary Table 1

Patients’ summary. Summary table of treated patients under analysis, showing details of the disease, the ID of the patient (Patient ID), the body weight at treatment, the cell dose expressed as millions of CD34+ cells 10 per kg, the transduction efficiency, the GT source (expressed as BM, MPB for mobilized PB or BM and MPB), the VCN at treatment, the age of the patient at treatment and the maximum follow-up available with molecular data, and the gender.

Supplementary Table 2

Patients’ samples and details with ISs. The table presents all details of each patient sample used in this work, using the following columns: Disease, Patient ID, Tissue of sample origin (PB and BM), Lineage, Cell Marker, Time point (expressed in months after GT), VCN, amount of genomic material (reported as ng of DNA), PCR Method for IS retrieval (between LAM-PCR and SLiM-PCR), sequencing technology and platform, Number of reads supporting the ISs and Number of ISs, population diversity index (using Shannon H index).

Supplementary Table 3

Normalized integration frequency by gene. Pair-wise comparison of gene normalized integration frequency for patients with MLD, WAS and β-Thal, after homogeneous subsampling and randomization. For each gene (column GeneName) and pair-wise comparison (column Comparison) we reported the average number of ISs for study and the final corrected P value (column corrected p-value avg).

Supplementary Table 4

Estimated number of active HSPC. For each available patient, the table reports the number of estimated HSPCs observed at early time points (less than 24 months) and from 24 months (column Pop. Size >24 m) calculated with the Chao1 model and corrected with the VCN (if VCN > 1). We also reported the number of IS retrieved less than 24 months and more than 24 months, and the number of shared IS between the two sets. The last column reports the fraction of the estimated HSPCs greater than 12 months on the total number of observed IS more than 24 months.

Supplementary Table 5

Mean depth coverage of somatic mutations data. The table shows the mean depth coverage reached for each patient with MLD and β-Thal coming from the Illumina’s AmpliSeq Myeloid Panel Targeted exome sequencing used for the somatic mutations analysis. Columns report: Disease, Patient ID, time point in days of the sample and mean depth coverage.

Supplementary Table 6

Somatic mutations. The table presents all the somatic mutations for each patient with MLD and β-Thal coming from the Illumina’s AmpliSeq Myeloid Panel Targeted exome sequencing. Columns report: Disease, Patient ID, time point in days of the sample, cell type, total number of reads in the position of the mutation, number of reads supporting the reference variant, number of reads supporting the mutated variance, VAF, chromosome, starting position, ending position, nucleotide reported by the reference, nucleotide reported by the mutation, functional annotation of the position (intron, exon), gene, exon modification, aminoacidic change, effect of the mutation reported by ClinVar, frequency of the mutation in the population reported by gnomAD and ID of the mutation reported by avsnp147.

Supplementary Table 7

Primers used for the first (Exponential) PCR. A list of primers used for the first exponential PCR with details about name, type and sequence.

Supplementary Table 8

Primers used for the second (Fusion) PCR. List of primers used for the second fusion PCR with details about name, type, sequence and barcode (eight nucleotides) of each primer.

Supplementary Table 9

Linker cassette sequences. The linker cassette sequences used in the SLiM-PCR procedure. Each linker cassette was generated by the annealing of the short oligo with a long oligo. The sequence of each oligo and its barcode (eight nucleotides) is reported.

Peer Review File

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Calabria, A., Spinozzi, G., Cesana, D. et al. Long-term lineage commitment in haematopoietic stem cell gene therapy. Nature 636, 162–171 (2024). https://doi.org/10.1038/s41586-024-08250-x

- Received: 16 September 2022

- Accepted: 18 October 2024

- Published: 23 October 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08250-x

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Adult skull bone marrow is an expanding and resilient haematopoietic reservoir

Bong Ihn Koh,

Vishal Mohanakrishnan,

Hyun-Woo Jeong,

Hongryeol Park,

Kai Kruse,

Young Jun Choi,

Melina Nieminen-Kelhä,

Rahul Kumar,

Raquel S. Pereira,

Susanne Adams,

Hyuek Jong Lee,

M. Gabriele Bixel,

Peter Vajkoczy,

Daniela S. Krause &

…

Ralf H. Adams

Nature volume 636, pages 172–181 (2024)

This article has been updated

Abstract

The bone marrow microenvironment is a critical regulator of haematopoietic stem cell self-renewal and fate[1]. Although it is appreciated that ageing, chronic inflammation and other insults compromise bone marrow function and thereby negatively affect haematopoiesis[2], it is not known whether different bone compartments exhibit distinct microenvironmental properties and functional resilience. Here we use imaging, pharmacological approaches and mouse genetics to uncover specialized properties of bone marrow in adult and ageing skull. Specifically, we show that the skull bone marrow undergoes lifelong expansion involving vascular growth, which results in an increasing contribution to total haematopoietic output. Furthermore, skull is largely protected against major hallmarks of ageing, including upregulation of pro-inflammatory cytokines, adipogenesis and loss of vascular integrity. Conspicuous rapid and dynamic changes to the skull vasculature and bone marrow are induced by physiological alterations, namely pregnancy, but also pathological challenges, such as stroke and experimental chronic myeloid leukaemia. These responses are highly distinct from femur, the most extensively studied bone marrow compartment. We propose that skull harbours a protected and dynamically expanding bone marrow microenvironment, which is relevant for experimental studies and, potentially, for clinical treatments in humans.

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Niche microenvironments composed of multiple cell types and molecular signals regulate the self-renewal and the ultimate fate of haematopoietic stem cells (HSCs) in bone marrow1,3 (BM). The skull, a flat bone encasing the brain, develops via intramembranous ossification and fundamentally differs in both composition and remodelling from long bones, which undergo endochondral ossification during development and sustain load-bearing mechanical stress4,5,6. The thin and flat mouse calvarium, the main component of the skull roof, has been successfully used as a platform for the imaging of HSC dynamics7,8. Potential functional differences between BM compartments, such as those in long and flat bone, however, remain little understood. It has been shown that HSCs are homogenously distributed throughout calvarium and long bone (namely diaphysis and epiphysis) under homeostatic conditions, whereas chimerism in mice transplanted with congenic haematopoietic stem and progenitor cells (HSPCs) is initially higher in calvarium and epiphysis relative to diaphysis[9]. The discovery of designated channels between the calvarial BM and the dura mater of the meninges indicates that skull BM acts as a haematopoietic reservoir for meningeal and central nervous system immunity10,11. Ageing causes an overall decline in haematopoietic function, with compromised lymphopoiesis and a bias toward dysfunctional myelopoiesis2,12. Again, it remains unknown whether all BM compartments are equally affected by ageing processes and therefore collectively contribute to haematopoietic decline. Here we show that calvarial BM is fundamentally different to the BM in long bone. Calvarial BM continuously expands during adulthood, is resistant to most major hallmarks of BM niche ageing, and gradually increases its systemic haematopoietic contribution throughout adult life and ageing. Our work also establishes that BM compartments exhibit unexpected functional differences, with implications for a range of physiological conditions such as pregnancy and ageing, as well as pharmacological treatments and disease settings.

Skull BM expands during adult life

To characterize changes in calvarial BM throughout adult life, we collected skulls from young adult (10–14 weeks; hereafter referred to as ‘young’), middle-aged (31–37 weeks), old (52–75 weeks) and geriatric (95+ weeks) mice. Initial stereoscopic observation showed that blood-filled structures, representing blood vessels and surrounding BM, occupy only limited areas of the frontal and parietal bone in young adults, whereas interparietal bone is mostly filled by blood cells (Fig. 1a). Although angiogenic growth of blood vessels is normally confined to development, regeneration or certain pathological conditions13,14, substantial expansion of the vasculature can be seen in calvaria from middle-aged mice (Fig. 1a–d). Further increases in vascular area and vessel diameter can be observed in old and geriatric skulls (Fig. 1a–d). Flow cytometric analyses show that the expansion of calvarial vasculature is accompanied by a profound increase in total haematopoietic cells, HSCs, HSPCs, committed haematopoietic progenitors and stromal cells (Supplementary Data 1; gating strategies shown in Supplementary Data 2). Skull cross-sections from young and geriatric mice show that the enlargement of calvarial BM occurs more rapidly in female mice than in males (Fig. 1a and Extended Data Fig. 1a,b). Expansion of calvarial BM results in an increase in total skull thickness (of approximately 70% between young and geriatric mice of both sexes) without decreasing the thickness of the cortical bone tables (Extended Data Fig. 1a,b). Immunofluorescence analysis of skull sections shows a profound increase in V-type proton ATPase-positive activated osteoclasts lining the surface of the inner and outer cortical bone tables in skulls from old relative to young mice (Extended Data Fig. 1c,d), indicating that bone resorption by osteoclasts contributes to BM expansion.

Fig. 1: Age-related expansion of blood vessels in adult skull.

a. Transverse view of mouse skull at the indicated stages of adulthood and ageing. Scale bars, 1 mm. b,c, In vivo immunofluorescence staining of blood vessels in skull showing substantial vascular expansion (b) and changes in vessel branching and morphology (c). Representative images from three independent experiments. Scale bars, 1 mm (b) and 500 μm (c). d, Quantification of vascular area, diameter and endomucin expression in different skull parts from young (Y), middle-aged (M), old (O) and geriatric (G) mice. For vascular area and endomucin expression, each dot indicates a value from 1 mouse and n = 4 mice per group from 3 independent experiments. For sinus diameter, each dot indicates a randomly selected vessel from all four mice. Data are mean ± s.d. P values from Tukey multiple comparison test (one-way analysis of variance (ANOVA)). MFI, mean fluorescence intensity.

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To address whether skull BM expansion also occurs in ageing humans, we analysed computed tomography (CT) head scan images obtained from 36 human patients evaluated for small cerebral aneurysms. This revealed significantly increased area enclosed by the inner and outer cortical bone tables in older (61–69 years of age) compared with younger (21–40 years of age) individuals (Extended Data Fig. 2a–c). A larger difference was seen in female skulls (83.2% increase versus 24.6% in males), showing that this sexual dichotomy occurs in both mice and humans. These results demonstrate a substantial expansion of the BM volume in both mouse and human skull during adulthood and ageing.

Vessel growth during skull BM expansion

Previous studies have shown that arterial and sinusoidal endothelial cells are important components of the HSC microenvironment15,16. To gain more insight into the organization of the calvarial vasculature, we first utilized an in vivo immunofluorescence staining method with CD31 antibody in Flk1-GFP knock-in mice[17] (Flk1 is also known as Kdr). This approach revealed profound differences in vascular architecture in young adult frontal, parietal and interparietal skull bones (Extended Data Fig. 3a,c). Specifically, larger FLK1+CD31+ sinusoidal vessels with distal sprouts dominate in frontal bone, thinner FLK1+CD31+ sinusoidal vessel loops connect to FLK1low/−CD31+ arterioles in parietal bone, and interparietal bone is completely filled by a dense network of sinusoidal vessels and arterioles (Extended Data Fig. 3a,c). Vascular leakage assays with Evans Blue corroborated local differences in vascular properties in different skull parts and revealed significantly higher leakage in frontal bone compared with the other two skull bones (Extended Data Fig. 3b,d).

To gain more insight into the organization of the calvarial vasculature and the changes during adulthood and ageing, we performed in vivo labelling with fluorescent CD31 and endomucin antibodies. In young adult mice, only interparietal bone was largely filled by a dense vascular network, whereas substantial and sustained vascular expansion was observed in frontal and parietal bones of middle-aged and old mice (Fig. 1b–d). Vascular area increased by 6.4- and 3.6-fold in frontal and parietal bones, respectively, between young and geriatric mice. Endomucin, which decorates calvarial sinusoidal vessels but not arteries and smaller arterioles, has been implicated in bone maintenance and HSC function18,19. Vascular endomucin expression significantly increased until the geriatric stages (3.1-fold increase in frontal bone and 7.0-fold increase in parietal bone). We next focused on parietal bone, which showed the largest BM expansion and a transition from thin looping sinusoidal vessels in young adult mice to a dense network at later stages (Fig. 1b,c). Sinus diameter increased 4.8-fold and dorsal–ventral vertical extension of the vascular network can be observed at the old stage (Fig. 1c,d and Extended Data Fig. 3e). Immunofluorescence staining of caveolin-119,20 showed a 2.0-fold increase of interconnecting arterioles during ageing (Extended Data Fig. 3f,g). In vivo immunofluorescence staining of haematopoietic cells using fluorescent CD45 antibody and flow cytometric analyses show that vascular growth is associated with a substantial increase in haematopoietic cells (13.1-fold increase between young and geriatric mice) within the expanding BM (Extended Data Fig. 4a,b and Supplementary Data 1). CD45+ cells were predominantly associated with large-calibre sinusoidal vessels, which were highly abundant in old and geriatric skull (Extended Data Fig. 4a). The link between vessel calibre and BM was further confirmed by a genetic approach; labelling of haematopoietic cells in the Vav-cre;Rosa26-mTmG background21,22 shows that haematopoietic cells expressing GFP surround large-calibre sinusoidal vessels (Extended Data Fig. 4c). Similarly, CD3e+ T lymphocytes, B220+ B lymphocytes and CD11b+ cells (including monocytes/macrophages, granulocytes and natural killer cells) are associated with large-calibre sinusoids in skull BM (Extended Data Fig. 4d). With the exception of interparietal bone, BM-associated sinusoidal vessels with strong endomucin immunostaining are comparably sparse in young adult mice and increase substantially throughout adult life and ageing (Fig. 1b–d).

Interparietal bone, which is largely filled by vessels and BM at the young adult stage, shows minimal age-related changes in vascular area (24.3% increase between young and geriatric samples) and sinus diameter (9.3% decrease between young and geriatric samples) but a significant increase in endomucin expression (2.0-fold increase between young and geriatric samples) (Fig. 1b,d). By contrast, vascular density in the metaphyseal region of the femoral BM decreased by 45.2% between the middle-aged and old stages, but rebounded at geriatric stages with the emergence of very thin vessels displaying high endomucin expression (Extended Data Fig. 5a,b), which have been recently characterized as dysfunctional sinusoidal vessels[23]. Electron micrographs of the luminal surface of endothelial cells in young and old skull BM show comparable patterns of endothelial fenestrations, which are a hallmark of vessels in primary lymphoid organs including BM. By contrast, endothelial cells in old femur BM show a highly irregular pattern of fenestrae and the emergence of larger pores in the endothelial surface (Extended Data Fig. 5c). In contrast to the increasing vasculature of skull marrow, vessel density in the dura mater, which is adjacent to the skull and connected directly to the skull BM via specialized channels[10], decreases significantly during ageing (Extended Data Fig. 5d,e). Collectively, these results show continuous expansion of the BM and vasculature in the adult and ageing calvarium but not in the adjacent dura mater. Moreover, ageing femoral BM displays features of compromised vascular integrity, which are not seen in ageing skull BM.

Skull BM responses in pathophysiology

High demand for certain blood cells leads to strongly increased haematopoiesis in a range of physiological but also pathological conditions1,24. Pregnancy, for example, induces extramedullary haematopoiesis and the rapid expansion of maternal blood volume and red blood cells25,26. Skull BM and associated vasculature increased substantially during pregnancy, namely 1.8-fold at 17 days postcoitum (dpc) and 2.3-fold at 2 days postpartum (dpp), which involves substantial vessel enlargement in frontal and parietal bone together with a strong increase in total haematopoietic cells, HSCs and HSPCs, committed haematopoietic progenitors and stromal cells (Fig. 2a,b and Supplementary Data 1). As reported previously[27], CD31hiendomucinhi type H vessels in femur increased strongly at late-stage pregnancy and decreased postpartum, but overall vascular density in the metaphyseal region showed more limited changes (26.2% increase at 17 dpc and 8.5% decrease at 2 dpp) relative to skull (Extended Data Fig. 6a,b). Although the total number of haematopoietic cells in femur was not significantly changed by pregnancy, flow cytometry showed increases in HSCs, HSPCs, certain committed haematopoietic progenitors and stromal cells (Supplementary Data 1), consistent with enhanced blood cell production.

Fig. 2: Pathophysiological regulation of vessel growth and BM expansion in adult skull.

a,b, In vivo immunofluorescence (a) and quantification (b) of skull blood vessels and BM in pregnant (17 dpc) and postpartum (2 dpp) female mice. n = 3 mice per group from 3 independent experiments. c,d, In vivo immunofluorescence (c) and quantification (d) of skull blood vessels and BM in mice 7 days after transient mid-cerebral artery occlusion. n = 6 (sham) and n = 8 (tMCAO) mice per group from 3 independent experiments. e,f, In vivo immunofluorescence (e) and quantification (f) of skull blood vessels and BM in mice with CML. n = 4 mice per group from 3 independent experiments. g,h, In vivo immunofluorescence (g) and quantification (h) of skull blood vessels and BM in mice with 28-day sustained PTH treatment. n = 4 mice per group from 3 independent experiments. Arrowheads indicate areas of substantial expansion. Scale bars, 1 mm. Data are mean ± s.d. P values by Tukey multiple comparison test (one-way ANOVA) and two-tailed unpaired Student’s t-test.

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Ischaemic stroke in a transient mid-cerebral artery occlusion model (tMCAO) was shown to activate BM haematopoietic stem cells[28]. Consistent with this finding, tMCAO induced a striking expansion of skull BM and associated vasculature (2.1-fold increase), filling a substantial portion of parietal bone within 7 days (Fig. 2c,d). These changes are accompanied by strong increases in calvarial total haematopoietic cells, HSCs, HSPCs, committed haematopoietic progenitors and stromal cells (Supplementary Data 1). By contrast, vascular density in femoral BM decreased by 26.0% and haematopoietic cells and subpopulations were either unchanged or reduced in the stroke condition (Extended Data Fig. 6c,d and Supplementary Data 1).

Haematological malignancies are known to lead to BM alterations, which, in turn, facilitate disease progression[29]. A chronic myeloid leukaemia (CML) model showed a modest but significant expansion of the skull BM and associated blood vessels (39.1% increase), which involved an increase in haematopoietic and stromal cells (Fig. 2e,f and Supplementary Data 1). By contrast, substantial loss of vascularity (50.4% decrease), severe vascular rarefaction and limited changes in total haematopoietic cell number were observed in CML femoral BM (Extended Data Fig. 6e,f and Supplementary Data 1).

Biologically active fragments of parathyroid hormone (PTH) are used to treat osteoporosis[30], and also lead to HSPC expansion[31]. We found that 4 weeks of daily PTH treatment resulted in substantial skull BM expansion (4.6-fold increase), increased large-calibre vessels and expansion of vessel-associated BM area (Fig. 2g,h). Flow cytometry confirmed the increase in total haematopoietic cell number, HSCs, HSPCs and certain committed progenitors, as well as in endothelial and stromal cells (Supplementary Data 1). By contrast, only very limited changes were observed in femoral BM immunostaining and flow cytometry (Extended Data Fig. 6g,h and Supplementary Data 1). These results demonstrate an unexpected dynamic plasticity of skull BM in a range of pathophysiological contexts and uncover differences between calvarial and femoral BM.

Skull HSPCs drive BM growth during ageing

HSPCs residing in BM change drastically during ageing, both in quantity and differentiation potential[32]. To address whether such cells might promote skull BM expansion, we isolated lineage-negative (Lin−) cells from young or old skull and femur and transplanted them into lethally irradiated young recipients (Fig. 3a). In vivo immunofluorescence staining showed substantial expansion (2.2-fold) of skull BM vessels in recipient mice that received cells from old donor skull BM compared with those that received cells from young donors (Fig. 3b,c). Transplantation of old donor femur BM also resulted in the expansion of skull BM vessels in recipient mice but to a lesser degree than old donor skull BM. Notably, vascular density in femur was not significantly different between recipients of transplants from young and old donors (Extended Data Fig. 7a,b), further suggesting divergent responsiveness of different BM compartments. Flow cytometric analyses of HSPCs showed that both long-term HSCs (LT-HSCs) and Lin−Sca1+KIT+ (LSK) HSPCs increase in number in the ageing skull BM (Fig. 3d,e), similar to reports for ageing long bone33,34. To further test whether HSPCs contribute to BM expansion and vascular growth in skull, we treated mice with prostaglandin E_235,36 (PGE_2) or AMD3100 (ref. [37]) to modulate HSPC number. PGE_2 treatment increased the number of HSPCs in the skull BM by 92.3% (Extended Data Fig. 7c,d), resulting in significant expansion of calvarial vasculature and vessel-associated BM relative to control mice (Fig. 3f,g and Supplementary Data 1). AMD3100 treatment mobilized HSPCs into the peripheral blood by 11.2-fold (Extended Data Fig. 7e,f) and induced a 60.0% reduction of skull BM and vasculature relative to control mice (Fig. 3h,i). Although both PGE_2 and AMD3100 treatments led to changes in vascular density in femoral BM, these alterations were confined to existing marrow without changing the BM area (Extended Data Fig. 7g–j). These results indicate that treatments acting on HSPCs can dynamically modulate the skull BM and its vasculature.

Fig. 3: Regulation of calvarial vessels by haematopoietic cells.

a, Experimental scheme for lethal irradiation and transplantation of haematopoietic lineage-depleted BM population isolated from young or old donor mice. b,c, In vivo immunofluorescence (b) and quantification (c) of skull blood vessels and BM in transplanted mice showing increased vascular expansion in recipients receiving Lin− cells from old donors. n = 4 mice per group from 3 independent experiments. Arrowheads indicate areas of substantial BM vascular expansion. YS, young adult skull; OS, old skull; YF, young adult femur; OF, old femur. d,e, Quantification of HSPCs in young adult, middle-aged, old and geriatric skull and femur by FACS. n = 5 (young adult), n = 3 (middle-aged and old) and n = 4 (geriatric) mice per group from 3 independent experiments. f–i, In vivo immunofluorescence staining (f,h) and quantification (g,i) of skull blood vessels and BM in mice treated with PGE_2 or AMD3100 to promote HSPC expansion or mobilization, respectively. n = 6 (PGE_2 control), n = 5 (PGE_2) and n = 4 (all other groups) mice per group from 3 independent experiments. Arrowheads indicate areas of substantial BM expansion (white) and regression (blue). Scale bars, 1 mm. Data are mean ± s.d. P values by two-tailed unpaired Student’s t-test and Tukey multiple comparison test (one-way ANOVA).

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VEGFA drives skull BM and vessel growth

VEGFA is a master regulator of vascular growth38,39, and enzyme-linked immunosorbent assays (ELISAs) showed that levels of the growth factor were significantly increased in the skull BM in response to pregnancy, stroke and PTH treatment, whereas no changes could be detected in the femoral BM under the same conditions (Extended Data Fig. 8a). VEGFA was significantly reduced in the femoral BM of mice with CML but was increased in the skulls of the same mice (Extended Data Fig. 8a).

Previous studies showed that HSPCs isolated from long bone express high levels of Vegfa transcripts compared with other BM cell types[40]. We performed quantitative PCR with reverse transcription (RT–qPCR) analyses of various haematopoietic and stromal cell populations after fluorescence-activated cell sorting (FACS) from enzymatically digested BM. Lin−Sca1+KIT+ HSPCs from skull indeed showed higher Vegfa expression compared with Lin−Sca1−KIT+ cells (4.72-fold), Lin−Sca1−KIT− stromal cells (5.97-fold) or Lin+ mature haematopoietic cells (5.92-fold) in young mice (Fig. 4a). HSPCs isolated from aged skull show higher Vegfa expression compared with Lin−Sca1−KIT+ cells (8.39-fold), Lin−Sca1−KIT− stromal cells (10.59-fold) and Lin+ mature haematopoietic cells (9.86-fold) (Fig. 4a). By contrast, HSPCs isolated from aged femur show significantly lower Vegfa expression (5.5-fold) compared with the same population from young mice (Extended Data Fig. 9a). These results are mirrored by the expression of transcripts encoding VEGFA_120 and VEGFA_164 isoforms, which are highest in HSPCs from old skull, but also substantially higher in HSPCs from young mice compared with other cell populations (Extended Data Fig. 9b).

Fig. 4: Role of VEGF in calvarial vascular growth.

a, RT–qPCR analyses of Vegfa mRNA expression in FACS-sorted LSK (Lin−Sca1+KIT+), KIT+ (Lin−Sca1−KIT+), lineage-negative (Lin−Sca1−KIT−) and lineage-positive (Lin+) populations isolated from young or old skull BM. n = 5 pooled mice per sample from 3 independent experiments. b, VEGFA protein concentrations in total BM lysates from skull or femur isolated at various stages of adulthood and ageing. n = 5 young, n = 4 middle-aged, n = 7 old and n = 3 geriatric mice per group from 3 independent experiments. c, Immunofluorescence of skull BM sections showing high anti-VEGFA signal in KIT+ HSPCs (arrowheads). Representative images from three independent experiments. Scale bars, 20 μm. d,e, Immunofluorescence (d) and quantification (e) of intravenously injected Hypoxyprobe in old skull or femoral BM. n = 5 mice per group from 2 independent experiments. Arrowheads indicate labelled cells. Scale bars, 200 μm. IFI, integrated fluorescence intensity. f,g, In vivo immunofluorescence (f) and quantification (g) of calvarial blood vessels in mice expressing bone-homing VEGFA. Note the substantial increase in BM and expansion of vessels (arrowheads) and vascular area. n = 4 mice per group from 3 independent experiments. Scale bars, 1 mm. h,i, In vivo immunofluorescence (h) and quantification (i) of calvarial blood vessels in mice treated with VEGFR2 blocking antibody (DC101) for 12 weeks, showing profound inhibition of BM expansion, suppressed vessel growth (arrowheads) and with decrease in vascular diameter. n = 5 mice per group from 2 independent experiments. Scale bars, 1 mm. Data are mean ± s.d. P values by Tukey multiple comparison test (one-way ANOVA) and two-tailed unpaired Student’s t-test.

Source Data

At the protein level, ELISA of whole-BM lysates shows that VEGFA levels are initially highest in young femur, probably reflecting expression by non-haematopoietic sources such as chondrocytes, and decrease by 40.2% during ageing (Fig. 4b). By contrast, the amount of VEGFA in calvarial BM increases 2.2-fold from young to geriatric stages (Fig. 4b). Immunofluorescence of skull sections confirms high expression of VEGFA by resident KIT+ HSPCs (Fig. 4c). As hypoxia is a critical regulator of VEGFA expression in angiogenesis41,42, we administered Hypoxyprobe (pimonidazole hydrochloride) to analyse age-related changes in the oxygenation of skull and femur (Extended Data Fig. 9c,d). The Hypoxyprobe signal was significantly higher in skull BM relative to femoral BM at old (3.3-fold) and geriatric stages (4.8-fold) (Fig. 4d,e and Extended Data Fig. 9c,d), consistent with the observed increase of VEGFA in ageing skull.

Next, we manipulated VEGFA-dependent VEGFR2 signalling in vivo to directly address the role of this pathway in the expansion of skull vasculature and BM. For gain-of-function experiments, a cDNA encoding a bone-homing version of VEGFA (Methods) was cloned into the vector pLIVE, which enables constitutive protein expression in liver after hydrodynamic tail vein injection[43]. Vegfa overexpression induced a 2.5-fold increase of skull BM, including HSCs and other haematopoietic and stromal cells, and associated vasculature (Fig. 4f,g and Supplementary Data 1). Conversely, inhibition of VEGFR2 signalling with the blocking antibody DC101 inhibited BM expansion and vascular growth during ageing, as shown by immunostaining and flow cytometry (Fig. 4h,i and Supplementary Data 1). By contrast, although we observed vascular density changes within the confines of the femoral BM, these alterations did not involve any substantial volumetric changes of the BM as in the skull (Extended Data Fig. 9e–j). These results demonstrate that VEGFA–VEGFR2 signalling regulates skull BM expansion during ageing and that the increase of HSPCs in ageing skull is likely to be a relevant source of VEGFA.

Resilience of ageing skull BM

The ageing BM microenvironment disrupts normal self-renewal and other essential functions of HSCs[44]. To assess whether hallmarks of ageing affect calvarial and femoral BM equally, we characterized adipogenesis and inflammation in both compartments in young and geriatric mice. Adipocytes have previously been reported to accumulate in the ageing BM and negatively regulate HSC function[45]. Whole-mount or cross-sectional staining of skull or femur, respectively, with BODIPY shows minimal changes in the number of lipid-filled mature adipocytes in the skull, whereas a substantial 5.9-fold increase in adipocytes can be observed in the metaphyseal region of the ageing femoral BM (Fig. 5a,b). Next, we used a bead-based multiplex array to quantify 12 pro-inflammatory cytokines in lysates from skull and femoral BM isolated from young and old mice. Of note, only IFNγ was significantly upregulated in old skull, whereas 7 out of 12 cytokines, including TNF and IL-6, showed increased expression in old femur relative to young femur (Fig. 5c). It should also be noted that the expression of most pro-inflammatory cytokines in old femur was substantially higher than in skull, further supporting the notion that detrimental processes associated with ageing are more prominent in femur.

Fig. 5: Properties and function of ageing skull marrow.

a,b, Staining (a) and quantification (b) of adipocytes (BODIPY) and blood vessels (CD31) in skull or femoral BM of young or geriatric mice. Scale bars, 1 mm. n = 4 (young skull), n = 6 (old skull), n = 5 (all other groups) mice per group from 4 independent experiments. F, frontal; P, parietal; i-P, interparietal. c, Quantification of inflammatory cytokines by multiplex array analysis of total BM lysates from young or old skull or femur. n = 4 (young skull), n = 5 (old skull), n = 4 (young femur), n = 5 (old femur) mice per group from 2 independent experiments. d,e, Quantification of myeloid progenitors and progeny by FACS (d; difference in percentage of live cells) and RT–qPCR analyses of myeloid determination factors (e) of geriatric skull versus femur BM. Lin−Sca1−KIT+, common myeloid progenitor (CMP), granulocyte-monocyte progenitor (GMP), megakaryocyte-erythrocyte progenitor (MEP). Each value is the fold change difference between geriatric femur sample and corresponding skull sample from the same mouse. n = 4 mice per group from 2 independent experiments. f,g, Scheme of shielding experiments (f) and FACS analysis of CD11b+ myeloid cells in peripheral blood (g) isolated from mice with head versus leg shielding. n = 5 (young) and n = 4 (old) mice per group from 3 independent experiments. h, Kaplan–Meier survival plot showing survival of mice after whole-body irradiation or shielding skull or hindlimbs. n = 8 mice per group from 2 independent experiments. i, Schematic showing skull BM photoconversion in Vav-cre;Rosa26-KikGR mice. j, FACS analysis of photoconverted CD45+ haematopoietic cells in peripheral blood. n = 3 mice per group from 3 independent experiments. Data are mean ± s.d. P values by two-tailed unpaired Student’s t-test, log-rank test and Tukey multiple comparison test (one-way ANOVA).

Source Data

Ageing HSCs exhibit a myeloid bias at the expense of lymphopoiesis[46]. To assess whether such myeloid bias occurs and determine which stages of the myeloid differentiation cascade are affected in the ageing skull, we analysed myeloid-committed progenitors and mature myeloid cells in the geriatric skull or femoral BM using flow cytometry. We detected significant proportional increases of Lin−Sca1−KIT+ multipotent, common myeloid and granulocyte-monocyte progenitors, but not megakaryocyte-erythroid progenitors, in the femur compared with the skull (Fig. 5d). Furthermore, RT–qPCR analyses of the key myeloid determination factors Cebpa and Spi1 (which encodes the transcription factor PU.1) show substantial upregulation in the geriatric femur BM versus skull BM (Fig. 5e). Finally, to compare the myeloid output from skull versus hindlimb BM into peripheral blood, we selectively shielded these areas during lethal irradiation of young and old mice (Fig. 5f). Whereas there was no difference in myelopoiesis between head or leg shielding in young mice, peripheral blood output from hindlimbs of old mice showed a strong myeloid bias compared with skull (Fig. 5f,g). Together, these results show that calvarial BM is remarkably resilient against key features of ageing and thereby maintains a healthy microenvironment for haematopoiesis even in old mice.

Old skull preserves functional BM niches

It has been established that skull BM contains functional HSCs that can fully reconstitute the entire haematopoietic system in a transplantation setting[9] and is the source of meningeal immune cells11,47. The effect of ageing on haematopoietic output by different BM compartments, however, remains unknown. Following lethal irradiation in a non-transplantation setting, all mice with whole-body exposure died within 12 days of BM failure (Fig. 5h). However, all young adult mice that were protected by shielding of the head or hindlimbs survived past 16 weeks without transplantation of exogenous BM, demonstrating that both bone compartments contain sufficient HSCs for survival (Fig. 5h). Secondary haematopoietic reconstitution following an additional round of lethal irradiation with head or hindlimb shielding also demonstrated the long-term reconstitution potential of HSCs derived from both BM compartments (Extended Data Fig. 10a,b). Whereas shielding of the head of old mice was also fully compatible with long-term survival after irradiation, the same was not the case for hindlimb shielding, and none of the mice in this group survived beyond 200 days (Fig. 5h).

The calvarial vasculature is also highly resilient against irradiation-induced changes. Consistent with our previous findings[40], vascular density (10.2-fold) and diameter (3.3-fold) were substantially increased in femoral BM at 7 days after irradiation. By contrast, vessels in skull showed minimal changes both with and without BM transplantation (Extended Data Fig. 10c–e). To explore the potential mechanisms that contribute to BM heterogeneity in ageing skull and femur, we performed single-cell RNA sequencing (scRNA-seq) of KIT+ HSPCs, and stromal and endothelial cells isolated from either BM compartment in young or geriatric mice (Extended Data Fig. 11a–d). We found significantly higher expression of factors associated with stress and inflammation such as Jund, Fosb and Dusp1, as well as myeloid differentiation factors such as Ngp, Ltf and S100a8, in geriatric femur HSCs compared with skull HSCs (Extended Data Fig. 12a–f). These differentially expressed genes correlate strongly with gene expression changes reported for IL-1β-treated HSCs or the Tet2-knockout model of clonal haematopoiesis23,48.

To further determine whether the observed differences in BM function between ageing skull and femur are owing to HSC- or HSPC-intrinsic or extrinsic, microenvironmental factors, we performed colony-forming unit (CFU) assays of HSPCs isolated from skull or femur of young or geriatric mice (Extended Data Fig. 13a,b). Although HSPCs derived from both geriatric skull and femur initially showed higher colony-forming potential than their younger counterparts in primary CFU plating, there were no significant differences in secondary plating or in HSPC self-renewal or myeloid differentiation potential in both rounds of plating, indicating a role for niche-derived factors. scRNA-seq analysis of endothelial cells from geriatric BM revealed differences in the expression of inflammatory and myeloid differentiation factors such as Hotairm1, Lgals3, Il6 and S100a6, which were significantly upregulated in geriatric femur compared with skull (Extended Data Fig. 14a–g). Conversely, endothelial cells from skull exhibited higher expression of HSC maintenance factors such as Ptn, Gpr182 and Spp1 (Extended Data Fig. 14h).

Finally, to assess the haematopoietic function of skull BM in a physiological context devoid of irradiation injury, we expressed the green-to-red photoconvertible fluorescent protein, Kikume green–red[49] (KikGR) in haematopoietic cells under control of Vav-cre[21] (Fig. 5i). Following in vivo photoconversion of haematopoietic cells in calvarial BM, flow cytometric analysis of peripheral blood revealed a marked age-dependent increase in systemic haematopoietic contribution from photoconverted skull BM (Fig. 5j). These results show that systemic haematopoietic output from skull increases relative to other BM compartments during ageing.

In summary, our findings reveal an unexpected level of heterogeneity between two major BM compartments that are well-established model systems in experimental studies. In contrast to nearly all other organ systems and long bone, which become fully vascularized during embryonic and postnatal development[50], blood vessel growth in skull persists throughout adult life. Lifelong angiogenesis in skull involves the continued formation of large-calibre sinusoidal vessels, which are strongly associated with a range of haematopoietic cell populations and thus represent a central landmark in the skull BM environment. Vascular growth and BM expansion in skull rely on VEGFA and its receptor VEGFR2, which are key regulators of developmental and regenerative angiogenesis in many organs39,51,52. Consistent with previous studies[40], skull BM-resident HSPCs express more VEGFA than Lin+ differentiated haematopoietic cells, but other relevant sources of VEGFA might exist, as has been shown for chondrocytes and osteoprogenitors in long bone[53]. Our experiments also establish that circulation-derived VEGFA can promote calvarial vessel growth and BM expansion, which is consistent with the finding that systemic VEGF can increase lifespan and delay ageing processes in mice[54]. Conversely, HSPC behaviour is controlled by the BM endothelium and associated reticular cells, which provide critical niche signals, such as stem cell factor[55] (also known as SCF or KIT ligand) or CXCL12[56] (also known as SDF-1). Several studies have shown that haematopoietic cell production is enhanced in response to various physiological and pathological stimuli, such as pregnancy and ischaemic stroke[57]. Our present findings indicate that these conditions lead to the expansion of calvarial BM but induce only limited alterations in load-bearing long bone, where BM growth may not be compatible with mechanical loading and structural integrity. Furthermore, our results establish that calvarial BM is more resistant to ageing-related degenerative processes, namely adipogenesis, the upregulation of inflammatory cytokines and compromised vascular integrity. This might, in part, reflect that calvarial BM is biologically ‘younger’ owing to its formation during adult life. Alternatively, unknown cellular or molecular features might differentially influence haematopoiesis in different BM compartments. Consistent with the latter, recent work has shown that skull BM has a distinct molecular profile in health and neurological disorders[58]. Notably, our findings also suggest that BM expansion occurs in human skull, which is consistent with previous observations59,60. Thus, our findings regarding the plasticity and resilience of the BM in skull may have wider relevance for ageing and disease processes in humans as well as pharmacological treatments that target angiogenic signalling pathways.

Methods

Animal models

C57BL/6J mice were used for all experiments involving wild-type mice and pharmacological treatments. Mice at the age of 10–14 weeks, 31–37 weeks, 52–75 weeks and >95 weeks were chosen for young adult, middle-aged, old and geriatric groups, respectively. Both female and male mice of all age groups were used for initial BM expansion analyses, while only female mice were used for remaining experiments. Flk1-GFP reporter mice[17] were used for initial blood vessel characterization. For genetic labelling of haematopoietic cells, Vav1-cre mice[21] were interbred with ROSA26-mTmG reporter mice[22] to generate Vav-mTmG mice. For photoconversion of haematopoietic cells, Vav1-cre mice were interbred with ROSA26-CAG-loxP-stop-loxP-KikGR knock-in mice[49] to generate Vav-KikGR mice. For pregnancy experiments, 10-week-old C57BL/6J female mice were paired with 10- to 12-week-old C57BL/6J male mice and the onset of pregnancy was determined by the presence of a vaginal plug in the morning. Ten-week-old mice received daily intraperitoneal injections of PTH (1–34) (Bachem, 0.1 mg kg−1 for 28 days), PGE_2 (Cayman Chemical, 2 mg kg−1 for 7 days), AMD3100 (Abcam, 5 mg kg−1 for 14 days) before they were euthanized. For DC101 treatment, 10-week-old mice received intraperitoneal injections of DC101 (BioXCell, 40 mg kg−1) every 2 days for 12 weeks.

Mice were kept in individually ventilated cages, with constant access to food and water under a 12 h light and 12 h dark cycle regime. Air flow, temperature (21–22 °C) and humidity (55–60%) were controlled by an air management system. Mice were checked daily and maintained in specific pathogen-free conditions. Sufficient nesting material and environmental enrichment was provided. All animal experiments were performed according to the institutional guidelines and laws, approved by local animal ethical committee and were conducted at the Max Planck Institute for Molecular Biomedicine (84-02.04.2016.A160, 81-02.04.2018.A171, 81-02.04.2020.A212, 81-02.04.2020.A416 and 81-02.04.2022.A198), Universitätsmedizin Berlin (G0220/17), Georg-Speyer-Haus (F123/2017) and the University Medical Center Mainz Institute of Transfusion Medicine (G23-1-067 A1TE) under the indicated permissions granted by the Landesamt für Natur, Umwelt und Verbraucherschutz (LANUV) of North Rhine-Westphalia, the State Office for Health and Social Affairs Berlin, Regierungspräsidium Darmstadt and the Landesuntersuchungsamt Rheinland-Pfalz, Germany.

Human subjects, CT acquisition and data analysis

The study was approved by the local ethics committee and the institutional review board (IRB) of Asan Medical Center, and the requirement for informed consent was waived due to the retrospective nature of the study (IRB number: 2023-0658). The study population consisted of 36 patients, divided into 4 groups according to age (between 20 and 40 years, over 60 years) and sex (male, female), with 9 individuals in each group. Patients who underwent CT for evaluation of small cerebral aneurysm from April to May 2023 were eligible. Participants were excluded if they had a previous history of surgery or radiation therapy to the head and neck, vascular or bone-related medical implants, or a suspicious disease other than small cerebral aneurysm.

All human patients underwent CT examinations on the same 128-channel multidetector CT system (Somatom Definition Edge; Siemens). Imaging variables were as follows: 100 kV; 100 effective mAs; axial scan mode; section thickness, 0.5 mm; display FOV, 20.5 cm; pitch, 1; gantry rotation time, 0.5 s; pixel matrix, 512 × 512. Images were obtained from the vertex to first cervical spine, without an intravenous injection of contrast media.

The CT data were digitally transferred to a personal computer and processed with ImageJ software (http://rsb.info.nih.gov/ij/). A representative image was selected on a coronal CT image perpendicular to the outermost convex area on an axial CT image. After whole-bone segmentation of the parietal bone, cortical bone and BM were defined by attenuation densities on CT scan: cortical bone as over 850 Hounsfield units and BM as less than 850 Hounsfield units and their areas were calculated.

Sample processing and immunostaining

Mice were euthanized by transcardial perfusion of PBS and 4% paraformaldehyde (PFA), skulls and femur were collected and fixed immediately in ice-cold 4% PFA for 6–8 h under gentle agitation. Bones were decalcified in 0.5 M EDTA for 3 days (for skulls) or 7 days (for femurs) at 4 °C under gentle shaking agitation, washed 5 times in PBS in 5 min intervals, followed by overnight incubation in cryoprotectant solution (20% sucrose, 2% polyvinylpyrrolidone) and embedding in bone embedding medium (8% gelatin, 20% sucrose, 2% polyvinylpyrrolidone). Samples were stored overnight at −80 °C. 80-μm-thick cryosections were prepared for immunofluorescence staining.

Bone sections were washed in PBS and permeabilized with 0.3% Triton X-100 in PBS for 10 min at room temperature. Samples were incubated in blocking solution (5% heat-inactivated donkey serum in 0.3% Triton X-100) for 1 h at room temperature. Primary antibodies (rat monoclonal anti-endomucin (V.7C7) (Santa Cruz, sc-65495, 1:200 dilution), rabbit monoclonal anti-vATPaseB1/B2 (Abcam, 200839, 1:200 dilution), goat polyclonal anti-osteopontin (R&D Systems, AF808, 1:200 dilution), goat polyclonal anti-CD31 (R&D, AF3628, 1:200 dilution), rabbit polyclonal anti-caveolin-1 (Cell Signaling, 3238, 1:100), goat polyclonal anti-VEGF_164 (R&D Systems, AF-493-NA, 1:200 dilution), and rat monoclonal APC-conjugated anti-CD117 (KIT) (BD Biosciences, 553356, 1:100 dilution) were diluted in PBS with 5% donkey serum and incubated overnight at 4 °C. Next, slides were washed 3–5 times in PBS in 5 min intervals. Species-specific Alexa Fluor-conjugated secondary antibodies Alexa Fluor 488 (Thermo Fisher Scientific, A21208), Alexa Fluor 594 (Thermo Fisher Scientific, A21209), Alexa Fluor 647 (Thermo Fisher Scientific, A31573 or A21447) diluted 1:500 in PBS with 5% donkey serum were added and incubated overnight at 4 °C. Slides were washed 3–5 times in PBS in 5 min intervals. Nuclei were counterstained with DAPI (Sigma-Aldrich, D9542, 1:1,000 dilution). Coverslips were mounted with FluoroMount-G (Southern Biotech, 0100-01).

In vivo immunostaining and Evans Blue leakage assay

Rat monoclonal anti-CD31 (BD Biosciences, 553708) was conjugated to Alexa Fluor 647 using the Alexa Fluor 647 Antibody Labeling Kit (Thermo Fisher Scientific, A20186) according to the manufacturer’s instructions. For blood vessel immunostaining, the conjugated anti-CD31 antibody and rat monoclonal PE-conjugated anti-endomucin (V.7C7) (Santa Cruz, 65495 PE) were diluted 1:10 in 200 μl PBS and injected intravenously into the tail vein. For haematopoietic cell immunostaining, rat monoclonal FITC-conjugated anti-CD45 (eBioscience, 11-0451-82), hamster monoclonal FITC-conjugated anti-CD3e (eBioscience, 16-0031-82), rat monoclonal PE-conjugated anti-CD45R/B220 (BD Biosciences, 553090), rat monoclonal FITC-conjugated anti-CD11b (BD Biosciences, 553310) were diluted 1:10 in PBS and injected intravenously into the tail vein. Mice were euthanized 1 h after injection with transcardial perfusion with PBS and 4% PFA and bones were collected and fixed immediately in ice-cold 4% PFA for 6–8 h under gentle agitation. The dura mater was carefully removed from the skull with forceps. Bones were decalcified in 0.5 M EDTA for 1 day (for skulls) or 7 days (for femurs) at 4 °C under gentle shaking agitation, and washed 5 times in PBS in 5 min intervals. Skulls were counterstained with DAPI (1:500 dilution) for 1 h and trimmed down to the calvarium before mounting with iSpacers (Sunjin Lab, IS011) in PBS. Femurs were cryosectioned, counterstained and mounted as described above.

For the Evans Blue leakage assay, mice were anaesthetized immediately prior to tail vein injection of 200 μl Evans Blue solution (Sigma-Aldrich, E2129, 1% v/w). Mice were euthanized via transcardial perfusion 5 min after injection as described above. In order to distinguish vascular leakage in the dura mater from the calvarial BM, dura mater tissues were separated from the calvarial bone before overnight decalcification.

Immunostained samples were imaged with a Zeiss LSM980 (Carl Zeiss). Images were analysed, quantified and processed using ZEN Black (Carl Zeiss, v2.3), ImageJ (NIH, v2.0.0) and IMARIS (Bitplane, v10.0.1). Tilescan overview images of skull BM were superimposed on top of a black background, filling empty corners without image data. Vessel diameter was measured by selecting the z-plane image with the widest vessel diameter from the z-stack of the individual vessel.

Scanning electron microscopy

Skull and femur from 12-week-old and 73-week-old mice were isolated and submerged in 4% PFA, 0.5% glutaraldehyde, 2 mM MgCl_2, 2 mM CaCl_2 in 0.1 M cacodylate buffer, pH 7.4, under agitation for 2 h at room temperature. Samples were fixed further overnight in 2% glutaraldehyde, 2 mM MgCl_2, 2 mM CaCl_2 in 0.1 M cacodylate buffer, pH 7.4 at 4 °C. Bones were then decalcified over 12 days, changing solution every other day in 5% EDTA in 0.1 M cacodylate buffer, pH 7.4 under rotation at 4 °C. Subsequently, 150 μm sections were generated with a vibratome (VT 1200, Leica). Sections were post-fixed in 1% OsmO_4, containing 2.5% PFA–glutaraldehyde mixture buffered with 0.1 M phosphate (pH 7.2) for 5 h and then were placed in graded ethanol for critical-point drying using E3000 (Polaron) critical-point dryer. Critical-point-dried bones were placed on a piece of carbon tape and sputter coated with gold in a SC502 Sputter Coater (Polaron). Specimens were imaged on a Quanta 250 Field Emission Scanning Electron microscope (FEI Quanta 250 FEG, FEI, Hillsboro, OR) installed at the Korea Research Institute of Bioscience and Biotechnology.

Dura mater whole-mount immunostaining

Mice were euthanized by transcardial perfusion of PBS and 4% PFA, skulls were collected and fixed immediately in ice-cold 4% PFA for 6–8 h under gentle agitation. Skulls were decalcified in 0.5 M EDTA for 24 h at 4 °C under gentle shaking agitation, washed 5 times in PBS in 5 min intervals, trimmed down to the calvarium, and incubated in blocking solution (5% heat-inactivated donkey serum in 0.3% Triton X-100) for 1 h at room temperature. Goat polyclonal anti-CD31 (R&D, AF3628, 1:100 dilution) was diluted in PBS with 5% donkey serum and incubated overnight at 4 °C with gentle agitation. Samples were washed 3–5 times in PBS in 10 min intervals. Alexa Fluor 647 (Thermo Fisher Scientific, A21447) diluted 1:500 in PBS with 5% donkey serum was added and incubated overnight at 4 °C with gentle agitation. Samples were washed 3–5 times in PBS in 10 min intervals and mounted with iSpacers (Sunjin Lab, IS011) in PBS.

tMCAO

Sixteen-week-old female C57BL6/J mice were used throughout the experiments. Mice were anaesthetized by intraperitoneal injection of a mixture of 10 mg kg−1 xylazine (cp-pharma) and 90 mg kg−1 ketamine hydrochloride (cp-pharma). Throughout the whole procedure and during recovery, body temperature was maintained at 37 °C via a heating pad. After ligation of the left proximal common carotid artery and external carotid artery, a 7.0-nylon monofilament (Doccol) with a 0.23-mm coated tip was introduced into the distal internal carotid artery via an incision in the ligated common carotid artery. The monofilament was advanced distal to the carotid bifurcation to occlude the middle cerebral artery. Arter topical application of the local anaesthetic lidocaine hydrochloride (Xylocain Spray 2%, Aspen) the neck wound was closed temporarily for a 45 min ischaemic period. At reperfusion, the monofilament was withdrawn from the carotid artery and the wound was stitched with 4-0 non-resorbable sutures (Ethibond Excel, Ethicon) and the single s.c. injection of Penicillin G 20 000 U (Benzylpenicillin-Natrium, InfectoPharm) was given. The mouse was returned to its cage to recover under observation.

Chronic myeloid leukaemia

Six-week-old female C57BL/6 mice were purchased from Charles River Laboratories and were used as donors and recipients in all transplants. The transplantation experiments were performed as previously described[61]. In brief, to induce CML-like myeloproliferative neoplasia, donor BM cells from donor mice pre-treated with 5-fluorouracil (200 mg kg−1 intravenously; 4 days prior to collection) were pre-stimulated overnight in medium containing SCF (50 ng ml−1), IL-6 (10 ng ml−1) and IL-3 (6 ng ml−1) and transduced on two consecutive days with murine stem cell virus (MSCV)-IRES-GFP-BCR-ABL1 to induce CML or MSCV-IRES-GFP control virus. Subsequently, transduced cells were intravenously transplanted (2.5 × 10^5 cells per mouse) into sublethally irradiated (900 cGy) recipient mice. Mice were euthanized 14 days after transplantation.

Lineage depletion and transplantation

In order to transplant lineage-negative BM cells, young (10- to 14-week-old) or old (52- to 75-week-old) donor mice were euthanized and skulls were collected. The calvarium was first chopped with scissors in FACS buffer (PBS with 2% fetal calf serum), then crushed with a mortar and pestle. Cell suspension was filtered through a 40-μm mesh filter (Falcon, 352340), resuspended in RBC lysing buffer (Sigma-Aldrich, R7757) for red blood cell lysis and washed with FACS buffer. Cells were resuspended in FACS buffer and incubated with a biotinylated anti-haematopoietic lineage antibody cocktail (Miltenyi-Biotec, 130-092-613, 1:10 dilution), followed by washing with FACS buffer and incubation with R-PE-conjugated streptavidin secondary antibody (Invitrogen, S866, 1:50 dilution). DAPI (1:1,000 dilution) was added to resuspended cells to distinguish live and dead cells and were FACS-sorted for live lineage-negative cells on a FACSAria Fusion (BD Biosciences). Sorted cells were intravenously transplanted (5×10^5 cells/mouse) into lethally irradiated (12 Gy, Best Theratronics, Gammacell 40 Exactor) recipient mice (12-week-old). Mice were euthanized 14 days after transplantation.

FACS analysis of BM and peripheral blood

Mice from each age group were euthanized and skull and femur were collected. Skulls were chopped with scissors in FACS buffer before crushed with mortar and pestle; femurs were crushed without chopping. BM stromal samples were dissociated with Collagenase I (Gibco, 17100-017, 2 mg ml−1) and Collagenase IV (Gibco, 17104-019, 2 mg ml−1) in PBS for 20 min at 37 °C with intermittent shaking. Cell suspensions were strained through a 40-μm mesh filter, resuspended in RBC lysing buffer (when applicable) and washed with FACS buffer. Cells were resuspended and incubated with the following primary antibodies in FACS buffer: biotinylated rat monoclonal anti-haematopoietic lineage antibody cocktail (Miltenyi-Biotec, 130-092-613, 1:50 dilution), APC-conjugated rat monoclonal anti-CD117 (BD Biosciences, 553356, 1:100 dilution), FITC-conjugated rat monoclonal anti-CD117 (Biolegend, 105806, 1:100 dilution), FITC-conjugated rat monoclonal anti-Ly6A/E (Sca1) (eBioscience, 11-5981-85, 1:100 dilution), PerCP-Cy5.5-conjugated rat monoclonal anti-Ly-6A/E (Invitrogen, 45-5981, 1:100), APC-Cy7-conjugated hamster monoclonal anti-CD48 (BD Biosciences, 561242, 1:100 dilution), PE-conjugated rat monoclonal anti-CD150 (SLAM) (Biolegend, 115904, 1:100 dilution), Alexa Fluor 647-conjugated rat monoclonal anti-CD150 (Biolegend, 115918, 1:100 dilution), PE-Cy7-conjugated rat monoclonal anti-CD45 (eBioscience, 25-0451-82, 1:100 dilution), BV421-conjugated rat monoclonal anti-TER-119 (Biolegend, 116234, 1:100 dilution), FITC-conjugated rat monoclonal anti-CD71 (Biolegend, 113806, 1:100 dilution), Alexa Fluor 647-conjugated rat monoclonal anti-CD31 (BD Biosciences, 553708, conjugation described above, 1:200 dilution), PE-conjugated rat monoclonal anti-endomucin (Santa Cruz, 65495 PE, 1:100 dilution), PE-Cy7-conjugated rat monoclonal anti-CD16/32 (eBioscience, 25-0161, 1:100 dilution), eFluor 450-conjugated rat monoclonal anti-CD34 (eBioscience, 48-0341, 1:100 dilution), PE-conjugated rat monoclonal anti-CD127 (eBioscience, 12-1271, 1:100 dilution), BV711-conjugated rat monoclonal anti-CD41 (BD Biosciences, 740712, 1:100 dilution), PE-conjugated rat monoclonal anti-CD105 (eBioscience, 12-1051-82, 1:100 dilution), APC-conjugated hamster monoclonal anti-CD3e (eBioscience, 17-0031, 1:100 dilution), PE-conjugated rat monoclonal anti-CD45R/B220 (BD Biosciences, 553090, 1:100 dilution), FITC-conjugated rat monoclonal anti-CD11b (BD Biosciences, 553310, 1:100 dilution). Cells were washed, resuspended in FACS buffer with Alexa Fluor 405-conjugated (Invitrogen, S32351, 1:100 dilution) or APC-Cy7-conjugated (BD Biosciences, 554063, 1:100 dilution) streptavidin secondary antibody, washed again before analysis with a FACSymphony A5 Cell Analyzer (BD Biosciences).

Peripheral blood was collected from the submandibular vein with lancets (Medipoint) into EDTA-coated tubes. Blood was resuspended in RBC lysing buffer and washed with FACS buffer. Cells were resuspended and incubated with the following primary antibodies in FACS buffer: biotinylated rat monoclonal anti-haematopoietic lineage antibody cocktail (Miltenyi-Biotgec, 130-092-613, 1:50 dilution), APC-conjugated rat monoclonal anti-CD117 (BD Biosciences, 553356, 1:100 dilution), FITC-conjugated rat monoclonal anti-Ly6A/E (Sca1) (eBioscience, 11-5981-85, 1:100 dilution), Pacific Blue-conjugated mouse monoclonal anti-CD45.2 (Biolegend, 109820, 1:100 dilution), APC-conjugated hamster monoclonal anti-CD3e (eBioscience, 17-0031, 1:100 dilution), PE-conjugated rat monoclonal anti-CD45R/B220 (BD Biosciences, 553090, 1:100 dilution), FITC-conjugated rat monoclonal anti-CD11b (BD Biosciences, 553310, 1:100 dilution). Cells were washed and resuspended in FACS buffer before analysis with a FACSymphony A5 Cell Analyzer (BD Biosciences).

RNA extraction and quantitative PCR

FACS-sorted cells from 10-week-old mouse skulls were lysed and RNA was extracted using a Monarch Total RNA Miniprep Kit (New England BioLabs, T2010S). Extracted RNA concentration was measured with a NanoDrop 8000 Spectrophotometer (Thermo Fisher Scientific) and cDNA was generated with a LuncaScript RT SuperMix Kit (New England BioLabs, E3010L). Quantitative PCR with reverse transcription was performed with a BioRad CFX96 real-time PCR system using FAM-conjugated Taqman probes for Vegfa (Mm00437306_m1) or using PowerUp SYBR Green Master Mix (Applied Biosystems, A25742) with primers designed using Pimer-BLAST or adopted from previously published studies: Vegfa_120 (5′-AACGATGAAGCCCTGGAGTG-3′; 5′-TGAGAGGTCTGGTTCCCGA-3′); Vegfa_164 (5′-AACGATGAAGCCCTGGAGTG; 5′-GACAAACAAATGCTTTCTCCG-3′); Vegfa_188 (5′-AACGATGAAGCCCTGGAGTG-3′; 5′-AACAAGGCTCACAGTGAACG-3′). Gene expression levels were normalized to the endogenous VIC-conjugated Gapdh probe (44326317E) as control.

ELISA

Mice from each age group were euthanized, bones were collected. Skulls were chopped before being crushed with a mortar and pestle in ice-cold RIPA lysis buffer; femurs were crushed without chopping. Supernatants of centrifuged lysates were further concentrated using an Ultra-0.5 Centrifugal Filter Unit with a 3 kDa cutoff (Millipore, UFC500396), resulting concentrations were measured using a Pierce BCA Protein Assay Kit (Thermo Fisher Scientific, 23225), and the concentrations of VEGFA in tissue extracts were measured using a Mouse VEGFA Quantikine ELISA Kit (R&D Systems, MMV00-1).

Hypoxia analysis

Hypoxic cells were detected with the hypoxia probe pimonidazole (Pimo, Hypoxyprobe) according to the manufacturer’s instructions. Mice were intraperitoneally injected with 60 mg kg−1 1 h before analysis.

VEGFA plasmid construction and overexpression

To generate the pLIVE-VEGFA_165-HA-MP-Asp8x bone-homing protein containing VEGF_165 fused to a HA tag, metalloprotease and 8x Asp peptide sequences, a cDNA fragment encoding amino acids 1–191 of human VEGFA was amplified via PCR using the following oligonucleotide primers: VEGFA-AscI-Fwd: 5′-ATGAACTTTCTGCTGTCT-3′ and VEGFA-XhoI-Rev: 5′-CCGCCTCGGCTTGTCACATCTGCA-3′ and annealed with the NEBuilder Assembly Cloning Kit.

Ten-week-old mice were used for hydrodynamic tail vein injection. Mice were injected with 0.5 μg g−1 (plasmid/body weight) pLIVE-Vegfa plasmid suspended in TransIT-EE hydrodynamic delivery solution (Mirus, MIR5340). The appropriate amount of plasmid was suspended in an injection volume of 10% of the body weight and injected into each individual mouse via the tail vein in 5–7 s as previously reported[62].

Adipocyte analysis

To stain for neutral lipids, the entire calvarium or femur cryosections were incubated in BODIPY 493/503 (Invitrogen, D3922; 1:1,000 dilution) for 1 h at room temperature with gentle agitation (only calvarium). Samples were washed with PBS 3–5 times at 5 min intervals before mounting.

Analysis of inflammatory cytokines

Mice from each age group were euthanized and bones were collected. Skulls were chopped before being crushed with a mortar and pestle in ice-cold RIPA lysis buffer; femurs were crushed without chopping. Supernatants of centrifuged lysates were further concentrated using an Ultra-0.5 Centrifugal Filter Unit with a 3 kDa cutoff (Millipore, UFC500396), resulting concentrations were measured using a Pierce BCA Protein Assay Kit (Thermo Fisher Scientific, 23225), and concentrations of inflammatory cyotokines were measured with LEGENDplex Mouse Inflammation Panel (13-plex) with V-bottom plates (Biolegend, 740446). Analysis on a FACSymphony (BD Biosciences) and quantification were performed according to the manufacturer’s protocol. Data analysis was performed using software provided by Biolegend. Manual gating was used to define beads A and B, and automatic gating strategy was used to gate individual cytokines in the APC–PE plot.

Irradiation with partial shielding

Mice were anaesthetized with ketamine (100 mg kg−1) and xylazine (10 mg kg−1) prior to irradiation. For partial shielding, the entire head or both legs of a mouse were inserted into the opening of the cylindrical 1-inch-thick lead shield (JRT Associates, PTI-50-P) and exposed to lethal irradiation (12 Gy). The mouse was returned to its cage to recover under observation.

Sample preparation for scRNA-seq

Mice from each age group were euthanized and skull and femur were collected. Skulls were chopped with scissors in FACS buffer before crushed with mortar and pestle; femurs were crushed without chopping. BM stromal samples were dissociated with Collagenase I (Gibco, 17100-017, 2 mg ml−1) and Collagenase IV (Gibco, 17104-019, 2 mg ml−1) in PBS for 20 min at 37 °C with intermittent shaking. Cell suspensions were strained through a 40-μm mesh filter and washed with FACS buffer. Cells were resuspended and incubated with biotinylated rat monoclonal anti-haematopoietic lineage antibody cocktail (Miltenyi-Biotec, 130-092-613, 1:50 dilution). Cells were washed, resuspended in FACS buffer with mouse monoclonal anti-Biotin MicroBeads (Miltenyi-Biotec, 130-105-637, 1:50 dilution) and incubated before being loaded into a magnetic-associated cell sorting (MACS) column (Miltenyi-Biotec, 130-042-201) for lineage depletion. Lin− cells were further incubated with rat monoclonal anti-CD45 MicroBeads (Miltenyi-Biotec, 130-052-301, 1:50 dilution), rat monoclonal anti-CD117 MicroBeads (Miltenyi-Biotec, 130-091-224, 1:50 dilution), biotinylated rat monoclonal anti-CD71 (Biolegend, 113803, 1:100). Cells were washed, resuspended in FACS buffer with mouse monoclonal anti-Biotin MicroBeads (Miltenyi-Biotec, 130-105-637, 1:50 dilution) and incubated before being loaded into a magnetic-associated cell sorting (MACS) column (Miltenyi-Biotec, 130-042-201) for further haematopoietic depletion. Single-cell suspensions were processed with BD Rhapsody and scRNA-seq libraries were evaluated and quantified by Agilent Bioanalyzer using High Sensitivity DNA Kit (Agilent Technologies, 5067-4626) and Qubit (Thermo Fisher Scientific, Q32851). Individual libraries were diluted to 4 nM and pooled for sequencing. Pooled libraries were sequenced by using High Output Kit (Illumina, TG-160-2002) with a NextSeq500 sequencer (Illumina).

scRNA-seq

Preprocessing: STAR version 2.7.10a (PMID: 23104886) was used to generate a reference genome index for GRCm39, with Gencode annotations vM29, subset to lncRNA and protein-coding genes.

FASTQ reads were mapped against the reference genome index using STAR with the settings “--soloType CB_UMI_Complex --soloCellFilter None --outSAMtype BAM SortedByCoordinate --soloFeatures GeneFull_Ex50pAS --soloCBmatchWLtype 1MM --soloUMIlen 8 --soloCBwhitelist BD_CLS1.txt BD_CLS2.txt BD_CLS3.txt --runRNGseed 1 --soloMultiMappers EM --readFilesCommand zcat --outSAMattributes NH HI AS nM NM MD jM jI MC ch CB UB GX GN sS CR CY UR UY”. Libraries using standard BD Rhapsody beads were mapped using the adapter parameters “--soloAdapterSequence NNNNNNNNNACTGGCCTGCGANNNNNNNNNGGTAGCGGTGACA --soloCBposition 2_0_2_8 2_21_2_29 3_1_3_9 --soloUMIposition 3_10_3_17”, libraries with BD Rhapsody enhanced beads with --soloAdapterSequence NNNNNNNNNGTGANNNNNNNNNGACA --soloCBposition 2_0_2_8 2_13_2_21 3_1_3_9 --soloUMIposition 3_10_3_17.

Raw counts were imported as AnnData[63] objects. We removed low complexity barcodes with the knee plot method, and further filtered out cells with a mitochondrial mRNA content, as well as unusually high total and gene counts using manually determined cutoffs for each sample. Doublets were scored with scrublet[64]. Finally, each sample’s gene expression matrix was normalized using scran[65] (1.22.1) with Leiden clustering[66] input at resolution 0.5.

G2M and S phase scores were assigned to each cell using gene lists from ref. [67] and the scanpy[68] (1.9.6) sc.tl.score_genes_cell_cycle function.

Embedding, clustering and annotation: different combinations of samples and cell populations (all, ECs, HSCs), were used as input for 2D embedding and clustering: the corresponding expression matrix was subset to the 2,000 most highly variable genes (sc.pp.highly_variable_genes, flavour “seurat”). The top 50 principal components were calculated, and batch-corrected using Harmony[69] (0.0.9). The principal components served as basis for k-nearest neighbour calculation (sc.pp.neighbors, n_neighbors=30), which were used as input for UMAP[70] layout (sc.tl.umap, min_dist=0.3). Cell populations were clustered using scanpy.tl.leiden, and a suitable resolution was chosen for a first-pass annotation. Here, contaminating cell populations, including multiplet clusters, were removed, and clustering was repeated. Cluster marker genes were calculated using a pseudobulk approach, comparing aggregate counts with 2 pseudoreplicates for each cluster to all remaining cells (pyDeSEQ2 0.4.8). Finally, expression of select marker genes was plotted using Matplotlib[71] (3.8.4) imshow, and clusters were annotated accordingly.

Differential expression analysis: Differentially expressed genes were calculated using a pseudobulk approach, comparing aggregate counts with two pseudoreplicates for each condition (pyDeSEQ2 0.4.8).

Skull BM photoconversion

Vav1-KikGR mice were anaesthetized with ketamine (100 mg kg−1) and xylazine (10 mg kg−1). A skin flap was generated to expose the calvarium, as previously described[72]. Each exposed area of the calvarium was then exposed to UV light from a Zeiss Axio Imager (Zeiss Microscopy) for 60 s, confirmed for photoconversion from green to red fluorescence, before exposing another area. The skin flap was sutured back together and peripheral blood was analysed by flow cytometry, as described above, to check for the presence of photoconverted cells, which were non-existent in the peripheral blood immediately after photoconversion. One week after photoconversion, peripheral blood was drawn, stained for Alexa Fluor-conjugated rat monoclonal anti-CD45, and was analysed by flow cytometry for CD45+ photoconverted haematopoietic cells derived from the skull BM.

Statistical analysis

No statistical methods were used to predetermine sample size. The experiments were randomized and investigators were blinded to allocation during experiments and outcome analyses. All values are presented as mean ± s.d. Statistical significance was determined by the two-tailed unpaired Student’s t-test between two groups or the Tukey multiple comparison test (one-way ANOVA) for multiple-group comparison. Statistical analyses were performed using GraphPad Prism 9.0 (GraphPad Software). Statistical significance was set at P < 0.05.

Reporting summary

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

Data availability

The scRNA-seq data generated in this study have been deposited in the Gene Expression Omnibus under accession number GSE275179. The mouse reference genome GRCm39 with GENCODE M26 annotation (https://www.gencodegenes.org/mouse/release_M26.html) was used for mapping the reads in this study. All individual mouse lines used in this study are commercially available at The Jackson Laboratory. Plasmid constructs are available, upon request, through the corresponding author. Source data are provided with this paper.

Change history

- 27 November 2024

In the Methods, two instances of “cGy” have been updated to instead read as “Gy”.

References

1. Hoggatt, J., Kfoury, Y. & Scadden, D. T. Hematopoietic stem cell niche in health and disease. Annu. Rev. Pathol. 11, 555–581 (2016).

2. Verovskaya, E. V., Dellorusso, P. V. & Passegue, E. Losing sense of self and surroundings: hematopoietic stem cell aging and leukemic transformation. Trends Mol. Med. 25, 494–515 (2019).

3. Comazzetto, S., Shen, B. & Morrison, S. J. Niches that regulate stem cells and hematopoiesis in adult bone marrow. Dev. Cell 56, 1848–1860 (2021).

4. van den Bos, T., Speijer, D., Bank, R. A., Bromme, D. & Everts, V. Differences in matrix composition between calvaria and long bone in mice suggest differences in biomechanical properties and resorption: Special emphasis on collagen. Bone 43, 459–468 (2008).

5. Sivaraj, K. K. & Adams, R. H. Blood vessel formation and function in bone. Development 143, 2706–2715 (2016).

6. Mills, W. A., Coburn, M. A. & Eyo, U. B. The emergence of the calvarial hematopoietic niche in health and disease. Immunol. Rev. 311, 26–38 (2022).

7. Lo Celso, C. et al. Live-animal tracking of individual haematopoietic stem/progenitor cells in their niche. Nature 457, 92–96 (2009).

8. Christodoulou, C. et al. Live-animal imaging of native haematopoietic stem and progenitor cells. Nature 578, 278–283 (2020).

9. Lassailly, F., Foster, K., Lopez-Onieva, L., Currie, E. & Bonnet, D. Multimodal imaging reveals structural and functional heterogeneity in different bone marrow compartments: functional implications on hematopoietic stem cells. Blood 122, 1730–1740 (2013).

10. Herisson, F. et al. Direct vascular channels connect skull bone marrow and the brain surface enabling myeloid cell migration. Nat. Neurosci. 21, 1209–1217 (2018).

11. Cugurra, A. et al. Skull and vertebral bone marrow are myeloid cell reservoirs for the meninges and CNS parenchyma. Science 373, eabf7844 (2021).

12. Nakamura-Ishizu, A., Ito, K. & Suda, T. Hematopoietic stem cell metabolism during development and aging. Dev. Cell 54, 239–255 (2020).

13. Potente, M., Gerhardt, H. & Carmeliet, P. Basic and therapeutic aspects of angiogenesis. Cell 146, 873–887 (2011).

14. Chung, A. S. & Ferrara, N. Developmental and pathological angiogenesis. Annu. Rev. Cell Dev. Biol. 27, 563–584 (2011).

15. Ramasamy, S. K. et al. Regulation of hematopoiesis and osteogenesis by blood vessel–derived signals. Annu. Rev. Cell Dev. Biol. 32, 649–675 (2016).

16. Ramalingam, P., Butler, J. M. & Poulos, M. G. Vascular regulation of hematopoietic stem cell homeostasis, regeneration, and aging. Current Stem Cell Rep. 7, 194–203 (2021).

17. Xu, Y. et al. Neuropilin-2 mediates VEGF-C–induced lymphatic sprouting together with VEGFR3. J. Cell Biol. 188, 115–130 (2010).

18. Kusumbe, A. P. et al. Age-dependent modulation of vascular niches for haematopoietic stem cells. Nature 532, 380–384 (2016).

19. Langen, U. H. et al. Cell–matrix signals specify bone endothelial cells during developmental osteogenesis. Nat. Cell Biol. 19, 189–201 (2017).

20. Yu, J. Direct evidence for the role of caveolin-1 and caveolae in mechanotransduction and remodeling of blood vessels. J. Clin. Invest. 116, 1284–1291 (2006).

21. de Boer, J. et al. Transgenic mice with hematopoietic and lymphoid specific expression of Cre. Eur. J. Immunol. 33, 314–325 (2003).

22. Muzumdar, M. D., Tasic, B., Miyamichi, K., Li, L. & Luo, L. A global double-fluorescent Cre reporter mouse. Genesis 45, 593–605 (2007).

23. Mitchell, C. A. et al. Stromal niche inflammation mediated by IL-1 signalling is a targetable driver of haematopoietic ageing. Nat. Cell Biol. 25, 30–41 (2023).

24. Olson, O. C., Kang, Y. A. & Passegue, E. Normal hematopoiesis is a balancing act of self-renewal and regeneration. Cold Spring Harb. Perspect. Med. 10, a035519 (2020).

25. Nakada, D. et al. Oestrogen increases haematopoietic stem-cell self-renewal in females and during pregnancy. Nature 505, 555–558 (2014).

26. Oguro, H. et al. 27-Hydroxycholesterol induces hematopoietic stem cell mobilization and extramedullary hematopoiesis during pregnancy. J. Clin. Invest. 127, 3392–3401 (2017).

27. Rodrigues, J. et al. Estrogen enforces the integrity of blood vessels in the bone during pregnancy and menopause. Nat. Cardiovasc. Res. 1, 918–932 (2022).

28. Courties, G. et al. Ischemic stroke activates hematopoietic bone marrow stem cells. Circ. Res. 116, 407–417 (2015).

29. Mendez-Ferrer, S. et al. Bone marrow niches in haematological malignancies. Nat. Rev. Cancer 20, 285–298 (2020).

30. Reid, I. R. & Billington, E. O. Drug therapy for osteoporosis in older adults. Lancet 399, 1080–1092 (2022).

31. Calvi, L. M. et al. Osteoblastic cells regulate the haematopoietic stem cell niche. Nature 425, 841–846 (2003).

32. Akunuru, S. & Geiger, H. Aging, clonality, and rejuvenation of hematopoietic stem cells. Trends Mol. Med. 22, 701–712 (2016).

33. Morrison, S. J., Wandycz, A. M., Akashi, K., Globerson, A. & Weissman, I. L. The aging of hematopoietic stem cells. Nat. Med. 2, 1011–1016 (1996).

34. Colom Diaz, P. A., Mistry, J. J. & Trowbridge, J. J. Hematopoietic stem cell aging and leukemia transformation. Blood 142, 533–542 (2023).

35. North, T. E. et al. Prostaglandin E_2 regulates vertebrate haematopoietic stem cell homeostasis. Nature 447, 1007–1011 (2007).

36. Porter, R. L. et al. Prostaglandin E_2 increases hematopoietic stem cell survival and accelerates hematopoietic recovery after radiation injury. Stem Cells 31, 372–383 (2013).

37. Hoggatt, J. et al. Rapid mobilization reveals a highly engraftable hematopoietic stem cell. Cell 172, 191–204.e110 (2018).

38. Simons, M., Gordon, E. & Claesson-Welsh, L. Mechanisms and regulation of endothelial VEGF receptor signalling. Nat. Rev. Mol. Cell Biol. 17, 611–625 (2016).

39. Apte, R. S., Chen, D. S. & Ferrara, N. VEGF in signaling and disease: beyond discovery and development. Cell 176, 1248–1264 (2019).

40. Chen, Q. et al. Apelin+ endothelial niche cells control hematopoiesis and mediate vascular regeneration after myeloablative injury. Cell Stem Cell 25, 768–783.e766 (2019).

41. Schipani, E., Wu, C., Rankin, E. B. & Giaccia, A. J. Regulation of bone marrow angiogenesis by osteoblasts during bone development and homeostasis. Front. Endocrinol. 4, 85 (2013).

42. Wong, B. W., Marsch, E., Treps, L., Baes, M. & Carmeliet, P. Endothelial cell metabolism in health and disease: impact of hypoxia. EMBO J. 36, 2187–2203 (2017).

43. Suda, T. & Liu, D. Hydrodynamic gene delivery: its principles and applications. Mol. Ther. 15, 2063–2069 (2007).

44. Ho, Y.-H. & Méndez-Ferrer, S. Microenvironmental contributions to hematopoietic stem cell aging. Haematologica 105, 38–46 (2020).

45. Ambrosi, T. H. et al. Adipocyte accumulation in the bone marrow during obesity and aging impairs stem cell-based hematopoietic and bone regeneration. Cell Stem Cell 20, 771–784.e776 (2017).

46. Ho, Y. H. et al. Remodeling of bone marrow hematopoietic stem cell niches promotes myeloid cell expansion during premature or physiological aging. Cell Stem Cell 25, 407–418.e406 (2019).

47. Brioschi, S. et al. Heterogeneity of meningeal B cells reveals a lymphopoietic niche at the CNS borders. Science 373, eabf9277 (2021).

48. McClatchy, J. et al. Clonal hematopoiesis related TET2 loss-of-function impedes IL1β-mediated epigenetic reprogramming in hematopoietic stem and progenitor cells. Nat. Commun. 14, 8102 (2023).

49. Tomura, M. et al. Tracking and quantification of dendritic cell migration and antigen trafficking between the skin and lymph nodes. Sci. Rep. 4, 6030 (2014).

50. Maes, C. Role and regulation of vascularization processes in endochondral bones. Calcif. Tissue Int. 92, 307–323 (2013).

51. Carmeliet, P. & Jain, R. K. Molecular mechanisms and clinical applications of angiogenesis. Nature 473, 298–307 (2011).

52. Hooper, A. T. et al. Engraftment and reconstitution of hematopoiesis is dependent on VEGFR2-mediated regeneration of sinusoidal endothelial cells. Cell Stem Cell 4, 263–274 (2009).

53. Hu, K. & Olsen, B. R. Osteoblast-derived VEGF regulates osteoblast differentiation and bone formation during bone repair. J. Clin. Invest. 126, 509–526 (2016).

54. Grunewald, M. et al. Counteracting age-related VEGF signaling insufficiency promotes healthy aging and extends life span. Science 373, eabc8479 (2021).

55. Ding, L., Saunders, T. L., Enikolopov, G. & Morrison, S. J. Endothelial and perivascular cells maintain haematopoietic stem cells. Nature 481, 457–462 (2012).

56. Greenbaum, A. et al. CXCL12 in early mesenchymal progenitors is required for haematopoietic stem-cell maintenance. Nature 495, 227–230 (2013).

57. Heidt, T. et al. Chronic variable stress activates hematopoietic stem cells. Nat. Med. 20, 754–758 (2014).

58. Kolabas, Z. I. et al. Distinct molecular profiles of skull bone marrow in health and neurological disorders. Cell 186, 3706–3725.e3729 (2023).

59. Sabanciogullari, V. et al. Diploe thickness and cranial dimensions in males and females in mid-Anatolian population: an MRI study. Forensic Sci. Int. 219, 289.e281–287 (2012).

60. Lillie, E. M., Urban, J. E., Lynch, S. K., Weaver, A. A. & Stitzel, J. D. Evaluation of skull cortical thickness changes with age and sex from computed tomography scans. J. Bone Miner. Res. 31, 299–307 (2016).

61. Krause, D. S. et al. Differential regulation of myeloid leukemias by the bone marrow microenvironment. Nat. Med. 19, 1513–1517 (2013).

62. Liu, F., Song, Y. & Liu, D. Hydrodynamics-based transfection in animals by systemic administration of plasmid DNA. Gene Ther. 6, 1258–1266 (1999).

63. Virshup, I., Rybakov, S., Theis, F. J., Angerer, P. & Wolf, F. A. anndata: annotated data. Preprint at bioRxiv https://doi.org/10.1101/2021.12.16.473007v1 (2021).

64. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291.e9 (2019).

65. Lun, A. T. L., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).

66. Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).

67. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

68. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

69. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

70. McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://doi.org/10.48550/arXiv.1802.03426 (2018).

71. Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).

72. Koh, B. I. et al. VEGFR2 signaling drives meningeal vascular regeneration upon head injury. Nat. Commun. 11, 3866 (2020).

Acknowledgements

The authors thank M. Stehling (MPI Münster), J. Kirsch (FCCF, DRFZ) and C. Conche (HI-TRON Mainz, DKFZ) for FACS technical assistance; F. Berkenfeld for supporting animal colony maintenance; and K. Müller for sequencing technical assistance. V.M. is a part of the Cells in Motion-International Max Planck Research School (CiM-IMPRS) and University of Münster, Germany. R.H.A. is supported by the Max Planck Society, the European Research Council (AdG 101139772, PROTECT), the DFG (CRC 1366, project no. 394046768), and the Leducq Foundation.

Funding

Open access funding provided by Max Planck Society.

Author information

Authors and Affiliations

1. Department of Tissue Morphogenesis, Max Planck Institute for Molecular Biomedicine, Münster, Germany

Bong Ihn Koh, Vishal Mohanakrishnan, Hongryeol Park, Susanne Adams, M. Gabriele Bixel & Ralf H. Adams

2. Sequencing Core Facility, Max Planck Institute for Molecular Biomedicine, Münster, Germany

Hyun-Woo Jeong

3. Bioinformatics Service Unit, Max Planck Institute for Molecular Biomedicine, Münster, Germany

Kai Kruse

4. Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

Young Jun Choi

5. Department of Neurosurgery, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

Melina Nieminen-Kelhä & Peter Vajkoczy

6. Institute of Transfusion Medicine, Transfusion Center, University Medicine Mainz, Mainz, Germany

Rahul Kumar & Daniela S. Krause

7. Georg-Speyer-Haus Institute for Tumor Biology and Experimental Medicine and Goethe University Frankfurt, Frankfurt, Germany

Raquel S. Pereira

8. Center for Vascular Research, Institute for Basic Science, Daejeon, Republic of Korea

Hyuek Jong Lee

Contributions

B.I.K. and R.H.A. designed the study and interpreted results. B.I.K. designed, conducted and analysed the majority of the experiments, including all pharmacological treatments, skull sample processing, immunostainings, transplantations, FACS sorting and analyses, RNA and protein analyses, and photoconversion surgeries. V.M. processed femur samples and provided technical assistance. H.-W.J. and H.P. provided technical assistance. K.K. performed scRNA-seq analyses. Y.J.C. conducted and analysed human head CT scans. M.N.-K. and P.V. designed and performed transient middle cerebral artery occlusion surgeries. R.K., R.S.P. and D.S.K. generated the CML mouse model and contributed to project design. S.A. generated pLIVE plasmids. H.J.L. conducted scanning electron microscopy sample processing and imaging. M.G.B. contributed to project design and data interpretation. R.H.A. supervised the project.

Corresponding authors

Correspondence to Bong Ihn Koh or Ralf H. Adams.

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Extended data figures and tables

Extended Data Fig. 1 Age-dependent expansion of BM in skull.

a, b. DAPI staining and quantification of mouse skull coronal cryosections showing expansion of BM cellular content during adulthood and aging (n = 4 mice/group from three independent experiments). Blue arrowheads indicate location of magnified inset. c, d. IF staining and quantification of skull coronal cryosections showing increased vATPase+ activated osteoclasts attached to Osteopontin+ bone surfaces in old versus young specimen (n = 4 mice/group from two independent experiments). Scale bars, 1 mm. Vertical bars indicate mean ± SD. P values were calculated using Tukey multiple comparison test (one-way ANOVA) and two-tailed unpaired Student’s t-test.

Source Data

Extended Data Fig. 2 Age-related expansion of diploic space in human skull.

a. Method for quantification of whole bone, cortex and bone marrow areas in human patient CT scans. b, c. Representative coronal CT images (b) and quantification (c) of young (21–40 years old) and old (61–69 years old) female and male human skulls showing enlargement of diploic space with aging (n = 9 patients/group). Scale bars, 2 cm. Vertical bars indicate mean ± SD. P values were calculated using two-tailed unpaired Student’s t-test.

Source Data

Extended Data Fig. 3 Regional differences and expansion of skull BM.

a, c. In vivo IF staining (a) and quantification of BM vessels (c) in Flk1-GFP reporter mice showing distinct blood vessel architecture in frontal (F), parietal (P) and interparietal (i-P) skull. Arrowheads indicate Flk1low/− CD31+ arterioles. n = 6 mice/group for vascular area, n = 20 randomly selected vessels from all samples/group for vessel diameter from two independent experiments. Boxed areas in overview image (left) indicate location of magnified images. Scale bar, 1 mm. b, d. Imaging (b) and quantification (d) of Evans Blue vascular leakage in different skull bone parts (n = 4 mice/group from three independent experiments). Boxed areas in overview image (left) indicate location of magnified images. Scale bar, 1 mm. e. IF staining of skull BM blood vessels showing increase in vascular network complexity with aging. Dotted lines demarcate dorsal and ventral skull boundaries. Representative images from four independent experiments. Scale bars, 100 μm. f, g. IF staining and quantification of caveolin-1-positive (Cav1+) blood vessels showing an increase with aging (n = 8 mice/group from two independent experiments). Dotted lines demarcate dorsal and ventral boundaries of the skull. Scale bars, 100 μm. Vertical bars indicate mean ± SD. P values were calculated using Tukey multiple comparison test (one-way ANOVA) and two-tailed unpaired Student’s t-test.

Source Data

Extended Data Fig. 4 Association of vessels and hematopoietic cells in skull BM.

a, b. In vivo IF staining (a) and quantification (b) of CD45+ hematopoietic cells in skull BM during aging (n = 4 mice/group from three independent experiments). Green arrowheads indicate dense clusters of CD45+ cells. Scale bars, 500 μm. c. Genetic labeling of hematopoietic cells in Vav-Cre Rosa26-mTmG mice (Vav-mTmG) shows association (arrowheads) of GFP-expressing cells with large CD31+ vessels. Representative images from three independent experiments. Scale bars, 150 μm. d. Confocal images showing association of CD3ɛ+ T lymphocytes (green), B220+ B lymphocytes (red) and CD11b+ cells (green) with large-caliber CD31+ BM vessel in aged skull. Representative images from three independent experiments. Vertical bars indicate mean ± SD. P values were calculated using Tukey multiple comparison test (one-way ANOVA). Scale bars, 500 μm.

Source Data

Extended Data Fig. 5 Age-related changes in the BM vasculature.

a, b. In vivo IF staining (a) and quantification (b) of femoral BM vessels showing vascular deterioration during aging by comparing young (Y), middle-aged (M), old (O), and geriatric (G) specimen (n = 5 mice/group for vascular density and Endomucin intensity, n = 10 randomly selected vessels from all samples/group from three independent experiments). Scale bars, 500 μm. c. Electron micrographs showing the surface of BM endothelial cells in young vs. old skull or femur, as indicated. Note regular pattern of fenestrations (black arrowheads) in young and old skull but disorganized pattern with larger gaps (yellow arrowheads) in old femur. Representative images from three independent experiments. Scale bars, 1 μm. d, e. IF staining (d) and quantification (e) of dural blood vessels revealing substantial decrease in vascular density with aging. n = 3 (young), n = 6 (old) mice/group from three independent experiments. Scale bars, 250 μm. Vertical bars indicate mean ± SD. P values were calculated using Tukey multiple comparison test (one-way ANOVA) and two-tailed unpaired Student’s t-test.

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Extended Data Fig. 6 Pathophysiological changes in the femoral BM vasculature.

a, b. In vivo IF staining (a) and quantification (b) of femoral BM blood vessels in pregnant (17 dpc) and post-partum (2 dpp) female mice. n = 3 mice/group from three independent experiments. c, d. In vivo IF staining (c) and quantification (d) of femoral BM blood vessels in mice 7 days after transient mid-cerebral artery occlusion (tMCAO). n = 6 (sham), n = 8 (tMCAO) from three independent experiments. e, f. In vivo IF staining (e) and quantification (f) of femoral BM blood vessels in mice with chronic myeloid leukemia. n = 4 mice/group from three independent experiments. g, h. In vivo IF staining (g) and quantification (h) of femoral BM blood vessels in mice with 28-day sustained parathyroid hormone (PTH) treatment. n = 4 mice/group from three independent experiments. Scale bars, 1 mm. Vertical bars indicate mean ± SD. P values were calculated using Tukey multiple comparison test (one-way ANOVA) and two-tailed unpaired Student’s t-test.

Source Data

Extended Data Fig. 7 Effect of treatments affecting HSPCs.

a, b. In vivo IF staining (a) and quantification (b) of femoral BM blood vessels in recipient mice receiving hematopoietic lineage-negative cells isolated from young or old donors (young skull (YS), old skull (OS), young femur (YF), old femur (OF)). n = 4 mice/group from three independent experiments. Scale bars, 1 mm. c, d. Diagram showing experimental scheme for PGE2 treatment for HSPC expansion (c) and quantification (d) of HSPCs in skull BM and spleen weight with PGE2 treatment (n = 3 mice/group from three independent experiments). e, f. Diagram showing experimental scheme for AMD3100 treatment for HSPC mobilization (e) and quantification (f) of HSPCs in peripheral blood and spleen weight with AMD3100 treatment (n = 5 mice/group from three independent experiments). g-j. In vivo IF staining (g, i) and quantification (h, j) of femoral BM blood vessels in mice treated with PGE2 (g, h) or AMD3100 (i, j) for HSPC expansion or mobilization, respectively. n = 6 (PGE2 Control), n = 5 (PGE2), n = 4 (all other groups) mice/group from three independent experiments. Scale bars, 1 mm. Vertical bars indicate mean ± SD. P values were calculated using two-tailed unpaired Student’s t-test.

Source Data

Extended Data Fig. 8 VEGF-A response to pathophysiological conditions.

a. VEGF-A protein concentrations in total BM lysates from skull or femur isolated from mice in various pathophysiological conditions, including pregnancy, stroke, chronic myeloid leukemia and sustained PTH treatment. n = 4 (Pregnancy, Stroke and PTH) and n = 5 (CML) mice/group from two independent experiments. Vertical bars indicate mean ± SD. P values were calculated using two-tailed unpaired Student’s t-test.

Source Data

Extended Data Fig. 9 VEGF expression and hypoxia in BM compartments.

a. qRT-PCR analyses of Vegfa mRNA in FACS-sorted LSK (LIN- Sca1+ cKit+), cKit+ (LIN- Sca1- cKit+), Lin neg (LIN- Sca1- cKit-) and Lin pos (LIN+) populations isolated from femur BM of young or old mice (n = 5 mice pooled/sample from three independent experiments). b. qRT-PCR analyses of Vegfa mRNA isoforms in populations shown in (a) isolated from skull or femur BM of young or old mice (n = 5 mice pooled/sample from three independent experiments). c, d. IF staining (c) and quantification (d) of IV-injected Hypoxyprobe in young, old and geriatric skull and femoral BM, as indicated. Note increase of Hypoxyprobe signal in skull but not in femur (arrowheads, n = 5 mice/group from two independent experiments). Scale bars, 1 mm. Vertical bars indicate mean ± SD. **P < 0.01, ***P < 0.001 versus Young LSK (a) or Young (d), #P < 0.05 versus Old by Tukey multiple comparison test (one-way ANOVA). e, h. Diagram showing experimental scheme for Vegfa overexpression or anti-VEGFR2 blocking antibody DC101 treatment. f-j. In vivo IF staining (f, i) and quantification (g, j) of femoral BM blood vessels in mice after hydrodynamic injection of bone-homing Vegfa construct (n = 4 mice/group from three independent experiments) (f, g) or treatment with anti-VEGFR2 blocking antibody (DC101) for 12 weeks (n = 5 mice/group from two independent experiments) (i, j). Scale bars, 1 mm. Vertical bars indicate mean ± SD. P values were calculated using two-tailed unpaired Student’s t-test.

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Extended Data Fig. 10 Long-term reconstitution potential of skull HSCs.

a. Diagram showing experimental scheme for serial hematopoietic reconstitution following serial partial irradiation with shielding. b. Monthly FACS analysis of T, B, and myeloid cells in peripheral blood isolated from animals with head versus leg shielding during primary and secondary hematopoietic reconstitution (n = 5 (Head shield), n = 4 (Leg shield) mice/group from two independent experiments). Vertical bars indicate mean ± SD. c-e. Fluorescence imaging (c) and quantification (d, e) of skull or femoral BM blood vessels in Cdh5-mTnG (mTomato, red) reporter mice at 7 days after lethal irradiation with (+) or without (-) bone marrow transplantation (BMT), as indicated. Note minimal vascular alterations in the skull BM compared to substantial vascular changes in femoral BM (n = 6 mice/group for vascular area/density, n = 10 randomly selected vessels from all samples/group for vessel diameter from three independent experiments). Vertical bars indicate mean ± SD. P values were calculated using Tukey multiple comparison test (one-way ANOVA).

Source Data

Extended Data Fig. 11 Single-cell RNA-sequencing analysis of HSPCs and stromal cells in skull and femur bone marrow during aging.

a. UMAP plot showing color-coded merged cell clusters of FACS-sorted cKit+ HSPCs and hematopoietic lineage-depleted bone marrow cells. Hematopoietic stem cell (HSC), multi-potent progenitor (MPP), myeloid progenitor (MyPro), lymphoid progenitor (LyPro), megakaryocyte-erythrocyte progenitor (MEP), pre-B-cell (Pre-B), sinusoidal endothelial cell (sEC), arterial endothelial cell (aEC), bone marrow mesenchymal stromal cell (bmMSC), metaphyseal mesenchymal stromal cell (mpMSC), smooth muscle cell (SMC), osteoblast (Ob). b. Heat map showing top cell marker genes of each cell population shown in (a). c. Group-selected color-coded cell clusters from young or old, skull or femur as indicated. d. Frequency plots of color-coded HSPCs (top) and endothelial cells (bottom) from young skull (YS), old skull (OS), young femur (YF), old femur (OF).

Extended Data Fig. 12 Single-cell RNA-sequencing analysis of HSCs in skull and femur bone marrow during aging.

a. UMAP plot showing color-coded merged cell clusters of HSCs identified in Extended Data Fig. 11. b. Heat map showing top cell marker genes of each cell population shown in (a). c. Frequency plots of color-coded HSCs from young skull (YS), old skull (OS), young femur (YF), old femur (OF). d. Differentially expressed genes (DEG) analysis comparing HSCs from geriatric skull versus femur. e. DEG analysis comparing each HSC subcluster from geriatric skull versus femur. f. Violin plots showing the expression of selected genes with log normalized values for inflammatory and myeloid determination factors in each HSC subcluster identified in (a). Skull HSC1 (n = 6216 cells), HSC2 (n = 3713 cells), HSC3 (n = 768 cells) and Femur HSC1 (n = 6726 cells), HSC2 (n = 3006 cells), HSC3 (n = 719 cells) isolated from n = 5 mice from two independent experiments. The box of each boxplot starts in the first quartile and ends in the third, with the line inside representing the median. Blue dots, p-adjusted value < 0.01 and Log_2 fold change <0.5; orange dots, p-adjusted value < 0.01 and Log_2 fold change > 0.5 by two-tailed unpaired Student’s t-test.

Extended Data Fig. 13 HSPCs are dependent on microenvironment during aging.

a, b. Brightfield image (a) and quantification (b) of primary and secondary colony forming unit (CFU) assays of 500 FACS-sorted LSK cells isolated from young or geriatric skull or femur bone marrow (n = 4 mice/group from two independent experiments). Scale bar, 5 mm. Vertical bars indicate mean ± SD. P values were calculated using Tukey multiple comparison test (one-way ANOVA).

Source Data

Extended Data Fig. 14 Single-cell RNA-sequencing analysis of ECs in skull and femur bone marrow during aging.

a. UMAP plot showing color-coded merged cell clusters of ECs identified in Extended Data Fig. 11. b. Heat map showing top cell marker genes of each cell population shown in (a). c. Frequency plots of color-coded ECs from young skull (YS), old skull (OS), young femur (YF), old femur (OF). d, e. Violin plots showing the expression of selected genes with log normalized values for vascular signaling factors in ECs (d) and each EC subcluster (e) from geriatric skull versus femur. Skull ECs (n = 2414 cells), Arterial (n = 1070 cells), Sinusoidal 1 (n = 329 cells), Sinusoidal 2 (n = 833 cells), Venous (n = 182 cells) and Femur ECs (n = 594 cells), Arterial (n = 430 cells), Sinusoidal 1 (n = 19 cells), Sinusoidal 2 (n = 58 cells), Venous (n = 87 cells) isolated from n = 5 mice from two independent experiments. The box of each boxplot starts in the first quartile and ends in the third, with the line inside representing the median. f. Differentially expressed genes (DEG) analysis comparing ECs from geriatric skull versus femur. g, h. Heat map showing the expression of selected genes with log normalized values for inflammatory factors (g) and HSC-regulating factors (h) identified in (f). Blue dots, p-adjusted value < 0.01 and Log_2 fold change <0.5; orange dots, p-adjusted value < 0.01 and Log_2 fold change > 0.5 by two-tailed unpaired Student’s t-test.

Supplementary information

Reporting Summary

Supplementary Data 1

Flow cytometric analyses of skull or femur BM in ageing, pregnancy, stroke, CML, PTH treatment, young versus old transplantation, PGE_2 treatment, AMD3100 treatment, pLIVE-Vegfa overexpression and DC101 treatment.

Supplementary Data 2

Gating strategies for all FACS isolation and flow cytometric analyses performed in the study.

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Source data

Source Data Figs. 1–5 and Source Data Extended Data Figs. 1–10 and 13.

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Koh, B.I., Mohanakrishnan, V., Jeong, HW. et al. Adult skull bone marrow is an expanding and resilient haematopoietic reservoir. Nature 636, 172–181 (2024). https://doi.org/10.1038/s41586-024-08163-9

- Received: 10 October 2023

- Accepted: 07 October 2024

- Published: 13 November 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08163-9

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Anti-viral defence by an mRNA ADP-ribosyltransferase that blocks translation

Christopher N. Vassallo,

Christopher R. Doering &

Michael T. Laub

Nature volume 636, pages 190–197 (2024)

Abstract

Host–pathogen conflicts are crucibles of molecular innovation1,2. Selection for immunity to pathogens has driven the evolution of sophisticated immunity mechanisms throughout biology, including in bacterial defence against bacteriophages[3]. Here we characterize the widely distributed anti-phage defence system CmdTAC, which provides robust defence against infection by the T-even family of phages[4]. Our results support a model in which CmdC detects infection by sensing viral capsid proteins, ultimately leading to the activation of a toxic ADP-ribosyltransferase effector protein, CmdT. We show that newly synthesized capsid protein triggers dissociation of the chaperone CmdC from the CmdTAC complex, leading to destabilization and degradation of the antitoxin CmdA, with consequent liberation of the CmdT ADP-ribosyltransferase. Notably, CmdT does not target a protein, DNA or structured RNA, the known targets of other ADP-ribosyltransferases. Instead, CmdT modifies the N6 position of adenine in GA dinucleotides within single-stranded RNAs, leading to arrest of mRNA translation and inhibition of viral replication. Our work reveals a novel mechanism of anti-viral defence and a previously unknown but broadly distributed class of ADP-ribosyltransferases that target mRNA.

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Main

ADP-ribosyltransferases are important enzymes found throughout biology that transfer the ADP ribose moiety of NAD+ onto other biomolecules, usually modifying an amino acid on a target protein5,6,7. ADP-ribosylation is one of the most common post-translational modifications in biology, and is used to regulate a wide variety of proteins involved in cellular signalling, chromatin and transcription, DNA repair, and other functions. At least six of the human ADP-ribosyltransferases (previously called poly-ADP-ribosyl polymerases[8] (PARPs)) are induced by interferon and are thus hypothesized to function in anti-viral defence9,10, but their precise roles and targets remain poorly defined. Although they have been studied for decades as protein modifiers, recent work has identified ADP-ribosyltransferases that target nucleic acids. DarT toxins of DarTG toxin–antitoxin systems can modify single-stranded DNA[11]. The Pseudomonas aeruginosa type VI secretion effector RhsP2 targets the 2′ hydroxyl of some double-stranded RNAs[12], and in Photorhabdus laumondii, a type VI secretion effector modifies 23S ribosomal RNA[13] (rRNA).

Here, we identify CmdT, an ADP-ribosyltransferase that functions in bacterial anti-phage defence by specifically modifying mRNAs to block phage translation and the production of mature virions. CmdT is part of a tripartite toxin–antitoxin–chaperone (TAC) system. Toxin–antitoxin systems have a major role in anti-phage defence[14]. These systems feature a protein toxin that is restrained from killing a cell or blocking cell growth by a cognate antitoxin[15]. For anti-phage toxin–antitoxin systems, phage infection must somehow liberate the toxin, but the mechanisms responsible remain incompletely understood[16]. TAC systems are common variants that feature a chaperone related to the conserved protein export chaperone SecB. The chaperones of TAC systems promote proteolytic stability of their cognate antitoxin and, consequently, neutralization of the toxin17,18,19,20. How the toxins of TAC systems are liberated is not known.

For CmdTAC from the Escherichia coli strain ECOR22[4], we find that during infection by T4 phage, the major capsid protein outcompetes the antitoxin CmdA for binding to the chaperone CmdC. Consequently, CmdA is degraded by the ClpP protease, leading to activation of the CmdT ADP-ribosyltransferase. Notably, CmdT does not target proteins, DNA or structured RNAs, the targets of other known ADP-ribosyltransferases. Instead, we find that CmdT selectively modifies mRNA to block translation and abort the phage infection. Biochemical analyses demonstrate that CmdT primarily modifies the N6 position of adenine in GA dinucleotides found in mRNAs. In sum, our work reveals a novel mechanism of anti-phage defence and a previously unknown molecular target of an ADP-ribosyltransferase. As some of the interferon-inducible human ADP-ribosyltransferases are reported to also modify RNA in vitro[10], our work suggests that ADP-ribosylation of RNA may be a common, previously unappreciated facet of innate immunity throughout biology.

CmdTAC protects against Tevenirinae phages

The cmdTAC system (Fig. 1a) was discovered in the genome of the wild E. coli isolate ECOR22 through a functional genetic screen for anti-phage defence systems[4]. Its homologues are found in diverse bacteria, including both Gram-negative and Gram-positive species (Extended Data Fig. 1a,b). In E. coli, cmdTAC provides robust defence against the Tevenvirinae, a major family of phages, which includes T4. Consistent with our prior work[4], deleting cmdTAC from ECOR22 improved the efficiency of plating (EOP) of T4 by at least 10^3-fold. Transformation of ECOR22 with a low-copy plasmid containing cmdTAC expressed from its native promoter (hereafter cmdTAC refers to this construct unless noted) fully restored defence (Fig. 1b). We challenged E. coli MG1655 containing cmdTAC with all 12 Myoviridae subfamily Tevenirinae phage from the BASEL collection[21]. CmdTAC decreased the EOP of most Tevenirinae phages between 10^2- and 10^5-fold with only two exceptions, Bas35 and Bas38 (Fig. 1c and Extended Data Fig. 1c). No protection was observed against phages from other taxa. We also measured burst size during a single round of infection and found that CmdTAC reduced the number of T4 progeny from around 110 phages to almost 0, indicating a lack of new viral particle production (Fig. 1d).

Fig. 1: The anti-phage defence system CmdTAC protects E. coli against infection by Tevenviridae.

a, Schematic of the cmdTAC operon (NCBI proteins RCP66309-11.1) indicating structural features predicted by HHpred. ART, ADP-ribosyltransferase. b, Plaquing of tenfold serially diluted T4 phage on lawns of E. coli strain ECOR22 and an ECOR22 ΔcmdTAC mutant without or with a low-copy plasmid containing cmdTAC under its native promoter. c, Efficiency of plating of the phages indicated on E. coli K-12 + cmdTAC relative to an empty vector control. T2, T4 and T6 data from ref. [4]. d, T4 burst size in E. coli K-12 + cmdTAC or an empty vector (EV) (1 h at 30 °C). Data are mean ± s.d.; n = 5 biological replicates. e, Growth of E. coli K-12 + cmdTAC or empty vector and infected with T4 at the indicated MOIs. The centre line indicates mean, shaded area represents 95% confidence interval; n = 6 technical replicates; representative of 3 biological replicates. f, AlphaFold2-predicted model of CmdTAC complex with insets showing structural alignments of CmdT to the ADP-ribosyltransferase exotoxin A (Protein Data Bank (PDB) 1AER, root mean square deviation (r.m.s.d.) 6.93 Å) and CmdC monomer to the SecB chaperone monomer (PBD 1OZB, r.m.s.d. 4.13 Å ). r.m.s.d. values were generated by Foldseek.

Source Data

To determine how CmdTAC provides defence, we examined growth of E. coli MG1655 containing cmdTAC following infection by T4 at different multiplicities of infection (MOI) (Fig. 1e). Direct defence systems provide protection at almost any MOI, whereas abortive infection (Abi) systems allow growth only when the MOI is less than 1. Abi systems typically inhibit the growth of infected cells to prevent phage replication and thus do not protect the individual cell, but prevent the spread of the infection to other cells in the population[22]. Indeed, cmdTAC protected against infection only at MOIs less than 1, with the survival of infected cells indistinguishable from an empty vector control (Extended Data Fig. 1e). By contrast, around 15% of control cells with a direct defence system (PD-T4-1[4]) survived. We also found that the number of infected cells that produced at least 1 progeny phage (that is, the efficiency of centre of infection (ECOI)) was reduced nearly 100-fold relative to cells lacking cmdTAC (Extended Data Fig. 1f). These results are consistent with CmdTAC functioning via an Abi mechanism.

CmdA helps fold and neutralize CmdT

To identify the potential functions of each component in the CmdTAC system, we used HHpred and AlphaFold2 together with FoldSeek to predict the function and structure of each component separately and in complex (Fig. 1f and Extended Data Fig. 2a–f). These analyses identified ADP-ribosyltransferase-like β-strands in CmdT and predicted a structure with similarity to the catalytic domain of P. aeruginosa exotoxin A, an ADP-ribosyltransferase of the diphtheria toxin (DTX) family[23]. A sequence logo built from CmdT homologues revealed several conserved residues including the HY-[E,D,Q] catalytic triad of the diphtheria toxin family of ADP-ribosyltransferases[24] (Extended Data Fig. 3a). Substituting alanine for the inferred catalytic residue of CmdT, Y41—a variant referred to here as CmdT*—abolished protection against T4 (Extended Data Fig. 1d). CmdA was predicted to have a pair of N-terminal α-helices followed by a long and unstructured C-terminal domain[20]. CmdC is homologous to SecB and has the same predicted tetrameric organization as SecB-like chaperones commonly found in TAC systems18,19. AlphaFold2 predicted that the three proteins form a hetero-hexameric complex with a 1:1:4 ratio of CmdT:CmdA:CmdC (Fig. 1f). To validate that CmdTAC forms a complex, we engineered cmdTAC under its native promoter to produce CmdT–Flag and then performed immunoprecipitation coupled to tandem mass spectrometry (IP–MS/MS). CmdT co-precipitated with both CmdA and CmdC (Extended Data Fig. 3b and Supplementary Table 1).

To experimentally test their predicted functions, we expressed each gene of the cmdTAC system in E. coli MG1655. Inducing any individual component did not strongly affect plating viability (Fig. 2a). However, co-expressing cmdT and cmdA led to a substantial loss in viability. This phenotype was abolished by the Y41A substitution in the putative catalytic domain of CmdT, indicating that CmdT is likely to be a toxic ADP-ribosyltransferase (Fig. 2a). Producing CmdT alone was not toxic, probably because the protein did not steadily accumulate, as measured by SDS–PAGE, and was insoluble, as indicated by a complete lack of protein detected in native gels (Fig. 2b and Extended Data Fig. 3c). By contrast, CmdT co-produced with CmdA steadily accumulated in both SDS–PAGE and native gel analysis. We then repeated the cmdTA co-expression experiment, but with CmdA containing a 3×HA epitope, and found that CmdA transiently accumulated and then was almost undetectable after 75 min of induction (Fig. 2c). Together, our results indicate that when co-expressing cmdTA, CmdA is initially needed for the folding or stabilization of CmdT, but that CmdA is subsequently cleared from cells, leaving behind stable, toxic CmdT.

Fig. 2: CmdTAC is a TAC system that is activated by the T4 major capsid protein.

a, Plating viability (tenfold serial dilutions) of strains containing empty vector or the indicated components and induced with 0.2% arabinose from an arabinose-inducible promoter (Para). b, Cells expressing cmdT or cmdTA with a Flag tag on the C terminus of CmdT were assessed for plating viability by growth on LB + arabinose (top) and CmdT expression by immunoblotting of native gels (middle) or SDS–PAGE (bottom). Loading control shown in Extended Data Fig. 3c. c, MG1655 Wild-type or MG1655 ΔclpP cells expressing cmdTA with a 3×HA tag at the N terminus of CmdA were assessed for CmdA levels by immunoblotting (left) and plating viability on LB + arabinose (right). d, Cells expressing cmdTA with CmdA harbouring an N-terminal 3×HA tag. The cells also contain empty vector or a vector with cmdC under the control of a tetracycline-inducible promoter. CmdA levels were measured by immunoblot (left) and cell viability was assessed by tenfold serial dilutions on LB with inducers (right). e, Immunoblots of cells harbouring cmdTAC with CmdT and CmdA engineered to have a Flag or 3×HA tag on their C and N terminus, respectively. Samples taken from cells infected with T4. Loading control shown in Extended Data Fig. 3e. f, Phage plaques of tenfold serially diluted T4 on MG1655 wild-type or MG1655 ΔclpP E. coli with empty vector or the indicated plasmids. g, Cells with cmdTAC (with N-terminally 3×HA tagged CmdA) and expressing an additional copy of cmdC (bottom) or not (top) were assessed for CmdA levels by immunoblot (left) and for defence by tenfold serially diluted T4 plaquing (right). h, IP–MS/MS analysis of proteins that co-precipitate with N-terminally Flag-tagged CmdC at 0 and 15 min after infecting cells harbouring cmdTAC with T4 phage. i, Growth of uninfected E. coli cells harbouring cmdTAC, with expression of Gp23 and Gp31 as indicated. The centre line indicates mean, shaded area represents 95% confidence intervals. n = 3 biological replicates.

Source Data

Similar to the antitoxins of other known TAC systems, the C terminus of CmdA ends with LAA, a ClpXP protease recognition sequence[25]. To determine whether CmdA is degraded by ClpP, we induced expression of cmdTA3×HA in a ΔclpP strain and monitored CmdA levels by immunoblot. Unlike in wild-type (MG1655) cells, CmdA accumulated to consistently high levels in the isogenic ∆clpP background (Fig. 2c). This accumulated CmdA was sufficient to neutralize CmdT, as reflected by the substantially improved plating viability of ΔclpP cells that express cmdTA compared with wild-type cells that express cmdTA.

We also found that co-expressing cmdC, either operonic with cmdTA (Fig. 2a) or separately from another plasmid (Fig. 2d), was sufficient to rescue the lethality of cmdTA, and immunoblotting confirmed that CmdC production promoted the accumulation of CmdA (Fig. 2d). Collectively, our findings indicate that CmdTAC is a TAC system with the following features: CmdT is a toxin that requires CmdA to fold and stably accumulate; CmdA serves as an antitoxin to neutralize CmdT in the presence of the SecB-like chaperone CmdC; and in the absence of CmdC, CmdA is degraded by the ClpP protease, leading to CmdT toxicity.

CmdTAC defence requires CmdA degradation

Upon phage infection, CmdT must somehow be released from the CmdTAC complex to abort the infection and prevent the production of new viruses. To understand the dynamics of the CmdTAC system, we used T4 to infect cmdTAC cells in which CmdT and CmdA encoded Flag and 3×HA epitope tags, respectively. We then monitored the abundance of each component by immunoblotting. CmdT was present throughout the course of phage infection, whereas CmdA levels steadily decreased (Fig. 2e and Extended Data Fig. 3d,e).

The decrease in CmdA during phage infection is likely to reflect its degradation by ClpXP and suggests that the ClpP protease is essential for CmdT activation and phage defence. To test this hypothesis, we challenged wild-type or ∆clpP cells harbouring cmdTAC with T4 phage. We found that T4 robustly infected ΔclpP cells harbouring either cmdTAC or an empty vector, in stark contrast to wild-type cells harbouring cmdTAC, which exhibited strong defence against T4 (Fig. 2f). The defect in defence seen with the ΔclpP strain was largely reversed by inducing expression of clpP in trans. As with ∆clpP cells, overproducing CmdC in cells harbouring cmdTAC led to the accumulation of CmdA and a loss of defence against T4 (Fig. 2g). Collectively, our results indicate that CmdTAC is likely to form a stable complex in the absence of phage, and infection then triggers the ClpP-dependent degradation of CmdA and release of CmdT.

The T4 capsid protein triggers CmdTAC

To identify the phage-derived trigger that activates CmdTAC, we selected for T4 mutants that could escape CmdTAC defence. All had one of two independent mutations that extended the C terminus of the protein encoded by alt.-3, an uncharacterized gene with no predicted function. The mutations extended the Alt.-3 coding sequence (normally 96 amino acids) to one of two alternative stop codons, leading to either a 117 (Alt.-3†) or 110 (Alt.-3††) amino acid protein that overcame anti-phage defence (Extended Data Fig. 4a,b). Ectopically producing Alt.-3 was not sufficient to activate CmdTAC in the absence of phage infection, and a T4 strain lacking alt.-3 did not escape CmdTAC defence (Extended Data Fig. 4c,d). Thus, we concluded that Alt.-3 is not an activator of CmdTAC and that the extended, mutant variants of Alt.-3 somehow inhibited CmdTAC activation, possibly by interfering with the proteolysis of CmdA. Indeed, we found that unlike wild-type Alt.-3, induction of Alt.-3† rescued CmdTA toxicity (Extended Data Fig. 4e) and promoted the accumulation of CmdA, similar to that observed during CmdC induction (compare Extended Data Fig. 4e to Fig. 2d). These results reinforce the conclusion that CmdT activation requires CmdA degradation and indicate that T4 phages can readily escape CmdTAC-mediated defence through mutations that inhibit this degradation.

To identify the bona fide activator of CmdTAC, we deleted alt.-3 from the T4 genome and attempted to identify additional escape mutants by directed evolution, but were unable to. Because CmdC is needed to protect CmdA from degradation (Fig. 2d), we suspected that CmdC might preferentially bind a phage product instead of CmdA during infection. This would lead to release and degradation of CmdA, and the consequent liberation of CmdT. To identify potential CmdC interaction partners, we Flag-tagged CmdC in the context of cmdTAC under its native promoter and performed IP–MS/MS at 0 and 15 min post-T4 infection. As expected, before phage infection CmdC co-precipitated both CmdA and CmdT (Fig. 2h and Supplementary Table 1). However, at 15 min post-infection, the major co-precipitating protein was Gp23, the major capsid protein of T4[26].

To validate our IP–MS/MS result, we induced Gp23 expression from a high-copy plasmid in cells also containing cmdTAC in the absence of phage infection. The ectopic production of Gp23 alone leads to protein aggregation and cellular toxicity, as Gp23 requires an additional T4-encoded co-chaperonin, Gp31, to fold properly27,28 (Fig. 2i). We therefore tested whether co-producing Gp23 and Gp31 was sufficient to activate CmdT. Indeed, co-producing Gp23 and Gp31 strongly inhibited the growth of cmdTAC-containing cells, but not an empty vector control (Fig. 2i). Of note, Gp31 alone does not result in CmdT-dependent growth inhibition. Collectively, these results strongly indicated that Gp23 activates the CmdTAC system.

CmdT is an mRNA ADP-ribosyltransferase

As noted above, the CmdT toxin is likely to be an ADP-ribosyltransferase. To test for ADP-ribosylation, we first collected protein from cells producing CmdTA or from CmdTAC-containing cells infected with T4. We performed western blots with an ADP ribose-specific antibody but did not detect any CmdT-dependent protein bands (Fig. 3a). An electrostatic model of CmdT has a large positively charged cleft surrounding the putative active site (Fig. 3b), suggesting that CmdT might bind nucleic acids. To test for DNA modifications, we isolated DNA from the cells in the same conditions as the previous experiment and performed a dot blot with an ADP ribose antibody. However, we did not detect a signal, except from cells expressing the known DNA-targeting ADP-ribosyltransferase DarT[29](Fig. 3a). We then repeated the procedure using total RNA, and observed strong signal on the anti-ADP ribose blot for cells harbouring cmdTAC and infected with T4 and for cells expressing cmdTA (Fig. 3a), but not in the empty vector controls, suggesting that CmdT was ADP-ribosylating RNA. Cells expressing cmdT*A, which produces CmdT with a putative active site mutation, lacked ADP-ribosyltransferase activity against RNA, as expected (Extended Data Fig. 5a).

Fig. 3: CmdT is an ADP-ribosyltransferase that specifically targets mRNA to block translation.

a, ADP ribose antibody was used for immunoblots (top) or DNA and RNA dot blots (bottom) as indicated. Top left, samples taken from cells expressing cmdTA or harbouring an empty vector, 60 min post-induction. Top right, samples taken from cells expressing cmdTAC under its native promoter or harbouring an empty vector and infected with T4 phage for 32 min. Proteins were separated by SDS–PAGE; DNA and RNA samples were spotted onto nylon membranes. Total RNA and DNA are indicated by methylene blue staining. b, AlphaFold2-predicted structure of CmdT (left) with conserved aromatic residues and putative catalytic residues (see Extended Data Fig. 3a) shown in green, with the corresponding electrostatic surface representation. Colour bar is in units of kcal/(mol·e) (right). c, RNA samples from indicated strains and conditions were resolved on agarose gels and then stained with ethidium bromide (EtBr) to visualize total RNA or blotted with anti-ADP ribose to visualize ADP-ribosylated RNA. d, Protein synthesis, as measured by 35S-labelled cysteine and methionine incorporation at the indicated timepoints during T4 infection of cells harbouring CmdTAC or CmdT*AC, which has a catalytically inactive CmdT. Proteins were resolved by SDS–PAGE before phosphorimaging. Representative image from two independent biological replicates. e, In vitro transcription–translation reactions with DNA encoding dihydrofolate reductase (DHFR) used as template. Purified CmdT and NAD+ were added, where indicated, with DHFR production visualized by Coomassie staining. c indicates control eluant (mock purification). The DHFR band was identified based on the ladder in Supplementary Fig. 1. f, In vitro transcription–translation reactions using a DNA template encoding DHFR or mRNA templates produced by T7 RNA polymerase and treated with CmdT (or eluent from a mock purification of untagged CmdT) prior to addition. The DHFR band was identified based on positive and negative controls in lanes 1 and 4, respectively.

To identify the RNA species that were being modified, we probed northern blots with the ADP ribose antibody (Fig. 3c). We detected strong signals in the regions corresponding to mRNA, but not those corresponding to rRNA or tRNA, indicating that CmdT preferentially ADP-ribosylates mRNA. We then probed total RNA collected at multiple timepoints over the course of T4 infection and observed ADP-ribosylation of RNA within 10 min after infection (Extended Data Fig. 5b). We also tested whether inducing Gp23 and Gp31 led to ADP-ribosylation of RNA in CmdTAC-containing cells. Among all combinations of cmdTAC, gp23 and gp31, we could detect RNA modification only when all three were expressed (Extended Data Fig. 5c). Collectively, our results indicate that CmdT is an RNA-targeting ADP-ribosyltransferase that is activated by the presence of T4 Gp23 and is dependent on Gp31.

CmdT blocks translation and T4 replication

To understand how mRNA ADP-ribosylation inhibits phage development, we measured translation by growing T4-infected cells producing CmdTAC or CmdT*AC in the presence of radiolabelled cysteine and methionine. By 10 min post-infection we observed a complete block in incorporation in cells with CmdTAC but not CmdT*AC (Fig. 3d). To further test whether CmdT activity blocks translation, we purified CmdT–His_6 and, as a control, we isolated protein from a mock purification of CmdT lacking an affinity tag. Purified CmdT, but not the control eluant, blocked translation of a model protein, DHFR, in a cell-free in vitro transcription–translation reaction, and inhibition was dependent on NAD+ (Fig. 3e). To ensure that CmdT had blocked DHFR translation and not transcription, we first in vitro transcribed DHFR and then treated the mRNA with purified CmdT and NAD+ (Extended Data Fig. 6a). Providing this template to the in vitro translation reaction again resulted in no DHFR protein (Fig. 3f). By contrast, purified DHFR mRNA treated with the control eluant was robustly translated (Fig. 3f). Together, these data suggest that CmdT modifies mRNA to block translation.

To understand the global effect of CmdT activation on the T4 life cycle in vivo, we performed RNA sequencing (RNA-seq) on cells containing cmdTAC or an empty vector at 15 and 30 min post-infection with T4. Compared with the empty vector control, cells with cmdTAC showed substantially decreased expression of late genes and, consequently, increased read counts from early and middle genes (Extended Data Fig. 6b). The various stages of T4 gene expression depend on the successful translation of gene products in earlier stages of the life cycle[30] suggesting that T4 replicating in cmdTAC-positive cells is unable to progress to the late stages.

We also performed RNA immunoprecipitation with sequencing (RIP–seq) by enriching for ADP-ribosylated RNAs with an ADP ribose-specific antibody, followed by deep sequencing. Transcripts per million reads (TPM) values for T4 mRNAs were significantly higher in the immunoprecipitated samples compared with the control RNA-seq sample 30 min post-infection (P = 0.04; two-sided Welch’s t-test). In addition, the TPM enrichment was significantly higher for T4 mRNAs than for tRNAs and rRNAs (Extended Data Fig. 6c). These findings reinforce our conclusion that CmdT is mRNA-specific. On the basis of our findings, we conclude that the ADP-ribosylation of mRNA by CmdT leads to a halt in translation, preventing the progression of T4 to the late stages of its life cycle and a failure to produce mature progeny.

CmdT modifies GA dinucleotides in ssRNA

To determine the specificity of nucleic acid modification by CmdT we performed in vitro ADP-ribosylation using purified CmdT, NAD+ and a 24-residue model mRNA oligonucleotide that contained a Shine-Dalgarno sequence and an AUG start codon. We found that CmdT treatment resulted in a shift in the migration of the single-stranded RNA (ssRNA) substrate on denaturing gels, indicative of covalent RNA modification (Fig. 4a). When a ssDNA oligonucleotide with the equivalent sequence was supplied as a substrate, a product was formed, but with substantially reduced kinetics (Fig. 4b). Supplying double-stranded RNA (dsRNA) substrates did not produce a higher mass product, even at high concentrations of CmdT (Fig. 4c).

Fig. 4: CmdT modifies the N6 methyl group of adenine in GA dinucleotides of ssRNA.

a, The indicated RNA oligonucleotide was incubated with 4 nM CmdT–His_6, protein from a mock purification of untagged CmdT (c) or no protein (−), with or without NAD+. Reaction products were resolved on a polyacrylamide TBE-urea gel and imaged by methylene blue staining. Red arrowheads indicate ADP-ribosylated products. b, ADP-ribosylation by 4 nM CmdT–His_6 of the RNA (top) or equivalent DNA (bottom) oligonucleotide at the indicated timepoints, visualized as in a. c, ADP-ribosylation by CmdT–His_6 of the ssRNA oligonucleotide from b or the corresponding dsRNA with increasing concentrations of CmdT–His_6 for 60 min. d, ADP-ribosylation by CmdT–His_6 of the indicated oligonucleotides, which lack A, U, G or C. e, ADP-ribosylation by CmdT–His_6 of the indicated oligonucleotide (bottom) with the identity of the variable dinucleotides (N_1N_2) indicated above each reaction. f, HPLC analysis of nucleosides isolated following incubation of the no-U and no-C RNA oligonucleotides in d with CmdT and NAD+, as indicated, and then treated with nuclease P1 and antarctic phosphatase, with or without SVPD, as indicated. Peaks corresponding to A, C, G and U nucleosides, NAD+, G-ribose, A-ribose and nicotinamide-riboside (nicotinamide-r, produced from SVPD cleavage of unused NAD+) are marked, along with species from the top row identified in Extended Data Fig. 7e as GA, A, AC and AU, with the first nucleotide in each case being ADP-ribosylated (ADPr). The y axis represents absorbance at 254 nm. g, ESI-MS analysis of the A-ribose peak fraction from f. h, MS/MS fragmentation of the A-ribose sodium adduct from g. Collision energy, 40 eV. Predicted fragments are annotated with structures and m/z is indicated. Grey, ribose from adenosine; red, ribose from ADP ribose; white, adenine. Structures of fragmentation products are shown in Extended Data Fig. 8a.

Source Data

To further test our hypothesis that mRNAs are preferentially targeted, we incubated purified CmdT with total RNA extracted from cells infected with T4, supplying 6-biotin-17-NAD+ as a substrate. We then enriched for ADP-ribosylated RNA by precipitation with streptavidin beads and sequenced RNA samples taken before and after streptavidin pulldown, without rRNA depletion (Extended Data Fig. 6d). The results of this experiment paralleled those of the in vivo RIP–seq (Extended Data Fig. 6e), with enrichment of mRNAs compared with tRNAs and rRNAs. Together, these data suggested that unstructured, ssRNA is the preferred substrate of ADP-ribosylation by CmdT.

To determine the nucleotide and sequence specificity of CmdT, we repeated the ADP-ribosylation reactions with CmdT, NAD+ and one of four ssRNA oligonucleotide substrates, each missing one base. Only the no-U and no-C oligonucleotides produced slower migrating products, suggesting that neither C nor U is required for modification (Fig. 4d). Notably, the no-U and no-C substrates produced four and three products, respectively, each of decreasing mobility in the gel (Fig. 4d and Extended Data Fig. 7a). This observation suggested that CmdT was adding multiple modifications to individual oligonucleotides. The formation of the slower migrating bands was dependent on reaction time (Extended Data Fig. 7a). We noticed that the number of bands formed was the same as the number of 5′-AG-3′ or 5′-GA-3′ dinucleotides in the no-U and no-C oligonucleotides (Fig. 4d). Similarly, the model RNA used in Fig. 4a–c contains one GA site and produced one reaction product.

To further test the sequence specificity of CmdT, we designed five ssRNA oligonucleotides with invariant C- and U-containing scaffolds. At two separate positions within the oligonucleotides, we introduced various dinucleotides: CC (control), AA, AG, GG or GA. During ADP-ribosylation by CmdT, substantial product was produced only with the substrate containing GA (Fig. 4e). Very faint bands appeared when the variable dinucleotide contained two purines other than GA, but none when the oligonucleotide contained two pyrimidines (CC). Taken together, our results indicate that CmdT preferentially recognizes GA dinucleotides.

To determine which nucleotides are covalently modified with ADP ribose, we performed a CmdT ADP-ribosylation reaction on an equimolar mixture of the no-U and no-C RNA oligonucleotides. We then digested the ADP-ribosylated RNA using nuclease P1, which hydrolyses 3′-5′ phosphodiester bonds, and antarctic phosphatase, which removes 5′ and 3′ phosphates, and resolved the products with high-performance liquid chromatography (HPLC) (Extended Data Fig. 7b and Methods). In addition to peaks corresponding to C, U, A and G nucleosides, we observed other peaks with longer retention times (Fig. 4f, top row). These additional peaks were not seen in control reactions lacking NAD+ or lacking CmdT. Notably, the absorbance of the adenosine peak dropped substantially in CmdT-treated samples compared with the control reactions, suggesting that the four new peaks were derived from modified adenosine (Extended Data Fig. 7c).

In parallel, we incubated the same reactions with snake venom phosphodiesterase I (SVPD), in addition to the nuclease and phosphatase. SVPD cleaves the phosphodiester bond in ADP ribose, in addition to some phosphodiester bonds that may be inaccessible to nuclease P1 (Extended Data Fig. 7b). For example, SVPD treatment of ADP-ribosylated adenosine would produce adenosine-ribose and adenosine, with the ribose and lone adenosine originating from SVPD cleavage of the ADP ribose moiety (Extended Data Fig. 7d). For the samples treated with CmdT and NAD+, SVPD treatment produced two peaks with relative retention times corresponding to adenosine-ribose and guanosine-ribose[12], with the former showing stronger signal (Fig. 4f and Extended Data Fig. 7e). To identify the original (pre-SVPD treatment) peaks other than A, C, G and U nucleosides (Fig. 4f, top row), we isolated fractions corresponding to each, treated them with SVPD and then re-ran HPLC. This analysis indicated that these other peaks were A, AC and AU, with the adenosine being ADP-ribosylated in each case, and a minor peak of GA with the G being ADP-ribosylated. Notably, the three major nuclease P1-resistant species each contained adenosine preceded by a G in the original substrate (Extended Data Fig. 7f), indicating that CmdT primarily ADP-ribosylates adenosine nucleotides at GA motifs in single-stranded mRNA.

To confirm the identity of the peak we inferred to be adenosine-ribose (Fig.  4f, second row), we performed electrospray ionization mass spectrometry (ESI-MS), which revealed peaks at 400 and 422 m/z, equivalent to the molecular weight of adenosine-ribose [M+H]+ and [M+Na]+ (Fig. 4g). There are two positions on adenosine that are most likely to react with NAD+ and become ADP-ribosylated, the 2′ hydroxyl of the ribose and N6 on the adenine base (Fig. 4g). To distinguish between these, we analysed the adenosine-ribose sodium adduct by ESI-MS/MS fragmentation (Fig. 4h and Extended Data Fig. 7g). We did not detect a fragment ion for ribose-2′-O-ribose (m/z 287) which would be produced if the 2′-OH was ADP-ribosylated[12]. Instead, we observed peaks at 200 and 230 m/z, which are likely to correspond to fragments in which the adenine base contains a portion of the ribose derived from ADP-ribosylation of N6 (Extended Data Fig. 8a,b).

Finally, we compared the UV absorbance (λmax) of adenosine and CmdT-generated adenosine-ribose. ADP-ribosylation at the 2′ hydroxyl of ribose does not shift the UV absorbance spectrum of adenosine[12], whereas modifications of the N6 position, such as N6-methylation or N6-isopentenylation, redshift the UV absorbance of adenosine by 6 or 8 nm, respectively[31]. The adenosine-ribose produced by CmdT shifted the λmax from approximately 258 nm to approximately 265 nm (Extended Data Fig. 8c). Given our mass spectrometry and UV spectroscopy data, along with the finding that CmdT can weakly modify ssDNA (which lacks a 2′ hydroxyl), we conclude that the site of ADP-ribosylation by CmdT is the N6 of adenine in GA dinucleotides within ssRNA (Fig. 5).

Fig. 5: Model for anti-phage defence by the CmdTAC system.

Top, CmdTAC produced by E. coli forms a complex prior to infection. Following T4 infection, newly synthesized capsid protein (Gp23) binds CmdC chaperone, leading to degradation of CmdA by ClpP and subsequent release of active CmdT toxin. CmdT ADP-ribosylates GA dinucleotides in ssRNAs thereby preventing their translation and the production of mature T4 virions. Bottom, structure of ADP-ribosylated adenosine.

Discussion

This work reveals an ADP-ribosyltransferase that specifically targets mRNA. Prior studies have identified numerous ADP-ribosyltransferases in phages, bacteria and eukaryotes that use the highly abundant and reactive molecule NAD+, or occasionally NAD-capped RNAs, to covalently modify a wide range of proteins, often to reversibly regulate their activities5,6,7,32. Protein ADP-ribosyltransferases are also featured in many biological conflicts with secreted toxins, such as pertussis and cholix toxins, that are capable of shutting down key cellular processes by modifying specific target proteins33,34. More recently, ADP-ribosyltransferases that target DNA and structured RNAs have been found11,12,13. Our findings, and other recent results[35], now extend the range of targets to include mRNA, with CmdT-catalysed ADP-ribosylation leading to a potent block in protein translation. Notably, in vitro studies have shown that human PARP10, PARP11, PARP14 and PARP15 can each modify ssRNA substrates10,36, although there is no evidence for RNA modification occurring in vivo. Nevertheless, these prior results suggest that ADP-ribosylation of mRNA may be a widespread, but previously unappreciated modification occurring throughout biology. PARP10, PARP11 and PARP14 are encoded by interferon-stimulated genes37,38,39 and several mammalian viruses, including SARS-CoV-2, encode ADP-ribosylhydrolases that offset poly-ADP-ribosyltransferase activity to promote viral replication40,41. Thus, the ADP-ribosylation of viral RNAs may be a broadly conserved and critical facet of anti-viral immunity.

Our results indicate that CmdT primarily modifies the N6 position of adenines within GA dinucleotides. Notably, GA nucleotides are almost always found within the Shine-Dalgarno sequences of bacterial (AGGAGG) and T4 (GAGG) transcripts. ADP-ribosylation of Shine-Dalgarno sequences may prevent modified transcripts from engaging ribosomes and initiating translation. Alternatively, or in addition, the modification of GA dinucleotides within an mRNA may disrupt base-pairing with tRNAs or may stall ribosomal translocation. Further investigation is required to understand exactly how the block to translation occurs.

Many anti-phage defence systems have been discovered recently[3], most of which function through an Abi mechanism in which infected cells kill themselves or stop growing to prevent the production of new phages. Abi-based systems must remain off in the absence of infection and then be rapidly triggered upon phage infection. Our work reveals the T4 major capsid protein Gp23 as the probable direct activator of the CmdTAC system (Fig. 5). Structural proteins appear to be common triggers of anti-phage defence systems42,43 possibly because they adopt folds that differ substantially from endogenous host proteins, thereby providing requisite specificity. Additionally, structural proteins accumulate to high levels, helping to ensure defence system activation. One potential downside to structural proteins as triggers is that most are encoded by late genes, raising the possibility that activation may not occur before new virions are produced. However, the ‘late’ classification is based on the peak time of expression, and both RNA-seq and mass spectrometry studies indicate that late genes are expressed at earlier timepoints44,45. Consistent with this idea, we detected CmdT activation and the ADP-ribosylation of mRNAs as early as 10 min post-infection. Precisely how the capsid protein interacts with CmdC and the specific regions involved will require further biochemical studies. Notably, despite extensive directed evolution we did not isolate any escape mutants of gp23, an essential gene. This could indicate that mutations to gp23 that escape binding to CmdC are lethal by preventing the production of stable, mature virions. In addition, CmdC may engage an extended region of the capsid protein such that multiple substitutions and complex mutations would be required to abrogate binding.

CmdTAC adds to the growing list of toxin–antitoxin systems shown to function in anti-phage defence[14] and represents one of the first such TAC systems[35]. TAC systems have been well-characterized outside the context of phages17,18,19,20, revealing principles such as the reliance of their antitoxins on C-terminal chaperone-addiction domains for folding and stability, and the use of SecB-like chaperones. The toxins of TAC systems show more variability, including several different families of endoribonucleases, which could, in principle, function similarly to CmdT to inhibit translation and prevent phage replication. More broadly, our discovery of ADP-ribosyltransferase activity in CmdT underscores the tremendous diversity in the mechanisms of action of toxins associated with toxin–antitoxin and TAC systems.

The intense conflict between phages and the bacteria they prey upon has led to the evolution of sophisticated anti-phage defence systems with a wide range of molecular innovations. Many of these systems have been identified in recent years, but the molecular mechanisms by which they inhibit phages remain incompletely defined. Our work reveals mRNA ADP-ribosylation as a potent defence mechanism used by bacteria to thwart phages. As several interferon-inducible mammalian PARPs—whose exact functions remain unclear—can also modify RNA, this mechanism may be broadly used in immunity throughout biology.

Methods

Statistics and reproducibility

Unless otherwise noted, representative images depict one of three biological replicates.

Bacterial and phage growth and culture conditions

E. coli was routinely grown in LB medium at 37 °C unless otherwise stated. Phages were propagated and handled as described previously[4].

Plasmid and strain construction

Primers, strains and plasmids are listed in Supplementary Tables 2, 3 and 4, respectively. In all cases, when plasmids were used as PCR templates, PCR samples were treated with DpnI at 37 °C for 1 h to eliminate the plasmid template before transformation. Finished DNA constructs were transformed into DH5α and verified with Sanger and/or long-read sequencing (Primordium) before transformation into the wild-type MG1655 background. For phage assays, the cmdTAC operon was present in low-copy pCD1 (Chloramphenicol-resistant (Cmr), pSC101 origin of replication) and expressed from its native promoter (Pnative). To construct the variant pCV49 (cmdT*AC) by site-directed mutagenesis, complementary primers containing the Y to A mutation (CV109 and CV110) were used to amplify pCD2 for 15 cycles with KAPA DNA polymerase. pCV39 was constructed using the same primer set with pCD4 as the plasmid template.

Low-copy, Pnative-cmdTANT-3×HAC (pCV43) was engineered using primers CV127 and CV128 to amplify pCD2 such that amplicon ends were located downstream of the CmdA start codon. A synthetic DNA fragment with three YPYDVPDYA codons plus GGGSGGG linker codons (3×HA tag, CV115) with ends complementary to the PCR-amplified vector was then ligated to this vector amplicon by Gibson assembly. Pnative-cmdTACNT-Flag (pCV44) (NT Flag indicates an N-terminal Flag tag) was constructed by amplification of pCD2 with outward facing and 5′-phosphorylated primers CV120 and CV122 which included DYKDDDDK codons followed by intramolecular blunt-end ligation with T4 DNA ligase. To construct Pnative-cmdTCT-FlagAC, primers CD41 and CD42 were used to amplify pCD2 and primers CD43 and CD44 were used to amplify C-terminally Flag-tagged (CT-Flag) CmdT sequence from pCD10 (see below for pCD10 construction) which was then ligated using Gibson assembly. To construct Pnative-cmdTCT-FlagANT-3×HACNT-His6 (pCV42), first, a His_6 epitope tag encoding fragment was added to cmdC on pCD2 by the intramolecular blunt-end ligation strategy using primers CV120 and CV122. Next, primers CD41 and CD42 were used to amplify this plasmid and primers CD43 and CD44 were used to amplify CT-Flag cmdT from pCD10 which was then assembled using Gibson assembly. Finally, to insert three tandem HA tags onto the N terminus of CmdA, this intermediate construct was amplified by PCR with CV123 and CV124 such that amplicon ends were located downstream of the CmdA ATG start codon. The synthetic DNA fragment CV115 (HA tag) was used as a PCR template with primers CV125 and CV126 which was then ligated to the vector amplicon by Gibson assembly to produce pCV42.

To construct pCV45 used for the deletion of alt.-3 from T4, complimentary oligonucleotides CV118 and CV119 with pCAS9 compatible overhanging sites were annealed by slow cooling from 98 °C in the presence of 50 mM NaCl to form a duplex spacer insert. pCAS9 and the annealed oligonucleotide were incubated with T4 DNA ligase and BsaI-v2 (NEB) in a one-pot reaction.

For pBAD30 constructs (kanamycin-resistant (Kmr), medium-copy p15a origin), primers CD5 and CD6 were used to amplify and linearize pBAD30. Insert fragments were amplified with the relevant primers (CD7-10, CD13-15, CD20-21, CD30-33, CD38-40) using T4 genomic DNA, plasmid DNA, or MG1655 genomic DNA as a template. pBAD-cmdTNT-HISA (pCD19) was created using PCR site-directed mutagenesis of pCD4 using primers CD45 and CD46. pBAD-cmdTNT-HIS (∆cmdA, pCD9) was created by using primers CV1 and CV2 (which exclude the open reading frame of cmdA) to amplify pCD19. This PCR amplicon was intramolecularly ligated with T4 DNA ligase. In some experiments, pAJM677 (Kmr, p15A origin), a variant of pBAD, was used to express CmdTA due to its higher expression after induction with arabinose and tighter repression (pCV41). To engineer pCV41, pAJ677 was amplified and linearized with primers CV113 and CV114. The insert containing cmdTA was amplified from pCD4 with primers CV116 and CV117. Plasmid and insert fragments were ligated by Gibson Assembly. To add the 3×HA tag to cmdA in this context, pCV41 was used as a PCR template with primers CV127 and CV128, and this amplicon was ligated to CV115 fragment by Gibson assembly. Anhydrotetracycline inducible (Ptet) pIF (carbenicillin-resistant (Cbr), low-copy pSC101 origin) and pKVS45 (Cbr, p15A origin) constructs were similarly constructed by PCR amplification of the vectors using primers CD24 and CD25 and inserts (CD26-29, CD36-37), followed by Gibson assembly.

To construct the gp23 expression plasmid pCD16, primers CD16 and CD17 or CD18 and CD19 were used to amplify the high-copy origin from pUC19 (pMB1* origin) and pBAD30 without its origin, respectively. These fragments were assembled using Gibson assembly to create a high-copy inducible vector. Subsequently, the backbone was amplified by PCR using primers CD5 and CD6 and gp23 was amplified by PCR from T4 genomic DNA using primers CD34 and CD35. The two fragments were ligated using Gibson assembly.

Plaque and phage assays

Overnight cultures of indicated strains were mixed 1:80 with melted LB with 0.5% agar and then overlaid on plates containing LB with 1.2% agar. For plaque assays done with induction of an arabinose-inducible promoter, base layer plates contained 0.2% w/w arabinose. A tenfold dilution series of the indicated phage was spotted onto plates and the plates grown at 30 °C overnight and plaque-forming units (PFU) were enumerated. log_10(protection) (Fig. 1c) was measured as −log_10 EOP, where EOP is the ratio PFUexperimental/PFUcontrol, where the subscript indicates the conditions. Unless otherwise noted, experiments were performed in biological triplicate and representative images are shown.

To measure survival of strains infected with T4, overnight cultures were diluted to OD_600 0.1. Cultures were grown to OD_600 0.3 and then adjusted to ~3 × 10^7 colony-forming units (CFUs) in a 1 ml volume in a 1.7 ml Eppendorf tube. Cells were infected with T4 at an MOI of 10 and incubated at 37 °C with rotation. At 0 and 18 min post-infection, cells were pelleted and washed twice with PBS to remove excess phages. One-hundred microlitres of tenfold dilutions were spread onto LB agar plates with chloramphenicol or kanamycin and CFUs were quantified. Survival was measured as CFU ml−1 at 18 min post-infection divided by CFU ml−1 at 0 min post-infection. To combat progeny phages in the empty vector strain inhibiting CFU formation, all samples were plated with ~10^7  chloramphenicol-treated, chloramphenicol-sensitive companion plating cells (MG1655).

ECOI assays were conducted by diluting overnight cultures to OD_600 0.1 in 20 ml LB. Cultures were grown until they reached OD_600 0.3–0.4 at which point they were infected with T4 at an MOI of 0.1. After 20 min, 1 ml was pelleted and washed twice with PBS. One-hundred microlitres of tenfold dilutions were mixed with 50 µl of indicator strain and 3 ml LB 0.5% agar and overlayed onto LB plates. To control for unadsorbed phages, a ∆ompC strain (OmpC is the receptor for T4) was assayed in parallel. CFUs were enumerated and ECOI was calculated as PFU ml−1 of the cmdTAC-containing strain divided by PFU ml−1 of empty vector, after subtracting PFU ml−1 of the ∆ompC control experiment from each value.

To determine burst size, cell cultures of empty vector and CmdTAC-containing strains were grown in LB + 20 µg ml−1 chloramphenicol in a water bath at 37 °C until OD_600 measured 0.5. l-Tryptophan was then added to 20 µg ml−1 to each culture to assist adsorption of T4. One hundred microlitres of a 10^7 PFU ml−1 T4 stock were added to 9.9 ml of each culture and incubated without shaking for 2 min to allow adsorption. Next, for each culture, 100 µl T4-infected culture from this adsorption flask was added to 9.9 ml LB + 20 µg ml−1 chloramphenicol (flask A). Flask A was again diluted 1:10 into flask B, and again 1:10 into flask C. Five hundred microlitres from flask A was added to 200 µl ice-cold chloroform and vortexed for 10 s. Viable PFUs from this chloroform-treated sample represent unadsorbed phage (adsorption control). Next, 100 µl from each flask A (time 0 sample) or the adsorption control was mixed with 3.5 ml LB 0.5% agar maintained at 50 °C to which was added 50 µl of an overnight culture of indicator strain. This mixture was vortexed briefly and overlayed onto LB + 20 µg ml−1 chloramphenicol + 1.2% agar plates. All flasks were then left to incubate in a shaking water bath at 30 °C. After 60 min, 100 µl from flask C of the empty vector strain and flask A of the +cmdTAC strain were overlayed with indicator strain on agar plates. After overnight incubation at 37 °C, plaques were enumerated, and normalized to the adsorption control. Burst size was recorded as the number of plaques from each plate multiplied by their dilution factor, and then divided by the number of plaques at time 0.

Growth curves

For measuring growth during T4 infection, overnight cultures of +cmdTAC and empty vector cells were back-diluted 1:200 in 96-well plates and infected with T4 at the indicated MOIs. Cultures were grown at 37 °C with orbital shaking on a plate reader (Biotek) for 6 h. For ectopic expression of Gp23 and Gp31 with CmdTAC, overnight cultures were back-diluted to OD_600 of 0.05 in M9L + 0.2% w/w glucose + 100 ng ml−1 anhydrotetracycline (aTc) and grown for 3 h at 37 °C to pre-induce Gp31. Cultures were then pelleted and resuspended at an OD_600 of 0.05 in fresh M9L + 0.2% w/w glucose + 100 ng ml−1 aTc or M9L + 0.2% w/w glucose + 100 ng ml−1 aTc. Cultures were grown at 37 °C with orbital shaking on a plate reader for 12 h.

RNA extraction following phage infection

Overnight cultures of +cmdTAC and empty vector cells were back-diluted and grown at 37 °C to OD_600 between 0.2 and 0.3 before being infected with T4 at a MOI of 10. RNA was extracted from cells at multiple timepoints post-infection as previously described[44]. In brief, 1 ml of cells was mixed with 1 ml of boiling lysis buffer (SDS 2%, 4 mM EDTA pH 8) and incubated at 100 °C for 5 min before flash freezing in liquid nitrogen. Two millilitres of acid-buffered phenol solution (pH 4.3, Sigma) heated to 67 °C was added to thawed samples, vortexed, and then incubated at 67 °C for 2 min. Samples were spun down at 20,000g for 10 min and hot phenol extraction repeated on the collected aqueous layer. A third extraction was then done using 2 ml of acid-buffered phenol-chloroform solution (Ambion). RNA from the final extraction was then precipitated at −20 °C for at least 1 h or at −80 °C overnight with 1× volume isopropanol, 1/10× volume 3 M sodium acetate (pH 5.5, Thermo Fisher), and 1/100× volume GlycoBlue. RNA was pelleted by centrifugation at 4 °C and 20,000g for 30 min. Pellets were washed twice with 800 ml of ice-cold 70% ethanol, air-dried, and resuspended in 90 μl RNAse-free H_2O (Thermo Fisher).

To remove DNA, 10 μl of 10× Turbo DNase buffer (Ambion) and 2 μl of Turbo DNase I (Ambion) was added to each sample and incubated at 37 °C for 20 min. An additional 2 μl of Turbo DNase I was then added, and samples again incubated at 37 °C for 20 min. RNA was extracted from this digest by precipitation with 3× volume ethanol, 1/10× volume 3 M sodium acetate (pH 5.5), and 1/100× volume GlycoBlue. Pelleting and washing were performed the same as described above. RNA yield was verified using a NanoDrop spectrophotometer.

RNA extraction from non-infected cells

Cells were grown until desired conditions and then 900 μl of culture was mixed with 100 μl of stop solution (5% acid phenol, 95% ethanol) and inverted to mix. Samples were then spun down at 13,000g for 30 s, the supernatant removed, and pellets flash frozen in liquid nitrogen. To each pellet, 400 μl of TRIzol Reagent (Invitrogen) heated to 65 °C was added and mixed using a thermomixer for 10 min at 65 °C and 2,000 rpm before freezing at −80 °C for at least 10 min. Samples were thawed and then centrifuged at 20,000g for 5 min at 4 °C to pellet any debris and the TRIzol solution moved to a new tube. RNA was purified using the Direct-zol RNA Miniprep kit (Zymo Research) following manufacture’s protocol including optional on-column DNAse treatment. RNA yield was verified using a NanoDrop spectrophotometer.

Immuno-northern blotting

Novex 6% TBE-urea gels in 1× TBE buffer (Invitrogen) were pre-run at 180 V for at least 50 min prior to sample loading. Each RNA sample was mixed with equal volume of Novex 2× TBE-urea sample buffer (Invitrogen), heated at 90 °C for 10 min, and then placed on ice for 2–3 min just before loading. Gels were run at 180 V for 30–50 min depending on expected product length. Gels were removed from casing and incubated in 40 ml 1× TBE with added 4 μl of SYBR Gold stain (Thermo Fisher) for 10 min. Gels were imaged on a ChemiDoc MP imaging system (Bio-Rad) set for SYBR Gold imaging. RNA was transferred from the gel to a Hybond-N+ nylon membrane (Cytiva) via semi-dry transfer at 0.38 A for 90 min. After transfer, RNA was bound to the membrane by exposure to 120,000 μJ of UV radiation in a Stratalinker UV Crosslinker. Membranes were then incubated with shaking in 0.2% iBlock (Invitrogen) in 1× PBST for 10 min at room temperature or overnight at 4 °C. Primary antibody treatment was done with Poly/Mono-ADP Ribose rabbit antibody (Cell Signaling Technologies) diluted 1:1,000 in 0.2% iBlock + 1× PBST either for 2 h at room temperature or overnight at 4 °C with shaking. Following primary antibody treatment, membranes were washed 3 times for 10 min each with 1× PBST. For secondary antibody treatment, membranes were incubated for 1 h with shaking at room temperature with goat anti-rabbit IgG (H + L) secondary antibody, HRP (Invitrogen) diluted 1:1,000 in 0.2% iBlock + 1× PBST. Membranes were then again washed 3 times for 10 min each in 1× PBST. Signal was developed using SuperSignal West Femto maximum sensitivity substrate (Thermo Fisher) and imaged on a ChemiDoc MP imaging system set for chemiluminescence detection. Dot blots were conducted identically except 250 ng DNA or 1 µg RNA were spotted on membranes.

For agarose immuno-northern blots, 0.8 g of agarose was melted in 66.7 ml of H_2O and allowed to cool to 65 °C. 8 ml of 0.2 M (10×) 3-(N-morpholino)propanesulfonic acid (MOPS) buffer, 5.4 ml of formaldehyde and 5 µl of 10 mg ml−1 ethidium bromide were added to the agarose, and a 14×12 cm gel was cast and allowed to cool. 4 µg RNA were added to 17 µl sample buffer (2 µl 10× MOPS, 4 µl formaldehyde, 10 µl de-ionized formamide, and 1 µl ethidium bromide) and samples were denatured at 80 °C for 10 min then cooled on ice for 5 min. Prior to sample loading, the empty gel was run at 115 V for 5 min. 2 µl of loading dye (50% glycerol, bromophenol blue and xylene cyanol) were added to each RNA sample. Samples were then electrophoresed at 100 V for 80 min in 1× MOPS buffer. The gel was visualized before soaking in H_2O for 10 min followed by a 20-minute equilibration in transfer buffer (3 M NaCl, 0.01 N NaOH). RNA was transferred onto Hybond-N+ nylon membrane by upward capillary transfer at room temperature for 75 min in transfer buffer. Immunoblotting was performed as described above. All immunoblotting experiments were performed in at least biological duplicate.

RNA immunoprecipitation and sequencing

Cells were collected and RNA collected as described above for infected cells. rRNA was removed using a previously described ribosomal RNA subtraction method[46]. rRNA-depleted RNA was then fragmented using sonication. For each sample to be sonicated, 4 μg of RNA was added to 100 μl 1× TE buffer (Sigma) in a 1.5 ml TPX microtube (Diagenode) and incubated on ice for 15 min. Tubes were then placed in a Bioruptor 300 sonicator water bath chilled to 4 °C for 60 cycles of 30 s on, 30 s off at high power setting. Every ten cycles tubes were briefly spun down in a microcentrifuge to ensure all liquid stayed below the water line in the sonicator. Each sample was then brought to a total volume of 200 μl with RNAse-free water and then precipitated at −20 °C for at least 1 h or at −80 °C overnight with 600 μl 100% ethanol, 20 μl of 3 M sodium acetate (pH 5.5), and 2 μl GlycoBlue. RNA was pelleted by centrifugation at 4 °C and 21,000g for 30 min. Pellets were washed twice with 800 ml of ice-cold 70% ethanol, air-dried, and resuspended in 90 μl RNAse-free H_2O.

ADP ribose RNA immunoprecipitation was based on a methylated RNA immunoprecipitation sequencing (MeRIP-seq) protocol for low-input samples[47]. One-hundred microlitres of Dynabeads Protein G beads were washed 3 times in IP buffer (150 mM NaCl, 10 mM pH 7.5 Tris-HCl, 0.1% NP-40 substitute). Ten microlitres of Poly/Mono-ADP Ribose rabbit antibody (Cell Signaling Technologies) was added to washed beads resuspended in 500 μl IP buffer and then incubated overnight at 4 °C with end-to-end rotation. Following incubation, antibody conjugated beads were washed twice with IP buffer and then resuspended in 500 μl IP buffer with 20 μg fragmented, rRNA-depleted RNA and 5 μl Superase-In RNAse inhibitor and incubated overnight at 4 °C with end-to-end rotation. Samples were then washed twice with 1 ml IP buffer, twice with 1 ml low-salt wash (50 mM NaCl, 10 mM pH 7.5 Tris-HCl, 0.1% NP-40 substitute), and twice with 1 ml high-salt wash (500 mM NaCl, 10 mM pH 7.5 Tris-HCl, 0.1% NP-40 substitute). For each wash, beads were incubated in the wash solution for 10 min at 4 °C with end-to-end rotation. After the final wash, beads were incubated in 200 μl RLT buffer from the Qiagen RNeasy kit for 2 min at room temperature with end-to-end rotation. Supernatant was separated from the beads using a magnetic rack, transferred to a new tube, and mixed with 200 μl of 100% ethanol. This mixture was passed through a RNeasy MiniElute spin column by centrifugation at 20,000g at 4 °C for 1 min. Spin columns were then washed once with 500 μl RNeasy RPE buffer and once with 500 μl 80% ethanol with each spin done at 20,000g for 1 min at 4 °C. Columns were then spun at 20,000g for 5 min to remove residual ethanol. RNA was eluted from the column in 15 μl RNAse-free H_2O with a spin at 20,000g for 5 min at 4 °C. RNA yield and integrity was verified using a NanoDrop spectrophotometer and a Novex 6% TBE-urea gel (Invitrogen), respectively.

Pre- and post-immunoprecipitation RNA (50–100 ng) was then used to make RNA-seq libraries using the NEBNext Ultra II RNA Library Prep Kit for Illumina following the manufacturer’s protocol for use with rRNA-depleted formalin-fixed, paraffin-embedded RNA. Paired-end sequencing of the libraries was performed on a Singular G4 machine at the MIT BioMicroCenter. FASTQ files were then mapped to the MG1655 genome (NC_00913.2), the T4 genome (NC_000866), and the plasmid pKVS45-CmdTAC as previously described44,48.

Library preparation for RNA-seq

Cells were collected and RNA collected as described above for infected cells. rRNA was removed using a previously described ribosomal RNA subtraction method[46]. One-hundred nanograms of each rRNA-depleted RNA sample was then used to make RNA-seq libraries using the NEBNext Ultra II RNA Library Prep Kit for Illumina following the manufacturer’s protocol for use with purified mRNA or rRNA-depleted RNA. Paired-end sequencing of the libraries was performed on an Illumina NextSeq 5000 machine at the MIT BioMicroCenter. FASTQ files were then mapped to the MG1655 genome (NC_00913.2), the T4 genome (NC_000866), and the plasmid pKVS45-CmdTAC, as previously described44,48.

Co-immunoprecipitation and LC–MS/MS

Overnight cultures of +cmdTAC and +cmdTA/Flag-C or +cmdT-Flag/AC cells were back-diluted in 250 ml LB and grown at 37 °C to an OD_600 of 0.3 and then for +cmdTAC and +cmdTA/Flag-C samples infected with T4 at an MOI of 10. At 0 min for all samples and 15 min post-infection for +cmdTAC and +cmdTA/Flag-C cultures 64 ml of sample was pelleted by centrifugation at 7,500g for 5 min. Pellets were decanted and resuspended in 1 ml of lysis buffer (25 mM Tris-HCL, 150 mM NaCl, 1 mM EDTA, 5% glycerol, 1% Triton X-100) supplemented with 1 μl ml−1 Ready-Lyse Lysozyme (Fischer Scientific), 1 μl ml−1 benzonase (Sigma), and cOmplete Protease Inhibitor Cocktail (Roche) and then flash frozen in liquid nitrogen. Samples were kept in liquid nitrogen until all timepoints were collected. Samples were subjected to two freeze-thaw cycles in liquid nitrogen to ensure complete lysis of cells. Additional lysis buffer was added to samples as needed to normalize sample concentrations by OD_600. Samples were spun at 20,000g for 10 min at 4 °C to pellet any debris. For each sample, 50 μl of Pierce Anti-DYKDDDDK magnetic agarose beads was mixed with 450 μl lysis buffer and then collected to the side of the tube using a magnetic rack. Beads were then washed twice with 500 μl lysis buffer. After the final wash, beads were mixed with 1 ml of sample and incubated for 20 min at room temperature on an end-to-end rotor. After incubation, beads were washed in wash buffer (1× PBS, 150 mM NaCl) twice and then once with MilliQ H_2O.

On-bead reduction, trypsin digest, and LC–MS/MS were done by the MIT Biopolymers and Proteomics Core as previously described[42]. In brief, proteins were reduced for 1 h at 56 °C with 10 mM dithiothreitol (Sigma) and then alkylated for 1 h at 25 °C in the dark with 20 mM iodoacetamide (Sigma). Proteins were digested with modified trypsin (Promega) overnight in 100 mM, pH 8 ammonium bicarbonate at a 1:50 enzyme:substrate ratio. Formic acid (99.9%, Sigma) was added to stop trypsin digest. Digested peptides were desalted using Pierce Peptide Desalting Spin Columns (Thermo) then lyophilized. Peptides were separated on a PepMap RSLC C18 column (Thermo) over 90 min by reverse phase HPLC (Thermo Ultimate 3000) before nano-electrospray with an Orbitrap Exploris 480 mass spectrometer (Thermo). Mass spectrometer run was done in data-dependent mode. Full scan parameters were resolution of 120,000 across 375–1600 m/z and maximum IT 25 ms. This was followed by MS/MS for as many precursor ions in a two second cycle with a resolution of 30,000, dynamic exclusion of 20 s, and a NCE of 28. Detected peptides were mapped to MG1655, plasmid, and T4 protein sequences and the abundance of proteins were estimated by number of spectrum counts/molecular mass to normalize for protein sizes. The ratio of spectral counts between the Flag pulldowns and untagged pulldowns at each timepoint were used to generate the data in the figures with a pseudocount added to each count.

In vitro transcription and translation

In vitro transcription–translation assays were conducted using the PURExpress kit (NEB) according to the manufacturer’s protocol with a 2-h incubation at 37 °C. Each reaction was supplemented with 1 U µl−1 Riboguard RNase inhibitor (LGC Biosearch Technologies), with or without 1 mM NAD+ and protein eluants as indicated. When supplying mRNA as a translation template, primers were used to amplify the DHFR gene using PCR from the PURExpress control DHFR plasmid. The PCR amplicon was purified using the DNA Clean & Concentrator Kit (Zymogen). Then, mRNA was synthesized from the PCR template by incubating 300 ng DNA with 200 U T7 RNA polymerase, 0.5 mM NTPs, and 5 mM DTT in a final reaction volume of 40 µl at 37 °C for 4.5 h. The resulting RNA was purified from the reaction using the RNA Clean and Concentrator Kit (Zymogen) with on-column DNase I treatment. Pure mRNA was then treated with CmdT or control mock purified protein in 1× ADPr buffer (20 mM Tris-HCl pH 8.0 and 150 mM NaCl) with 1 mM NAD+ and 1 U µl−1 Riboguard at 37 °C for 2 h. RNA was again purified as before, and 1 µg was supplied in the PURExpress reaction for 4 h at 37 °C. From this reaction, 2.5 µl was then denatured in Laemmli buffer and run on a 8–16% polyacrylamide gel by SDS–PAGE and stained with either Brilliant Blue R250 or Coomassie Fluor Orange (Molecular Probes) and visualized on a Bio-Rad ChemiDoc MP imager.

Protein immunoblotting

Cell cultures were grown overnight and diluted 1:200 in fresh LB containing the appropriate antibiotics. Cultures were grown at 37 °C to mid-exponential phase and then treated with T4 at an MOI of 10, or the appropriate inducers as dictated by the experiment. At various timepoints, cells were pelleted, flash frozen and subsequently resuspended in Laemmli buffer with 2.5% 2-mercaptoethanol in a volume normalized to culture turbidity (100 µl OD600−1 ml−1). Samples were run by standard SDS–PAGE on 12% polyacrylamide gels. Transfer onto 0.2 µm PVDF membranes was done at 90 V for 40 min for CmdA–HA, and otherwise was done at 100 V for 60 min. Membranes were blocked in Tris-buffered saline with 0.05% Tween-20 (TBST) and 5% non-fat milk for 60 min at room temperature and incubated with primary monoclonal antibody (1:1,000 rabbit anti-Flag or anti-HA, Cell Signaling Technologies) overnight at 4 °C. Membranes were washed with TBST and incubated with HRP-conjugated goat anti-rabbit IgG (Invitrogen) in blocking buffer for 60 min at room temperature. Membranes were again washed and incubated with SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Scientific) before exposure on a Bio-Rad ChemiDoc MP imager. Membranes were stained with Brilliant blue R250 as a loading control. To quantify band intensity, we used the gel analysis tool in ImageJ. Pixel intensity from the antibody signal was normalized to pixel intensity of total protein stain.

For immunoblots, membrane chemiluminescence was imaged directly followed by imaging of pre-stained molecular weight markers. Images were aligned, as shown in Supplementary Fig. 1, to relate chemiluminescent bands to molecular weight markers, as shown in main figures.

Non-denaturing blots were performed by lysing cells with a buffer composed of 50% BPER-II (Thermo Scientific), 0.1 mg ml−1 lysozyme, cOmplete protease inhibitor (Sigma-Aldrich), 6 U DNase I (NEB) and 3 µl RNase A (NEB) in volumes normalized to OD_600 of culture samples. Samples were incubated until clear at room temperature and then spun at max speed in a table-top centrifuge for 5 min to pellet insoluble material. Native loading dye (6×; 600 mM Tris-HCl, 50% glycerol, 0.02% bromophenol blue) was added to samples and loaded onto a 12% polyacrylamide Mini-Protean TGX pre-cast gel (Bio-Rad, does not contain SDS). Samples were electrophoresed in 25 mM Tris, 192 mM glycine running buffer at 75 V for 90 min. Transfer was conducted onto 0.2 µm PVDF membranes as described in this section at 100 V for 1 h at 4 °C. Blots were processed as described above. Blots shown are representative images of at least two biological replicates.

T4 genome engineering and evolution

The evolution of T4 on cmdTAC-containing cells was performed as described previously[49] for four rounds, resulting in the alt.-3 C-terminal extensions in all five replicates. To generate alt.-3 mutants for further evolution experiments, T4 stock was overlayed onto strains containing Cas9 and spacers directed toward alt.-3 or a control plasmid with no spacer (ML4233 and ML4234). The number of plaques formed on the spacer-containing strain was compared to the control to determine whether there was any selection imposed by the spacer. Despite attempting eight potential spacers, no selection was observed. To mitigate this, we repeated the experiment with T4 ∆agt ∆bgt (DNA contains non-glucosylated, 5-hydroxymethyl cytosine) on an E. coli ∆mcrA ∆mcrBC background required for viability of T4 ∆agt ∆bgt. This T4 formed fewer plaques in the presence of the alt.-3 spacer, suggesting that selection for alt.-3 mutants was imposed in this condition. The alt.-3 region was amplified by PCR and Sanger sequenced from plaques that were able to form on the spacer-containing strain. Of those plaques, we isolated a strain that harboured a mutation encompassing nearly the entire open reading frame of alt.-3. The T4 ∆alt.-3 strain was propagated in the presence of the spacer and stored as a stock at 4 °C. Evolution of this T4 strain on cmdTAC-containing cells was conducted the same as before, for 17 rounds, without observing mutations that increased plaquing ability.

Radiolabel incorporation assays

Overnight cultures of +cmdTAC and +cmdT*AC cells were back-diluted in LB + 20 µg ml−1 chloramphenicol and grown at 37 °C to an OD_600 between 0.2 and 0.3. An aliquot of each culture was collected before T4 infection at an MOI of 10 and again at each indicated timepoint (t = 10, 20, 30, 40 min post-infection). Each collected sample was incubated with EasyTag EXPRESS-35S protein labelling mix (Perkin Elmer) at 23 µCi ml−1 for 2 min at 37 °C. Labelling was chased with an unlabelled cysteine/methionine mixture at 5 mM and then samples precipitated in 13% w/v ice-cold TCA. Samples were pelleted by centrifugation at 16,000g for 10 min at 4 °C, washed twice with 500 µl ice-cold acetone, and then resuspended in resuspension buffer (100 mM Tris pH 11.0, 3% w/v SDS). Samples were run on 4–20% SDS–PAGE gels (Bio-Rad), the gels incubated in Gel-Dry Drying Solution (Invitrogen) for 10 min, and then dried on a vacuum gel dryer for 2 h at 80 °C. Dried gels were exposed to a phosphorimaging screen overnight before imaging on an Amersham Typhoon imager.

Protein purification

Five millilitres of cultures of ML4207 and ML4232 were grown overnight at 37 °C in LB + 0.2% glucose. The following day, 5 ml of each culture was washed of glucose twice and used to inoculate 495 ml of LB + 25 µg ml−1 kanamycin. After 1 h of additional growth, arabinose was added to a final concentration of 0.2%. Cultures were grown an additional 95 min, pelleted, washed with H_2O, again pelleted, and flash frozen in liquid N_2. The following day, cell pellets were resuspended in 4 ml lysis buffer (50 mM Tris pH 7.5, 500 mM NaCl, 0.05% Tween-20, EDTA-free protease inhibitor, 0.5 mM PMSF, 0.5 mg ml−1 lysozyme, 5 mM imidazole, and 5% glycerol) on ice. Cells were then lysed by sonication in a Bioruptor 300 for two rounds of 10 cycles each, high setting, 30 s on/30 s off. One millilitre of Ni-NTA agarose resin (Qiagen, 0.5 ml bed volume) was equilibrated in lysis buffer. Cell lysate was clarified by centrifugation then incubated with the Ni resin for 1 h at 4 °C with gentle rocking. The following steps were conducted at 4 °C. The resin was then passed through a 10 ml chromatography column and then washed 5× with 2.5 ml of wash buffer (same as lysis buffer but without lysozyme, and imidazole at 20 mM). Protein was then eluted 5× with 2.5 ml elution buffer (wash buffer with imidazole concentration at 300 mM). Eluted proteins were buffer-exchanged into Tris pH 7.4 using Micro Bio-Spin chromatography columns (Bio-Rad) and concentrated using Amicon Ultra 0.5 ml centrifugal filters with a 3-kDa cutoff.

In vitro ADP-ribosylation by CmdT

A typical reaction was assembled on ice as follows. To a buffer composed of 20 mM Tris-HCl pH 8.0 and 150 mM NaCl, we added 1 U µl−1 Riboguard RNase inhibitor, 1 mM NAD+ (NEB), 4 µg of DNA or RNA oligonucleotide, and protein at the concentrations indicated. The reactions were then incubated in a thermocycler at 37 °C. To stop the reaction, an equal volume of 2× 6 M urea sample buffer (Novex) was added. RNA was denatured at 95 °C for 10 min and then immediately placed on ice. One microgram of RNA samples were then subject to electrophoresis in 15% polyacrylamide TBE-urea gels at 180 V for 75 min. Gels were stained both with SYBR Gold and with a concentrated solution of 0.2% methylene blue in 0.1× TBE buffer for 15 min, de-stained with several changes of H_2O and imaged.

In vitro ADP ribose RNA pulldown

Twenty micrograms of total RNA were ADP-ribosylated with CmdT as described above with 0.25 mM 6-Biotin-17-NAD+ (Cayman Chemical) for 4 h at 37 °C and then continued at 4 °C overnight. Two control reactions were set up identically except with mock purified protein, or with standard NAD+ in place of 6-Biotin-17-NAD+. Ten micrograms of each reaction were kept at −80 °C as the pre-pulldown sample. The remaining 10 µg were incubated with streptavidin conjugated superparamagnetic beads (Dynabeads MyOne Streptavidin C1) following the manufacturer’s protocol. RNA was stripped from the beads by addition of 0.5 ml Trizol and incubation at 25 °C for 15 min on a thermomixer at 1,000 rpm. Beads were then precipitated with a magnet and 100 µl of chloroform were added. The phases were separated by centrifugation at 14,000g for 15 min. Finally, the aqueous phase was purified using the RNA Clean and Concentrate Kit (Zymogen). Pre- and post-pulldown samples were electrophoresed on a 6% TBE-urea gel for 45 min, stained with SYBR Gold and imaged. The samples from pre- and post-pulldown reactions containing 6-Biotin-17-NAD+ and CmdT were subject to RNA-seq as described in this section, but without rRNA depletion.

HPLC analysis of ribonucleosides

Ten micrograms each of no-U and no-C RNA oligonucleotides (Fig. 4d) were subjected to ADP-ribosylation as described above. Controls were included in which purified CmdT was replaced by a mock purification, or in which NAD+ was omitted. Next, samples were split and treated with either 100 U Nuclease P1 (NEB) and 10 U antarctic phosphatase, or, the same with the addition of 1 U Phosphodiesterase I from Crotalus adamanteus venom (SVPD, Millipore Sigma). Reactions were incubated in digest buffer (25 mM Tris-HCl pH 7.6, 50 mM NaCl_2, 1 mM ZnCl_2, and 10 mM MgCl_2) at 37 °C for 3.5 h in a total volume of 110 µl. One-hundred microlitres of digested and dephosphorylated nucleosides (10 µg) were injected onto a Vydac C18 4.6 ×250 mm reverse phase silica column (218TP54) equilibrated with 90% buffer A (0.1 M triethylammonium acetate (TEAA), pH 7.0)/10% buffer B (0.1 M TEAA, 20% acetonitrile, pH 7.0). HPLC was run with a mobile phase gradient composed of buffer A and B, 10–60% B from 1–21 min and 60–97% B from 21–26 min at a flow rate of 1 ml min−1. Analytes were measured at A_254. On a replicate run, samples without SVPD treatment were collected as fractions and relevant fractions were lyophilized. The samples were then resuspended in digest buffer, and again incubated for 3.5 h with 10 U antarctic phosphatase and 1 U SVPD and analysed by HPLC as described above.

Mass spectrometry of modified nucleosides

Fractions collected from HPLC analysis were dried in a speed-vac and resuspended in 200 µl of 50% acetonitrile in 0.1% formic acid. The fractions, or a buffer blank, were directly infused by syringe pump into a Thermo Q Exactive with an API source and electrospray ionization probe at a flow rate of 5 µl min−1. The instrument was operated in positive ion mode. MS/MS was conducted at collision energies of both 25 and 40 CE. Instrument parameters were as follows: spray voltage, 3.8 kV; capillary tube temperature, 280 °C; sheath gas, 20; auxiliary gas, 5; sweep gas, 5.

Bioinformatic analyses

The CmdT sequence logo was generated from the CmdT hmm file from DefenseFinder[50] using skylign.org.

Hidden Markov model profiles of CmdT and CmdC were downloaded from DefenseFinder[50] and searched against the RefSeq non-redundant protein database using hmmscan and default parameters. Protein hits were then identified in all available RefSeq bacterial genomes and CmdTAC was called if both CmdT and CmdC were present within two proteins of each other in the genome. CmdA was not included in calling as it both has higher sequence variability and is often unannotated in clearly homologous systems. All datasets were downloaded in July 2023. The complete taxonomic lineage of RefSeq genomes was created and filtered to include bacteria of current interest (genera with greater than 1,000 sequenced genomes). A taxonomic relationship of these genera was produced with NCBI Common Tree, and presence/absence was recorded from the taxonomic profiles of the CmdT/C hmmscan.

Structural predictions

Protein homology was assessed using HHpred[51]. Predictions of the structures of individual components and CmdTAC as a complex were done using AlphaFold2[52] with the multimer module and default parameters on the reduced database with 1 prediction generated per model. Structural homology searches based on the AlphaFold2-predicted structures of CmdT and CmdC were done using Foldseek[53] and the top hit for each search used for subsequent analyses. Electrostatics modelling was done using the coulombic function in ChimeraX. All predicted structure visualization was done using ChimeraX.

RNA-seq and RIP-seq read mapping

FASTQ files for each sample were trimmed using cutadapt[54] (version 1.15) and then mapped to the E. coli MG1655 genome (NC_00913.2), the T4 genome (NC_000866), and the plasmid pKVS45-CmdTAC using bowtie2[55] (version 2.3.4.1) with the following arguments: -D 20, -I 40, -X 300, -R 3, -N 0, -L 20, -i S,1,0.50. Sam files generated from bowtie2 mapping were then converted to bam files using samtools[56] (version 1.7) and then further converted to numpy arrays using the genomearray3 python library[57] for use in downstream analyses. For in vivo RIP–seq analyses only highly expressed transcripts as determined by transcripts with an RNA TPM for both replicates greater than or equal to the minimum mean TPM of any T4 transcript were used. For statistical comparisons of TPM ratios between RNA types a Welch’s t-test was used.

Reporting summary

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

Data availability

Summary spectra and raw data for IP–MS/MS of CmdC and CmdT pulldowns were deposited at MassIVE and can be accessed under accession MSV000093692; a summary table of high confidence hits is provided as Supplementary Table 1. Raw data for nucleotide MS and ESI-MS/MS were deposited at MassIVE and can be accessed under accession MSV000093878. RNA-seq and RIP–seq data are available at GEO under accession number GSE253514. All other data are available in the manuscript or the supplementary materials. Source data are provided with this paper.

Code availability

Code used for RNA-seq and in vivo RIP–seq analysis is available at https://doi.org/10.5281/zenodo.10522487 (ref. [58]).

References

1. Daugherty, M. D. & Malik, H. S. Rules of engagement: molecular insights from host-virus arms races. Annu. Rev. Genet. 46, 677–700 (2012).

2. McLaughlin, R. N. & Malik, H. S. Genetic conflicts: the usual suspects and beyond. J. Exp. Biol. 220, 6–17 (2017).

3. Georjon, H. & Bernheim, A. The highly diverse antiphage defence systems of bacteria. Nat. Rev. Microbiol. 21, 686–700 (2023).

4. Vassallo, C. N., Doering, C. R., Littlehale, M. L., Teodoro, G. I. C. & Laub, M. T. A functional selection reveals previously undetected anti-phage defence systems in the E. coli pangenome. Nat. Microbiol. 7, 1568–1579 (2022).

5. Cohen, M. S. & Chang, P. Insights into the biogenesis, function, and regulation of ADP-ribosylation. Nat. Chem. Biol. 14, 236–243 (2018).

6. Suskiewicz, M. J., Prokhorova, E., Rack, J. G. M. & Ahel, I. ADP-ribosylation from molecular mechanisms to therapeutic implications. Cell 186, 4475–4495 (2023).

7. Kim, D.-S., Challa, S., Jones, A. & Kraus, W. L. PARPs and ADP-ribosylation in RNA biology: from RNA expression and processing to protein translation and proteostasis. Genes Dev. 34, 302–320 (2020).

8. Lüscher, B. et al. ADP-ribosyltransferases, an update on function and nomenclature. FEBS J. 289, 7399–7410 (2022).

9. Lüscher, B., Verheirstraeten, M., Krieg, S. & Korn, P. Intracellular mono-ADP-ribosyltransferases at the host-virus interphase. Cell. Mol. Life Sci. 79, 288 (2022).

10. Suskiewicz, M. J. et al. Updated protein domain annotation of the PARP protein family sheds new light on biological function. Nucleic Acids Res. 51, 8217–8236 (2023).

11. Jankevicius, G., Ariza, A., Ahel, M. & Ahel, I. The toxin–antitoxin system DarTG catalyzes Reversible ADP-ribosylation of DNA. Mol. Cell 64, 1109–1116 (2016).

12. Bullen, N. P. et al. An ADP-ribosyltransferase toxin kills bacterial cells by modifying structured non-coding RNAs. Mol. Cell 82, 3484–3498.e11 (2022).

13. Jurėnas, D. et al. Photorhabdus antibacterial Rhs polymorphic toxin inhibits translation through ADP-ribosylation of 23S ribosomal RNA. Nucleic Acids Res. 49, 8384–8395 (2021).

14. LeRoux, M. & Laub, M. T. Toxin–antitoxin systems as phage defense elements. Annu. Rev. Microbiol. 76, 21–43 (2022).

15. Harms, A., Brodersen, D. E., Mitarai, N. & Gerdes, K. Toxins, targets, and triggers: an overview of toxin–antitoxin biology. Mol. Cell 70, 768–784 (2018).

16. Laub, M. T. & Typas, A. Principles of bacterial innate immunity against viruses. Curr. Opin. Immunol. 89, 102445 (2024).

17. Texier, P. et al. ClpXP-mediated degradation of the TAC antitoxin is neutralized by the SecB-like chaperone in Mycobacterium tuberculosis. J. Mol. Biol. 433, 166815 (2021).

18. Bordes, P. et al. SecB-like chaperone controls a toxin–antitoxin stress-responsive system in Mycobacterium tuberculosis. Proc. Natl Acad. Sci. USA 108, 8438–8443 (2011).

19. Sala, A., Calderon, V., Bordes, P. & Genevaux, P. TAC from Mycobacterium tuberculosis: a paradigm for stress-responsive toxin–antitoxin systems controlled by SecB-like chaperones. Cell Stress Chaperones 18, 129–135 (2013).

20. Bordes, P. et al. Chaperone addiction of toxin–antitoxin systems. Nat. Commun. 7, 13339 (2016).

21. Maffei, E. et al. Systematic exploration of Escherichia coli phage–host interactions with the BASEL phage collection. PLoS Biol. 19, e3001424 (2021).

22. Aframian, N. & Eldar, A. Abortive infection antiphage defense systems: separating mechanism and phenotype. Trends Microbiol. 31, 1003–1012 (2023).

23. Jørgensen, R. et al. Exotoxin A–eEF2 complex structure indicates ADP ribosylation by ribosome mimicry. Nature 436, 979–984 (2005).

24. Mikolčević, P., Hloušek-Kasun, A., Ahel, I. & Mikoč, A. ADP-ribosylation systems in bacteria and viruses. Comput. Struct. Biotechnol. J. 19, 2366–2383 (2021).

25. Flynn, J. M., Neher, S. B., Kim, Y. I., Sauer, R. T. & Baker, T. A. Proteomic discovery of cellular substrates of the ClpXP protease reveals five classes of ClpX-recognition signals. Mol. Cell 11, 671–683 (2003).

26. Rao, V. B. & Black, L. W. Structure and assembly of bacteriophage T4 head. Virol. J. 7, 356 (2010).

27. Revel, H. R., Stitt, B. L., Lielausis, I. & Wood, W. B. Role of the host cell in bacteriophage T4 development. I. Characterization of host mutants that block T4 head assembly. J. Virol. 33, 366–376 (1980).

28. Bakkes, P. J., Faber, B. W., van Heerikhuizen, H. & van der Vies, S. M. The T4-encoded cochaperonin, gp31, has unique properties that explain its requirement for the folding of the T4 major capsid protein. Proc. Natl Acad. Sci. USA 102, 8144–8149 (2005).

29. LeRoux, M. et al. The DarTG toxin-antitoxin system provides phage defence by ADP-ribosylating viral DNA. Nat. Microbiol. 7, 1028–1040 (2022).

30. Miller, E. S. et al. Bacteriophage T4 genome. Microbiol. Mol. Biol. Rev. 67, 86–156 (2003).

31. Basanta-Sanchez, M., Temple, S., Ansari, S. A., D’Amico, A. & Agris, P. F. Attomole quantification and global profile of RNA modifications: Epitranscriptome of human neural stem cells. Nucleic Acids Res. 44, e26 (2016).

32. Wolfram-Schauerte, M. et al. A viral ADP-ribosyltransferase attaches RNA chains to host proteins. Nature 620, 1054–1062 (2023).

33. Honjo, T., Nishizuka, Y., Hayaishi, O. & Kato, I. Diphtheria toxin-dependent adenosine diphosphate ribosylation of aminoacyl transferase II and inhibition of protein synthesis. J. Biol. Chem. 243, 3553–3555 (1968).

34. Jørgensen, R. et al. Cholix toxin, a novel ADP-ribosylating factor from Vibrio cholerae. J. Biol. Chem. 283, 10671–10678 (2008).

35. Mets, T. et al. Mechanism of phage sensing and restriction by toxin–antitoxin–chaperone systems. Cell Host Microbe 32, 1059–1073.e8 (2024).

36. Munnur, D. et al. Reversible ADP-ribosylation of RNA. Nucleic Acids Res. 47, 5658–5669 (2019).

37. Daugherty, M. D., Young, J. M., Kerns, J. A. & Malik, H. S. Rapid evolution of PARP genes suggests a broad role for ADP-ribosylation in host-virus conflicts. PLoS Genet. 10, e1004403 (2014).

38. Shaw, A. E. et al. Fundamental properties of the mammalian innate immune system revealed by multispecies comparison of type I interferon responses. PLoS Biol. 15, e2004086 (2017).

39. Parthasarathy, S. & Fehr, A. R. PARP14: A key ADP-ribosylating protein in host–virus interactions? PLoS Pathog. 18, e1010535 (2022).

40. Alhammad, Y. M. et al. SARS-CoV-2 Mac1 is required for IFN antagonism and efficient virus replication in cell culture and in mice. Proc. Natl Acad. Sci. USA 120, e2302083120 (2023).

41. Grunewald, M. E. et al. The coronavirus macrodomain is required to prevent PARP-mediated inhibition of virus replication and enhancement of IFN expression. PLoS Pathog. 15, e1007756 (2019).

42. Zhang, T. et al. Direct activation of a bacterial innate immune system by a viral capsid protein. Nature 612, 132–140 (2022).

43. Gao, L. A. et al. Prokaryotic innate immunity through pattern recognition of conserved viral proteins. Science 377, eabm4096 (2022).

44. Guegler, C. K. & Laub, M. T. Shutoff of host transcription triggers a toxin–antitoxin system to cleave phage RNA and abort infection. Mol. Cell 81, 2361–2373.e9 (2021).

45. Wolfram-Schauerte, M., Pozhydaieva, N., Viering, M., Glatter, T. & Höfer, K. Integrated omics reveal time-resolved insights into T4 Phage infection of E. coli on proteome and transcriptome levels. Viruses 14, 2502 (2022).

46. Culviner, P. H., Guegler, C. K. & Laub, M. T. A simple, cost-effective, and robust method for rRNA depletion in RNA-sequencing studies. mBio 11, e00010–e00020 (2020).

47. Zeng, Y. et al. Refined RIP-seq protocol for epitranscriptome analysis with low input materials. PLoS Biol. 16, e2006092 (2018).

48. Culviner, P. H. & Laub, M. T. Global analysis of the E. coli toxin MazF reveals widespread cleavage of mRNA and the inhibition of rrna maturation and ribosome biogenesis. Mol. Cell 70, 868–880.e10 (2018).

49. Srikant, S., Guegler, C. K. & Laub, M. T. The evolution of a counter-defense mechanism in a virus constrains its host range. eLife 11, e79549 (2022).

50. Tesson, F. et al. Systematic and quantitative view of the antiviral arsenal of prokaryotes. Nat. Commun. 13, 2561 (2022).

51. Zimmermann, L. et al. A completely reimplemented MPI bioinformatics toolkit with a new HHpred server at its core. J. Mol. Biol. 430, 2237–2243 (2018).

52. Bryant, P., Pozzati, G. & Elofsson, A. Improved prediction of protein–protein interactions using AlphaFold2. Nat. Commun. 13, 1265 (2022).

53. van Kempen, M. et al. Fast and accurate protein structure search with Foldseek. Nat. Biotechnol. 42, 243–246 (2024).

54. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

55. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

56. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

57. Culviner, P. H., Nocedal, I., Fortune, S. M. & Laub, M. T. Global analysis of the specificities and targets of endoribonucleases from Escherichia coli toxin–antitoxin systems. mBio 12, e02012–e02021 (2021).

58. Doering, C. Anti-viral defense by an ADP-ribosyltransferase that targets mRNA to block translation. Zenodo https://doi.org/10.5281/zenodo.10522488 (2024).

Acknowledgements

The authors thank D. Saxton, T. Zhang, S. Srikant and C. Beck for comments on the manuscript; B. Imperiali for help with interpreting the mass spectrometry data; C. Eickmann for help with AlphaFold2 predictions; the MIT BioMicroCenter and its staff for their support with sequencing; the MIT Biopolymers and Proteomics Core and its staff for assisting in HPLC and mass spectrometry experiments; and S. Srikant for help with T4 reverse genetics. M.T.L. is an Investigator of the Howard Hughes Medical Institute. This work was also supported by a Howard Hughes Medical Institute Gilliam Fellowship awarded to C.R.D. and a National Institutes of Health NIGMS grant 5F32 GM139231-02 to C.N.V.

Author information

Author notes

1. These authors contributed equally: Christopher N. Vassallo, Christopher R. Doering

Authors and Affiliations

1. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

Christopher N. Vassallo, Christopher R. Doering & Michael T. Laub

2. Howard Hughes Medical Institute, Cambridge, MA, USA

Michael T. Laub

Contributions

C.R.D. and C.N.V. cultured bacteria and phages, created plasmids and strains, and conducted bacterial growth and phage plating assays. C.R.D. and C.N.V. performed RNA extractions and immuno-northern blots. RNA-seq, RIP–seq and all RNA-seq library preparation was performed by C.R.D. In vitro ADPr RNA-seq was performed by C.N.V. IP–MS was performed by C.R.D. In vitro transcription and translation, protein immunoblotting, and T4 genome engineering and evolution were performed by C.N.V. Radiolabel incorporation assays were performed by C.R.D. Protein purification, in vitro ADPr assays, HPLC and ESI-MS were conducted by C.N.V. Bioinformatic analyses were performed by C.R.D. and C.N.V. Structural predictions were performed by C.R.D. C.R.D., C.N.V. and M.T.L. designed experiments, analysed data, prepared figures and wrote the manuscript.

Corresponding author

Correspondence to Michael T. Laub.

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Extended data figures and tables

Extended Data Fig. 1 Taxonomic distribution of cmdTAC and efficiency of plaquing for BASEL and T-even phages.

(a) Presence or absence of CmdTAC homologs in bacterial genera with > 1000 sequenced genomes (see Methods). (b) Examples of CmdTAC homologs found in diverse bacterial species. Grey bars between genes capture percent identity, defined by the color bar below. (c) Plaquing of the phages indicated on E. coli K12 harboring cmdTAC under control of its native promoter or an empty vector control. Data used to generate EOP data in Fig. 1c. (d) Plaquing of T4 phage on EV, cmdTAC+, or cmdT*AC+. (e) Survival assay of empty vector, cmdTAC+ cells or cells expressing a direct defense system (PD-T4-1) from its native promoter on the same low-copy plasmid. Values represent the number of CFUs of each strain after 18 min of infection divided by the CFUs pre-infection. Boxplots represent the median and quartile ranges of n = 4 biological replicates, with whiskers indicating max/min values. p-values are indicated at the top and represent a two-sided independent t-test. (f) Efficiency of center of infection assays with cmdTAC+ cells which measures the number of infected cells that go on to produce > 0 progeny phage. Value is measured relative to the empty vector control. Boxplots represent the median and quartile ranges of n = 4 biological replicates, with whiskers indicating max/min values.

Source Data

Extended Data Fig. 2 AlphaFold2-based prediction of CmdTAC structures.

(a-c) AlphaFold2 predicted structures of CmdT (A), CmdA (B), and tetrameric CmdC (C). For the CmdC tetramer in panel (c) each subunit is labeled with a letter (A-D) where noted in panel (d). (d) PAE plot and per-residue model confidence score (pLDDT) for AlphaFold2-predicted CmdC tetramer. For the upper triangle of the plot, each subunit-subunit interaction is boxed and labeled with the corresponding subunit interactions corresponding to the subunits labeled in (c). (e) PAE plot and per-residue model confidence score (pLDDT) for the AlphaFold2-predicted complex of CmdTAC. Outlined in black and labeled within the plot are regions corresponding to notable features in the CmdTAC complex. (f) Ribbon diagrams of predicted CmdTAC complex, color-coded based on per-residue model confidence score (pLDDT).

Extended Data Fig. 3 Sequencing logo and IP-MS/MS interacting partners of CmdT, loading controls of for immunoblots, and quantification of CmdA degradation.

(a) Sequence logo showing conservation of putative catalytic residues in CmdT homologs. Shaded residues indicate active site residues in known ADP-ribosyltransferases. Blue color indicates conserved, aromatic residues. Amino acid numbers for CmdTECOR22 listed across the top. (b) IP-MS/MS of proteins co-precipitating with N-terminally FLAG-tagged CmdT. Data points corresponding to CmdC and CmdA are labeled. The x-axis indicates the ratio of spectral counts for cmdTFLAGAC and cmdTAC containing cells, both on low copy plasmids under the cmdTAC native promoter. A pseudocount is added to each sample set. (c) Coomassie stained gels used as loading controls for immunoblots shown in Fig. 2b. (d) Western blot for CmdTFLAG and CmdA3xHA at 0 and 25 min post T4 infection of ML4220. Left, quantification of CmdA to CmdT ratios as measured by pixel intensity of the Western blot on the right and normalized to total protein stain, bottom right. The normalized ratios were adjusted so that the mean time 0 value is equal to 1.0. Box plots display the values, median, and quartiles of three biological replicates, with whiskers indicating max/min values. Two-sided independent t-test p-value is shown. (e) Coomassie stained gel used as loading control for immunoblot shown in Fig. 2e.

Source Data

Extended Data Fig. 4 Mutations to alt.-3 inhibit CmdTAC defense by blocking CmdA degradation.

(a) The alt.-3† and alt.-3†† mutations isolated as cmdTAC escape mutants are summarized below a diagram of the alt.-3 region of the T4 genome. The region deleted in T4 Δagt Δbgt Δalt.-3 is shown below. The C-termini of Alt.-3, Alt.-3†, and Alt.-3†† are shown on the right. (b) Plaquing of 10-fold serially diluted T4 phage harboring the alt.-3† mutation on lawns of E. coli expressing cmdTAC or carrying an empty vector. (c) Plating viability of strains harboring cmdTAC or an empty vector and expressing the wild-type alt.-3 gene. (d) Plaquing of 10-fold serially diluted T4 Δagt Δbgt Δalt.-3 phage on lawns of E. coli harboring cmdTAC or an empty vector. The bacterial genotype was ∆mcrA ∆mcrBC, as required for T4 ∆agt ∆bgt. (e) Cells expressing cmdTA from an inducible promoter with CmdA engineered to have an N-terminal 3xHA tag. Cells also express alt.-3 or alt.-3† from a tetracycline-inducible promoter. CmdA levels were measured by immunblot (left), with cell viability assessed by 10-fold serial dilutions (right).

Extended Data Fig. 5 Anti-ADP ribose immuno-northern blots and total RNA staining of TBE-urea gels for varying methods of CmdT expression.

(a) Immuno-northern blot (left) and total RNA (right) of RNA samples taken from cells harboring arabinose-inducible cmdTA, cmdT*A, or an empty vector harvested at the times indicated post-induction. RNA was resolved on polyacrylamide gels. (b) Same as panel (a) but for cells harboring cmdTAC infected with T4 and harvested post-infection at the times indicated. (c) Same as panel (a) but for cells harboring cmdTAC, or an empty vector, and producing the combinations of Gp23 and Gp31 indicated.

Extended Data Fig. 6 TBE-urea gel resolution of in vitro modified mRNA and RNA-seq and RIP-seq analysis of ADP-ribosylated RNA.

(a) TBE-urea gel showing in vitro transcribed DHFR mRNA used as a translation template for the in vitro translation conducted in Fig. 3f. The DHFR transcript was either treated with a control eluant or CmdT. CmdT-treatment resulted in molecular weight increase equivalent to approximately 80 nucleotides. Sizes were determined based on a low-range ssRNA marker. (b) Heat maps summarizing, for each T4 transcript, the ratio of TPM values in cells harboring cmdTAC or an empty vector. Transcripts are ordered from top to bottom based on peak time of expression, as determined previously[44]. Data are the average of two biological replicates. (c) Ratio of ADP-ribose IP enriched TPM to baseline RNA TPM values for each transcript. Transcripts are separated by RNA type and T4 vs MG1655 origin. Note that the T4 genome does not contain rRNA. Boxplots represent the median and quartile ranges, with whiskers indicating 1.5x interquartile range. Asterisks indicate p-value < 0.01 for a two-sided Welch’s t-test. Exact p-values for tests shown (all in comparison to T4 coding transcripts) are: E. coli coding, p = 9.9E-35; T4 tRNA, p = 4.9E-4; E. coli tRNA, p = 1.3E-11; E. coli rRNA, p = 9.0E-5. Data are the average of n = 2 biological replicates. (d) TBE-urea gel showing total RNA from T4-infected cells after treatment +/- CmdT and with 6-biotin-17-NAD+ or NAD+. RNA is shown pre- and post- streptavidin enrichment, with arrows indicating samples that were sequenced. (e) Ratio of TPM after streptavidin enrichment of biotinylated RNA to TPM pre-enrichment for each transcript. Transcripts are separated by RNA type and T4 vs MG1655 origin. Note that the T4 genome does not contain rRNA. Two-sided Welch’s t-test p-values are indicated. Boxplots represent the median and quartile ranges, with whiskers indicating 1.5x interquartile range.

Extended Data Fig. 7 Additional analyses of ADP-ribosylation of RNA by CmdT.

(a) The no-U oligo (see Fig. 4d) incubated with CmdT for the times indicated, with all reactions visualized as in Fig. 4. (b) Schematic showing enzymatic digestion of ADP-ribosylated oligos with nuclease PI and antarctic phosphatase, with or without snake venom phosphodiesterase (SVPD). (c) Overlay of HPLC traces from Fig. 4f derived from analysis of nucleosides isolated following incubation of the no-U and no-C RNA oligos with CmdT and NAD+ (or without NAD+, as indicated) and then treated with nuclease P1 and antarctic phosphatase. Arrow highlights the decrease in adenosine for reaction containing CmdT and NAD+. Peaks corresponding to A, C, G, and U nucleosides are marked. (d) Schematic summarizing enzymatic digestion activities of nuclease P1, antarctic phosphatase, and snake venom phosphodiesterase on ADP-ribosylated RNA oligos. (e-f) HPLC analysis of nucleosides produced after treating the numbered peaks from top row of Fig. 4f (reproduced at the top) with SVPD. The products are labeled and the inferred parent molecule indicated on the far right with the origin of the peaks on the oligo substrates shown in (f). Gr = G-ribose; Ar = A-ribose; ADPr = ADP-ribose. (g) MS/MS fragmentation of the A-ribose sodium adduct. Collision energy = 25 eV, as used previously[12]. Note the absence of a ribose-ribose peak at m/z = 287.

Source Data

Extended Data Fig. 8 Mass spectrometry and UV-Vis analysis of CmdT-dependent ADP-ribosylated adenosine.

(a) Predicted fragmentation of adenosine ribosylated on the N6 position (far left) to produce ribosylated adenine (second from left), which is predicted to further fragment into three species (right), with m/z values of 200, 230, and 158, corresponding to the peaks seen in Fig. 4h. (b) Predicted fragmentation of adenosine ribosylated on the 2′-OH (left) to produce the ribosylated adenine (right), with an m/z value of 287. This peak was not seen in Fig. 4h, but was seen with prior analysis of RhsP2 (ref. [12]). (c) UV-Vis spectroscopy of adenosine and CmdT-produced adenosine-ribose from Fig. 4f.

Source Data

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Vassallo, C.N., Doering, C.R. & Laub, M.T. Anti-viral defence by an mRNA ADP-ribosyltransferase that blocks translation. Nature 636, 190–197 (2024). https://doi.org/10.1038/s41586-024-08102-8

- Received: 10 February 2024

- Accepted: 23 September 2024

- Published: 23 October 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08102-8

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Leptin-activated hypothalamic BNC2 neurons acutely suppress food intake

Han L. Tan,

Luping Yin,

Yuqi Tan,

Jessica Ivanov,

Kaja Plucinska,

Anoj Ilanges,

Brian R. Herb,

Putianqi Wang,

Christin Kosse,

Paul Cohen,

Dayu Lin &

…

Jeffrey M. Friedman

Nature volume 636, pages 198–205 (2024)

Abstract

Leptin is an adipose tissue hormone that maintains homeostatic control of adipose tissue mass by regulating the activity of specific neural populations controlling appetite and metabolism[1]. Leptin regulates food intake by inhibiting orexigenic agouti-related protein (AGRP) neurons and activating anorexigenic pro-opiomelanocortin (POMC) neurons[2]. However, whereas AGRP neurons regulate food intake on a rapid time scale, acute activation of POMC neurons has only a minimal effect3,4,5. This has raised the possibility that there is a heretofore unidentified leptin-regulated neural population that rapidly suppresses appetite. Here we report the discovery of a new population of leptin-target neurons expressing basonuclin 2 (Bnc2) in the arcuate nucleus that acutely suppress appetite by directly inhibiting AGRP neurons. Opposite to the effect of AGRP activation, BNC2 neuronal activation elicited a place preference indicative of positive valence in hungry but not fed mice. The activity of BNC2 neurons is modulated by leptin, sensory food cues and nutritional status. Finally, deleting leptin receptors in BNC2 neurons caused marked hyperphagia and obesity, similar to that observed in a leptin receptor knockout in AGRP neurons. These data indicate that BNC2-expressing neurons are a key component of the neural circuit that maintains energy balance, thus filling an important gap in our understanding of the regulation of food intake and leptin action.

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Leptin is an adipose tissue-derived hormone that functions as the afferent signal in a negative feedback loop that maintains homeostatic control of adipose tissue mass1,6. Leptin regulates food intake and maintains energy balance, in part by inhibiting orexigenic agouti-related protein (AGRP)/neuropeptide Y (NPY) neurons, expressing leptin receptor (LepR), and activating anorexigenic pro-opiomelanocortin (POMC) neurons, which also express LepR1,2. These neurons are located in the arcuate nucleus (ARC) of the hypothalamus. POMC is a protein precursor that is cleaved proteolytically to generate α-melanocyte stimulating hormone (αMSH), which reduces food intake by activating the melanocortin 4 receptor (MC4R)[7]. AGRP/NPY neurons increase food intake through projections to many of the same sites as POMC neurons where the AGRP protein diminishes αMSH signalling at the MC4R[7]. AGRP/NPY neurons also directly inhibit POMC neurons[8]. These findings have indicated that food intake is reciprocally regulated by these two neural populations. This proposed ‘yin–yang’ dynamic between these neural populations is a consistent feature in nearly all models of how the homeostatic control of food intake and body weight is maintained[2]. However, the functional effects and dynamics of POMC and AGRP/NPY neurons diverge in several important respects and several lines of evidence have indicated that there may be other leptin-responsive populations that are crucial for leptin-driven control of food intake and body weight9,10.

First, while activating AGRP/NPY neurons rapidly leads to food seeking and consumption, activating POMC neurons has only a minimal effect on acute food intake3,4,5. Second, mutations in POMC, POMC processing enzymes or the MC4R cause obesity7,11, whereas mutations in AGRP and NPY have not been reported to alter body weight, although ablation of these neurons in adulthood has been reported to cause anorexia12,13,14,15,16. In addition, while activation of AGRP/NPY neurons is associated with negative valence, POMC neuron activation is not associated with positive valence and also elicits negative valence17,18. The role of these populations in leptin signalling has also been evaluated and, whereas a knockout of the LepR in AGRP/NPY neurons of adult mice causes extreme obesity, deletion of the LepR from adult POMC neurons has only a minimal effect[19]. Finally, acute activation of AGRP/NPY neurons alters glucose metabolism—a response not observed with acute manipulation of POMC neurons[20]. Thus, while POMC and AGRP/NPY neurons have opposite effects on weight, their effects are not equivalent in other respects, indicating that they are not precise counterparts. This has raised the possibility that there might be a missing population of LepR-expressing neurons that acutely suppress food intake with a timecourse similar to the effects of AGRP/NPY neurons. Indeed, a previous report indicated that there is a missing population of GABAergic neurons that express LepR and regulate weight[9].

To address this, we conducted a systematic profiling of the transcriptomes of ARC neurons using single-nucleus RNA sequencing (snRNA-seq) and identified a new population of LepR-expressing ARC neurons that co-express the basonuclin 2 (Bnc2) gene. Further studies showed that BNC2 neurons rapidly induce satiety with similar kinetics to the activation of feeding after AGRP/NPY neural activation. This discovery adds an important new cellular component to the neural network that regulates hunger and satiety and sheds new light on how leptin regulates energy balance.

Identification of BNC2/LepR neurons

We performed snRNA-seq on ARC that was micro-dissected from adult male mice (Fig. 1a). Nuclei were isolated, stained with a fluorescent antibody to NeuN—a specific marker of neuronal nuclei—and subjected to fluorescence-activated sorting followed by single nucleus sequencing using the 10x single-cell analysis platform[21]. A total of 3,557 cells were profiled, of which 3,481 were identified as neurons on the basis of expression levels of neuronal markers including Tubb3, Snap25, Syt1 and Elavl4, and the absence of non-neuronal markers (Extended Data Fig. 1a,b). Leiden clustering analysis identified 21 distinct neuron clusters (Fig. 1b,c). To confirm that these neurons were from the ARC rather than from adjacent regions, we analysed the clusters for specific marker genes that define known ARC populations. This validation process confirmed that 19 of the clusters were indeed located within the ARC with the exception of Clusters 9 and 15, which express Nr5a1, indicating their origin from the region of the adjacent ventromedial hypothalamus22,23 (Fig. 1c). These clusters included one that expresses Agrp/Npy (Cluster 0), two expressing Pomc (Clusters 3 and 12) neurons and 16 more clusters (Fig. 1d). Clusters 0 and 3 expressed the Lepr, as did Cluster 17, which did not express Agrp, Npy or Pomc but instead showed specific expression of the Bnc2 gene (Fig. 1d). Several of the genes in BNC2 Cluster 17 including Lepr were also found in a single cluster (n11.Trh/Cxcl12) in a previously published dataset of ARC neuron types[22] (Extended Data Fig. 1c). However, Bnc2 was not reported as being expressed in this cluster and thyrotropin-releasing hormone (Trh) gene—encoding the canonical marker TRH—was also expressed in other clusters that did not express Lepr[22]. Thus, BNC2 serves as a specific marker for a new Lepr cluster that does not include Agrp, Npy or Pomc.

Fig. 1: Identification of new LepR-expressing neurons in the ARC.

a, Micro-dissection of mouse ARC. b, Uniform manifold approximation and projection (UMAP) plot of neuronal nuclei from the male ARC (n = 6 adult wild-type (WT) mice). c, Dotplot showing marker genes of individual neuronal clusters, including the Nr5a1 gene. d, Heatmap illustrating the expression of indicated genes across clusters. Neuronal clusters are categorized as excitatory (Slc17a6 positive) and inhibitory (Slc321a/Gad1/Gad2 positive). e, RNA ISH of Lepr, Bnc2, Agrp, Pomc and Slc32a1 in the ARC of adult male WT mice (n = 3 mice). White arrows, Agrp- or Pomc-positive cells; red arrows, Bnc2-positive cells. f, Percentage of Bnc2-positive cells co-expressing Lepr, Pomc, Agrp and Slc32a1 (n = 3 mice). g, UMAP plots of Lepr/Pomc, Lepr/Bnc2 and Lepr/Npy neurons in adult human ARC neurons. h, Co-localization of Lepr/Bnc2, Lepr/Agrp/Npy and Lepr/Pomc neurons in adult human ARC neurons. Data are presented as mean ± s.e.m. Scale bar, 10 μm. Max., maximum; min., minimum.

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The total number of cells in the BNC2 cluster (Cluster 17, n = 35) was lower than those in the AGRP cluster (Cluster 0, n = 650) or the POMC cluster (Cluster 3, n = 271). However, the level of LepR expression in this cluster was significantly higher than in the POMC cluster and equivalent to that in the AGRP cluster (Cluster 0, 1.32; Cluster 3, 0.52; Cluster 17, 1.09). We confirmed the co-expression of Lepr and Bnc2 using multiplex in situ hybridization (ISH) of adult mouse hypothalamus. The results showed that more than 90% of Bnc2 cells in the ARC co-expressed Lepr (90.4%) and Slc32a1 (96.6%)—a marker for inhibitory GABAergic neurons—whereas less than 5% co-expressed either Agrp (2.6%) and Pomc (3.3%) (Fig. 1e,f). We also determined whether Bnc2 co-localized with Lepr in human hypothalamus ARC[24] and found a significant overlap between Bnc2 and Lepr (Fisher’s exact test; P = 0.0218; Fig. 1g). Here again, the LepR/BNC2 cells in human hypothalamus were distinct from AGRP/NPY and POMC neurons, as 82.1% (23 of 28) of Lepr/Bnc2 neurons were not co-localized with Agrp, Npy or Pomc (Fig. 1h). Altogether, these results show that BNC2 neurons are a new population of LepR-expressing neurons in mouse and human that does not overlap with the previously characterized LepR AGRP/NPY and POMC populations.

Activation of BNC2 neurons by feeding

To further study the dynamics and function of BNC2 neurons in ARC, we generated a BNC2-P2A-iCre knockin mouse line (referred to hereafter as BNC2-Cre) by inserting a P2a-iCre fusion in frame at the C terminus of the BNC2 protein (Extended Data Fig. 2a,b). The correct insertion site of iCre was confirmed by PCR of genomic DNA (Extended Data Fig. 2c). Eutopic expression of Cre in BNC2 neurons in ARC was verified by injecting an adeno-associated virus (AAV) with a Cre-dependent mCherry into the ARC of adult BNC2-Cre mice. ISH showed nearly complete co-localization of mCherry and Bnc2 RNA (Extended Data Fig. 2d). Leptin binding to LepR activates Janus tyrosine kinase 2 (JAK2), leading to STAT3 phosphorylation—a canonical marker of leptin activation[25]—and we confirmed leptin signalling in BNC2 neurons by immunostaining for pSTAT3 after leptin treatment. An AAV-DIO-mCherry was injected into the ARC of adult BNC2-Cre mice and animals subsequently received a single injection of leptin (3 mg kg−1) after an overnight fast. This resulted in a significant increase in pSTAT3 in mCherry-labelled BNC2 neurons at 3 h, which was not seen in control animals that received PBS injections (Fig. 2a,b). Refeeding mice after an overnight fast also led to a significant increase of pSTAT3 amounts in BNC2 neurons (Fig. 2a,b). Consistent with this, we also found that the amounts of FOS—a neuron activity marker[26]—increased significantly in BNC2 neurons following refeeding or leptin injection (Fig. 2c,d).

Fig. 2: Activation of BNC2 neurons by feeding.

a,c, Representative pSTAT3 (a) and FOS (c) immunostaining in male mice under three conditions: overnight fasting (FD), fasting followed by 3-h refeeding with chow (Ref) and fasting followed by leptin injection with 3-h wait (Lep) (n = 4 mice per group). Arrowheads indicate double-positive cells. b,d, Percentage of pSTAT3/mCherry (b) and FOS/mCherry (d) double-positive cells in mCherry-positive cells (n = 4 mice per group, one-way analysis of variance (ANOVA)). e, Spontaneous APs of ARC BNC2 neurons from overnight-fasted adult male BNC2-Cre mice injected with AAV-DIO-GFP. Leptin was added at the time indicated. f,g, Spontaneous AP frequency (f) and resting membrane potential (RMP, g) before, during and after leptin application (n = 17 cells from four mice per group, Friedman test). h–k, Calcium recordings from male mice expressing GCaMP6s in BNC2 neurons exposed to an inedible plastic tube, chow or PB (h). i, Average calcium traces in fasted mice aligned to presentation (left) and fluorescence quantification (0–300 s) (right, n = 8 mice per group, paired Wilcoxon test). j, Average calcium traces in mice fed ad libitum aligned to presentation (left) and quantification (0–300 s) (right, n = 8 mice per group, paired Student’s t-test or Wilcoxon test). k, Individual calcium trace from an overnight-fasted mouse presented with PB. l,m, Average calcium traces in fasted mice presented with PB, before (l) and after (m) eating, aligned to eating (left) and quantification (right, n = 8 mice per group, paired Student’s t-test). n,o, Recordings from fasted male mice presented with chow inside or outside a container (n). o, Average calcium traces aligned to presentation (left) and quantification (0–100 and 500–600 s) (right, n = 8 mice per group, paired Student’s t-test or Wilcoxon test). Data are presented as mean ± s.e.m. NS, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. Statistical details in Source Data. Scale bars, 50 μm. Illustrations in h and n were created using BioRender (https://biorender.com).

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We then performed whole-cell patch-clamp recordings in hypothalamic slices prepared from adult male BNC2-Cre mice injected with an AAV-DIO-EGFP in the ARC. Application of leptin (100 nM) to brain slices prepared from mice that were either fasted or fed ad libitum resulted in a significant depolarization and a substantial increase in action potential (AP) firing rates in green fluorescent protein (GFP)-labelled BNC2 neurons (Fig. 2e–g and Extended Data Fig. 3a–d). These effects were reversible after leptin washout (Fig. 2e–g and Extended Data Fig. 3a–d). In addition, a higher percentage of cells responded to leptin in the slices prepared after an overnight fast (16 of 17) compared with those responding in slices prepared from mice fed ad libitum (7 of 12) (Extended Data Fig. 3e). The greater effect of leptin on neurons from fasted hypothalamus is consistent with the FOS data showing that the baseline activity of BNC2 neurons is lower after an overnight fast (Fig. 2d).

To assess the in vivo responses, we used fibre photometry to record the effect of food cues on the activity of BNC2 neurons. We injected AAV-DIO-GCaMP6s into the ARC of adult male BNC2-Cre mice and implanted a fibre above the ARC to record neural activity. Mice were fasted overnight and then given access to one of the following: an inedible plastic tube, a pellet of standard chow or peanut butter (PB), all of which they had been exposed to previously. The inedible tube did not have a discernible effect on BNC2 neuron activity (Fig. 2h,i and Extended Data Fig. 4a–c). However, even before consumption, relative to the plastic tube, exposure to standard chow robustly activated BNC2 neurons in seconds (Fig. 2h,i and Extended Data Fig. 4a–c) and exposure to PB, which has a significantly higher fat content, elicited a significantly greater response than did chow (Fig. 2h,i and Extended Data Fig. 4a–c). By contrast, BNC2 neurons from mice fed ad libitum showed little or no response to the inedible tube and standard chow (Fig. 2j and Extended Data Fig. 4d–f) while PB still significantly activated BNC2 neurons (Fig. 2j and Extended Data Fig. 4d–f). These results show that, in fasted mice, the activity of BNC2 neurons is increased by the presence of food and that this is further augmented by palatability and is sensitive to nutritional state (that is, fed versus fasted).

We further noted that the increased fluorescence of BNC2 neurons preceded the initiation of food intake, indicating that they were activated by sensory cues (Fig. 2k and Supplementary Video 1). To assess the impact of food consumption, we analysed the changes in BNC2 neuron activity during the consumption phase. Our analysis showed that BNC2 neuron activity further increased during food consumption compared with its level when animals were exposed to food cues but had not yet consumed it (Fig. 2l,m). These data indicate that BNC2 neurons respond to food cues as well as signals generated during food consumption. To directly test the effect of sensory cues on BNC2 neuron activity, we presented fasted mice with standard chow in a container that allowed them to see and smell the pellet without being able to consume it (Fig. 2n). Under these conditions, BNC2 neurons still showed significant activation, albeit of lesser magnitude and duration compared with the experiments in which the food was not caged (Fig. 2o and Extended Data Fig. 4g,h). This indicates that BNC2 neurons respond to sensory cues and that accessibility to food and its consumption further increases the strength and duration of neural activation. To test whether the sensory-dependent regulation of BNC2 neuron activity depends on experience, we presented new foods, such as PB and sucrose tablets, to naive mice that had never encountered them before after an overnight fast. In this case, we did not observe significant changes in BNC2 neuron activity in the naive mice after the presentation of the food (Extended Data Fig. 4i–l). However, after a delay, the animals began to consume the food, at which point the activity of BNC2 neurons significantly increased (Extended Data Fig. 4i–l). Collectively, these results indicate that BNC2 neurons respond to food-related sensory cues in an experience-dependent manner and that food consumption further activates these neurons.

Finally, we examined whether the removal of food extinguished the neural activation seen after food presentation. In this experiment, mice that had been fasted overnight were presented with accessible chow which was removed 2 or 10 min after it was provided. Removal of food led to a rapid decrease in the activity of BNC2 neurons (Extended Data Fig. 4m–p). By contrast, the BNC2 neurons remained active when food was continuously present (Extended Data Fig. 4m–p).

BNC2 neurons acutely suppress appetite

We next assessed the function of BNC2 neurons using chemogenetics and optogenetics. AAVs expressing a Cre-dependent stimulatory hM3Dq (Gq-coupled human muscarinic M3 designer receptors exclusively activated by designer drugs, DREADDs) or an inhibitory hM4Di DREADD were injected into the ARC of BNC2-Cre mice (Extended Data Fig. 5a). Mice receiving the AAV-DIO-mCherry were used as controls. We then selectively activated or inhibited these neurons using clozapine-N-oxide (CNO)[27]. Activating BNC2 neurons at the beginning of the dark cycle resulted in a significant reduction in food intake and body weight compared with control mice of both sexes (Fig. 3a,b and Extended Data Fig. 5c,d). By contrast, silencing BNC2 neurons during the light cycle significantly increased food consumption and body weight in both male and female mice (Fig. 3c,d and Extended Data Fig. 5e,f).

Fig. 3: Rapid and sustained satiety driven by BNC2 neurons.

a,b, Hourly food intake (a) and body weight (BD) change at 3 h (b) in male mice receiving PBS or CNO injection at the onset of the night dark phase (n = 5 mCherry-injected mice, n = 4 hM3Dq-injected mice; two-way ANOVA). c,d, Hourly food intake (c) and body weight change at 3 h (d) in male mice receiving PBS or CNO injection during the daytime (n = 6 mCherry-injected mice, n = 8 hM4Di-injected mice; two-way ANOVA). e, Unilateral AAV virus injection into the ARC region of adult male BNC2-Cre mice followed by fibre optic implant. f, Food intake before, during and after laser stimulation in overnight-fasted mice (n = 7 GFP-injected mice, n = 12 ChR2-injected mice; two-way ANOVA). g, Food intake before, during and after laser stimulation in mice fed ad libitum during the daytime (n = 7 GFP-injected mice, n = 5 GtACR2-injected mice; two-way ANOVA). h–j, Closed-loop place preference (h,i) assay to evaluate the valence of BNC2 neurons. i, Representative heatmaps of time spent in each chamber by overnight-fasted adult male BNC2-Cre mice injected with GFP or ChR2 during the initial phase and the closed-loop paired phase. j, Quantification of place preference (time spent in the pair-stimulated chamber, n = 6 GFP-injected mice, n = 8 ChR2-injected mice; two-way ANOVA). Data are presented as mean ± s.e.m. NS, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. Statistical details in Source Data. Illustrations in e and h were created using BioRender (https://biorender.com).

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We further evaluated the dynamics of the feeding response using optogenetics[28]. AAVs with Cre-dependent versions of the activating channelrhodopsin 2 (ChR2)-GFP or inhibitory soma-targeted Guillardia theta anion-conducting channelrhodopsins (stGtACR2)-FusionRed were injected into the ARC of male BNC2-Cre mice, after which an optical fibre was implanted over the ARC[28] (Fig. 3e and Extended Data Fig. 5b). Optogenetic activation of BNC2 neurons at a frequency of 20 Hz for 20 min led to a significant reduction in food intake in mice that were fasted overnight compared with controls expressing GFP, and the decreased food intake persisted for up to 20 min after cessation of light stimulation (Fig. 3f). Activating ARC BNC2 neurons did not affect locomotion (Extended Data Fig. 5g). Consistent with the results from chemogenetic inhibition, optogenetic silencing of BNC2 neurons at 20 Hz significantly increased food intake in mice during the light cycle but this did not persist when the photoinhibition ceased (Fig. 3g).

We also used optogenetics to assess the effect of BNC2 neural activation on valence using a real-time place preference assay (Fig. 3h). We found a significant preference of mice for the chamber associated with BNC2 activation after an overnight fast compared with GFP expressing controls (Fig. 3i,j). However, there was no preference for the photostimulation-paired chamber in sated mice that were tested in the light cycle (Fig. 3j and Extended Data Fig. 5h).

BNC2 neurons inhibit AGRP/NPY neurons

Food deprivation leads to the activation of AGRP/NPY neurons that is associated with negative valence, raising the possibility that BNC2 activation is associated with positive valence because they inhibit AGRP/NPY neurons17,29. We evaluated this using ChR2-assisted circuit mapping (CRACM) on brain slices[30]. A Flp-dependent mCherry AAV (fDIO-mCherry) and a Cre-dependent ChR2-GFP AAV (DIO-ChR2-GFP) were co-injected into the ARC of BNC2-Cre::NPY-FlpO mice[31]. We found dense GFP expression in BNC2 terminals within the ARC, in proximity to AGRP/NPY soma expressing mCherry (Fig. 4a). Evoked inhibitory postsynaptic currents (oIPSCs) were recorded in mCherry-labelled AGRP/NPY neurons before and after optogenetic activation of BNC2 neurons (Fig. 4b). We found synchronized oIPSCs after light activation in approximately 81% of AGRP/NPY neurons (25 of 31) with a rapid onset and a latency of 5.12 ms (Fig. 4c,d) indeed indicating that BNC2 neurons directly inhibit AGRP/NPY neurons. Consistent with this, the oIPSCs were abolished after the application of the sodium channel blocker tetrodotoxin (TTX), which blocks APs in all neurons, but inhibitory currents onto AGRP/NPY neurons were restored by the co-application of 4-aminopyride (4-AP)—a potassium channel antagonist that enables ChR2-mediated depolarization of local presynaptic terminals (Fig. 4c,e). Finally, the oIPSCs observed in the presence of TTX and 4-AP were blocked after the application of the GABAA receptor antagonist picrotoxin (PTX) (Fig. 4c,e). In aggregate, these data show that BNC2 neurons directly inhibit AGRP/NPY neurons through the GABAA receptor.

Fig. 4: BNC2 neurons monosynaptically inhibit AGRP/NPY neurons.

a, Representative image of GFP and mCherry immunostaining in the ARC of adult BNC2-Cre::NPY-Flpo mice injected with AAV-DIO-ChR2-GFP and AAV-fDIO-mCherry (n = 4 mice). b,f, Schematics of ChR2-assisted circuity mapping to assess projections from BNC2 neurons to NPY neurons (b) or from NPY neurons to BNC2 neurons (f). c, Representative oIPSCs from NPY neurons following activation of BNC2 neurons with different blockers. d, oIPSC latency upon light stimulation (n = 29 cells from four mice). e, Quantification of oIPSC amplitude shown in c (n = 7 cells from four mice; Friedman test). g, Representative oIPSCs from BNC2 neurons following activation of NPY neurons. h, Quantification of oIPSC amplitudes under different conditions (n = 7 cells from four mice; two-way ANOVA). i, Experimental design and hypothesis. j, Average calcium traces from NPY neurons aligned to the time of chow presentation in mice receiving PBS (blue) or CNO (red). k, Quantification of fluorescence changes within the 0–200 s timeframe shown in j (n = 4 mice, paired Student’s t-test). Data are presented as mean ± s.e.m. NS, not significant; *P < 0.05; ***P < 0.001. Statistical details in Source Data. Scale bars, 50 μm (a, left), 10 μm (a, right). ACSF, artificial cerebrospinal fluid.

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We also tested whether AGRP/NPY neurons could inhibit BNC2 neurons by injecting AAV-fDIO-ChR2-GFP and AAV-DIO-mCherry into the ARC of BNC2-Cre::NPY-FlpO mice. However, in contrast to the effects of BNC2 activation to increase oIPSCs in AGRP/NPY neurons, we failed to see an effect of AGRP/NPY activation on postsynaptic currents in mCherry-labelled BNC2 neurons (0 of 25) (Fig. 4f–h). This was despite the fact that brief blue light pulses triggered APs in GFP-labelled AGRP/NPY neurons (Extended Data Fig. 6a,b). Thus, while BNC2 neurons directly inhibit AGRP/NPY neurons, AGRP/NPY neurons do not alter the activity of BNC2 neurons.

Previous reports have shown that AGRP/NPY neuronal activity is inhibited by the presence of food cues17,29. This raised the possibility that BNC2 neuronal activation might mediate some or all of the effect of food cues to inhibit AGRP/NPY neurons. We tested this by injecting AAV-DIO-hM4Di-mCherry, an inhibitory DREADD, together with an AAV-fDIO-GCaMP6s into the ARC of BNC2-Cre::NPY-FlpO (Fig. 4i). After an overnight fast, we administered PBS or CNO 30 min before providing the animal with chow (Extended Data Fig. 6c). Consistent with the previous results (Fig. 3), inhibition of BNC2 neurons significantly increased food intake relative to PBS-treated controls (Extended Data Fig. 6d). We then imaged the activity of AGRP/NPY neurons after refeeding using fibre photometry with or without inhibition of BNC2 neurons. As reported previously, control mice receiving PBS showed a robust reduction in calcium signals in AGRP/NPY neurons when given chow food (Fig. 4j,k). We then measured the activity of AGRP/NPY neurons after giving CNO and found that the inhibition of BNC2 neurons significantly attenuated the decrease in AGRP/NPY activity seen after refeeding (Fig. 4j,k). These findings indicate that a portion of the sensory inputs that suppress AGRP/NPY neurons after refeeding are conveyed by BNC2 neurons.

LepR knockout in BNC2 neurons causes obesity

BNC2 neurons express LepR (Fig. 1) and are activated by leptin (Fig. 2), indicating that they mediate some of leptin’s effects. We assess this possibility by knocking out LepR in BNC2 neurons using CRISPR–Cas9 genome editing technology. BNC2-Cre mice were bred to LSL-Cas9-GFP mice[32], and an AAV carrying two single guide RNAs (sgRNAs) designed to target the mouse Lepr (sgLepr) locus, or two control guide RNAs (sgCtrl)19,33, were injected bilaterally into the ARC. The specific deletion of the Lepr gene was confirmed by showing that leptin no longer increased the amounts of pSTAT3 in BNC2 neurons that received injections of the sgLepr guide RNAs compared with those receiving sgCtrl guide RNAs (Fig. 2 and Extended Data Fig. 7).

Male and female mice with a LepR knockout in BNC2 neurons gained significantly more weight compared with the sgCtrl-injected mice (Fig. 5a and Extended Data Fig. 8a) when fed on chow. Eight weeks after the guide RNA injection, fat mass within the sgLepr group was also significantly increased relative to the sgCtrl group with a small increase in lean mass (Fig. 5b and Extended Data Fig. 8b). Daily food intake was significantly higher in sgLepr mice at week 8, compared with their sgCtrl counterparts (Fig. 5c and Extended Data Fig. 8c). Total energy expenditure (EE) was increased significantly in the BNC2 LepR knockout mice, and this was still evident even when indexed to lean mass (Extended Data Fig. 8d–f). However, this difference was no longer significant when the regression analysis of EE was calculated relative to body weight (Extended Data Fig. 8g). This indicates that the primary effect of the knockout to increase weight is a result of increased food intake. There was no difference in locomotor activity between the two groups (Extended Data Fig. 8h,i). The respiratory exchange ratio was higher in the sgLepr group in comparison with the sgCtrl group (Extended Data Fig. 8j,k), indicating that there is increased carbohydrate use. We next evaluated the response of the BNC2 LepR knockout mice to a highly palatable diet. BNC2 LepR male knockouts fed a standard high-fat diet (HFD, 60 kcal% fat) gained significantly more weight compared with sgCtrl-injected mice (Extended Data Fig. 8l). The sgLepr group also exhibited a substantial increase in fat mass, a small increase in lean mass and higher HFD consumption compared with controls (Extended Data Fig. 8m,n).

Fig. 5: LepR knockout in BNC2 neurons causes obesity.

a, Body weight of male mice on chow diet following sgCtrl or sgLepr injection (n = 14 mice per group; two-way ANOVA). b, Body composition of the two groups 8 weeks after virus injection (n = 11 mice per group, unpaired Student’s t-test). c, Daily chow intake 8 weeks after injection (n = 13 sgCtrl-injected mice, n = 14 sgLepr-injected mice; Mann–Whitney test). d, Food intake 3 h after PBS or leptin injection at the onset of the night dark phase in male mice (n = 7 mice per group; two-way ANOVA). e, Food intake 3 h after PBS, leptin or Sema administration at the night phase onset in male mice (n = 6 mCherry-injected mice, n = 7 dtA-injected mice; two-way ANOVA). f, Glucose concentrations at 0 and 1 h after CNO injection following 16-h overnight fasting in male mice (n = 7 mCherry-injected mice, n = 8 hM3Dq-injected mice; paired Student’s t-test or Wilcoxon test). g,h, GTT of two groups 1 h after CNO injection (n = 7 mCherry-injected mice, n = 8 hM3Dq-injected mice; Mann–Whitney test). GTT curves (g) and area under curve (AUC) (h). i,j, ITT of two groups of mice 1 h after CNO injection (n = 8 mCherry-injected mice, n = 8 hM3Dq-injected mice, unpaired Student’s t-test). ITT curves (i) and AUC (j). k, Glucose concentrations at 0 and 1 h after CNO injection after 16-h overnight fasting in male mice (n = 6 mCherry-injected mice, n = 5 hM4Di-injected mice, paired Student’s t-test). l,m, GTT 1 h after CNO injection (n = 8 mCherry-injected mice, n = 8 hM4Di-injected mice, unpaired Student’s t-test). GTT curves (l) and AUC (m). n,o, ITT 1 h after CNO injection (n = 5 mCherry-injected mice, n = 7 hM4Di-injected mice, unpaired Student’s t-test). ITT curves (n) and AUC (o). Data are presented as mean ± s.e.m. NS, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.001. Statistical details in Source Data.

Source Data

To further assess the role of BNC2 neurons in mediating leptin signalling, we examined how a LepR knockout in BNC2 neurons affects the response to leptin. In these studies, we assessed the response to leptin 2 weeks after virus injection, which was before the time that the BNC2 LepR knockout mice developed obesity. We administered PBS or leptin (5 mg kg−1) to sgCtrl-injected or sgLepr-injected male mice immediately before the onset of the dark phase and measured food intake over the ensuing 3 h. Although leptin injection significantly reduced food intake in both groups, the sgLepr-injected mice consumed significantly more chow than did the sgCtrl-injected mice (Fig. 5d). These data show that LepR in BNC2 neurons is required for the full effect of leptin. In analogous studies, we ablated BNC2 neurons in the ARC and evaluated their response to leptin and glucagon-like peptide-1 (GLP-1). AAV-mCherry-flex-dtA was injected bilaterally into the ARC of adult male BNC2-Cre mice to ablate BNC2 neurons. Mice injected with AAV-DIO-mCherry served as controls. Here again, we found that the effect of leptin was decreased significantly in the dtA-injected mice versus the mCherry control mice receiving leptin (Fig. 5e). We also tested the effect of semaglutide (Sema)—a GLP-1 receptor agonist—in these animals. In contrast to the response to leptin, we found that Sema (10 nmol kg−1) decreased food intake in both groups to a similar extent (Fig. 5e). This shows that, although BNC2 neurons are required for the complete response to leptin, they do not account for the effects of Sema.

Finally, we assayed glucose homeostasis in the BNC2 LepR knockouts and found that sgLepr male mice fed a chow diet for 9 weeks had increased fasting blood glucose levels (Extended Data Fig. 8o). The BNC2 LepR knockout mice also showed impaired glucose tolerance during a glucose tolerance test (GTT), and a significantly reduced response to insulin compared with sgCtrl in an insulin tolerance test (ITT) (Extended Data Fig. 8p–s). To evaluate whether the impaired glucose tolerance and insulin action in BNC2 LepR knockout mice was a direct effect or secondary to their obesity, we modulated the activity of BNC2 neurons using chemogenetics. AAVs expressing Cre-dependent activating or inhibitory DREADDs were injected into the ARC of BNC2-Cre male mice. Normal chow-fed mice receiving the activating hM3Dq DREADDs were fasted overnight, after which CNO was injected. At 1 h, the treated mice showed significantly decreased blood glucose levels compared with control mCherry-injected controls (Fig. 5f). We also observed an improvement in glucose tolerance and enhanced insulin sensitivity in the mice after activation of BNC2 neurons compared with control mice (Fig. 5g–j). Similar to the effects on valence, this effect is opposite to that seen after AGRP neuron activation[20]. Consistent with this, chemogenetic silencing of BNC2 neurons in chow-fed mice increased blood glucose levels and impaired glucose tolerance and insulin sensitivity (Fig. 5k–o). These data show that BNC2 neurons can acutely regulate peripheral glucose homeostasis independent of their effect on food intake and body weight.

Discussion

Leptin regulates food intake and body weight by modulating the activity of discrete neural populations expressing LepR in the ARC and elsewhere[1], including orexigenic AGRP/NPY neurons and anorexigenic POMC neurons. However, several lines of evidence have raised the possibility that there are other, as yet unknown leptin-regulated neurons that suppress appetite9,10,34. However, the nature of this putative population(s) was not known.

Here we report the use of snRNA-seq to identify a new LepR-expressing neuron cluster in the ARC that expresses the Bnc2 marker gene. Although there are fewer BNC2 neurons compared with those expressing POMC and AGRP/NPY, the amount of LepR in BNC2 neurons is as high or higher than in these other populations. The data further show that BNC2 neurons are activated rapidly by leptin or the presence of food and that the level of activation is further modulated by food palatability, nutritional status and food consumption (Extended Data Fig. 9). BNC2 neurons directly inhibit AGRP/NPY neurons and BNC2 activation reduces food intake for a sustained period. Activating BNC2 neurons also decreases peripheral glucose concentrations and increases insulin sensitivity while silencing them has the opposite effects. Finally, in contrast to anorexigenic POMC neurons, a BNC2-specific knockout of LepR causes significant hyperphagia and obesity. These findings establish a role for BNC2 neurons to acutely induce satiety in response to leptin and thus add an important new cellular component to the neural pathways that maintain homeostatic control of energy balance.

The Bnc2 gene encodes a conserved transcription factor expressed during development and has been reported to play a role in cell development, proliferation, pigmentation and liver fibrosis35,36,37,38. In support of a potential role to regulate weight, genome-wide association studies have consistently linked the Bnc2 gene to body mass index, fat distribution and diabetes39,40, although the underlying mechanisms have not been explored previously. In the current paper, we used Bnc2 as a marker gene, but these genetic data together with the data reported here also raises the possibility that Bnc2 itself could have a role in the development or function of the BNC2/LepR neurons we identified. Further studies including knocking out the Bnc2 gene exclusively in BNC2/LepR neurons will be necessary to assess this possibility and are now underway. While LepR clusters not expressing AGRP/NPY or POMC in the ARC have been identified previously in other studies, TRH, the canonical marker expressed in this cluster is also expressed in non-LepR clusters[22]. By contrast, BNC2 expression in the ARC is restricted to LepR neurons, making it particularly useful for probing the function of the neurons in this cluster. This enabled us to establish an important role for these neurons to regulate food intake and mediate leptin action. A recent report has indicated that TRH neurons in the ARC can also reduce food intake and body weight but the data indicate that these neurons are regulated by GLP-1, not leptin[41]. By contrast, as shown in this report, BNC2 neurons in ARC are regulated by leptin but not GLP-1, indicating that BNC2 and TRH neurons in ARC are functionally distinct.

The canonical ‘yin–yang’ model of feeding regulation has emphasized the opposing roles of POMC neurons (suppressing hunger) and AGRP/NPY neurons (promoting hunger)[2]. However, as described above, the characteristics of these neural populations are markedly different in several respects, indicating that there might be a missing leptin-regulated neuronal population that acutely suppresses food intake. Consistent with this, a knockout of LepR in GABAergic neurons had a greater effect than did a knockout of LepR in AGRP or POMC neurons[9]. The identity of this ‘missing’ GABAergic population was not known and the evidence reported here showing that BNC2/LepR neurons rapidly suppress feeding indicates that they serve this function. However, these data do not exclude the possibility that other populations might also contribute.

Optogenetic activation of BNC2 neurons potently reduces food intake for a sustained period even after photoactivation has ceased. This is analogous to the sustained effect of AGRP/NPY neuronal activation to increase in food intake through the release of neuropeptide Y42,43. The sustained satiety effect of activating BNC2 neurons lasted at least 20 min, possibly longer. Previous research indicates that sustained hunger after AGRP/NPY neuron activation can extend up to 1 h (ref. [42]). BNC2 neurons are GABAergic and directly inhibit AGRP/NPY neurons through the GABAA receptor although, as is the case for other feeding neurons, it is possible that neuropeptides also contribute. The snRNA-seq data shows expression of several putative neuropeptides in BNC2 neurons including Scg2, Chga and Trh, which have been implicated in feeding control and energy balance44,45,46,47. Future studies will be necessary to determine whether, in addition to GABA, these neuropeptides mediate some of the effects of BNC2 neurons.

BNC2 neurons suppress appetite, at least in part, by directly inhibiting AGRP/NPY neurons through monosynaptic connections. However, as is the case for POMC neurons, this interaction is not reciprocal48,49 as AGRP/NPY neurons do not seem to modulate the activity of BNC2 neurons. Similar to AGRP/NPY neurons, BNC2 neurons are activated by sensory cues associated with food and may function as coincidence detectors for both sensory and interoceptive inputs such as leptin. The rapid regulation of AGRP/NPY and BNC2 neurons by food cues is probably mediated by associative sensory inputs, the precise source of which is unknown at present. It is plausible that these sensory inputs synapse onto both BNC2 neurons and AGRP/NPY neurons, exerting distinct effects on these two neuronal populations. If true, mapping the afferent inputs to both may reveal anatomic sites that convey associative sensory inputs. Alternatively, the food cues may target primarily BNC2 neurons, which subsequently relay the information to AGRP/NPY neurons. In support of the former, we find that inhibiting BNC2 neurons only partially blunts the effect of food cues to suppress AGRP/NPY activity, indicating that both direct and indirect (through BNC2 neurons) pathways contribute. It has also been shown that while food cues can transiently suppress AGRP/NPY activity, caloric content and consumption are required for a sustained reduction in their activity[50]. This is also the case for BNC2 neurons because the level of BNC2 neuron activity decreases markedly when the food is removed.

In addition to markedly increasing weight, deletion of LepR in adult BNC2 neurons resulted in abnormal GTTs and ITTs, indicating a potential role of BNC2 neurons to also regulate glucose metabolism. Indeed, activating BNC2 neurons in the ARC decreases peripheral glucose concentrations and enhances insulin sensitivity, whereas inhibiting them has the opposite effect, extending their role beyond feeding regulation. Here again, some of these effects could be a result of the effect of BNC2 neurons on AGRP/NPY neural activity.

In summary, we have identified BNC2 neurons as a fast-acting population of neurons that acutely regulate feeding and energy balance bidirectionally. These findings add an important new component to the neural circuit that regulates appetite and adiposity, while also shedding new light on the mechanisms by which leptin regulates this system. Finally, pharmacologic activation of these neurons could have therapeutic implications to reduce weight or suppress the negative valence associated with hunger.

Methods

Mice

All animal experiments were approved by the Institutional Animal Care and Use Committee at Rockefeller University and were carried out in accordance with the National Institutes of Health guidelines. Mice were group housed in a 12-h light/12-h dark cycle at 22 °C and 30–60% humidity with ad libitum access to a normal chow diet and water. We used the following mouse genotypes: C57BL/6J (wild type (WT); stock no. 000664, The Jackson Laboratory), NPY-IRES2-FlpO-D (The Jackson Laboratory, stock no. 030211), Rosa26-LSL-Cas9 (The Jackson Laboratory, stock no. 024857) and BNC2-P2A-iCre (see below). For all Cre or Flp mouse line experiments, only heterozygous animals were used. Sample sizes were decided on the basis of experiments from similar studies. Littermates of the same sex were assigned randomly to either experimental or control groups. The experiments were performed using both male and female mice, as indicated.

Generation of BNC2-P2A-iCre mouse line

The BNC2-P2A-iCre mouse line was generated by the CRISPR and Genome Editing Resource Center and Transgenic and Reproductive Technology Resource Center at Rockefeller University using CRISPR–Cas9 technology[51]. Briefly, a custom-designed long single-stranded DNA (lssDNA) donor, containing homology arms of Bnc2 locus flanking the P2A-iCre sequence, was inserted near the endogenous STOP codon. Two guide RNAs (sgRNAs) were used to induce site-specific double-stranded breaks. The lssDNA donor with the pre-assembled Cas9 protein–gRNA complexes was mixed and microinjected into C57BL/6J mouse zygotes following standard CRISPR genome engineering protocols. The resulting live offspring were genotyped by PCR with two sets of primers that specifically amplified the mutant allele. Validation was ensured by Sanger sequencing. The BNC2-P2A-iCre transgenic animals were bred to C57BL/6J mice for maintenance.

Viruses

AAVs used in these studies were obtained from Addgene, UNC Vector Core, or generated by Janelia Viral Tools Service. We used the following viruses: AAV5-hSyn-DIO-hM3D(Gq)-mCherry (Addgene, catalogue no. 44361, 2.2 × 10^13 vg ml−1), AAV5-hSyn-DIO-hM4Di(Gi)-mCherry (Addgene, catalogue no. 44362, 2.5 × 10^13 vg ml−1), AAV5-hSyn-DIO-mCherry (Addgene, catalogue no. 50459, 2.2 × 10^13 vg ml−1), AAV5-Ef1a-DIO-EYFP (Addgene, catalogue no. 27056, 1.6 × 10^13 vg ml−1), AAV5-hSyn-Flex-GCaMP6s-WPRE (Addgene, catalogue no. 100845, 2.9 × 10^13 vg ml−1), AAV5-EF1a-DIO-hChR2(H134R)-EYFP (UNC Vector Core, 2.7 × 10^12 vg ml−1), AAV1-hSyn1-SIO-stGtACR2-FusionRed (Addgene, catalogue no. 105677, 2.1 × 10^13 vg ml−1), AAV5-Ef1a-fDIO-mCherry (Addgene, catalogue no. 114471, 2.3 × 10^13 vg ml−1), AAV8-Ef1a-fDIO-GCaMP6s (Addgene, catalogue no. 105714, 2.1 × 10^13 vg ml−1), AAV5&DJ-EF1a-fDIO-hChR2(H134R)-EYFP-WPRE (UNC Vector Core, 1.4 × 10^12 vg ml−1), AAV5-Ef1a-mCherry-flex-dtA (Addgene, catalogue no. 58536, 3.88 × 10^12 vg ml−1). For Lepr deletion, AAV viral vectors were cloned inhouse and packaged with the AAV5 serotype using Janelia Viral Tools Service. The sequences of sgLepR are: 5′-GAGTCATCGGTTGTGTTCGG-3′, 5′-TGCCGGCGGTTGGATG GACT-3′ (virus titre, 4.9 × 10^12 vg ml−1); The sequence of sgCtrl is: 5′-TTTTTTTTTTTTTTGAATTC-3′ (virus titre, 8.5 × 10^12 vg ml−1). Viral aliquots were stored at −80 °C before stereotaxic injection.

Chemicals and diet

The following chemicals were used in this study: Leptin (ThermoFisher Scientific, catalogue no. 498OB05M, 3 mg kg−1 or 5 mg kg−1, intraperitoneal injection), Sema (Millipore Sigma, catalogue no. AT35750, 10 nmol kg−1, subcutaneous injection), CNO dihydrochloride (Tocris, catalogue no. 6329, 3 mg kg−1, intraperitoneal injection), sucrose tablets 20 mg (TestDiet, catalogue no. 1811555) and HFD (Research Diets, 60% kcal% Fat).

Stereotactic surgery

Mice (8–10 weeks old) were anaesthetized using isoflurane anaesthesia (induction 5%, maintenance 1.5–2%) and positioned on a stereotaxic rig (Kopf Instruments, Model 1900). Viruses were delivered into the brains through a glass capillary using a Drummond Scientific Nanoject III Programmable Nanoliter Injector. For the ARC region, the following coordinates relative to the bregma were used: anterior–posterior, −1.65 mm to −1.70 mm; medial–lateral (ML), ±0.25 mm to 0.30 mm and dorsal–ventral (DV), −5.9 mm. For chemogenetics experiments, Bnc2 neuron labeling and Lepr deletion, 30–50 nl of the virus was injected bilaterally at a rate of 1 nl s−1. For optogenetics, 30 nl of the virus was injected unilaterally at a rate of 1 nl s−1 followed by the implant of an optical fibre (ThorLabs, catalogue no. CFM12U-20) at 200 µm above the ARC (anterior–posterior, −1.65 mm; ML, 0.3 mm; DV, −5.7 mm). For fibre photometry experiments, 30 nl of the virus was injected unilaterally followed by the implant of an optical fibre cannula (Doric, catalogue no. MFP_400/430/1100-0.57_1m_FCM-MF2.5_LAF) at 150 µm above the ARC (anterior–posterior, −1.65 mm; ML, 0.3 mm; DV, −5.75 mm). For CRACM experiments, the two viruses were mixed at the ratio of 1:1, and 50 nl of the mixed virus was injected bilaterally into the ARC.

Isolation of nuclei and snRNA-seq

Male C57BL/6J mice aged 10–12 weeks were euthanized by transcardial perfusion using ice-cold HEPES-Sucrose Cutting Solution containing NaCl (110 mM), HEPES (10 mM), glucose (25 mM), sucrose (75 mM), MgCl_2 (7.5 mM) and KCl (2.5 mM) at pH 7.4 (ref. [52]). Subsequently, brains were dissected quickly in the same solution, frozen using liquid nitrogen and stored at −80 °C until nuclei isolation. To isolate nuclei, as described previously53,54, the samples were thawed on ice, resuspended in HD buffer containing tricine KOH (10 mM), KCl (25 mM), MgCl_2 (5 mM), sucrose (250 mM), 0.1% Triton X-100, SuperRNaseIn (0.5 U ml−1), RNase Inhibitor (0.5 U ml−1). Samples were homogenized using a 1 ml dounce homogenizer. The resulting homogenates were filtered using a 40 μM filter, centrifuged at 600g for 10 min and resuspended in nucleus storage buffer containing sucrose (166.5 mM), MgCl_2 (10 mM), Tris buffer (pH 8.0, 10 mM), SuperRNaseIn (0.05 U ml−1), RNase Inhibitor (0.05 U ml−1) for subsequent staining. Nucleus quality and number were assessed using an automated cell counter (Countess II, ThermoFisher). For staining, nuclei were labelled with Hoechst 33342 (ThermoFisher Scientific, catalogue no. H3570; 0.5 µl per million nuclei), anti-NeuN Alexa Fluor 647-conjugated antibody (Abcam, catalogue no. ab190565; 0.5 µl per million nuclei) and TotalSeq anti-Nuclear Pore Complex Proteins Hashtag antibody (BioLegend, catalogue no. 682205; 0.5 mg per million nuclei) for 15 min at 4 °C. Following staining, samples were washed with 10 ml 2% BSA (in PBS) and centrifuged at 600g for 5 min. Nuclei were then resuspended in 2% BSA (in PBS) with RNase inhibitors (SuperRNaseIn 0.5 U ml−1, RNase Inhibitor 0.5 U ml−1) for subsequent fluorescence-activated cell sorting. The samples were gated on the basis of Hoechst fluorescence to identify nuclei and then further sorted on the basis of high Alexa Fluor 647 expression, designating NeuN+ nuclei as neurons.

Nuclei were captured and barcoded using 10x Genomics Chromium v.3 following the manufacturer’s protocol. The processing and library preparation were carried out by the Genomics Resource Center at Rockefeller University, and sequencing was performed by Genewiz using Illumina sequencers.

SnRNA-seq analysis

The FASTQ file was analysed with Cell Ranger v.5.0. The snRNA-seq data for ARC (WT) was preprocessed individually using the Seurat v.4 (v.4.0.3)[55]. Cells with more than 800 and fewer than 5,000 RNA features were selected for further analysis. Cells with greater than 1% mitochondrial genes and greater than 12,000 total RNA counts were also removed. Genes detected in fewer than three cells were excluded. We then demultiplexed the cells on the basis of their hashtag count (positive quantile = 0.8) using the built-in function in Seurat v.4. Only the cells with singlet Hashtag assignment were kept for downstream analysis. The data was then log-normalized with a scale factor of 10,000. After the initial quality control, demultiplexing and normalization steps, all the singlets were then scaled and reduced dimensionally with principal component analysis and uniform manifold approximation and projection (UMAP). Leiden clustering (resolution = 0.55) was applied to identify clusters. We used known cell-type specific gene expression to annotate the clusters.

We analysed co-expression of marker genes within the human ARC using previously published human adult samples, and the data can be accessed through the NeMO archive (https://assets.nemoarchive.org/dat-917e9vs). A cell was considered to express the marker gene if at least two unique molecular identifiers were detected. The identification of arcuate cells was achieved by clustering and the expression of canonical markers, as detailed in the earlier study. Co-expression of genes such as Lepr, Bnc2, Agrp, Npy and Pomc was tabulated in R, and two-tailed Fisher’s tests were calculated to assess the significance of co-expression of gene pairs within the 16,819 arcuate cells in the human dataset.

Chemogenetics for activation or inhibition

AAV viruses were delivered bilaterally into the ARC of male BNC2-Cre mice aged 8–10 weeks. Mice were then allowed to recover and express DREADDs for at least 3 weeks. For activation or inhibition, animals were injected intraperitoneally with 3 mg kg−1 of CNO or PBS (control).

Optogenetics for activation or inhibition

AAV viruses were delivered unilaterally into the ARC of male BNC2-Cre mice aged 8–10 weeks followed by the implantation of an optic fibre. Subsequently, the mice were given a recovery period of at least 3 weeks to allow for gene expression. Before the experiments, the mice were habituated to patch cables over a period of 5 days. The implanted optic fibres were connected to patch cables using ceramic sleeves (Thorlabs) and linked to a 473 nm laser (OEM Lasers/OptoEngine). The output of the laser was verified at the beginning of each experiment. A blue light, generated by a 473 nm laser diode (OEM Lasers/OptoEngine) with a power of 15 mW, was used. The light pulse (10 ms) and frequency (20 Hz) were controlled by a waveform generator (Keysight) to either activate or inhibit BNC2 neurons in the ARC. In the activation feeding experiments, mice were allowed to acclimate to the cage for 20 min. Subsequently, three feeding sessions, each lasting 20 min, were initiated. During these sessions, the light was turned off for the initial 20 min, switched on for the subsequent 20 min and then turned off again for the remaining 20 min. In the inhibition feeding experiments, following the 20 min acclimation, each feeding session was extended to 30 min. The amount of food consumed during each feeding session was recorded manually. Animals were euthanized at the end of the experiments to confirm viral expression and fibre placement using immunohistochemistry.

Real-time place preference

A custom-made two-chamber box (50 × 50 × 25 cm black plexiglass) with an 8.5 cm gap enabling animals to move freely between the chambers was used for this assay. To evaluate the initial preference of the mice, they were introduced into the box for a 10 min session without any photostimulation. Subsequently, in the second 10 min session following the initial one, photostimulation (15 mW, 20 Hz) was paired with the chamber for which the mice exhibited less preference during the initial session. The Ethovision XT v.13 software, coupled with a CCD camera, facilitated the recording of the percentage of time spent by the mice in each chamber.

Fibre photometry

Mice were acclimated to tethering and a home-cage-style arena for 5 min daily over the course of 5 days before the experiment. Data acquisition was conducted using a fibre photometry system from Tucker-Davis Technologies (catalogue no. RZ5P, Synapse) and Doric components, with recordings synchronized to video data in Ethovision by transistor–transistor logic triggering. A dual fluorescence Mini Cube (Doric) combined light from 465 nm and isosbestic 405 nm light-emitting diodes (LEDs), which were transmitted through the recording fibre connected to the implant. GCaMP6s fluorescence, representing the calcium-dependent signal (525 nm), and isosbestic control (430 nm) were detected using femtowatt photoreceivers (Newport, catalogue no. 2151) and a lock-in amplifier at a sampling rate of 1 kHz. Analysis was conducted using a Matlab script involving the removal of bleaching and movement artifacts using a polynomial least square fit applied to the 405 nm signal, adjusting it to the 465 nm trace (405fitted), and then calculating the GCaMP signal as %ΔF/F = (465signal − 405fitted)/405fitted. The resulting traces were filtered using a moving average filter and down-sampled by a factor of 20. The code is available upon request.

In situ hybridization

Mice were briefly transcardially perfused with ice-cold RNase-free PBS. Brains were then quickly collected, embedded in optimal cutting temperature embedding medium on dry ice, and stored at −80 °C until cryostat sectioning (15 µm thickness) onto Superfrost Plus Adhesion Slides (ThermoFisher). The RNAscope Fluorescent Multiplex assay (Advanced Cell Diagnostics Bio) was based on the manufacturer’s protocol. All reagents were purchased from Advanced Cell Diagnostics (ACDbio). Probes for the following mRNAs were used: Agrp (catalogue no. 400711-C3), Pomc (catalogue no. 314081-C3), Lepr (catalogue no. 402731), Slc31a1 (catalogue no. 319191) and Bnc2 (catalogue no. 518521-C2). Briefly, brain sections were fixed in 4% paraformaldehyde (PFA) at 4 °C for 15 min followed by serial submersion in 50% ethanol, 70% ethanol, and twice in 100% ethanol for 5 min each at room temperature. Sections were treated with Protease IV for 30 min at room temperature followed by a 2 h incubation with specific probes at 40 °C using a HyBez oven. Signal amplification was achieved through successive incubations with Amp-1, Amp-2, Amp-3 and Amp-4 for 30, 15, 30 and 15 min, respectively, at 40 °C using a HyBez oven. Each incubation step was followed by two 2 min washes in RNAscope washing buffer. Nucleic acids were counterstained with DAPI Fluoromount-G (SouthernBiotech) mounting medium before coverslipping. The slides were visualized using an inverted Zeiss LSM 780 laser scanning confocal microscope with a ×20 lens. The acquired images were imported into Fiji for further analysis.

Immunohistochemistry

Mice were perfused transcardially with PBS first and then 4% PFA for fixation. Brains were collected and immersed in 4% PFA overnight at 4 °C for more fixation. Fixed brains were immersed sequentially in 10% sucrose, 20% sucrose and 30% sucrose buffers for 1 h, 1 h and overnight, respectively, all at 4 °C. After this, the brains were embedded in optimal cutting temperature embedding medium and stored at −80 °C until cryostat sectioning (30–50 µm thickness). For the staining process, brain sections were first blocked in a blocking buffer containing 3% BSA, 2% goat serum and 0.1% Triton X-100 in PBS for 30 min at room temperature followed by an overnight incubation with primary antibodies in the cold room. After washing in PBS, the sections were incubated with fluorescence-conjugated secondary antibodies (Invitrogen) for 1 h at room temperature. Stained sections were mounted onto SuperFrost (Fisher Scientific catalogue no. 22-034- 980) slides and then visualized with an inverted Zeiss LSM 780 laser scanning confocal microscope with a ×10 or ×20 lens. The acquired images were imported to Fiji for further analysis. The following antibodies were used: FOS antibody (1:1,000; Synaptic systems, catalogue no. 226308), pSTAT3 antibody (1:1,000; Cell Signaling Technology, catalogue no. 9145 s), GFP (1:1,000; abcam, catalogue no. ab13970), RFP (1:1,000; Rockland, catalogue no. 600-401-379).

Electrophysiology and CRACM

Adult mice were euthanized by transcardial perfusion using ice-cold cutting solution containing choline chloride (110 mM), NaHCO_3 (25 mM), KCl (2.5 mM), MgCl_2 (7 mM), CaCl_2 (0.5 mM), NaH_2PO_4 (1.25 mM), glucose (25 mM), ascorbic acid (11.6 mM) and pyruvic acid (3.1 mM). Subsequently, brains were quickly dissected in the same solution and sectioned using a vibratome into 275 µm coronal sections. These sections were then incubated in artificial cerebral spinal fluid containing NaCl (125 mM), KCl (2.5 mM), NaH_2PO_4 (1.25 mM), NaHCO_3 (25 mM), MgCl_2 (1 mM), CaCl_2 (2 mM) and glucose (11 mM) at 34 °C for 30 min, followed by room temperature incubation until use. The intracellular solution for current-clamp recordings contained K-gluconate (145 mM), MgCl_2 (2 mM), Na_2ATP (2 mM), HEPES (10 mM) and EGTA (0.2 mM, 286 mOsm, pH 7.2). The intracellular solution for the voltage-clamp recording contained CsMeSO_3 (135 mM), HEPES (10 mM), EGTA (1 mM), QX-314 (chloride salt, 3.3 mM), Mg-ATP (4 mM), Na-GTP (0.3 mM) and sodium phosphocreatine (8 mM, pH 7.3 adjusted with CsOH). Signals were acquired using the MultiClamp 700B amplifier and digitized at 20 kHz using DigiData1550B (Molecular Devices). The recorded electrophysiological data were analysed using Clampfit (Molecular Devices) and MATLAB (Mathworks).

For CRACM experiments, voltage-clamp recordings were conducted on BNC2 and NPY neurons. To record oIPSCs, the cell membrane potential was held at 0 mV. ChR2-expressing axons were activated using brief pulses of full-field illumination (0.5 ms, 0.1 Hz, ten times) onto the recorded cell with a blue LED light (pE-300 white; CoolLED). Subsequently, TTX (1 µM), 4-AP (100 mM) and PTX (1 µM) were applied sequentially through the bath solution, each for 10–20 min. Data acquisition started at least 5 min after each drug application.

Indirect calorimetry

Indirect calorimetry was performed using the Phenomaster automated home cage phenotyping system (TSE Systems). Mice were housed individually in environmentally controlled chambers maintained at 22 °C, following a 12 h light/12 h dark cycle, and at 40% humidity, with ad libitum access to food and water. O_2 and CO_2 measurements were collected at 15 min intervals with a settling time of 3 min and a sample flow rate of 0.25 l min−1. The raw data obtained were analysed using CalR[56].

Blood glucose, GTT and ITT

Blood glucose levels were measured using a OneTouch Ultra meter and glucose test strips. For GTTs, mice were fasted overnight followed by a 20% glucose injection (2 g kg−1) and glucose measurements at 0, 15, 30, 60 and 120 min. ITTs were conducted after a 4 h fast, with insulin injection (0.75 U kg−1) and glucose measurements at 0, 15, 30, 45 and 60 min. To test how BNC2 neuron activity affected glucose metabolism, CNO was injected for 1 h before the start of GTT and ITT experiments.

Statistical analysis

All statistical analyses used GraphPad Prism v.9. Data distribution was tested for normality (Shapiro–Wilk test) and then comparisons were made using parametric or non-parametric tests, as appropriate. Two-tailed statistical tests were used, and statistical significance was determined by Student’s t-test, Mann–Whitney test, Fisher’s exact test, one-way or 2-way ANOVA, and Friedman test as indicated in the Source Data.

Reporting summary

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

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. The raw sequencing data of adult mouse ARC have been deposited in the Gene Expression Omnibus and are available under the accession number GSE249564. The snRNA-seq data for adult human ARC are accessible through the NeMO archive (https://assets.nemoarchive.org/dat-917e9vs). Source Data are provided with this paper.

Code availability

The analytic code for processing data is available at GitHub (https://github.com/yuqiyuqitan/BNC2).

References

1. Friedman, J. M. Leptin and the endocrine control of energy balance. Nat. Metab. 1, 754–764 (2019).

2. Andermann, M. L. & Lowell, B. B. Toward a wiring diagram understanding of appetite control. Neuron 95, 757–778 (2017).

3. Krashes, M. J. et al. Rapid, reversible activation of AgRP neurons drives feeding behavior in mice. J. Clin. Invest. 121, 1424–1428 (2011).

4. Aponte, Y., Atasoy, D. & Sternson, S. M. AGRP neurons are sufficient to orchestrate feeding behavior rapidly and without training. Nat. Neurosci. 14, 351–355 (2011).

5. Zhan, C. et al. Acute and long-term suppression of feeding behavior by POMC neurons in the brainstem and hypothalamus, respectively. J. Neurosci. 33, 3624–3632 (2013).

6. Zhang, Y. et al. Positional cloning of the mouse obese gene and its human homologue. Nature 372, 425–432 (1994).

7. Yeo, G. S. H. et al. The melanocortin pathway and energy homeostasis: from discovery to obesity therapy. Mol. Metab. 48, 101206 (2021).

8. Cowley, M. A. et al. Leptin activates anorexigenic POMC neurons through a neural network in the arcuate nucleus. Nature 411, 480–484 (2001).

9. Vong, L. et al. Leptin action on GABAergic neurons prevents obesity and reduces inhibitory tone to POMC neurons. Neuron 71, 142–154 (2011).

10. Rupp, A. C. et al. Specific subpopulations of hypothalamic leptin receptor-expressing neurons mediate the effects of early developmental leptin receptor deletion on energy balance. Mol. Metab. 14, 130–138 (2018).

11. Lindberg, I. & Fricker, L. D. Obesity, POMC, and POMC-processing enzymes: surprising results from animal models. Endocrinology 162, bqab155 (2021).

12. Ste Marie, L., Luquet, S., Cole, T. B. & Palmiter, R. D. Modulation of neuropeptide Y expression in adult mice does not affect feeding. Proc. Natl Acad. Sci. USA 102, 18632–18637 (2005).

13. Erickson, J. C., Clegg, K. E. & Palmiter, R. D. Sensitivity to leptin and susceptibility to seizures of mice lacking neuropeptide Y. Nature 381, 415–421 (1996).

14. Corander, M. P., Rimmington, D., Challis, B. G., O’Rahilly, S. & Coll, A. P. Loss of agouti-related peptide does not significantly impact the phenotype of murine POMC deficiency. Endocrinology 152, 1819–1828 (2011).

15. Luquet, S., Perez, F. A., Hnasko, T. S. & Palmiter, R. D. NPY/AgRP neurons are essential for feeding in adult mice but can be ablated in neonates. Science 310, 683–685 (2005).

16. Gropp, E. et al. Agouti-related peptide-expressing neurons are mandatory for feeding. Nat. Neurosci. 8, 1289–1291 (2005).

17. Betley, J. N. et al. Neurons for hunger and thirst transmit a negative-valence teaching signal. Nature 521, 180–185 (2015).

18. Qu, N. et al. A POMC-originated circuit regulates stress-induced hypophagia, depression, and anhedonia. Mol. Psychiatry 25, 1006–1021 (2020).

19. Xu, J. et al. Genetic identification of leptin neural circuits in energy and glucose homeostases. Nature 556, 505–509 (2018).

20. Steculorum, S. M. et al. AgRP neurons control systemic insulin sensitivity via myostatin expression in brown adipose tissue. Cell 165, 125–138 (2016).

21. Gaublomme, J. T. et al. Nuclei multiplexing with barcoded antibodies for single-nucleus genomics. Nat. Commun. 10, 2907 (2019).

22. Campbell, J. N. et al. A molecular census of arcuate hypothalamus and median eminence cell types. Nat. Neurosci. 20, 484–496 (2017).

23. Steuernagel, L. et al. HypoMap-a unified single-cell gene expression atlas of the murine hypothalamus. Nat. Metab. 4, 1402–1419 (2022).

24. Herb, B. R. et al. Single-cell genomics reveals region-specific developmental trajectories underlying neuronal diversity in the human hypothalamus. Sci. Adv. 9, eadf6251 (2023).

25. Buettner, C. et al. Critical role of STAT3 in leptin’s metabolic actions. Cell Metab. 4, 49–60 (2006).

26. Minatohara, K., Akiyoshi, M. & Okuno, H. Role of immediate-early genes in synaptic plasticity and neuronal ensembles underlying the memory trace. Front. Mol. Neurosci. 8, 78 (2015).

27. Roth, B. L. DREADDs for neuroscientists. Neuron 89, 683–694 (2016).

28. Emiliani, V. et al. Optogenetics for light control of biological systems. Nat. Rev. Methods Primers 2, 55 (2022).

29. Chen, Y., Lin, Y. C., Kuo, T. W. & Knight, Z. A. Sensory detection of food rapidly modulates arcuate feeding circuits. Cell 160, 829–841 (2015).

30. Rost, B. R., Wietek, J., Yizhar, O. & Schmitz, D. Optogenetics at the presynapse. Nat. Neurosci. 25, 984–998 (2022).

31. Daigle, T. L. et al. A suite of transgenic driver and reporter mouse lines with enhanced brain-cell-type targeting and functionality. Cell 174, 465–480 e422 (2018).

32. Platt, R. J. et al. CRISPR–Cas9 knockin mice for genome editing and cancer modeling. Cell 159, 440–455 (2014).

33. Wang, P. et al. A leptin-BDNF pathway regulating sympathetic innervation of adipose tissue. Nature 583, 839–844 (2020).

34. Leshan, R. L., Greenwald-Yarnell, M., Patterson, C. M., Gonzalez, I. E. & Myers, M. G. Jr. Leptin action through hypothalamic nitric oxide synthase-1-expressing neurons controls energy balance. Nat. Med. 18, 820–823 (2012).

35. Vanhoutteghem, A. et al. Basonuclin 2 has a function in the multiplication of embryonic craniofacial mesenchymal cells and is orthologous to disco proteins. Proc. Natl Acad. Sci. USA 106, 14432–14437 (2009).

36. Romano, R. A., Li, H., Tummala, R., Maul, R. & Sinha, S. Identification of Basonuclin2, a DNA-binding zinc-finger protein expressed in germ tissues and skin keratinocytes. Genomics 83, 821–833 (2004).

37. Bobowski-Gerard, M. et al. Functional genomics uncovers the transcription factor BNC2 as required for myofibroblastic activation in fibrosis. Nat. Commun. 13, 5324 (2022).

38. Vanhoutteghem, A. et al. The importance of basonuclin 2 in adult mice and its relation to basonuclin 1. Mech. Dev. 140, 53–73 (2016).

39. Paterson, A. D. et al. A genome-wide association study identifies a novel major locus for glycemic control in type 1 diabetes, as measured by both A1C and glucose. Diabetes 59, 539–549 (2010).

40. Pant, S. D. et al. Comparative analyses of QTLs influencing obesity and metabolic phenotypes in pigs and humans. PLoS ONE 10, e0137356 (2015).

41. Webster, A. N. et al. Molecular connectomics reveals a glucagon-like peptide 1 sensitive neural circuit for satiety. Preprint at bioRxiv https://doi.org/10.1101/2023.10.31.564990 (2023).

42. Chen, Y. et al. Sustained NPY signaling enables AgRP neurons to drive feeding. eLife 8, e46348 (2019).

43. Krashes, M. J., Shah, B. P., Koda, S. & Lowell, B. B. Rapid versus delayed stimulation of feeding by the endogenously released AgRP neuron mediators GABA, NPY, and AgRP. Cell Metab. 18, 588–595 (2013).

44. Trebak, F. et al. A potential role for the secretogranin II-derived peptide EM66 in the hypothalamic regulation of feeding behaviour. J. Neuroendocrinol. https://doi.org/10.1111/jne.12459 (2017).

45. Zhang, X. & van den Pol, A. N. Thyrotropin-releasing hormone (TRH) inhibits melanin-concentrating hormone neurons: implications for TRH-mediated anorexic and arousal actions. J. Neurosci. 32, 3032–3043 (2012).

46. Chavez, J. et al. Hypothalamic TRH mediates anorectic effects of serotonin in rats. eNeuro 9, ENEURO.0077-22.2022 (2022).

47. Bandyopadhyay, G. K. & Mahata, S. K. Chromogranin A regulation of obesity and peripheral insulin sensitivity. Front. Endocrinol. 8, 20 (2017).

48. Roseberry, A. G., Liu, H., Jackson, A. C., Cai, X. & Friedman, J. M. Neuropeptide Y-mediated inhibition of proopiomelanocortin neurons in the arcuate nucleus shows enhanced desensitization in ob/ob mice. Neuron 41, 711–722 (2004).

49. Atasoy, D., Betley, J. N., Su, H. H. & Sternson, S. M. Deconstruction of a neural circuit for hunger. Nature 488, 172–177 (2012).

50. Su, Z., Alhadeff, A. L. & Betley, J. N. Nutritive, post-ingestive signals are the primary regulators of AgRP neuron activity. Cell Rep. 21, 2724–2736 (2017).

51. Shola, D. T. N., Yang, C., Han, C., Norinsky, R. & Peraza, R. D. Generation of mouse model (KI and CKO) via Easi-CRISPR. Methods Mol. Biol. 2224, 1–27 (2021).

52. Yao, Z. et al. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. Nature 598, 103–110 (2021).

53. Martin, C. et al. Frozen tissue nuclei extraction (for 10xV3 snSEQ). protocols.io https://doi.org/10.17504/protocols.io.bi62khge (2020).

54. Kamath, T. et al. Single-cell genomic profiling of human dopamine neurons identifies a population that selectively degenerates in Parkinson’s disease. Nat. Neurosci. 25, 588–595 (2022).

55. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e1821 (2019).

56. Mina, A. I. et al. CalR: a web-based analysis tool for indirect calorimetry experiments. Cell Metab. 28, 656–666.e651 (2018).

Acknowledgements

We thank all members of the laboratory of J.M.F. for discussions and support, H. Duan from the Genomics Resource Center and Flow Cytometry Resource Center for technical assistance with snRNA-seq experiments, C. Yang from CRISPR and Genome Editing Center for generating the BNC2-P2A-iCre mouse line, and the Bio-Imaging Resource Center. This work was supported by the JPB Foundation and the Howard Hughes Medical Institute (to J.M.F.), the Kavli Neural Systems Institute Postdoctoral Fellowship (to H.L.T.), and the Maurice R. and Corinne P. Greenberg Center for the Advancement of Translational Research Pilot Project Grant (to H.L.T.). Figures 2h,n and 3e,h and Extended Data Figs. 2a,c and 6c were created with Biorender (https://biorender.com).

Author information

Authors and Affiliations

1. Laboratory of Molecular Genetics, Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA

Han L. Tan, Jessica Ivanov, Anoj Ilanges, Putianqi Wang, Christin Kosse & Jeffrey M. Friedman

2. Department of Psychiatry, Neuroscience Institute, New York University Langone Medical Center, New York, NY, USA

Luping Yin & Dayu Lin

3. Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA

Yuqi Tan

4. Laboratory of Molecular Metabolism, The Rockefeller University, New York, NY, USA

Kaja Plucinska & Paul Cohen

5. Department of Pharmacology, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA

Brian R. Herb

Contributions

H.L.T. conceived the study, designed the experiments and performed all experiments with input from all authors. H.L.T. also led the analysis of the research data. L.Y. conducted the electrophysiology experiments and analysed the data. Y.T. and P.W. analysed the mouse snRNA-seq data. A.I. performed the mouse snRNA-seq experiments. K.P. performed indirect calorimetry experiments. J.I. performed the immunohistochemistry experiments. B.R.H. analysed the human snRNA-seq data. P.C. and D.L. provided inputs on experimental design. C.K. provided the code for fibre photometry data analysis. H.L.T. and J.M.F. wrote the manuscript.

Corresponding author

Correspondence to Jeffrey M. Friedman.

Ethics declarations

Competing interests

J.M.F. receives royalty payments from the sale of leptin through Rockefeller University, according to the university’s policy for distributing proceeds from inventions to their inventors. All other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 SnRNA-seq analysis of mouse ARC neurons.

a, Single-cell UMAP plot of the male ARC (N = 6 adult WT mice). b, Violin plot showing known markers of different cell types: neurons (Tubb3, Snap25, Syt1, Elavl4), tanycytes (Rax), ependymocytes (Ccdc153), oligodendrocyte cells (Mag, Olig1, Cspg4, Cd9, Pllp), macrophages (Aif1, C1qa), endothelial cells (Slco1c1, Cldn5), mural cells (Mustn1), astrocyte (Gfap, Sox9), vascular and leptomeningeal cells (Lum), pituitary cells (Tshb), pars tuberalis cells (Cck), ependymal cells (Vim). Clusters 20/21/23/24 are non-neuronal clusters. c, Dot plot displaying the expression of cluster-specific marker genes (identified in this study) across neuronal clusters with the ARC from Campbell et al.[22].

Extended Data Fig. 2 Generation of BNC2-P2A-iCre knockin mouse line.

a, Schematic of the BNC2-P2A-iCre locus. b, Sequence information of gRNAs and lssDNA. c, Genomic PCR using specific primers (N = 3 mice). d, Cre expression was assessed using a Cre-dependent mCherry viral construct. Colocalization with endogenous Bnc2 mRNA was confirmed through RNA ISH (N = 3 mice). Scale bar, 20 μm. Illustrations in a and c were created using BioRender (https://biorender.com).

Extended Data Fig. 3 Activation of BNC2 neurons by leptin.

a, Representative images of a patched GFP-labeled BNC2 neuron. b, Spontaneous APs of BNC2 neurons in the ARC from ad-libitum-fed adult male BNC2-Cre mice (injected with DIO-GFP). Leptin was added to the bath solution at the indicated time window. c,d, Spontaneous AP frequency (c) and RMP (d) of BNC2 neurons before, during, and after leptin application (N = 12 cells from 4 mice, Friedman test or one-way ANOVA). e, Percentage of leptin-responsive BNC2 neurons under the overnight fasting and ad-libitum-fed conditions. Data are presented as mean ± SEM. *p < 0.05; **p < 0.01. Statistical details in Source Data.

Source Data

Extended Data Fig. 4 Rapid responses of BNC2 neurons to food cues.

a-c, Heatmaps of normalized ΔF/F in individual overnight-fasted male mice aligned to the presentation of an inedible tube (a), chow (b), or peanut butter (c). d-f, Heatmaps of normalized ΔF/F in individual ad-libitum-fed male mice aligned to the presentation of an inedible tube (d), chow (e), or peanut butter (f). g,h, Heatmaps of normalized ΔF/F in individual overnight-fasted male mice aligned to the presentation of chow inside (g) or outside (h) the container. i,k, Individual calcium trace of an overnight-fasted naive male mouse presented with novel peanut butter (i) or sucrose tablets (k). j,l, Quantification of fluorescence changes before and after the presentation of peanut butter (j) or sucrose tablets (l), as well as during consumption (N = 4 mice, paired Student’s t-test). m-p, Recordings from overnight-fasted male mice presented with chow for 2 or 10 min. m, Average calcium signal traces aligned to the time of presentation. n, Quantification of fluorescence changes in the indicated timeframes shown in m (N = 8 mice per group, paired Student’s t-test). o,p, Heatmaps of normalized ΔF/F in individual overnight-fasted male mice aligned to the presentation of chow for 2 min (o) or 10 min (p). Data are presented as mean ± SEM. ns, not significant; *p < 0.05; **p < 0.01. Statistical details in Source Data.

Source Data

Extended Data Fig. 5 Rapid satiety driven by BNC2 neurons.

a, Representative image showing bilateral hM3Dq-mcherry expressing (red) in ARC BNC2 neurons with DAPI staining (blue) (N = 6 mice). Scale bar, 100 μm. b, Representative image showing unilateral ChR2-GFP expression in ARC BNC2 neurons and the optical fibre implant site (N = 12 mice). c, d, Food intake (c) and body weight change at 3 h (d) in female mice receiving PBS or CNO injection at the onset of the night dark phase (N = 4 mCherry-injected mice, N = 5 hM3Dq-injected mice, two-way ANOVA). e, f, Food intake (e) and body weight change at 3 h (f) in female mice receiving PBS or CNO injection during the daytime (N = 4 mCherry-injected mice, N = 4 hM4Di-injected mice, two-way ANOVA). g, Distance traveled before, during, and after laser stimulation in overnight-fasted male mice (N = 4 mice per group, two-way ANOVA). h, Heatmaps of time spent in each chamber during the initial phase and the closed-loop paired phase in sated adult male BNC2-Cre mice injected with GFP or ChR2. Data are presented as mean ± SEM. ns, not significant. **p < 0.01; ****p < 0.0001. Statistical details in Source Data.

Source Data

Extended Data Fig. 6 Light-induced activation of NPY neurons.

a, Representative images of patched ChR2-GFP-labeled NPY neurons. b, Detection of light-induced APs in ChR2-GFP-labeled NPY neurons. c, Schematic of PBS or CNO administration timing during fibre photometry recordings of NPY neuron activity. d, Food consumption during the 10 min after food presentation in male mice injected with PBS or CNO (N = 4 mice, paired Student’s t-test). *p < 0.05. Statistical details in Source Data. Illustration in c was created using BioRender (https://biorender.com).

Source Data

Extended Data Fig. 7 High efficiency of LepR deletion mediated by sgLepr.

Brain slices from overnight-fasted adult male BNC2-Cre::LSL-Cas9-GFP mice, injected unilaterally in the ARC with either sgCtrl or sgLepr, were analyzed 3 h post-leptin injection. The slices underwent mCherry and pSTAT3 immunostaining to assess the deletion efficiency (N = 3 mice per group). Scale bar, 50 μm.

Extended Data Fig. 8 LepR knockout in BNC2 neurons causes obesity.

a, Body weight of female mice on chow diet post sgCtrl or sgLepr injection (N = 4 mice per group, two-way ANOVA). b, Body composition of female mice 8 weeks post-injection (N = 4 mice per group, unpaired Student’s t-test). c, Daily chow intake 8 weeks post-injection (N = 4 mice per group, unpaired Student’s t-test). d, e, EE om male mice from Fig. 5a measured by indirect calorimetry at 10–12 weeks post-injection (N = 11 mice per group, unpaired Student’s t-test). f,g, Regression analysis of EE vs. lean mass (f) and body weight (g) from e (N = 11 mice per group, ANCOVA). h,i, Locomotion for male mice from Fig. 5a (N = 11 mice per group, unpaired Student’s t-test). j,k, RER in male mice from Fig. 5a (N = 11 mice per group, unpaired Student’s t-test). l, Body weight of male mice on an HFD post sgCtrl or sgLepr injection (N = 8 sgCtrl-injected mice, N = 9 sgLepr-injected mice, two-way ANOVA). m, Body composition from l 8 weeks post-injection (N = 8 sgCtrl-injected mice, N = 9 sgLepr-injected mice, unpaired Student’s t-test). n, Daily HFD intake from l 8 weeks post-injection (N = 8 sgCtrl-injected mice, N = 9 sgLepr-injected mice, Mann-Whitney test). o, Fasting glucose levels in male mice from Fig. 5a (N = 11 mice per group, unpaired Student’s t-test). p,q, GTT of male mice from Fig. 5a at 10–12 weeks post-injection (N = 11 mice per group, unpaired Student’s t-test). r,s, ITT of male mice from Fig. 5a at 10–12 weeks post-injection (N = 6 mice per group, unpaired Student’s t-test). Data are presented as mean ± SEM. ns, not significant; *p < 0.05; **p < 0.01; ***p < 0.001, ****p < 0.0001. Statistical details in Source Data.

Source Data

Extended Data Fig. 9 Model of leptin regulation of feeding and energy balance in ARC microcircuits.

Leptin-activated BNC2 neurons acutely promote satiety by suppressing AGRP/NPY neurons, which rapidly stimulates hunger. POMC neurons restrict weight gain by modulating energy balance over the long term with minimal short-term effects on food intake.

Supplementary information

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Supplementary Video 1

PB effect on BNC2 neurons in fasted mice. Calcium signal trace from overnight-fasted male mice expressing GCaMP6s in BNC2 neurons exposed to PB.

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Tan, H.L., Yin, L., Tan, Y. et al. Leptin-activated hypothalamic BNC2 neurons acutely suppress food intake. Nature 636, 198–205 (2024). https://doi.org/10.1038/s41586-024-08108-2

- Received: 12 December 2023

- Accepted: 24 September 2024

- Published: 30 October 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08108-2

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Rhythmic IL-17 production by γδ T cells maintains adipose de novo lipogenesis

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Miguel Rendas,

Harry Kane,

Evan Lynch,

Britta Kunkemoeller,

Karl Wessendorf-Rodriguez,

Emily A. Day,

Caroline Sutton,

Martin Brennan,

Katie O’Brien,

Ayano C. Kohlgruber,

Hannah Prendeville,

Amanda E. Garza,

Luke A. J. O’Neill,

Kingston H. G. Mills,

Christian M. Metallo,

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…

Lydia Lynch

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Abstract

The circadian rhythm of the immune system helps to protect against pathogens1,2,3; however, the role of circadian rhythms in immune homeostasis is less well understood. Innate T cells are tissue-resident lymphocytes with key roles in tissue homeostasis4,5,6,7. Here we use single-cell RNA sequencing, a molecular-clock reporter and genetic manipulations to show that innate IL-17-producing T cells—including γδ T cells, invariant natural killer T cells and mucosal-associated invariant T cells—are enriched for molecular-clock genes compared with their IFNγ-producing counterparts. We reveal that IL-17-producing γδ (γδ17) T cells, in particular, rely on the molecular clock to maintain adipose tissue homeostasis, and exhibit a robust circadian rhythm for RORγt and IL-17A across adipose depots, which peaks at night. In mice, loss of the molecular clock in the CD45 compartment (Bmal1∆Vav1) affects the production of IL-17 by adipose γδ17 T cells, but not cytokine production by αβ or IFNγ-producing γδ (γδIFNγ) T cells. Circadian IL-17 is essential for de novo lipogenesis in adipose tissue, and mice with an adipocyte-specific deficiency in IL-17 receptor C (IL-17RC) have defects in de novo lipogenesis. Whole-body metabolic analysis in vivo shows that Il17a−/−Il17f−/− mice (which lack expression of IL-17A and IL-17F) have defects in their circadian rhythm for de novo lipogenesis, which results in disruptions to their whole-body metabolic rhythm and core-body-temperature rhythm. This study identifies a crucial role for IL-17 in whole-body metabolic homeostasis and shows that de novo lipogenesis is a major target of IL-17.

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Main

Circadian rhythms allow organisms to anticipate and adjust to predictable changes in the external environment, such as light[8], temperature[9] and feeding behaviour[10]. At the cellular level, circadian rhythms are dictated by the molecular clock, an auto-regulatory system of transcriptional activators (BMAL1 and CLOCK) and repressors (PER1–PER3, CRY1 and CRY2 and REV-ERBα and REV-ERBβ), expressed by all mammalian cells[11]. Cellular rhythms in a tissue are kept in synchrony by a combination of neuronal, hormonal and metabolic cues. These, in turn, give rise to circadian rhythms in physiological processes such as endocrine signalling[12], immune cell function[1] and systemic metabolism[13].

Maintenance of core body temperature is one such physiological process, which oscillates daily over 24 h and is governed by metabolic rate. In mice, body temperature is highest at night while active and lowest during the day while inactive, and is important for optimal whole-body homeostasis[14]. White adipose tissue (WAT) and brown adipose tissue (BAT) are crucial for whole-body metabolic homeostasis, and are influenced by daily fluctuations in energy demands such as feeding, fasting and activity[15]. Light and feeding govern the circadian rhythm of adipose lipid metabolism, including lipolysis, lipogenesis and thermogenesis. Lipolysis peaks during the inactive phase to maintain body temperature, whereas lipogenesis peaks during feeding for lipid uptake and storage from the diet16,17. The molecular clock tightly regulates lipid metabolism in adipose tissue, and circadian disruption can lead to impaired lipid homeostasis and subsequent metabolic disorder. For example, circadian disruption, often observed in shift workers, can impair lipid homeostasis and increase the risk of metabolic disease[18]. Thus, it is important to understand how circadian rhythms interact, especially in the setting of metabolic disease.

Adipose tissue has a unique immune environment that is enriched for innate T cells, including γδ T cells, mucosal-associated invariant T (MAIT) cells and invariant natural killer T (iNKT) cells4,5. The adipose tissue immune system is crucial for maintaining homeostasis, and holds the potential for treating metabolic disease6,7. Cytokines, including TNF[19], IL-6 (ref. [20]), IL-17A[21] and IL-17F[22], from adipose immune cells regulate local homeostasis and systemic metabolic processes. We have previously found that clonal IL-17-producing Vγ6+ γδ T cells reside in adipose tissue, and that mice lacking γδ T cells or IL-17A are susceptible to hypothermia[21]. A subsequent study also showed that IL-17RC signalling in adipocytes was important for thermogenesis[22]. These studies highlight members of the IL-17 family as key mediators of adipose thermogenesis.

The molecular clock regulates both T helper 17 (TH17) cells and type-3 innate lymphoid cells (ILC3s) in the gut, which produce IL-17 and IL-22 (refs. 23,24). Here we investigated whether type-17 innate T cells are also regulated by the molecular clock. We show that type-17 innate T cells (γδ17, MAIT17 and NKT17) are enriched for molecular-clock genes, compared with type-1 innate T cells (γδIFNγ, MAIT1 and NKT1), particularly in adipose tissue, and that this mediates the circadian fluctuation of RORγt and IL-17A, which peaks at night. We identify adipose de novo lipogenesis (DNL) as a major target of diurnal IL-17 production in adipose tissue. Furthermore, loss of IL-17 signalling in adipocytes disrupts circadian DNL, ultimately impairing systemic metabolic rhythm and circadian body-temperature regulation. We identify a crucial role for adipose IL-17, which is controlled by the molecular clock. In turn, IL-17 regulates circadian physiological processes to support whole-body metabolism. This pathway could provide a therapeutic target for metabolic disease.

Molecular-clock enrichment in innate IL-17+ T cells

Adaptive CD4+ TH17 cells and innate gut ILC3s, which produce IL-17, express a rhythmic molecular clock23,24,25,26. Whether this is a specific trait of type-17 cells, or a trait of innate T cells, remains unknown. We performed single-cell RNA sequencing (scRNA-seq) in innate T cells from epididymal adipose tissue (epiWAT), and compiled publicly available scRNA-seq data on innate T cells from the lungs, spleen, thymus, skin and lymph nodes of wild-type mice, including iNKT, MAIT and γδ T cells (Fig. 1a). Notably, uniform manifold approximation and projection (UMAP) analysis first separated innate T cells into IL-17-producing (type-17) or IFNγ-producing (type-1) cells, and then separated on the basis of tissue origin and cell subtype (Fig. 1a,b). This highlights the distinct transcriptional program of TH17-like cells, regardless of tissue or cell type. We then generated a ‘molecular-clock score’ using the average expression of the molecular-clock genes Arntl (also known as Bmal1), Clock, Nr1d1, Nr1d2, Cry1, Cry2, Per1, Per2, Per3 and Rora (Fig. 1c and Extended Data Fig. 1a), and found enriched expression in type-17 innate T cells compared with their type-1 counterparts (Fig. 1d). In addition, tissue-resident innate T cells (adipose and lung) had higher expression of molecular-clock genes than innate T cells in lymphoid organs (spleen, lymph nodes and thymus; Fig. 1d and Extended Data Fig. 1b).

Fig. 1: Innate IL-17+ T cells are enriched for molecular-clock genes.

a, UMAP of mouse innate T cells across several organs, reanalysed from GSE142845, GSE141895, E-MTAB-7704, E-MTAB-8732, GSE123400 and GSE147262 (all publicly available scRNA-seq datasets). PLN, peripheral lymph node. b, UMAP of mouse innate T cells subclustered on the basis of functionality (type 1 (TH1-like) or type 17 (TH17-like)). c, Density plot of the cumulative expression of all molecular-clock genes across innate T cell subsets, designated the molecular-clock score. d, Heat map of the averaged gene expression of molecular-clock genes with hierarchal clustering of data from a. e, Heat map of the averaged gene expression of molecular-clock genes from adipose tissue innate lymphocytes with hierarchal clustering of data generated in this laboratory. f, Spider plot showing that adipose γδ17 T cells have the highest cumulative expression of molecular-clock genes. g, Top, diagram of the isolation and PCR analysis of adipose γδ T cells. Bottom, relative expression of Rev-erba (also known as Nr1d1) in epiWAT γδ T cells across 24 h. h, Diagram of Per1Venus oscillations in cells isolated from adipose tissue. i–k, Circadian time plots of Per1Venus expression by αβ (grey) and γδ (red) T cells across 24 h in epiWAT (i), scWAT (j) and mesWAT (k). MFI, mean fluorescence intensity. g,i–k, White and grey panels represent light and dark periods, respectively. i–k, Data are representative of two independent experiments. g,i–k, Data are mean ± s.e.m., n = 4–5 mice per group. g,i–k, Significance was calculated using cosinor analysis, with cosine fitted curves; amplitude (amp) and acrophase (acro) were extracted from the cosinor model. NS, not significant, *P < 0.05, **P < 0.01, ***P < 0.001.

Source Data

To account for time-of-day variation in the scRNA-seq datasets, we analysed each dataset separately for in-run comparisons, comparing innate T cells collected from the same mice at the same time of day only (Fig. 1e). Regardless of the time at which the sequencing was run, type-17 innate T cells had higher expression of molecular-clock genes (Fig. 1e and Extended Data Fig. 1c–g), with adipose γδ T cells showing the highest expression overall (Fig. 1f). Therefore, to investigate the role of the molecular clock in innate type-17 cells, we focused on adipose γδ17 T cells. To confirm that γδ T cells have rhythmic expression of circadian clock genes, we isolated adipose γδ17 T cells across 24 h, and found robust circadian expression of Nr1d1, which peaked before the active phase (Fig. 1g). Next, we used Venus reporter mice to observe Per1 oscillations (Per1Venus; Fig. 1h) in adipose T cells over 24 h. Adipose γδ T cells have rhythmic expression of Per1, with peak expression as the mouse dark cycle is entered (Fig. 1i–k), which was not observed in adipose αβ T cells (Fig. 1i–k). In addition, adipose γδ17 T cells had higher expression of Per1 than adipose αβ T cells in all depots throughout the entire 24-h period, providing further evidence for higher levels of molecular-clock expression in innate type-17 adipose T cells (Fig. 1i–k).

The molecular clock regulates γδ IL-17A

Having established molecular-clock enrichment in γδ T cells, we investigated whether the production of IL-17A and its transcriptional regulator RORγt exhibit a circadian rhythm over 24 h. We first measured the circadian production of IFNγ and IL-17A ex vivo by collecting lymph-node γδ T cells at zeitgeber time (ZT) 0, then splitting them into eight groups for stimulation at 3-h intervals over 24 h (Extended Data Fig. 2a). Ex vivo stimulated γδ17 T cells had a robust circadian rhythm for IL-17A production, which peaked during the night (Extended Data Fig. 2b–e). By contrast, γδIFNγ T cells had an opposing diurnal rhythm that peaked during the day (Extended Data Fig. 2f–i). Previous studies on CD4+ T cells showed that NFIL3 antagonizes IL-17 circadian rhythm[23]. NFIL3 was expressed almost exclusively in γδ17 T cells, not in γδIFNγ T cells, and exhibited a circadian rhythm in γδ17 T cells (Extended Data Fig. 2j–l). Although lymph-node γδ17 T cells express higher levels of molecular-clock genes than γδIFNγ T cells, adipose tissue γδ17 T cells have the highest expression of these genes (Fig. 1d,f). Therefore, we investigated whether adipose γδ17 T cells expressed a circadian rhythm for IL-17A. There was no change in the total number of γδ17 T cells over a 24-h period (Extended Data Fig. 3a,b). Although adipose γδ17 T cells exhibit a circadian rhythm to their proportions, no rhythm was observed in absolute cell number (Extended Data Fig. 3a,b), suggesting that the fluctuations in proportions are due to changes in circulating γδIFNγ T cells, which are higher in the blood. We found a robust circadian rhythm for IL-17A production, and RORγt expression by adipose γδ17 T cells (Fig. 2a–c). Together, these data show that γδ17 T cells have a circadian rhythm for IL-17A production, which can be maintained intrinsically for at least 24 h ex vivo.

Fig. 2: IL-17A expression by adipose γδ T cells is regulated by the molecular clock.

a, IL-17A and RORγt histograms from adipose (epiWAT) γδ T cells across 24 h. b, Percentage of IL-17A+ cells. c, RORγt MFI. d, RORγt flow plots from adipose (epiWAT) γδ T cells stimulated with PMA–ionomycin and 40 µM SR9009. e, Percentage of RORγt+ cells. f, IL-17A flow plots from adipose (epiWAT) γδ T cells stimulated with PMA–ionomycin and 40 µM SR9009. g, Percentage of IL-17A+ cells. h, IL-17F flow plots from adipose (epiWAT) γδ T cells stimulated with IL-1β, IL-23 and 40 µM SR9009. i, Percentage of IL-17F+ cells. j, Percentage of γδ T cells and αβ T cells among total lymphocytes in Arntlfl (grey) and Arntl∆Vav1 (red) mice. k,l, Absolute cell numbers of γδ (k) and αβ (l) T cells in adipose tissue of Arntlfl (grey) and Arntl∆Vav1 (red) mice. m, IL-17A flow plots from Arntlfl and Arntl∆Vav1 adipose γδ T cells. n, Percentage of IL-17A+ cells. o, IL-17A+ γδ T cell numbers per gram of adipose tissue in Arntlfl (grey) and Arntl∆Vav1 (red) mice. p, IL-17A+ cells as a percentage of the total adipose αβ T cells. q, IFNγ expression by adipose αβ and γδ T cells from Arntlfl (grey) and Arntl∆Vav1 (red) mice. b,c, White and grey panels represent light and dark periods, respectively. Data are representative of three (a–c,j–q) or two (d–i) independent experiments. b,c,e,g,i–l,n–q, Data are mean ± s.e.m., n = 4–15 mice per group. b,c, Significance was calculated using cosinor analysis, with cosine fitted curves, e,g,i–l,n–q, Two-tailed unpaired student’s t-test. *P < 0.05, **P < 0.01, ***P < 0.001.

Source Data

To determine the influence of the molecular clock on IL-17A expression by adipose γδ17 T cells, we used SR9009, a REV-ERBα agonist that inhibits RORγt-mediated transcription by competing for DNA-binding sites[27]. We observed that SR9009 has no effect on RORγt expression by adipose or lymph-node γδ17 T cells (Fig. 2d,e and Extended Data Fig. 3e–g). However, SR9009 completely inhibited IL-17A and IL-17F expression by adipose γδ17 T cells (Fig. 2f–i). SR9009 did not affect lymph-node lymphocyte viability or γδ17 T cell proportions (Extended Data Fig. 3c,d), but it caused a dose-dependent decrease in the production of IL-17A and IL-17F by lymph-node γδ17 T cells (Extended Data Fig. 3h–m). These data demonstrate the molecular-clock modulation of IL-17A expression by γδ17 T cells, particularly in adipose tissue.

To further investigate the role of the molecular clock in IL-17A expression, mice were generated with a specific deletion of Arntl in CD45-expressing cells (Arntl∆Vav1 mice). We found that deletion of Arntl decreased the percentage and number of γδ T cells in mesenteric adipose tissue (mesWAT) and epiWAT (Fig. 2j,k), but did not have this effect in γδ intraepithelial lymphocytes (IELs) (Extended Data Fig. 3n). Deletion of Arntl in CD45+ cells also did not affect the proportion or number of adipose αβ T cells, or IELs (Fig. 2j,l and Extended Data Fig. 3o). Notably, loss of Arntl increased both the proportion and the cell number of adipose IL-17A+ γδ T cells, compared with Arntlfl controls (Fig. 2m–o), but had no effect on IL-17A or IFNγ expression in adipose αβ T cells (Fig. 2p,q). γδ17 T cells have the highest expression of IL-7R in adipose tissue (Extended Data Fig. 3p), so we specifically deleted Arntl in IL-7R expressing cells (Arntl∆Il7r mice). Similarly to the Arntl∆Vav1 mice, there were decreased levels of adipose γδ T cells in the Arntl∆Il7r mice, but no difference in the lungs and spleen (Extended Data Fig. 3q). Again, adipose γδ T cells lacking Arntl had increased IL-17A expression (Extended Data Fig. 3r). These data suggest that the molecular clock is a major regulator of IL-17A and IL-17F, whereby activation of Rev-erba (also known as Nr1d1) decreases IL-17 expression, whereas loss of Bmal1 (Arntl) increases it. Together, these data show that the molecular clock regulates the homeostatic function of adipose γδ17 T cells.

Environmental factors regulate γδ IL-17A

Light is a major entrainment signal for the circadian regulation of whole-body metabolic rhythm. Therefore, we investigated whether light is an entrainment signal for circadian IL-17A production by adipose γδ T cells. Using metabolic cages, mice were housed under normal light conditions (lights on 07:00–19.00; control) or on a reversed light–dark cycle (lights on 19:00–07:00 for three weeks; Fig. 3a). After three weeks of inverted light, energy expenditure and metabolic rate rhythms were completely reversed (Fig. 3b,d and Extended Data Fig. 4a). Adipose tissue was then collected from both groups at ZT0, ZT6, ZT12 and ZT18 (Fig. 3a). IL-17A production by adipose γδ17 T cells had a circadian rhythm, which was reversed in the light-inverted group (Fig. 3g). Thus, light is a major entrainment signal for IL-17A expression by γδ17 T cells.

Fig. 3: Light entrains whole-body metabolism and IL-17A production by adipose γδ T cells.

a, Reverse-light-cycle experimental set-up. b, Energy expenditure of regular (grey) and inverted-light-cycle (red) mice after three weeks. c,d, Circadian plots of energy expenditure (c) and oxygen consumption (d). e, Food consumption of regular (grey) and inverted-light-cycle (red) mice after three weeks. f, Circadian plot of food consumption. g, Percentage of IL-17A+ cells among adipose γδ T cells of normal (grey) and reverse-light-cycle (red) mice over 24 h. h, Reverse-feeding experimental set-up. i, Energy expenditure of regular (grey) and reverse-fed (blue) mice after three weeks. j,k, Circadian plots of energy expenditure (j) and oxygen consumption (k). l, Cumulative food consumption of regular (grey) and reverse-fed (blue) mice after three weeks. m, Averaged food consumption. n, Percentage of IL-17A+ cells among adipose γδ T cells of regular (grey) and reverse-fed (blue) mice over 24 h. o, Experimental set-up for 50% CR feeding. p,q, Energy expenditure (p) and oxygen consumption (q) of ad-libitum-fed (grey) and 50%-CR-fed (orange) mice over 24 h. r, IL-17A flow plots of adipose γδ T cells from mice fed ad libitum (AL) and mice fed a 50% CR diet. s, Percentage of IL-17A+ cells. t, Energy expenditure from 50%-CR-fed (orange) and reverse-fed (blue) mice over 24 h. u, Percentage of IL-17A+ cells among adipose γδ T cells. v, HFD experimental set-up. w, RER SFD-fed (grey) and HFD-fed (red) mice after three weeks. x, Circadian plot of RER. y, Bar plot of food intake. z, Left, IL-17A histograms of adipose γδ T cells from SFD-fed and HFD-fed mice. Right, percentage of IL-17A+ cells. White and grey panels represent light and dark periods, respectively. a–u, Data are representative of three independent experiments; w–z, two independent experiments. c,d,f,j,k,p,q,t,x, Data are mean; b,e,g,i,l–n,s,u,w,y,z, Data are mean ± s.e.m.; n = 4–6 mice per group. b–g,i–l,n,p,q,t,x,z, Significance was calculated using cosinor analysis, with cosine fitted curves; amplitude (amp) and acrophase (acro) were extracted from the cosinor model. m, Two-group analysis with ANCOVA. s,u, Two-way ANOVA. y, Two-tailed unpaired student’s t-test. NS, not significant, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Source Data

Previous studies have shown an increase in circulating IL-17A in both mice and humans after eating[28]. Adipose tissue is sensitive to feeding behaviour; it expands and contracts during feeding (active) and fasting (inactive) periods, respectively[29]. A caveat of reversing the light cycle of mice is that the feeding cycle is also reversed (Fig. 3e,f); thus, we investigated whether the adipose IL-17A rhythm was entrained by light as a result of reverse feeding. Mice were placed into metabolic cages under normal light conditions but with reversed feeding schedules, with access to food from 07:00–19.00 (reverse feeding), or from 19.00–07:00 (control) for three weeks (Fig. 3h). Of note, energy expenditure and metabolic rate rhythms did not completely reverse, but rather, were disrupted in reverse-fed mice compared with controls (Fig. 3i–k and Extended Data Fig. 4b). Furthermore, reverse-fed mice had disrupted circadian expression of IL-17A by adipose γδ17 T cells, with a decreased amplitude and an altered acrophase compared with controls (Fig. 3n), suggesting that adipose IL-17 is linked with feeding. However, reverse-fed mice consumed approximately 50% fewer calories than the control mice did (Fig. 3l,m), which could have affected adipose IL-17 production. To investigate whether the disruption to IL-17A expression was due to decreased food intake or the time of feeding, mice were fed ad libitum or fed a 50% calorie-restricted diet (50% CR), reducing the amount of food consumed to match the amount of food eaten by the reverse-fed mice (Fig. 3o). Although the calorie-restricted diet decreased energy expenditure and metabolic rate at all times of day, compared with the ab-libitum-fed mice, the calorie-restricted group maintained the rhythm (amplitude) of energy expenditure and metabolic rate (Fig. 3p,q and Extended Data Fig. 4c,d). Likewise, calorie restriction reduced IL-17 expression (Fig. 3r,s), but did not affect the rhythm of IL-17 production (Fig. 3r,s). Comparing the circadian rhythm of energy expenditure in calorie-restricted and reverse-fed mice, both consuming the same amount of food, revealed differences in amplitude and acrophase between the feeding regimens (Fig. 3t). Furthermore, this was associated with a loss of IL-17 rhythm by adipose γδ17 T cells only in the reverse-fed group (Fig. 3u). Together, these data suggest that light entrains the production of IL-17A by adipose γδ17 T cells, and that this effect occurs mostly through feeding behaviour, because disrupted feeding behaviour disturbs IL-17A expression in adipose γδ17 T cells.

A high-fat diet disrupts circadian IL-17A expression

Mice exhibit a circadian rhythm in fuel use, switching between carbohydrate and lipid burning across dark and light cycles. This can be measured in metabolic cages by the respiratory exchange ratio (RER), in which 0.7 indicates lipid burning and 1 indicates carbohydrate use. Switching to a 60% high-fat diet (HFD) for 3 weeks disrupts the RER rhythm, causing mice to use lipids throughout both light and dark cycles, as previously described[30] (Fig. 3v–x). In addition, a one-week HFD was sufficient to disrupt the normal feeding rhythm of mice; it caused them to feed more during the day, indicating increased grazing, as previously reported31,32 (Fig. 3y and Extended Data Fig. 4e). We found that a three-week HFD increased the expression of IL-17A and RORγt by adipose γδ17 T cells throughout 24 h, disrupting the IL-17A rhythm (Fig. 3z and Extended Data Fig. 4f). Inhibition of the IL-17A signalling axis in adipose tissue has been shown to prevent diet-induced obesity[33]. We also found that Il17a/f−/− mice were resistant to weight gain after 16 weeks on a HFD (Extended Data Fig. 4g,h). Together, these results suggest that an increase in caloric intake throughout 24 h by HFD feeding increases the expression of IL-17A by adipose γδ17 T cells, and that loss of IL-17 signalling prevents diet-induced weight gain.

Previous studies have shown that IL-17A signalling has a pathogenic role in neurodegenerative disease, including multiple sclerosis[34]. In the mouse model of experimental autoimmune encephalomyelitis (EAE), IL-17A produced by γδ T cells has a pathological role in the development and onset of EAE[34]. We examined the pathological role of disrupted IL-17A from a HFD feeding regimen using the EAE mouse model. HFD-fed mice had faster disease progression, compared with controls that were fed a standard-fat diet (SFD) (Extended Data Fig. 5a–d).

γδ IL-17A regulates DNL

Adipose γδ T cells are the main source of IL-17A in adipose tissue, contributing 75% of the IL-17A in adipose tissue (Fig. 4a and Extended Data Fig. 6a). Loss of adipose γδ T cells decreased the levels of IL-17A protein, and the levels recovered after readministration of γδ T cells (Fig. 4b). Using IL-17A-GFP mice, we found diurnal expression of IL-17A across all adipose depots including BAT, epiWAT and subcutaneous (scWAT) adipose tissue, which peaked at night during the active phase (Fig. 4c). Studies from our laboratory and others have shown that γδ17 T cells have a key role in maintaining body temperature and thermogenesis21,22. We next wondered why fluctuations in IL-17A expression might be important in adipose tissue. Adipose lipolysis and lipogenesis are under strict circadian control, with lipolysis occurring during the day in mice, and lipogenesis happening at night. Overlaying adipose IL-17A expression, we found that IL-17 peaks when lipogenesis peaks[15] (Fig. 4d). Because adipose IL-17 was influenced by food intake, we hypothesized that adipose IL-17A might have a role in DNL (Fig. 4e). Using the public BAT proteome database, OPABAT, we found that the levels of IL-17RC protein directly correlated with the abundance of the DNL proteins FASN, ACACA, ACACB, ACLY and SCD1 (Fig. 4f,g and Extended Data Fig. 6b), but not with proteins involved in lipolysis[35] (Extended Data Fig. 7a). In addition, DNL protein abundance did not correlate with other cytokine receptor levels in BAT (Extended Data Fig. 7b–f). We isolated adipose tissue across 24 h from mice lacking γδ T cells (Tcrδ−/− mice), and found that the circadian expression of the DNL genes Fasn, Acc2 (also known as Acacb) and Scd1 was disrupted in Tcrδ−/− mice compared with wild-type controls (Fig. 4h,i and Extended Data Fig. 6c). We next investigated the direct effects of IL-17 on DNL, by treating primary brown adipocytes with recombinant (r)IL-17A or vehicle, and found increased expression of a transcriptional regulator of DNL genes, Mlxipl, and fatty acid synthase Fasn, by rIL-17A (Fig. 5a,b). Next, we administered IL-17A in vivo over three days, which increased the expression of all BAT DNL genes (Mlxipl, Acaca, Fasn, Scd1, Scd2 and Elovl6; Fig. 5c). These experiments support the conclusion that γδ-T-cell-derived IL-17 regulates adipose DNL.

Fig. 4: γδ T cells are the main source of adipose tissue IL-17A, and maintain adipose tissue DNL.

a, Representative flow plot showing that γδ T cells are the main producer of IL-17A in adipose tissue. b, Adipose IL-17A protein levels by enzyme-linked immunosorbent assay (ELISA) from wild-type (WT) mice (grey), Tcrδ−/− mice (red) and Tcrδ−/− mice reconstituted with wild-type γδ T cells (γδs; open circles). c, Diurnal expression of IL-17A–GFP by γδ17 T cells across the adipose tissue depots BAT, scWAT and epiWAT. d, Diagrammatic representation of circadian whole-body metabolism by RER, showing the most active metabolic processes during the day and night, with the IL-17A circadian rhythm overlaid. e, Representation of the DNL pathway. f, Correlation analysis of BAT IL-17RC protein levels with FASN abundance from OPABAT. g, Correlation heat map of IL-17RC protein levels with DNL protein abundance from OPABAT. h,i, Circadian time plots showing the relative expression of the DNL genes Fasn (h) and Acc2 (i) from BAT of wild-type (grey) and Tcrδ−/− (red) mice. d,h,i, White and grey panels represent light and dark periods, respectively. a–d,h,i, Data are representative of two independent experiments. b–d,h,i, Data are mean ± s.e.m., n = 4–12 mice per group. Significance was calculated using a one-way ANOVA (b), two-tailed unpaired student’s t-test (c), simple linear regression (f,g) or cosinor analysis, with cosine fitted curves (h,i); amplitude (amp) and acrophase (acro) were extracted from the cosinor model. NS, not significant, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Source Data

Fig. 5: IL-17A signalling is necessary for adipocyte DNL, and for whole-body metabolism.

a,b, Expression of Mlxipl (a) and Fasn (b) in brown adipocytes treated with IL-17A (2 µg). c, BAT DNL gene expression from PBS- or IL-17A-treated mice. d, Diagram showing the increased expression of IL-17A in Arntl∆Il7r mice. e, Acly, Fasn, Acaca and Scd1 gene expression from wild-type or Arntl∆Il7r BAT. f,g, Acly, Fasn, Acaca and Scd1 gene expression from wild-type or Il17a/f−/− BAT (f), and from wild-type or Il17a−/− BAT (g). h, Left, western blots for the circadian expression of SCD1 from wild-type (grey) and Il17a−/− (knockout; KO) BAT. Right, circadian plot. a.u., arbitrary units. i, Heavy-labelled-water protocol for palmitate labelling. j,k, Palmitate abundance (j) and palmitate synthesis (k) in wild-type (grey) and Il17a/f−/− (red) BAT and liver. l, Left, experimental set-up for high-sucrose feeding. Right, weight gain after three weeks. m, Left, volcano plot of DNL-associated gene expression in BAT from wild-type and AdIl17rc−/− mice, from GSE144255. log_2(FC), log_2-transformed fold change. Right, differential expression heat map of DNL-associated genes. n, DNL-associated genes from skin biopsies of patients with psoriasis who were treated with placebo or ixekizumab (150 mg). o, RER averaged by light or dark cycle. p, Circadian rhythmicity analysis. q, Wild-type (grey) and Il17a/f−/− body temperature. r, Core-temperature data averaged by light or dark cycle. s, Circadian rhythmicity analysis. White and grey panels represent light and dark periods, respectively. Data are representative of one independent experiment (h–k), two independent experiments (a–g,l) or four independent experiments, with two experiments pooled (o–s). c,e-h,j-i,o,q,r Data are mean ± s.e.m., n = 3–6 mice per group. Significance was calculated using two-tailed paired student’s t-test (a,b), two-tailed unpaired student’s t-test (c,e–g), two-way ANOVA (l), repeated measures ANOVA, (j,k,o,r) or cosinor analysis, with cosine fitted curves (h,p,s); amplitude (amp) and acrophase (acro) were extracted from the cosinor model. NS, not significant, *P < 0.05, **P < 0.01, ***P < 0.001 ****P < 0.0001.

Source Data

Because we found an increase in IL-17A expression in adipose γδ T cells from Arntl∆Il7r mice (Extended Data Fig. 3r and Fig. 5d), we investigated whether there was also a corresponding increase in the expression of DNL genes. Indeed, expression of Acly, Acaca and Scd1 was increased in the BAT of Arntl∆Il7r mice, compared with Arntlfl controls, specifically at ZT18, but not at ZT6 (Fig. 5e and Extended Data Fig. 8a). Previous studies have shown that Il17a−/− and Il17f−/− mice have impaired thermogenesis21,22. Because adipose γδ T cells produce both IL-17A and IL-17F, which can compensate for one another, we used IL-17A and IL-17F double-knockout mice (Il17a/f−/−). BAT taken from Il17a/f−/− mice at ZT6 and ZT18, in the middle of the inactive and the active phases, respectively, showed lower expression of DNL genes at ZT18, compared with BAT from wild-type mice, but this was not the case at ZT6 (Fig. 5f and Extended Data Fig. 8b). This effect was also observed in Il17a−/− single-knockout mice (Fig. 5g). Furthermore, we found that Il17a/f−/− mice had decreased expression of thermogenesis and lipolysis genes at ZT6 and ZT18, compared with wild-type mice (Extended Data Fig. 8c,d). However, wild-type and Il17a/f−/− mice upregulated DNL genes when administered rIL-17A or rIL-17F (Extended Data Fig. 8e–h). No differences were observed between wild-type and Il17a/f−/− groups that were treated with phosphate-buffered saline (PBS), because injections were given during the day, when DNL and IL-17A expression are low (Extended Data Fig. 8e–h). At the protein level, circadian expression of SCD1 was disrupted in Il17a−/− mice, compared with wild-type controls (Fig. 5h). Next, heavy-labelled water (2H_2O) was used to determine the ability of wild-type and Il17a/f−/− BAT to synthesize palmitate. We found decreased lipid synthesis in Il17a/f−/− BAT compared with wild-type, but no significant difference in the liver (Fig. 5i–k). Owing to decreased DNL in the BAT of IL-17-deficient mice, we placed wild-type and Il17a/f−/− mice on a three-week high-sucrose regime that promotes DNL, and found that Il17a/f−/− mice were resistant to high-sucrose-induced weight gain (Fig. 5l).

We next took advantage of publicly available transcriptomic data of adipocyte-specific deletion of the IL-17 receptor, IL-17RC. We found that the DNL genes Mlxipl, Acaca, Fasn, Scd1, Scd2 and Srebf1 were significantly downregulated in BAT lacking IL-17RC, compared with wild-type BAT (Fig. 5m). Furthermore, publicly available clinical data from skin biopsies of patients with psoriasis who were receiving either a placebo or anti-IL-17 therapy show that individuals who were treated with brodalumab[36] (anti-IL-17RA; Extended Data Fig. 8i) or ixekizumab[37] (anti-IL-17A; Fig. 5n) exhibited a decrease in the expression of DNL genes. Combined, these data show that IL-17 signalling has a role in regulating DNL in BAT and human skin.

Finally, we performed whole-body metabolic analysis in wild-type and Il17a/f−/− mice at thermoneutrality (30 °C). Thermoneutrality was chosen to remove any influence from adaptive thermogenesis, because mice that are housed at room temperature expend approximately 40% of their energy to keep warm at room temperature38,39. A previous study suggested that BAT DNL is crucial for the maintenance of body temperature in mice[40]. We found that Il17a/f−/− mice have an altered RER, particularly at night (Fig. 5o), and show a blunted circadian RER rhythm (Fig. 5p). Notably, the body temperature of Il17a/f−/− mice was decreased at all times of day, but especially during the active phase (Fig. 5r,s). Circadian analysis found that Il17a/f−/− mice had a significantly blunted circadian fluctuation of body temperature over 24 h (Fig. 5s). Combined, our results show that circadian IL-17A from adipose-tissue-resident γδ T cells is important for physiological whole-body metabolism and body temperature, and that it targets the adipose tissue DNL pathway.

Discussion

Here we show that type-17 innate T cells—particularly adipose γδ17 T cells—are enriched for the molecular clock. The molecular clock controls adipose γδ17 T cell maintenance and IL-17A production, which is essential for adipose tissue circadian homeostasis, whole-body metabolism and body-temperature regulation, through the circadian regulation of DNL. Tissue-resident innate lymphocytes (γδ T cells[21], iNKT cells[7] and ILCs[41]) produce effector cytokines that are important for host defence, but also for the normal maintenance of tissue physiology. We show that the molecular clock is more prominent in type-17 tissue-resident innate T cells than it is in type-1 cells, particularly in adipose tissue. The molecular clock has been shown to regulate IL-17A responses in ILC3s[26] and TH17 cells[23]; however, circadian rhythms in innate T cells have not been described. In the small intestine, genetic deletion of Nfil3 or Rev-erba disrupted IL-17A expression, but had no effect on the expression of IFNγ by TH1 cells[23]. Furthermore, IL-17A-producing ILC3s also had higher expression of molecular-clock genes than ILC1s[26], ILC2s[25] and ILC progenitors[24]. Our data show that γδ17 T cells have a daily rhythm to IL-17A and RORγt expression, which is negatively regulated by REV-ERBα activity. We found that all adipose depots, including BAT, had a diurnal rhythm to IL-17A expression by γδ17 T cells. These studies, combined with our findings, suggest that enriched expression of the molecular clock is a conserved feature of IL-17A-producing lymphocytes.

Previous work by our laboratory and others have described a key thermogenic role for IL-17A—and/or IL-17F—signalling in adipose tissue21,22. However, these studies were performed in cold-challenged mice, and here we describe a circadian homeostatic role of IL-17A. Given the cold-challenge results, it was at first puzzling that we found peaks of IL-17A expression by adipose γδ17 T cells at night during feeding, rather than during the day when lipolysis is highest in mice. At night, when mice feed, energy is stored as triacylglycerides in adipose tissue through DNL[42]. Another study found that DNL and lipolysis in adipose tissue are intricately connected. The research showed that loss of DNL, through the BAT-specific disruption of SREBP activity, was sufficient to disrupt the circadian body temperature of mice, providing evidence that BAT DNL has an essential role in maintaining body temperature[40]. Therefore, loss of DNL circadian rhythm in Il17a/f−/− mice might blunt lipolysis owing to a reduction in the lipid stores that act as fuel for circadian lipolysis and body-temperature regulation. We show that disrupted DNL could be restored by administering IL-17A and IL-17F in vitro and in vivo. A reanalysis of a previously published dataset found that loss of IL-17RC specifically in adipocytes significantly reduced BAT DNL gene expression[22]. The authors concluded that IL-17F, rather than IL-17A, mediates the pro-thermogenic effect on adipose tissue because of the IL-17RC deletion. However, both IL-17A and IL-17F are known to signal through IL-17RC heterodimers with IL-17RA. Thus, both cytokines might act in synergistic or compensatory mechanisms to promote thermogenesis, and maintain body temperature43,44,45. Thus, here we analysed Il17a/f−/− double-knockout mice, and found a marked defect in the daily fluctuations of body-temperature rhythm.

A previous study found that BMAL1 deficiency specifically in RORγt-expressing cells (Bmal1∆Rorc) led to an increase in the expression of genes that are involved in lipid metabolism in gut epithelial cells[24]. This study attributed the phenotype to a loss of IL-22 in ILC3s. A similar study found that IL-17A expression was enhanced in Bmal1∆Rorc mice, suggesting that REV-ERBα negatively regulates IL-17 expression, in line with our data. Furthermore, Bmal1∆Rorc mice had an increase in the mass of gonadal and subcutaneous adipose tissue[24], which supports our data linking IL-17 to lipid metabolism and to enhanced lipid synthesis and storage. This suggests that increased IL-17, rather than decreased IL-22 from gut ILC3s, drives the enhanced expression of lipid-metabolism genes in the gut epithelial cells of Bmal1∆Rorc mice, although further work is needed to investigate this. Together, these data highlight that the IL-17 cytokine family is important for lipid metabolism in both adipocytes and gut epithelial cells, where innate T cells and ILCs reside.

IL-17A derived from γδ T cells, among others, has a pathological role in the initiation of EAE[34]. In addition, a HFD exacerbates EAE pathology, and increases brain infiltration by TH17 cells, enhancing the production of IL-17A by TH17 cells. We found that a three-week HFD, before the onset of obesity, enhanced EAE and disrupted the circadian rhythm of IL-17A, leading to constantly elevated levels over 24 h (ref. [46]). Shift workers are reported to have an increased preference for high-fat, high-calorie foods, in addition to irregular meal timings47,48,49. In modern living, all-day grazing and constant food availability have changed the way many people eat in the Western world. In mice, a HFD disrupts feeding behaviour, and increases grazing during the inactive phase as well as the active phase[50]. Furthermore, SFD-fed mice that were forced to eat during the day (inactive phase) gained weight over time51,52. Shift work has been associated with an increased risk of developing multiple sclerosis and metabolic disorders[53]. Our data suggest that adipose IL-17 production is regulated by food intake, which would enhance DNL and fat storage with all-day grazing. Given the rising number of shift workers[54], understanding how feeding disruption and timing affect immunometabolism could help to reduce shift work-related diseases.

Our data reveal that IL-17 has a circadian rhythm, with nightly peaks. This rhythm is controlled by the molecular clock, entrained by light and affected by feeding. A major target of IL-17 in adipose tissue is DNL, and the absence of IL-17A and IL-17F results in defects in DNL and a reduced ability to store fat from food, as is also seen[33] in mice with an adipocyte-specific deletion of Il17rc. It is becoming increasingly clear that immune cells have a crucial role in regulating local tissue homeostasis, which affects whole-body circadian metabolism. Given the ever-increasing burden of metabolic disease, a better understanding of the links between immune function, circadian rhythm and whole-body metabolic health is essential to combat conditions such as obesity, cachexia and metabolic syndrome. Here we provide evidence for the IL-17 family as potent metabolic modulators that could be therapeutic targets in metabolic disease.

Methods

Mice

In all experiments, male C57BL/6J-OlaHsd mice between 8 and 12 weeks of age were used. Mice were bred in-house under specific-pathogen-free conditions in accordance with Irish and European Union regulations. Il17a/f−/− mice were received from the laboratories of V. K. Kuchroo and Y. Iwakura. Il17a−/− mice were received from the laboratory of K.H.G.M. IL-17A-GFP mice were purchased from The Jackson Laboratory. The Vav1Cre, Il7rCre, Per1Venus and Arntlfl mouse lines were bred and maintained at the Champalimaud Centre for the Unknown (CCU) animal facility. All mouse work was performed in compliance with the L.L. laboratory project licence, with ethical approval from the Trinity College Dublin ethics committee and the Animal Research Ethics Committee from the Health Products Regulatory Authority (HPRA), and the Brigham and Women’s Hospital Institutional Animal Care and Use Committee guidelines. Experiments involving the Vav1Cre (ref. [55]), Il7rCre, Per1Venus (ref. [56]) and Arntlfl (ref. [57]) mouse lines were approved by national and local institutional review boards, the Direção Geral de Veterinária and CCU ethical committees. For experiments using rIL-17A, mice were administered 100 µl of rIL-17A (2 µg per mouse) or PBS control by intraperitoneal (i.p.) injection three times over five days.

Environmental modulation

Inversion of light–dark cycles

For experiments manipulating light cycles, mice were placed inside a ventilated, light-tight cabinet at room temperature, and light was adjusted such that control mice had lights on from 07:00–19:00, and test mice had lights on from 19.00–07:00.

Diet models

For HFD feeding experiments, mice were fed a 45% or 60% HFD for 3 weeks or 16 weeks ad libitum, compared with control mice that were fed a SFD, in which 10% of the calories were from fat. For reverse-feeding experiments, mice were fed a SFD ad libitum from 07:00–19:00 during the light cycle, compared with controls that were fed a SFD ad libitum from 19:00 to 07:00 during the dark cycle. For isocaloric feeding experiments, mice were placed into metabolic cages, and food consumption was measured over two days. The total calories per day was calculated, and mice were then fed 50% of the total calories consumed per day, for one week, compared with ad-libitum-fed controls that had 24-h access to food. For high-sucrose feeding experiments, mice were provided a 20% sucrose solution as their drinking water ad libitum for three weeks.

Tissue processing

Mice were euthanized by CO_2 inhalation and adipose tissue was removed, minced with a razor and digested in 1 mg ml−1 collagenase type II (Worthington) dissolved in RPMI, in a shaking incubator for 25–30 min at 37 °C. Digested cells were filtered through a 70-μm nylon mesh and centrifuged at 300g for 5 min to pellet the stromovascular fraction (SVF). Lymph nodes were disrupted through a 70-μm filter and centrifuged for 5 min at 300g to pellet. After processing, the adipose SVF and lymph-node leukocytes were stimulated with phorbol 12-myristate 13-acetate (PMA; 10 ng ml−1; Sigma), ionomycin (500 ng ml−1; Sigma) and brefeldin A (BFA; 5 µg ml−1; Sigma) in cRPMI medium, and incubated at 37 °C for 3 h. For cytokine stimulations, adipose SVF was stimulated with rIL-23 (10 ng ml−1; Miltenyi) and rIL-1β (10 ng ml−1; Immunotools) with BFA (5 µg ml−1) for 3 h.

For the circadian cytokine production of lymph-node γδ T cells, all lymph nodes were collected from mice at 07:00 (ZT0) and plated at 3-h time points over 24 h. Each time point was then stimulated with PMA and ionomycin in BFA for 3 h, leading up to the respective time point, before being collected for fluorescent staining for flow cytometry analysis.

For treatments with the REV-ERBβ agonist SR9009 (Sigma), cells were stimulated with PMA–ionomycin or with the recombinant cytokines rIL-1β and rIL-23 as described above, in the presence or absence of SR9009. For dose–response graphs, lymph-node cells were stimulated in the presence of 0 µM, 5 µM, 10 µM, 20 µM, 40 µM and 80 µM doses of SR9009. Adipose SVF was stimulated with 40 µM of SR9009 only.

ELISA

γδ T cells were pre-expanded in wild-type mice using IL-7 and IL-15 cytokines and sorted to purity. A total of 50,000 γδ T cells were adoptively transferred into Tcrδ−/− mice. After one day, wild-type, Tcrδ−/− and Tcrδ−/− age-matched mice reconstituted with 50,000 γδ T cells were euthanized and adipose tissue lysates were assayed for IL-17A protein levels by ELISA. Processed adipose SVF lysates were diluted 1:2 in reagent diluent (1% bovine serum albumin in PBS) and IL-17A protein levels were quantified using the Mouse IL-17 Quantikine ELISA kit (M1700, R&D Systems).

Culturing of mouse adipocytes

Interscapular BAT was isolated, minced and incubated in Dulbecco’s modified Eagle’s medium (DMEM) (glucose-free) supplemented with 10% fetal calf serum (FCS), 2.5 mM l-glutamine and 5 mM glucose. Tissue explants were then treated with or without rIL-17A (50 ng ml−1; R&D Systems) for 18 h at 37 °C. Once the medium was removed, explants were snap-frozen in liquid nitrogen for RNA extraction.

Flow cytometry analysis

For intracellular and intranuclear staining, cells were washed with 1 ml PBS and incubated in ZombieAqua Cell Viability Dye (Biolegend; 1:1,000 in PBS) for 20 min at room temperature. Cells were incubated with an extracellular fluorochrome-labelled antibody cocktail with Fc block (1:200; BD Biosciences) in FACS buffer (PBS + 2% FCS) for 20 min at room temperature. Cells were then washed with 2% FACS buffer resuspended in 100 µl of Foxp3 staining kit (eBioscience) and incubated at room temperature for 20 min. Cells were washed with 1× permeabilization buffer (eBioscience) and then incubated with an intracellular fluorochrome-labelled antibody cocktail in 1× permeabilization buffer (eBioscience) for 20 min at room temperature. Cells were incubated on ice for 30 min and subsequently washed in 2% FACS buffer. Cells were acquired on a FACS Canto or LSR Fortessa cytometer (BD Biosciences). Data were analysed with FlowJo v.10 software. Cell sorting was performed using a FACSAria (BD Biosciences). Sorted populations were more than 95% pure. For a list of flow cytometry antibodies, see Supplementary Table 1.

Induction and assessment of EAE

Mice were fed either ad libitum or a 60% HFD three weeks before EAE induction. EAE was induced in male mice by subcutaneous immunization with MOG35–55 peptide (150 µg per mouse; GenScript) emulsified in complete Freund’s adjuvant (Condrex) containing 4 mg ml−1 heat-killed Myocobacterium tuberculosis (MTB). Mice were injected i.p. with pertussis toxin (200 ng per mouse) (Kaketsuken) on day 0 to induce EAE development. Disease severity was monitored and assessed by clinical scores as follows: no clinical signs, 0; limp tail, 1; ataxic gait, 2; hind limb weakness, 3; hind limb paralysis, 4; tetra paralysis or moribund, 5. A weight loss of more than 20% constituted an additional humane end-point.

RNA extraction and quantitative PCR analysis

RNA extraction from isolated cells

Adipose γδ17 T cells were isolated and sorted from adipose tissue. RNA and cDNA were generated from isolated cells using the SYBR Green Fast Advanced Cells-to-CT Kit (Invitrogen) following the manufacturer’s specific instructions. To quantify the relative mRNA expression of genes of interest, quantitative PCR was performed in a 96- or 384-well plate format (Thermo Fisher Scientific) using PowerUp SYBR Green Master Mix (Invitrogen)-based detection (eBioscience). Relative mRNA levels were calculated using the ∆∆ cycle threshold (∆∆Ct) method and normalized to corresponding endogenous controls (Actb).

RNA extraction from tissues

Tissues were snap-frozen in liquid nitrogen, defrosted at room temperature and transferred to a 2-ml tube containing a 5-mm stainless steel bead. Tissues were homogenized in 1 ml trizol reagent (Thermo Fisher Scientific) in a tissue lyser for 2.5 min, 25 pulses per second. Then, 200 µl chloroform was added to each tube, and they were inverted once and left at room temperature for 2–3 min, before centrifuging at 12,000g for 15 min. The aqueous phase containing RNA was transferred into a new Eppendorf tube and 500 µl isopropanol was added to precipitate the RNA. Tubes were inverted ten times and left at room temperature for 10 min, and then centrifuged at 12,000g for 10 min. Supernatants were discarded and RNA pellets were washed in 1 ml 75% ethanol, diluted in RNAse free dH_2O. Tubes were centrifuged at 12,000g for 5 min and supernatants were discarded by inverting the tube. The RNA pellet was left to dry at room temperature for 20–30 min, until transparent, and the pellets were resuspended in 50 µl RNAse free water. RNA was left on ice for 30 min, then in a heat block set at 55 °C for 15 min. RNA quality and concentration were determined using a Nanodrop 2000 UV spectrophotometer (Thermo Fisher Scientific). Twenty microlitres of cDNA was synthesized from 2 µg of isolated RNA using the High-Capacity cDNA Reverse Transcription Kit (Biosciences) in a MiniAmp Thermal Cycler (BD Biosciences). To quantify the relative mRNA expression of genes of interest, quantitative PCR was performed in a 96- or 384-well plate format (Thermo Fisher Scientific) using SYBR Green-based detection (eBioscience). Relative mRNA levels were calculated using the ∆∆ cycle threshold (∆∆Ct) method and normalized to corresponding endogenous controls (Ppib or Rpl18). For a list of primers, see Supplementary Table 2.

Protein analysis by western blotting

BAT from wild-type and Il17a−/− mice was collected over 24 h and snap-frozen. The BAT was then lysed and centrifuged at 14,000g, and the pellet was discarded. The amount of protein was quantified using a BCA kit. Tissue lysates were subsequently boiled at 95 °C for 5 min to denature the proteins. Proteins were resolved, on the basis of their molecular weight, through SDS–PAGE gels in 1× running buffer. Proteins were electro-transferred onto PVDF membranes (Merck) in 1× transfer buffer containing 10% methanol. Membranes were blocked in 5% milk in 1× Tris-buffered saline with Tween-20 and incubated with SCD1, or α-tubulin primary antibodies (Cell Signaling Technologies) overnight at 4 °C and HRP-conjugated secondary antibodies (Jackson Immunoresearch). Membranes were incubated in ECL substrate (BioRad), and images were developed on a Biorad Gel Doc imaging system. Images were quantified using ImageJ. For raw uncropped blots, see Supplementary Fig. 1.

Plasma 2H_2O enrichment analysis and DNL calculations

For measurements of DNL in vivo, wild-type or Il17a/f−/− mice were placed overnight on 8% enriched D_2O drinking water, with subsequent collection and snap-freezing of BAT and liver. The 2H labelling of water from samples or standards was determined by deuterium acetone exchange, and using equations as previously described[58].

Metabolic cage analysis

Indirect calorimetry experiments were performed with the Promethion metabolic cage system, or the comprehensive lab animal monitoring system (CLAMS), essentially as described[59]. Mice were singly housed and allowed at least 12 h of acclimatization to the new environment. O_2 consumption, CO_2 emission, energy expenditure, body weight, food and water intake and locomotor activity were monitored throughout the experiment. For experiments involving the manipulation of light cycles, mice were placed inside the metabolic cages at room temperature, and light was adjusted such that the control mice had lights on from 07:00–19:00, and test mice had lights on from 19:00–07:00. Analysis was performed using the online indirect calorimetry vignettes CalR (ref. [60]); online software is available at https://calrapp.org/.

Bulk RNA-seq and scRNA-seq data analysis

The bulk RNA-seq dataset of BAT from wild-type and AdIl17rc−/− mice was downloaded from the GEO repository GSE144255 as cuff gene counts[22]. The bulk RNA-seq datasets of skin biopsies from patients with psoriasis who were receiving brodalumab[36] and ixekizumab[37] were downloaded from the GEO repositories GSE117468 and GSE31652, respectively. Heat maps were generated using the online platform Heatmapper[61] (http://heatmapper.ca/).

scRNA-seq was performed on single-cell suspensions of sorted γδ T cells and iNKT cells from the visceral adipose tissue of C57BL/6J mice using the 10x Genomics platform. A total of five visceral adipose tissue deposits from five independent biological replicates were pooled for sequencing. Single-cell suspensions were loaded onto a 10x Chromium Controller to generate gel beads-in-emulsion (GEMS), and GEMs were processed to generate unique molecular identifier (UMI)-based libraries according to the 10X Genomics Chromium Single Cell 3’ protocol. Libraries were sequenced using a NextSeq 500 sequencer. Raw BCL files were demultiplexed using Cell Ranger v.3.0.2 mkfastq to generate fastq files with default parameter. Fastq files were aligned to the mm10 genome (v.1.2.0) and feature reads were quantified simultaneously using the Cell Ranger count for feature barcoding. Filtered feature-barcode UMI count matrices containing quantification of gene expression were used for downstream analysis.

Downstream scRNA-seq data analysis

A total of 22,748 cells mouse γδ T cells, iNKT cells and MAIT cells expressing a median of 1,423 genes and 3,556 UMIs per cell were loaded from feature-barcode UMI count matrices using the Seurat v.4.1.0 package[62]. Adipose and splenic iNKT cells were reanalysed from GSE142845 (ref. [63]), thymic iNKT cells were reanalysed from GSE141895 (ref. [64]), thymic MAIT cells were reanalysed from E-MTAB-7704 (ref. [65]), pulmonary γδ T cells were reanalysed from E-MTAB-8732 (ref. [66]), peripheral lymph node (PLN) and dermal γδ T cells were reanalysed from GSE123400 (ref. [67]) and meningeal γδ T cells were reanalysed from GSE147262 (ref. [68]). Cells expressing more than 11% of mitochondrial genes as a percentage of total gene counts were considered to represent apoptotic or dead cells and were therefore removed from the analysis. Cells were also filtered on the basis of total UMI counts and total gene counts on a per-sample basis to remove empty droplets, poor quality cells and doublets, with a minimum cut-off of at least 300 genes per cell across all samples. UMI counts were normalized using regularized negative binomial regression using sctransform v.0.3.3 (ref. [69]).

Dimensionality reduction was performed using principal component analysis (PCA) with n = 100 dimensions and 2,000 or 3,000 variable features, and an elbow plot was used to determine the number of PCA dimensions used as input for UMAP[70]. For collective analysis of cells from different batches and cells sequenced using different scRNA-seq technologies the Harmony v.1.0 package[71] was used with default settings to remove batch effects, and batch-corrected harmony embeddings were used for UMAP. UMAP was performed using a minimum distance of 0.3 and a spreading factor of 1. Shared nearest neighbour graphs were calculated using k = 20 nearest neighbours. Graph-based clustering was performed using the Louvain algorithm. In some cases, overclustering was performed and clusters were manually collapsed, and/or the first two dimensions of the UMAP reduction were used as input for graph-based clustering instead of PCA or harmony embeddings. Adipose γδ T cells were distinguished from adipose iNKT cells by expression of Trdc and graph-based clustering, and analysed separately for γδ1 versus γδ17 cell identification. Type-1 and type-17 innate T cells were individually identified in each dataset using graph-based clustering and gene-expression analysis, and cycling cells were excluded. For thymic innate T cells, CD44− progenitor populations were also excluded. Raw counts from type-1 and type-17 cell populations were then merged and normalized together into a single file for comparative analysis.

Gene-expression analysis was performed using the FindMarkers() or FindAllMarkers() Seurat functions and the Wilcoxon rank sum test. Heat maps were generated using the Complex Heatmap v.2.7.11 and circlize v.0.4.14 packages[72], and module scores were calculated using the AddModuleScore() Seurat function with n = 10 control features. Density plots were produced using the Nebulosa v.1.1.1 package[73]. Log-normalized RNA counts were used for all gene-expression analysis and visualization. All computational analysis was performed using R v.4.1.2 and RStudio Desktop v.1.4.1712 on an Ubuntu 20.04 Linux GNU (64 bit) system.

Statistical analysis

GraphPad Prism 9.3.0 was used for statistical analysis. For all experiments, a 95% confidence interval was used and P ≤ 0.05 was considered statistically significant. A D’Agostino–Pearson omnibus normality test was first performed to test whether the data were normally distributed (Gaussian distribution). If data were normally distributed, parametric testing was performed. If data were not normally distributed, non-parametric testing was performed. When comparing two groups, an unpaired or paired two-tailed student’s t-test was used. When comparing more than two groups, an ordinary one-way ANOVA with Dunnett’s test was used. When comparing data with two variables, a two-way ANOVA with Bonferroni test was used. Cosine curves were fitted in GraphPad Prism and circadian rhythmicity was evaluated using the cosinor regression model using the cosinor and cosinor2 R packages in RStudio. The circadian period was assumed to be 24 h for all analysis and the significance of the circadian fit was assessed by a zero-amplitude test with 95% confidence. Amplitude and acrophase were extracted from the cosinor model. In all figures, *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001.

Graphical representations

All diagrams and graphical representations were created using BioRender.

Reporting summary

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

Data availability

The data supporting the findings of this study are publicly available through the Gene Expression Omnibus (GEO) repository at GSE144255, GSE117468 and GSE31652 for the bulk RNA-seq datasets and GSE142845, GSE141895, GSE123400, GSE147262, or from ArrayExpress at E-MTAB-7704 and E-MTAB-8732 for the scRNA-seq datasets. Raw data that support the findings of this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.

References

1. Scheiermann, C., Gibbs, J., Ince, L. & Loudon, A. Clocking in to immunity. Nat. Rev. Immunol. 18, 423–437 (2018).

2. Man, K., Loudon, A. & Chawla, A. Immunity around the clock. Science 354, 999–1003 (2016).

3. Ince, L. M. et al. Influence of circadian clocks on adaptive immunity and vaccination responses. Nat. Commun. 14, 476 (2023).

4. Trim, W. V. & Lynch, L. Immune and non-immune functions of adipose tissue leukocytes. Nat. Rev. Immunol. 22, 371–386 (2022).

5. Kane, H. & Lynch, L. Innate immune control of adipose tissue homeostasis. Trends Immunol. 40, 857–872 (2019).

6. Goldberg, E. L. et al. Ketogenesis activates metabolically protective γδ T cells in visceral adipose tissue. Nat. Metab. 2, 50–61 (2020).

7. Lynch, L. et al. Adipose tissue invariant NKT cells protect against diet-induced obesity and metabolic disorder through regulatory cytokine production. Immunity 37, 574–587 (2012).

8. Brown, T. M. & Piggins, H. D. Electrophysiology of the suprachiasmatic circadian clock. Prog. Neurobiol. 82, 229–255 (2007).

9. Buhr, E. D., Yoo, S. H. & Takahashi, J. S. Temperature as a universal resetting cue for mammalian circadian oscillators. Science 330, 379–385 (2010).

10. Panda, S. Circadian physiology of metabolism. Science 354, 1008–1015 (2016).

11. Takahashi, J. S. Transcriptional architecture of the mammalian circadian clock. Nat. Rev. Genet. 18, 164–179 (2016).

12. Hastings, M., O’Neill, J. S. & Maywood, E. S. Circadian clocks: regulators of endocrine and metabolic rhythms. J. Endocrinol. 195, 187–198 (2007).

13. Adamovich, Y. et al. Oxygen and carbon dioxide rhythms are circadian clock controlled and differentially directed by behavioral signals. Cell Metab. 29, 1092–1103 (2019).

14. Seebacher, F. Responses to temperature variation: integration of thermoregulation and metabolism in vertebrates. J. Exp. Biol. 212, 2885–2891 (2009).

15. Kiehn, J.-T., Koch, C. E., Walter, M., Brod, A. & Oster, H. Circadian rhythms and clocks in adipose tissues: current insights. Chronophysiol. Ther. 7, 7–17 (2017).

16. Yamamuro, D. et al. Peripheral circadian rhythms in the liver and white adipose tissue of mice are attenuated by constant light and restored by time-restricted feeding. PLoS One 15, e0234439 (2020).

17. Bray, M. S. & Young, M. E. Circadian rhythms in the development of obesity: potential role for the circadian clock within the adipocyte. Obes. Rev. 8, 169–181 (2007).

18. Antunes, L. C., Levandovski, R., Dantas, G., Caumo, W. & Hidalgo, M. P. Obesity and shift work: chronobiological aspects. Nutr. Res. Rev. 23, 155–168 (2010).

19. Sethi, J. K. & Hotamisligil, G. S. Metabolic messengers: tumour necrosis factor. Nat. Metab. 3, 1302–1312 (2021).

20. Qing, H. et al. Origin and function of stress-induced IL-6 in murine models. Cell 182, 372–387 (2020).

21. Kohlgruber, A. C. et al. γδ T cells producing interleukin-17A regulate adipose regulatory T cell homeostasis and thermogenesis. Nat. Immunol. 19, 464–474 (2018).

22. Hu, B. et al. γδ T cells and adipocyte IL-17RC control fat innervation and thermogenesis. Nature 578, 610–614 (2020).

23. Yu, X. et al. TH17 cell differentiation is regulated by the circadian clock. Science 342, 727–730 (2013).

24. Godinho-Silva, C. et al. Light-entrained and brain-tuned circadian circuits regulate ILC3s and gut homeostasis. Nature 574, 254–258 (2019).

25. Teng, F. et al. A circadian clock is essential for homeostasis of group 3 innate lymphoid cells in the gut. Sci. Immunol. 4, eaax1215 (2019).

26. Wang, Q. et al. Circadian rhythm-dependent and circadian rhythm-independent impacts of the molecular clock on type 3 innate lymphoid cells. Sci. Immunol. 4, eaay7501 (2019).

27. Chang, C. et al. The nuclear receptor REV-ERBΑ modulates Th17 cell-mediated autoimmune disease. Proc. Natl Acad. Sci. USA 116, 18528–18536 (2019).

28. Nogueira, G. et al. Interleukin-17 acts in the hypothalamus reducing food intake. Brain. Behav. Immun. 87, 272–285 (2020).

29. Aggarwal, A. et al. The circadian clock regulates adipogenesis by a Per3 crosstalk pathway to Klf15. Cell Rep. 21, 2367–2375 (2017).

30. Lopes, N. et al. Distinct metabolic programs established in the thymus control effector functions of γδ T cell subsets in tumor microenvironments. Nat. Immunol. 22, 179–192 (2021).

31. Kohsaka, A. et al. High-fat diet disrupts behavioral and molecular circadian rhythms in mice. Cell Metab. 6, 414–421 (2007).

32. Chrobok, L. et al. Rhythmic neuronal activities of the rat nucleus of the solitary tract are impaired by high-fat diet—implications for daily control of satiety. J. Physiol. 600, 751–767 (2022).

33. Teijeiro, A., Garrido, A., Ferre, A., Perna, C. & Djouder, N. Inhibition of the IL-17A axis in adipocytes suppresses diet-induced obesity and metabolic disorders in mice. Nat. Metab. 3, 496–512 (2021).

34. McGinley, A. M. et al. Interleukin-17A serves a priming role in autoimmunity by recruiting IL-1β-producing myeloid cells that promote pathogenic T cells. Immunity 52, 342–356 (2020).

35. Xiao, H. et al. Architecture of the outbred brown fat proteome defines regulators of metabolic physiology. Cell 185, 4654–4673 (2022).

36. Tomalin, L. E. et al. Short-term transcriptional response to IL-17 receptor-A antagonism in the treatment of psoriasis. J. Allergy Clin. Immunol. 145, 922–932 (2020).

37. Krueger, J. G. et al. IL-17A is essential for cell activation and inflammatory gene circuits in subjects with psoriasis. J. Allergy Clin. Immunol. 130, 145–154 (2012).

38. Kokolus, K. M. et al. Baseline tumor growth and immune control in laboratory mice are significantly influenced by subthermoneutral housing temperature. Proc. Natl Acad. Sci. USA 110, 20176–20181 (2013).

39. Fischer, A. W., Cannon, B. & Nedergaard, J. Optimal housing temperatures for mice to mimic the thermal environment of humans: an experimental study. Mol. Metab. 7, 161–170 (2018).

40. Adlanmerini, M. et al. Circadian lipid synthesis in brown fat maintains murine body temperature during chronic cold. Proc. Natl Acad. Sci. USA 116, 18691–18699 (2019).

41. Talbot, J. et al. Feeding-dependent VIP neuron–ILC3 circuit regulates the intestinal barrier. Nature 579, 575–580 (2020).

42. Ameer, F., Scandiuzzi, L., Hasnain, S., Kalbacher, H. & Zaidi, N. De novo lipogenesis in health and disease. Metabolism 63, 895–902 (2014).

43. Goepfert, A., Lehmann, S., Wirth, E. & Rondeau, J. M. The human IL-17A/F heterodimer: a two-faced cytokine with unique receptor recognition properties. Sci. Rep. 7, 8906 (2017).

44. McGeachy, M. J., Cua, D. J. & Gaffen, S. L. The IL-17 family of cytokines in health and disease. Immunity 50, 892–906 (2019).

45. Douglas, A., Stevens, B. & Lynch, L. Interleukin-17 as a key player in neuroimmunometabolism. Nat. Metab. 5, 1088–1100 (2023).

46. Ji, Z. et al. Obesity promotes EAE through IL-6 and CCL-2-mediated T cells infiltration. Front. Immunol. 10, 1881 (2019).

47. Cain, S. W., Filtness, A. J., Phillips, C. L. & Anderson, C. Enhanced preference for high-fat foods following a simulated night shift. Scand. J. Work Environ. Health 41, 288–293 (2015).

48. Farías, R., Sepúlveda, A. & Chamorro, R. Impact of shift work on the eating pattern, physical activity and daytime sleepiness among Chilean healthcare workers. Saf. Health Work 11, 367–371 (2020).

49. Samhat, Z., Attieh, R. & Sacre, Y. Relationship between night shift work, eating habits and BMI among nurses in Lebanon. BMC Nurs. 19, 25 (2020).

50. Pendergast, J. S. et al. High-fat diet acutely affects circadian organisation and eating behavior. Eur. J. Neurosci. 37, 1350–1356 (2013).

51. Arble, D. M., Bass, J., Laposky, A. D., Vitaterna, M. H. & Turek, F. W. Circadian timing of food intake contributes to weight gain. Obesity 17, 2100–2102 (2009).

52. Stucchi, P. et al. Circadian feeding drive of metabolic activity in adipose tissue and not hyperphagia triggers overweight in mice: is there a role of the pentose-phosphate pathway? Endocrinology 153, 690–699 (2012).

53. Alfredsson, L. & Olsson, T. Lifestyle and environmental factors in multiple sclerosis. Cold Spring Harb. Perspect. Med. 9, a028944 (2019).

54. Puttonen, S., Viitasalo, K. & Härmä, M. Effect of shiftwork on systemic markers of inflammation. Chronobiol. Int. 28, 528–535 (2011).

55. de Boer, J. et al. Transgenic mice with hematopoietic and lymphoid specific expression of Cre. Eur. J. Immunol. 33, 314–325 (2003).

56. Cheng, H. Y. M. et al. Segregation of expression of mPeriod gene homologs in neurons and glia: possible divergent roles of mPeriod1 and mPeriod2 in the brain. Hum. Mol. Genet. 18, 3110–3124 (2009).

57. Storch, K. F. et al. Intrinsic circadian clock of the mammalian retina: importance for retinal processing of visual information. Cell 130, 730–741 (2007).

58. Muta, Y. et al. Enhanced SREBP2-driven cholesterol biosynthesis by PKCλ/ι deficiency in intestinal epithelial cells promotes aggressive serrated tumorigenesis. Nat. Commun. 14, 8075 (2023).

59. Okada, K. et al. Thioesterase superfamily member 1 suppresses cold thermogenesis by limiting the oxidation of lipid droplet-derived fatty acids in brown adipose tissue. Mol. Metab. 5, 340–351 (2016).

60. Mina, A. I. et al. CalR: a web-based analysis tool for indirect calorimetry experiments. Cell Metab. 28, 656–666 (2018).

61. Babicki, S. et al. Heatmapper: web-enabled heat mapping for all. Nucleic Acids Res. 44, W147–W153 (2016).

62. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

63. LaMarche, N. M. et al. Distinct iNKT cell populations use IFNγ or ER stress-induced IL-10 to control adipose tissue homeostasis. Cell Metab. 32, 243–258 (2020).

64. Baranek, T. et al. High dimensional single-cell analysis reveals iNKT cell developmental trajectories and effector fate decision. Cell Rep. 32, 108116 (2020).

65. Legoux, F. et al. Molecular mechanisms of lineage decisions in metabolite-specific T cells. Nat. Immunol. 20, 1244–1255 (2019).

66. McIntyre, C. L. et al. 2β2 Integrins differentially regulate γδ T cell subset thymic development and peripheral maintenance. Proc. Natl Acad. Sci. USA 117, 22367–22377 (2020).

67. Tan, L. et al. Single-cell transcriptomics identifies the adaptation of Scart1+ Vγ6+ T Cells to skin residency as activated effector cells. Cell Rep. 27, 3657–3671 (2019).

68. Alves de Lima, K. et al. Meningeal γδ T cells regulate anxiety-like behavior via IL-17a signaling in neurons. Nat. Immunol. 21, 1421–1429 (2020).

69. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

70. McInnes, L. et al. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3, 861 (2018).

71. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

72. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

73. Alquicira-Hernandez, J. & Powell, J. E. Nebulosa recovers single-cell gene expression signals by kernel density estimation. Bioinformatics 37, 2485–2487 (2021).

Acknowledgements

The authors acknowledge the work of B. Moran of the flow cytometry facility, in addition to R. Rakhmatullin and all other staff of the animal facilities at our institutions. We thank the past and present members of the laboratories, who have provided invaluable input over the years, and A. Muñoz Zamora, C. Ortega-de San Luis, E. Stewart and J. O’Leary for proofreading and feedback on the manuscript. This work was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (StG_679173 to L.L.), Science Foundation Ireland (SFI) (16/FRL/3865 to L.L.) and the NIH (R01 AI134861 and metabolic core grant S10 OD020100 to L.L.). This work was also supported by the Irish Research Council (grant GOIPG/2019/421 to A.D.)

Author information

Authors and Affiliations

1. School of Biochemistry and Immunology, Trinity Biomedical Science Institute, Trinity College Dublin, Dublin, Ireland

Aaron Douglas, Brenneth Stevens, Harry Kane, Evan Lynch, Emily A. Day, Caroline Sutton, Martin Brennan, Katie O’Brien, Hannah Prendeville, Luke A. J. O’Neill, Kingston H. G. Mills & Lydia Lynch

2. Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal

Miguel Rendas & Henrique Veiga-Fernandes

3. Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

Harry Kane, Britta Kunkemoeller, Ayano C. Kohlgruber, Amanda E. Garza & Lydia Lynch

4. Molecular and Cell Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA

Karl Wessendorf-Rodriguez & Christian M. Metallo

5. Molecular Biology, Princeton University, Princeton, NJ, USA

Lydia Lynch

6. Ludwig Cancer Research Institute, Princeton Branch, Princeton University, Princeton, NJ, USA

Lydia Lynch

Contributions

L.L. provided funding for the study. L.L., L.A.J.O., K.H.G.M., C.M.M. and H.V.-F. supervised the study. A.D. and L.L. conceived and designed the original study. A.D. and L.L. managed the project administration. A.D., B.S. and M.R. performed most of the experiments. E.A.D., C.S., H.P., A.E.G., B.K., A.C.K. and K.W.-R. performed some experiments. M.B., K.O. and E.L. provided technical assistance in some experiments. H.K. and A.D., performed bioinformatic analysis. A.D. analysed and interpreted most of the data. B.S., C.S., E.A.D., B.K. and M.R. analysed some of the data. A.D. and L.L. wrote and edited the manuscript.

Corresponding author

Correspondence to Lydia Lynch.

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Extended data figures and tables

Extended Data Fig. 1 Innate IL-17+ T cells are enriched for molecular-clock genes within tissues.

a, Density plot expression of individual molecular-clock genes across innate T cell subsets. b, Violin plot showing expression of the molecular-clock score of type-17 versus type-1 innate lymphocytes, further divided by tissue-resident or lymphoid. c, Violin plot showing expression of the molecular-clock score of adipose innate lymphocytes. d, Violin plot showing expression of the molecular-clock score of thymic innate lymphocytes (left), heat map of averaged gene expression of molecular-clock genes with hierarchal clustering of data generated from GSE141895 and E-MTAB-7704 (right). e, Violin plot showing expression of the molecular-clock score of lung innate lymphocytes (left), heat map of averaged gene expression of molecular-clock genes with hierarchal clustering of data generated from E-MTAB-8732 (right). f, Violin plot showing expression of the molecular-clock score of meningeal innate lymphocytes (left), heat map of averaged gene expression of molecular-clock genes with hierarchal clustering of data generated from GSE147262 (right). g, Violin plot showing expression of the molecular-clock score of splenic innate lymphocytes (left), heat map of averaged gene expression of molecular-clock genes with hierarchal clustering of data generated from GSE142845 (right). h, Representative gating strategy for adipose tissue T cells.

Extended Data Fig. 2 Lymph-node γδ T cells express IL-17A and IFNγ rhythmically.

a, Diagrammatic representation of experimental set-up. b, Representative flow plots of IL-17A expression at nadir (ZT5), and peak (ZT15). c, IL-17A expression by lymph node (LN) γδ T cells stimulated ex vivo for 3 h with PMA/Ionomycin, d, displaying diurnal rhythm. e, Circadian fluctuation of IL-17A production by LN γδ T cells over 24 h. f, Representative flow plots of IFNγ expression at peak (ZT6), and nadir (ZT18). g,h, IFNγ expression by LN γδ T cells stimulated ex vivo for 3 h with PMA/Ionomycin, displaying diurnal rhythm (h). i, Circadian fluctuation of IFNγ production by LN γδ T cells over 24 h. j, Representative flow plots of NFIL3 expression at nadir (ZT12), and peak (ZT0). k, NFIL3 expression by LN γδ T cells stimulated ex vivo for 3 h with PMA/Ionomycin. l, Circadian fluctuation of NFIL3 expression (blue), overlaid with IL-17A production (red), by LN γδ T cells over 24 h. Data are representative of two independent experiments. White and grey panels represent light and dark periods respectively. c-e,g-i,k,l, Data shown as mean ± SEM, n = 4 mice per group. c,d,g,h,k, Significance was calculated using two-tailed unpaired students t-test, e,i,l, Cosinor analysis, with cosine fitted curves, l, amplitude (Amp) and acrophase (Acro) were extracted from the cosinor model. NS = Non-Significant, * p < 0.05, ** p < 0.01.

Source Data

Extended Data Fig. 3 SR9009 dose-dependently decreases IL-17A and IL-17F expression by lymph-node γδ T cells.

a, Circadian time plot of percentage CD27- and CD27+ γδ T cell subsets in adipose tissue, with b, subset cell numbers of 24 h. c, Lymph-node lymphocyte viability with increasing concentration of SR9009. d, Percentage of lymph-node γδ T cell of total lymphocytes with increasing concentrations of SR9009. e, Representative flow plots of RORγt expression by lymph-node γδ T cell with increasing concentration of SR9009, with f, percentage and g, MFI of RORγt expressed by lymph-node γδ T cells. h, Representative flow plots of IL-17A expression by lymph-node γδ T cell with increasing concentration of SR9009, with i, percentage and j, MFI of IL-17A expressed by lymph-node γδ T cells. k, Percentage and l, MFI of IL-17AF expressed by lymph-node γδ T cells with increasing concentration of SR9009. m, Percentage of IL-17A expressed by lymph-node γδ T cells with increasing concentration of vehicle. n, γδ T cell and o, αβ T cell percentage of total lymphocytes in Arntlfl (grey) and Arntl∆Vav1 (red) mice. p, IL-7R histogram of adipose T cells. q, γδ T cell percentage of total lymphocytes in Arntlfl (grey) and Arntl∆Il7r (red) mice. r, Percentage IL-17A of adipose γδ T cells from Arntlfl (grey) and Arntl∆Vav1 (red) mice. a,b, White and grey panels represent light and dark periods respectively. a,b,n–r, Data shown as mean ± SEM, n = 4–8 mice per group. c,d,f,g,i-o, Data shown as mean ± SEM, c–m, n = 9 technical replicates from 3 mice per group. a,b, Significance was calculated using cosinor analysis, with cosine fitted curves, amplitude (Amp) and acrophase (Acro) were extracted from the cosinor model, c,d,f,g,i-m,p, One-way ANOVA, s,t, Two-tailed unpaired students t-test. NS = Non-Significant, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

Source Data

Extended Data Fig. 4 Whole-body metabolic rate is entrained by light and disrupted by irregular feeding.

a, Time plot of metabolic rate parameter (oxygen consumption) of mice on a regular light cycle (grey; lights on 7am – 7pm), versus mice on an inverted light cycle (red; lights on 7pm – 7am) after 3 weeks on these light cycles. b, Time plot of metabolic rate parameter (oxygen consumption) of mice on a regular feeding cycle (grey; feeding 7pm – 7am), versus mice on a reverse feeding cycle (blue; feeding 7am – 7pm) after 3 weeks on these feeding regimens. c, Time plot of metabolic rate parameter (oxygen consumption) of mice on an ad libitum (grey), versus mice on a 50% CR feeding regimen (orange) after 1 week on these feeding regimens. d, Time plot of energy expenditure of mice on an ad libitum (grey), versus mice on a 50% CR feeding regimen (orange) after 1 week on these feeding regimens. e, Per cent feeding during day or night of SFD and HFD-fed mice. f, Representative histograms (left), of adipose γδ T cells from SFD (grey) or HFD (red) fed mice with circadian time plots of RORγt MFI (right). g, Diagrammatic representation of experimental set-up for long-term HFD feeding regimen experiments. h, Time plot of weight gain over a 16-week HFD feeding regime. White and grey panels represent light and dark periods respectively. Data are representative of a–d three independent experiments, or e,f, two independent experiments. a–h, data shown as mean ± SEM, n = 4–9 mice per group. f, Significance was calculated using cosinor analysis, with cosine fitted curves, amplitude (Amp) and acrophase (Acro) were extracted from the cosinor model, e, Two-tailed unpaired students t-test, h, two-way repeated measures ANOVA followed by Bonferroni’s multiple comparisons test. NS, Non-Significant, ** p < 0.01, *** p < 0.001.

Source Data

Extended Data Fig. 5 A three-week HFD feeding regime increases the onset and severity of EAE.

a, EAE scores of SFD-fed (grey), and b, HFD-fed (red) mice. c, Disease progression measured by rate of change (disease onset to disease peak) between feeding regimes. d, Time to clinical end-point post EAE induction between feeding regimes. Data are representative of two independent experiments. a–d, data shown as mean ± SEM, n = 6 mice per group. Significance was calculated using b, two-way repeated measures ANOVA followed by Bonferroni’s multiple comparisons test, c, Two-tailed unpaired students t-test, d, Simple survival analysis with Gehan-Breslow-Wilcoxon test. * p < 0.05, ** p < 0.01.

Source Data

Extended Data Fig. 6 γδ T cells are the main source of adipose tissue IL-17A, and maintain adipose tissue DNL.

a, Pie chart depicting adipose lymphocytes contributing to total IL-17A expression, γδ T cells (red), non-T lymphocytes (dark grey) and TH17 cells (light grey). b, Correlation analysis of BAT IL-17RC protein levels with ACLY abundance from OPABAT. c, Circadian time plots showing relative expression of the DNL gene Scd1 from BAT of wild-type (grey) and Tcrδ−/− (red) mice. c, White and grey panels represent light and dark periods respectively. c, Data are representative of two independent experiments. c, data shown as mean ± SEM, n = 4–14 mice per group. b, Significance was calculated using a simple linear regression, c, cosinor analysis, with cosine fitted curves, amplitude (Amp) and acrophase (Acro) were extracted from the cosinor model. ** p < 0.01.

Source Data

Extended Data Fig. 7 BAT DNL proteins do not correlate with the expression of other cytokine receptors.

a, Correlation heat map of IL-17RC protein levels with adipocyte lipolysis-associated protein abundance from OPABAT. b, Correlation heat map of IL-10RB protein levels with DNL protein abundance from OPABAT. c, Correlation heat map of IL-1R1 protein levels with DNL protein abundance from OPABAT. d, Correlation heat map of IL-6ST protein levels with DNL protein abundance from OPABAT. e, Correlation heat map of IL-17RE protein levels with DNL protein abundance from OPABAT. f, Correlation heat map of IL-4R protein levels with DNL protein abundance from OPABAT. a–f, Significance was calculated using simple linear regression. * p < 0.05, ** p < 0.01.

Source Data

Extended Data Fig. 8 IL-17A regulates DNL and lipolysis gene expression.

a, Relative expression of DNL genes Mlxipl, Fasn, Acaca, Scd1 and Elovl6 in the BAT of Arntlfl mice compared to BAT of Arntl∆Il7r mice at ZT6. b, Relative expression of DNL genes Mlxipl, Fasn, Acaca, Scd1 and Elovl6 in the BAT of wild-type mice compared to BAT of Il17a/f−/− mice at ZT6. c, Relative expression of lipolysis/thermogenesis genes Ucp1, Cidea, Ppargc1a, Atgl and Hsl in the BAT of wild-type mice compared to BAT of Il17a/f−/− mice at ZT6. d, Relative expression of lipolysis/thermogenesis genes Ucp1, Cidea, Ppargc1a, Atgl and Hsl in the BAT of Arntlfl mice compared to BAT of Arntl∆Il7r mice at ZT18. e–h, Relative expression of Mlxipl (e), Acly (f), Fasn (g) and Scd1 (h) after administration of IL-17A (1 µg) and IL-17F (1 µg) to wild-type and Il17a/f−/− mice compared to PBS administered controls. i, Expression of ACACA and SCD from skip biopsies of patients with psoriasis who were treated with placebo and brodalumab. a–i, Data shown as mean ± SEM, n = 2–5 mice per group, or n = 27–33 patients per group i. a–d,i, Significance was calculated using Two-tailed unpaired student’s t-test. * p < 0.05, ** p < 0.01, *** p < 0.001.

Source Data

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Douglas, A., Stevens, B., Rendas, M. et al. Rhythmic IL-17 production by γδ T cells maintains adipose de novo lipogenesis. Nature 636, 206–214 (2024). https://doi.org/10.1038/s41586-024-08131-3

- Received: 26 May 2022

- Accepted: 27 September 2024

- Published: 30 October 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08131-3

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RAS-mutant leukaemia stem cells drive clinical resistance to venetoclax

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Maria Sirenko,

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Abstract

Cancer driver mutations often show distinct temporal acquisition patterns, but the biological basis for this, if any, remains unknown. RAS mutations occur invariably late in the course of acute myeloid leukaemia, upon progression or relapsed/refractory disease1,2,3,4,5,6. Here, by using human leukaemogenesis models, we first show that RAS mutations are obligatory late events that need to succeed earlier cooperating mutations. We provide the mechanistic explanation for this in a requirement for mutant RAS to specifically transform committed progenitors of the myelomonocytic lineage (granulocyte–monocyte progenitors) harbouring previously acquired driver mutations, showing that advanced leukaemic clones can originate from a different cell type in the haematopoietic hierarchy than ancestral clones. Furthermore, we demonstrate that RAS-mutant leukaemia stem cells (LSCs) give rise to monocytic disease, as observed frequently in patients with poor responses to treatment with the BCL2 inhibitor venetoclax. We show that this is because RAS-mutant LSCs, in contrast to RAS-wild-type LSCs, have altered BCL2 family gene expression and are resistant to venetoclax, driving clinical resistance and relapse with monocytic features. Our findings demonstrate that a specific genetic driver shapes the non-genetic cellular hierarchy of acute myeloid leukaemia by imposing a specific LSC target cell restriction and critically affects therapeutic outcomes in patients.

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Main

The co-occurrence and relative timing of acquisition of driver mutations in cancers follow non-random patterns, but the biological basis for this and its effect on clinical outcomes remain unexplained7,8. RAS is among the most frequently mutated oncogenes across all cancers[9]. In contrast to their occurrence as truncal initiating mutations during epithelial carcinogenesis, RAS mutations (RASmut, most frequently in the NRAS or KRAS genes) are always acquired late in the course of acute myeloid leukaemia (AML), presenting as subclonal mutations upon refractory or relapsed disease or upon progression of an antecedent myelodysplastic syndrome1,2,3,4,5,6.

AML is an aggressive blood malignancy that can be driven by a variety of genetic lesions and originates from haematopoietic stem and progenitor cells (HSPCs). Although leukaemic cells are unable to differentiate normally, leukaemias retain some semblance of normal hematopoietic development, forming cellular hierarchies with leukaemia stem cells (LSCs) residing on the apex10,11. Different hierarchies result in leukaemias with a predominant immature stem cell phenotype or more mature myeloid features12,13,14. Associations between specific hierarchical organizations and AML genetics, as well as drug responses and disease relapse, have been described[14] and stem cell signatures have been associated with poor outcomes after therapy[15]. However, how these hierarchies are determined and the mechanisms by which they affect clinical outcomes remain obscure.

The BH3-mimetic drug venetoclax (VEN) is a recently US Food and Drug Administration (FDA)-approved therapeutic agent for the treatment of AML. VEN is a selective inhibitor of BCL2 and has now become the standard of care, in combination therapies, for patients with AML who are older or unfit to receive intensive chemotherapy16,17,18. Nevertheless, 20–30% of patients are refractory to VEN-based combination regimens, and more than 40% of those responding ultimately relapse17,19,20,21. An association between poor outcomes to VEN and AML with mature myelomonocytic immunophenotypic features was reported and attributed to an intrinsic resistance to VEN conferred by the monocytic maturation state22,23,24. However, this association was not found consistently in other studies25,26. Associations between VEN resistance and RAS pathway mutations have also been reported24,25.

Here, through synthetic leukaemogenesis studies using CRISPR–Cas9-mediated gene editing in human induced pluripotent stem (iPS) cells and primary HSPCs, we show that N/KRAS mutations occur late in AML pathogenesis because mutant RAS transforms granulocyte–monocyte progenitor cells (GMPs) with pre-existing oncogenic mutations. Through single-cell transcriptomics and multi-omics in iPS-cell-derived xenografts and primary AML cells, we show that RAS-mutant (MT) GMP-type LSCs preferentially generate monocytic leukaemic cells and are resistant to VEN, and that it is this resistance of the RAS-MT LSCs, rather than the differentiation state of the LSCs or that of their progeny, that drives clinical relapse and resistance to VEN-containing therapies. This is a new paradigm whereby a specific oncogenic driver targets a specific cell type for transformation into an LSC with altered sensitivity to targeted therapies, with profound effect on therapeutic outcomes.

RASmut is insufficient for leukaemogenesis

To interrogate the contribution of the prototypical AML RAS pathway mutation, NRASG12D, to leukaemogenesis, we generated a set of isogenic iPS cell lines with single, double and triple mutations through CRISPR–Cas9-mediated gene editing (Extended Data Fig. 1a). The NRAS mutation (R) increased cell proliferation and cycling of iPS-cell-derived HSPCs (iPS-HSPCs) in vitro (Extended Data Fig. 1b–g). Conversely, ASXL1 (A) and SRSF2P95L (S) mutations impaired differentiation without affecting proliferation. The triple-mutant SRSF2P95L/ASXL1/NRASG12D (SAR) iPS-HSPCs were consistently engraftable in transplantation assays at levels greater than 1%, generating immature blast-like myeloid cells, consistent with an AML, as previously shown[27] (Extended Data Fig. 1h–j and Supplementary Table 1). By contrast, single NRASG12D iPS-HSPCs showed non-detectable or infrequent minimal (0.1%) engraftment and double mutant iPS-HSPCs had engraftment levels of generally less than 1% (Extended Data Fig. 1h–j). Other engineered iPS cell lines with NRASG12D mutation and other co-mutations also showed absent or occasional minimal (0.1%) engraftment (Extended Data Fig. 2a–e). As haematopoietic cells derived from normal iPS cells are non-transplantable, this lack of engraftment is reflective of a non-transformed state and absence of leukaemia-initiating potential[28].

Collectively, these results indicate that the NRASG12D mutation alone cannot induce leukaemic features, and that it can only do so in cooperation with certain, but not other, co-mutations.

RASmut are obligatory late events in AML

To next test the effects of mutational order in the triple-mutant SAR model, we developed models to temporally control the acquisition of the NRASG12D mutation, before or after the SRSF2 and ASXL1 mutations in iPS-HSPCs (R + SA or SA + R, respectively; Fig. 1a and Extended Data Fig. 2f–h). In all cases, NRASG12D did not generate engraftable cells as the initiating mutation, but only when acquired in iPS-HSPCs with SRSF2 and ASXL1 mutations (Fig. 1b and Extended Data Fig. 2i,j). We corroborated these findings in primary human HSPCs, using a lentiviral strategy to induce NRASG12D with doxycycline (Dox) either before or after transduction with mutant SRSF2P95L and ASXL1del1900–1922 in cord blood (CB) CD34+ cells, followed by transplantation (R + SA or SA + R cohorts, respectively; Fig. 1c and Extended Data Fig. 3a). The R + SA group, as well as control groups SA (without NRASG12D) and mCherry/GFP, showed limited or absent engraftment, as expected after prolonged in vitro culture (Fig. 1d). By contrast, all mice transplanted with SA + R CB cells had robust human engraftment and succumbed to a lethal myeloid leukaemia (Fig. 1d–h, Extended Data Fig. 3b–d and Supplementary Table 1). This leukaemia was dependent on the RASmut, as SA + R transplanted mice in which NRASG12D expression was silenced by means of Dox withdrawal remained healthy and without leukaemic human cells in their bone marrow (Extended Data Fig. 3e,f).

Fig. 1: RASmut are obligatory late events in AML and need to be acquired after specific cooperating mutations.

a, iPS cells with heterozygous SRSF2 and ASXL1 (top) or NRAS (bottom) mutations were differentiated into HSPCs and transduced with lentiviral vectors encoding NRASG12D (top) or SRSF2P95L and either a truncated dominant-negative ASXL1 transgene (ASXL1del1900–1922) or a gRNA targeting exon 12 of ASXL1 (bottom) and transplanted intravenously into NSGS mice. b, Human engraftment in the bone marrow of mice 13–15 weeks post-transplantation. Each data point represents one mouse: n = 3 (R + mCherry), 4 (R + SAdel), 8 (R + SAgRNA) and 6 (SA + R) from two experiments. Mean and s.e.m. are shown. P values were calculated with a two-tailed unpaired t-test. c, CB CD34+ cells were transduced with the lentiviral vectors shown at the indicated time intervals of in vitro culture, with Dox added to the culture at the indicated time point to induce NRASG12D expression. The cells were prestimulated for 4 days before and were injected into NSGS mice 7 days after the first transduction. d, Human engraftment in the bone marrow of NSGS mice transplanted with CB CD34+ cells shown in c. P values were calculated with one-way ANOVA; n = 2 (mCherry/GFP), 3 (SA), 6 (R + SA) and 8 (SA + R) mice. Mean and s.d. are shown. e, Survival of mice from c; n = 5 (SA + R) and 3 (R + SA) mice. f, Bone marrow and spleen images from a mouse transplanted with CB SA + R cells representative of at least three experiments. Left, haematoxylin and eosin (H&E) staining. Middle and right, immunohistochemistry for hCD45 (pan-haematopoietic) and hCD33 (myeloid) markers. g, Wright–Giemsa-stained human cells retrieved from the bone marrow of a mouse transplanted with SA + R CB cells. Image representative of at least three independent experiments. h, Representative images of spleens from mice transplanted as shown in c. BM, bone marrow; UT, untransplanted. Scale bars, 500 μm (f, lower magnification panels), 100 μm (f, higher magnification panels), 50 μm (g).

Source data

These experiments establish that RASmut can promote leukaemogenesis only as a late event following previously acquired cooperating mutations.

RAS-MT AML LSCs originate from GMPs

To understand the requirement for NRASG12D to be acquired after the other mutations, we examined the immunophenotypic HSPC populations present in CB HSPCs transduced with R + SA, SA + R, all three mutant genes simultaneously (SAR), SA alone, R alone or with a control empty (mCherry/GFP) vector (Fig. 2a). R, R + SA and SAR cells (that is, all groups transduced with R early, hereafter referred to as ‘R-early’) showed marked reduction of the progenitors committed to the myelomonocytic lineage (GMPs) with concomitant increase of the more immature common myeloid progenitor (CMP) population and megakaryocyte-erythrocyte progenitor (MEP) population (Fig. 2a). By contrast, SA + R cells (R-late) retained the GMP population (Fig. 2a). Single-cell RNA sequencing (scRNA-seq) analyses confirmed decrease of GMPs, with concomitant increase in CMPs and erythroid and megakaryocytic progenitors in all R-early groups (Fig. 2b,c and Supplementary Fig. 1). By contrast, the GMP population was preserved in the SA and SA + R groups (Fig. 2c). These results, which corroborate the immunophenotypic analyses, indicate that acquisition of NRASG12D mutation before, or at the same time as, the other two mutations causes arrest at the CMP stage with loss of the GMP population and failure to induce leukaemia.

Fig. 2: RAS-MT AML LSCs originate from GMPs harbouring pre-existing mutations.

a, Experimental scheme. CB CD34+ cells were transduced with lentiviral vectors as indicated and analysed by flow cytometry (right panels) on the day of transplantation, that is, 7 days after the first transduction (day 8). b, Uniform manifold approximation and projection (UMAP) representation of integrated single-cell transcriptome data from the six groups of cells shown in a, on day 8. c, Stacked barplots showing fraction of cells in each cluster. With the exception of clusters marked as not significant (NS), all other cluster sizes were significantly different from the respective cluster size in the Ctrl (mCherry/GFP) group (logistic regression). d, iPS-HSPCs with NRAS (R) or SRSF2 and ASXL1 mutations (SA) were transduced with lentiviral vectors as indicated. Right panels, principal component analysis of RNA-seq and ATAC-seq data from sorted CD34+CD45+ cells; n = 3 independent experiments for all groups. e, Normalized enrichment scores (NES) and adjusted P values derived from gene set enrichment analysis (GSEA) for gene sets corresponding to the human AML developmental hierarchy from ref. [14] using gene lists ranked by the −log_10(Padjusted (padj)) × log_2FC from the indicated differential expression comparisons. f, Heatmap showing Pearson correlation values for the ATAC-seq peak normalized read counts in the iPS-HSPC dataset from d and overlapping peaks in primary normal haematopoietic cell subpopulations from ref. [29]. g, Experimental scheme (left) and survival (right) of animals transplanted with CMPs and GMPs transduced with SA and sorted prior to induction of R with Dox. n = 1 for each CMP group (±Dox); n = 3 for each GMP group (±Dox). h, Experimental scheme (left) and survival (right) of mice transplanted with CMPs and GMPs sorted prior to SAR transduction. n = 4 for all groups. i, FACS-sorted CMPs and GMPs from g. CLP, common lymphoid progenitor; DC P, dendritic cell progenitor; EBM, eosinophil/basophil/mast cell; Ery/Meg P, erythrocyte/megakaryocyte progenitor; Ery P, erythrocyte progenitor; LMPP, lymphoid-primed MPP; Meg P, megakaryocyte progenitor; Mono, monocyte; Mono P, monocyte progenitor; ProMono, promonocyte.

Source data

To further compare cellular states between HSPCs acquiring NRASG12D as an early versus late mutation, we performed bulk RNA-seq and assay for transposase-accessible chromatin using sequencing (ATAC-seq) analyses in SA + R versus R + SA iPS-HSPCs (Figs. 1a and 2d). R-early cells (R + SA and R + control (Ctrl); Fig. 2d) were very distinct from those with initial SA mutations in both their transcriptome and chromatin landscape, regardless of subsequent mutational acquisition (Fig. 2d). Acquisition of SA mutations following R resulted in only modest change of cell state, consistent with a maturation arrest by early R in our functional experiments (Fig. 2d and Extended Data Fig. 4a,b; left panels). By contrast, acquisition of R after SA resulted in profound changes (Fig. 2d and Extended Data Fig. 4a,b; right panels). SA + R cells showed enrichment for gene expression signatures of primary human AML (Extended Data Fig. 4c), as well as for GMP-like and leukaemia stem and progenitor cells (LSPC)-cycle signatures derived from primary human AML, which were, conversely, depleted in R-early cells[14] (Fig. 2e and Extended Data Fig. 4d). SA + R cells were also more similar to GMPs and monocytes in their accessible chromatin landscape and distal cis-regulatory elements, whereas R-early cells showed higher similarity to primitive cells—haematopoietic stem cell (HSC) or multipotent progenitor (MPP) (HSC/MPP) and CMP—as well as progenitors of the megakaryocytic and erythroid lineage[29] (Fig. 2f and Extended Data Fig. 4e,f).

As R + SA cells are immunophenotypically and transcriptionally depleted of GMPs and do not cause leukaemia in vivo, whereas SA + R HSPCs preserve the GMP population and cause leukaemia, we next tested whether the SA + R GMPs are the leukaemia-initiating cells. Indeed, fluorescence-activated cell sorting (FACS) of SA + R HSPCs showed that only GMPs, and not CMPs, had leukaemia-initiating activity despite expressing comparable levels of all three transgenes (Extended Data Fig. 5a–c). We then reasoned that the typical acquisition pattern of RASmut late in the course of human AML may be accounted for by their acquisition by a GMP, produced by HSC/MPPs harbouring ancestral mutations. Indeed, GMPs, but not CMPs, in which R was induced after sorting, with SA transduction either before (SA + R) or at the same time as R (SAR), could initiate a lethal myeloid leukaemia (Fig. 2g–i, Extended Data Fig. 5d–l and Supplementary Table 1). This leukaemia was serially transplantable, indicating that SA + R endows GMPs with self-renewal potential (Extended Data Fig. 5g,h). CMPs and GMPs without R (−Dox) exhibited minimal or no engraftment, as expected from non-transformed committed progenitors (Extended Data Fig. 5i,j). These results indicate that, not only are RAS-MT GMPs able to initiate and maintain leukaemia in vivo, but also that GMPs derived from ancestral AML clones with previously acquired cooperating driver mutations can be the target cell of transformation by RASmut.

We next sought to understand the signalling and genomic underpinnings of the selective transformation of GMPs by RASmut. Mutant RAS activated ERK at comparable or lower levels in sorted GMPs than in CMPs or HSC/MPPs transduced with SA + R and expressing similar levels of all transgenes (Extended Data Fig. 6a,b). We next defined ‘RAS-late genes’ as the genes upregulated in iPS-HSPCs selectively in SA + R (Extended Data Fig. 6c and Supplementary Table 2) and ‘RAS-late peaks’ as the peaks gaining accessibility selectively in SA + R cells (Extended Data Fig. 6d and Supplementary Table 3). The latter were enriched for motifs of transcription factors classically associated with MAPK signalling, such as AP1, CREB and ETS family (Extended Data Fig. 6e). ‘RAS-late genes’ were enriched for pathways related primarily to inflammatory responses (Extended Data Fig. 6f). These were also enriched in genes differentially expressed between SA + R and Ctrl CB cells in the GMP cluster in the scRNA-seq analyses (Extended Data Fig. 6g).

Notably, of the genes classically associated with RAS signalling activation (‘HALLMARK KRAS signalling’), those upregulated by RAS as late mutation were distinct, with almost no overlap with the genes upregulated by RAS as an early mutation (Extended Data Fig. 6h and Supplementary Table 4). The 65 genes selectively activated by late but not by early RAS were more accessible in the SA + Ctrl and SA + R groups—as shown earlier, these represent GMPs (Fig. 2f and Extended Data Fig. 4f)—and less accessible in the R + Ctrl and R + SA groups that resemble CMPs (Extended Data Fig. 6i,j).

These results support a scenario whereby mutant RAS leads to comparable ERK signalling activation in GMPs as in more primitive HSPCs, but with different transcriptional output, upregulating a distinct set of genes that are primed at the chromatin level in the GMP state.

RAS-MT LSCs generate monocytic leukaemia

Recent studies have hinted at an association between RASmut and a mature phenotype of cells of a patient with AML3,14. We thus hypothesized that RAS-MT GMP-like LSCs produce leukaemic blasts with mature phenotype. To test this, we interrogated the leukaemic cell output of RAS-MT LSCs, compared with that of RAS-WT LSCs in the same patient. First, we generated xenografts from a pair of AML-iPS cell lines—AML-4.10 and AML-4.24, with or without a KRASG12D mutation, respectively—derived from the same patient with AML28,30 (Fig. 3a). RAS-MT cells contained a higher fraction of proliferating cells, a smaller proportion of immature CD34+ cells and a larger proportion of cells with monocytic markers, such as CD14, CD64 (FCGR1A) and CLEC7A, than the RAS-WT clone (Fig. 3b–d and Extended Data Fig. 7a). Conversely, a smaller fraction of RAS-MT cells expressed neutrophil markers, such as CD13 (ANPEP), CD15 (FUT4) and ELANE (Fig. 3d).

Fig. 3: RAS-MT AML LSCs produce leukaemic cells with monocytic features.

a, Two iPS cell lines derived from a patient with AML with a clonal t(1;7;14) translocation and a subclonal KRASG12D mutation, one capturing the RASWT major clone (AML-4.24) and one the KRASG12D subclone (AML-4.10) of the patient AML were differentiated to HSPCs, transplanted into NSGS mice, allowed to generate lethal leukaemias, collected, sorted and subjected to scRNA-seq analysis. b, UMAP representation of single-cell transcriptome data. The dashed line delineates the monocytic metacluster. c, Cell density across the UMAP coordinates from b. Cells coloured by sample (top panel) or phase of the cell cycle (bottom panel). d, Percentage of cells expressing the indicated genes (normalized counts > 0.5) or contained in the monocytic metacluster (shown in b). e, Schematic of the GoT experiment. f, Number of cells that could be genotyped as NRAS-WT (423 cells) or MT (576 cells) by GoT. g,h, Fraction (g) and absolute number (h) of cells belonging to each cell type assigned from transcriptome data in the NRAS-WT and NRAS-MT cells (NA, not assigned to a NRAS genotype). Cells belonging to the NRAS-MT clone contain a higher fraction of monocytic cells (Fisher’s exact test P value = 0.00028, odds ratio = 3.255) and lower fraction of immature HSC/MPP-like cells (Fisher’s exact test P value = 3.044 × 10−11, odds ratio = 0.3345) than NRAS-WT cells. i, Expression of a monocytic priming gene module (IRF7/IRF8) from ref. [32] in NRAS-WT and MT cells. The whiskers denote the 1.5× interquartile range (IQR). The lower and upper hinges of the boxes represent the first and third quartiles, respectively. The middle line represents the median. Points represent values outside the 1.5× IQR. The P value was calculated with a two-sided Wilcoxon test.

We next examined data obtained from a patient with AML with a subclonal NRASG12D mutation with the Genotyping of Transcriptomes (GoT) method[31] (Fig. 3e–g and Extended Data Fig. 7b). Cells belonging to the NRAS-MT clone contained a higher fraction of monocytic cells and lower fraction of immature HSC/MPP-like cells and had significantly higher expression of a monocytic priming gene module[32] than NRAS-WT cells (Fig. 3g–i).

These results establish that RAS-MT AML subclones generate more mature monocytic cells than more ancestral AML clones in the same patient.

In addition, in a cohort of 599 patients with AML[33], patients with RASmut had a significantly higher fraction of CD14+ monocytic blasts and higher frequency of AML with myelomonocytic (FAB M4) or monoblastic/monocytic (FAB M5) morphology, compared with those without RASmut (Extended Data Fig. 7c,d). Furthermore, lentiviral expression of either NRASG12D or KRASG12D in CD34+CD45+ cells from four patient-derived AML-iPS cell lines of different genetic groups[30] induced myeloid maturation in vitro (Extended Data Fig. 7e). These results corroborate the association between RASmut and monocytic differentiation in diverse AML genotypes at the population level.

In contrast to RASmut, SRSF2 and ASXL1 mutations or their co-mutation (SA) showed no association with monocytic disease in patients with AML (Extended Data Fig. 7f,g). SRSF2 and ASXL1 co-mutation is a characteristic feature of chronic myelomonocytic leukaemia (CMML)—a myeloproliferative neoplasm characterized by monocytosis. In an international cohort of 399 patients with CMML[34], we found N/KRAS mutations to frequently co-occur with SA mutations (Extended Data Fig. 7h). To test a potential contribution of the SRSF2 and ASXL1 mutations to the N/KRAS mutation-driven monocytic phenotype, sorted CB GMPs with SRSF2 and ASXL1 (SA) or an IDH1R132H transgene were transduced with lentiviral vectors expressing NRASG12D, KRASG12D or FLT3-ITD (Extended Data Fig. 7i,j). Both RASmut, but not the FLT3-ITD mutation, potently drove monocytic differentiation of GMPs regardless of the initiating mutations (SRSF2 + ASLX1 or IDH1) (Extended Data Fig. 7k). SA or IDH1 mutations alone did not cause monocytic differentiation (Extended Data Fig. 7k). Finally, examination of our CB scRNA-seq data (Fig. 2b) showed increase in expression of several monocytic lineage genes and concomitant decrease in expression of granulocytic genes in the GMP cluster in all groups transduced with R, but not in the SA alone group (Extended Data Fig. 7l).

These results collectively show that RAS pathway mutations and not the SRSF2 or ASXL1 mutations drive monocytic differentiation of RAS-MT GMPs.

RAS-MT AML LSCs are resistant to VEN

In view of our findings so far, establishing that RASmut cause monocytic AML, we sought to re-evaluate previously reported associations between poor responses to VEN-containing regimens and monocytic disease and, independently, RAS pathway mutations22,23,24,25.

To this end, we reanalysed data from a cohort of older or unfit patients with newly diagnosed AML treated on a prospective clinical trial with VEN and decitabine (DEC)[19]. A total of 118 patients were included, of whom 31 could be classified as having monocytic AML, determined by flow cytometric assessment, esterase positivity or FAB M4 or M5 morphology[35] (Supplementary Table 5). Overall survival (OS) and all other outcomes were comparable between the monocytic and non-monocytic groups (Fig. 4a,b and Supplementary Table 6). By contrast, comparing outcomes in patients with or without N/KRAS mutations, including or excluding TP53-mutated cases, known to be associated with poor prognosis, showed that patients with TP53-WT AML harbouring mutations in N/KRAS treated with VEN and DEC had significantly increased risk of relapse, shorter duration of response (DOR) and reduced OS, compared with those without N/KRAS mutations (Fig. 4c,d, Extended Data Fig. 8a,b and Supplementary Tables 5 and 6). These analyses do not support the monocytic stage as a determinant or predictor of clinical response to VEN + hypomethylating agent regimens, but establish the presence of N/KRAS mutations as a predictor of inferior responses.

Fig. 4: RASMT LSCs drive clinical resistance to VEN.

a–d, Outcome data from 118 older or unfit patients with newly diagnosed AML treated in a prospective trial with 10-day DEC and VEN (DEC10-VEN). DOR (a) and OS (b) in patients with monocytic versus non-monocytic AML. DOR (c) and OS (d) in patients with AML with TP53-WT with versus without N/KRAS mutations. log-rank test, two-tailed unadjusted P values. e, Two iPS cell lines derived from a patient with AML, one capturing the RAS-WT major clone (AML-4.24) and one the KRASG12D subclone (AML-4.10), were differentiated in vitro to LSCs and to monocytic blasts. f, HSPCs and monocytes derived from normal iPS cells and from the indicated AML-iPS cell lines were treated with VEN and viability was measured by CellTiter-Glo. Viability compared with dimethylsulfoxide (DMSO)-treated is shown. HSPCs, n = 3 normal, 5 AML-4.24, 7 AML-4.10 and 2 AML-9.9; monocytes, n = 3 normal, 4 AML-4.24, 2 AML-4.10 and 1 AML-9.9 independent experiments; mean and s.d. are shown. P values were calculated with a two-tailed unpaired t-test. g, CB CD34+ cells transduced with SA + R, as shown in Fig. 2a were transplanted into NSGS mice. The mice were treated with VEN (100 mg kg−1 day−1 by oral gavage) or vehicle, starting 1 week post-transplant, daily, for 3 weeks. h, Survival of mice from the experiment shown in g; n = 3 (VEN) and 4 (Vehicle). P value was calculated with a log-rank (Mantel–Cox) test. i, NRASG12D expression in hCD45+ cells from the bone marrow of a moribund mouse treated with VEN. CI, confidence interval; CR, complete remission; CRi, CR with incomplete haematologic recovery; HR, hazard ratio; NR, not reached.

Source data

To test the effects of RAS mutational status, independently from the effects of monocytic differentiation state, to VEN responses in a tightly controlled experimental setting, we used directed in vitro differentiation of the two patient-derived iPS cell lines AML-4.24 and AML-4.10 into immature CD34+ LSCs and monocytes (Fig. 4e and Extended Data Fig. 8c–f). A normal iPS cell line (N-2.12)[36] and an independent RAS-WT AML-iPS cell line derived from a different patient (AML-9.9)[30] were also used as further controls. Monocytes derived from all AML and normal iPS cells were resistant to VEN (Fig. 4f, bottom panel). By contrast, CD34+ LSCs and HSPCs from all RAS-WT lines—AML and normal—were sensitive to VEN (Fig. 4f, top panel). However, notably, LSCs from the AML-4.10 KRAS-MT line were consistently VEN-resistant (Fig. 4f, top panel).

Resistance to BCL2 inhibition at the cellular level can be mediated by downregulation of BCL2 with concomitant upregulation of other anti-apoptotic BCL2 family proteins (such as MCL1, BCL2L1/BCL-xL and BCL2A1) and/or downregulation of pro-apoptotic members (such as BAX)16,24,26,37,38,39. Monocytic blasts of the iPS-cell-derived xenografts expressed very low amounts of BCL2, while they highly expressed MCL1, regardless of genotype (Extended Data Fig. 9a, bottom panels). By contrast, LSCs expressed high amounts of BCL2 and much lower amounts of MCL1 than monocytic cells (Extended Data Fig. 9a–e). Notably, the RAS-MT LSCs had reduced BCL2 expression compared with the LSCs of the RAS-WT clone, and a trend towards higher MCL1 and BCL2L1 and lower BAX expression (Extended Data Fig. 9a, top panels).

In the GoT data, monocytic cells also expressed negligible BCL2 and high MCL1 amounts, regardless of NRAS genotype, mirroring our findings in the iPS-cell-derived xenografts (Extended Data Fig. 9f). Immature cells—MPP-like and GMP-like—expressed higher BCL2 (average log_2 fold change (FC) 0.075) and lower MCL1 (average log_2 FC −0.54) than monocytic leukaemia cells (Extended Data Fig. 9f), again corroborating our data in the iPS cell model. Again, NRAS-MT immature MPP-like cells had markedly lower expression of BCL2 than their NRAS-WT counterparts (average log_2 FC −0.27) (Extended Data Fig. 9f). As in the iPS-cell-derived cells, BAX expression was also lower in the NRAS-MT, as opposed to the NRAS-WT, MPPs (average log_2 FC −0.14). Finally, expression of BCL2 was decreased in SA + R iPS-HSPCs, which also showed concomitant increase in MCL1 and BCL2L1 and decrease in BAX expression (Extended Data Fig. 9g).

These data collectively demonstrate that RAS-MT LSCs are resistant to VEN. This VEN resistance of RAS-MT LSCs could be accounted for by altered BCL2 family expression, and drive the clinical resistance to VEN in patients with AML with RASmut (Fig. 4c,d). To further test whether RAS-MT LSCs are VEN-resistant in vivo, we treated mice transplanted with CB SA + R cells with VEN for 3 weeks (Fig. 4g). All mice succumbed to lethal leukaemia, which was accelerated in VEN-treated, compared with control vehicle-treated, animals (Fig. 4h). Almost all leukaemic cells retrieved from these mice expressed the NRASG12D transgene (Fig. 4i).

GMP state does not drive VEN resistance

As RAS-MT LSCs originate from GMPs and retain genomic features (transcriptome and chromatin accessibility) of GMPs, we next asked whether their resistance to VEN can be attributed to the RASmut and its signalling and transcriptional consequences or to the gene regulatory and metabolic wiring of the GMP cell state.

To test this, we performed bulk and scRNA-seq analyses in sorted CB SA + R CMPs and GMPs expressing equivalent amounts of the transgenes (Fig. 5a and Extended Data Fig. 5a, left panels). Expression of BCL2, MCL1, BCL2L1 and BAX was comparable between CMPs and GMPs (Fig. 5b and Extended Data Fig. 10a–c). Consistent with this, sorted SA + R CMPs and GMPs had comparable sensitivity to VEN (Fig. 5c). To then ask whether expression of pro- and anti-apoptotic genes is modulated by the RASmut in GMPs, we identified NRASG12D positive cells, by means of ΔLNGFR expression (most of which also expressed SA) and WT cells not expressing any of the transgene-linked reporters (Fig. 5d and Extended Data Fig. 10d). MCL1 was increased in NRASG12D versus WT GMPs (Fig. 5e).

Fig. 5: Resistance of RAS-MT GMP-like LSCs to VEN is due to the RASmut and not to the GMP state.

a, Experimental design. b, UMAP representation of integrated single-cell transcriptome data from FACS-sorted SA + R CMPs and GMPs. c, Viability of FACS-sorted CMPs, GMPs and HSC/MPPs, untransduced (UT) or transduced with SA + R, treated with VEN. **P < 0.01, ****P < 0.0001, NS (one-way ANOVA). Mean and s.d. from n = 3 independent experiments are shown. d, Cells expressing ΔLNGFR-NRASG12D (NRASG12D+, red) or none of the transgenes (WT, green) projected in the UMAP from b. e, Expression of MCL1 and BCL2 in NRASG12D+ versus WT cells belonging to the GMP cluster. P values were calculated with a two-sided Wilcoxon test. f, Viability of CD34+ LSCs from the indicated patient-derived AML-iPS cell lines with or without (Control) ectopic lentiviral expression of NRASG12D or KRASG12D, as indicated, treated with VEN and/or RASi. Viability compared with DMSO-treated group is shown. Mean and s.d. from n = 3 or 4 independent experiments is shown. P values were calculated with a two-tailed unpaired t-test. g, Detection of the indicated proteins by western blotting in CD34+ LSCs from the indicated patient-derived AML-iPS cell lines with or without (Control) ectopic lentiviral expression of NRASG12D or KRASG12D, with or without treatment with RASi. Samples were derived from the same experiment and processed in parallel. β-Actin controls were run on different gels as sample processing controls. For source data, see Supplementary Fig. 4. CBF, core binding factor; SF-mutated, splicing factor-mutated; MLLr, MLL-rearranged.

These results collectively indicate that it is the RASmut, rather than the GMP state, that confers VEN resistance to the RAS-MT LSCs. Furthermore, this VEN resistance endowed by RASmut does not seem to be restricted to GMPs, but occurs across HSPC types (Fig. 5c).

Consistent with our previous results (Extended Data Fig. 7l), monocytic lineage genes were upregulated and granulocytic lineage genes downregulated in NRASG12D+, compared with WT, GMPs (Extended Data Fig. 10e). Many genes encoding ribosomal proteins were also downregulated in NRASG12D+ GMPs (Extended Data Fig. 10e). Pathway analysis was suggestive of altered protein synthesis and metabolic processes consistent with metabolic re-wiring of GMPs by RASmut (Extended Data Fig. 10f).

Finally, to further confirm that N/KRAS mutations confer VEN resistance and to test whether the latter is effected by both NRAS and KRAS mutations and generalizable across diverse AML genetic types, we ectopically expressed NRASG12D or KRASG12D in CD34+CD45+ LSCs from patient-derived AML-iPS cell lines of different genetic groups[30]. Mutant NRAS and KRAS significantly decreased VEN sensitivity of all LSCs (Fig. 5f and Extended Data Fig. 10g). This was reversed by treatment with an active state-selective RAS multi inhibitor (RASi)40,41 (Fig. 5f). Furthermore, and consistent with this, expression of both NRASG12D and KRASG12D led to increase in MCL1 and BCL-xL and decrease in BCL2 in all groups (Fig. 5g). The RASi reversed the increase of MCL1 and BCL-xL in all cases, with more variable effects on BCL2 amounts (Fig. 5g).

Discussion

Here we provide evidence that the LSCs sustaining RAS-MT subclones in AML originate from GMPs. We thus demonstrate that the subclonal RAS-mutated AML LSC can emerge from a different and more mature cell type than the cell-of-origin of the major AML clone, which, in most cases, is a primitive HSC/MPP (Extended Data Figs. 10h and 11a).

Our data strongly point to monocytic differentiation and VEN resistance in AML being two independent effects with a common cause, RASmut. In addition, we show here that leukaemic transformation by RASmut is dependent on the GMP cellular milieu and chromatin landscape, whereas VEN resistance is conferred more broadly in all HSPC types by RASmut, pointing to different mechanistic underpinnings of these processes.

Our data shed new light on recent observations related to clinical responses to VEN in AML. Although we confirm that mature monocytes are resistant to VEN, in agreement with previous findings[22], we show that this resistance at the cellular level has no effect on the clinical outcome, which is instead determined by the response of LSCs (Extended Data Fig. 11a,b). This is in line with evidence showing that the targeting of LSCs is essential to achieving lasting therapeutic responses in AML15,42,43. In addition, in view of our data, the monocytic subclones that have been observed to outgrow and be selected upon VEN treatment in patients are likely to, at least in many cases, correspond to RAS-MT subclones22,24,44. As RAS-MT AML subclones contain a higher fraction of monocytic cells than antecedent RAS-WT clones, selection of the RAS-MT subclone at the LSC level can give the appearance of an expansion of cells with monocytic features upon relapse or resistance, with the latter being an epiphenomenon and not causative to relapse or resistance (Extended Data Fig. 11c).

Recently identified ‘monocytic LSCs’ shown to generate monocytic AML, to have a distinct transcriptome and to downregulate BCL2 may also conceivably correspond, at least in a fraction of the cases, to RAS-MT LSCs22,44. The association of monocytic features with poor outcomes after VEN therapy in some cohorts22,24 versus lack of association in others25,26, including the one we present here, may be explained by variable degrees of enrichment of the respective monocytic cohorts in AML cases with RASmut. Thus, our findings can synthesize and reconcile previous seemingly contradictory observations into a coherent model.

Our findings have important implications for clinical practice. The resistance of N/KRAS-MT LSCs to VEN implies that combination therapy with VEN may have limited benefit for patients with pre-existing N/KRAS mutations, and may even accelerate disease progression by promoting the growth of the N/KRAS-MT subclone (Fig. 4g–i and Extended Data Fig. 11c). Consistent with this, we recently reported rapid selection of RAS-MT subclones in patients with AML treated with VEN[45]. Our findings also provide strong rationale for combining VEN with MCL1 inhibitors and potentially BCL-xL inhibitors, as well as RASi, as frontline therapy in patients with detectable RASmut or all eligible patients46,47.

The malignant cells in AML are the product of two orthogonal processes: one that stems from the clonal evolution of genetic clones and subclones through the sequential acquisition of driver genetic lesions; and one that arises from a differentiation hierarchy in each genetic clone and subclone wherein AML LSCs give rise to more differentiated progeny[48]. Our study shows how these intersect and affect each other, specifically how a specific cell differentiation state along the myeloid lineage is selected by mutant RAS as the target cell of transformation and how this, in turn, changes the hierarchical structure of the resulting leukaemia to one more biased towards mature monocytic output. It has long been debated whether the phenotype of leukaemic blasts is determined by the degree of differentiation of the LSC cell-of-origin or, alternatively, by the transforming event and its effects on the developmental program of the LSCs[49]. Our results propose a new paradigm, whereby the oncogenic event (RASmut) selects for a specific differentiation state of a progenitor cell (a GMP) that is the target cell of transformation, with the resulting blast phenotype (monocytic differentiation) being the result of the interaction between both the target cell type and the mutational event. Furthermore, we show that this interaction between the genetic and developmental AML hierarchy determines not only the phenotype, but also critical properties of the disease, with far-reaching implications for its treatment. In view of our findings, more such dependencies between genetic and non-genetic determinants of AML pathogenesis are likely to exist that await discovery and can potentially inform clinical practice.

Methods

Gene editing of human iPS cells

We used the previously described normal iPS cell line N-2.12-D-1-1 as the parental line to generate all CRISPR–Cas9-edited lines described in this study, unless otherwise specified[36]. For the edited lines described in Extended Data Fig. 2d, we used a patient-derived RUNX1-FPD line harbouring a heterozygous RUNX1 mutation (NM_00100189: c.533-1G>T) as parental line50,51. The gene editing strategies used to generate ASXL1C terminus truncation, SRSF2P95L mutation, NRASG12D mutation and FLT3-ITD were described previously27,28,52. Several independent clones with each mutation were isolated after each gene editing step and, following genetic and preliminary phenotypic characterization to exclude potential outliers, one clone was selected for each subsequent editing step.

We used CRISPR–Cas9-mediated homology-directed repair (HDR) to introduce the DNMT3AR882H mutation using co-delivery of a mutant and a WT donor template (Extended Data Fig. 2a) as previously described[27]. Nucleofection of a plasmid expressing the gRNA and Cas9 with mCitrine and clone selection by restriction fragment length polymorphism analysis were performed as previously described[27]. In brief, the N-2.12-D-1-1 iPS cell line was cultured in hESC medium containing 10 mM Y-27632 for at least 1 h before nucleofection. The cells were dissociated into single cells with accutase and 1 million cells were used for nucleofection with 5 µg of gRNA/Cas9 plasmid and 5 µg of each donor plasmid (WT and G12D) using Nucleofector II (Lonza). mCitrine+ cells were FACS-sorted 48 h after transfection and plated at clonal density. Single colonies were screened by PCR and restriction fragment length polymorphism analysis with DdeI restriction enzyme.

An NRASG12D iPS cell line (NRAS-66) was engineered to introduce a TRE-driven Cas9 and the M2rtTA in the two alleles of the AAVS1 locus (Extended Data Fig. 2f) by TALEN-mediated gene targeting, as described[53].

Human iPS cell culture and haematopoietic differentiation

Derivation of the AML-9.9, AML-4.10, AML-4.24, N-2.12, AML-37.16 and AML-47.1 iPS cell lines has been described previously28,30,36. Human iPS cells were cultured on mitotically inactivated mouse embryonic fibroblasts as described previously[52]. Haematopoietic differentiation used a spin-EB protocol as described previously[52]. For monocytic differentiation, day 11–16 HSPCs were transferred to StemPro-34 SFM medium with 1% non-essential amino acids, 1 mM l-glutamine and 0.1 mM β-mercaptoethanol, supplemented with 100 ng ml−1 macrophage colony-stimulating factor and 25 ng ml−1 interleukin-3 for 3–30 days with medium changes every 2 days. In the end of the differentiation culture, the cells were collected and dissociated with accutase into single cells and used for flow cytometry, cytological analyses, VEN treatment or transplantation into immunodeficient mice.

Cytological analyses

Approximately 200,000 cells from liquid haematopoietic differentiation cultures were washed twice with PBS containing 2% FBS and resuspended in PBS. Cytospins were prepared on slides using a Shandon CytoSpin III cytocentrifuge (Thermo Electron). Slides were then air-dried for 30 mins and stained with the Hema 3 staining kit (Fisher Scientific). The slides were read on a Nikon Eclipse Ci microscope and digital images were taken with a Nikon DS-Ri2 camera and NIS-Elements D4.40.00 software.

Flow cytometry and FACS-sorting

The following antibodies were used: CD34-PE (clone 563, catalogue no. 550761, BD Pharmingen, 1:100 dilution), CD34-BV711 (clone 563, catalogue no. 740803, BD Biosciences, 1:100 dilution), CD45-APC (clone HI30; catalogue no. 555485, BD Pharmingen, 1:100 dilution), mCD45-PE-Cy7 (clone 30-F11, catalogue no. 552848, BD Pharmingen, 1:100 dilution), CD33-BV421 (clone WM53, catalogue no. 562854, BD Biosciences, 1:100 dilution), CD19-PE (clone HIB19, catalogue no. 561741, BD Biosciences, 1:100 dilution), CD19-BV650 (clone HIB19, catalogue no. 740568, BD Biosciences, 1:100 dilution), CD38-PE-Cy7 (clone HIT2, catalogue no. 980312, Biolegend, 1:100 dilution), CD123-BV421 (clone 7G3, catalogue no. 563362, BD Biosciences, 1:20 dilution), CD45RA-APC (clone MEM-56, catalogue no. MHCD45RA05, ThermoFisher Scientific, 1:100 dilution), CD68-PE-Cy7 (clone Y1/82 A, catalogue no. 565595, BD Pharmingen, 1:100 dilution), CD11b-BB515 (clone ICRF44, catalogue no. 564517, BD Biosciences, 1:100 dilution), CD11b-BV650 (clone ICRF44, catalogue no. 301336, Biolegend, 1:100 dilution), CD14-APC (clone M5E2, catalogue no. 555399, BD Biosciences, 1:100 dilution), CD14-BV421 (clone M5E2, catalogue no. 565283, BD Biosciences, 1:100 dilution) and CD271 (LNGFR)- APC-Cy7 (clone ME20.4; catalogue no. 345125, Biolegend, 1:2,000 dilution). Cell viability was assessed with 4,6-diamidino-2-phenylindole (DAPI; Life Technologies). Cells were assayed on a BD Fortessa or BD Symphony A5 SE and data were analysed with FlowJo software (Tree Star). Cells were sorted on a BD FACS Aria II.

iPS- and CB-derived HSPC culture and lentiviral transduction

CB CD34+ cells were purchased from AllCells and cultured in X-VIVO 15 medium with 1% non-essential amino acids, 1 mM l-glutamine, 0.1 mM β-mercaptoethanol and 20% BIT 9500 serum substitute (Stem Cell Technologies) and supplemented with 100 ng ml−1 stem cell factor, 100 ng ml−1 Flt3 ligand, 100 ng ml−1 thrombopoietin and 20 ng ml−1 interleukin-3 for 1–4 days. Lentiviral vector packaging and cell transduction with viral supernatants in the presence of 4 µg ml−1 polybrene were performed as described previously[36].

Transplantation into NSG and NSGS mice

All mouse studies were performed in compliance with Icahn School of Medicine at Mount Sinai laboratory animal care regulations and approved by an Institutional Animal Care and Use Committee. NSG (NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ) and NSGS (NOD.Cg-PrkdcscidIl2rgtm1WjlTg(CMV IL3,CSF2,KITLG)1Eav/MloySzJ) mice were purchased from Jackson Laboratories and housed at the Center for Comparative Medicine and Surgery at Icahn School of Medicine at Mount Sinai. Female mice at 6–8 weeks of age were used and were assigned randomly to treatment and control groups. Numbers of mice per group were determined on the basis of historical observations. The smallest sample size estimated to provide more than 80% power to detect differences in leukaemic potential was used. Investigators were not blinded. At 1 day before transplantation, the mice were injected intraperitoneally with 30 mg kg−1 busulfan solution. Gene-edited iPS-cell-derived HSPCs from days 12–14 of haematopoietic differentiation, AML-iPS-cell-derived LSCs from days 14–16 of haematopoietic differentiation or cultured CB HSPCs were resuspended in StemPro-34 and injected through the tail vein using a 25G needle at 1 × 10^6 (for iPS-cell-derived cells) or 2–3 × 10^5 (for CB cells) per mouse in 100 µl. For Dox administration, mice were fed with Dox chow. For VEN administration, VEN was formulated for oral dosing in 60% phosal 50 propylene glycol, 30% polyethylene glycol 400 and 10% ethanol; 100 mg kg−1 was administered daily for 3 weeks. All mice were euthanized promptly once they showed signs of illness, according to Institutional Animal Care and Use Committee guidelines. Bone marrow was collected from the femurs and tibia. Bone marrow and spleen cells were haemolysed with ACK lysis buffer. Human engraftment was assessed by flow cytometric evaluation using hCD45-APC (clone HI30, BD PharMingen) and mCD45-PE-Cy7 (clone 30-F11, BD Biosciences) antibodies. Human cells were isolated using magnetic activated cell sorting (MACS), using CD45 microBeads (catalogue no. 130-045-801, Miltenyi Biotec) or mouse cell depletion kit (catalogue no. 130-104-694, Miltenyi Biotec), and cryopreserved for subsequent scRNA-seq analyses. For secondary transplantation, cells were obtained from the bone marrow of a primary NSGS recipient on week 6 post-transplantation (endpoint due to lethal disease). Following mouse cell depletion by means of MACS, 2 × 10^5 cells were injected intravenously in a secondary NSGS mouse, which was euthanized 7 weeks later.

Bulk RNA-seq

Three independent transductions of iPS-HSPCs for each of the four groups (R + SA, R + Ctrl, SA + R, SA + Ctrl; Fig. 2d) were performed. CD34+CD45+ HSPCs were obtained after MACS-sorting of CD45+ cells on a day of differentiation when all cells are CD34+ to obtain double positive CD34+CD45+ HSPCs using the MACS cell separation microbeads and reagents (Miltenyi Biotec). A total of 200,000 sorted cells were used for RNA extraction with the RNeasy mini kit (Qiagen) and 50,000 cells were used for ATAC-seq. PolyA-tailed mRNA was selected with beads from 1 μg total RNA using the NEBNext Poly(A) mRNA Magnetic Isolation Module (New England Biolabs). cDNAs were generated using random hexamers and ligated to barcoded Illumina adaptors with the NEXTflex Rapid Directional RNA-seq Library Prep Kit (Bioo Scientific); 75-nucleotide-long single-end reads were sequenced in a NextSeq-500 (Illumina).

ATAC-seq

A total of 50,000 MACS-sorted CD34+CD45+ cells from the same iPS-HSPC samples used for RNA-seq were processed as follows: nuclei were isolated by lysing with 50 µl of ATAC lysis buffer (10 mM Tris pH 7.4, 10 mM NaCl, 3 mM MgCl_2, 0.1% NP40, 0.1% Tween-20 and 0.01% Digitonin) and washing with 1 ml of ATAC wash buffer (10 mM Tris pH 7.4, 10 mM NaCl, 3 mM MgCl_2, 0.1% Tween-20). Cell lysates were spun to obtain nuclear pellets, which were subjected to transposase reaction using the Illumina Nextera DNA Sample Preparation Kit according to the manufacturer’s instructions. The final libraries were quantified using the Agilent BioAnalyzer; 75-nucleotide-long paired-end reads were sequenced in a NextSeq-500 (Illumina).

Bulk RNA-seq data processing and analysis

FastQC (v.0.11.8, RRID:SCR_014583) was used for quality control. Trim Galore! (v.0.6.6, RRID:SCR_011847) was used to trim the adapter sequences with a quality threshold of 20. The human reference genome GRCh38 and GENCODE release 36 was used as the transcriptome reference (RRID:SCR_014966). Alignment used STAR aligner (v.2.7.5b, RRID:SCR_004463). Gene-level read counts were obtained using Salmon (v.1.2.1, RRID:SCR_017036) for all libraries. Sample normalization was carried out using the median-ratios normalization method from DESeq2 R package (v.1.30.1, RRID:SCR_015687), and differential expression analysis used DESeq2. Genes with fewer than five reads in total across all samples were filtered out. A gene was considered differentially expressed if the Benjamini–Hochberg adjusted P value was less than 0.05 and the absolute log_2FC was greater than 1. Heatmaps were prepared using pheatmap (v.1.0.12) with hierarchical clustering. Barplots were prepared with ggplot2 (v.3.4.3). Over-representation for ‘RAS-late genes’ was analysed using the clusterProfiler R package (v.3.16.0).

Bulk ATAC-seq data processing and analysis

FastQC (v.0.11.8, RRID:SCR_014583) was used for quality control. Trim Galore! (v.0.6.6, RRID:SCR_011847) was used to trim the adapter sequences with default parameters. For each individual sample, paired-end 75-base-pair reads were aligned to the human reference genome (GRCh38/GENCODE release 36, RRID:SCR_014966) using Bowtie2 (v.2.1.0, RRID:SCR_016368) with default parameters and –X 2000. Reads were sorted using SAMtools (v1.11, RRID:SCR_002105), and mitochondrial and pseudo-chromosomal alignments were removed. Picard (v2.2.4, RRID:SCR_006525) was used to remove duplicates (Picard Toolkit 2019). To generate a universe of regions, all samples were merged using SAMtools merge function, followed by peaks calling using MACS (v.2.1.0, RRID:SCR_013291) with parameters –nomodel –nolambda –slocal 10000. Reads for each sample at the universe of regions were quantified using BedTools multicov with the corresponding filtered bam files (v.2.29.2, RRID:SCR_006646). Sample normalization was carried out using the median-ratios normalization method from DESeq2 R package (v.1.30.1, RRID:SCR_015687). Regions with fewer than 750 normalized reads in total across all samples were filtered out and differential peak analysis was carried out using DESeq2 (adjusted P value < 0.05 and absolute log_2FC ≥ 2. Coverage tracks (Bigwig files) were generated from filtered BAM files for individual replicates using deepTools (v.3.2.1, RRID:SCR_016366) bamCoverage with parameters –normalizeUsing RPKM –binsize 1.

Cell-type-specific regulatory elements were obtained from ref. [29] The liftOver function from rtracklayer package (v.1.60.1) was used to convert hg19 coordinates to hg38. Distal elements specific to cell types of interest were plotted in heatmap format using deepTools (v.3.2.1) computeMatrix and plotHeatmap functions. Transcription factor motifs were analysed with the Homer (v.4.10) findMotifsGenome function. Data were visualized with ggplot2 (v.3.4.3) and dcCompareCurves function from deepStats (v.0.4). The CI threshold for bootstraps was set to 0.95.

Gene set enrichment analysis

GSEA was carried out on all 6,495 C2 curated gene sets from the Molecular Signatures Database (MSigDB, http://www.broadinstitute.org/msigdb) using the ‘fgsea’ R package (v.1.22 RRID:SCR_020938). Genes were ranked on the basis of log_2FC multiplied by −log_10FDR (false discovery rate). GSEA P values were adjusted to control for FDR using the Benjamini–Hochberg method. Gene sets with FDR < 0.05 were considered to show significant enrichment.

GSEA was also applied to gene sets derived from ref. [14], corresponding to the populations LSPC-Quiescent, LSPC-Primed, LSPC-Cycle, GMP-like, ProMono-like, Mono-like and cDC-like.

scRNA-seq

Chromium 10x Genomics 3′ protocol (v.3.0) was used for scRNA-seq in cells from MACS-sorted iPS-cell-derived xenografts and on FACS-sorted CB HSPCs.

scRNA-seq data quality control and preprocessing

The FASTQ files were aligned, filtered, barcoded and unique molecular identifier (UMI) counted using CellRanger Chromium Single Cell RNA-seq by 10x Genomics (v.7.1.0 or v.5.0.1), with GRCh38 database (v.2020-A) as the human genome reference. Each dataset was filtered to retain cells with at least 1,000 UMIs, at least 1,000 genes expressed and less than 15% of the reads mapping to the mitochondrial genome. UMI counts were then normalized so that each cell had a total of 10,000 UMIs across all genes, and these normalized counts were log-transformed with a pseudocount of 1 using the ‘LogNormalize’ function in the Seurat package. The top 2,000 most highly variable genes were identified using the ‘vst’ selection method of ‘FindVariableFeatures’ function and counts were scaled using the ‘ScaleData’ function. Datasets were processed using the Seurat package (v.4.0.3)[54].

scRNA-seq data dimensionality reduction and integration

Principal component analysis was carried out using the top 2,000 highly variable features (‘RunPCA’ function) and the top 30 principal components were used in the downstream analysis. Diffusion maps were generated as implemented in the destiny (v.3.4.0) R package[55] with default parameters and using 10,000 subsampled cells from each integrated dataset. Datasets for each patient were integrated separately by using the ‘RunHarmony’ function in the harmony package (v.0.1.0). K-nearest neighbour graphs were obtained by using the ‘FindNeighbors’ function, whereas the UMAPs were obtained by the ‘RunUMAP’ function[56]. The Louvain algorithm was used to cluster cells on the basis of expression similarity. Cell density estimations were performed using the stat_density_2d function of the ggplot2 (v.3.3.5) package.

scRNA-seq data cell type annotation

Differential markers for each cluster were identified using the Wilcox test (‘FindAllMarkers’ function) with adjusted P value < 0.01, absolute log_2FC > 0.25 and greater than 10% of cells expressing the gene in both comparison groups using 1,000 random cells to represent each cluster. The top upregulated genes and curated genes from the literature were used to assign cell types to the clusters. Metaclusters were obtained by merging the manually annotated cell types into groups. Cell type frequencies between samples were compared using logistic regression (GLM R function, stats package v.4.3.1).

Genotyping of transcriptomes

A bone marrow mononuclear cell sample was obtained from a patient with AML with written informed consent under a protocol approved by a local Institutional Review Board at Memorial Sloan-Kettering Cancer Center. The sample was FACS-sorted to deplete lymphoid cells and enrich blast populations (DAPI−CD45+CD3−CD20−CD19−CD34+CD117+) and processed using the 10x Genomics 5′ V1 Gene Expression protocol. The FASTQ files were aligned and the cell-by-gene count matrix was generated with CellRanger v.5.0.1 with GRCh38 as the human genome reference using default parameters. The data were filtered to retain cells with less than 20% of reads mapping to the mitochondrial genome, at least 200 genes detected, and at least 4,000 UMIs. Downstream analyses were performed using Seurat v.4 in R 4.0.

Full length cDNA from the 10x library preparation was set aside for GoT. Amplicon libraries for NRAS G12D were generated and sequenced on an Illumina MiniSeq 300 cycle mid-output kit in 147:8:16:147 configuration. Resulting FASTQ files were processed as described above for the gene expression library. Mutation status of single cells was determined by performing a pileup of both gene expression and GoT data at the NRASG12 genomic location. Cells were assigned as NRAS MT if one or more mutant UMIs was detected, and as WT if two or more UMIs with only WT alleles were detected. This increased stringency for WT cells was used to account for allelic dropout. The genotyping efficiency was 8.3%, with 576 cells genotyped as NRAS-MT and 423 as NRAS-WT.

Association of SAR mutations with phenotypic AML features in a patient cohort

Bulk RNA-seq data from 599 adult patients with AML from the Alliance Cohort[33], for whom FAB subtype clinical annotations and bulk targeted DNA sequencing data were also available, were used to analyse cell type composition. Cell type fractions were determined using cell-type-specific gene expression profiles derived from a scRNA-seq AML dataset (GSE230559) using dampened weighted least squares[57].

Clinical trial

The analysis presented in this manuscript included patients with AML who received frontline therapy with DEC and VEN on a prospective clinical trial at the University of Texas MD Anderson Cancer Center, Houston, TX (NCT03404193). Patients were included if they were 60 years old or older, or unfit to receive intensive chemotherapy. Patients with European LeukaemiaNet (ELN) favorable risk cytogenetics and previous BCL2 inhibitor exposure were excluded. DEC was dosed at 20 mg m−2 for 10 days for induction, followed by 5 days after achievement of a response. VEN was dosed at 400 mg daily or equivalent in conjunction with azole antifungals. Endpoints and outcomes were assessed per the ELN 2017 or ELN 2022 (extended cohort) guidelines[58]. The full protocol has been described previously[19]. Monocytic differentiation was determined by flow cytometric assessment, esterase positivity, myelomonocytic (FAB M4) or monoblastic/monocytic (FAB M5) morphology[35]. Measurable residual disease was assessed using multiparametric flow cytometry with sensitivity of 0.1% (ref. [59]). All studies were conducted with informed consent in accordance with Declaration of Helsinki ethical guidelines.

VEN treatment and viability assay

iPS-cell-derived HSPCs, iPS-cell-derived monocytes and CB-derived HSPCs were plated on 96-well tissue culture-treated clear flat-bottom plates (Corning, catalogue nos. 3903 or 3603, respectively) at a density of 20,000 per well. VEN was purchased from Selleckchem and dissolved in DMSO for stock solutions at a concentration of 10 mM and subsequently diluted in StemPro medium and added to a total volume of 100 μl of medium per well at a final concentration of 6 μM in triplicate wells. RASi was obtained from Chemed (catalogue no. C-1418) and was added to the cultures at a final concentration of 100 nM in triplicate wells. After 3 days, cell viability was measured using the CellTiter-Glo Luminescent Cell Viability Assay (Promega, catalogue no. G7570) per the manufacturer’s suggested conditions. Per cent viability at each compound concentration was calculated as: (Signal)/(DMSO control) × 100. Half-maximal inhibitory concentration value calculations and generation of half-maximal inhibitory concentration curves were performed using the Prism v.8 software (Graphpad, RRID:SCR_002798).

Western blotting

A total of 1–2 × 10^5 iPS-cell- or CB-derived HSPCs were lysed with SDS sample buffer (ThermoFisher Scientific) supplemented with protease inhibitor and phosphatase inhibitor. Protein concentration was determined by bicinchoninic acid assay (Pierce Biotechnology Inc.) and 1–2 μg of protein from each extract was diluted in Laemmli SDS sample buffer, resolved by electrophoresis on Bolt 10% Bis-Tris precast gels (Invitrogen) and blotted on nitrocellulose membranes. The membranes were blocked with PVDF blocking reagent (TOYOBO) and incubated with primary antibodies: P-p44/42 MAPK (ERK1/2, clone D13.14.4E, catalogue no. 4370S, Cell Signaling Technologies, 1:5,000 dilution), p44/42 MAPK (ERK1/2, clone L34F12, catalogue no. 4696S, Cell Signaling Technologies, 1:5,000 dilution,), BCL2 (clone 124, catalogue no. M0887, DAKO, 1:5,000 dilution), MCL1 (clone D35A5, catalogue no. 5453S, Cell Signaling Technologies, 1:1,000 dilution), BCL-xL (clone 54H6, catalogue no. 2764S, Cell Signaling Technologies,1:5,000 dilution), β-actin (clone 13E5, catalogue no. 5125S, Cell Signaling Technologies, 1:10,000 dilution). After washing, blots were incubated with horseradish peroxidase-conjugated secondary antibody and developed using Western Blotting Detection Reagent (Western HRP Substrate, Millipore). For each antibody, independent blots were used from the same cell lysate with identical loading conditions.

Statistical analysis

Statistical analysis used GraphPad Prism software. Pairwise comparisons between different groups were performed using a two-sided unpaired unequal variance t-test (Stats R package v.4.3.1), unless stated otherwise. For all analyses, P < 0.05 was considered statistically significant. Investigators were not blinded to the different groups.

Reporting summary

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

Data availability

RNA-seq, ATAC-seq and scRNA-seq data have been deposited in the NCBI Gene Expression Omnibus under the accession number GSE253715. Alignment was performed using the GRCh38 reference genome (v.36; https://www.gencodegenes.org/human/release_36.html). Source data are provided with this paper.

References

1. Papaemmanuil, E. et al. Genomic classification and prognosis in acute myeloid leukemia. N. Engl. J. Med. 374, 2209–2221 (2016).

2. Papaemmanuil, E. et al. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood 122, 3616–3627 (2013).

3. Miles, L. A. et al. Single-cell mutation analysis of clonal evolution in myeloid malignancies. Nature 587, 477–482 (2020).

4. Morita, K. et al. Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics. Nat. Commun. 11, 5327 (2020).

5. Menssen, A. J. et al. Convergent clonal evolution of signaling gene mutations is a hallmark of myelodysplastic syndrome progression. Blood Cancer Discov. 3, 330–345 (2022).

6. Makishima, H. et al. Dynamics of clonal evolution in myelodysplastic syndromes. Nat. Genet. 49, 204–212 (2017).

7. Levine, A. J., Jenkins, N. A. & Copeland, N. G. The roles of initiating truncal mutations in human cancers: the order of mutations and tumor cell type matters. Cancer Cell 35, 10–15 (2019).

8. Kent, D. G. & Green, A. R. Order matters: the order of somatic mutations influences cancer evolution. Cold Spring Harb. Perspect. Med. 7, a027060 (2017).

9. Prior, I. A., Hood, F. E. & Hartley, J. L. The frequency of Ras mutations in cancer. Cancer Res. 80, 2969–2974 (2020).

10. Lapidot, T. et al. A cell initiating human acute myeloid leukaemia after transplantation into SCID mice. Nature 367, 645–648 (1994).

11. Bonnet, D. & Dick, J. E. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat. Med. 3, 730–737 (1997).

12. Bennett, J. M. et al. Proposals for the classification of the acute leukaemias. French-American-British (FAB) co-operative group. Br. J. Haematol. 33, 451–458 (1976).

13. Shlush, L. I. et al. Tracing the origins of relapse in acute myeloid leukaemia to stem cells. Nature 547, 104–108 (2017).

14. Zeng, A. G. X. et al. A cellular hierarchy framework for understanding heterogeneity and predicting drug response in acute myeloid leukemia. Nat. Med. 28, 1212–1223 (2022).

15. Ng, S. W. et al. A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature 540, 433–437 (2016).

16. Konopleva, M. et al. Efficacy and biological correlates of response in a phase II study of venetoclax monotherapy in patients with acute myelogenous leukemia. Cancer Discov. 6, 1106–1117 (2016).

17. DiNardo, C. D. et al. Azacitidine and venetoclax in previously untreated acute myeloid leukemia. N. Engl. J. Med. 383, 617–629 (2020).

18. Souers, A. J. et al. ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nat. Med. 19, 202–208 (2013).

19. DiNardo, C. D. et al. 10-day decitabine with venetoclax for newly diagnosed intensive chemotherapy ineligible, and relapsed or refractory acute myeloid leukaemia: a single-centre, phase 2 trial. Lancet Haematol. 7, e724–e736 (2020).

20. Wei, A. H. et al. Venetoclax plus LDAC for newly diagnosed AML ineligible for intensive chemotherapy: a phase 3 randomized placebo-controlled trial. Blood 135, 2137–2145 (2020).

21. Kadia, T. M. et al. Phase II study of venetoclax added to cladribine plus low-dose cytarabine alternating with 5-azacitidine in older patients with newly diagnosed acute myeloid leukemia. J. Clin. Oncol. 40, 3848–3857 (2022).

22. Pei, S. et al. Monocytic subclones confer resistance to venetoclax-based therapy in patients with acute myeloid leukemia. Cancer Discov. 10, 536–551 (2020).

23. Kuusanmaki, H. et al. Phenotype-based drug screening reveals association between venetoclax response and differentiation stage in acute myeloid leukemia. Haematologica 105, 708–720 (2020).

24. Zhang, H. et al. Integrated analysis of patient samples identifies biomarkers for venetoclax efficacy and combination strategies in acute myeloid leukemia. Nat. Cancer 1, 826–839 (2020).

25. Stahl, M. et al. Clinical and molecular predictors of response and survival following venetoclax therapy in relapsed/refractory AML. Blood Adv. 5, 1552–1564 (2021).

26. Waclawiczek, A. et al. Combinatorial BCL2 family expression in acute myeloid leukemia stem cells predicts clinical response to azacitidine/venetoclax. Cancer Discov. 13, 1408–1427 (2023).

27. Wang, T. et al. Sequential CRISPR gene editing in human iPSCs charts the clonal evolution of myeloid leukemia and identifies early disease targets. Cell Stem Cell 28, 1074–1089 e1077 (2021).

28. Kotini, A. G. et al. Stage-specific human induced pluripotent stem cells map the progression of myeloid transformation to transplantable leukemia. Cell Stem Cell 20, 315–328 e317 (2017).

29. Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).

30. Kotini, A. G. et al. Patient-derived iPSCs faithfully represent the genetic diversity and cellular architecture of human acute myeloid leukemia. Blood Cancer Discov. 4, 318–335 (2023).

31. Nam, A. S. et al. Somatic mutations and cell identity linked by genotyping of transcriptomes. Nature 571, 355–360 (2019).

32. Velten, L. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19, 271–281 (2017).

33. Lasry, A. et al. An inflammatory state remodels the immune microenvironment and improves risk stratification in acute myeloid leukemia. Nat Cancer 4, 27–42 (2023).

34. Bernard, E. et al. Molecular international prognostic scoring system for myelodysplastic syndromes. NEJM Evid. 1, EVIDoa2200008 (2022).

35. Matarraz, S. et al. Introduction to the diagnosis and classification of monocytic-lineage leukemias by flow cytometry. Cytometry B Clin. Cytom. 92, 218–227 (2017).

36. Kotini, A. G. et al. Functional analysis of a chromosomal deletion associated with myelodysplastic syndromes using isogenic human induced pluripotent stem cells. Nat. Biotechnol. 33, 646–655 (2015).

37. Bhatt, S. et al. Reduced mitochondrial apoptotic priming drives resistance to BH3 mimetics in acute myeloid leukemia. Cancer Cell 38, 872–890 e876 (2020).

38. Pan, R. et al. Selective BCL-2 inhibition by ABT-199 causes on-target cell death in acute myeloid leukemia. Cancer Discov. 4, 362–375 (2014).

39. Maiti, A. et al. Outcomes of relapsed or refractory acute myeloid leukemia after frontline hypomethylating agent and venetoclax regimens. Haematologica 106, 894–898 (2021).

40. Schulze, C. J. et al. Chemical remodeling of a cellular chaperone to target the active state of mutant KRAS. Science 381, 794–799 (2023).

41. Holderfield, M. et al. Concurrent inhibition of oncogenic and wild-type RAS-GTP for cancer therapy. Nature 629, 919–926 (2024).

42. Pearce, D. J. et al. AML engraftment in the NOD/SCID assay reflects the outcome of AML: implications for our understanding of the heterogeneity of AML. Blood 107, 1166–1173 (2006).

43. Eppert, K. et al. Stem cell gene expression programs influence clinical outcome in human leukemia. Nat. Med. 17, 1086–1093 (2011).

44. Pei, S. et al. A novel type of monocytic leukemia stem cell revealed by the clinical use of venetoclax-based therapy. Cancer Discov. 13, 2032–2049 (2023).

45. Zhang, Q. et al. Activation of RAS/MAPK pathway confers MCL-1 mediated acquired resistance to BCL-2 inhibitor venetoclax in acute myeloid leukemia. Signal Transduct. Target Ther. 7, 51 (2022).

46. Carter, B. Z. et al. Maximal activation of apoptosis signaling by cotargeting antiapoptotic proteins in BH3 mimetic-resistant AML and AML stem cells. Mol. Cancer Ther. 21, 879–889 (2022).

47. Caenepeel, S. et al. AMG 176, a selective MCL1 inhibitor, is effective in hematologic cancer models alone and in combination with established therapies. Cancer Discov. 8, 1582–1597 (2018).

48. Kreso, A. & Dick, J. E. Evolution of the cancer stem cell model. Cell Stem Cell 14, 275–291 (2014).

49. Wang, J. C. & Dick, J. E. Cancer stem cells: lessons from leukemia. Trends Cell Biol. 15, 494–501 (2005).

50. Song, W. J. et al. Haploinsufficiency of CBFA2 causes familial thrombocytopenia with propensity to develop acute myelogenous leukaemia. Nat. Genet. 23, 166–175 (1999).

51. Krutein, M. C. et al. Restoring RUNX1 deficiency in RUNX1 familial platelet disorder by inhibiting its degradation. Blood Adv. 5, 687–699 (2021).

52. Chang, C. J. et al. Dissecting the contributions of cooperating gene mutations to cancer phenotypes and drug responses with patient-derived iPSCs. Stem Cell Rep. 10, 1610–1624 (2018).

53. Gonzalez, F. et al. An iCRISPR platform for rapid, multiplexable, and inducible genome editing in human pluripotent stem cells. Cell Stem Cell 15, 215–226 (2014).

54. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

55. Angerer, P. et al. destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 32, 1241–1243 (2016).

56. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

57. Tsoucas, D. et al. Accurate estimation of cell-type composition from gene expression data. Nat. Commun. 10, 2975 (2019).

58. Dohner, H. et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood 129, 424–447 (2017).

59. Xu, J., Jorgensen, J. L. & Wang, S. A. How do we use multicolor flow cytometry to detect minimal residual disease in acute myeloid leukemia? Clin. Lab. Med. 37, 787–802 (2017).

Acknowledgements

This work was supported by US National Institutes of Health (NIH) grants R01CA271418, R01CA260711 and R01CA271331, by a Leukemia and Lymphoma Society (LLS) Blood Cancer Discoveries Grant, by an Edward P. Evans Foundation Discovery Research Grant, by a RUNX1 Research Program grant and by a 2021 AACR-MPM Oncology Charitable Foundation Transformative Cancer Research Grant (grant number 21-20-45-PAPA) to E.P.P. This work was also supported by US NIH grants R01CA235622 to M.K. and R01HL157387 and R33CA267219 to D.A.L. M.S. was supported by US NIH grant F31CA254130. S.S.C. was supported by NIH grant R37CA273232, Department of Defense Grant W81XWH-22-1-0857 and Cancer Prevention Institute of Texas grant RR180046. P.L. was supported in part by NIH grants R01CA23074501, R01CA23026701, R01CA279264 and P01CA129243. A.M. was supported by a grant from the Gateway for Cancer Research. C.D.D. was supported by a LLS Scholar in Clinical Research Award. This study was supported in part by the MD Anderson Cancer Center Support Grant CA016672 from the US NIH National Cancer Institute. This work was supported in part by the Bioinformatics for Next Generation Sequencing (BiNGS) shared resource facility of the Tisch Cancer Institute at the Icahn School of Medicine at Mount Sinai, which is partially supported by US NIH Cancer Center Support grant P30CA196521. This work was also supported in part by the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai, supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this paper was also supported by the NIH Office of Research Infrastructure award S10OD026880.

Author information

Author notes

1. These authors contributed equally: Junya Sango, Saul Carcamo, Maria Sirenko, Abhishek Maiti

Authors and Affiliations

1. Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Junya Sango, Saul Carcamo, Hager Mansour, Gulay Ulukaya, Nataly Cruz-Rodriguez, Tiansu Wang, Malgorzata Olszewska, Emmanuel Olivier, Manon Jaud, Elvin Wagenblast, Deniz Demircioglu, Dan Hasson & Eirini P. Papapetrou

2. Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Junya Sango, Saul Carcamo, Hager Mansour, Gulay Ulukaya, Lewis E. Tomalin, Nataly Cruz-Rodriguez, Tiansu Wang, Malgorzata Olszewska, Emmanuel Olivier, Manon Jaud, Lewis Silverman, Elvin Wagenblast, Deniz Demircioglu, Dan Hasson & Eirini P. Papapetrou

3. Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Junya Sango, Saul Carcamo, Hager Mansour, Gulay Ulukaya, Lewis E. Tomalin, Nataly Cruz-Rodriguez, Tiansu Wang, Malgorzata Olszewska, Emmanuel Olivier, Manon Jaud, Elvin Wagenblast, Dan Hasson & Eirini P. Papapetrou

4. Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Junya Sango, Saul Carcamo, Hager Mansour, Gulay Ulukaya, Lewis E. Tomalin, Nataly Cruz-Rodriguez, Tiansu Wang, Malgorzata Olszewska, Emmanuel Olivier, Manon Jaud, Lewis Silverman & Eirini P. Papapetrou

5. Center for Advancement of Blood Cancer Therapies, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Junya Sango, Saul Carcamo, Hager Mansour, Gulay Ulukaya, Lewis E. Tomalin, Nataly Cruz-Rodriguez, Tiansu Wang, Malgorzata Olszewska, Emmanuel Olivier, Manon Jaud, Lewis Silverman, Elvin Wagenblast, Deniz Demircioglu, Dan Hasson & Eirini P. Papapetrou

6. Bioinformatics for Next Generation Sequencing Shared Resource Facility, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Saul Carcamo, Gulay Ulukaya, Lewis E. Tomalin, Deniz Demircioglu & Dan Hasson

7. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Maria Sirenko & Elli Papaemmanuil

8. Louis V. Gerstner Jr Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Maria Sirenko

9. Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Abhishek Maiti & Courtney D. DiNardo

10. Department of Medicine, Division of Precision Medicine, NYU Grossman School of Medicine, New York, NY, USA

Bettina Nadorp

11. Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA

Bettina Nadorp

12. Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA

Benjamin Kroger & Stephen S. Chung

13. Medical Scientist Training Program, University of Texas Southwestern Medical Center, Dallas, TX, USA

Benjamin Kroger

14. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Feng Hu & Piro Lito

15. Children’s Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA

Stephen S. Chung

16. Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA

Ronan Chaligne & Dan A. Landau

17. New York Genome Center, New York, NY, USA

Ronan Chaligne & Dan A. Landau

18. Clara D. Bloomfield Center for Leukemia Outcomes Research, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA

Ann-Kathrin Eisfeld

19. Department of Medicine (Oncology), Albert Einstein College of Medicine, Bronx, NY, USA

Marina Konopleva

20. Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY, USA

Marina Konopleva

21. Montefiore Einstein Comprehensive Cancer Center, Bronx, NY, USA

Marina Konopleva

Contributions

J.S. performed most of the experiments, analysed data and prepared the figures. H.M., N.C.-R., T.W., M.O., E.O., M.J., B.K., F.H., S.S.C. and E.W. performed experiments and analysed data. S.C., G.U., L.E.T., B.N., D.D. and D.H. performed bioinformatics analyses and prepared figures. M.S., R.C., D.A.L. and E.P. performed the GoT experiments and analysed data. A.M., C.D.D. and M.K. analysed clinical trial data. L.S. and A.-K.E. provided materials and patient datasets. P.L. provided the RASi. M.K. and E.P.P. designed the study and interpreted data. E.P.P. wrote the manuscript and supervised the study. J.S., S.C., M.S. and A.M. contributed equally as first authors; H.M., G.U., L.E.T., N.C.-R. & T.W. contributed equally as second authors.

Corresponding author

Correspondence to Eirini P. Papapetrou.

Ethics declarations

Competing interests

M.K. receives research support from Klondike, AbbVie and Janssen, and consulting fees from AbbVie, Syndax, Novartis, Servier, AbbVie, Menarini-Stemline Therapeutics, Adaptive, Dark Blue Therapeutics, MEI Pharma, Legend Biotech, Sanofi Aventis, Auxenion GmbH, Vincerx, Curis, Intellisphere and Janssen. D.A.L. is an advisory board member of Mission Bio, Veracyte, Pangea and Alethiomics and has received research support from Ultima Genomics unrelated to this work. A.M. receives research support from Chimeric Therapeutics, Lin Biosciences, Hibercell Inc., Qurient and Molecular Templates, Inc. C.D.D. has received consulting fees from Abbvie, AstraZeneca, Astellas, GenMab, Notable Labs, Rigel and Servier. E.P. is cofounder of, and holds a fiduciary role in Isabl Inc. P.L. has received grants to his Institution from Amgen, Mirati, Revolution Medicines, Boehringer Ingelheim and Virtec Pharmaceuticals. P.L. is an advisory board member of Frontier Medicines, Ikena, Biotheryx and PAQ-Tx (consulting fees and equity in each) and has received consulting fees or honoraria from Black Diamond Therapeutics, AmMax, OrbiMed, PAQ-Tx, Repare Therapeutics, Boehringer Ingelheim and Revolution Medicines. EPP has received consulting fees or honoraria from Janssen, Daiichi Sankyo and Cellarity, unrelated to the current study. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Peter van Galen, Dominique Bonnet and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Characterization of in vitro and in vivo leukemic properties of edited iPS-HSPCs with single, double and triple mutations.

a, Isogenic single, double and triple-mutant iPS cell lines generated through sequential CRISPRCas9-mediated gene editing of a normal iPS cell line (Parental). b, Overview of in vitro and in vivo phenotypic assessment of iPS-HSPCs. c, Fraction of CD34−/CD45+ cells, i.e. hematopoietic cells that have lost CD34 expression upon maturation, on day 14 of hematopoietic differentiation. Mean and SEM from n = 8(P), 10(A, SA), 12(S), 7(R), 3(AR, SR), and 19(SAR) independent differentiation experiments with 2 (A, S, SA, AR, SR, SAR) or 3(R) iPS cell lines per genotype are shown. *P < 0.05, ****P < 0.001, ns: not significant (two-tailed unpaired t test). d, Number of methylcellulose colonies obtained from iPS-HSPCs on day 14 of hematopoietic differentiation. Mean and SEM from n = 6(P, A, S, SA, SAR), 3 (R, AR) and 4(SR) independent differentiation experiments with 2 iPS cell lines per genotype are shown. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns: not significant (two-tailed unpaired t test). e, Cell counts of iPS-HSPCs in liquid hematopoietic differentiation culture. Mean and SEM of n = 2(P, R, AR, SR), 4(A, SAR), 6(S) and 11(SA) independent differentiation experiments with 1 (P, R, SAR) or 2 (A, S, AR, SR, SA) iPS cell lines per genotype are shown. f, Competitive growth assay. The cells were mixed 1:1 at the onset of hematopoietic differentiation with an isogenic normal iPS cell line stably expressing GFP. The relative population size was estimated as the percentage of GFP- cells (calculated by flow cytometry) at each time point relative to the population size on day 2. Mean and SEM from n = 3(P), 4(A), 6(S, R), 5(SA), 2(AR,SR) and 8(SAR) independent differentiation experiments with 2 (P, A, R, SA, AR, SR, SAR) or 3 (S) iPS cell lines per genotype are shown. P values were calculated with a two-tailed unpaired t test. g, Cell cycle analyses of iPS-HSPCs. Mean and SEM from 3 (P, A, S, SA, SAR) and 4 (R) independent differentiation experiments with one line per genotype are shown. *P < 0.05 (R vs P: P = 0.0336; SAR vs P: P = 0.0279), ns: not significant (two-tailed unpaired t test). h, Human engraftment in the BM of NSG mice 13-15 weeks after transplantation with HSPCs derived from the indicated gene-edited mutant iPS cell lines (1 or 2 lines per genotype). Error bars show mean and SEM of values from individual mice. n = 2 (P); 2(A); 2(S); 8(R); 2(SA); 6(AR); 8(SR); 27(SAR). P values were calculated with a two-tailed unpaired t test. i, Representative flow cytometry for evaluation of human engraftment in mouse BM. j, Wright-Giemsa-stained BM cells retrieved from a mouse transplanted with SAR iPS-HSPCs. Scale bar, 25 μm. P: Parental; A: ASXL1-mutant; S: SRSF2-mutant; R: NRAS-mutant; SA: SRSF2-ASXL1 double mutant; AR: ASXL1-NRAS- double mutant; SR: SRSF2-NRAS double mutant; SAR: SRSF2-ASXL1-NRAS triple mutant.

Source data

Extended Data Fig. 2 Generation and characterization of additional edited iPS cell lines.

a, Gene editing strategy to generate a heterozygous DNMT3AR882H mutation in the same normal parental iPS cell line used to generate the lines shown in Extended Data Fig. 1, through homology-directed repair with simultaneous delivery of one mutant and one WT donor templates. Schematic representation of the DNMT3A locus with the position of the gRNA target sequence and the PCR primers used for RFLP analysis shown. Silent mutations introduced in the donor to create the DdeI restriction site (underlined) and inactivate the PAM motif are indicated in green font. The G→A mutation giving rise to the R882H amino acid substitution is shown in red font. b, Sanger sequencing confirming the G→A heterozygous point mutation giving rise to the R882H amino acid substitution in one edited DNMT3AR882H iPS cell line selected after screening. c,d, Schematic of gene editing steps to generate the iPS cell lines with single, double and triple driver mutations starting from the parental WT (c) or an iPS cell line derived from a RUNX1- familial platelet disorder (FPD) patient harboring a germline RUNX1V118Gfs*11 mutation (d). e, Human engraftment in the BM of NSG mice 13-15 weeks after transplantation with gene-edited iPS-HSPCs. Mean and SEM is shown. RAR: RUNX1-ASXL1-NRAS triple mutant (n = 5 mice); DFR: DNMT3A-FLT3-NRAS triple mutant (n = 2 mice). f, Gene targeting strategy used to introduce a tetracycline response element (TRE)-driven Cas9 and the reverse tetracycline transactivator (rtTA), respectively, into the two alleles of the AAVS1 locus using TALEN-mediating targeting. g, Karyotype of iPS cell line NRASG12D-iCas9-10 confirming a normal diploid karyotype. h, Confirmation of induction of iCas9 expression by DOX in the NRASG12D-iCas9-10 iPS cells by qRT-PCR. i, Representative flow cytometric evaluation of engraftment in mice transplanted with the iPS-HSPCs shown in Fig. 1a,b. j, Representative flow cytometric evaluation of transduction efficiency of iPS-HSPCs with the lentiviral constructs shown in Fig. 1a, co-expressing the indicated fluorescent protein genes.

Source data

Extended Data Fig. 3 Leukemogenesis in CB CD34+ cells.

a, Transduction efficiency of CB CD34 + R + SA and SA + R cells prior to transplantation (day 8 depicted in Fig. 1c). b, Percentage of CD33+ myeloid cells (of hCD45+ cells) in the BM of mice transplanted with CB CD34+ cells shown in Fig. 1c. Mean and SD of values from individual mice is shown. n = 2 (mCherry/GFP), 3 (SA), 3 (R + SA) and 8 (SA + R) mice. c, Percentage of hCD45+ cells from transplanted mice expressing each lentiviral transgene (based on expression of the linked fluorescent protein). Mean and SD of values from individual mice is shown. n = 2 (mCherry/GFP), 3 (SA), 3 (R + SA) and 4 (SA + R) mice. d, Spleen weight of transplanted mice. n = 2 (mCherry/GFP), 3 (SA), 3 (R + SA) and 4 (SA + R) mice. UT: untransplanted. Mean and SD are shown. P values were calculated with one way ANOVA. e,f, Survival (e) and BM engraftment (f) of mice injected with SA + R CB CD34+ cells under continuous Dox administration or following Dox withdrawal 14 days after transplantation. Mean and SD of values from 4 individual mice per group are shown. P value was calculated with a two-tailed unpaired t test.

Source data

Extended Data Fig. 4 Genomic analyses of iPS-HSPCs.

a,b, Differentially expressed genes (a) and differentially accessible peaks (b) between the indicated iPS-HSPC groups. c, Significantly enriched (GSEA) AML gene sets. NES: Normalized enrichment score. d, Cumulative enrichment scores for a GMP-like signature derived from human primary AML[14]. e, Cell-type-specific regulatory elements from Corces et al. HSC: hematopoietic stem cell; MPP: multi-potent progenitor; CMP: common myeloid progenitor; GMP: granulocyte-monocyte progenitor; LMPP: lymphoid-primed multipotent progenitor; CLP: common lymphoid progenitor; MEP: megakaryocyte-erythrocyte progenitor; Mono: monocyte; Ery: erythroid cell; NK: natural killer. f, Accessibility (Reads Per Kilobase per Million mapped ATAC reads) of the regulatory elements specific to the indicated cell types (CMP/MEP, GMP, GMP/Mono and Mono) from e. The X axis shows distance from the transcriptional start site.

Extended Data Fig. 5 Leukemogenesis from CB GMPs.

a, Transduction efficiency of SA + R CB CMPs and GMPs from two independent experiments. b, Sorted SA + R CB CMPs and GMPs were injected into NSGS mice. A mouse that received SA + R GMPs succumbed to a lethal disease 11 weeks after transplantation, while a mouse transplanted with SA + R CMPs showed no signs of illness. c, Engraftment in mice transplanted with SA + R CMPs or GMPs 11 weeks after transplantation. d,e, Sorted CMPs (upper panels) and GMPs (lower panels) from the experiments depicted in Fig. 2g (d) and Fig. 2h (e), cultured with Dox and assayed on the day of transplantation (day 8). f, Wright-Giemsa-stained human cells with blast morphology retrieved from the BM of a mouse transplanted with SA + R GMPs. Image representative of 3 independent experiments. Scale bar, 50 μm. g, BM engraftment in a secondary recipient mouse upon serial transplantation of SA + R GMPs from the experiment shown in Fig. 2g. h, Wright-Giemsa-stained cells from the BM of a secondary recipient mouse transplanted with SA + R GMPs. The larger cells are human blasts. The smaller cells with segmented nuclei correspond to murine neutrophils. Scale bar, 50 μm. i,j, Engraftment in the BM of mice transplanted with SA + R from Fig. 2g (i) or SAR from Fig. 2h (j) CMPs or GMPs at the endpoint of the experiment. n = 1 mouse for each SA + R CMP group; 3 mice for each SA + R GMP group; 4 mice for each SAR CMP group; 3 for GMP+Dox and 2 for GMP-Dox. Mean and SD are shown. k, Percentage of CD33+ myeloid cells (of hCD45+ cells) in the BM of mice (n = 3) transplanted with SA + R GMPs from Fig. 2g. Mean and SD are shown. l, Flow cytometry analysis of a representative mouse transplanted with sorted SA + R or SAR GMPs from the experiments schematically depicted in Fig. 2g,h, respectively, showing that the leukemic cells co-express all 3 mutant transgenes.

Source data

Extended Data Fig. 6 Genomics analyses of genetically engineered iPS- and CB- HSPCs.

a, Transgene expression in sorted GMPs and HSC/MPPs transduced with SA + R. b, Assessment of ERK activation (phospho-ERK, pERK) by Western blotting in total CB CD34+ cells or FACS-sorted CMPs, GMPs and HSC/MPPs transduced with the indicated lentiviral vectors or untransduced (UT). Shown is one representative experiment out of 2. Samples were derived from the same experiment and processed in parallel. β-actin controls were run on different gels as sample processing controls. For source data, see Supplementary Fig. 4. c, Hierarchical clustering of expression values of differentially expressed genes (DEGs) from the SA + R vs SA+Ctrl and SA + R vs R + SA comparisons. Genes belonging to clusters 8 and 10 were designated as “RAS-late genes”. d, Hierarchical clustering of accessibility scores of differentially accessible peaks (DAPs) from the SA + R vs SA+Ctrl and SA + R vs R + SA comparisons. The peaks of cluster 4 were designated as “RAS-late peaks”. e, Top statistically significant transcription factor (TF) motifs (identified using the Homer motif discovery package) enriched in the “RAS-late” peaks from d, grouped by TF families. f, Selected top enriched (over-representation analysis) HALLMARK pathways in the “RAS-late genes” from c. Count: number of “RAS-late genes” in the gene set. Adjusted p values were derived from GSEA. g, Selected enriched (FDR < 0.1) HALLMARK pathways in SA + R vs Ctrl cells of the GMP cluster from Fig. 2b. NES: normalized enrichment score. h, Expression of the genes belonging to the “KRAS signaling up” HALLMARK gene set. Cluster 4 contains 65 genes that are upregulated specifically in the SA + R group. i, Aggregate accessibility of the 65 genes related to RAS signaling that are specifically upregulated in SA + R iPS-HSPCs (cluster 4 genes from h). *P < 0.05, **P < 0.01, ***P < 0.001, ns: not significant (two-tailed unpaired t-test). The top and bottom lines of the whiskers denote the highest and lowest values, respectively. The box spans the interquartile range (25th-75th percentile) and the line represents the median. j, Accessibility (Reads Per Kilobase per Million mapped ATAC reads, RPKM) within 1 kb on either side of the transcription start site (TTS) of the 65 genes related to RAS signaling that are specifically upregulated in SA + R iPS-HSPCs (cluster 4 genes from h), showing higher accessibility in SA + R and SA+Ctrl cells, compared to the R + SA and R+Ctrl groups. The X axis shows distance from the TTS. One representative replicate per condition is shown.

Extended Data Fig. 7 RAS mutations drive monocytic differentiation.

a, UMAP of integrated single-cell transcriptome data from Fig. 3a,b, at resolution 0.4. b, Expression of selected marker genes in each annotated cluster from the GoT data. c, Fraction of CD14+ monocytic blasts in AML patients with mutations in RAS pathway genes (NRAS, KRAS or PTPN11) or without any RAS pathway mutation (RAS WT). “Any RAS MT” denotes cases with mutations in either of the 3 genes NRAS, KRAS or PTPN11. The whiskers denote the 1.5* IQR (interquartile range). The lower and upper hinges of the boxes represent the first and third quartiles, respectively. The middle line represents the median. Points represent values outside of the 1.5* IQR. The P value was calculated with a two-sided Wilcoxon test. d, FAB subtype of AML patients with mutations (MT) in RAS pathway genes (NRAS, KRAS or PTPN11) or without any RAS pathway mutation (RAS WT). “Any RAS MT” denotes cases with mutations in either of the 3 genes NRAS, KRAS or PTPN11. Two-tailed Fisher test, ns: not significant. e, Flow cytometry for myelomonocytic markers CD68 and CD11b in CD34+ cells from 4 patient-derived AML-iPS cell lines with or without (Control) lentiviral expression of NRASG12D or KRASG12D. MLLr: MLL-rearranged; SF: splicing factor; CBF: core binding factor. f, Fraction of CD14+ monocytic blasts in AML patients with or without mutations (MT) in SRSF2 and ASXL1 genes. SA denotes cases with double SRSF2 and ASXL1 mutations; S/A WT denotes cases without SRSF2 or ASXL1 mutations. The whiskers denote the 1.5* IQR (interquartile range). The lower and upper hinges of the boxes represent the first and third quartiles, respectively. The middle line represents the median. Points represent values outside of the 1.5* IQR. ns: not significant (two-sided Wilcoxon test). g, FAB subtype of AML patients with or without mutations (MT) in SRSF2 and ASXL1 genes. SA denotes cases with double SRSF2 and ASXL1 mutations; S/A WT denotes cases without SRSF2 or ASXL1 mutations. Two-tailed Fisher test, ns: not significant. h, Proportion of cases with or without NRAS or KRAS mutations (RASMT and RAS–WT, respectively) with or without combined SRSF2 and ASXL1 mutations (SA and not SA, respectively) among 399 CMML patients from the MDS International Working Group cohort (Bernard et al. 2022). (P value: one tail Fisher test). i,j, Experimental scheme. Schematic of lentiviral vectors used (i). Vectors N, K and F are DOX-inducible. k, Myelomonocytic markers CD11b and CD14 in GMPs with various transgene combinations, shown in i,j, cultured for 5 days after sorting. N: NRASG12D, K: KRASG12D, F: FLT3-ITD, IDH: IDH1R132H, S: SRSF2P95L; A: ASXL1Del. l, Heatmap showing differential expression of the indicated granulocytic (MPO, AZU1, ELANE) and monocytic (CD14, CD52, S100A6, S100A8, S100A9, CCL2, CCL3, CCL4) lineage genes in the GMP cluster, in the indicated comparisons, from the single-cell transcriptome data from Fig. 2a,b. *P < 0.05, ***P < 0.001 (two-sided Wilcoxon test).

Extended Data Fig. 8 Differentiation of normal and AML- iPS cells into HSPCs and monocytes.

a,b, Duration of response (DOR) (a) and overall survival (OS) (b) in AML patients with N/KRAS mutations vs without N/KRAS mutations. Log-rank test, two-tailed unadjusted P values. CR: complete remission; CRi: CR with incomplete hematologic recovery; HR: hazard ratio; CI: confidence interval. c, Schematic of protocol for in vitro directed differentiation of human iPS cells into HSPCs and monocytes. d, Representative flow cytometry assessment of HSPC (CD34) and monocytic (CD68, CD11b, CD14) markers in normal iPS cell and AML iPS cell-derived HSPCs and monocytes. e, Representative Wright-Giemsa-stained cytospin preparations of HSPCs and monocytic cells derived from normal iPS cell and AML iPS cell lines, showing immature morphology (upper panels) and typical monocytic morphology (lower panels). Images are from one experiment out of at least 3 repeats. Scale bars, 50 μm. f, Dose-response curve of AML-4.24 HSPCs treated with VEN at the indicated doses. Cells were treated for 48 h and viability was assessed using the CellTiter-Glo assay. Mean and SD from n = 2 independent experiments is shown. 6 μM was selected as the dose for subsequent assays.

Extended Data Fig. 9 Single-cell transcriptomic analyses of AML-iPS cell-LSCs.

a, Violin plots showing expression of anti- and pro-apoptotic genes of the BCL2 family in monocytic blasts (monocytic metacluster generated by merging all monocytic and dendritic cell clusters shown in Fig. 3b and Extended Data Fig. 7a) or LSCs (cluster 28 shown in b-d) within the AML-4.10 and AML-4.24 leukemia cells from xenografts. P values were calculated with a two-sided Wilcoxon test. b, Expression of anti- and pro-apoptotic genes in the LSC cluster shown in Fig. 3b without subclustering in the iPS cell-derived leukemia cells from xenografts. P values were calculated with a two-sided Wilcoxon test. c, Expression of HSC markers SPINK2 and HOPX projected onto the integrated UMAP. The red squares indicate the LSC subcluster (cluster 28 shown in c,d). d, UMAP representation of single-cell transcriptome data in resolution 3.2, yielding 46 clusters. e, Left panel: UMAP representation of the LSC cluster (from resolution 0.4 clustering shown in Fig. 3b) subdivided into 5 clusters (from resolution 3.2 clustering shown in c). Middle and right panels: Expression of HSC markers CD34, HOPX and SPINK2 projected onto the LSC cluster UMAP. f, Split-violin plots showing expression of anti- and pro-apoptotic genes in monocytic blasts or immature MPP-like and GMP-like cells of the NRASMT and NRASWT genetic clones from GoT data. **P < 0.01, ****P < 0.0001, ns: not significant (two-tailed Wilcoxon test). g, Normalized expression of the indicated pro- and anti- apoptotic genes in the genetically engineered iPS-HSPCs shown in Fig. 2d (n = 3 independent experiments for all groups). *P < 0.05, **P < 0.01, ***P < 0.001, ns: not significant (two-tailed unpaired t-test).

Extended Data Fig. 10 Genomics analyses of genetically engineered sorted CMPs and GMPs.

a, Expression of the indicated pro- and anti- apoptotic genes in FACS-sorted SA + R CMPs and GMPs measured by bulk RNA-Seq. Mean and SD from n = 3 independent experiments are shown. ns: not significant (two-tailed unpaired t-test). b, UMAP representation of integrated single-cell transcriptome data from FACS-sorted SA + R CMPs (left) and GMPs (right) from Fig. 5a,b. c, Expression of the indicated pro- and anti- apoptotic genes in the different clusters. d, Flow cytometry analysis of SA + R CB cells from the experiment shown in Fig. 5a on day 6, showing that the vast majority of ΔLNGFR-NRASG12D+ cells also express the other two transgenes (GFP-ASXL1del1900-1922 and mCherry-SRSF2P95L). e, Volcano plot showing differentially expressed genes between NRASG12D+ and WT cells of the GMP cluster from Fig. 5b,d. Significantly upregulated and downregulated genes (Wilcoxon test) are shown in red and blue, respectively. Granulocytic (MPO, AZU1, ELANE) and monocytic (S100A8, S100A9, S100A12, CD52, CCL2) lineage genes, downregulated and upregulated, respectively, are highlighted. Downregulated genes encoding ribosomal proteins are shown in green. f, Top 20 most enriched HALLMARK pathways in NRASG12D+ vs WT cells belonging to the GMP cluster from Fig. 5b,d. NES: normalized enrichment score. g, Viability of CD34+ LSCs from the indicated patient-derived AML-iPS cell lines with or without ectopic lentiviral expression of NRASG12D, treated with VEN at the indicated concentrations. %Viability compared to DMSO-treated group is shown. n = 3 for AML-4.24 treated with 12 μM VEN and n = 4 for all other groups. Mean and SD are shown. P values were calculated with a two-tailed unpaired t test. h, Summary schematic of the effects of RAS mutation acquisition in different HSPC types. RAS mutations acquired by more primitive HSPCs (HSC/MPPs or CMPs) result in reduction of GMP formation and reciprocal increase in megakaryocyte and erythroid progenitors (MEP) (left panel). Acquisition of RAS mutations in GMPs drives their differentiation towards the monocytic and away from the granulocytic lineage (right panel).

Extended Data Fig. 11 Summary models for the role of RAS mutations in leukemic transformation.

a, Model of emergence of RAS-MT LSCs based on the findings of this study. RASmut acquired by a GMP harboring previously acquired driver mutations can give rise to an LSC. The latter generates leukemic cells with mature monocytic immunophenotype, whereas the major AML clone without RASmut gives rise to leukemic cells with more immature features. Thus, the LSC of the RAS-MT subclone originates from a different and more mature type of cell in the hematopoietic hierarchy (a GMP) than the LSC of the major ancestral RAS-WT clone, which originates from an HSC/MPP/CMP. b, Mechanism of VEN resistance in AML with subclonal N/KRAS mutations. RAS-WT LSCs express high levels of BCL-2 and are the targets of VEN therapy, whose elimination translates into a clinical response. In contrast, monocytic blasts, regardless of genotype, are uniformly VEN-resistant, as they lack expression of BCL-2 and instead rely on MCL-1 expression for survival. However, resistance of the monocytes has no impact on the clinical response, which is instead dependent on the elimination of LSCs – the cells with self-renewal potential that can maintain and regenerate the leukemia. Critically, RAS-MT LSCs downregulate BCL-2 and upregulate MCL-1 and BCL-xL and are thus resistant to VEN. It is the VEN resistance of these RAS-MT LSCs, rather than the resistance of the monocytic blasts, that is the determinant of clinical relapse and resistance. VEN S: VEN-sensitive; VEN R: VEN-resistant. c, Impact of VEN treatment on the size of immature and monocytic AML populations within RAS-WT and RAS-MT clones. Treatment with VEN imposes selection pressure at the level of the LSCs. RAS-MT LSCs are resistant to VEN – in contrast to RAS-WT LSCs, which are VEN sensitive – and are thus selected for and expand upon VEN treatment. Because RAS-MT LSCs produce more monocytic blasts than RAS-WT LSCs, expansion of the RAS-MT LSC compartment is also accompanied by an increase in the fraction of monocytic blasts. However, it is the LSCs and not the monocytic cells that mediate clinical resistance and relapse, with the increase in monocytic cells being a byproduct of RAS-MT LSC expansion without relevance to the clinical outcome.

Supplementary information

Supplementary Information

Supplementary Discussion, Figs. 1–4, Tables 1, 5, 6 and References.

Reporting Summary

Supplementary Table 2

List of RAS-late genes.

Supplementary Table 3

List of RAS-late peaks.

Supplementary Table 4

List of 65 genes related to RAS signalling found selectively upregulated by late RAS (Extended Data Fig. 6h, cluster 4).

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Source Data Extended Data Fig. 1

Source Data Extended Data Fig. 2

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Source Data Extended Data Fig. 5

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Sango, J., Carcamo, S., Sirenko, M. et al. RAS-mutant leukaemia stem cells drive clinical resistance to venetoclax. Nature 636, 241–250 (2024). https://doi.org/10.1038/s41586-024-08137-x

- Received: 26 July 2023

- Accepted: 30 September 2024

- Published: 30 October 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08137-x

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Nucleosome flipping drives kinetic proofreading and processivity by SWR1

Paul Girvan,

Adam S. B. Jalal,

Elizabeth A. McCormack,

Michael T. Skehan,

Carol L. Knight,

Dale B. Wigley &

…

David S. Rueda

Nature volume 636, pages 251–257 (2024)

This article has been updated

Abstract

The yeast SWR1 complex catalyses the exchange of histone H2A–H2B dimers in nucleosomes, with Htz1–H2B dimers1,2,3. Here we used single-molecule analysis to demonstrate two-step double exchange of the two H2A–H2B dimers in a canonical yeast nucleosome with Htz1–H2B dimers, and showed that double exchange can be processive without release of the nucleosome from the SWR1 complex. Further analysis showed that bound nucleosomes flip between two states, with each presenting a different face, and hence histone dimer, to SWR1. The bound dwell time is longer when an H2A–H2B dimer is presented for exchange than when presented with an Htz1–H2B dimer. A hexasome intermediate in the reaction is bound to the SWR1 complex in a single orientation with the ‘empty’ site presented for dimer insertion. Cryo-electron microscopy analysis revealed different populations of complexes showing nucleosomes caught ‘flipping’ between different conformations without release, each placing a different dimer into position for exchange, with the Swc2 subunit having a key role in this process. Together, the data reveal a processive mechanism for double dimer exchange that explains how SWR1 can ‘proofread’ the dimer identities within nucleosomes.

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Main

A canonical nucleosome contains two copies each of the four histones H2A, H2B, H3 and H4 around which approximately 147 bp of DNA are wrapped[4]. However, additional variants have been discovered for each of these histones and, when present, that have special roles in cells, such as at centromeres, and in processes including transcription and DNA repair[5]. Most of these variants are laid down into chromatin during replication, but an exception in yeast is the H2A histone variant Htz1 (H2A.Z in higher eukaryotes). Htz1 is specifically incorporated into nucleosomes by the SWR1 complex1,2,3. In humans, there are two large multi-subunit complexes that incorporate H2A.Z into nucleosomes, SRCAP[6] and TIP60 (ref. [7]), the latter complex also being able to acetylate histones, as well as other proteins, as a part of DNA damage signalling[7]. In addition to being signals of DNA damage, nucleosomes that contain Htz1 (or H2A.Z) also have a role in transcriptional regulation[8].

SWR1 is a 14-subunit complex that is a member of the INO80 remodeller family[9]. Cryo-electron microscopy (cryo-EM) structures of INO80 and SWR1 complexes bound to nucleosomes have been reported10,11,12. Despite significant similarity between the complexes in terms of subunits and sequence homology, the two complexes engage with nucleosomes in a very different manner[13]. The ATPase domains of the INO80 subunit engage at superhelical location 6 (SHL6), whereas those of SWR1 are located at SHL2. Both complexes unwrap significant sections of DNA from the nucleosome, but this is stabilized by the motor domains of INO80 (refs. 10,11) and the Arp6–Swc6 subunits in SWR1 (ref. [12]). These differences may relate to the differing activities of the two complexes because SWR1, unlike INO80, lacks the ability to slide nucleosomes[14], although ATP-dependent DNA translocation within the context of the nucleosome wrap is required for activity[14]. Instead, SWR1 catalyses the ATP-dependent exchange of H2A–H2B dimers with those comprising Htz1–H2B1,2,3. The exchange takes place in a stepwise manner with both dimers being exchanged[15]. For canonical nucleosomes, SWR1 shows specificity for exchange of H2A–H2B dimers with Htz1–H2B and will not catalyse the reverse exchange under any conditions so far identified15,16. How this remarkable specificity is achieved is unknown, although sequence differences between the α2 helix of H2A and Htz1 probably contribute to this[14]. Acetylation of K56 on H3 in nucleosomes appears to reduce the specificity of histone exchange by interfering with Swc2 function[17].

Single-molecule studies have begun to reveal aspects of the complex process of histone exchange. In the initial complex, when nucleosomes first bind to SWR1, the DNA wrap becomes dynamic with small, but rapid, unwrapping events in addition to the significant unwrapping by Arp6–Swc6 subunits[12]. However, to progress towards dimer exchange, a larger unwrapping occurs18,19,20, presumably to fully expose the dimer, although the nature and full extent of this unwrapping remain unclear, as well as which subunits contribute to this process. It is also unclear whether dimer exchange is a processive process, with both dimers exchanged in a nucleosome after a single SWR1-binding event, or is distributive with nucleosome release between dimer exchanges12,18,20. If histone exchange is processive, this would suggest a higher propensity for double-exchanged dimer nucleosomes than for single exchanges, although the significance of double-exchanged versus single-exchanged nucleosomes is also unknown.

Single-molecule histone exchange by SWR1

We have previously developed a fluorescence resonance energy transfer (FRET)-based assay to monitor histone exchange by SWR1 in bulk phase by monitoring loss of FRET when labelled H2A–H2B dimers are exchanged for unlabelled Htz1–H2B dimers[16]. We have adapted this methodology for single-molecule analysis by changing the dimer labelling so that a gain of FRET was observed when an unlabelled H2A–H2B dimer is exchanged for a labelled Htz1–H2B dimer (Fig. 1a). This makes interpretation of the data less ambiguous, because in a loss of FRET assay, it can be hard to distinguish between histone exchange and dye photobleaching[19]. This assay allows us to monitor two histone dimer exchanges on surface-immobilized nucleosomes as consecutive step increases in FRET (Fig. 1b and Extended Data Fig. 1). A histogram of the average FRET value between the first and second exchange reveals two possible intermediate states at approximately 0.6 and approximately 0.4 FRET (Fig. 1c), which corresponds to donor-proximal and donor-distal exchange, respectively. Control experiments in the presence of non-hydrolysable ATPγS analogue revealed no exchange (Extended Data Fig. 1). A dwell time analysis of the intermediate state (Fig. 1d) shows that the time between the first and the second exchange is τ_2 = 246 ± 25 s, which is independent of the initial exchange (proximal or distal) and consistent with slow histone exchange observed in other single-molecule studies18,19 and in ensemble-averaged measurements2,15,16.

Fig. 1: Double-exchange events can be observed by smFRET.

a, Schematic of the assay. Nucleosomes (113N2.AF488) labelled with AF488 (blue) on the short 2-bp overhang are surface immobilized on a PEGylated microscope slide. SWR1, ATP and AF555–Htz1–H2B dimers (green) are flowed in to start the exchange reaction. Histone exchange is detected as a FRET increase between AF488 and AF555. b, Intensity trajectory (top) and corresponding FRET trajectory (bottom) for a single nucleosome showing a stepwise gain in FRET signal following each dimer exchange. c, Idealized FRET histogram of the first-exchange event shows two approximately equal populations of approximately 0.4 and approximately 0.6 FRET corresponding to either dye-distal or dye-proximal exchange. d, Dwell time distribution between the first and second exchanges yields a second-exchange time τ_2 = 246 ± 25 s. Reported errors are the error of the fit.

Source Data

Double exchange can be processive

The lifetime of SWR1–nucleosome complexes has been shown to be long (several tens of minutes[18]), although is reduced in the presence of ATP18,19. We have also determined lifetimes of SWR1–nucleosome complexes to be on the order of tens of minutes (Extended Data Fig. 2). Such long lifetimes, longer than that required for a single histone exchange (2–3 min (refs. 18,19)), raise the possibility of a processive mechanism for double histone exchange, as hinted at previously[19]. However, structural studies[12] have strongly suggested that dimer exchange takes place in the position facing the enzyme complex, which has the DNA wrap partially unwound by the Arp6–Swc6 subunits. For double dimer exchange to be processive, different mechanisms for exchange would need to occur for each dimer, or a mechanism must exist to rotate the bound nucleosome in situ. An alternative, and seemingly more plausible, possibility is a distributive mechanism that allows the singly exchanged nucleosome to dissociate and then rebind to SWR1 with the appropriate face oriented for dimer exchange. However, it is important to note that any processive enzyme reactions that are observed need to be explained by a different mechanism.

To evaluate these alternatives directly, we expanded our single-molecule FRET (smFRET) assay to three colours with an additional dye (Atto647N) on SWR1 (see Methods) to colocalize enzyme binding and dissociation dynamics (Fig. 2a). The labelled SWR1 did not affect enzyme activity in bulk (Extended Data Fig. 2). Using alternating laser excitation, we can selectively follow histone exchange as stepwise FRET increases, while monitoring SWR1 binding by fluorescence intensity. The resulting single-molecule trajectories showed molecules that undergo single (43%; Fig. 2b) and double (57%; Fig. 2d) exchanges during a SWR1-binding event. In these trajectories, SWR1 binding precedes the first histone exchange by 36 ± 2 s (Fig. 2c), whereas the second exchange is approximately sixfold slower, taking 227 ± 11 s (Fig. 2e), consistent with the value measured in Fig. 1d. Ultimately, SWR1 dissociates or photobleaches (Fig. 2b,d).

Fig. 2: SWR1 processively exchanges H2A–H2B for Htz1–H2B.

a, Three-colour smFRET assay with surface-immobilized AF488–nucleosome (blue), AF555–Htz1–H2B dimers (green) and SWR1(647N) (red). SWR1 binding is monitored by red fluorescence; histone exchange is detected as a FRET increase between AF488 and AF555. b, Example trace showing SWR1 binding (red; top) followed by a single-exchange event (bottom) after the exchange time (τ_1). Asterisk indicates photobleaching or dissociation. c, Dwell time distribution between SWR1 binding and the first exchange (for both single and double exchanges) yields a τ_1 = 36 ± 2 s. d, Example trace showing SWR1 binding (red; top) followed by two processive exchange events (bottom) with a second-exchange time (τ_2). Asterisk indicates photobleaching or dissociation. e, Dwell time distribution between the first and second exchanges yields a second-exchange time τ_2 = 227 ± 11 s. Reported errors are the error of the fit.

Source Data

These data demonstrate that SWR1 can exchange two histone dimers in a single-binding event, strongly supporting a processive exchange mechanism. A small fraction (approximately 10%) of distributive exchanges is observed, but this is expected for processive enzymes, as all processive enzymes are expected to exhibit a fraction of distributive events depending on experimental conditions. In some trajectories, we cannot observe the presence of SWR1, probably due to Atto647N photobleaching or incomplete labelling (Extended Data Fig. 3).

Nucleosomes flip between two bound states

Having recapitulated and monitored the complete double-exchange reaction at the single-molecule level, and established that this can be processive, we then sought to delve more deeply into the different steps of the reaction pathway. We set out to answer two questions: first, how SWR1 determines which dimer to exchange so that H2A–H2B dimers are always replaced with Htz1–H2B dimers and never the reverse; and second, how consecutive exchange reactions are carried out processively without release of the nucleosome.

To answer the first question, we labelled the nucleosome (on the short DNA overhang) with a FRET donor and SWR1 complex with a FRET acceptor (Fig. 3a) to monitor nucleosome dynamics when bound to the complex. The resulting single-molecule FRET trajectories revealed two conformations for bound nucleosomes with different FRET efficiencies (approximately 0.1 and approximately 0.4; Fig. 3b and Extended Data Fig. 4a–d). The cryo-EM structure of the SWR1–nucleosome complex[12] was used to evaluate the nature of the complexes, and the simplest interpretation is that binding of the nucleosome is in two pseudo-symmetric conformations, with each conformation presenting a different dimer to the surface of the SWR1 complex (Fig. 3a). These two bound states place the two dyes either close or further apart, termed dye-proximal and dye-distal conformations, respectively. Most molecules (68%; n = 154) showed that nucleosomes can flip between the distal and proximal states, although a small proportion remained in either the distal (10%) or proximal (22%) states (Fig. 3b). To rule out the possibility that the observed dynamic FRET stems from movement of DNA, we relocated the donor to H2A (Extended Data Fig. 4e–g), and observed similar dynamic FRET transitions that showed the same slight preference for the dye-proximal dimer. Alternative explanations for flipping, such as DNA unwrapping or SWR1 diffusing along the DNA overhang, were ruled out because unwrapping[12] and diffusion[21] require ATP binding, whereas flipping is not dependent on ATP.

Fig. 3: SWR1 flips between each face of a nucleosome.

a, Schematic of the assay. Nucleosomes (113N2.Cy3) labelled with Cy3 on the short 2-bp overhang are surface immobilized on a PEGylated microscope slide. SWR1, labelled with Atto647N on the N terminus of the Arp6 subunit (SWR1(647N)) is flowed in and allowed to bind to the nucleosome. Interactions between the nucleosome and SWR1(647N) are monitored via smFRET between the donor (green circle) and acceptor (red circle). b, Examples of typical smFRET (grey) and idealized (black) traces. Some molecules display a static FRET of either 0.4 or 0.1, whereas other molecules dynamically flip between these two FRET states. c, Idealized FRET histogram shows two major populations of SWR1(647N) bound to a nucleosome: a low-FRET (0.1) population corresponding to SWR1(647N) bound to the dye-distal side of the nucleosome, and a mid-FRET (approximately 0.4) population corresponding to SWR1(647N) bound to the dye-proximal side of the nucleosome. d, Dwell time plots for the distal-to-proximal (left) and proximal-to-distal (right) transition. The average dwell times (τave) for SWR1 bound in the distal and proximal orientations are approximately equal. Reported errors are the error of the fit.

Source Data

A time-binned FRET histogram of all the trajectories (Fig. 3c) showed that each state is sampled with approximately equal probability with a slight preference for the dye-proximal state. A dwell time analysis of the dynamic molecules showed that the nucleosome flips with approximately equal average time (3–4 s; Fig. 3d) from either the proximal or the distal face of the nucleosome. This observation is consistent with previous data from our group (and that in Fig. 1) that show an approximately equal propensity for exchange of each of the dimers in a yeast nucleosome in the first step[12]. The dwell time analysis further revealed biphasic exponential kinetics with a slow (4–5 s) and a fast (0.6–0.8 s) population, indicating the presence of flipping intermediates that cannot be distinguished by FRET alone (Extended Data Fig. 4b). A possible explanation for the biphasic kinetics is that SWR1 engages the nucleosome in either a more (slow) or less (fast) stable conformation. The corrected exponential amplitudes showed that the molecules spend approximately 80% of the time in the slow (more engaged) configuration.

SWR1 senses heterotypic nucleosome dimer

Although each dimer in a canonical yeast nucleosome containing two H2A–H2B dimers has, in principle, an equal likelihood to be exchanged, the second-exchange reaction is exclusively of the remaining H2A-containing dimer[15]. Furthermore, a nucleosome in which both H2A–H2B dimers have been exchanged for Htz1–H2B cannot undergo SWR1-catalysed replacement by Htz1, and even futile cycling, in which one Htz1–H2B dimer is exchanged for another, does not seem to occur[15]. These observations indicate that the SWR1 complex has a mechanism to distinguish between Htz1 and H2A within a nucleosome.

Having determined that the bound nucleosome flips between conformations and that each presents a different nucleosome face, and hence dimer, to the SWR1 complex, we then sought to test whether this mechanism allowed SWR1 to probe the identity of the dimer with which it was presented. We prepared nucleosomes with a single copy each of H2A and Htz1 (Fig. 4a) and then repeated the experiments described above to monitor the flipping process. The data show that these ‘heterotypic’ nucleosomes bind to SWR1 in a similar manner to the canonical nucleosomes and are able to flip between both distal and proximal orientations (Fig. 4b and Extended Data Fig. 4h). However, in contrast to canonical nucleosomes, the static trajectories were almost exclusively in the proximal orientation that presents the H2A–H2B dimer to SWR1 (Fig. 4b). Furthermore, dwell time analysis for each orientation revealed clear kinetic differences between the two states (Fig. 4d and Extended Data Fig. 4i). The side containing the H2A–H2B dimer exhibits almost identical kinetics to the canonical nucleosome (complare with Fig. 3d). By contrast, the side containing the Htz1–H2B dimer exhibits only a single fast exponential decay (0.63 ± 0.02 s), comparable with the fast component of the canonical dimer face. This is also reflected in the time-binned FRET histogram (Fig. 4c), which shows a clear preference for the proximal (H2A–H2B) face.

Fig. 4: Histone composition regulates SWR1 flipping kinetics.

a, Cy3-labelled surface-immobilized heterotypic nucleosomes (113N2.Cy3) containing Htz1–H2B (green) and canonical H2A–H2B dimers (orange). SWR1(647N) is flowed in and nucleosome binding is monitored via smFRET between the donor (green circle) and acceptor (red circle). b, Characteristic smFRET (grey) and idealized (black) trajectories. Some molecules (37%; ntotal = 118) display static 0.4 FRET, whereas others (59%) flip dynamically between 0.4 and 0.1 FRET. c, Idealized FRET histogram showing two populations corresponding to SWR1(647N) bound to the dye-distal Htz1–H2B (0.1 FRET) or to the dye-proximal H2A–H2B (approximately 0.4 FRET). The dashed line indicates the canonical nucleosome distribution from Fig. 3c. A small (0.04) shift of the low-FRET population may indicate altered binding to the Htz1 side. d, Dwell time plots for the distal-to-proximal (left) and proximal-to-distal (right) transition for a heterotypic nucleosome. Average dwell time (τave) on the Htz1–H2B side (distal) is shorter than the H2A–H2B side (proximal). e, Surface-immobilized hexasomes (113H2.Cy3) lacking the dye-distal H2A–H2B dimer (dashed orange line). SWR1(647N) is flowed in and hexasome binding is monitored via smFRET. f, Characteristic smFRET (grey) and idealized (black) trajectories. Molecules display a low (0.1) FRET. A small number of molecules show infrequent transitions from 0.1 to 0.4 FRET. g, Idealized FRET histogram showing one major population of SWR1(647N) bound to a hexasome. Only the low-FRET (0.1) population corresponding to SWR1(647N) bound to the vacant (dye-distal) side of the hexasome is present. The dashed line indicates the canonical nucleosome distribution from Fig. 3c. A small (0.03) shift of the low-FRET population may indicate altered binding when SWR1 faces the empty side. Reported errors are the error of the fit.

Source Data

To rule out the possibility that the observed kinetic differences stem from the asymmetric DNA overhangs, we prepared heterotypic nucleosomes with swapped DNA overhangs (Extended Data Fig. 4k–n). The data show that the observed biased flipping kinetics is maintained, confirming that the SWR1 selection against the Htz1–H2B side is based on histone content rather than on DNA overhang. These data show that SWR1 is able to distinguish between H2A and Htz1 within the context of the nucleosome, discriminating against Htz1 by rapidly flipping back to the canonical side. We thus propose that a form of kinetic proofreading22,23 places the appropriate face of the nucleosome into the position proficient for dimer removal and exchange, thus contributing to the exquisite selectivity of the enzyme for replacing H2A with Htz1 and not the reverse. However, the ratio of the two dwell times (Fig. 4d) only gives a selectivity of sixfold, which is less than the apparent selectivity reported for SWR1 (refs. 15,16), so although this kinetic proofreading contributes significantly to specificity, additional steps (such as the multiple ATP hydrolysis events during dimer exchange or selective binding of Htz1–H2B versus H2A–H2B dimers for insertion) probably increase this selectivity further.

The hexasome vacant site hinders flipping

A necessary intermediate in the histone dimer exchange reaction is a hexasome intermediate in which one H2A–H2B dimer has been removed but has not yet been replaced with an Htz1–H2B dimer. We next prepared hexasomes labelled in the same way as nucleosomes (Fig. 4e and Extended Data Fig. 5) to determine whether there was any effect on the kinetics and distribution of binding orientations. The single-molecule trajectories exhibit an almost complete loss of the proximal orientation with almost all hexasomes bound in the distal orientation, with the fraction of molecules flipping between the distal and the proximal sites decreasing to 14% (Fig. 4f). The distal orientation places the ‘empty dimer’ site against the SWR1 complex surface ready for insertion by an incoming Htz1–H2B dimer, consistent with structural data suggesting that this is the face of the nucleosome that undergoes histone exchange[12]. Although we did observe occasional flipping into the proximal conformation, this state is very short lived and reverts quickly to the distal configuration. A time-binned histogram of all trajectories confirms that the proximal orientation becomes almost undetectable (Fig. 4g). These results are consistent with SWR1 placing the nucleosome empty site in position ready to accept the incoming Htz1–H2B dimer. This change in the flipping dynamics suggests an active stabilization of the hexasome intermediate, produced on-enzyme, retaining the orientation that places the empty site in position to accept the incoming dimer. Indeed, our recent cryo-EM structure of the hexasome-bound SWR1 complex demonstrates that Swc5 has a role in complex stabilization[24].

Structural basis for nucleosome flipping

To gain a better understanding of the flipping mechanism, we used cryo-EM analysis to examine different states of SWR1 complexed with nucleosomes. Our previous cryo-EM structures have shown one major state for the complex but also revealed several minor states, the function of which was not evident at that time[12]. However, in light of the new single-molecule data above, we re-examined these less-populated structural states in an expanded dataset to see whether these provided information about how nucleosomes might flip between different conformations. Further analysis and processing focused on these minor classes, revealing additional details (Extended Data Fig. 6). Two classes were of particular interest and resulted in structures at 3.8 Å and 4.7 Å, respectively (Fig. 5, Extended Data Table 1, Extended Data Figs. 6 and 7 and Supplementary Videos 1 and 2). One of these classes (described briefly in our previous work[12]) was very similar to the major structure, but a longer section of overhang DNA is evident that emanates from the lower gyre of the nucleosome and binds across the surface of SWR1 (configuration I; Fig. 5a,b and Supplementary Video 1). The DNA extending from the upper gyre is unwrapped from the nucleosome and binds to Arp6–Swc6 in the same manner as that described for the main structure[12]. The second structure (configuration II; Fig. 5c,d and Supplementary Video 2) also showed a longer section of DNA overhang bound to SWR1, but this time, it was the DNA that extended from the upper gyre, which is released from Arp6–Swc6, that now binds across the same surface of SWR1 as the DNA from the lower gyre in configuration I. The DNA overhang from the lower gyre is released in this structure. Thus, the same DNA-binding surface on SWR1 binds different overhangs in each structure (Extended Data Figs. 7 and 8).

Fig. 5: Structural basis of SWR1-mediated nucleosome flipping.

a, The built-in coordinates of SWR1 in complex with a canonical nucleosome at 3.8 Å resolution in configuration I. Note that the DNA emanating from the lower gyre of the nucleosome (highlighted in blue) is bent up and binding across the surface of SWR1. b, A bottom view of the SWR1–nucleosome structure in configuration I. For clarity, only Swr1 (HD1 and HD2), Swc2 and the Arp6–Swc6 complex of SWR1 are shown. c, The built-in coordinates of SWR1 in complex with a canonical nucleosome at 4.7 Å resolution in configuration II. Note that the DNA emanating from the upper gyre of the nucleosome (highlighted in red) is binding across the surface of SWR1. d, A bottom view of the SWR1–nucleosome structure in configuration II. For clarity, only Swr1 (HD1 and HD2), Swc2 and the Arp6–Swc6 complex of SWR1 are shown. e, Three representative 2D class averages of SWR1–nucleosome in the canonical conformation. A cartoon representation of each 2D class is shown beneath. f, Three representative 2D class averages of SWR1-mediated nucleosome flipping with SWR1 orientated as in panel e. A cartoon representation of each 2D class is shown beneath. g, Cartoon summary of SWR1-mediated nucleosome flipping. h, Cartoon summary of kinetic proofreading and processivity of histone exchange by the SWR1 remodeller.

The improved resolution, combined with the availability of an AlphaFold model for Swc2 (ref. [25]) allows us to assign, locate and build regions of the Swc2 subunit that we were previously unable to assign confidently. Swc2 has an essential role in SWR1-mediated histone exchange12,26. The N-terminal region of Swc2 binds to Htz1–H2B dimers26,27; however, we are still unable to assign that part of Swc2 in our structure. The central portion of yeast Swc2 (residues 136–345) is a DNA-binding module that probably localizes the SWR1 complex towards the nucleosome-depleted region21,28 (Extended Data Fig. 8a). We can confidently build a portion of this DNA-binding region (residues 195–329) into the density (Extended Data Fig. 8b). Our structure indicates that there are three contacts between this region of Swc2 and the DNA (Extended Data Fig. 8b). Several positively charged residues in these regions are conserved across Swc2 subunits from different species (Extended Data Fig. 9a), consistent with a role in interacting with DNA. Two of these contact regions have been previously observed[12], although specific residue contacts could not be unambiguously determined. The AlphaFold model of Swc2 now allows us to better define these regions, which both contact the DNA wrap of the nucleosome (Extended Data Fig. 8b). A third contact region, involving eight conserved basic residues (K319–K322, R325, K326, K328 and K329), are in a loop that sits across the surface of the SWR1 complex (Extended Data Figs. 8 and 9). This surface contacts the DNA overhang adjacent to the nucleosome wrap (Extended Data Fig. 8), but a different overhang in structures emanating from either the lower (configuration I) or the upper (configuration II) DNA gyre.

These two states suggest a simple mechanism to allow flipping of nucleosome orientations between the proximal and distal states (Fig. 5g). By swapping which DNA overhang is bound to SWR1, and then releasing the nucleosome but without releasing the DNA overhang, the nucleosome can flip and rebind in the opposite orientation but remain tethered to SWR1 by the DNA contact with the Swc2 subunit (Supplementary Video 3). Owing to the symmetry of the histone octamer, we cannot distinguish whether the nucleosome orientation relative to SWR1 switches between configurations I and II. An alternative explanation could be that SWR1 remains bound to the same face of the nucleosome in both configurations, and only the DNA overhang interacting with Swc2 is swapped. However, the smFRET experiments in which the donor is located on H2A still exhibit the nucleosome flipping dynamics (Extended Data Fig. 4e–g), thereby ruling out this possibility. Consequently, we interpret that the two major states that we observed by cryo-EM represent intermediates on the flipping pathway. In principle, SWR1 could also use a single approximately 35-bp or longer DNA overhang while flipping the nucleosome (Fig. 5c), as shown in our single-molecule experiments (Fig. 3a). In the cell, however, the chromatin context (that is, the presence or absence of a neighbouring nucleosome) would dictate whether one or two overhangs can be used for flipping.

The single-molecule analysis presented above shows that although nucleosomes bound to SWR1 flip between configurations I and II, they only spend a very small amount of time flipping between these configurations. We would, therefore, expect to see very few complexes caught in this process in our cryo-EM dataset and these would probably be in various conformations with the nucleosome only interacting via the DNA overhangs that are so hard to define and average. Nonetheless, by careful classification of the particle dataset where bound nucleosome could not be visualized (Extended Data Fig. 6), we were able to identify several 2D classes in which the nucleosome could be observed in a flipped state where the nucleosome was disengaged from SWR1 but the flanking DNA remained bound. Three particularly well-defined examples are shown in Fig. 5 (compare Fig. 5e and 5f), but others were also visible (Extended Data Fig. 9b). We interpret these as direct visualization of nucleosomes frozen in the act of flipping between configurations I and II. The dynamic nature of the flipping, however, precluded a 3D analysis of intermediate flipped states.

Discussion

The ATP-dependent exchange of H2A–H2B dimers for those containing Htz1–H2B is a two-step process that replaces each dimer in turn[15]. Structural data[12] have strongly suggested that the dimer to be exchanged makes close contacts with SWR1, and unwrapping of the DNA from around this dimer begins upon binding and then progresses in an ATP-dependent process18,19,20. However, both dimers in a nucleosome can be exchanged, implying dissociation of the nucleosome to allow it to rebind with the appropriate dimer exposed for exchange in a distributive mechanism. On the basis of ensemble-averaged experiments, we have previously suggested that histone exchange proceeds via a distributive mechanism[12]. However, this conclusion was based on the assumptions that both exchanges proceeded with comparable rates, and that SWR1 would be completely processive or distributive. The three-colour single-molecule exchange experiments (Fig. 2) showed that, under these conditions, the second exchange is much slower and that not all exchanges are processive, illustrating the need for such single-molecule experiments to unambiguously show processivity of the SWR1 complex. Why the second exchange is slower than the first one remains unclear. One possibility is that heterotypic nucleosomes have slower exchange rates. To test this possibility, we performed single-molecule exchange assays using a heterotypic nucleosome substrate (Extended Data Fig. 3). The resulting exchange time is approximately 100 s, still threefold slower than the first exchange (approximately 36 s), confirming that exchange is slower on heterotypic nucleosomes and in agreement with previous results20,29.

However, demonstration that dimer exchange is processive raises a conundrum. We questioned how SWR1 accesses both faces of a single nucleosome without dissociation or by utilizing different mechanisms for each exchange given the asymmetric manner of association with the SWR1 complex. The answer is by a partial release of the nucleosome, while retaining a hold on the flanking DNA, to allow it to flip 180° and then rebind with the opposite face towards the enzyme to allow the second-exchange event (Fig. 5g,h and Supplementary Video 3). This simple, yet elegant, mechanism also explains the exquisite specificity of dimer exchange by SWR1 through dynamic, kinetic proofreading that favours placing a nucleosome face containing H2A rather than Htz1 in the position for exchange without releasing the substrate.

In cells, the Htz1–H2B variant is enriched at the +1 nucleosome at transcription start sites, which is typically flanked by a long (approximately 140 bp) nucleosome-free region (NFR) on one side[30]. SWR1 has been found, by crosslinking, to reside on the NFR-proximal side of the +1 nucleosome, which led the authors to question how SWR1 might exchange NFR-distal dimers[31]. At certain genes, chromatin immunoprecipitation-exo data from yeast cells have shown a preference for Htz1 insertion into the NFR-distal side of the nucleosome[32]; however, at the genome-wide level, the NFR-distal preference was closer to approximately 60:40, suggesting the opposite to be the case at other transcription start sites, despite the location of the SWR1 complex on the opposite face of the nucleosome[31]. In vitro, linker-distal or linker-proximal dimer preference for the first-exchange reaction has been somewhat controversial, as discussed[18]. Our initial studies[12] have shown a weak exchange preference (between 50:50 and 60:40) for the linker-distal (dye-proximal) dimer. Our new single-molecule exchange data (Fig. 1c) are consistent with those results (approximately 55:45 linker-distal). Data from other laboratories are also consistent with a weak linker-distal preference, particularly at physiological temperatures[29]. Conversely, others have reported a stronger preference for the linker-distal dimer based on more frequent and faster kinetics for linker-distal exchange18,19. The origin of these differences is not clear. The nature of the enzyme or histone source (for example, recombinant versus native, and yeast versus frog/Drosophila) or variations of the nucleosome positioning sequence could, in principle, explain these differences, but the data presented here do not resolve this issue.

Finally, we speculate that nucleosome flipping might have a role in other nucleosome remodelling activities. Flipping could be used to recognize and modify different histone components on both faces of nucleosomes or to monitor the histone composition of different faces of nucleosomes as part of regulation processes. Furthermore, the sliding directionality of nucleosomes on DNA could be swapped by such a flipping mechanism, as proposed for Chd1 (ref. [33]), allowing processive sliding of nucleosomes to space them evenly along DNA or to position them in gene regulation and/or DNA repair.

Methods

Purification of wild-type SWR1

Recombinant SWR1 was produced as previously described12,16 with minor modifications. Baculoviruses encoding SWR1 genes were initially amplified in Sf9 cells, before using the amplified baculoviruses to infect BTI-TN-5B1-4 (High Five) cells for expression, which were harvested after 72 h. Cells were lysed by sonication in 50 mM HEPES (pH 8.0), 0.5 M NaCl, 1 mM TCEP, 10% glycerol, 1 mM benzamidine-HCL supplemented with 1 protease inhibitor tablet and 10 µl of benzonase per litre of cell culture. Lysate was clarified by centrifugation at 30,000g for 60 min at 4 °C. The supernatant was filtered before being injected onto a StrepTrap HP (Cytiva) column. The column was washed with buffer A (25 mM HEPES (pH 7.5), 0.3 M NaCl, 1 mM TCEP and 10% glycerol) before being eluted with buffer A supplemented with 5 mM desthiobiotin. The eluted protein was combined and diluted 1:1 with buffer B (25 mM HEPES (pH 7.5), 0.1 M NaCl, 1 mM TCEP and 10% glycerol) to dilute the salt before being loaded onto a HiTrap Q HP (Cytiva) column. The protein was eluted with a linear gradient from buffer B to buffer C (25 mM HEPES (pH 7.5), 2 M NaCl, 1 mM TCEP and 10% glycerol). The relevant fractions were pooled and diluted again 1:1 with buffer B to reduce the salt before being injected onto a Heparin HP (Cytiva) column. Protein was eluted with a linear gradient from buffer B to buffer C. Finally, the protein was concentrated, snap frozen in liquid nitrogen and stored at −80 °C.

Purification of fluorescently labelled SWR1

To site specifically label the SWR1 complex, we made use of the ybbR-labelling approach34,35. The 11-amino acid ybbR tag was fused to the N terminus of the Arp6 subunit of SWR1. The ybbR–Arp6 mutant was used in place of the wild-type Arp6 gene when assembling the SWR1 genes using the MultiBac system[16]. The SWR1(ybbR–Arp6) complex was expressed and purified in an analogous way to wild-type SWR1 with the ybbR-labelling reaction taking place after elution from the HiTrap Q HP column. The labelling reaction was carried out overnight at 4 °C. Typically, SWR1(ybbR–Arp6; approximately 1 µM) was labelled with CoA-Atto647N (approximately 10 µM) using recombinant Sfp transferase (approximately 0.2 µM) in buffer B supplemented with 10 mM MgCl_2. The labelled SWR1 complex was separated from free dye and Sfp transferase using a Heparin HP (Cytiva) column, eluting with a linear gradient from buffer B to buffer C. Finally, SWR1(Atto647N–Arp6) (referred to as SWR1(647N) in the text) was concentrated, snap frozen in liquid nitrogen and stored at −80 °C.

Purification of S. cerevisiae histones

All nucleosomes or hexasomes used in this study were composed of S. cerevisiae histones assembled on DNA containing the 601 Widom sequence.

S. cerevisiae octamers with and without Alexa Fluor 555 on H2A K119C were prepared as previously described[16].

S. cerevisiae H2A–H2B, Htz1–H2B (with and without Alexa Fluor 555 on Htz1 K125C) or Htz1–H2B(3×Flag) histone dimers were expressed in E. coli and purified as soluble dimers. Cells were lysed by sonication in buffer D (20 mM Tris (pH 7.5), 0.5 M NaCl, 0.1 mM EDTA and 1 mM TCEP) plus protease inhibitor tablets (Roche; 2 tablets per 100 ml). Dimers were purified by loading the cleared lysate onto tandem HiTrap Q FF and HiTrap Heparin HP columns in buffer E (20 mM Tris (pH 7.5), 0.5 M NaCl, 1 mM EDTA and 1 mM TCEP). The HiTrap Q FF column was removed before elution from the HiTrap Heparin HP column via a gradient to buffer F (20 mM Tris (pH 7.5), 2 M NaCl, 1 mM EDTA and 1 mM TCEP), followed by gel filtration on a Superdex S200 in buffer F.

S. cerevisiae histone H3(Q120M, K121P and K125Q) and histone H4 were co-expressed in E. coli and purified as soluble tetramers. Cells were lysed by sonication in buffer D plus protease inhibitor tablets (Roche; 2 tablets per 100 ml). Tetramers were purified using a HiTrap Heparin HP column in buffer E and eluted via a gradient to buffer F, followed by gel filtration on a Superdex S200 in buffer F.

Preparation of nucleosomes

Biotinylated DNA containing the Widom 601 sequence was generated as previously described[12]. Salt gradient dialysis of the S. cerevisiae octamers with DNA was carried out to form a ‘core’ nucleosome. A biotinylated DNA overhang was ligated to the core nucleosome as previously described[12]. This resulted in nucleosomes with one long overhang of 113 bp and a short overhang of 2 bp, which we refer to as 113N2 (‘N’ representing the Widom 601 nucleosome positioning sequence). The biotin was present on the long 113-bp linker. For nucleosomes where the DNA was labelled, the fluorophore was attached at the end of the 2-bp short overhang.

Preparation of hexasomes

To facilitate the formation of yeast hexasomes, three amino acid substitutions were introduced into the S. cerevisiae H3 histone (Q120M, K121P and K125Q)[36]. These substitutions (MPQ) are the corresponding amino acids found in human and Xenopus laevis H3.

To form hexasomes, S. cerevisiae H2A–H2B dimers were mixed with S. cerevisiae H3(MPQ)–H4 tetramers. The amount of H2A–H2B dimers used was limited to 0.6× the amount of tetramers to ensure only partial H2A–H2B occupancy. Hexasomes were assembled onto the same DNA that was used for nucleosomes by salt gradient dialysis to generate ‘core’ hexasomes. Core hexasomes were separated from tetrasomes, nucleosomes and free DNA using a MonoQ column, loaded in buffer G (20 mM Tris (pH 7.5), 1 mM EDTA, 1 mM TCEP and 200 mM NaCl) eluting with a gradient into buffer H (as buffer G with 2 M NaCl). The fractions were immediately diluted into 4× volume of 20 mM Tris (pH 7.5) to reduce the salt concentration. A biotinylated DNA overhang was ligated to the core hexasome in the same way as was used for nucleosomes. This resulted in a hexasome with one long overhang of 113 bp and a short overhang of 2 bp, which we refer to as 113H2 (‘H’ representing a hexasome assembled on the Widom 601 sequence). The biotin was present on the long 113-bp linker. For hexasomes where the DNA was labelled, the fluorophore was attached at the end of the 2-bp short overhang.

As is the case for hexasomes prepared with X. laevis histones[37], yeast hexasomes prepared in this way exploit the inherent asymmetry of the Widom 601 sequence. Because of this asymmetry, the H2A–H2B dimer present in a hexasome is preferentially located on the ‘TA-rich’ side of the Widom 601 sequence, leaving the vacant site on the ‘TA-poor’ side. We orientated our Widom 601 sequence with the TA-rich side closest to the 2-bp short overhang. This resulted in the vacant H2A–H2B site being located next to the 113-bp linker.

Preparation of heterotypic nucleosomes

Core hexasomes, prepared as described above, were mixed with S. cerevisiae Htz1–H2B dimers to form heterotypic nucleosomes. Htz1–H2B dimers were added at an amount equal to 0.3× the amount of hexasome present. Core heterotypic nucleosomes were then purified in the same way as canonical nucleosomes. A biotinylated DNA overhang was ligated to the core heterotypic nucleosomes as described above. Resulting heterotypic nucleosomes contain the Htz1–H2B dimer next to the long 113-bp overhang and the conical H2A–H2B dimer next to the short 2-bp overhang.

Bulk histone exchange assay

SWR1 (100 nM; wild type or SWR1(647N)), 200 nM nucleosomes and 400 nM Htz1–H2B(3×Flag) were mixed in exchange buffer (25 mM Tris-HCl (pH 7.8), 100 mM KCl, 0.2 mM EDTA and 2 mM MgCl_2), with or without 1 mM ATP. The exchange reaction was carried out at 30 °C. At the indicated time points, 8 µl of the reaction was removed and quenched by the addition of 4 µl of a stopping solution (0.5 mg ml−1 salmon sperm DNA, 30 mM EDTA and 3× ficoll loading buffer) and placed on ice. The ‘no ATP’ control was taken at the longest indicated time point. After all time points had been taken, the reaction products were separated by 6% native PAGE, run at 110 V in 0.5× TBE at 4 °C and visualized using fluorescence of the nucleosome.

Two-colour smFRET microscope

smFRET measurements looking at the flipping of nucleosomes by SWR1 were performed on an Olympus IX-71 microscope equipped with a homebuilt prism-TIRF module. Excitation was provided by a 532-nm laser (Stradus, Vortran) or a 637-nm laser (Stradus, Vortran). Fluorescence was collected through a 1.2 NA, 60× water objective (Olympus) and filtered through a dual bandpass filter (FF01-577/690-25, Semrock). The fluorescence was spectrally separated using a OptoSplit II (Cairn Research) to separate donor and acceptor emission. The donor and acceptor emissions were further filtered through ET585/65M and ET700/75M (Chroma) bandpass filters, respectively. The donor and acceptor images were then projected side-by-side onto an electron-multiplying charge-coupled device (EMCCD) (Andor iXon Ultra 897). Data were collected as raw movies using a custom LabVIEW script.

Single-molecule fluorescence spots from the raw movies were localized using custom IDL scripts and converted into raw fluorescence trajectories. Raw fluorescence trajectories were corrected for bleed through of the donor fluorescence into the acceptor channel. Apparent FRET efficiencies were calculated as the ratio of acceptor intensity divided by the sum of the donor and acceptor intensities.

Two mechanical shutters (LS-3, Uniblitz, Vincent Associates) were placed in the excitation path for alternating laser excitation (Extended Data Fig. 4e–g). Frame acquisition and shutter synchronization were obtained using a homebuilt negative-edge-triggered JK flip–flop circuit (SN74LS112AN, Texas Instruments) using the ‘Fire’ output of the EMCCD as the input clock. IDL scripts were modified accordingly to locate single molecules and extract fluorescence trajectories.

Three-colour smFRET microscope

smFRET measurements looking at histone exchange coupled with SWR1 binding were performed on an Olympus IX-71 microscope equipped with a homebuilt prism-TIRF module. Alternating laser excitation was provided by a 488-nm laser (OBIS, Coherent) or a 637-nm laser (OBIS, Coherent). Alternation of the lasers and synchronization of the lasers with the camera were controlled by a custom LabVIEW script and a DAQ (USB-6341, National Instruments). Fluorescence was collected through a 1.2 NA, 60× water objective (Olympus) and filtered through ET500lp (Chroma) and NF03-642E-25 (Semrock) filters. The fluorescence was spectrally separated using a MultiSplit (Cairn Research) housing the following dichroic filters: T500lpxr UF2, T635lpxr UF2 and T725lpxr UF2 (Chroma). The separated fluorescent emission was projected onto quadrants of a sCMOS (ORCA Fusion, Hammamatsu) camera. Data were collected as raw movies using HCImage Live (Hammamatsu).

Single-molecule fluorescence spots from the raw movies were localized using custom IDL scripts and converted into raw fluorescence trajectories. Raw fluorescence trajectories were corrected for bleed through of the donor fluorescence into the acceptor channel. Apparent FRET efficiencies were calculated as the ratio of acceptor intensity divided by the sum of the donor and acceptor intensities.

Microscope slide passivation and flow chamber assembly

Quartz slides (UQC optics) and glass coverslips were aminosilinized with N-(2-aminoethyl)-3-aminopropyltrimethoxysilane, then passivated using methoxy-PEG-SVA (relative molecular mass = 5,000; Laysan Bio, Inc.) containing 5% biotin-PEG-SVA (relative molecular mass = 5,000, Laysan Bio, Inc.) in 100 mM sodium bicarbonate as previously described[38] with minor modifications. Following passivation, slides and coverslips were stored under nitrogen in the dark at −20 °C. Before use, slides and coverslips were warmed to room temperature and assembled into flow chambers using 0.12-mm thick double-sided adhesive sheets (Grace Bio-Labs SecureSeal). Flow chambers were sealed with epoxy glue.

Nucleosome or hexasome immobilization

Nucleosomes or hexasomes were surface immobilized as previously described[12]. In brief, neutravidin (0.1 mg ml−1) in T50 buffer (50 mM Tris-HCl (pH 7.5) and 50 mM NaCl) was injected into the assembled flow chamber and incubated for 5 min to allow binding to the biotinylated PEG surface. Excess neutravidin was washed out with reaction buffer (25 mM Tris-HCl (pH 7.8), 100 mM KCl, 4% glycerol, 1 mM EDTA, 2 mM MgCl_2 and 0.2 mg ml−1 BSA). Biotinylated nucleosomes or hexasomes were diluted to 10 pM in reaction buffer before injecting into the flow chamber and allowed to bind to the neutravidin for 5 min. Excess nucleosomes or hexasomes were flushed out using imaging buffer (reaction buffer with Trolox, 2.5 mM protocatechuic acid and 0.25 µM protocatechuate-3,4-dioxygenase) and imaged immediately.

smFRET between nucleosome or hexasome and SWR1 data collection

Nucleosomes or hexasomes labelled with a Cy3 donor on the short end of the DNA overhang (113N2.Cy3 or 113H2.Cy3) were immobilized in a flow chamber and imaged. SWR1(647N), 10 nM in imaging buffer (25 mM Tris-HCl (pH 7.8), 100 mM KCl, 4% glycerol, 1 mM EDTA, 2 mM MgCl_2, 0.2 mg ml−1 BSA, Trolox, 2.5 mM protocatechuic acid and 0.25 µM protocatechuate-3,4-dioxygenase) was injected. Imaging was performed by first directly exciting the acceptor with a 637-nm laser for approximately 15 s to localize SWR1(647N), before switching to 532-nm excitation to observe FRET between the nucleosome or hexasome and SWR1. All single-molecule measurements were carried out at room temperature, data were acquired with a 100-ms frame time.

smFRET between nucleosome or hexasome and SWR1 data analysis

Manual inspection of the donor intensity, acceptor intensity and apparent FRET from each molecule was carried out using custom MATLAB scripts. For a molecule to be included in downstream analysis, it needed to have a constant signal from the acceptor under direct acceptor excitation to indicate that SWR1(647N) was bound and display a single step photobleaching event of either the donor or acceptor under donor excitation. All molecules that satisfied these criteria were truncated to just the FRETing region preceding the photobleaching event.

Truncated FRET traces were analysed with a hidden Markov model using vbFRET, using default parameters[39]. The idealized FRET from vbFRET was used to generate FRET histograms, plotted using Igor Pro 8 (Wavemetrics). Dwell times from the idealized FRET trajectories were extracted using custom MATLAB scripts. Only dwell times in which the idealized FRET transitioned between proximal and distal states (or the reverse) were included. Dwell time plots were generated in MATLAB and plotted in Igor Pro 8. The lifetime of the proximal-bound and distal-bound states was determined by fitting the dwell time plots to a double exponential function in Igor Pro 8. The slow and fast exponential phases probably correspond to a fully or partially engaged SWR1 complex, respectively. The average lifetimes (τave) for proximal-bound and distal-bound states were calculated using the pre-exponential factors (A) and lifetimes (τ) determined from the double exponential fit as follows:

$${\tau }_{{\rm{ave}}}=({A}_{1}{{\tau }_{1}}^{2}+{A}_{2}{{\tau }_{2}}^{2})/({A}_{1}{\tau }_{1}+{A}_{2}{\tau }_{2})$$

In all cases, we observed both static and dynamic trajectories when probing the FRET between nucleosomes or hexasomes and SWR1. Only dynamic trajectories were used for determining the kinetics. For both the canonical and the heterotypic nucleosomes, static trajectories represent a minority of the observed molecules. Short static traces may be due to dye photobleaching or SWR1 diffusion before a flipping event can take place. However, longer static traces are also observed. This heterogeneity is summarized in Extended Data Fig. 5. Long static trajectories suggest that a proportion of SWR1 molecules are stably engaged on one side of the nucleosome and not dynamically checking the histone identity of each nucleosome face. The nature of this stable SWR1 binding, compared with binding that allows nucleosome flipping, is unknown, as is the method by which SWR1 could transition from a static (stable binding) to a flipping (checking histone identity) state.

smFRET real-time imaging of histone exchange and SWR1-binding data collection

A quartz flow cell was prepared as described above. Neutravidin (0.01 mg ml−1) in T50 buffer (50 mM Tris-HCl (pH 7.5) and 50 mM NaCl) was injected into the flow chamber and incubated for 5 min to allow binding to the biotinylated PEG surface. Excess neutravidin was washed out and the flow cell further passivated by incubation with Pluronic F127 (0.5% w/v) in T50 buffer. Excess Pluronic F127 was washed out with reaction buffer (25 mM Tris-HCl (pH 7.8), 100 mM KCl, 4% glycerol, 0.2 mM EDTA, 2 mM MgCl_2 and 0.2 mg ml−1 BSA).

To follow the insertion of variant histones in real time at the single-molecule level, a ‘gain of FRET’ assay was used. Nucleosomes labelled with Alexa Fluor 488 (FRET donor) on the short 2-bp overhang (113N2.AF488) were immobilized in a flow chamber and imaged. To start the reaction, 1 nM SWR1, 4 nM Chz1–Htz1(AF555)–H2B and 1 mM ATP in imaging buffer (25 mM Tris-HCl (pH 7.8), 100 mM KCl, 4% glycerol, 0.2 mM EDTA, 2 mM MgCl_2, 0.2 mg ml−1 BSA, Trolox, 2.5 mM protocatechuic acid and 0.25 µM protocatechuate-3,4-dioxygenase) was injected into the chamber using a syringe pump. Exchange can be monitored by stepwise FRET increases as the AF555-labelled (FRET acceptor) Htz1–H2B dimer is exchanged into the immobilized AF488-labelled nucleosome. To reduce nonspecific binding of the Htz1(AF555)–H2B dimer, the dimer was first complexed with its natural chaperone, Chz1 (ref. [40]).

For experiments that simultaneously followed exchange and SWR1 binding, the experiment was conducted as described but with SWR1(647N) using the three-colour smFRET microscope described above. The two excitation lasers (488 nm and 637 nm) were alternated at a frequency of 1 Hz. All experiments were carried out at room temperature (22 °C).

smFRET real-time imaging of histone exchange and SWR1-binding data analysis

Visualization of single-molecule trajectories was carried out using custom MATLAB scripts. For each single molecule, the intensity of the donor (Alexa Fluor 488), acceptor (Alexa Fluor 555) and corresponding FRET, along with the colocalized SWR1-binding intensity (Atto647N) were inspected. Nucleosomes that underwent exchange were identified by stepwise increases in the FRET trajectory. SWR1 binding was identified as an increase in the Atto647N intensity. Nucleosomes where the signal for SWR1 binding overlapped with at least one exchange event were included for further analysis. Dwell times were collected by manual inspection of the trajectories. Data were obtained by measuring several regions of interest from at least three independent slides. Dwell time plots were generated in MATLAB and plotted and fit in Igor Pro 8.

Single-molecule measurements of SWR1 nucleosome lifetime

Nucleosomes labelled with Alexa Fluor 488 on the short 2-bp overhang (113N2.AF488) were immobilized in a flow chamber as described above. Of SWR1(647N), 5 nM in imaging buffer (25 mM Tris-HCl (pH 7.8), 100 mM KCl, 4% glycerol, 1 mM EDTA, 2 mM MgCl_2, 0.2 mg ml−1 BSA, Trolox, 2.5 mM protocatechuic acid and 0.25 µM protocatechuate-3,4-dioxygenase with 1 mM ATP) was injected. The three-colour smFRET microscope described above was used. The two excitation lasers (488 nm and 637 nm) were alternated at a frequency of 1 Hz. Experiments were carried out at room temperature (22 °C). Trajectories in which SWR1(647N) colocalized with a nucleosome were selected and further processed using tMAVEN[41] to determine the time for SWR1 to bind and the time SWR1 remained bound to a nucleosome.

Preparation of the SWR1–nucleosome complex for cryo-EM

Recombinant SWR1 was produced in BTI-TN-5B1-4 (High Five) insect cells, and the SWR1–nucleosome complex was assembled as previously described[12]. SWR1–nucleosome grids were prepared as previously described, except instead of glow discharge, the grids were cleaned by washing with water and ethyl acetate. Cryo-EM data acquisition, image acquisition and structure reconstruction were conducted using a similar procedure as previously described[12]. Data processing and refinement statistics for the two cryo-EM structures are summarized in Extended Data Table 1.

Cryo-EM data collection

A total of 35,076 micrographs were collected using a Titan KRIOS microscope operated at 300 kV. Images were collected on a Falcon IV direct electron detector with a pixel size of 1.1 Å px−1. Images were collected with a defocus range of −0.7 to −1.9 µm, with 1.0 s exposure time and a total dose of 40 e− Å−2 fractionated over 39 frames.

Cryo-EM data processing

Movie frames were aligned using MotionCor2 (ref. [42]), as previously described[12]. Contrast transfer function parameters were determined using Gctf[43] as previously described[12]. Particle picking was performed in cryoSPARC[44], as previously described[12]. Global-resolution and local-resolution estimates were calculated based on the gold-standard Fourier shell correlation (FSC = 0.143) criterion.

The cryo-EM processing workflow for the 3.8 Å SWR1–nucleosome map in configuration I is summarized in Extended Data Fig. 6. First, in the recently collected SWR1–nucleosome dataset, 2D classification in cryoSPARC for 2D classes containing density for SWR1 or the nucleosome resulted in a working particle pool of 1,918,312 particles[44]. These were subdivided into three classes via heterogeneous refinement in cryoSPARC, resulting in class 1 (SWR1–nucleosome complex (15%)), class 2 (SWR1-apo (55%)) and class 3 (nucleosome only (30%)). The subset of 268,805 particles in class 1 (SWR1–nucleosome) was then further classified into five classes via heterogeneous refinement in cryoSPARC, resulting in class 1.1 (SWR1–nucleosome in configuration I (68%)), class 1.2 (SWR1–nucleosome configuration II (17%)), class 1.3 (poorly aligned class (9%)), class 1.4 (poorly aligned class (2%)) and class 1.5 (poorly aligned class (4%)). The particles in class 1.1 were then imported and subjected to 3D refinement in RELION before one round of 3D classification without alignment (T = 30), with a soft mask overlapping the Swc2–bottom gyre DNA interface[45]. This generated two classes: class 1.1.1 (no density for bottom gyre DNA (63%)) and class 1.1.2 (clear density for bottom gyre DNA (37%)). Particles in class 1.1.2 were further selected for 3D refinement in RELION.

Next, in the previously collected dataset, 2D classification in cryoSPARC for 2D classes containing density for SWR1 or the nucleosome resulted in a working particle pool of 296,061 particles. These were subdivided into three classes via heterogeneous refinement in cryoSPARC, resulting in a class 1.1 (SWR1–nucleosome complex (33%)), class 1.2 (SWR1-apo (39%)) and class 1.3 (nucleosome only (28%))[44]. The subset of 96,648 SWR1–nucleosome particles were then further classified into five classes via heterogeneous refinement in cryoSPARC, resulting in class 1.1 (SWR1–nucleosome in configuration I (68%)), class 1.2 (SWR1–nucleosome configuration II (23%)), class 1.3 (poorly aligned class (5%)), class 1.4 (poorly aligned class (2%)) and class 1.5 (poorly aligned class (2%)). Particles in class 1.1 were imported and refined in RELION before one round of 3D classification without alignment (T = 30), with a soft mask overlapping the Swc2–bottom gyre DNA interface. This generated two classes: class 1.1.1 (no density for bottom gyre DNA (16%)) and class 1.1.2 (clear density for bottom density (84%)). Particles in class 1.1.2 were further selected for 3D refinement in RELION[45]. Particles from classes 1.1.2 in the recently collected dataset and 1.1.2 in the previously collected dataset were then merged to generate a working pool of 123,591 particles. The resulting particles were then subjected to 3D refinement and contrast transfer function refinement in RELION with a mask corresponding to the SWR1 subcomplex of Swr1, Arp6, Swc6, Swc2, RuvBL1 and RuvBL2, and the nucleosome to generate the final 3.8 Å SWR1–nucleosome map in configuration I[45].

The cryo-EM processing workflow for the 4.7 Å SWR1–nucleosome map in configuration II is summarized in Extended Data Fig. 6. First, in the recently collected SWR1–nucleosome dataset, particles in class 1.2 were selected, generating a working pool of 35,102 particles. The subset of particles was further classified into two classes in RELION using 3D classification with alignment (T = 6) in the absence of a mask[45]. This generated class 1.2.1 (SWR1–nucleosome with poor density for the upper gyre DNA (39%)) and class 1.2.2 (SWR1–nucleosome with clearer density of upper gyre DNA (61%)). The particles in class 1.2.2 were selected, generating a working pool of 20,990 particles for 3D refinement in RELION.

Next, in the previously collected SWR1–nucleosome dataset, particles in class 1.2 were selected, generating a working pool of 21,054 particles. The subset of particles was further classified in two classes in RELION using 3D classification with alignment (T = 6) in the absence of a mask[45]. This generated class 1.2.1 (SWR1–nucleosome with poor density for the upper gyre DNA (40%)) and class 1.2.2 (SWR1–nucleosome with clearer density of upper gyre DNA (60%)). The particles in class 1.2.2 were selected, generating a working pool of 12,605 particles for 3D refinement in RELION[45]. Particles from classes 1.2.2 in the recently collected dataset and 1.2.2 in the previously collected dataset were then merged to generate a working pool of 33,595 particles. The resulting particles were then subjected to 3D refinement and contrast transfer function refinement in RELION with a mask corresponding to the SWR1 subcomplex of Swr1, Arp6, Swc6, Swc2, RuvBL1 and RuvBL2, and the nucleosome to generate the final 4.7 Å SWR1–nucleosome map in configuration II.

Model building

For the Swc2 subunit, an initial template was generated using AlphaFold[25]. Different regions corresponding to secondary structures of the template were manually truncated and docked separately into the recently generated 3.8 Å SWR1–nucleosome map in configuration I in Chimera12,46, before being further built in Coot[47]. The final coordinates were subjected to real-space refinement in Phenix[48].

For the 3.8 Å SWR1–nucleosome configuration I map, first the SWR1–nucleosome complex from the previously solved 3.6 Å SWR1–nucleosome structure (Protein Data Bank (PDB) ID 6GEJ) was docked into the density using Chimera12,46. The coordinates for the DNA were then omitted. Next, the SWR1–nucleosome complex from the previously solved 4.5 Å SWR1–nucleosome structure (PDB ID 6GEN) was superimposed onto the docked structure using RuvBL1 and RuvBL2 as a reference. Coordinates for the superimposed structure were then omitted, with exception to the coordinates for the DNA, which was kept and docked into the 3.8 Å SWR1–nucleosome configuration I map in Chimera, before merging the two PDB models: SWR1–nucleosome DNA omitted and DNA only together. The coordinates corresponding to the previously built Swc2 subunit were then omitted, and the coordinates for the newly built Swc2 model were docked into the map. Additional DNA overhang was then built manually in Coot12,46,47. The final coordinates were then subjected to real-space refinement in Phenix[48].

For the 4.7 Å SWR1–nucleosome configuration II map, SWR1 from the previously solved 3.6 Å SWR1–nucleosome structure (PDB ID 6GEJ) was docked into the density using Chimera[46]. The coordinates corresponding to Swc2 were omitted, and the recently built Swc2 was docked together into the density using Chimera and further built in Coot46,47. The additional DNA overhang was then built manually in Coot. The final coordinates were then subjected to real-space refinement in Phenix[48].

2D classification of SWR1-mediated nucleosome flipping

First, in the recently collected SWR1–nucleosome dataset, particles in class 2 (SWR1-apo (55%)) were selected, generating a working pool of 594,100 particles. The subset of particles was then further classified into four classes via heterogeneous refinement in cryoSPARC, resulting in class 2.1 (RuvBL1–RuvBL2 only (21%)), class 2.2 (a poorly aligned class (20%)), class 2.3 (SWR1-apo with additional density underneath SWR1 (38%)) and class 2.4 (a poorly aligned class (21%)). Particles in class 2.3 were then selected for 2D classification in RELION[45].

Next, in the previously collected SWR1–nucleosome dataset, particles in class 2 (SWR1-apo (39%)) were selected, generating a working pool of 115,463 particles. The subset of particles was then further classified into four classes via heterogeneous refinement in cryoSPARC, resulting in class 2.1 (RuvBL1–RuvBL2 only (25%)), class 2.2 (a poorly aligned class (20%)), class 2.3 (SWR1-apo with additional density underneath SWR1 (30%)) and class 2.4 (a poorly aligned class (25%)). Particles in class 2.3 were then selected for 2D classification in RELION. The particles in class 2.3 in the recently collected SWR1–nucleosome dataset and the particles in class 2.3 in the previously collected SWR1–nucleosome dataset were then merged and subjected to multiple rounds of 2D classification in RELION to obtain 2D classes of SWR1-mediated nucleosome flipping.

Statistics and reproducibility

For data relating to Fig. 1, the total number of traces used in each dataset is indicated in each panel and was derived from three independent experiments. For data relating to Fig. 2, the total number of traces used for each dataset is indicated in each panel and was derived from four independent experiments. For data relating to Figs. 3 and 4, two independent experiments were performed, one of which is shown. The total number of traces used for each dataset is indicated in each panel. All gels were independently and successfully repeated twice.

Reporting summary

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

Data availability

Electron density maps have been deposited at the Electron Microscopy Database (accession codes EMDB-18471 and EMDB-18472), and atomic coordinates have been deposited at the PDB (PDB ID codes 8QKU and 8QKV). Initial models used for model building include PDB ID 6GEN and 6GEJ, as well as an AlphaFold-generated model of Swc2. Correspondence and requests for materials should be addressed to D.B.W. or D.S.R. All unique materials are available on request with completion of a standard Materials Transfer Agreement. Source data are provided with this paper.

Code availability

Code for the single-molecule data analysis is freely available (https://github.com/singlemoleculegroup).

Change history

- 19 November 2024

In the version of the article initially published, the label “Vacant H2A–H2B site” pointed to the solid orange section, and has now been amended to point at the orange line in the HTML and PDF versions of the article.

References

1. Krogan, N. J. et al. A Snf2 family ATPase complex required for recruitment of the histone H2A variant Htz1. Mol. Cell 12, 1565–1576 (2003).

2. Mizuguchi, G. et al. ATP-driven exchange of histone H2AZ variant catalyzed by SWR1 chromatin remodeling complex. Science 303, 343–348 (2004).

3. Kobor, M. S. et al. A protein complex containing the conserved Swi2/Snf2-related ATPase Swr1p deposits histone variant H2A.Z into euchromatin. PLoS Biol. 2, e131 (2004).

4. Luger, K., Mäder, A. W., Richmond, R. K., Sargent, D. F. & Richmond, T. J. Crystal structure of the nucleosome core particle at 2.8 Å resolution. Nature 389, 251–260 (1997).

5. Talbert, P. B. & Henikoff, S. Histone variants at a glance. J. Cell Sci. 134, jcs244749 (2021).

6. Ruhl, D. D. et al. Purification of a human SRCAP complex that remodels chromatin by incorporating the histone variant H2A.Z into nucleosomes. Biochemistry 45, 5671–5677 (2006).

7. Kusch, T. et al. Acetylation by Tip60 is required for selective histone variant exchange at DNA lesions. Science 306, 2084–2087 (2004).

8. Giaimo, B. D., Ferrante, F., Herchenröther, A., Hake, S. B. & Borggrefe, T. The histone variant H2A.Z in gene regulation. Epigenetics Chromatin 12, 37 (2019).

9. Clapier, C. R. & Cairns, B. R. The biology of chromatin remodeling complexes. Annu. Rev. Biochem. 78, 273–304 (2009).

10. Eustermann, S. et al. Structural basis for ATP-dependent chromatin remodelling by the INO80 complex. Nature 556, 386–390 (2018).

11. Ayala, R. et al. Structure and regulation of the human INO80–nucleosome complex. Nature 556, 391–395 (2018).

12. Willhoft, O. et al. Structure and dynamics of the yeast SWR1–nucleosome complex. Science 362, eaat7716 (2018).

13. Willhoft, O. & Wigley, D. B. INO80 and SWR1 complexes: the non-identical twins of chromatin remodelling. Curr. Opin. Struct. Biol. 61, 50–58 (2020).

14. Ranjan, A. et al. H2A histone-fold and DNA elements in nucleosome activate SWR1-mediated H2A.Z replacement in budding yeast. eLife https://doi.org/10.7554/eLife.06845.001 (2015).

15. Luk, E. et al. Stepwise histone replacement by SWR1 requires dual activation with histone H2A.Z and canonical nucleosome. Cell 143, 725–736 (2010).

16. Lin, C. L. et al. Functional characterization and architecture of recombinant yeast SWR1 histone exchange complex. Nucleic Acids Res. 45, 7249–7260 (2017).

17. Watanabe, S., Radman-Livaja, M., Rando, O. J. & Peterson, C. L. A histone acetylation switch regulates H2A.Z deposition by the SWR-C remodeling enzyme. Science 340, 195–199 (2013).

18. Poyton, M. F. et al. Coordinated DNA and histone dynamics drive accurate histone H2A.Z exchange. Sci. Adv. 8, eabj5509 (2022).

19. Fan, J., Moreno, A. T., Baier, A. S., Loparo, J. J. & Peterson, C. L. H2A.Z deposition by SWR1C involves multiple ATP-dependent steps. Nat. Commun. 13, 7052 (2022).

20. Singh, R. K. et al. Transient kinetic analysis of SWR1C-catalyzed H2A.Z deposition unravels the impact of nucleosome dynamics and the asymmetry of histone exchange. Cell Rep. 27, 374–386.e4 (2019).

21. Carcamo, C. C. et al. ATP binding facilitates target search of SWR1 chromatin remodeler by promoting one-dimensional diffusion on DNA. eLife 11, e77352 (2022).

22. Hopfield, J. J. Kinetic proofreading: a new mechanism for reducing errors in biosynthetic processes requiring high specificity. Proc. Natl Acad. Sci. USA 71, 4135–4139 (1974).

23. Ninio, J. Kinetic amplification of enzyme discrimination. Biochimie 57, 587–595 (1975).

24. Jalal, A. S. B. et al. Stabilization of the hexasome intermediate during histone exchange by yeast SWR1 complex. Mol. Cell 84, 3871–3884.e9 (2024).

25. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

26. Wu, W. H. et al. Swc2 is a widely conserved H2AZ-binding module essential for ATP-dependent histone exchange. Nat. Struct. Mol. Biol. 12, 1064–1071 (2005).

27. Liang, X. et al. Structural basis of H2A.Z recognition by SRCAP chromatin-remodeling subunit YL1. Nat. Struct. Mol. Biol. 23, 317–323 (2016).

28. Ranjan, A. et al. Nucleosome-free region dominates histone acetylation in targeting SWR1 to promoters for H2A.Z replacement. Cell 154, 1232–1245 (2013).

29. Sun, L., Pierrakeas, L., Li, T. & Luk, E. Thermosensitive nucleosome editing reveals the role of DNA sequence in targeted histone variant deposition. Cell Rep. 30, 257–268.e5 (2020).

30. Jiang, C. & Pugh, B. F. Nucleosome positioning and gene regulation: advances through genomics. Nat. Rev. Genet. 10, 161–172 (2009).

31. Yen, K., Vinayachandran, V. & Pugh, B. F. SWR-C and INO80 chromatin remodelers recognize nucleosome-free regions near +1 nucleosomes. Cell 154, 1246–1256 (2013).

32. Rhee, H. S., Bataille, A. R., Zhang, L. & Pugh, B. F. Subnucleosomal structures and nucleosome asymmetry across a genome. Cell 159, 1377–1388 (2014).

33. Qiu, Y. et al. The Chd1 chromatin remodeler shifts nucleosomal DNA bidirectionally as a monomer. Mol. Cell 68, 76–88.e6 (2017).

34. Belan, O. et al. Single-molecule analysis reveals cooperative stimulation of Rad51 filament nucleation and growth by mediator proteins. Mol. Cell 81, 1058–1073.e7 (2021).

35. Yin, J., Lin, A. J., Golan, D. E. & Walsh, C. T. Site-specific protein labeling by Sfp phosphopantetheinyl transferase. Nat. Protoc. 1, 280–285 (2006).

36. Leung, A. et al. Unique yeast histone sequences influence octamer and nucleosome stability. FEBS Lett. 590, 2629–2638 (2016).

37. Levendosky, R. F., Sabantsev, A., Deindl, S. & Bowman, G. D. The Chd1 chromatin remodeler shifts hexasomes unidirectionally. eLife 5, e21356 (2016).

38. Brenlla, A., Markiewicz, R. P., Rueda, D. & Romano, L. J. Nucleotide selection by the Y-family DNA polymerase Dpo4 involves template translocation and misalignment. Nucleic Acids Res. 42, 2555–2563 (2014).

39. Bronson, J. E., Fei, J., Hofman, J. M., Gonzalez, R. L. & Wiggins, C. H. Learning rates and states from biophysical time series: a Bayesian approach to model selection and single-molecule FRET data. Biophys. J. 97, 3196–3205 (2009).

40. Luk, E. et al. Chz1, a nuclear chaperone for histone H2AZ. Mol. Cell 25, 357–368 (2007).

41. Verma, A. R., Ray, K. K., Bodick, M., Kinz-Thompson, C. D. & Gonzalez, R. L. Increasing the accuracy of single-molecule data analysis using tMAVEN. Biophys. J. https://doi.org/10.1016/j.bpj.2024.01.022 (2024).

42. Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).

43. Zhang, K. Gctf: CTF real-time determination and correction. J. Struct. Biol. 193, 1–12 (2016).

44. Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. CryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).

45. Scheres, S. H. W. RELION: implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180, 519–530 (2012).

46. Pettersen, E. F. et al. UCSF Chimera — a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

47. Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D 60, 2126–2132 (2004).

48. Afonine, P. V. et al. Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr. D 74, 531–544 (2018).

Acknowledgements

We thank the Wigley and Rueda laboratories for comments and critical reading of the manuscript; Diamond for access and support of the cryo-EM facilities at the UK National Electron Bio-imaging Centre (eBIC), funded by the Wellcome Trust, Medical Research Council and BBSRC, and LonCEM facility; and N. Cronin for assistance with data collection at the LonCEM facility. The work was funded by the Wellcome Trust (095519/Z/11/Z and 209327/Z/17/Z (to D.B.W.)), Cancer Research UK (C6913/A21608 (to D.B.W.)), the Medical Research Council (MR/N009258/1 and MR/R009023/1 (to D.B.W.)), and a core grant from the MRC Laboratory of Medical Sciences (UKRIMC-A658-5TY10 (to D.S.R.)).

Author information

Author notes

1. These authors contributed equally: Paul Girvan, Adam S. B. Jalal

Authors and Affiliations

1. Section of Structural Biology, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, UK

Paul Girvan, Adam S. B. Jalal, Elizabeth A. McCormack, Michael T. Skehan, Carol L. Knight & Dale B. Wigley

2. Single Molecule Biophysics Group, MRC Laboratory of Medical Sciences, London, UK

Paul Girvan & David S. Rueda

3. Section of Virology, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, UK

David S. Rueda

Contributions

P.G., A.S.B.J., D.S.R. and D.B.W. designed the studies. P.G. conducted and analysed the single-molecule experiments. A.S.B.J. performed the cryo-EM analysis. A.S.B.J., P.G., E.A.C., M.T.S. and C.L.K. prepared the samples. P.G., A.S.B.J., D.B.W. and D.S.R. analysed the data and wrote the manuscript with input from all the authors.

Corresponding authors

Correspondence to Dale B. Wigley or David S. Rueda.

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Competing interests

The authors declare no competing interests.

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Nature thanks Blaine Bartholomew, Zhucheng Chen 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 Additional examples of smFRET trajectories relating to the experiment described in Fig. 1 of the main text.

a, Three examples of double exchange events where the first exchange is on the dye proximal side. b, Three examples of double exchange events where the first exchange is on the dye distal side. c, Idealized FRET histogram of an exchange reaction carried out in the presence of ATPγS. No stepwise FRET increases like the examples shown in (a) and (b) are observed. Most molecules exhibit static low FRET.

Extended Data Fig. 2 Fluorescently labelled SWR1 and measuring nucleosome bound lifetime.

a, SWR1 was specifically labelled with Atto647N on the N-terminus of the Arp6 subunit. Coomassie stained gel of the purified complex shows the presence of all expected SWR1 subunits (left). The same gel imaged for fluorescence shows that only the Arp6 subunit has been fluorescently labelled (right). Representative gel of three independent preparations. For gel source data, see Supplementary Fig. 1. b, Bulk activity assay using the insertion of a FLAG tagged Htz1–H2B dimer as a readout for exchange. Exchange activity of the labelled SWR1 complex is retained. Representative gel of two independent experiments using enzyme from separate purifications. For gel source data, see Supplementary Fig. 1. c, Three example single molecule intensity trajectories of SWR1(647N) colocalization to surface immobilized nucleosomes. d, Dwell time plot of SWR1(647N) binding times. Data is shown fit to a single exponential decay (with residuals below). On average SWR1 takes 6.58 ± 0.02 min to bind under our experimental conditions. e, Dwell time plot of the lifetime of SWR1(647N) bound to a nucleosome. Data is shown fit to a double exponential decay (with residuals below). Two types of bound complex are present, one stably bound (lifetime 19 ± 2 min) and one more transiently bound (lifetime 1.91 ± 0.01 min). We tentatively assign the transiently bound species to SWR1(647N) interacting with the extranucleosomal DNA, and the stably bound species to SWR1(647N) engaging properly with the nucleosome. Reported errors are the error of the fit.

Extended Data Fig. 3 Additional examples of trajectories relating to the experiment described in Fig. 2 of the main text, and exchange of a heterotypic nucleosome.

a, & b, Single exchange events, where the exchange event is preceded by SWR1 binding. c, & d, Processive double exchange events. A single SWR1 binding event is followed by two consecutive exchange events. (Data in (c) is from Fig. 2d of the main text, replotted here to additionally show the donor and acceptor trajectories.) e, Distributive double exchange event. Following the first exchange SWR1 dissociates. The second exchange is preceded by a SWR1 binding event. f, Ambiguous double exchange example. SWR1 either dissociates or photobleaches between the first and second exchange events. g, Schematic of three-color smFRET assay using a heterotypic nucleosome as the substrate. Schematic is colored similarly to Fig. 2a of the main text. h, & i, Example trajectories showing SWR1 binding and histone exchange of a heterotypic nucleosome substrate. j, Histogram showing the FRET before (white bars) and after (grey bars) exchange, for a heterotypic nucleosome. k, Distribution of the time between SWR1 binding and histone exchange yields an exchange time of 106 ± 5 s for a heterotypic nucleosome. Reported errors are the error of the fit.

Extended Data Fig. 4 Additional data and controls relating to the experiments in Figs. 3 and 4 of the main text.

a, Example fluorescence intensity trajectory (top) and corresponding FRET trajectory (bottom) resulting from SWR1(647N) bound to a surface immobilized nucleosome. The excitation scheme used is illustrated with the magenta and green bars (top). After locating SWR1(647N) bound nucleosomes with red excitation, FRET between the nucleosome and SWR1(647N) is monitored using green excitation. Single step photobleaching of the acceptor and donor indicate a single FRET pair. b, Dwell time plot of the dwell times in the proximal or distal bound configurations for a nucleosome containing two H2A–H2B dimers (data from Fig. 3d, replotted here to show additional details of the fit). Dwell time is fit to a double exponential decay. The lifetimes in the fast (τfast) and slow (τslow) phases are indicated, along with the corresponding amplitudes (Afast, Aslow). The lifetimes are approximately equal regardless of SWR1 orientation. c, FRET histogram of nucleosome (donor) only control displaying zero FRET in the absence of any SWR1(647N) (acceptor). d, Example fluorescence intensity trajectory (top) and corresponding FRET trajectory (bottom) showing SWR1(647N) binding to a surface immobilized nucleosome (indicated by *), and subsequently flipping between dye-distal and dye-proximal orientations. SWR1(647N) binding results in a small but detectable non-zero FRET. (Note: such a trajectory would not be included in subsequent analysis as it does not satisfy the criterion of having SWR1(647N) bound at the start of data acquisition, but is shown here to illustrate detection of SWR1(647N) binding and flipping.) e, Schematic of the assay where the donor fluorophore is placed on one of the H2A histones: Nucleosomes (113N2) labelled with Cy3B on the linker-distal H2A are surface immobilized. SWR1(647N) is flowed in and allowed to bind the nucleosomes. SWR1(647N)–nucleosome interactions are monitored via FRET. Repositioning the FRET donor from the short DNA overhang (as used throughout the rest of this work) to the linker-distal H2A results in lower FRET efficiencies. To identify these true low-FRET values we employed alternating laser excitation throughout the entire acquisition. f, Trajectory of a dynamic SWR1(647N) bound nucleosome showing donor emission upon donor excitation (DD, green trace, top); acceptor emission upon donor excitation (DA, magenta trace, top); acceptor emission upon acceptor excitation (AA, gray trace, top). DD and DA are used for calculating apparent FRET efficiency (gray trace, bottom). g, Idealized FRET histogram shows two major populations of SWR1(647N)–bound nucleosomes. We observe similar ratios of the two states regardless of FRET donor position (c.f. Fig. 3). h, Additional smFRET traces from the experiment described in Fig. 4b. i, Dwell time plot of the dwell times in the proximal or distal bound configurations for a heterotypic nucleosome containing one Htz1–H2B and one H2A–H2B dimer (data from Fig. 4d, replotted here to show additional details of the fit). While the time spent on the distal side (i.e., the side containing Htz1) is well described by a single exponential decay, the proximal (H2A containing side) is best fit to a double exponential decay. Compare with (b). The lifetimes in the fast (τfast) and slow (τslow) phases are indicated, along with the corresponding amplitudes (Afast, Aslow). j, Bulk assay showing the exchange of a canonical H2A–H2B nucleosome (AA) compared to a heterotypic nucleosome containing one Htz1–H2B and one H2A–H2B dimer (ZA). The insertion of a FLAG tagged Htz1–H2B dimer is used as a readout for exchange. The AA nucleosome undergoes two consecutive rounds of exchange (indicated by the appearance of a double band shift). However, the ZA nucleosome can only be exchanged once (single band shift) indicating that SWR1 does not remove Htz1–H2B dimers from a nucleosome. Representative gel of two independent experiments using enzyme from separate purifications. For gel source data, see Supplementary Fig. 1. k, Cartoons of 113N2 heterotypic nucleosome (top) and 2N113 swapped DNA overhang heterotypic nucleosome (bottom). The position of the Cy3 fluorophore (green circle) and biotin (orange circle) are shown. Swapping the DNA overhang orientation with respect to the 601 positioning sequence results in the Htz1–H2B variant histone either being adjacent or opposite to the long DNA overhang. l, Swapped DNA overhang heterotypic nucleosomes (Cy3.2N113) containing one Htz1–H2B dimer (green) and one canonical H2A–H2B dimer (orange) Cy3-labeled on the 2 bp overhang are surface immobilized. SWR1647N is flowed in and allowed to bind to the nucleosome. SWR1(647N)–nucleosome interactions are monitored via FRET. m, Idealized FRET histogram shows a main population at low (0.1) FRET (c.f. Fig. 4c). n, Dwell time plots for the distal to proximal (red) and proximal to distal (black) transition for a 2N113 heterotypic nucleosome. Binding to the H2A–H2B face of the nucleosome is more stable than binding to the Htz1–H2B face, irrespective of the location of the long DNA overhang.

Extended Data Fig. 5 Rastergrams summarising the nucleosome flipping data for different nucleosomes/hexasome, and preparation of yeast hexasomes.

a-d, Each horizontal line represents a smFRET trajectory, ordered by photobleaching/dissociation time. Color indicates whether SWR1 is bound in the dye-distal (green) or dye-proximal (yellow) orientations. Thresholding (at 0.25 FRET) of the idealized FRET trajectories was used to determine the two states. Data is shown for: a, Canonical H2A–H2B 113N2 nucleosomes. b, Hexasomes 113H2 containing only one H2A–H2B dimer. c, Heterotypic nucleosomes 113N2 containing both H2A–H2B and Htz1–H2B histones. d, Swapped linker heterotypic nucleosomes 2N113 containing both H2A–H2B and Htz1–H2B histones. e, Native PAGE comparing a nucleosome and hexasome sample. Representative gel of two independent preparations. f, H3MPQ mutations required for formation of S. cerevisiae hexasomes and heterotypic nucleosomes (see Methods) have no effect on SWR1 exchange activity as measured by bulk FRET decrease.

Extended Data Fig. 6 Schematic overview of the cryoEM processing.

Schemes for the 3.8 Å SWR1–nucleosome complex in configuration I (cyan) and the 4.7 Å SWR1–nucleosome complex in configuration II (green) datasets.

Extended Data Fig. 7 Cryo-EM analysis of the SWR1–nucleosome in configuration I (3.8 Å) and SWR1–nucleosome in configuration II (4.7 Å) volumes.

a, Representative micrograph out of 35,076 micrographs from the SWR1–nucleosome dataset. A scale bar is shown at the bottom left. b, Four representative 2D classes of SWR1–nucleosome complex in configuration I. c, Four representative 2D classes of SWR1–nucleosome complex in configuration II. d, gFSC curve of the SWR1–nucleosome in configuration I volume. e, gFSC curve of the SWR1–nucleosome configuration II volume. f, Local resolution of the SWR1–nucleosome complex in configuration I. g, Local resolution of SWR1–nucleosome in configuration II. h, Overview of the SWR1–nucleosome complex in configuration I at 3.8 Å. i, Overview of the SWR1–nucleosome complex in configuration II at 4.7 Å.

Extended Data Fig. 8 Details of the interaction between Swc2 and the nucleosome in the SWR1–nucleosome in configuration I.

a, Linearized cartoon of the Swc2 subunit, the built-in coordinates are represented in yellow. The Htz1–H2B binding domain (residues 1–89) and the DNA-binding domain (residues 136–345) are highlighted. b, The residues of Swc2 that binds the DNA or interact with the H2A–H2B histones are highlighted The interaction between Swc2 and the nucleosome in the SWR1–nucleosome complex in configuration I. For simplicity, only the built in coordinates of Swc2 and the nucleosome is shown. c, Representative density for Swc2 at contact 1 (contoured at 2σ) in the SWR1–nucleosome in configuration I complex. d, Representative density for Swc2 at contact 2 (contoured at 2.5σ) in the SWR1–nucleosome in configuration I complex. The side chains of Swc2 that interacts with nucleosomal DNA is shown. e, Representative density for Swc2 at contact #3 (contoured at 5.5σ). in the SWR1–nucleosome in configuration I complex. The side chains of Swc2 that interacts with the nucleosomal DNA is shown. f, Representative density for Swc2 at contact #4 (contoured at 3σ). in the SWR1–nucleosome in configuration I complex. The side chains of Swc2 that interacts with the bottom gyre nucleosomal DNA is shown.

Extended Data Fig. 9 Residues that interact with the nucleosome are conserved between Swc2-like proteins, and additional 2D classes of SWR1-mediated nucleosome flipping.

a, The interaction between Swc2 and the nucleosome in the SWR1–nucleosome complex in configuration I. For simplicity, only the built-in coordinates of Swc2 and the nucleosome are shown. The four different contacts between Swc2 and the nucleosome are highlighted. An alignment of 116 Swc2-like proteins across various species was used to generate a sequence logo to display sequence conservation. The residues of Swc2 that bind the DNA or interact with the H2A–H2B histones are highlighted. b, Five additional intermediates of SWR1-mediated nucleosome flipping are visible after 2D classification.

Extended Data Table 1 Cryo-EM data collection, refinement and validation statistics

Supplementary information

Supplementary Information

Uncropped raw gels for Extended Data Figs. 2a, 2b and 4j and legends for Supplementary Videos

Reporting Summary

Peer Review file

Supplementary Video 1

180° rocking movie of SWR1–nucleosome complex in configuration I coordinates fitted into the 3.8 Å volume. The atomic coordinates are colored as in Extended Data Fig. 7h, and the 3.8 Å volume is colored to match the fitted atomic coordinates.

Supplementary Video 2

180° rocking movie of SWR1–nucleosome complex in configuration II coordinates fitted into the 4.7 Å volume. The atomic coordinates are colored as in Extended Data Fig. 7i, and the 4.7 Å volume is colored to match the fitted atomic coordinates.

Supplementary Video 3

SWR1-mediated nucleosome flipping. Distal (orange) and proximal (blue) H2A–H2B. Two intermediate coordinates of SWR1–nucleosome flipping were modelled using the 2D classes (Figure 5f and Extended data Fig. 9b). A third modelled intermediate of SWR1–nucleosome complex in configuration I (before DNA at SHL6-7 stabilization by Arp6–Swc6) was built with the nucleosome coordinates inverted, and DNA matching that in configuration II. Two morphs were generated: First between configuration II and the first modelled coordinates. Second, between the first modelled coordinates, followed by the second modelled coordinates and ending with the third modelled configuration I. Morphs were spliced to generate the summary movie.

Source data

Source Data Fig. 1

Source Data Fig. 2

Source Data Fig. 3

Source Data Fig. 4

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Girvan, P., Jalal, A.S.B., McCormack, E.A. et al. Nucleosome flipping drives kinetic proofreading and processivity by SWR1. Nature 636, 251–257 (2024). https://doi.org/10.1038/s41586-024-08152-y

- Received: 11 October 2023

- Accepted: 04 October 2024

- Published: 06 November 2024

- Issue Date: 05 December 2024

- DOI: https://doi.org/10.1038/s41586-024-08152-y

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Nucleosome flipping drives kinetic proofreading and processivity by SWR1