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Single-cell technologies now let us profile entire transcriptomes in individual cells. But how do we make sense of this complexity in a biologically meaningful way? Many methods summarise cells into a single embedding, but this often comes at the cost of interpretability, especially when multiple gene programs are active...

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Mo Lotfollahi

13,007 subscribers

22,578 Aufrufe • vor 7 Monaten •via X (Twitter)

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7 Kommentare

Profilbild von Kaven
Kavenvor 7 Monaten

AI link for summary and cited qa

Profilbild von Mo Lotfollahi
Mo Lotfollahivor 7 Monaten

Ahh this is beautiful 😊

Profilbild von Kaven
Kavenvor 7 Monaten

I love them, you can generate these editable pptx on nouswise for free.

Profilbild von Dr. Zeynep Hülya Gümüş 🕊️
Dr. Zeynep Hülya Gümüş 🕊️vor 7 Monaten

Congrats, Mo! 🔥

Profilbild von Jamie Timmons
Jamie Timmonsvor 7 Monaten

That first line isn't really true. They provide a partial, part biased, part stochastic, SAMPLE of the transcriptome - and the impact of these limitations is as of yet isn't fully understood.

Profilbild von Aiden M Sababi
Aiden M Sababivor 7 Monaten

Great work, congrats.

Profilbild von Jaime
Jaimevor 7 Monaten

@HaoYin20 Clean approach — representing cells through multiple gene programs instead of a single embedding makes a lot of sense. Could the key question be not just which programs are active, but whether they stay dynamically accessible under perturbation?

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58,572 Aufrufe • vor 2 Jahren

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Brad Krajina

48,156 Aufrufe • vor 3 Jahren

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Mo Lotfollahi

61,805 Aufrufe • vor 2 Jahren

Excited to share TERRA, a tissue world model 🧬 Over ~1.5 years we ran a large data-generation + modelling effort to build a world model for human tissues, pretrained on 112M cells from spatial transcriptomics (mostly Xenium 5000-plex + public data). It's built on one of the largest human spatial transcriptomics corpora assembled to date, spanning 20 tissues across development, health and 26 disease conditions, ~two-thirds newly generated in-house. Why a "world model" for tissue? Images have universal representations (ViT/DINOv3), so do proteins (ESM, Alex Rives) and pathology (UNI, Faisal Mahmood). We've worked hard to build something similar for human tissue: one model that captures its multi-scale logic, genes → cells → their native microenvironments. Like the JEPA approach Yann LeCun has championed, TERRA learns by prediction in embedding space, but for human tissue. How it works: it tokenises each cell together with its nearest neighbours into one sequence while keeping every gene's identity, then masks part of a neighbourhood and predicts the representation of the hidden part, not raw noisy counts. From one backbone it reads out three scales, gene embeddings (what a gene is doing in a cell and its niche), cell embeddings (cell type and state) and neighbourhood embeddings (the niche), and because it keeps gene-level resolution it can knock a gene out in silico and predict the response. Applied entirely zero-shot, TERRA maps and perturbs human tissue across unseen organs, diseases and technologies, outperforming existing spatial approaches. Three take-homes: 1️⃣ One model, any tissue. A single pretrained backbone provides tissue representations zero-shot, handling genes, cells and niches across organs and platforms, off the shelf. 2️⃣ New biology, development to clinic. We built a new spatial atlas of the developing human pancreas and found an islet-associated capillary state that looks like a precursor of mature islet vasculature. In kidney, TERRA's in silico knockouts predicted the tissue-injury programme from cancer immunotherapy (checkpoint blockade), confirmed in treated kidneys, detected in blood, and linked to declining kidney function. 3️⃣ A grammar of tissue architecture. By coupling each cell's state to its niche, TERRA defines recurring cross-organ "archetypes" of macrophage neighbourhoods, including a tumour-boundary niche that tracks poor survival in kidney cancer. TERRA is already in use: it powered our recent skin atlas of hidden immune-memory niches ( with more studies coming soon. This was an amazing collaboration between clinicians, machine-learning scientists and cell biologists 🙏 Led by Sebastian Birk, Vali Sanian Amirhosein Vahidi, Samuel Ogden, Daniyal Jafree, Adib Miraki, Carlo Leonardi and Arpit Merchant, with Lassi Paavolainen, Menna Clatworthy, Omer Ali Bayraktar, Muzlifah Haniffa, Tom Mitchell and Mostafa Bakhti. Huge thanks too to everyone who shared data and helped along the way. What excites me most is seeing how the community builds on this. The model, code and tutorials are all public, so anyone can run TERRA on their own tissues, extend it, or build new models on top. Huge thanks to the whole team across Wellcome Sanger Institute and our many collaborators. 📄 Paper: 💻 Code: 🤗 Model: #SpatialTranscriptomics #SpatialGenomics #FoundationModels #AI4Science #MachineLearning #ComputationalBiology #SingleCell #WorldModels
2:15

Excited to share TERRA, a tissue world model 🧬 Over ~1.5 years we ran a large data-generation + modelling effort to build a world model for human tissues, pretrained on 112M cells from spatial transcriptomics (mostly Xenium 5000-plex + public data). It's built on one of the largest human spatial transcriptomics corpora assembled to date, spanning 20 tissues across development, health and 26 disease conditions, ~two-thirds newly generated in-house. Why a "world model" for tissue? Images have universal representations (ViT/DINOv3), so do proteins (ESM, Alex Rives) and pathology (UNI, Faisal Mahmood). We've worked hard to build something similar for human tissue: one model that captures its multi-scale logic, genes → cells → their native microenvironments. Like the JEPA approach Yann LeCun has championed, TERRA learns by prediction in embedding space, but for human tissue. How it works: it tokenises each cell together with its nearest neighbours into one sequence while keeping every gene's identity, then masks part of a neighbourhood and predicts the representation of the hidden part, not raw noisy counts. From one backbone it reads out three scales, gene embeddings (what a gene is doing in a cell and its niche), cell embeddings (cell type and state) and neighbourhood embeddings (the niche), and because it keeps gene-level resolution it can knock a gene out in silico and predict the response. Applied entirely zero-shot, TERRA maps and perturbs human tissue across unseen organs, diseases and technologies, outperforming existing spatial approaches. Three take-homes: 1️⃣ One model, any tissue. A single pretrained backbone provides tissue representations zero-shot, handling genes, cells and niches across organs and platforms, off the shelf. 2️⃣ New biology, development to clinic. We built a new spatial atlas of the developing human pancreas and found an islet-associated capillary state that looks like a precursor of mature islet vasculature. In kidney, TERRA's in silico knockouts predicted the tissue-injury programme from cancer immunotherapy (checkpoint blockade), confirmed in treated kidneys, detected in blood, and linked to declining kidney function. 3️⃣ A grammar of tissue architecture. By coupling each cell's state to its niche, TERRA defines recurring cross-organ "archetypes" of macrophage neighbourhoods, including a tumour-boundary niche that tracks poor survival in kidney cancer. TERRA is already in use: it powered our recent skin atlas of hidden immune-memory niches ( with more studies coming soon. This was an amazing collaboration between clinicians, machine-learning scientists and cell biologists 🙏 Led by Sebastian Birk, Vali Sanian Amirhosein Vahidi, Samuel Ogden, Daniyal Jafree, Adib Miraki, Carlo Leonardi and Arpit Merchant, with Lassi Paavolainen, Menna Clatworthy, Omer Ali Bayraktar, Muzlifah Haniffa, Tom Mitchell and Mostafa Bakhti. Huge thanks too to everyone who shared data and helped along the way. What excites me most is seeing how the community builds on this. The model, code and tutorials are all public, so anyone can run TERRA on their own tissues, extend it, or build new models on top. Huge thanks to the whole team across Wellcome Sanger Institute and our many collaborators. 📄 Paper: 💻 Code: 🤗 Model: #SpatialTranscriptomics #SpatialGenomics #FoundationModels #AI4Science #MachineLearning #ComputationalBiology #SingleCell #WorldModels

Mo Lotfollahi

51,634 Aufrufe • vor 3 Monaten

The Human Cell Atlas, a global effort to map and profile the body’s cells, will advance our understanding of the human body in ways not previously possible. How? Muzlifah Haniffa from the Wellcome Sanger Institute tells us more about this ambitious project we're proud to fund 📽️
0:52

The Human Cell Atlas, a global effort to map and profile the body’s cells, will advance our understanding of the human body in ways not previously possible. How? Muzlifah Haniffa from the Wellcome Sanger Institute tells us more about this ambitious project we're proud to fund 📽️

Wellcome

10,158 Aufrufe • vor 3 Jahren

CHOIR is now online Nature Genetics! CHOIR is a new clustering method for single-cell data that evaluates whether clusters represent statistically distinct cell populations. CHOIR scales to millions of cells and works with single-/multi-omic data of any type!
1:42

CHOIR is now online Nature Genetics! CHOIR is a new clustering method for single-cell data that evaluates whether clusters represent statistically distinct cell populations. CHOIR scales to millions of cells and works with single-/multi-omic data of any type!

Cathrine Sant

55,466 Aufrufe • vor 1 Jahr

This is why science is the GOAT. I’m officially a super fan of SpudCell. Scientists at the University of Minnesota just built a cell entirely from nonliving chemical components and then watched it grow, replicate its DNA, and divide. A complete cell cycle for the first time in history. No one has ever gotten a synthetic cell to divide before. This news is making big waves in the scientific community in both directions. For example, the paper was submitted to the journal Cell, but was rejected with one of the reviewers stating that SpudCells “weren’t real biology.” But here’s what IS true: for the first time, a fully defined chemical system, where every single molecule is known, executed a complete cell cycle. Chemical components were induced to become biology, in a lab, on purpose. And Adamala is releasing all 190 pages of her methodology and data so any synthetic biologist in the world can build their own. #science #biology #mindblown #breakingnews
2:56

This is why science is the GOAT. I’m officially a super fan of SpudCell. Scientists at the University of Minnesota just built a cell entirely from nonliving chemical components and then watched it grow, replicate its DNA, and divide. A complete cell cycle for the first time in history. No one has ever gotten a synthetic cell to divide before. This news is making big waves in the scientific community in both directions. For example, the paper was submitted to the journal Cell, but was rejected with one of the reviewers stating that SpudCells “weren’t real biology.” But here’s what IS true: for the first time, a fully defined chemical system, where every single molecule is known, executed a complete cell cycle. Chemical components were induced to become biology, in a lab, on purpose. And Adamala is releasing all 190 pages of her methodology and data so any synthetic biologist in the world can build their own. #science #biology #mindblown #breakingnews

Dr. Harini Bhat, PharmD

455,300 Aufrufe • vor 4 Monaten

A few months ago, researchers made a synthetic cell that grows, eats, and divides. But it only grows for about five generations, and then it “breaks down” completely and stops moving. This is the reason it’s not yet considered “alive.” When I asked Kate Adamala, the lead author behind SpudCell, why she thinks this happens, she said something really simple and somewhat surprising: The cell cannot break down its own waste. In other words, it has all these proteins and RNA and DNA, but has no way to break them down and reuse the building blocks when they inevitably fail. So when a ribosome stops working, it just sits inside the cell and remains there for many generations. With each generation, more of the cell is filled up with broken pieces. Now, you could add a protease to these cells, but proteases are blunt-force instruments. They would destroy both working proteins *and* the broken ones, and so probably kill the cell. And when I asked Kate why we can't just use the enzymes and chaperones that natural cells use, she said there are too many of them. If you try to recreate all of biology's quality control processes, you end up with a hugely bloated metabolism inside what is supposed to be a minimal cell. The issue, then — the fundamental bottleneck — is that we do not have good tools to recognize WHAT is broken inside a cell and *then* destroy them while leaving healthy variants unscathed. My hunch here is that we need better molecular sensors. If we could distinguish between different forms of a protein, without destroying a cell entirely, then perhaps we could couple that recognition to a selective protease and recycle only the things that are broken. We also just don’t understand, at a high enough level, what happens when a ribosome breaks, and thus we can’t design good probes to sense those broken molecules. In trying to create a synthetic cell, then, we might also arrive at ideas for much better tools. Trying to create life is a really good way to figure out all the obstacles and stuff we don’t yet understand. But when Kate says she thinks they'll achieve perpetual cell division by 2030 (in the interview), I'm kind of skeptical. There's a lot of work to do here, and the tools we tend to use as biologists are often much broader than the precise, highly-evolved systems we find in nature.
2:55

A few months ago, researchers made a synthetic cell that grows, eats, and divides. But it only grows for about five generations, and then it “breaks down” completely and stops moving. This is the reason it’s not yet considered “alive.” When I asked Kate Adamala, the lead author behind SpudCell, why she thinks this happens, she said something really simple and somewhat surprising: The cell cannot break down its own waste. In other words, it has all these proteins and RNA and DNA, but has no way to break them down and reuse the building blocks when they inevitably fail. So when a ribosome stops working, it just sits inside the cell and remains there for many generations. With each generation, more of the cell is filled up with broken pieces. Now, you could add a protease to these cells, but proteases are blunt-force instruments. They would destroy both working proteins *and* the broken ones, and so probably kill the cell. And when I asked Kate why we can't just use the enzymes and chaperones that natural cells use, she said there are too many of them. If you try to recreate all of biology's quality control processes, you end up with a hugely bloated metabolism inside what is supposed to be a minimal cell. The issue, then — the fundamental bottleneck — is that we do not have good tools to recognize WHAT is broken inside a cell and *then* destroy them while leaving healthy variants unscathed. My hunch here is that we need better molecular sensors. If we could distinguish between different forms of a protein, without destroying a cell entirely, then perhaps we could couple that recognition to a selective protease and recycle only the things that are broken. We also just don’t understand, at a high enough level, what happens when a ribosome breaks, and thus we can’t design good probes to sense those broken molecules. In trying to create a synthetic cell, then, we might also arrive at ideas for much better tools. Trying to create life is a really good way to figure out all the obstacles and stuff we don’t yet understand. But when Kate says she thinks they'll achieve perpetual cell division by 2030 (in the interview), I'm kind of skeptical. There's a lot of work to do here, and the tools we tend to use as biologists are often much broader than the precise, highly-evolved systems we find in nature.

Niko McCarty.

11,388 Aufrufe • vor 1 Monat

Will we ever be able to simulate a living cell? In theory. Molecules collide, proteins fold, and all of these things can be modeled on a computer. But there are so many unknowns, and so much compute would be required, that a mechanistic model of the cell remains a distant dream. Still, many research groups are trying to build cells "from the bottom up," mostly by stringing together mathematical equations that represent different parts of the cell. By *trying* to build an accurate model of the cell, they hope to improve our own understanding of how biology works. In doing so, they also maintain legibility, meaning that humans can understand and interpret all the equations used to construct the model. But Adam Green argues that legibility is a constraint on our models. He thinks that "human legibility, our ability to understand a system...has been limiting us." To truly accelerate biomedical progress, Green thinks that we should discard assumptions about legibility in favor of "more black box" models. Neural networks can make sense of biological data in a way that hard-coded equations cannot. This is not a new argument. A 2019 essay by Bert Hubert, called "Is biology too complex to ever understand?" makes much the same point. In that essay, Hubert writes: "There is no rule that says nature cannot be more complex than our brains can handle." Instead of trying to make sense of biology via reductionist observations and mechanisms, Hubert argues that we should just gather everything into databases "that might enable computers to make sense of what we have learned." (This vision is now playing out in many research groups.) This "black box" approach might be useful (in terms of modeling cells to cure diseases, say) but would be unsatisfying in the sense that it doesn't deepen our own understanding of the universe. And isn't that a key part of science? Is science for humans -- an act that satisfies some itch -- or rather a means to an end? I think both approaches are useful. The black box models may help us cure diseases faster by making useful predictions that our brains cannot (currently) comprehend, but the mechanistic models deepen our own understanding of how cells work.
2:26

Will we ever be able to simulate a living cell? In theory. Molecules collide, proteins fold, and all of these things can be modeled on a computer. But there are so many unknowns, and so much compute would be required, that a mechanistic model of the cell remains a distant dream. Still, many research groups are trying to build cells "from the bottom up," mostly by stringing together mathematical equations that represent different parts of the cell. By *trying* to build an accurate model of the cell, they hope to improve our own understanding of how biology works. In doing so, they also maintain legibility, meaning that humans can understand and interpret all the equations used to construct the model. But Adam Green argues that legibility is a constraint on our models. He thinks that "human legibility, our ability to understand a system...has been limiting us." To truly accelerate biomedical progress, Green thinks that we should discard assumptions about legibility in favor of "more black box" models. Neural networks can make sense of biological data in a way that hard-coded equations cannot. This is not a new argument. A 2019 essay by Bert Hubert, called "Is biology too complex to ever understand?" makes much the same point. In that essay, Hubert writes: "There is no rule that says nature cannot be more complex than our brains can handle." Instead of trying to make sense of biology via reductionist observations and mechanisms, Hubert argues that we should just gather everything into databases "that might enable computers to make sense of what we have learned." (This vision is now playing out in many research groups.) This "black box" approach might be useful (in terms of modeling cells to cure diseases, say) but would be unsatisfying in the sense that it doesn't deepen our own understanding of the universe. And isn't that a key part of science? Is science for humans -- an act that satisfies some itch -- or rather a means to an end? I think both approaches are useful. The black box models may help us cure diseases faster by making useful predictions that our brains cannot (currently) comprehend, but the mechanistic models deepen our own understanding of how cells work.

Niko McCarty.

16,417 Aufrufe • vor 4 Monaten

Introducing Tahoe-x1 (Tx1) by Tahoe. A 3-billion-parameter, single-cell foundation model that learns unified representations of genes, cells, and drugs, achieving state-of-the-art performance across cancer-relevant cell biology benchmarks, open-sourced on Hugging Face. 🧵
0:31

Introducing Tahoe-x1 (Tx1) by Tahoe. A 3-billion-parameter, single-cell foundation model that learns unified representations of genes, cells, and drugs, achieving state-of-the-art performance across cancer-relevant cell biology benchmarks, open-sourced on Hugging Face. 🧵

Nima Alidoust

172,248 Aufrufe • vor 1 Jahr