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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

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22,402 次观看 • 3 个月前 •via X (Twitter)

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Excited to share our new work on building a multimodal atlas of human skin in health and inflammatory disease — a project I’m especially proud of, bringing together AI, high-throughput genomics, and clinical science to accelerate discovery. Over the past decade, single-cell genomics has transformed how we map cells in human tissues. But a major challenge remains: can we systematically decode how cells organize into functional niches in situ — including those invisible to standard histopathology? To address this, we integrated large-scale scRNA-seq, spatial transcriptomics, histopathology, and AI-driven modeling frameworks to build an in situ atlas of human skin across health and disease. Led by Lloyd Steele, an MD/PhD student working between Haniffa Lab and my lab at Wellcome Sanger Institute and Cambridge University . Another amazing collaboration with Muzz Haniffa, the mastermind behind the work as part of Human Cell Atlas. A key part of this study is that we didn’t build everything from scratch — we leveraged and combined AI methods that actually work! and showed how they can be used together to extract biological insight at scale. We used: • scArches to build and map into a reference scRNA-seq atlas of human skin: • NicheCompass to identify and characterize spatial niches: • MINT-Flow to extract microenvironment-induced cell states and gene programs: Together, these enabled an end-to-end workflow from atlas construction to spatial mapping, niche discovery, and cell state decoding. At scale, we integrated ~5 million cells and 100+ spatial sections, enabling a systematic view of tissue organization. Using this framework, we identified 26 niches in skin, including known histopathologic structures as well as hidden disease-associated niches not visible on H&E. Among the most striking findings were a resident memory T cell-rich sebaceous gland niche and a plasma cell-rich sweat gland niche, suggesting that appendageal structures act as active immunological microenvironments and may contribute to inflammatory memory and disease persistence. Importantly, this atlas is not just descriptive — it is usable. It can support mapping of new datasets, resolve finer cell types and niches, extract microenvironment-driven programs, and enable predictive analyses at scale. More broadly, this work shows what becomes possible when AI, spatial genomics, and atlas-scale data are integrated end-to-end: not just mapping tissues, but systematically decoding them. This was a massive collaboration, and I’m very grateful to the amazing scientists April Foster, Kenny Roberts, and Chloe Admane. Lloyd is an amazing scientist, and I’m especially excited for the community to see more of his work soon — stay tuned. The data and pre-trained models will be released soon. Preprint:
1:09

Excited to share our new work on building a multimodal atlas of human skin in health and inflammatory disease — a project I’m especially proud of, bringing together AI, high-throughput genomics, and clinical science to accelerate discovery. Over the past decade, single-cell genomics has transformed how we map cells in human tissues. But a major challenge remains: can we systematically decode how cells organize into functional niches in situ — including those invisible to standard histopathology? To address this, we integrated large-scale scRNA-seq, spatial transcriptomics, histopathology, and AI-driven modeling frameworks to build an in situ atlas of human skin across health and disease. Led by Lloyd Steele, an MD/PhD student working between Haniffa Lab and my lab at Wellcome Sanger Institute and Cambridge University . Another amazing collaboration with Muzz Haniffa, the mastermind behind the work as part of Human Cell Atlas. A key part of this study is that we didn’t build everything from scratch — we leveraged and combined AI methods that actually work! and showed how they can be used together to extract biological insight at scale. We used: • scArches to build and map into a reference scRNA-seq atlas of human skin: • NicheCompass to identify and characterize spatial niches: • MINT-Flow to extract microenvironment-induced cell states and gene programs: Together, these enabled an end-to-end workflow from atlas construction to spatial mapping, niche discovery, and cell state decoding. At scale, we integrated ~5 million cells and 100+ spatial sections, enabling a systematic view of tissue organization. Using this framework, we identified 26 niches in skin, including known histopathologic structures as well as hidden disease-associated niches not visible on H&E. Among the most striking findings were a resident memory T cell-rich sebaceous gland niche and a plasma cell-rich sweat gland niche, suggesting that appendageal structures act as active immunological microenvironments and may contribute to inflammatory memory and disease persistence. Importantly, this atlas is not just descriptive — it is usable. It can support mapping of new datasets, resolve finer cell types and niches, extract microenvironment-driven programs, and enable predictive analyses at scale. More broadly, this work shows what becomes possible when AI, spatial genomics, and atlas-scale data are integrated end-to-end: not just mapping tissues, but systematically decoding them. This was a massive collaboration, and I’m very grateful to the amazing scientists April Foster, Kenny Roberts, and Chloe Admane. Lloyd is an amazing scientist, and I’m especially excited for the community to see more of his work soon — stay tuned. The data and pre-trained models will be released soon. Preprint:

Mo Lotfollahi

11,759 次观看 • 3 个月前

Excited to share our new work. Over the past decade, single-cell genomics has transformed our ability to map cellular systems. But a major question remains: Can we predict how perturbations reshape cellular trajectories over time? In 2018, we first showed that it is possible to predict cellular responses to perturbations — ranging from disease signals to chemical treatments — even in unseen contexts. In 2022, we introduced CPA (MSB 2022; NeurIPS 2022), extending this idea to predict responses to unseen chemical and genetic perturbations, including their combinations. Since then, the field of perturbation modeling has grown enormously. The community has pushed the space forward with many creative ideas and powerful models. It’s exciting to see how fast things are moving — even though many fundamental challenges remain. One of the biggest is that cells are not static. They move through trajectories during development, immune responses, and disease. Yet most current models still predict perturbation effects within a single state, rather than how early perturbations propagate across future states and reshape downstream outcomes. To address this, we developed PerturbGen, a trajectory-aware generative AI model that predicts how genetic perturbations reshape downstream cellular states. Huge credit to the people who made this work possible. Thanks to co-first authors Kevin Ly, Adib Miraki, Tomoya Isobe, AmirHoss3in Vahidi, Delshad Vaghari & Anthony Rostron. Special recognition to Kevin Ly and Adib Miraki for driving this work over the finish line. Grateful for our outstanding collaborators from Haniffa Lab, Bertie Gottgens lab Gosia Trynka and many others — a true cross-institute effort across Cambridge Stem Cell Institute, Open Targets ,Wellcome Sanger Institute and Cambridge University.🎉 PerturbGen learns transcriptional dynamics across cellular trajectories. By introducing perturbations at an early source state, it can simulate how these effects propagate into future states along differentiation trajectories. Scaling this across genes enables the creation of dynamic in silico perturbation atlases — maps of how perturbations reshape biological trajectories over time. We explored this idea across three biological questions. First, in a human in vivo LPS immune challenge, PerturbGen predicted that perturbing a transient IL1B signal dampens downstream inflammatory programs in myeloid cells, with pathway changes reversing signatures observed in an independent IL-1β stimulation experiment. Second, in human hematopoiesis, PerturbGen predicted transcriptional responses to CRISPR transcription factor knockouts and enabled construction of perturbation atlases revealing lineage- and age-specific regulatory programs. These programs could also be linked to human genetics and blood diseases, including recapitulation of signatures associated with ETV6-related thrombocytopenia. Finally, we asked whether perturbation modeling could help improve complex tissue models. We built a dynamic perturbation atlas of human skin organoids to identify perturbations that could guideorganoid cells towardhuman fetal skin states. PerturbGen prioritized activation of Wnt signaling via GSK3β inhibition. Experimental validation confirmed the prediction: treatment with CHIR99021 induced stromal gene programs and shifted organoid fibroblasts toward transcriptional states observed in fetal skin stroma. Together, these results show how trajectory-aware perturbation modeling can connect gene perturbations to developmental programs, human genetics, disease mechanisms, and experimental interventions. More broadly, we think these point toward a future where single-cell atlases become predictive systems. As atlases expand across tissues, developmental windows, and modalities, models like PerturbGen could enable dynamic, virtual perturbation atlases— allowing us to simulate interventions, generate hypotheses, and design experiments before stepping into the lab. Preprint Code Excited to see how the community builds on this work.
2:46

Excited to share our new work. Over the past decade, single-cell genomics has transformed our ability to map cellular systems. But a major question remains: Can we predict how perturbations reshape cellular trajectories over time? In 2018, we first showed that it is possible to predict cellular responses to perturbations — ranging from disease signals to chemical treatments — even in unseen contexts. In 2022, we introduced CPA (MSB 2022; NeurIPS 2022), extending this idea to predict responses to unseen chemical and genetic perturbations, including their combinations. Since then, the field of perturbation modeling has grown enormously. The community has pushed the space forward with many creative ideas and powerful models. It’s exciting to see how fast things are moving — even though many fundamental challenges remain. One of the biggest is that cells are not static. They move through trajectories during development, immune responses, and disease. Yet most current models still predict perturbation effects within a single state, rather than how early perturbations propagate across future states and reshape downstream outcomes. To address this, we developed PerturbGen, a trajectory-aware generative AI model that predicts how genetic perturbations reshape downstream cellular states. Huge credit to the people who made this work possible. Thanks to co-first authors Kevin Ly, Adib Miraki, Tomoya Isobe, AmirHoss3in Vahidi, Delshad Vaghari & Anthony Rostron. Special recognition to Kevin Ly and Adib Miraki for driving this work over the finish line. Grateful for our outstanding collaborators from Haniffa Lab, Bertie Gottgens lab Gosia Trynka and many others — a true cross-institute effort across Cambridge Stem Cell Institute, Open Targets ,Wellcome Sanger Institute and Cambridge University.🎉 PerturbGen learns transcriptional dynamics across cellular trajectories. By introducing perturbations at an early source state, it can simulate how these effects propagate into future states along differentiation trajectories. Scaling this across genes enables the creation of dynamic in silico perturbation atlases — maps of how perturbations reshape biological trajectories over time. We explored this idea across three biological questions. First, in a human in vivo LPS immune challenge, PerturbGen predicted that perturbing a transient IL1B signal dampens downstream inflammatory programs in myeloid cells, with pathway changes reversing signatures observed in an independent IL-1β stimulation experiment. Second, in human hematopoiesis, PerturbGen predicted transcriptional responses to CRISPR transcription factor knockouts and enabled construction of perturbation atlases revealing lineage- and age-specific regulatory programs. These programs could also be linked to human genetics and blood diseases, including recapitulation of signatures associated with ETV6-related thrombocytopenia. Finally, we asked whether perturbation modeling could help improve complex tissue models. We built a dynamic perturbation atlas of human skin organoids to identify perturbations that could guideorganoid cells towardhuman fetal skin states. PerturbGen prioritized activation of Wnt signaling via GSK3β inhibition. Experimental validation confirmed the prediction: treatment with CHIR99021 induced stromal gene programs and shifted organoid fibroblasts toward transcriptional states observed in fetal skin stroma. Together, these results show how trajectory-aware perturbation modeling can connect gene perturbations to developmental programs, human genetics, disease mechanisms, and experimental interventions. More broadly, we think these point toward a future where single-cell atlases become predictive systems. As atlases expand across tissues, developmental windows, and modalities, models like PerturbGen could enable dynamic, virtual perturbation atlases— allowing us to simulate interventions, generate hypotheses, and design experiments before stepping into the lab. Preprint Code Excited to see how the community builds on this work.

Mo Lotfollahi

17,003 次观看 • 4 个月前

Scientists just figured out how to reverse aging using AI. And this is a massive breakthrough. We can now reprogram any human cell back to age 20. Heart cells, brain cells, skin cells, all reset to their biological prime. And here’s the wildest part…the technology to do this, has already existed since 2012 (it won the Nobel Prize). But the real breakthrough wasn’t possible until this year, when they supercharged it with AI. It’s a wild story. So in 2006, scientists discovered Yamanaka factors. They’re proteins that can basically convert any normal cell into a universal stem cell. Now this was a huge deal, because these stem cells are basically like magic healers. If you have torn muscle tissue, you could inject these stem cells into the area and they will turn into the youthful muscle cells you need. So Yamanaka factors were this insane breakthrough, because they allowed any human to turn any cell you already have into these magic healers. But, there was one big problem… It turns out, the original Yamanaka factors weren’t very good at this stem cell conversion. They could do it, but they just weren’t very reliable. Enter OpenAI...and this is where things get crazy. OpenAI designed a special AI model built specifically to create new proteins. Think of it like ChatGPT but for protein engineering. So they took all the Yamanaka research and asked this new AI to go ham on improving it. And get this… Their version was 50x more effective than the original. They tested it on 50 year old cells and it successfully started repairing 30% of their cells in just 7 days. This is just science fiction…it actually happened. And it sounds crazy, but in a few years, humans will be able to take a shot that will literally reverse the age of their cells.
1:32

Scientists just figured out how to reverse aging using AI. And this is a massive breakthrough. We can now reprogram any human cell back to age 20. Heart cells, brain cells, skin cells, all reset to their biological prime. And here’s the wildest part…the technology to do this, has already existed since 2012 (it won the Nobel Prize). But the real breakthrough wasn’t possible until this year, when they supercharged it with AI. It’s a wild story. So in 2006, scientists discovered Yamanaka factors. They’re proteins that can basically convert any normal cell into a universal stem cell. Now this was a huge deal, because these stem cells are basically like magic healers. If you have torn muscle tissue, you could inject these stem cells into the area and they will turn into the youthful muscle cells you need. So Yamanaka factors were this insane breakthrough, because they allowed any human to turn any cell you already have into these magic healers. But, there was one big problem… It turns out, the original Yamanaka factors weren’t very good at this stem cell conversion. They could do it, but they just weren’t very reliable. Enter OpenAI...and this is where things get crazy. OpenAI designed a special AI model built specifically to create new proteins. Think of it like ChatGPT but for protein engineering. So they took all the Yamanaka research and asked this new AI to go ham on improving it. And get this… Their version was 50x more effective than the original. They tested it on 50 year old cells and it successfully started repairing 30% of their cells in just 7 days. This is just science fiction…it actually happened. And it sounds crazy, but in a few years, humans will be able to take a shot that will literally reverse the age of their cells.

Whiplash347

68,643 次观看 • 8 个月前

How does an embryo reliably "compute" its form - "cell by cell" - using only local interactions and mechanics, yet produce a precise global body plan? I’m excited to share our Nature Methods paper "MultiCell: geometric learning in multicellular development", presenting #AIxBiology research led by Haiqian Yang and the result of a great collaboration with Ming Guo, George Roy, Tomer Stern, Anh Nguyen and Dapeng Bi. A long-standing challenge in developmental biology is to predict how thousands of cells collectively self-organize as tissues fold, divide, and rearrange. In MultiCell, we represent a developing embryo as a dual graph that unifies two complementary views of tissue mechanics with single-cell resolution: cells as moving points (granular) and cells as a connected foam (junction network). This lets the model learn dynamics from both geometry and cell–cell connectivity. On whole-embryo 4D light-sheet movies of Drosophila gastrulation (~5,000 cells), our model predicts key cell behaviors and the timing of events, including junction loss, rearrangements, and divisions with high accuracy, at single-cell resolution. Beyond prediction, the same representation supports robust time alignment across embryos and offers interpretable activation maps that highlight the morphogenetic "drivers" of development. The broader goal is a foundation for cell-by-cell forecasting in more complex tissues, and eventually for detecting subtle dynamical signatures of disease. Kudos to the team for this inspiring collaboration with brilliant researchers to push the boundary of AI for biology! Citation: Yang, H., Roy, G., Nguyen, A.Q., Buehler, M.J., et al. MultiCell: geometric learning in multicellular development. Nature Methods (2025), DOI: 10.1038/s41592-025-02983-x Code/data links are in the manuscript.
0:46

How does an embryo reliably "compute" its form - "cell by cell" - using only local interactions and mechanics, yet produce a precise global body plan? I’m excited to share our Nature Methods paper "MultiCell: geometric learning in multicellular development", presenting #AIxBiology research led by Haiqian Yang and the result of a great collaboration with Ming Guo, George Roy, Tomer Stern, Anh Nguyen and Dapeng Bi. A long-standing challenge in developmental biology is to predict how thousands of cells collectively self-organize as tissues fold, divide, and rearrange. In MultiCell, we represent a developing embryo as a dual graph that unifies two complementary views of tissue mechanics with single-cell resolution: cells as moving points (granular) and cells as a connected foam (junction network). This lets the model learn dynamics from both geometry and cell–cell connectivity. On whole-embryo 4D light-sheet movies of Drosophila gastrulation (~5,000 cells), our model predicts key cell behaviors and the timing of events, including junction loss, rearrangements, and divisions with high accuracy, at single-cell resolution. Beyond prediction, the same representation supports robust time alignment across embryos and offers interpretable activation maps that highlight the morphogenetic "drivers" of development. The broader goal is a foundation for cell-by-cell forecasting in more complex tissues, and eventually for detecting subtle dynamical signatures of disease. Kudos to the team for this inspiring collaboration with brilliant researchers to push the boundary of AI for biology! Citation: Yang, H., Roy, G., Nguyen, A.Q., Buehler, M.J., et al. MultiCell: geometric learning in multicellular development. Nature Methods (2025), DOI: 10.1038/s41592-025-02983-x Code/data links are in the manuscript.

Markus J. Buehler

387,846 次观看 • 6 个月前

We are thrilled to share that our first paper from my new lab, Spateo ( for spatiotemporal modeling of molecular holograms, is now online in Cell: Spateo is a comprehensive analytical framework for 3D whole-embryo spatiotemporal modeling. Its advanced features include: • 3D alignment and reconstruction at the whole-mouse-embryo scale (see the animation). • 3D spatial domain digitization and cell-cell communication analysis to understand spatial gene expression gradients and both inter- and intracellular communication. • 3D morphometric and volumetric analyses along with 3D morphogenesis vector field modeling to quantify dynamics such as surface area, volume, and cell density across organs, and to dissect the interplay between morphogenesis factors and cell migration. • A “Google Earth”-like browser, Spateo-viewer ( and for interactive and intuitive exploration of 3D spatial data. • Additional features, such as RNA signal-based single-cell segmentation. We are also honored that Nature “News and Views” has highlighted this work as well: This is really an amazing outcome after two years' heroic revision process that rewrite the entire paper using a new data ( for whole mouse embryos.
1:24

We are thrilled to share that our first paper from my new lab, Spateo ( for spatiotemporal modeling of molecular holograms, is now online in Cell: Spateo is a comprehensive analytical framework for 3D whole-embryo spatiotemporal modeling. Its advanced features include: • 3D alignment and reconstruction at the whole-mouse-embryo scale (see the animation). • 3D spatial domain digitization and cell-cell communication analysis to understand spatial gene expression gradients and both inter- and intracellular communication. • 3D morphometric and volumetric analyses along with 3D morphogenesis vector field modeling to quantify dynamics such as surface area, volume, and cell density across organs, and to dissect the interplay between morphogenesis factors and cell migration. • A “Google Earth”-like browser, Spateo-viewer ( and for interactive and intuitive exploration of 3D spatial data. • Additional features, such as RNA signal-based single-cell segmentation. We are also honored that Nature “News and Views” has highlighted this work as well: This is really an amazing outcome after two years' heroic revision process that rewrite the entire paper using a new data ( for whole mouse embryos.

evo-devo

58,302 次观看 • 1 年前

I'm creating 3D tours of open single cell RNA-seq data in UMAP space. Here, using data from 50k brain cells by the allen institute . ( The UMAP position and class of every cell is read into Blender, realized as a stylized, class-specific model I sculpted
1:06

I'm creating 3D tours of open single cell RNA-seq data in UMAP space. Here, using data from 50k brain cells by the allen institute . ( The UMAP position and class of every cell is read into Blender, realized as a stylized, class-specific model I sculpted

Brad Krajina

48,127 次观看 • 3 年前

A whole new wave of disease therapies have started to arrive. They're based on lab made stem cells that can reprogram the body. Folks like Jeff Bezos, Sam Altman and Brian Armstrong have invested billions of dollars into this technology. @nabsicle is trying to mass produce these new stem cells to make the therapies cheaper and more widespread. This is our full episode with her where we get deep into iPSCs and how this technology works. The Core Memory podcast is on all major platforms and on YouTube down below. Brex and @e1ventures are very smart and help us bring the world's smartest podcast to you. Enjoy! Timestamps 00:00 Intro 01:22 Cell Rejuvenation 02:30 AI and Biotech 06:18 Lab-Made vs Natural Stem Cells 08:25 Programming Cell States 10:07 Reversing Parkinson's 12:15 Japan’s Stem Cell Trials 16:46 Personalized Cells 21:19 Cellino’s Laser Bubble Technology 31:34 Stem Cell Fabs 38:33 Personalized Medicine 43:02 Lowering Stem Cell Therapy Costs 46:31 Can Humans Live Forever?
1:17:51

A whole new wave of disease therapies have started to arrive. They're based on lab made stem cells that can reprogram the body. Folks like Jeff Bezos, Sam Altman and Brian Armstrong have invested billions of dollars into this technology. @nabsicle is trying to mass produce these new stem cells to make the therapies cheaper and more widespread. This is our full episode with her where we get deep into iPSCs and how this technology works. The Core Memory podcast is on all major platforms and on YouTube down below. Brex and @e1ventures are very smart and help us bring the world's smartest podcast to you. Enjoy! Timestamps 00:00 Intro 01:22 Cell Rejuvenation 02:30 AI and Biotech 06:18 Lab-Made vs Natural Stem Cells 08:25 Programming Cell States 10:07 Reversing Parkinson's 12:15 Japan’s Stem Cell Trials 16:46 Personalized Cells 21:19 Cellino’s Laser Bubble Technology 31:34 Stem Cell Fabs 38:33 Personalized Medicine 43:02 Lowering Stem Cell Therapy Costs 46:31 Can Humans Live Forever?

Ashlee Vance

21,616 次观看 • 3 个月前

1/n: 🔬We introduce NicheCompass (NC) a graph learning model to build spatial atlases across millions of cells, quantitatively characterize cell niches, infer cellular communication, and map new data into these atlases. NC can be used with various spatial technologies, ranging from cellular to subcellular resolution including even multimodal approaches. We have explored interesting biology by analyzing cell niches spanning from development to cancer. Led by Sebastian Birk in a fun colab with @cartalop: paper:
1:04

1/n: 🔬We introduce NicheCompass (NC) a graph learning model to build spatial atlases across millions of cells, quantitatively characterize cell niches, infer cellular communication, and map new data into these atlases. NC can be used with various spatial technologies, ranging from cellular to subcellular resolution including even multimodal approaches. We have explored interesting biology by analyzing cell niches spanning from development to cancer. Led by Sebastian Birk in a fun colab with @cartalop: paper:

Mo Lotfollahi

61,763 次观看 • 2 年前

🚨Here's what a lot of people misunderstand about cancer treatment, says drpaulmarik: "Cancer is not homogeneous. The somatic mutation theory—which is the current theory in which treatment is based—posits that you have a mutation in a single cell, and that gives rise to a whole population of cells that look the same and have the same mutation. But the Cancer Genome Atlas has shown that that theory is completely wrong. The cancer cells are very heterogeneous, so they're made up of very different populations of cells with different mutations, and one of the populations is the cancer stem cell. It's a sub-population of the cancer. These are generally slow-growing, but they're distinct in that they have the ability to divide indefinitely and grow indefinitely, and can change their characteristics. Basically, if you get rid of the fast-dividing cells, which is the cancer, you're left with the stem cells, which then become the roots, which grow back to form the tumor" sometimes years later. Conventional chemotherapy gets rid of the fast-dividing regular cancer cells but *NOT* the stem cells. So the key question is: how do you get rid of the stem cells? “There are a number of repurposed drugs that do it, and this has been well-established in scientific medical literature. One of the most effective treatments to knock out the stem cell is the famous horse deworming medicine," says drpaulmarik. Yes, ivermectin. Independent Medical Alliance
6:30

🚨Here's what a lot of people misunderstand about cancer treatment, says drpaulmarik: "Cancer is not homogeneous. The somatic mutation theory—which is the current theory in which treatment is based—posits that you have a mutation in a single cell, and that gives rise to a whole population of cells that look the same and have the same mutation. But the Cancer Genome Atlas has shown that that theory is completely wrong. The cancer cells are very heterogeneous, so they're made up of very different populations of cells with different mutations, and one of the populations is the cancer stem cell. It's a sub-population of the cancer. These are generally slow-growing, but they're distinct in that they have the ability to divide indefinitely and grow indefinitely, and can change their characteristics. Basically, if you get rid of the fast-dividing cells, which is the cancer, you're left with the stem cells, which then become the roots, which grow back to form the tumor" sometimes years later. Conventional chemotherapy gets rid of the fast-dividing regular cancer cells but *NOT* the stem cells. So the key question is: how do you get rid of the stem cells? “There are a number of repurposed drugs that do it, and this has been well-established in scientific medical literature. One of the most effective treatments to knock out the stem cell is the famous horse deworming medicine," says drpaulmarik. Yes, ivermectin. Independent Medical Alliance

Jan Jekielek

96,002 次观看 • 1 年前

RFK Jr. says he is working with insurance companies to start covering treatment that can cure sickle cell. “I spent the day with a young woman who was in agony because of sickle cell… and she now has a full life because she received this treatment.” “If we can give that to people that’s almost providential power.” “I was in South Carolina inking the final documents on a deal that we've put together… to make this particular cell and gene therapy available to every person in the state who has sickle cell with it being fully paid for.” “We're negotiating now with 38 other states and by the end of next year we hope to have this available to every American.” “It's saving the insurance companies money… if they look at the total cost over the lifetime of that child it's an enormous saving because even though it's a very expensive drug, it's a one time treatment.”
2:13

RFK Jr. says he is working with insurance companies to start covering treatment that can cure sickle cell. “I spent the day with a young woman who was in agony because of sickle cell… and she now has a full life because she received this treatment.” “If we can give that to people that’s almost providential power.” “I was in South Carolina inking the final documents on a deal that we've put together… to make this particular cell and gene therapy available to every person in the state who has sickle cell with it being fully paid for.” “We're negotiating now with 38 other states and by the end of next year we hope to have this available to every American.” “It's saving the insurance companies money… if they look at the total cost over the lifetime of that child it's an enormous saving because even though it's a very expensive drug, it's a one time treatment.”

End Tribalism in Politics

35,435 次观看 • 1 年前

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

55,040 次观看 • 1 年前

the process of mitosis, a type of cell division where a single cell divides to produce two identical cells, essential for growth and repair in multicellular organisms.
0:29

the process of mitosis, a type of cell division where a single cell divides to produce two identical cells, essential for growth and repair in multicellular organisms.

Science girl

43,666 次观看 • 1 年前

Introducing Tahoe-x1 (Tx1) by Tahoe Therapeutics. 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 Therapeutics. 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

171,805 次观看 • 8 个月前

How do we go from a single cell to a complex organism? For 50 yrs experimentally meeting the nutritional demands of a growing organism has been a bottleneck in development. For the first time, we built placental support and predictive tools to make it possible. Watch ↓
4:05

How do we go from a single cell to a complex organism? For 50 yrs experimentally meeting the nutritional demands of a growing organism has been a bottleneck in development. For the first time, we built placental support and predictive tools to make it possible. Watch ↓

Becoming

866,502 次观看 • 6 个月前

"Spatial interactions modulate tumor growth and immune infiltration" Our latest preprint: Sadegh Marzban in collab w/ Amelio Lab! We apply methods from artificial life, Bert Chan's #Lenia, as a model of tumor-immune: - spatial growth dynamics - cell-cell competition & - cell migration Application to head & neck squamous cell carcinomas to elucidate the role of collagen on immune infiltration
0:20

"Spatial interactions modulate tumor growth and immune infiltration" Our latest preprint: Sadegh Marzban in collab w/ Amelio Lab! We apply methods from artificial life, Bert Chan's #Lenia, as a model of tumor-immune: - spatial growth dynamics - cell-cell competition & - cell migration Application to head & neck squamous cell carcinomas to elucidate the role of collagen on immune infiltration

Jeffrey West

12,314 次观看 • 2 年前

All cells make “sounds” healthy cells “sound” one ways and cancerous cells “sound”completely different. In the future we will be able to detect a single cancer cell, by its “sound”. Electronic medicine is the future.
1:23

All cells make “sounds” healthy cells “sound” one ways and cancerous cells “sound”completely different. In the future we will be able to detect a single cancer cell, by its “sound”. Electronic medicine is the future.

Brian Roemmele

188,882 次观看 • 2 年前

Together, we can give the gift of life this #Ramadan. Eesa needed a stem cell transplant after being diagnosed with aplastic anaemia - and thankfully a donor was found. Now, his family want even more people to join the stem cell register, and help others just like Eesa 💚
0:59

Together, we can give the gift of life this #Ramadan. Eesa needed a stem cell transplant after being diagnosed with aplastic anaemia - and thankfully a donor was found. Now, his family want even more people to join the stem cell register, and help others just like Eesa 💚

Anthony Nolan

20,230 次观看 • 1 年前

Today in nature, we report a spatial single-cell atlas of human cortical development, revealing surprisingly early specification of human cortical layers and areas. We built an interactive browser to explore the spatial data: Paper link below👇
0:57

Today in nature, we report a spatial single-cell atlas of human cortical development, revealing surprisingly early specification of human cortical layers and areas. We built an interactive browser to explore the spatial data: Paper link below👇

Xuyu Qian

53,308 次观看 • 1 年前

We’re delighted to hear that exa-cel has been approved by NICE to treat sickle cell disorder! This innovative gene therapy is the first of its kind, offering a potential cure to people without a stem cell donor.
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We’re delighted to hear that exa-cel has been approved by NICE to treat sickle cell disorder! This innovative gene therapy is the first of its kind, offering a potential cure to people without a stem cell donor.

Anthony Nolan

20,039 次观看 • 1 年前