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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... at once. We developed Tripso, a self-supervised transformer model that represents cells through multiple gene program-specific embeddings, while also uncovering new programs directly from the data. Instead of collapsing biology into a single vector, Tripso decomposes cell state into multiple representations, each reflecting a different gene program. We explored this across multiple systems. In human hematopoiesis, spanning development to aging, Tripso identified distinct age-associated program activity, including stronger JAK-STAT signalling in early life and dynamic IKZF1-related changes during B cell maturation. By comparing in vitro culture conditions with in vivo hematopoietic stem cell states, Tripso suggested that targeting the SEC61 translocon could enhance stem cell maintenance ex vivo, a prediction that we subsequently validated experimentally. In parallel, we identified a previously uncharacterised tissue-resident memory T-cell program associated with atopic dermatitis and mapped it to distinct spatial immune niches Together, these results show how modelling cells through gene programs can lead to interpretable and experimentally testable insights. More broadly, this work points toward a more interpretable and biologically grounded models of cell state. As single-cell datasets continue to grow, we hope approaches like Tripso will help bridge the gap between data-driven representations and biological insight. This work wouldn’t have been possible without the contributions of an amazing team. Thank you to co-first authors Marie, Tomoya Isobe, Amirhosein Vahidi, Carlo Leonardi, and everyone from roser's Lab, Haniffa Lab, Nicola Wilson and Bertie Gottgens's Lab, bringing together expertise across Cambridge Stem Cell Institute, Open Targets, Wellcome Sanger Institute and Cambridge University. Marie is one of the very best PhD students I have ever supervised. She is truly a force of nature, exceptionally resourceful, deeply innovative, and one of the most impressive scientists I have worked with. I am immensely proud of her and all that she has accomplished. As she begins her internship at Genentech , I have no doubt she will do amazing work there and continue to make her mark. paper: code:show more

Mo Lotfollahi

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22,525 views • 5 months ago •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:

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

11,832 views • 5 months ago

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.

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

17,165 views • 6 months ago

Evolution says you can turn anything into anything if you tweak it long enough. But that house of cards collapses immediately when you realize what it takes to make even a single cell. They say new cell types arise one mutation at a time. Is that plausible? Here is the issue: Different cell types are created through a massive coordinated stack of genetic information, and it's not all just about DNA sequences. First you need a whole suite of new genetic systems related to the cell: new genes for the cell’s internal systems, instructions for its specific functional role in the body, the regulators that coordinate everything, markers to tell them where to go and when, and the controls that keep the wrong programs off. But the DNA structure itself must also be manipulated. You see, every cell in your body contains the DNA instructions to make any other cell. So how does a bone cell become a bone cell instead of a skin cell? Because of how the DNA is folded. DNA is folded in certain ways for different cell types, so that only the "bone cell instructions" are able to be read by the "cell construction program." The DNA fold decides which programs are open for each cell type. This is critical. If DNA is not folded properly so that specific cell-type information can be read, catastrophe ensues. Then you need placement blueprints, so the cells form in the right place, in the right number, and lock together to form a functional tissue. These developmental instructions are passed from parent to offspring - but research shows that tweaking these master developmental programs ends badly for the child. One mutation at a time cannot invent that entire programming stack and keep it coordinated. Research has confirmed this. To convert one cell type into another in the lab, researchers have to force many changes to happen at the same time in coordination. And even when directed by the researcher, cell conversion almost always fails. Change the sequence without the fold and you cannot get a new cell type. Fold the DNA without the new systems and you can only rearrange old programs. Make the cell without the map and it shows up in the wrong place. Break one layer in the stack and the others do not patiently wait - they fail. So creating a new cell type is not simply "tweak a gene one at a time." It requires a coordinated stack of entirely new programmed instructions: > New suite of internal systems, programmed into the DNA > New functional DNA fold so only the right systems are activated > New mapping blueprints so everything goes where it's needed Step-by-step mutation does not coordinate those layers. It produces broken cells before it produces new ones. Coordination of this level of complexity has only ever been known to come from one place: Intelligence.

Evolution says you can turn anything into anything if you tweak it long enough. But that house of cards collapses immediately when you realize what it takes to make even a single cell. They say new cell types arise one mutation at a time. Is that plausible? Here is the issue: Different cell types are created through a massive coordinated stack of genetic information, and it's not all just about DNA sequences. First you need a whole suite of new genetic systems related to the cell: new genes for the cell’s internal systems, instructions for its specific functional role in the body, the regulators that coordinate everything, markers to tell them where to go and when, and the controls that keep the wrong programs off. But the DNA structure itself must also be manipulated. You see, every cell in your body contains the DNA instructions to make any other cell. So how does a bone cell become a bone cell instead of a skin cell? Because of how the DNA is folded. DNA is folded in certain ways for different cell types, so that only the "bone cell instructions" are able to be read by the "cell construction program." The DNA fold decides which programs are open for each cell type. This is critical. If DNA is not folded properly so that specific cell-type information can be read, catastrophe ensues. Then you need placement blueprints, so the cells form in the right place, in the right number, and lock together to form a functional tissue. These developmental instructions are passed from parent to offspring - but research shows that tweaking these master developmental programs ends badly for the child. One mutation at a time cannot invent that entire programming stack and keep it coordinated. Research has confirmed this. To convert one cell type into another in the lab, researchers have to force many changes to happen at the same time in coordination. And even when directed by the researcher, cell conversion almost always fails. Change the sequence without the fold and you cannot get a new cell type. Fold the DNA without the new systems and you can only rearrange old programs. Make the cell without the map and it shows up in the wrong place. Break one layer in the stack and the others do not patiently wait - they fail. So creating a new cell type is not simply "tweak a gene one at a time." It requires a coordinated stack of entirely new programmed instructions: > New suite of internal systems, programmed into the DNA > New functional DNA fold so only the right systems are activated > New mapping blueprints so everything goes where it's needed Step-by-step mutation does not coordinate those layers. It produces broken cells before it produces new ones. Coordination of this level of complexity has only ever been known to come from one place: Intelligence.

Divinely Designed

22,570 views • 14 days ago

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.

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 views • 10 months ago

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.

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

388,322 views • 8 months ago

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.

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

58,480 views • 1 year ago

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

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

50,993 views • 1 month ago

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.

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.

16,382 views • 2 months ago

Science Corner: David Friedberg Explains Recent Mitotherapy Breakthroughs ⚡️ On E224, david friedberg broke down what mitotherapy is and how recent discoveries could unleash this treatment for many diseases: "So mitochondria are the powerhouse of the cell." "Every cell in our body gets its energy, which is what it uses to function, from the mitochondria." "And so there's been a lot of research into the relationship between mitochondria and aging, and that dysfunctional mitochondria may actually be a key driver for many diseases." "Including many cancers, Alzheimer's, Parkinson's, ALS, features of autism, muscle tissues being weak, etc." " So, as the cells get older and the mitochondria stop working, we make new mitochondria." "But over time, the DNA degrades and the mitochondria become less effective and there are fewer functional mitochondria per cell." Friedberg highlighted three recent papers: 1) "The power and potential of mitochondria transfer" (nature) -- "these folks identified and demonstrated that mitochondria can actually transfer from one cell to another" -- " So, if you've got a cell that's got damaged, or dysfunctional mitochondria, they've identified three mechanisms by which mitochondria can move into a cell that needs more mitochondria that are working and are more functional." -- "And as a result, it can rejuvenate or provide energy to a dysfunctional cell, which might improve dysfunctional tissue or improve disease." 2) "A human brain map of mitochondrial respiratory capacity and diversity" (nature) -- " this was the first mapping of the mitochondria in the human brain" -- " what it showed was that different parts of the brain, different cells, had different amounts of mitochondria and different mitochondrial function." -- "(This) starts to highlight how that difference in energy production in different cells in different parts of the brain may actually cause some of the things like memory loss or speech impairment," -- "the mitochondrial dysfunction in the brain might actually be the key driver of that aging symptomology." 3) "Organelle-tuning condition robustly fabricates energetic mitochondria for cartilage regeneration" (nature | Bone Research) -- " (the researchers) figured out a way to treat stem cells so that (they) would start to make an excess amount of mitochondria than they normally would make" -- " So they created highly energetic mitochondria and they made a lot of them." -- " the idea that we can put mitochondria into our body or into tissue in our body to heal it or repair it has been something that folks have been trying to do research around for a long time" -- " but the limiting factor is access to enough mitochondria" -- " so this mechanism that they developed opens up the door to this whole new therapeutic modality, a new type of therapy called mitotherapy" Conclusion: " ... based on the series of papers that we're seeing coming out recently, I believe (this) could end up becoming a really incredible new therapy that may ultimately lead to the treatment for many diseases that we're dealing with right now."

Science Corner: David Friedberg Explains Recent Mitotherapy Breakthroughs ⚡️ On E224, david friedberg broke down what mitotherapy is and how recent discoveries could unleash this treatment for many diseases: "So mitochondria are the powerhouse of the cell." "Every cell in our body gets its energy, which is what it uses to function, from the mitochondria." "And so there's been a lot of research into the relationship between mitochondria and aging, and that dysfunctional mitochondria may actually be a key driver for many diseases." "Including many cancers, Alzheimer's, Parkinson's, ALS, features of autism, muscle tissues being weak, etc." " So, as the cells get older and the mitochondria stop working, we make new mitochondria." "But over time, the DNA degrades and the mitochondria become less effective and there are fewer functional mitochondria per cell." Friedberg highlighted three recent papers: 1) "The power and potential of mitochondria transfer" (nature) -- "these folks identified and demonstrated that mitochondria can actually transfer from one cell to another" -- " So, if you've got a cell that's got damaged, or dysfunctional mitochondria, they've identified three mechanisms by which mitochondria can move into a cell that needs more mitochondria that are working and are more functional." -- "And as a result, it can rejuvenate or provide energy to a dysfunctional cell, which might improve dysfunctional tissue or improve disease." 2) "A human brain map of mitochondrial respiratory capacity and diversity" (nature) -- " this was the first mapping of the mitochondria in the human brain" -- " what it showed was that different parts of the brain, different cells, had different amounts of mitochondria and different mitochondrial function." -- "(This) starts to highlight how that difference in energy production in different cells in different parts of the brain may actually cause some of the things like memory loss or speech impairment," -- "the mitochondrial dysfunction in the brain might actually be the key driver of that aging symptomology." 3) "Organelle-tuning condition robustly fabricates energetic mitochondria for cartilage regeneration" (nature | Bone Research) -- " (the researchers) figured out a way to treat stem cells so that (they) would start to make an excess amount of mitochondria than they normally would make" -- " So they created highly energetic mitochondria and they made a lot of them." -- " the idea that we can put mitochondria into our body or into tissue in our body to heal it or repair it has been something that folks have been trying to do research around for a long time" -- " but the limiting factor is access to enough mitochondria" -- " so this mechanism that they developed opens up the door to this whole new therapeutic modality, a new type of therapy called mitotherapy" Conclusion: " ... based on the series of papers that we're seeing coming out recently, I believe (this) could end up becoming a really incredible new therapy that may ultimately lead to the treatment for many diseases that we're dealing with right now."

The All-In Podcast

58,533 views • 1 year ago

🚨🚨🚨 Dutch Cancer Researcher and Erasmus Medical Centre Assc Prof Maarten Fornerod discusses DNA contamination in COVID Vaccines. ---------------------------- ...for last 35 years or so I've been working uh in the areas of molecular biology, gene expression, biology, cancer biology and recently in the last years, maybe last 10 years in the context of big data and computational biology... ...when we use genetic vaccines what we do essentially is we're making a complex intervention in a very complex system. It's impossible to predict what happens if you combine these two complex systems, and you get unpredictable effects. .. And the only way to go about this is to do genotoxic research, when you want to introduce a genetic medicine into a human being and that genetotic research has to be independent, it has to be double blind, has to be long lasting. AND ALL THESE HAVE NOT BEEN DONE WITH THE GENETIC CORONA VACCINES! if there's a vaccine, there's a little bit of DNA in there upon injection that is very, very rapidly degraded by the human body. However if it's protected and in a lipid nanoarticle it can very efficiently be transduced in the cell... I've been doing this this many, many times. This is called lipofection and it's a very efficient way to introduce DNA into a cell. ...Many people think that this is not possible. But I've been working in nuclear transport for many years, I think more than 10 years. So I've been exposed to a lot of molecular cell biology of nuclear cytoplasmic transport. And it's clear that the DNA can enter the nucleus. Now to make things worse, the mRNA vaccine doesn't stay in the arm but it's detected in , in all different organs including the reproductive system. And so partly this is based on animal models, of course we know from Michael Morz that he has detected uh the spike protein in brain, in the heart and it's for sure it's detected in the blood and even in breast milk. ..So there's NO DOUBT that this mRNA vaccine spreads widely in the human body. ..Now from a genetic point of view, there are possible consequences uh of this and the consequences could be a 1. long term disruption of cellular processes that could lead to disease. 2. there's a risk of insertional mutageenesis in somatic cells that can lead to cancer. 3. the insertion mutogenesis takes place in a germ cell which would be a hereditary burden uh on the human population. 4. And you could also think that these um DNAs, could transfect the microbiome and it could possibly lead to bacterial resistance. So these are all possible..the consequences of these genetic vaccines in my view, the shortcomings in this rollout of these vaccines were there was no genotoxic research performed at all. There was no safety studies, on carcinogenic potential of these vaccines. My personal opinion is that it's now not a question of whether, it will integrate in recipient's DNA, but how often it occurs... It's just a numbers game. If you do this in many cells, many persons it will no doubt integrate in cells in human recipients. So the question does it affect the human genome? I would say it probably, I think it most certainly does. And you see Kevin McKernan here and he represented ah a preliminary data where he detected a possible Pfizer DNA in colon cancer biopsy one year after vaccination.

🚨🚨🚨 Dutch Cancer Researcher and Erasmus Medical Centre Assc Prof Maarten Fornerod discusses DNA contamination in COVID Vaccines. ---------------------------- ...for last 35 years or so I've been working uh in the areas of molecular biology, gene expression, biology, cancer biology and recently in the last years, maybe last 10 years in the context of big data and computational biology... ...when we use genetic vaccines what we do essentially is we're making a complex intervention in a very complex system. It's impossible to predict what happens if you combine these two complex systems, and you get unpredictable effects. .. And the only way to go about this is to do genotoxic research, when you want to introduce a genetic medicine into a human being and that genetotic research has to be independent, it has to be double blind, has to be long lasting. AND ALL THESE HAVE NOT BEEN DONE WITH THE GENETIC CORONA VACCINES! if there's a vaccine, there's a little bit of DNA in there upon injection that is very, very rapidly degraded by the human body. However if it's protected and in a lipid nanoarticle it can very efficiently be transduced in the cell... I've been doing this this many, many times. This is called lipofection and it's a very efficient way to introduce DNA into a cell. ...Many people think that this is not possible. But I've been working in nuclear transport for many years, I think more than 10 years. So I've been exposed to a lot of molecular cell biology of nuclear cytoplasmic transport. And it's clear that the DNA can enter the nucleus. Now to make things worse, the mRNA vaccine doesn't stay in the arm but it's detected in , in all different organs including the reproductive system. And so partly this is based on animal models, of course we know from Michael Morz that he has detected uh the spike protein in brain, in the heart and it's for sure it's detected in the blood and even in breast milk. ..So there's NO DOUBT that this mRNA vaccine spreads widely in the human body. ..Now from a genetic point of view, there are possible consequences uh of this and the consequences could be a 1. long term disruption of cellular processes that could lead to disease. 2. there's a risk of insertional mutageenesis in somatic cells that can lead to cancer. 3. the insertion mutogenesis takes place in a germ cell which would be a hereditary burden uh on the human population. 4. And you could also think that these um DNAs, could transfect the microbiome and it could possibly lead to bacterial resistance. So these are all possible..the consequences of these genetic vaccines in my view, the shortcomings in this rollout of these vaccines were there was no genotoxic research performed at all. There was no safety studies, on carcinogenic potential of these vaccines. My personal opinion is that it's now not a question of whether, it will integrate in recipient's DNA, but how often it occurs... It's just a numbers game. If you do this in many cells, many persons it will no doubt integrate in cells in human recipients. So the question does it affect the human genome? I would say it probably, I think it most certainly does. And you see Kevin McKernan here and he represented ah a preliminary data where he detected a possible Pfizer DNA in colon cancer biopsy one year after vaccination.

aussie17

126,273 views • 1 year ago

This is crazy! This is some of the complex systems working inside your cells. God's Design inside every single cell in your body. Cells cannot arise through evolution. The minimum viable cell requires: - Around ~500,000 lines of coded information (DNA) - Close to ~500 unique protein products (the building blocks of all those cellular machines and systems) - Total of ~20k-50k total proteins all working perfectly together - Around ~30-40 regulatory systems guiding all those interactions The cell requires all those parts & systems, or it doesn't function. If we do the math, there are about ~10^70,000 possible interactions in this cell. Interactions are things like: - energy production - waste removal - protein creation - system repair The odds are incomprehensible. Evolutionists will argue the odds are misleading, because it evolves gradually via step-by-step trial & error. But the cell REQUIRES a minimum set of parts & systems to function - without all of these in place, together, from the beginning, it dies. Therefore, the cell cannot evolve through step-by-step evolutionary processes, because there is no reproduction + mutation to drive evolutionary change until the cell is complete. Cells are a massive problem for Evolutionism, because they are such obvious signs of Intelligent Design. Even the famous atheist Richard Dawkins admitted that, "Biology is the study of complicated things that have the appearance of having been designed with a purpose." Biology seems designed because it IS designed. God's Divine Design becomes more obvious, the closer we look.

This is crazy! This is some of the complex systems working inside your cells. God's Design inside every single cell in your body. Cells cannot arise through evolution. The minimum viable cell requires: - Around ~500,000 lines of coded information (DNA) - Close to ~500 unique protein products (the building blocks of all those cellular machines and systems) - Total of ~20k-50k total proteins all working perfectly together - Around ~30-40 regulatory systems guiding all those interactions The cell requires all those parts & systems, or it doesn't function. If we do the math, there are about ~10^70,000 possible interactions in this cell. Interactions are things like: - energy production - waste removal - protein creation - system repair The odds are incomprehensible. Evolutionists will argue the odds are misleading, because it evolves gradually via step-by-step trial & error. But the cell REQUIRES a minimum set of parts & systems to function - without all of these in place, together, from the beginning, it dies. Therefore, the cell cannot evolve through step-by-step evolutionary processes, because there is no reproduction + mutation to drive evolutionary change until the cell is complete. Cells are a massive problem for Evolutionism, because they are such obvious signs of Intelligent Design. Even the famous atheist Richard Dawkins admitted that, "Biology is the study of complicated things that have the appearance of having been designed with a purpose." Biology seems designed because it IS designed. God's Divine Design becomes more obvious, the closer we look.

Divinely Designed

32,319 views • 6 months ago

Dr. Patrick Soon-Shiong on the Cancer-Fighting Immune Breakthrough: Awakening the 450 Million-Year-Old Natural Killer Cell After decades of pioneering work since discovering the natural killer cell in the 1990s—a remarkable immune cell created by God and preserved through 450 million years—Dr. Patrick Soon-Shiong reveals an extraordinary breakthrough. This cell, unknown to many until the 1970s, serves as the body's first line of defense, designed to destroy cancer cells, infections like COVID, sepsis, bacteria, and fungus. Dr. Soon-Shiong emphasizes that this is not an invention, but an enlightenment: activating the body’s own natural killer cells to combat disease naturally, shifting away from toxic chemotherapy and radiation. With a single jab, they have unlocked the secret to empowering this primal cellular protector. This is evolution’s own weapon against illness, a game-changer that taps into ancient biology to revolutionize modern medicine. This insight reframes how we think about health and cancer treatment by harnessing the immune system's innate power, moving beyond harsh treatments, and embracing nature’s original disease-fighter from within. The discovery is a call to awaken the body’s own god-given defense system for a future of empowered healing.

Dr. Patrick Soon-Shiong on the Cancer-Fighting Immune Breakthrough: Awakening the 450 Million-Year-Old Natural Killer Cell After decades of pioneering work since discovering the natural killer cell in the 1990s—a remarkable immune cell created by God and preserved through 450 million years—Dr. Patrick Soon-Shiong reveals an extraordinary breakthrough. This cell, unknown to many until the 1970s, serves as the body's first line of defense, designed to destroy cancer cells, infections like COVID, sepsis, bacteria, and fungus. Dr. Soon-Shiong emphasizes that this is not an invention, but an enlightenment: activating the body’s own natural killer cells to combat disease naturally, shifting away from toxic chemotherapy and radiation. With a single jab, they have unlocked the secret to empowering this primal cellular protector. This is evolution’s own weapon against illness, a game-changer that taps into ancient biology to revolutionize modern medicine. This insight reframes how we think about health and cancer treatment by harnessing the immune system's innate power, moving beyond harsh treatments, and embracing nature’s original disease-fighter from within. The discovery is a call to awaken the body’s own god-given defense system for a future of empowered healing.

Camus

33,407 views • 10 months ago

🚨David Sinclair: “The first potential human age-reversal technology is in people right now.” ROGAN: “What is the current state of science as applied to longevity treatments?” SINCLAIR: “We have a human clinical trial.” ROGAN: “If people get old and we understand the mechanism, why couldn’t we reverse it?” SINCLAIR: “My colleagues and I found that aging has a root cause that’s the same in pretty much every cell in the body, and that we could control that.” “In 2020, we had a big publication that showed we could actually safely, for the first time, reverse that process, like reinstalling software in the cell to make it work again.” ---- The treatment is ER-100, an experimental epigenetic reprogramming gene therapy being developed by Life Biosciences, a company co-founded by Harvard geneticist Dr. David Sinclair. In the current FDA clinical trial, the therapy is delivered by injection into the eye. If this works in humans, it could be a major step toward actually reversing aspects of biological aging rather than simply slowing them down. Joe Rogan David Sinclair

🚨David Sinclair: “The first potential human age-reversal technology is in people right now.” ROGAN: “What is the current state of science as applied to longevity treatments?” SINCLAIR: “We have a human clinical trial.” ROGAN: “If people get old and we understand the mechanism, why couldn’t we reverse it?” SINCLAIR: “My colleagues and I found that aging has a root cause that’s the same in pretty much every cell in the body, and that we could control that.” “In 2020, we had a big publication that showed we could actually safely, for the first time, reverse that process, like reinstalling software in the cell to make it work again.” ---- The treatment is ER-100, an experimental epigenetic reprogramming gene therapy being developed by Life Biosciences, a company co-founded by Harvard geneticist Dr. David Sinclair. In the current FDA clinical trial, the therapy is delivered by injection into the eye. If this works in humans, it could be a major step toward actually reversing aspects of biological aging rather than simply slowing them down. Joe Rogan David Sinclair

KanekoaTheGreat

1,234,391 views • 1 month ago

This is how DNA is organized in a cell. And it's more evidence that Life was intelligently designed. Each human cell contains about 6.5ft (2m) of DNA packed into a dense microscopic structure. But it's not packed into a random pile. It's packed into precise loops scientists call 'cohesin loops.' DNA is wound up around spool-like proteins called histones, which are then further wound together into clusters called nucleosomes, which are then organized into shapes called chromatins, which are then folded into large structures called chromosomes. Four levels of very specific, complex organization. Without a specific & controlled organization system, DNA would be an unreadable, useless mess. Which means DNA would have to be organized immediately upon creation, or Life couldn't arise. And this is the craziest part... The way DNA is organized in a eukaryotic cell directly controls which genes are turned on or off. This organization is what determines cell-type. Every cell in your body contains the exact same DNA blueprint. The only reason a heart cell beats and a skin cell protects you is due to DNA organization. A heart cell packs away all "skin genes" into tight, unreadable section that doesn't get activated, while keeping "heart genes" open & active. If a cell doesn't have specific DNA organization, it loses its identity. When that happens, it results in cancer. Which means DNA organization must be specifically planned out from the start to prevent catastrophe. Life doesn't have time to tinker and figure this out via evolution. DNA requires organization from the start, or it's useless. DNA organizarion requires multiple systems all working together to function. Without them, it fails. Only intelligence has ever been shown to engineer specifically organized, complex informational systems with obvious signs of preplanning and intentionality. Life was Divinely Designed. Biology proves it over & over again.

This is how DNA is organized in a cell. And it's more evidence that Life was intelligently designed. Each human cell contains about 6.5ft (2m) of DNA packed into a dense microscopic structure. But it's not packed into a random pile. It's packed into precise loops scientists call 'cohesin loops.' DNA is wound up around spool-like proteins called histones, which are then further wound together into clusters called nucleosomes, which are then organized into shapes called chromatins, which are then folded into large structures called chromosomes. Four levels of very specific, complex organization. Without a specific & controlled organization system, DNA would be an unreadable, useless mess. Which means DNA would have to be organized immediately upon creation, or Life couldn't arise. And this is the craziest part... The way DNA is organized in a eukaryotic cell directly controls which genes are turned on or off. This organization is what determines cell-type. Every cell in your body contains the exact same DNA blueprint. The only reason a heart cell beats and a skin cell protects you is due to DNA organization. A heart cell packs away all "skin genes" into tight, unreadable section that doesn't get activated, while keeping "heart genes" open & active. If a cell doesn't have specific DNA organization, it loses its identity. When that happens, it results in cancer. Which means DNA organization must be specifically planned out from the start to prevent catastrophe. Life doesn't have time to tinker and figure this out via evolution. DNA requires organization from the start, or it's useless. DNA organizarion requires multiple systems all working together to function. Without them, it fails. Only intelligence has ever been shown to engineer specifically organized, complex informational systems with obvious signs of preplanning and intentionality. Life was Divinely Designed. Biology proves it over & over again.

Divinely Designed

14,875 views • 2 months ago