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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...

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Markus J. Buehler

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387,977 görüntüleme • 7 ay önce •via X (Twitter)

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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

58,343 görüntüleme • 1 yıl önce

🔬 Exciting News! Our manuscript, "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" is now finally published in Nature Methods (Nature Methods) 🎉 !!! (Re-)Introducing scGPT: A transformative foundation model engineered for single-cell omics analysis. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities. Since its preprint in May 2023, scGPT has significantly impacted the field, evidenced by 13K+ installations, 600+ GitHub stars 🌟, and 40+ citations before its official publication! scGPT has been validated by numerous benchmark studies as a leading foundation model in single-cell analysis. Its pre-trained embeddings extend its utility beyond single-cell studies, enhancing a variety of downstream tasks including protein enrichment and genetic perturbation predictions. Some key updates lately: ---Expanded zero-shot applications for efficient reference mapping and integration, now with CellXGene census integration. ---Advanced perturbation analysis capabilities, including genome-scale perturb-seq data analysis and bulk sequencing data generalization. ---Upgraded scGPT package, offering versatile model loading compatible with PyTorch and flash-attn, for both GPU and CPU. ---Cloud-based scGPT applications for reference mapping, cell annotation, and gene regulatory network inference are available on ---Integration with Hugging Face for easier model training. Limitations: scGPT is an early foray into foundation models for single-cell omics, facing challenges like limited zero-shot learning in some tasks, pretraining constraints, data quality issues, and evaluation limitations. See our Supplementary Notes for details. 🚀 Future Work? Short-Term Goals: 1. Releasing a Mouse Model for broader analysis. 2. Developing a comprehensive evaluation suite for foundation models in single-cell analysis. 3. Creating a foundation model for single-cell spatial omics. 4. Enhancing zero-shot capacity by integrating scGPT with RAG (e.g., knowledge graphs). Long-Term Goals: 1. Expanding scGPT for comprehensive single-cell multi-omics analysis. 2. Developing an in-silico perturbation model for predicting genetic perturbation effects. 3. Merging scGPT with multi-modal genomic sequence models for a deeper understanding of cell biology. 📚 Access the paper on Nature Methods: 🔬Preprint in Bioarixv: 💻 All our codes/data/weights are open source: Wholehearted congratulations to all the authors, especially the two co-first authors, Haotian (Haotian Cui ) and Chloe (ChloeXWang), who are really the emerging superstars in AI and biology! Vector Institute Peter Munk Cardiac Centre AI U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology University Health Network University of Toronto #scGPT #GenerativeAI #AI4Science #Combio #opensource
0:52

🔬 Exciting News! Our manuscript, "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" is now finally published in Nature Methods (Nature Methods) 🎉 !!! (Re-)Introducing scGPT: A transformative foundation model engineered for single-cell omics analysis. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities. Since its preprint in May 2023, scGPT has significantly impacted the field, evidenced by 13K+ installations, 600+ GitHub stars 🌟, and 40+ citations before its official publication! scGPT has been validated by numerous benchmark studies as a leading foundation model in single-cell analysis. Its pre-trained embeddings extend its utility beyond single-cell studies, enhancing a variety of downstream tasks including protein enrichment and genetic perturbation predictions. Some key updates lately: ---Expanded zero-shot applications for efficient reference mapping and integration, now with CellXGene census integration. ---Advanced perturbation analysis capabilities, including genome-scale perturb-seq data analysis and bulk sequencing data generalization. ---Upgraded scGPT package, offering versatile model loading compatible with PyTorch and flash-attn, for both GPU and CPU. ---Cloud-based scGPT applications for reference mapping, cell annotation, and gene regulatory network inference are available on ---Integration with Hugging Face for easier model training. Limitations: scGPT is an early foray into foundation models for single-cell omics, facing challenges like limited zero-shot learning in some tasks, pretraining constraints, data quality issues, and evaluation limitations. See our Supplementary Notes for details. 🚀 Future Work? Short-Term Goals: 1. Releasing a Mouse Model for broader analysis. 2. Developing a comprehensive evaluation suite for foundation models in single-cell analysis. 3. Creating a foundation model for single-cell spatial omics. 4. Enhancing zero-shot capacity by integrating scGPT with RAG (e.g., knowledge graphs). Long-Term Goals: 1. Expanding scGPT for comprehensive single-cell multi-omics analysis. 2. Developing an in-silico perturbation model for predicting genetic perturbation effects. 3. Merging scGPT with multi-modal genomic sequence models for a deeper understanding of cell biology. 📚 Access the paper on Nature Methods: 🔬Preprint in Bioarixv: 💻 All our codes/data/weights are open source: Wholehearted congratulations to all the authors, especially the two co-first authors, Haotian (Haotian Cui ) and Chloe (ChloeXWang), who are really the emerging superstars in AI and biology! Vector Institute Peter Munk Cardiac Centre AI U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology University Health Network University of Toronto #scGPT #GenerativeAI #AI4Science #Combio #opensource

Bo Wang

199,711 görüntüleme • 2 yıl önce

The Bitter Lesson for Biology — Adam Green on Scaling Laws for Virtual Cells In this episode, the founder of Markov Biosciences, Adam Green, explains the "bitter lesson" for biology, the idea borrowed from Richard Sutton that large unbiased datasets and the right training objective tend to outcompete models with hard-coded rules and human priors. We talk a lot about virtual cells, sources of bias in data, and his evidence that virtual cells pre-trained on plain observational data show clean scaling laws, getting monotonically better at predicting unseen perturbations as the models grow, and beating a state-of-the-art model built specifically for that task. Our interview was about 3 hours long, but cut to 1.5 hours. My main goal was to understand Adam's perspective on virtual cells and his goal to "solve biology." Search for "The New Biology" on YouTube, Spotify, and Apple Podcasts. Timestamps: 00:00 — Cold open 01:58 — Clinical predictions from a virtual cell 05:38 — What is a "virtual cell"? 08:01 — The problems with single-cell RNA-seq 11:31 — The urns analogy 19:54 — Observational vs. perturbation 23:29 — The bitter lesson for biology 29:06 — Geometric Plackett-Luce 38:27 — Ablations and loss function 47:23 — Cells as specimens 59:26 — Antibody-Drug conjugates 1:11:16 — Will we ever understand the cell fully?
1:29:33

The Bitter Lesson for Biology — Adam Green on Scaling Laws for Virtual Cells In this episode, the founder of Markov Biosciences, Adam Green, explains the "bitter lesson" for biology, the idea borrowed from Richard Sutton that large unbiased datasets and the right training objective tend to outcompete models with hard-coded rules and human priors. We talk a lot about virtual cells, sources of bias in data, and his evidence that virtual cells pre-trained on plain observational data show clean scaling laws, getting monotonically better at predicting unseen perturbations as the models grow, and beating a state-of-the-art model built specifically for that task. Our interview was about 3 hours long, but cut to 1.5 hours. My main goal was to understand Adam's perspective on virtual cells and his goal to "solve biology." Search for "The New Biology" on YouTube, Spotify, and Apple Podcasts. Timestamps: 00:00 — Cold open 01:58 — Clinical predictions from a virtual cell 05:38 — What is a "virtual cell"? 08:01 — The problems with single-cell RNA-seq 11:31 — The urns analogy 19:54 — Observational vs. perturbation 23:29 — The bitter lesson for biology 29:06 — Geometric Plackett-Luce 38:27 — Ablations and loss function 47:23 — Cells as specimens 59:26 — Antibody-Drug conjugates 1:11:16 — Will we ever understand the cell fully?

Niko McCarty.

27,682 görüntüleme • 1 ay önce

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:
1:25

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:

Mo Lotfollahi

22,440 görüntüleme • 4 ay önce

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 görüntüleme • 4 ay önce

A video article from 2016 on the role of histamine and mast cells in pattern hair loss: Antihistamines for Male Pattern Baldness? [2016] "The role of mast cells in male-pattern baldness is unknown, but the large numbers often present is a striking feature." "Following from my understanding of the implantation process of an embryo in the uterus, and the function of mast cells there, leading to formation of the placenta, I’m inclined to think of them as potential agents of tissue renewal or regeneration. I think their activation by estrogen, and quieting by progesterone, suggests that they are probably activators and guides for stem cell formation and differentiation, depending on the availability of support. Their presence in cancers has always seemed to me to indicate that both allergies and cancer are mainly systemic energy problems." — Ray Peat (2015)
10:25

A video article from 2016 on the role of histamine and mast cells in pattern hair loss: Antihistamines for Male Pattern Baldness? [2016] "The role of mast cells in male-pattern baldness is unknown, but the large numbers often present is a striking feature." "Following from my understanding of the implantation process of an embryo in the uterus, and the function of mast cells there, leading to formation of the placenta, I’m inclined to think of them as potential agents of tissue renewal or regeneration. I think their activation by estrogen, and quieting by progesterone, suggests that they are probably activators and guides for stem cell formation and differentiation, depending on the availability of support. Their presence in cancers has always seemed to me to indicate that both allergies and cancer are mainly systemic energy problems." — Ray Peat (2015)

Danny Roddy

21,631 görüntüleme • 2 yıl önce

📡🧵 Excited to release two #bioRxiv preprints! The first preprint is: "Zebrahub-Multiome: Uncovering Gene Regulatory Network Dynamics During Zebrafish Embryogenesis" We present a comprehensive time-resolved single-cell multiomic atlas of zebrafish development. Integrating scATAC-seq and scRNA-seq from 94,000+ cells, we map gene regulatory networks across six key stages of embryogenesis (10 to 24 hpf). A major accomplishment by YangJoon Kim co-led by Merlin Lange with experiments by Shruthi VijayKumar #Zebrahub-Multiome is a sequel to our transcriptomic and imaging atlas of zebrafish development. #zebrafish #development #ATACseq #cell #biology #sequencing Chan Zuckerberg Biohub Network #CZBiohubSF Biohub Note: We sent two preprints simultaneously yesterday, and this is the first one to come out... I will append to this thread when the second one is out...
0:38

📡🧵 Excited to release two #bioRxiv preprints! The first preprint is: "Zebrahub-Multiome: Uncovering Gene Regulatory Network Dynamics During Zebrafish Embryogenesis" We present a comprehensive time-resolved single-cell multiomic atlas of zebrafish development. Integrating scATAC-seq and scRNA-seq from 94,000+ cells, we map gene regulatory networks across six key stages of embryogenesis (10 to 24 hpf). A major accomplishment by YangJoon Kim co-led by Merlin Lange with experiments by Shruthi VijayKumar #Zebrahub-Multiome is a sequel to our transcriptomic and imaging atlas of zebrafish development. #zebrafish #development #ATACseq #cell #biology #sequencing Chan Zuckerberg Biohub Network #CZBiohubSF Biohub Note: We sent two preprints simultaneously yesterday, and this is the first one to come out... I will append to this thread when the second one is out...

Loïc A. Royer 💻🔬⚗️

39,790 görüntüleme • 1 yıl önce

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.
1:58

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 görüntüleme • 8 ay önce

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."
2:46

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

58,412 görüntüleme • 1 yıl önce

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.
1:28

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

32,233 görüntüleme • 4 ay önce

From 3D imaging to therapy Using our DISCO tissue clearing and whole organ 3D imaging platform, we revealed skull to meninges microchannels connecting calvarial bone marrow with the brain borders in mice (Cai...Ertürk, Nature Neuroscience,2018, We then extended this biology to humans, discovering distinct skull bone marrow programs and structural skull meninges connections in health and neurological disease (Kolabas...Ertürk, Cell, 2023). Now, a new study reports a first therapeutic application of this route: intracalvariosseous (calvarial) injection of drug loaded albumin nanoparticles to “hijack” calvarial immune cells, which then migrate via skull meninges microchannels to CNS lesions, bypassing the blood brain barrier and improving outcomes in stroke models, with encouraging clinical feasibility data (Gao et al., Cell, 2026) We are proud that DISCO enabled biology that is now turning into concrete therapeutic strategies for devastating neurological diseases. More to come. #DISCO #TissueClearing #Neuroimmunology #Stroke #Nanomedicine #DrugDelivery #BBB #CNS Refs (for linking in comments): Cai et al. Nat Neuroscience, Kolabas et al. Cell 2023, the video from Gao et al. Cell 2026 (online Jan 16, 2026).
0:29

From 3D imaging to therapy Using our DISCO tissue clearing and whole organ 3D imaging platform, we revealed skull to meninges microchannels connecting calvarial bone marrow with the brain borders in mice (Cai...Ertürk, Nature Neuroscience,2018, We then extended this biology to humans, discovering distinct skull bone marrow programs and structural skull meninges connections in health and neurological disease (Kolabas...Ertürk, Cell, 2023). Now, a new study reports a first therapeutic application of this route: intracalvariosseous (calvarial) injection of drug loaded albumin nanoparticles to “hijack” calvarial immune cells, which then migrate via skull meninges microchannels to CNS lesions, bypassing the blood brain barrier and improving outcomes in stroke models, with encouraging clinical feasibility data (Gao et al., Cell, 2026) We are proud that DISCO enabled biology that is now turning into concrete therapeutic strategies for devastating neurological diseases. More to come. #DISCO #TissueClearing #Neuroimmunology #Stroke #Nanomedicine #DrugDelivery #BBB #CNS Refs (for linking in comments): Cai et al. Nat Neuroscience, Kolabas et al. Cell 2023, the video from Gao et al. Cell 2026 (online Jan 16, 2026).

Ali Max Erturk

15,901 görüntüleme • 6 ay önce

The Pillars of Cancer Treatment Are a Lie. Chemo and Radiation Aren't Just Brutal—They're Actively Destroying Your Natural Killer Cells, the Very Thing Standing Between You and Cancer. A stunning revelation from Dr. Patrick Soon-Shiong that reframes our entire battle against cancer. He shares a truth 460 million years in the making. Within every one of us is an ancient gift, a cell bestowed by the very process of creation: the Natural Killer (NK) cell. Dr. Soon-Shiong explains that this cell is our fundamental biological shield, the key reason humanity has survived infection, trauma, and cancer across millennia. Yet, for decades, modern medicine has waged a devastating war on this very protector. How? With the very pillars of our oncological arsenal: high-dose chemotherapy, radiation, steroid therapy, and even newer modalities. These treatments, while aimed at cancer, systematically destroy the NK cells designed to defend us. A single dose of radiation can obliterate this natural defense for a year, leaving patients vulnerable. Dr. Soon-Shiong presents a paradigm-shattering question: What if, instead of attacking the body’s innate protection, we learned to unlock and unleash it? The answer is here. After a lifetime of research, a method has been discovered and approved to activate the body as its own factory. A single intervention—a “bio-shield”—that proliferates these natural killer cells, empowering them to do what they were designed to do: protect you from cancer. The results? Real patients with bladder cancer, free of disease for a decade. This is not a future promise. This is a present reality, approved in 2024. Dr. Soon-Shiong credits the current administration for its role in moving this breakthrough forward for the entire nation. The era of destroying the body to save it is ending. The era of activating our 460-million-year-old innate defense has begun.
2:58

The Pillars of Cancer Treatment Are a Lie. Chemo and Radiation Aren't Just Brutal—They're Actively Destroying Your Natural Killer Cells, the Very Thing Standing Between You and Cancer. A stunning revelation from Dr. Patrick Soon-Shiong that reframes our entire battle against cancer. He shares a truth 460 million years in the making. Within every one of us is an ancient gift, a cell bestowed by the very process of creation: the Natural Killer (NK) cell. Dr. Soon-Shiong explains that this cell is our fundamental biological shield, the key reason humanity has survived infection, trauma, and cancer across millennia. Yet, for decades, modern medicine has waged a devastating war on this very protector. How? With the very pillars of our oncological arsenal: high-dose chemotherapy, radiation, steroid therapy, and even newer modalities. These treatments, while aimed at cancer, systematically destroy the NK cells designed to defend us. A single dose of radiation can obliterate this natural defense for a year, leaving patients vulnerable. Dr. Soon-Shiong presents a paradigm-shattering question: What if, instead of attacking the body’s innate protection, we learned to unlock and unleash it? The answer is here. After a lifetime of research, a method has been discovered and approved to activate the body as its own factory. A single intervention—a “bio-shield”—that proliferates these natural killer cells, empowering them to do what they were designed to do: protect you from cancer. The results? Real patients with bladder cancer, free of disease for a decade. This is not a future promise. This is a present reality, approved in 2024. Dr. Soon-Shiong credits the current administration for its role in moving this breakthrough forward for the entire nation. The era of destroying the body to save it is ending. The era of activating our 460-million-year-old innate defense has begun.

Camus

150,903 görüntüleme • 9 ay önce

"The real dangerous thing about the Pfizer DNA". "It's like one of these self-amplifying RNA's". Genomics expert Kevin McKernan has seen DNA contamination 10- to 100-fold above safety limits and they haven't even tested the "E Lots" containing the super dangerous vials. "[In] a study from Professor Ulrike Kämmerer in Germany [she] transfected cell lines with the vaccine in petri dishes [and] she watched those through several cell divisions, and the SV40 was staying with them throughout the cell divisions." Transcript, Kevin McKernan: "That's the really dangerous thing about the Pfizer DNA. Unlike the Moderna DNA, they left a mammalian origin of replication in there. So, if it gets into a mammalian cell, it's capable of making more copies of itself once it's in there. In essence, it's like one of these self-amplifying RNAs that everyone's ludicrously thinking about releasing right now. There has been a study from Professor Ulrike Kämmerer in Germany where she transfected cell lines with the vaccine in petri dishes. Not the virus—I'm sorry, the vaccine. I might as well call it a virus at this stage, and she watched those through several cell divisions, and the SV40 was staying with them throughout the cell divisions. That's a sign that it is replication-competent to some degree. That's still yet to be done with the really egregious lots—the E lots. I bet if we did those, there would be full-length plasmids in those, and they would stay permanently in those cell lines. But we don't have access to those. For those not familiar with these E lots, there's a Schmeling et al. paper that showed that about four percent of the lots are responsible for about 77% of the adverse events. None of those have been surveyed yet for this. We're finding contamination 10- to 100-fold above the limit in the lots that had low adverse events. Wait until we get to the ones that are really hurting people."
1:24

"The real dangerous thing about the Pfizer DNA". "It's like one of these self-amplifying RNA's". Genomics expert Kevin McKernan has seen DNA contamination 10- to 100-fold above safety limits and they haven't even tested the "E Lots" containing the super dangerous vials. "[In] a study from Professor Ulrike Kämmerer in Germany [she] transfected cell lines with the vaccine in petri dishes [and] she watched those through several cell divisions, and the SV40 was staying with them throughout the cell divisions." Transcript, Kevin McKernan: "That's the really dangerous thing about the Pfizer DNA. Unlike the Moderna DNA, they left a mammalian origin of replication in there. So, if it gets into a mammalian cell, it's capable of making more copies of itself once it's in there. In essence, it's like one of these self-amplifying RNAs that everyone's ludicrously thinking about releasing right now. There has been a study from Professor Ulrike Kämmerer in Germany where she transfected cell lines with the vaccine in petri dishes. Not the virus—I'm sorry, the vaccine. I might as well call it a virus at this stage, and she watched those through several cell divisions, and the SV40 was staying with them throughout the cell divisions. That's a sign that it is replication-competent to some degree. That's still yet to be done with the really egregious lots—the E lots. I bet if we did those, there would be full-length plasmids in those, and they would stay permanently in those cell lines. But we don't have access to those. For those not familiar with these E lots, there's a Schmeling et al. paper that showed that about four percent of the lots are responsible for about 77% of the adverse events. None of those have been surveyed yet for this. We're finding contamination 10- to 100-fold above the limit in the lots that had low adverse events. Wait until we get to the ones that are really hurting people."

Humanspective

15,938 görüntüleme • 1 yıl önce

A new Nature paper from Johns Hopkins (by Prof. Lin Dingchang Lin ) just solved one of the hardest problems in biology: how do you record what every cell in a tissue experienced over time, not just what it looks like right now? The answer: GEMINI — Granularly Expanding Memory for Intracellular Narrative Integration. It works exactly like tree rings. Cells are genetically engineered to express a computationally designed protein assembly. As the assembly grows inside the cell, it captures cellular activity as fluorescent ring patterns — each ring a timestamp, each ring's properties encoding signal intensity. Look at a cross-section under a microscope and you can read the cell's history backward, with ~15-minute resolution. The key: cells build the recorder themselves. GEMINI doesn't interfere with normal function — it just quietly writes. What they demonstrated: In a full tumor xenograft, GEMINI captured every cancer cell's activity history across the entire tumor while it continued to grow normally. For the first time, researchers can look back and see how different regions of the same tumor responded differently to therapy over time — not snapshots, but film. In a mouse brain, GEMINI recorded neural activity dynamics without disrupting behavior, coordination, or memory. It could temporally resolve the history of a brain seizure. Why this matters: Every tool we have in biology gives you state — what the cell looks like now. Sequencing, imaging, proteomics — all snapshots. GEMINI gives you trajectory. It's the difference between a photograph and a video, applied to every cell in an organ simultaneously. The team is explicit that AI-based decoding tools will be central to reading GEMINI's output at whole-brain scale. This is the data layer that makes temporal single-cell atlases possible. Paper: Congratulations Dingchang Lin
1:51

A new Nature paper from Johns Hopkins (by Prof. Lin Dingchang Lin ) just solved one of the hardest problems in biology: how do you record what every cell in a tissue experienced over time, not just what it looks like right now? The answer: GEMINI — Granularly Expanding Memory for Intracellular Narrative Integration. It works exactly like tree rings. Cells are genetically engineered to express a computationally designed protein assembly. As the assembly grows inside the cell, it captures cellular activity as fluorescent ring patterns — each ring a timestamp, each ring's properties encoding signal intensity. Look at a cross-section under a microscope and you can read the cell's history backward, with ~15-minute resolution. The key: cells build the recorder themselves. GEMINI doesn't interfere with normal function — it just quietly writes. What they demonstrated: In a full tumor xenograft, GEMINI captured every cancer cell's activity history across the entire tumor while it continued to grow normally. For the first time, researchers can look back and see how different regions of the same tumor responded differently to therapy over time — not snapshots, but film. In a mouse brain, GEMINI recorded neural activity dynamics without disrupting behavior, coordination, or memory. It could temporally resolve the history of a brain seizure. Why this matters: Every tool we have in biology gives you state — what the cell looks like now. Sequencing, imaging, proteomics — all snapshots. GEMINI gives you trajectory. It's the difference between a photograph and a video, applied to every cell in an organ simultaneously. The team is explicit that AI-based decoding tools will be central to reading GEMINI's output at whole-brain scale. This is the data layer that makes temporal single-cell atlases possible. Paper: Congratulations Dingchang Lin

Bo Wang

85,133 görüntüleme • 4 ay önce

This video is one of the first times I thought biology was “cool.” It shows a neutrophil cell chasing a bacterium. Originally recorded in the 1950s by David Rogers at Vanderbilt University, the video gave me a deeper appreciation for life, even at the level of a single cell, because the neutrophil's movements seem so intentful, purposeful, aware. It wasn’t until recently, though, that I actually tried to demystify the neutrophil’s movements and understand how they happen. Here's what I learned: 1. The neutrophil's surface has thousands of protein receptors. Molecules secreted by the bacteria collide with these receptors. When that happens, the proteins change shape, slightly, and initiate a signaling cascade. 2. The neutrophil “knows” where to go because of a discrepancy in bound vs. unbound receptors. The side of the cell closest to the microbe will, probabilistically, have more "bound" receptors than the other side (because the molecules secreted by the microbe have a concentration gradient). This is how the neutrophil figures out which way to move. 3. Each bound receptor activates several G proteins located inside the cell membrane. Each G protein, in turn, switches on PI3K enzymes. In this way, the original signal is amplified; a single "activated" receptor might cause ~100 copies of PI3K to get switched on downstream. 4. The PI3K enzymes stick phosphates onto lipids in the cell membrane. The side of the neutrophil facing the bacterium now has more phosphates than the "back" side. Phosphate-binding proteins, such as GEF, accumulate and then recruit Rac, thus activating it. Rac, in turn, acts like a molecular switch, ultimately recruiting Arp2/3. (TL;DR: A bunch of proteins get activated, and the high phosphate concentration at the leading edge is the key signal for all this.) 5. At any given moment, the neutrophil has millions of actin molecules. These are the proteins used to build the cytoskeleton. Half of the actins are already “assembled” into filaments, but the other half are just floating around. Arp2/3 acts as a nucleator, grabbing onto actin and then starting a new cytoskeletal branch. More actin is assembled at the leading edge (where the Arp2/3 has accumulated), where they each push on the cell membrane with ~2 piconewtons of force. Hundreds of actin chains, pushing together, causes the cell to form protrusions. 6. The assembling actin chains push the cell at a speed of ~20 micrometers per minute (the length of about ten E. coli cells placed end-to-end.) As all of this is happening, another signaling cascade, nucleated at the back end of the cell, is dismantling actin filaments and recycling them. All this happens over a span of about 30 seconds. Much of this process is invisible; what we see, instead, is "just" a cell chasing its prey. But that's the wonderful thing about biology: A singular observation is usually more than enough fodder for a lifetime of work. The well is deep. There is always more to learn.
0:28

This video is one of the first times I thought biology was “cool.” It shows a neutrophil cell chasing a bacterium. Originally recorded in the 1950s by David Rogers at Vanderbilt University, the video gave me a deeper appreciation for life, even at the level of a single cell, because the neutrophil's movements seem so intentful, purposeful, aware. It wasn’t until recently, though, that I actually tried to demystify the neutrophil’s movements and understand how they happen. Here's what I learned: 1. The neutrophil's surface has thousands of protein receptors. Molecules secreted by the bacteria collide with these receptors. When that happens, the proteins change shape, slightly, and initiate a signaling cascade. 2. The neutrophil “knows” where to go because of a discrepancy in bound vs. unbound receptors. The side of the cell closest to the microbe will, probabilistically, have more "bound" receptors than the other side (because the molecules secreted by the microbe have a concentration gradient). This is how the neutrophil figures out which way to move. 3. Each bound receptor activates several G proteins located inside the cell membrane. Each G protein, in turn, switches on PI3K enzymes. In this way, the original signal is amplified; a single "activated" receptor might cause ~100 copies of PI3K to get switched on downstream. 4. The PI3K enzymes stick phosphates onto lipids in the cell membrane. The side of the neutrophil facing the bacterium now has more phosphates than the "back" side. Phosphate-binding proteins, such as GEF, accumulate and then recruit Rac, thus activating it. Rac, in turn, acts like a molecular switch, ultimately recruiting Arp2/3. (TL;DR: A bunch of proteins get activated, and the high phosphate concentration at the leading edge is the key signal for all this.) 5. At any given moment, the neutrophil has millions of actin molecules. These are the proteins used to build the cytoskeleton. Half of the actins are already “assembled” into filaments, but the other half are just floating around. Arp2/3 acts as a nucleator, grabbing onto actin and then starting a new cytoskeletal branch. More actin is assembled at the leading edge (where the Arp2/3 has accumulated), where they each push on the cell membrane with ~2 piconewtons of force. Hundreds of actin chains, pushing together, causes the cell to form protrusions. 6. The assembling actin chains push the cell at a speed of ~20 micrometers per minute (the length of about ten E. coli cells placed end-to-end.) As all of this is happening, another signaling cascade, nucleated at the back end of the cell, is dismantling actin filaments and recycling them. All this happens over a span of about 30 seconds. Much of this process is invisible; what we see, instead, is "just" a cell chasing its prey. But that's the wonderful thing about biology: A singular observation is usually more than enough fodder for a lifetime of work. The well is deep. There is always more to learn.

Niko McCarty.

127,178 görüntüleme • 6 ay önce

David Sinclair's lab just discovered that the same technology that reverses aging also kills cancer cells. His team has been using epigenetic reprogramming to reset old cells back to a younger state. It works. Old cells become young again. But when they tried it on cancer cells, something unexpected happened. The cancer cells didn't get younger. They killed themselves. Sinclair (David Sinclair) explained why. Cancer cells survive by ignoring the DNA damage inside them. They're filled with it. But they've shut down the part of the cell that would normally detect it and trigger self-destruction. Epigenetic reprogramming wakes that system back up. Sinclair put it this way: the cancer cell wakes up from its zombie-like state, looks at its own chromosomes, realizes they're destroyed, and says "I better kill myself." And it does. A normal cell gets reprogrammed and becomes young again. A cancer cell gets reprogrammed and destroys itself. Same technology. Two opposite outcomes. Both exactly what you'd want. His team has shown this works across many types of cancer. Side note: David Sinclair is speaking on May 6th at SynBioBeta this year - discussing the science of slowing and reversing aging. If longevity is the world you're in, the investors, partners, and scientists who matter in this space will be in the room. Link for tickets below.
1:02

David Sinclair's lab just discovered that the same technology that reverses aging also kills cancer cells. His team has been using epigenetic reprogramming to reset old cells back to a younger state. It works. Old cells become young again. But when they tried it on cancer cells, something unexpected happened. The cancer cells didn't get younger. They killed themselves. Sinclair (David Sinclair) explained why. Cancer cells survive by ignoring the DNA damage inside them. They're filled with it. But they've shut down the part of the cell that would normally detect it and trigger self-destruction. Epigenetic reprogramming wakes that system back up. Sinclair put it this way: the cancer cell wakes up from its zombie-like state, looks at its own chromosomes, realizes they're destroyed, and says "I better kill myself." And it does. A normal cell gets reprogrammed and becomes young again. A cancer cell gets reprogrammed and destroys itself. Same technology. Two opposite outcomes. Both exactly what you'd want. His team has shown this works across many types of cancer. Side note: David Sinclair is speaking on May 6th at SynBioBeta this year - discussing the science of slowing and reversing aging. If longevity is the world you're in, the investors, partners, and scientists who matter in this space will be in the room. Link for tickets below.

John Cumbers

95,716 görüntüleme • 3 ay önce

PantheonOS allows any biologist to perform complex data analyses of emerging single cell, multi-omics and spatial transcriptomics datasets end to end through AI agent and human collaboration. We are releasing six replays of use cases trajectories. Each "trajectory" is a complete end-to-end run, from prompt to analysis, to figures, and finally to report, and in fully inspectable and reproducible manner. See the six user cases in our PantheonOS Gallery: 1. 3D mouse embryo analysis: Tangram deconvolution and PyVista-based 3D visualization of E6 mouse embryo spatial transcriptomics data 2. 3D human fetal heart analysis: Spatial mapping of heart disease gene patterns using MERFISH data 3. Multi-omics spatial integration: Single cell multi-omics-to-spatial mapping with MOSCOT optimal transport 4. Gene panel design: 1000-plex immune-oncology MERFISH gene panel optimization 5. Cell segmentation benchmarking: Comparative evaluation of Cellpose-SAM, InstanSeg, StarDist, and other tools 6. Spatial disease biology: Ligand–receptor analysis of disease-associated tissue microenvironments We would love to hear how you can use PantheonOS for your research! Got an interesting agent run of your own and want to share? In the Pantheon UI, click Export Bundle (top-right of any chat) to package the full trajectory — chat history, code, figures, report — then submit it here:
0:58

PantheonOS allows any biologist to perform complex data analyses of emerging single cell, multi-omics and spatial transcriptomics datasets end to end through AI agent and human collaboration. We are releasing six replays of use cases trajectories. Each "trajectory" is a complete end-to-end run, from prompt to analysis, to figures, and finally to report, and in fully inspectable and reproducible manner. See the six user cases in our PantheonOS Gallery: 1. 3D mouse embryo analysis: Tangram deconvolution and PyVista-based 3D visualization of E6 mouse embryo spatial transcriptomics data 2. 3D human fetal heart analysis: Spatial mapping of heart disease gene patterns using MERFISH data 3. Multi-omics spatial integration: Single cell multi-omics-to-spatial mapping with MOSCOT optimal transport 4. Gene panel design: 1000-plex immune-oncology MERFISH gene panel optimization 5. Cell segmentation benchmarking: Comparative evaluation of Cellpose-SAM, InstanSeg, StarDist, and other tools 6. Spatial disease biology: Ligand–receptor analysis of disease-associated tissue microenvironments We would love to hear how you can use PantheonOS for your research! Got an interesting agent run of your own and want to share? In the Pantheon UI, click Export Bundle (top-right of any chat) to package the full trajectory — chat history, code, figures, report — then submit it here:

evo-devo

13,318 görüntüleme • 2 ay önce

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 görüntüleme • 8 ay önce

🚀 We are hiring! 𝗣𝗵𝗗 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 𝗶𝗻 𝗖𝗔𝗥-𝗧 𝗖𝗲𝗹𝗹𝘂𝗹𝗮𝗿 𝗧𝗵𝗲𝗿𝗮𝗽𝘆 𝗮𝗻𝗱 𝗖𝗮𝗻𝗰𝗲𝗿 𝗡𝗲𝘂𝗿𝗼𝗶𝗺𝗺𝘂𝗻𝗼𝗹𝗼𝗴𝘆 We are looking for a passionate and motivated PhD student to join an exciting project focused on how nerve, immune and cancer cell interactions shape metastatic tumor behavior and resistance to cellular therapies, especially CAR-T cells. The project combines: • Whole-body tissue clearing • Light-sheet microscopy • wildDISCO-based immunolabeling • CAR-T and immune cell tracking in metastatic cancer • Spatial proteomics and 3D spatial biology • AI-based analysis of large-scale imaging datasets This is a great opportunity for someone with a background in cancer biology, immunology, neuroscience, biomedical sciences, biomedical engineering or a related field who wants to work at the interface of experimental biology, advanced imaging and AI. Apply here: Please share with excellent candidates who may be interested. #Hiring #PhDPosition #CARTcells #CellularTherapy #CancerResearch #Neuroimmunology #Immunotherapy #Metastasis #SpatialBiology #AIinBiology #Microscopy #LMUMunich #HelmholtzMunich #ErturkLab
0:49

🚀 We are hiring! 𝗣𝗵𝗗 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 𝗶𝗻 𝗖𝗔𝗥-𝗧 𝗖𝗲𝗹𝗹𝘂𝗹𝗮𝗿 𝗧𝗵𝗲𝗿𝗮𝗽𝘆 𝗮𝗻𝗱 𝗖𝗮𝗻𝗰𝗲𝗿 𝗡𝗲𝘂𝗿𝗼𝗶𝗺𝗺𝘂𝗻𝗼𝗹𝗼𝗴𝘆 We are looking for a passionate and motivated PhD student to join an exciting project focused on how nerve, immune and cancer cell interactions shape metastatic tumor behavior and resistance to cellular therapies, especially CAR-T cells. The project combines: • Whole-body tissue clearing • Light-sheet microscopy • wildDISCO-based immunolabeling • CAR-T and immune cell tracking in metastatic cancer • Spatial proteomics and 3D spatial biology • AI-based analysis of large-scale imaging datasets This is a great opportunity for someone with a background in cancer biology, immunology, neuroscience, biomedical sciences, biomedical engineering or a related field who wants to work at the interface of experimental biology, advanced imaging and AI. Apply here: Please share with excellent candidates who may be interested. #Hiring #PhDPosition #CARTcells #CellularTherapy #CancerResearch #Neuroimmunology #Immunotherapy #Metastasis #SpatialBiology #AIinBiology #Microscopy #LMUMunich #HelmholtzMunich #ErturkLab

Ali Max Erturk

14,767 görüntüleme • 2 ay önce