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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...

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Ali Max Erturk

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15,941 просмотров • 8 месяцев назад •via X (Twitter)

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Комментарии: 3

Фото профиля Stefan F. Wirth
Stefan F. Wirth8 месяцев назад

A fantastic #study has shown how calvarial #immunecells migrate through #skull meninges #microchannels to the #brain, thus overcoming the blood-brain barrier. This process was visualized using whole-organ #3Dimaging and is expected to aid in the future development of stroke models. The study was conducted on the brain borders of #mice.

Фото профиля David Wang
David Wang8 месяцев назад

Using natural scaffolds as transport mechanism does make sense as a way to get through bbb

Фото профиля Ani Wifool
Ani Wifool8 месяцев назад

I’m so curious about how this strategy evolves for other neurological conditions. That’s actually wild! Okay, I see you

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REPORT: The COVID “vaccines” damage the brain and DEVASTATE mental health, as confirmed by a recent wave of eye-opening studies. The shots increase your risk of: 1. Ischemic stroke (+44%) 2. Hemorrhagic stroke (+50%) 3. Transient ischemic attack (+67%) 4. Myelitis (+165%) 5. Myasthenia gravis (+71%) 6. Alzheimer’s (+22.5%) 7. Cognitive impairment (+137.7%) 8. Depression (+68.3%) 9. Anxiety disorders (+43.9%) 10. Sleep disorders (+93.4%) Epidemiologist Nicolas Hulscher writes, “The most probable mechanism behind this damage is likely toxic Spike protein accumulation and persistence in the skull-meninges-brain axis, as evidenced by Rong et al., Morz and Mikami et al., and over 300 other studies, which can be found in the Spike Protein Pathogenicity Research Library.” “Using mRNA to hijack cells in various organ systems to produce a highly toxic [spike] protein that persists in the body for months to years was one of the worst ideas in human history,” Hulscher says. Ryan Cole
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Dr Dave Cartland BMedSc MBChB Ex-MRCGP

436,488 просмотров • 1 год назад

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.
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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 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

388,342 просмотров • 9 месяцев назад

⚡️📣👇Tremendously excited to share our new Cell article, where we develop TriPath, a method for analyzing 3D pathology samples using weakly supervised AI. Article: TriPath enables 3D computational pathology via 3D multiple instance learning allowing AI models to capture intricate morphological details from pathology volumes. Code: Blog post: Tested on two different imaging modalities, and patient cohorts from two institutions. Our superstar Andrew H. Song put in a monumental effort of leading the study, in a fantastic collaboration with Jonathan Liu at University of Washington . Interesting aspects: - Utilizing the whole tissue volume and leveraging 3D deep learning enable superior risk prediction performance compared to 2D deep learning baselines based on a few sampled tissue sections that emulate standard clinical practice. This indicates TriPath can harness additional information provided by 3D tissue morphology. - The performance is also superior to clinical baselines from a reader study that involved six expert pathologists. - The morphologically heterogeneous tissue volume could lead to opposing patient-level outcome predictions, dependent on which portion of the tissue volume is used. This concurs with current clinical literature warning that tissue sampling bias can lead to misdiagnosis. Some limitations: - While the 3D pathology cohort size is unprecedented, it is smaller than typical 2D pathology cohorts. Further large-scale studies will be required for validation. Nevertheless, we believe that this study will initiate a positive cycle, encouraging academic institutions and pharmaceutical companies to contribute large banks of human tissue blocks with paired clinical outcomes, thus speeding up advancements in 3D computational pathology. Concluding insights: We believe that 3D pathology is just around the corner - It has the huge potential to not only augment/improve the current clinical practice centered around 2D examination of human tissue, but also help reveal novel biomarkers for prognosis and therapeutic response.. Harvard Medical School Harvard Data Science Initiative Mass General Brigham Broad Institute
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Faisal Mahmood

65,581 просмотров • 2 лет назад

New research from MIT has identified a single amino acid that enhances the gut’s ability to repair itself — the first evidence that one nutrient alone can drive this kind of regenerative response. In this mechanistic study, researchers tested all twenty amino acids and found one that stood out. It activated immune cells in the intestinal lining to release interleukin-22 (IL-22), a signaling molecule that stimulates stem cells responsible for rebuilding the gut after injury. Mice fed a diet enriched with this amino acid showed greater stem-cell activity, more organoid formation, and faster repair of the intestinal lining after radiation damage. This was not a clinical trial, but it offers a detailed look at how diet communicates with the immune system to shape tissue regeneration at the cellular level. Study: Chi et al., Dietary cysteine enhances intestinal stemness via CD8⁺ T cell–derived IL-22 (MIT, 2025)
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New research from MIT has identified a single amino acid that enhances the gut’s ability to repair itself — the first evidence that one nutrient alone can drive this kind of regenerative response. In this mechanistic study, researchers tested all twenty amino acids and found one that stood out. It activated immune cells in the intestinal lining to release interleukin-22 (IL-22), a signaling molecule that stimulates stem cells responsible for rebuilding the gut after injury. Mice fed a diet enriched with this amino acid showed greater stem-cell activity, more organoid formation, and faster repair of the intestinal lining after radiation damage. This was not a clinical trial, but it offers a detailed look at how diet communicates with the immune system to shape tissue regeneration at the cellular level. Study: Chi et al., Dietary cysteine enhances intestinal stemness via CD8⁺ T cell–derived IL-22 (MIT, 2025)

William A. Wallace, Ph.D.

14,324 просмотров • 11 месяцев назад

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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

58,533 просмотров • 1 год назад

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

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

Niko McCarty.

16,417 просмотров • 3 месяцев назад

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

22,553 просмотров • 6 месяцев назад

🚀 How Recursion used AI to identify a novel approach for potentially treating the rare disease Familial Adenomatous Polyposis (FAP). A new video shares the story of Recursion’s investigational drug REC-4881 for the rare disease FAP which affects more than 50,000 people in the US and EU5 and has no approved medicines. Patients with this disease develop hundreds to thousands of polyps in their early teens to 20s that, if left untreated, have a nearly 100% risk of developing into colon cancer. ⬇️ Here’s how we did it: ▪️ Using the Recursion OS platform to analyze cell morphology, we tested thousands of compounds to find drugs that restored FAP-diseased cells to a healthy state. ▪️ This led us to REC-4881 — a drug that blocks a biological pathway called MEK1/2 – a new approach for treating FAP that had not been investigated clinically before. ▪️ Now, we’re using the full Recursion OS 2.0 platform — including our ClinTech real-world evidence – to fully understand disease burden and guide our clinical strategy, allowing us to expand patient eligibility for our trial. 💡 The takeaway: This program is the first clinical validation of the Recursion OS, and demonstrates our ability to translate unbiased phenotypic insights into potentially differentiated treatments for diseases with high unmet need. 👉 Read the full summary data in our December 8, 2025 press release here:
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🚀 How Recursion used AI to identify a novel approach for potentially treating the rare disease Familial Adenomatous Polyposis (FAP). A new video shares the story of Recursion’s investigational drug REC-4881 for the rare disease FAP which affects more than 50,000 people in the US and EU5 and has no approved medicines. Patients with this disease develop hundreds to thousands of polyps in their early teens to 20s that, if left untreated, have a nearly 100% risk of developing into colon cancer. ⬇️ Here’s how we did it: ▪️ Using the Recursion OS platform to analyze cell morphology, we tested thousands of compounds to find drugs that restored FAP-diseased cells to a healthy state. ▪️ This led us to REC-4881 — a drug that blocks a biological pathway called MEK1/2 – a new approach for treating FAP that had not been investigated clinically before. ▪️ Now, we’re using the full Recursion OS 2.0 platform — including our ClinTech real-world evidence – to fully understand disease burden and guide our clinical strategy, allowing us to expand patient eligibility for our trial. 💡 The takeaway: This program is the first clinical validation of the Recursion OS, and demonstrates our ability to translate unbiased phenotypic insights into potentially differentiated treatments for diseases with high unmet need. 👉 Read the full summary data in our December 8, 2025 press release here:

Recursion

11,967 просмотров • 8 месяцев назад

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

11,832 просмотров • 6 месяцев назад

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

85,294 просмотров • 7 месяцев назад

We’re thrilled to share that our MERFISH+ preprint is now live on bioRxiv!👉 In this work, the Bintu and Zhu labs (UCSD) developed MERFISH+, a next-generation spatial genomics platform that combines genome-wide RNA and epigenetic imaging over a large field of view. By introducing acrydite-modified probes covalently anchored to hydrogels, MERFISH+ achieves remarkable imaging stability and enables >1,800-gene, multi-modal, and multi-month experiments. With this platform, they, together with the Chi lab at UCSD, profiled a whole developing human heart at 12 post-conception week with merely two slides, resulting in a total of 53 slides, 3.1 million single cells and more than 30 cell types. Building upon our previous 3D reconstruction and modeling framework, Spateo ( we reconstruct the 3D human heart that nicely captures the anatomical structure of the heart, including the intricate vasculature network. Sophisticated analyses provide a holistic view of an entire organ and enable systematic characterization of 3D cellular neighborhoods and transcriptional gradients of substructures such as the descending arteries. Furthermore, using a generative integration framework for spatial multimodal data (Spateo-VI), we harmonized these MERFISH+ transcriptomic and chromatin data to reconstruct a 3D spatially-resolved multi-omics atlas of the developing human heart, shared at and MERFISH+ thus sets a new standard for large-format, multi-omic spatial profiling, enabling holistic, 3D characterization of organs at subcellular resolution. Huge congratulations to first authors Colin Kern, qingquan Zhang, Yifan Lu , and Jacqueline Eschbach, and to all collaborators from the Bintu, Zhu, Chi, and Qiu labs for this amazing team effort. Thanks for your diligence, creativity, and hard work on this project. We’re grateful for support from Arc Institute and our generous donors. Our lab is expanding—if you’re excited about building the next generation of single-cell and spatial genomics techniques and predictive single cell and spatial foundation models, we’re hiring! If you are interested, please reach out to me via direct message or email at xiaojie@stanford.edu. We are excited for any potential collaborations along this line of research in Stanford, UCSF and Berkeley and other labs as well.
0:42

We’re thrilled to share that our MERFISH+ preprint is now live on bioRxiv!👉 In this work, the Bintu and Zhu labs (UCSD) developed MERFISH+, a next-generation spatial genomics platform that combines genome-wide RNA and epigenetic imaging over a large field of view. By introducing acrydite-modified probes covalently anchored to hydrogels, MERFISH+ achieves remarkable imaging stability and enables >1,800-gene, multi-modal, and multi-month experiments. With this platform, they, together with the Chi lab at UCSD, profiled a whole developing human heart at 12 post-conception week with merely two slides, resulting in a total of 53 slides, 3.1 million single cells and more than 30 cell types. Building upon our previous 3D reconstruction and modeling framework, Spateo ( we reconstruct the 3D human heart that nicely captures the anatomical structure of the heart, including the intricate vasculature network. Sophisticated analyses provide a holistic view of an entire organ and enable systematic characterization of 3D cellular neighborhoods and transcriptional gradients of substructures such as the descending arteries. Furthermore, using a generative integration framework for spatial multimodal data (Spateo-VI), we harmonized these MERFISH+ transcriptomic and chromatin data to reconstruct a 3D spatially-resolved multi-omics atlas of the developing human heart, shared at and MERFISH+ thus sets a new standard for large-format, multi-omic spatial profiling, enabling holistic, 3D characterization of organs at subcellular resolution. Huge congratulations to first authors Colin Kern, qingquan Zhang, Yifan Lu , and Jacqueline Eschbach, and to all collaborators from the Bintu, Zhu, Chi, and Qiu labs for this amazing team effort. Thanks for your diligence, creativity, and hard work on this project. We’re grateful for support from Arc Institute and our generous donors. Our lab is expanding—if you’re excited about building the next generation of single-cell and spatial genomics techniques and predictive single cell and spatial foundation models, we’re hiring! If you are interested, please reach out to me via direct message or email at [email protected]. We are excited for any potential collaborations along this line of research in Stanford, UCSF and Berkeley and other labs as well.

evo-devo

42,340 просмотров • 11 месяцев назад

🚀 We’re hiring! Staff Scientist / Postdoc – Tissue Clearing & 3D Image Analysis (m/f/d) (LMU Munich) Are you a great fit, or do you know someone outstanding, please reach out 🔁 If you want to at the frontier of whole-organ / whole-body 3D imaging, and help generate truly beautiful datasets that drive major biological discoveries and therapeutic development, see below ✨ We’re building the next-generation pipeline for tissue clearing + light-sheet microscopy + quantitative 3D analysis in the SyNergy Excellence Cluster (Mesoscale Hub) and we’re looking for someone excited to push this forward with us. 🧠🔬📈 🎥 I’m also attaching a short video showing the kind of high-quality imaging and datasets you’d be working with. What you’ll do 🛠️ 🔹 Lead and evolve tissue clearing + light-sheet workflows across collaborative SyNergy projects 🔹 Turn complex 3D datasets into robust quantitative insights (visualization, atlas registration, readouts) 🔹 Develop new methods and analysis pipelines together with our AI team 🤖 🔹 Maintain and optimize cutting-edge light-sheet systems (optional: support animal license writing) What we’re looking for 🎯 ✅ Strong hands-on experience in tissue clearing and/or fluorescence microscopy ✅ Solid experience with light-sheet microscopy and 3D imaging workflows ✅ Familiarity with 3D tools like Imaris / arivis Vision4D, stitching (e.g., BigStitcher), and quantitative analysis in cleared tissues ✅ Service mindset, great organization, and strong scientific English How to apply 📩 Apply via the LMU Klinikum online application form Please also send your application to: farida.hellal@med.uni-muenchen.de CC: ali.ertuerk@med.uni-muenchen.de 📎 Include one PDF: short cover letter, CV, 2–3 referees, and earliest start date. 📍 Campus Großhadern (Munich) and Helmholtz Munich | 🕒 Full-time | 📅 Start: 01 January 2026 If you love high-quality imaging, cutting-edge biology, and building something that will matter, we’d love to hear from you. 🌍✨ #hiring #StaffScientist #Postdoc #TissueClearing #LightSheetMicroscopy #ImageAnalysis #SpatialBiology #Neuroscience #SyNergy #LMU #Munich
0:45

🚀 We’re hiring! Staff Scientist / Postdoc – Tissue Clearing & 3D Image Analysis (m/f/d) (LMU Munich) Are you a great fit, or do you know someone outstanding, please reach out 🔁 If you want to at the frontier of whole-organ / whole-body 3D imaging, and help generate truly beautiful datasets that drive major biological discoveries and therapeutic development, see below ✨ We’re building the next-generation pipeline for tissue clearing + light-sheet microscopy + quantitative 3D analysis in the SyNergy Excellence Cluster (Mesoscale Hub) and we’re looking for someone excited to push this forward with us. 🧠🔬📈 🎥 I’m also attaching a short video showing the kind of high-quality imaging and datasets you’d be working with. What you’ll do 🛠️ 🔹 Lead and evolve tissue clearing + light-sheet workflows across collaborative SyNergy projects 🔹 Turn complex 3D datasets into robust quantitative insights (visualization, atlas registration, readouts) 🔹 Develop new methods and analysis pipelines together with our AI team 🤖 🔹 Maintain and optimize cutting-edge light-sheet systems (optional: support animal license writing) What we’re looking for 🎯 ✅ Strong hands-on experience in tissue clearing and/or fluorescence microscopy ✅ Solid experience with light-sheet microscopy and 3D imaging workflows ✅ Familiarity with 3D tools like Imaris / arivis Vision4D, stitching (e.g., BigStitcher), and quantitative analysis in cleared tissues ✅ Service mindset, great organization, and strong scientific English How to apply 📩 Apply via the LMU Klinikum online application form Please also send your application to: [email protected] CC: [email protected] 📎 Include one PDF: short cover letter, CV, 2–3 referees, and earliest start date. 📍 Campus Großhadern (Munich) and Helmholtz Munich | 🕒 Full-time | 📅 Start: 01 January 2026 If you love high-quality imaging, cutting-edge biology, and building something that will matter, we’d love to hear from you. 🌍✨ #hiring #StaffScientist #Postdoc #TissueClearing #LightSheetMicroscopy #ImageAnalysis #SpatialBiology #Neuroscience #SyNergy #LMU #Munich

Ali Max Erturk

14,807 просмотров • 9 месяцев назад

When I caught Covid, my friends rushed doses of Ivermectin to me — kicked Covid in 24 hours. Here’s a cancer surgeon revealing her discoveries about the impact of Ivermectin on CANCER… 9 min. must see: Also — Compelling Evidence for Ivermectin in Treating Covid: · Front Line COVID-19 Critical Care Alliance Review — This review, which summarized findings from 27 studies, concluded that ivermectin demonstrates a strong signal of therapeutic efficacy against COVID-19. This includes both prevention and treatment aspects. · Meta-Analysis on Ivermectin — A meta-analysis covering 96 studies and over 135,000 patients suggests that ivermectin has shown effectiveness in combating COVID-19. This analysis points towards ivermectin's role in reducing severity and improving health outcomes. · Antiviral Effect Study — A study aimed at assessing the antiviral effect of high-dose ivermectin found it to have an impact on viral load, suggesting its potential in reducing the severity of the disease by decreasing the viral replication. Evidence for Ivermectin in Treating Cancer as summarized by Dr. William Makis, Radiologist, Oncologist, Cancer Researcher. NEW ARTICLE: IVERMECTIN and Protocols for CANCER Ivermectin has a dozen anti-cancer mechanisms but they can be summarized into two main ones: 1. Inhibits cancer proliferation signaling pathways (Akt, mTOR, Wnt) 2. Inhibits Cancer Stem Cells IVERMECTIN will act against regular CANCER as well as Pfizer and Moderna COVID-19 mRNA Vaccine Induced TURBO CANCER (which is highly resistant to chemo) Here are recent studies on IVERMECTIN use in certain types of cancer: BLADDER CANCER - (2024 Fan et al) - Ivermectin Inhibits Bladder Cancer Cell Growth and Induces Oxidative Stress and DNA Damage LUNG CANCER - (2024 Man-Yuan Li et al) - Ivermectin induces nonprotective autophagy by downregulating PAK1 and apoptosis in lung adenocarcinoma cells GLIOMA - (2024 Xing Hu et al) - Ivermectin as a potential therapeutic strategy for glioma MULTIPLE MYELOMA - (2024 Yang Song et al) - Gene signatures to therapeutics: Assessing the potential of ivermectin against t(4;14) multiple myeloma OVARIAN CANCER - (2023 Jawad et al) - Ivermectin augments the anti-cancer activity of pitavastatin in ovarian cancer cells PROSTATE CANCER - (2022 Lu et al) - Integrated analysis reveals FOXA1 and Ku70/Ku80 as targets of ivermectin in prostate cancer COLON CANCER - (2022, Alghamdi et al) - Efficacy of ivermectin against colon cancer induced by dimethylhydrazine in male wistar rats PANCREATIC CANCER - (2022 Lee et al) - Ivermectin and gemcitabine combination treatment induces apoptosis of pancreatic cancer cells via mitochondrial dysfunction MELANOMA - (2022 Zhang et al) - Drug repurposing of ivermectin abrogates neutrophil extracellular traps and prevents melanoma metastasis IVERMECTIN has proven anti-cancer activity against some 20 cancer types, although these are pre-clinical studies. We will never see clinical studies because Ivermectin is off patent and cheap. Merck, which used to have a patent on Ivermectin, has partnered with Moderna on mRNA Cancer Vaccines. IVERMECTIN is so safe, that in much of the civilized world, it is available over the counter, no prescription needed. Ivermectin is annually taken by close to 250 million people.
9:41

When I caught Covid, my friends rushed doses of Ivermectin to me — kicked Covid in 24 hours. Here’s a cancer surgeon revealing her discoveries about the impact of Ivermectin on CANCER… 9 min. must see: Also — Compelling Evidence for Ivermectin in Treating Covid: · Front Line COVID-19 Critical Care Alliance Review — This review, which summarized findings from 27 studies, concluded that ivermectin demonstrates a strong signal of therapeutic efficacy against COVID-19. This includes both prevention and treatment aspects. · Meta-Analysis on Ivermectin — A meta-analysis covering 96 studies and over 135,000 patients suggests that ivermectin has shown effectiveness in combating COVID-19. This analysis points towards ivermectin's role in reducing severity and improving health outcomes. · Antiviral Effect Study — A study aimed at assessing the antiviral effect of high-dose ivermectin found it to have an impact on viral load, suggesting its potential in reducing the severity of the disease by decreasing the viral replication. Evidence for Ivermectin in Treating Cancer as summarized by Dr. William Makis, Radiologist, Oncologist, Cancer Researcher. NEW ARTICLE: IVERMECTIN and Protocols for CANCER Ivermectin has a dozen anti-cancer mechanisms but they can be summarized into two main ones: 1. Inhibits cancer proliferation signaling pathways (Akt, mTOR, Wnt) 2. Inhibits Cancer Stem Cells IVERMECTIN will act against regular CANCER as well as Pfizer and Moderna COVID-19 mRNA Vaccine Induced TURBO CANCER (which is highly resistant to chemo) Here are recent studies on IVERMECTIN use in certain types of cancer: BLADDER CANCER - (2024 Fan et al) - Ivermectin Inhibits Bladder Cancer Cell Growth and Induces Oxidative Stress and DNA Damage LUNG CANCER - (2024 Man-Yuan Li et al) - Ivermectin induces nonprotective autophagy by downregulating PAK1 and apoptosis in lung adenocarcinoma cells GLIOMA - (2024 Xing Hu et al) - Ivermectin as a potential therapeutic strategy for glioma MULTIPLE MYELOMA - (2024 Yang Song et al) - Gene signatures to therapeutics: Assessing the potential of ivermectin against t(4;14) multiple myeloma OVARIAN CANCER - (2023 Jawad et al) - Ivermectin augments the anti-cancer activity of pitavastatin in ovarian cancer cells PROSTATE CANCER - (2022 Lu et al) - Integrated analysis reveals FOXA1 and Ku70/Ku80 as targets of ivermectin in prostate cancer COLON CANCER - (2022, Alghamdi et al) - Efficacy of ivermectin against colon cancer induced by dimethylhydrazine in male wistar rats PANCREATIC CANCER - (2022 Lee et al) - Ivermectin and gemcitabine combination treatment induces apoptosis of pancreatic cancer cells via mitochondrial dysfunction MELANOMA - (2022 Zhang et al) - Drug repurposing of ivermectin abrogates neutrophil extracellular traps and prevents melanoma metastasis IVERMECTIN has proven anti-cancer activity against some 20 cancer types, although these are pre-clinical studies. We will never see clinical studies because Ivermectin is off patent and cheap. Merck, which used to have a patent on Ivermectin, has partnered with Moderna on mRNA Cancer Vaccines. IVERMECTIN is so safe, that in much of the civilized world, it is available over the counter, no prescription needed. Ivermectin is annually taken by close to 250 million people.

Bobby

160,046 просмотров • 1 год назад

🚨🚨🚨 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.
6:25

🚨🚨🚨 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 просмотров • 1 год назад