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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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387,760 次观看 • 6 个月前 •via X (Twitter)

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

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

Science girl

43,666 次观看 • 1 年前

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

58,297 次观看 • 1 年前

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

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

Brad Krajina

48,127 次观看 • 3 年前

🔬 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

199,638 次观看 • 2 年前

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

Cathrine Sant

55,032 次观看 • 1 年前

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.

26,334 次观看 • 18 天前

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

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

Nima Alidoust

171,755 次观看 • 8 个月前

🚨Big news! Five years in the making, our Zebrahub paper is now published in #Cell 🎉. We’ve built a timecourse atlas of zebrafish embryonic development, combining #scRNAseq data and #lightsheet live imaging, and delved deep into the dynamics of key progenitors. Explore the datasets here: Led by Merlin Lange with key contributions by Alejandro Granados, Shruthi VijayKumar, Jordão Bragantini, Sarah Ancheta and YangJoon Kim #zebrahub #devbio #cell #Biology #imaging Cell @czbbiohub #CZBiohubS
0:42

🚨Big news! Five years in the making, our Zebrahub paper is now published in #Cell 🎉. We’ve built a timecourse atlas of zebrafish embryonic development, combining #scRNAseq data and #lightsheet live imaging, and delved deep into the dynamics of key progenitors. Explore the datasets here: Led by Merlin Lange with key contributions by Alejandro Granados, Shruthi VijayKumar, Jordão Bragantini, Sarah Ancheta and YangJoon Kim #zebrahub #devbio #cell #Biology #imaging Cell @czbbiohub #CZBiohubS

Loïc A. Royer 💻🔬⚗️

83,705 次观看 • 1 年前

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

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

Mo Lotfollahi

61,763 次观看 • 2 年前

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

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

Jeffrey West

12,314 次观看 • 2 年前

We made an explainer on the power of single cell technology to help understand biology and disease at a single cell level. Thanks Illumina for production support. Clip below and full video at:
1:03

We made an explainer on the power of single cell technology to help understand biology and disease at a single cell level. Thanks Illumina for production support. Clip below and full video at:

OzSingleCell Omics

19,797 次观看 • 3 年前

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

22,402 次观看 • 3 个月前

The newest course on Peterson Academy, Benjamin Bikman's six-hour course: Basics of Biology is available now. In this course, Dr. Benjamin Bikman provides a comprehensive introduction to cellular biology, focusing on the cell as the basic unit of life and exploring essential biological processes. The lectures cover key topics such as cellular transport mechanisms, the structure and function of biomolecules, the flow of genetic information through the central dogma, cell division processes, and cellular metabolism. By integrating molecular and cellular biology concepts, the course explains how cells maintain homeostasis and function as the foundation of life, while highlighting their practical applications in human health, disease, and life on Earth.
1:23

The newest course on Peterson Academy, Benjamin Bikman's six-hour course: Basics of Biology is available now. In this course, Dr. Benjamin Bikman provides a comprehensive introduction to cellular biology, focusing on the cell as the basic unit of life and exploring essential biological processes. The lectures cover key topics such as cellular transport mechanisms, the structure and function of biomolecules, the flow of genetic information through the central dogma, cell division processes, and cellular metabolism. By integrating molecular and cellular biology concepts, the course explains how cells maintain homeostasis and function as the foundation of life, while highlighting their practical applications in human health, disease, and life on Earth.

Peterson Academy

63,462 次观看 • 1 年前

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

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

Mo Lotfollahi

11,759 次观看 • 3 个月前

Gender Dysphoria is heavily encouraged by environmental factors, but I also believe it’s heavily influenced by aborted fetal tissue present in all vaccines. If anyone is skeptical of these ingredients being present in vaccines, I encourage you to watch the nine-hour deposition of Stanley Plotkin and dig deeper. Human fetal cell lines are used to culture vaccines. The CDC's Vaccine Excipient list includes WI-38, MRC-5, HEK293, and PERC.6. WI-38 is a diploid human cell culture line composed of fibroblasts derived from the lung tissue of an aborted female fetus. MRC-5 (Medical Research Council cell strain 5) is a diploid human cell culture line composed of fibroblasts derived from the lung tissue of a 14-week-old aborted male fetus. Human embryonic kidney cells 293, also often referred to as HEK 293, HEK-293, 293 cells, or less precisely as HEK cells, are a specific cell line originally derived from human embryonic kidney cells grown in a tissue culture. PERC.6 cell line was derived from human embryonic retinal cells taken from an elective abortion. The newest cell line created in 2015 for vaccines: WALVAX 2 is taken from the lung tissue of a 3-month gestation female who was ultimately selected from among 9 aborted babies. The scientists noted how they followed specific guidelines to mimic WI-38 and MRC-5 in selecting the aborted babies, ranging from 2-4 months gestation. They further noted how they induced labor using a “water bag” abortion to shorten the delivery time and prevent the death of the fetus to ensure live intact organs which were immediately sent to the labs for cell preparation. (Source:
2:00

Gender Dysphoria is heavily encouraged by environmental factors, but I also believe it’s heavily influenced by aborted fetal tissue present in all vaccines. If anyone is skeptical of these ingredients being present in vaccines, I encourage you to watch the nine-hour deposition of Stanley Plotkin and dig deeper. Human fetal cell lines are used to culture vaccines. The CDC's Vaccine Excipient list includes WI-38, MRC-5, HEK293, and PERC.6. WI-38 is a diploid human cell culture line composed of fibroblasts derived from the lung tissue of an aborted female fetus. MRC-5 (Medical Research Council cell strain 5) is a diploid human cell culture line composed of fibroblasts derived from the lung tissue of a 14-week-old aborted male fetus. Human embryonic kidney cells 293, also often referred to as HEK 293, HEK-293, 293 cells, or less precisely as HEK cells, are a specific cell line originally derived from human embryonic kidney cells grown in a tissue culture. PERC.6 cell line was derived from human embryonic retinal cells taken from an elective abortion. The newest cell line created in 2015 for vaccines: WALVAX 2 is taken from the lung tissue of a 3-month gestation female who was ultimately selected from among 9 aborted babies. The scientists noted how they followed specific guidelines to mimic WI-38 and MRC-5 in selecting the aborted babies, ranging from 2-4 months gestation. They further noted how they induced labor using a “water bag” abortion to shorten the delivery time and prevent the death of the fetus to ensure live intact organs which were immediately sent to the labs for cell preparation. (Source:

Dr. Ben Tapper

51,242 次观看 • 1 年前

Over 4 years in the making, I am excited to share our pre-print on the “In-cell structure and snapshots of copia retrotransposons in intact tissue by cryo-electron tomography”. We used cryo-ET to resolve the copia capsid structure to subnanometer resolution inside cells. A 🧵
1:09

Over 4 years in the making, I am excited to share our pre-print on the “In-cell structure and snapshots of copia retrotransposons in intact tissue by cryo-electron tomography”. We used cryo-ET to resolve the copia capsid structure to subnanometer resolution inside cells. A 🧵

Sven Klumpe

57,843 次观看 • 2 年前

The dream of every cell is to become two cells.” François Jacob This is what cell division of algae looks like, this is a Micrasterias rotata algae cell dividing
0:20

The dream of every cell is to become two cells.” François Jacob This is what cell division of algae looks like, this is a Micrasterias rotata algae cell dividing

Science girl

131,790 次观看 • 7 个月前

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 次观看 • 2 年前

📡🧵 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...
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📡🧵 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 💻🔬⚗️

39,773 次观看 • 1 年前