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🚀 Introducing scGPT-spatial! 🧬🌍 A game-changing spatial-omic foundation model, built on the powerful scGPT framework with MoE (mixture of experts) and continually pretrained on a massive 30 million spatial single-cell profiles! 🧠 What’s the challenge? Spatial transcriptomics is next-level complex—not only must we model single-cell/spot profiles, but we also...

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Bo Wang

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59,110 görüntüleme • 1 yıl önce •via X (Twitter)

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Derya Unutmaz, MD profil fotoğrafı
Derya Unutmaz, MD1 yıl önce

Amazing as always!

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AndaSeat1 yıl önce

🍀 Mercury retrograde-proof your gaming life! ⚔️ Kaiser 3's anti-bad-luck features: 💪 5,000+ durability cycles (tested!) 💧 Water-resistant surface 🛡️ Anti-spill protection ✨ Murphy's Law defying design 🌟 Because life happens, gaming shouldn't stop! 🪑⚡ Guard your gameplay: 🎲 Lucky deal: Chase away bad luck with $20 off! #AndaSeat #GamerLife #DurabilityGoals #Kaiser3 🎮✨

Tanishq Mathew Abraham, Ph.D. profil fotoğrafı
Tanishq Mathew Abraham, Ph.D.1 yıl önce

Looks interesting, congrats!

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Gökbörü1 yıl önce

awesome

Aiden profil fotoğrafı
Aiden1 yıl önce

Awesome, looks interesting

Steven ten Holder — e/bio profil fotoğrafı
Steven ten Holder — e/bio1 yıl önce

This + RegFormer insights…

Patricia Cano profil fotoğrafı
Patricia Cano1 yıl önce

scGPT-spatial’s integration of MoE and 30M spatial single-cell profiles could transform spatial biology, especially in zoonotic disease research. A huge step for public health and One Health!

steven hoffman profil fotoğrafı
steven hoffman1 yıl önce

@qicy11 Awesome

Greg Cook profil fotoğrafı
Greg Cook1 yıl önce

Hey @DeryaTR_ FYI

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Hao Yin1 yıl önce

Wowo 🤯 Absolutely a must read 🫡

Vivek Das profil fotoğrafı
Vivek Das1 yıl önce

Looks interesting. Congratulations to your team @BoWang87 .

Benzer Videolar

🔥😀Super-super excited to report the publication of "Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression" in Nature Genetics by Amin Abedini and Jon Levinsohn #kidney #diabetes #spatial transcriptomics
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🔥😀Super-super excited to report the publication of "Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression" in Nature Genetics by Amin Abedini and Jon Levinsohn #kidney #diabetes #spatial transcriptomics

Katalin Susztak

16,299 görüntüleme • 2 yıl önce

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

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

evo-devo

13,466 görüntüleme • 5 ay önce

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

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

Bo Wang

199,830 görüntüleme • 2 yıl önce

Excited to share our latest work on 🎧spatial audio-driven human motion generation. We aim to tackle a largely underexplored yet important problem of enabling virtual humans to move naturally in response to spatial audio—capturing not just what is heard, but also where the sound is coming from. To this end, we introduce the Spatial Audio-Driven Human Motion (SAM) dataset—the first comprehensive dataset featuring paired high-quality human motion and spatial audio recordings. For benchmarking, we develop a generative framework for human MOtion generation driven by SPAtial audio, termed MOSPA, which learns to synthesize realistic and diverse human motions conditioned on spatial audio input. We hope this research could provide a foundation for future research in spatial perception, virtual characters, and embodied AI. The dataset and model will be open-sourced soon. A big thank you to our intern, Shuyang Xu, for the wonderful collaboration! Congratulations, Shuyang! Project page: Paper: Video: #Animation #CG #CV #AIGC #DL #Deeplearning #Motion #Graphics #AI #GenerativeAI
3:58

Excited to share our latest work on 🎧spatial audio-driven human motion generation. We aim to tackle a largely underexplored yet important problem of enabling virtual humans to move naturally in response to spatial audio—capturing not just what is heard, but also where the sound is coming from. To this end, we introduce the Spatial Audio-Driven Human Motion (SAM) dataset—the first comprehensive dataset featuring paired high-quality human motion and spatial audio recordings. For benchmarking, we develop a generative framework for human MOtion generation driven by SPAtial audio, termed MOSPA, which learns to synthesize realistic and diverse human motions conditioned on spatial audio input. We hope this research could provide a foundation for future research in spatial perception, virtual characters, and embodied AI. The dataset and model will be open-sourced soon. A big thank you to our intern, Shuyang Xu, for the wonderful collaboration! Congratulations, Shuyang! Project page: Paper: Video: #Animation #CG #CV #AIGC #DL #Deeplearning #Motion #Graphics #AI #GenerativeAI

Zhiyang (Frank) Dou

14,728 görüntüleme • 1 yıl önce

We NOETIK built a new foundation model for spatial biology: OCTO-VirtualCell. This model is prompted to simulate single cell gene expression in "virtual cells," which are placed within real, intact patient tissue. Read about & explore these simulations below:
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We NOETIK built a new foundation model for spatial biology: OCTO-VirtualCell. This model is prompted to simulate single cell gene expression in "virtual cells," which are placed within real, intact patient tissue. Read about & explore these simulations below:

Daniel Bear

40,273 görüntüleme • 1 yıl önce

New research with Tsinghua University: Spatial-TTT. A framework for streaming visual-based spatial intelligence with test-time training (TTT). Spatial-TTT adapts fast weights to capture and organize spatial evidence from long video streams, enabling models to build structured 3D spatial memory over time. Highlights: 🔹Efficient streaming memory. Fast weights act as compact spatial memory with sublinear memory growth over 7000+ frames and more than 40% lower compute. 🔹Spatial-predictive mechanism. TTT layers with 3D spatiotemporal convolution capture geometric correspondence and temporal continuity. 🔹SOTA results on long-horizon video spatial understanding (VSI-Bench). The paper ranked #1 on Hugging Face Daily Papers on March 13. Project page: GitHub: Paper: Model & Data:
0:47

New research with Tsinghua University: Spatial-TTT. A framework for streaming visual-based spatial intelligence with test-time training (TTT). Spatial-TTT adapts fast weights to capture and organize spatial evidence from long video streams, enabling models to build structured 3D spatial memory over time. Highlights: 🔹Efficient streaming memory. Fast weights act as compact spatial memory with sublinear memory growth over 7000+ frames and more than 40% lower compute. 🔹Spatial-predictive mechanism. TTT layers with 3D spatiotemporal convolution capture geometric correspondence and temporal continuity. 🔹SOTA results on long-horizon video spatial understanding (VSI-Bench). The paper ranked #1 on Hugging Face Daily Papers on March 13. Project page: GitHub: Paper: Model & Data:

Tencent Hy

20,792 görüntüleme • 7 ay önce

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

42,365 görüntüleme • 11 ay önce

"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
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"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

12,332 görüntüleme • 2 yıl önce

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

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

Xuyu Qian

53,412 görüntüleme • 1 yıl önce

Inspired by some gesture-based point cloud controllers I've seen on here, I vibe coded a similar web app to explore the relationship between spatial, UMAP, and PCA embeddings for spatial transcriptomics data. Next level interactivity via🖐️ Try it out:
0:42

Inspired by some gesture-based point cloud controllers I've seen on here, I vibe coded a similar web app to explore the relationship between spatial, UMAP, and PCA embeddings for spatial transcriptomics data. Next level interactivity via🖐️ Try it out:

Dr. Jean Fan

17,664 görüntüleme • 9 ay önce

What if a foundation model could align histology, spatial biology & clinical data to reveal latent biomedical insights? 🚀 Introducing Haiku — a tri-modal foundation model trained on 26.7M+ spatial proteomics patches with matched H&E and clinical text, aligned in one shared embedding space. 📄 🧵👇
1:10

What if a foundation model could align histology, spatial biology & clinical data to reveal latent biomedical insights? 🚀 Introducing Haiku — a tri-modal foundation model trained on 26.7M+ spatial proteomics patches with matched H&E and clinical text, aligned in one shared embedding space. 📄 🧵👇

Zhi Huang

13,450 görüntüleme • 5 ay önce

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

58,552 görüntüleme • 1 yıl önce

Thanks to the authors for sharing all components of this mouse embryo spatial transcriptomics data from cell gene counts to per-molecule coordinates: 🥳 So I vibe coded an app to explore the 3D subcellular transcript organization:
0:20

Thanks to the authors for sharing all components of this mouse embryo spatial transcriptomics data from cell gene counts to per-molecule coordinates: 🥳 So I vibe coded an app to explore the 3D subcellular transcript organization:

Dr. Jean Fan

50,652 görüntüleme • 9 ay önce

Spatial-omics goes 3D🤗! Out at Cell Cell 👉🏼 We developed DISCO-MS with Matthias Mann Lab, a spatial proteomics technology for specimens fully imaged in 3D. DISCO-MS is aided by robotics and enables the study of diseases at their early stages.
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Spatial-omics goes 3D🤗! Out at Cell Cell 👉🏼 We developed DISCO-MS with Matthias Mann Lab, a spatial proteomics technology for specimens fully imaged in 3D. DISCO-MS is aided by robotics and enables the study of diseases at their early stages.

Ali Max Erturk

217,718 görüntüleme • 3 yıl önce

Excited to share TERRA, a tissue world model 🧬 Over ~1.5 years we ran a large data-generation + modelling effort to build a world model for human tissues, pretrained on 112M cells from spatial transcriptomics (mostly Xenium 5000-plex + public data). It's built on one of the largest human spatial transcriptomics corpora assembled to date, spanning 20 tissues across development, health and 26 disease conditions, ~two-thirds newly generated in-house. Why a "world model" for tissue? Images have universal representations (ViT/DINOv3), so do proteins (ESM, Alex Rives) and pathology (UNI, Faisal Mahmood). We've worked hard to build something similar for human tissue: one model that captures its multi-scale logic, genes → cells → their native microenvironments. Like the JEPA approach Yann LeCun has championed, TERRA learns by prediction in embedding space, but for human tissue. How it works: it tokenises each cell together with its nearest neighbours into one sequence while keeping every gene's identity, then masks part of a neighbourhood and predicts the representation of the hidden part, not raw noisy counts. From one backbone it reads out three scales, gene embeddings (what a gene is doing in a cell and its niche), cell embeddings (cell type and state) and neighbourhood embeddings (the niche), and because it keeps gene-level resolution it can knock a gene out in silico and predict the response. Applied entirely zero-shot, TERRA maps and perturbs human tissue across unseen organs, diseases and technologies, outperforming existing spatial approaches. Three take-homes: 1️⃣ One model, any tissue. A single pretrained backbone provides tissue representations zero-shot, handling genes, cells and niches across organs and platforms, off the shelf. 2️⃣ New biology, development to clinic. We built a new spatial atlas of the developing human pancreas and found an islet-associated capillary state that looks like a precursor of mature islet vasculature. In kidney, TERRA's in silico knockouts predicted the tissue-injury programme from cancer immunotherapy (checkpoint blockade), confirmed in treated kidneys, detected in blood, and linked to declining kidney function. 3️⃣ A grammar of tissue architecture. By coupling each cell's state to its niche, TERRA defines recurring cross-organ "archetypes" of macrophage neighbourhoods, including a tumour-boundary niche that tracks poor survival in kidney cancer. TERRA is already in use: it powered our recent skin atlas of hidden immune-memory niches ( with more studies coming soon. This was an amazing collaboration between clinicians, machine-learning scientists and cell biologists 🙏 Led by Sebastian Birk, Vali Sanian Amirhosein Vahidi, Samuel Ogden, Daniyal Jafree, Adib Miraki, Carlo Leonardi and Arpit Merchant, with Lassi Paavolainen, Menna Clatworthy, Omer Ali Bayraktar, Muzlifah Haniffa, Tom Mitchell and Mostafa Bakhti. Huge thanks too to everyone who shared data and helped along the way. What excites me most is seeing how the community builds on this. The model, code and tutorials are all public, so anyone can run TERRA on their own tissues, extend it, or build new models on top. Huge thanks to the whole team across Wellcome Sanger Institute and our many collaborators. 📄 Paper: 💻 Code: 🤗 Model: #SpatialTranscriptomics #SpatialGenomics #FoundationModels #AI4Science #MachineLearning #ComputationalBiology #SingleCell #WorldModels
2:15

Excited to share TERRA, a tissue world model 🧬 Over ~1.5 years we ran a large data-generation + modelling effort to build a world model for human tissues, pretrained on 112M cells from spatial transcriptomics (mostly Xenium 5000-plex + public data). It's built on one of the largest human spatial transcriptomics corpora assembled to date, spanning 20 tissues across development, health and 26 disease conditions, ~two-thirds newly generated in-house. Why a "world model" for tissue? Images have universal representations (ViT/DINOv3), so do proteins (ESM, Alex Rives) and pathology (UNI, Faisal Mahmood). We've worked hard to build something similar for human tissue: one model that captures its multi-scale logic, genes → cells → their native microenvironments. Like the JEPA approach Yann LeCun has championed, TERRA learns by prediction in embedding space, but for human tissue. How it works: it tokenises each cell together with its nearest neighbours into one sequence while keeping every gene's identity, then masks part of a neighbourhood and predicts the representation of the hidden part, not raw noisy counts. From one backbone it reads out three scales, gene embeddings (what a gene is doing in a cell and its niche), cell embeddings (cell type and state) and neighbourhood embeddings (the niche), and because it keeps gene-level resolution it can knock a gene out in silico and predict the response. Applied entirely zero-shot, TERRA maps and perturbs human tissue across unseen organs, diseases and technologies, outperforming existing spatial approaches. Three take-homes: 1️⃣ One model, any tissue. A single pretrained backbone provides tissue representations zero-shot, handling genes, cells and niches across organs and platforms, off the shelf. 2️⃣ New biology, development to clinic. We built a new spatial atlas of the developing human pancreas and found an islet-associated capillary state that looks like a precursor of mature islet vasculature. In kidney, TERRA's in silico knockouts predicted the tissue-injury programme from cancer immunotherapy (checkpoint blockade), confirmed in treated kidneys, detected in blood, and linked to declining kidney function. 3️⃣ A grammar of tissue architecture. By coupling each cell's state to its niche, TERRA defines recurring cross-organ "archetypes" of macrophage neighbourhoods, including a tumour-boundary niche that tracks poor survival in kidney cancer. TERRA is already in use: it powered our recent skin atlas of hidden immune-memory niches ( with more studies coming soon. This was an amazing collaboration between clinicians, machine-learning scientists and cell biologists 🙏 Led by Sebastian Birk, Vali Sanian Amirhosein Vahidi, Samuel Ogden, Daniyal Jafree, Adib Miraki, Carlo Leonardi and Arpit Merchant, with Lassi Paavolainen, Menna Clatworthy, Omer Ali Bayraktar, Muzlifah Haniffa, Tom Mitchell and Mostafa Bakhti. Huge thanks too to everyone who shared data and helped along the way. What excites me most is seeing how the community builds on this. The model, code and tutorials are all public, so anyone can run TERRA on their own tissues, extend it, or build new models on top. Huge thanks to the whole team across Wellcome Sanger Institute and our many collaborators. 📄 Paper: 💻 Code: 🤗 Model: #SpatialTranscriptomics #SpatialGenomics #FoundationModels #AI4Science #MachineLearning #ComputationalBiology #SingleCell #WorldModels

Mo Lotfollahi

51,560 görüntüleme • 2 ay önce

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

61,805 görüntüleme • 2 yıl önce

Apple and Meta are still missing the point when it comes to spatial productivity. Going monitor-free or showing the weather in 3d space isn't enough. There's not enough value in replacing a frictionless setup like a laptop and mouse unless the spatial environment unlocks something radically more powerful: a creative work space so fluid and enabling that you wouldn't want to work any other way. I created this "Spatial AI Workbench" mockup a while ago to explore that exact idea.
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Apple and Meta are still missing the point when it comes to spatial productivity. Going monitor-free or showing the weather in 3d space isn't enough. There's not enough value in replacing a frictionless setup like a laptop and mouse unless the spatial environment unlocks something radically more powerful: a creative work space so fluid and enabling that you wouldn't want to work any other way. I created this "Spatial AI Workbench" mockup a while ago to explore that exact idea.

Greg Madisᯅn

75,936 görüntüleme • 1 yıl önce

WE DID IT! +100,000 OVRMAPS 🚀 A major milestone in decentralized spatial computing. A huge thank you to our incredible community of mappers and our technical team! ↳ 30M sqm of 3D-mapped terrain ↳ 55TB of image data processed with cutting-edge AI models ↳ Powered by DePIN and a global network of mappers This is the future of Spatial Computing. Let’s break it down: 🧵👇
0:28

WE DID IT! +100,000 OVRMAPS 🚀 A major milestone in decentralized spatial computing. A huge thank you to our incredible community of mappers and our technical team! ↳ 30M sqm of 3D-mapped terrain ↳ 55TB of image data processed with cutting-edge AI models ↳ Powered by DePIN and a global network of mappers This is the future of Spatial Computing. Let’s break it down: 🧵👇

Over the Reality 🌐

493,928 görüntüleme • 1 yıl önce

Announcing the Video Editor in Spatial Media Toolkit. Effortlessly create spatial videos or photo slideshows with music, all privately on-device. Seamlessly integrates with our powerful 2D-to-3D conversion for stunning spatial storytelling.
0:47

Announcing the Video Editor in Spatial Media Toolkit. Effortlessly create spatial videos or photo slideshows with music, all privately on-device. Seamlessly integrates with our powerful 2D-to-3D conversion for stunning spatial storytelling.

Ryan Gordon

12,353 görüntüleme • 1 yıl önce

For those attending AACR26 in San Diego, I will demonstrate how CODA can map whole tumors, organs, and organisms in 3D and at single-cell resolution. I will also show how CODA can integrate spatial transcriptomics, spatial proteomics, IHC, IMC, etc. April 17, 3:30 pm
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For those attending AACR26 in San Diego, I will demonstrate how CODA can map whole tumors, organs, and organisms in 3D and at single-cell resolution. I will also show how CODA can integrate spatial transcriptomics, spatial proteomics, IHC, IMC, etc. April 17, 3:30 pm

Denis Wirtz

27,724 görüntüleme • 6 ay önce