Our virtual spatial transcriptomics atlas of 11,000 patients across 32 cancer types is featured in the AI for Science collection on @hf.co, together with DeepSpot-M.

Try it: www.auroraomics.org
Explore the dataset: huggingface.co/datasets/rat...
Manuscript: www.medrxiv.org/content/10.6...

🚀 Check out our new population-scale virtual spatial transcriptomics atlas, generated with DeepSpot-M by computationally augmenting TCGA retrospective histopathology archives spanning 28,664 images from 10,865 patients across 32 cancer types. huggingface.co/datasets/rat...

DeepSpot-M: a multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1

Most tumours are never profiled with spatial transcriptomics, but nearly all are imaged. DeepSpot-M bridges that gap and as a first demonstration, we used it to build a virtual spatial transcriptomics atlas of 28,664 TCGA slides across 32 cancers, the largest resource of its kind

After years of research and continuous refinement, we’re thrilled to share that our paper on the MetaGraph framework — enabling Petabase-scale search across sequencing data — has been published today in Nature (www.nature.com/articles/s41...)

Older spot-level spatial transcriptomics datasets shouldn't be forgotten now that new single-cell methods exist. 🧬

Instead of discarding this rich resource, we can bridge the gap.
DeepSpot2Cell helps bridge the gap 👇

DeepSpot2Cell: Predicting Virtual Single-Cell Spatial Transcriptomics from H&E images using Spot-Level Supervision [new]
Pred. sc gene expr. via DeepSet & spot sup. for spatial transcriptomics.

Internship Opportunity: Multimodal AI Research Scientist at the Biomedical Informatics Group at ETH Zurich 🚀

Interested in working at the intersection of computational pathology, spatial transcriptomics, LLM representation learning, and tissue generation?

Just presented our new multimodal histopathology method "SpotWhisperer" at ICML, one of the largest AI conference.

SpotWhisperer enables spatially resolved annotation of histopathology images using natural language. We achieved this by "transferring" annotations from transcriptomic data. More soon!

🤝 Great collaboration between @bocklab.bsky.social (@moritzbaio.bsky.social, Animesh, Jake), @nonchev.bsky.social, @gxxxr.bsky.social, and pathologist Viktor Kölzer.

SpotWhisperer is at #ICML25 FM4LS workshop. Visit our poster on Saturday (19 July 2025) if you're interested & attending ICML. (6/6)

🔬 Toward histopathology 2.0: spatial transcriptomes inferred from routine diagnostic H&E images + a chat interface for cell-resolution histopathology through English language. (1/6)

Excited to share an update to D3 (DNA Discrete Diffusion) — an application of score-entropy discrete diffusion model for regulatory genomics!

🧬 Paper: biorxiv.org/content/10.110…

(See thread below 👇) (1/n)

🚀 Excited to share that we've generated the largest digital spatial transcriptomics dataset using DeepSpot - over 56 million spatial transcriptomics spots from 3 780 TCGA samples across skin melanoma, renal cell carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma cohorts. #pathology

First place award at the Autoimmune Disease Machine Learning Challenge organized by the @broadinstitute.org and CrunchDAO. Our approach outperformed competitors worldwide in predicting single-cell spatial transcriptomics from H&E images. 🎉

How can we predict spatial transcriptomics from histology images to enable simple, affordable, and reliable analysis of spatially resolved gene expression in routine clinical use? 🤔 Introducing DeepSpot – a deep learning model designed to tackle this! 🧵👇 www.medrxiv.org/content/10.1...

Where RNA Science Meets AI, May 4–8, 2025, Ascona. Invited speakers: @evamarianovoa.bsky.social @fabiantheis.bsky.social @rivaselenarivas.bsky.social, Sterling Churchman, Barbara Treutlein, Rahul Satijia,
Registration open www.rna-ai.org
@hagentilgner.bsky.social @quaidmorris.bsky.social