Sevda ÖĞÜT

[Mo Lotfollahi @mo_lotfollahi]

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, @alexrives) and pathology (UNI, @AI4Pathology). 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 @ylecun 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 (https://t.co/LeWxOKkgMt), with more studies coming soon.

This was an amazing collaboration between clinicians, machine-learning scientists and cell biologists 🙏 Led by @SebastianBirk_, @ValiSanian @AmirhVahidi, Samuel Ogden, @daniyal_jafree, @Adib_m_, @CarloLeonardi7 and Arpit Merchant, with Lassi Paavolainen, Menna Clatworthy, @bayraktar_lab, @Muzz_Haniffa, Tom Mitchell and @bakhti_mostafa. 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 @sangerinstitute and our many collaborators.

📄 Paper: https://t.co/AOOIcwuTnq
💻 Code: https://t.co/ZBwTmd6SNI
🤗 Model: https://t.co/gyQA79lVfW

#SpatialTranscriptomics #SpatialGenomics #FoundationModels #AI4Science #MachineLearning #ComputationalBiology #SingleCell #WorldModels

[Manuel Madeira @manuelmlmadeira]

On the Role of Structure in Hierarchical Graph Neural Networks
with Luca Sbicego, Sevda Öğüt, @qinym710 , @DorinaThanou , and @pafrossard
🔗 https://t.co/QPV4acWnCJ
ICBINB Workshop

[Sevda ÖĞÜT @sevdoche]

@boyazwolf @shail_hash 🤩🤩