A cell atlas foundation model for scalable search of similar human cells

Graham Heimberg (), Tony Kuo, Daryle J. DePianto, Omar Salem, Tobias Heigl, Nathaniel Diamant, Gabriele Scalia, Tommaso Biancalani, Shannon J. Turley, Jason R. Rock, Héctor Corrada Bravo, Josh Kaminker (), Jason A. Vander Heiden () and Aviv Regev ()
Additional contact information
Graham Heimberg: Genentech
Tony Kuo: F. Hoffmann-La Roche
Daryle J. DePianto: Genentech
Omar Salem: Genentech
Tobias Heigl: Genentech
Nathaniel Diamant: Genentech
Gabriele Scalia: Genentech
Tommaso Biancalani: Genentech
Shannon J. Turley: Genentech
Jason R. Rock: Genentech
Héctor Corrada Bravo: Genentech
Josh Kaminker: Genentech
Jason A. Vander Heiden: Genentech
Aviv Regev: Genentech

Nature, 2025, vol. 638, issue 8052, 1085-1094

Abstract: Abstract Single-cell RNA sequencing has profiled hundreds of millions of human cells across organs, diseases, development and perturbations to date. Mining these growing atlases could reveal cell–disease associations, identify cell states in unexpected tissue contexts and relate in vivo biology to in vitro models. These require a common measure of cell similarity across the body and an efficient way to search. Here we develop SCimilarity, a metric-learning framework to learn a unified and interpretable representation that enables rapid queries of tens of millions of cell profiles from diverse studies for cells that are transcriptionally similar to an input cell profile or state. We use SCimilarity to query a 23.4-million-cell atlas of 412 single-cell RNA-sequencing studies for macrophage and fibroblast profiles from interstitial lung disease1 and reveal similar cell profiles across other fibrotic diseases and tissues. The top scoring in vitro hit for the macrophage query was a 3D hydrogel system2, which we experimentally demonstrated reproduces this cell state. SCimilarity serves as a foundation model for single-cell profiles that enables researchers to query for similar cellular states across the human body, providing a powerful tool for generating biological insights from the Human Cell Atlas.

Date: 2025
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DOI: 10.1038/s41586-024-08411-y

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