Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution.
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Zhang Jesse, Ubas Airol A, Svensson Valentine, Thomas Nicole, Subramanyam Vishvak, Carpenter Christopher, Winters Aidan, de Borja Richard, Teyssier Noam, Thakar Neha, Khan Umair, Akella Pooja V, Green Harper J, Thompson John D, Tran Vuong, Pangallo Joseph, Papalexi Efthymia, Sapre Ajay, Nguyen Hoai, Sanderson Oliver, Nigos Maria, Kaplan Olivia, Schroeder Sarah, Hariadi Bryan, Marrujo Simone, Curca Crina, Salvino Alec, Gallareta Olivares Guillermo, Koehler Ryan, Rosenberg Alexander, Roco Charles, Ricci-Tam Chiara, Jones Matthew G, Dobin Alexander, Plosky Brian S, Lyons Danielle E, Merico Daniele, Alidoust Nima, Goodarzi Hani, Yu Johnny
Аннотация
We present Tahoe-100M, a giga-scale single-cell perturbation atlas comprising 100 million transcriptomes from 50 diverse cancer cell lines treated with 1,100 drug-dose conditions. This parallel profiling of thousands of perturbations at single-cell resolution with minimal batch effects is enabled by the Mosaic platform, which multiplexes genetically distinct cell models into balanced "cell villages." Beyond cataloging transcriptomic shifts, Tahoe-100M systematically quantifies cellular phenotypes, including proliferation, cytotoxicity, lineage-specific vulnerabilities, and cell-cycle changes. It captures population-level transcriptomic heterogeneity, characterizing whether drug responses drive cells toward divergent fates or convergent states. Pathway-based signatures define drug-induced expression programs, classify mechanisms of action, reveal off-target activities, and expose adaptive stress responses associated with resistance. By unifying cellular and molecular readouts, this broadly applicable perturbation atlas advances our ability to model gene regulation, drug response, and network dynamics. Its public release enables the training of AI frameworks to advance predictive models of cell behavior.