Jesse Zhang, Airol A Ubas, Valentine Svensson, Nicole Thomas, Vishvak Subramanyam, Christopher Carpenter, Aidan Winters, Richard de Borja, Noam Teyssier, Neha Thakar, Umair Khan, Pooja V Akella, Harper J Green, John D Thompson, Vuong Tran, Joseph Pangallo, Efthymia Papalexi, Ajay Sapre, Hoai Nguyen, Oliver Sanderson, Maria Nigos, Olivia Kaplan, Sarah Schroeder, Bryan Hariadi, Simone Marrujo, Crina Curca, Alec Salvino, Guillermo Gallareta Olivares, Ryan Koehler, Alexander Rosenberg, Charles Roco, Chiara Ricci-Tam, Matthew G Jones, Alexander Dobin, Brian S Plosky, Danielle E Lyons, Daniele Merico, Nima Alidoust, Hani Goodarzi, Johnny Yu
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.