C. Liu, A. Hillsley, M. Sekhar, C. A. Jones, G. Sturm, T. Fujimori, D. M. Wiener, K. W. Cheng, T. Chandler, A. Lin, D. Peng, M. Frank, L. C. Dorman, I. Jeyakumar, I. E. Ivanov, G. Courville, C. B. Charlton, E. Hirata-Miyasaki, S. Ripsky, L. Luan, Z. Liu, R. Vasan, K. I. Harrington, K. Awayan, T. Le, Y. Rao, G. Palla, V. Turon-Lagot, M. A. Cid-Rosas, C. Arias, J. E. Elias, B. C. DeFelice, N. F. Neff, A. R. Lowe, S. B. Mehta, L. A. Royer, R. Gomez-Sjoberg, M. D. Leonetti
Modeling cellular behavior requires measurements that capture how cells evolve across time, environments, and interventions. Microscopy is uniquely suited to this goal: it is non-destructive and can be applied to living cells in their native context. Yet its phenotypic resolving power remains incompletely characterized relative to molecular assays. Here, we present a multimodal perturbation atlas of 1,000 pooled CRISPR knockouts in A549 cells, profiled by fluorescence microscopy (42 live, 13 fixed markers), label-free quantitative phase imaging of the same live cells (at single timepoints), and single-cell RNA sequencing (scRNA-seq). We develop deep learning frameworks to interpret the rich cell-biological signatures in these ~65M single-cell profiles. At matched reagent cost, phase imaging exceeds the phenotypic resolution of both fluorescence imaging and scRNA-seq, and more reliably recovers higher-order pathway organization. These results establish intrinsic morphology as a high-precision readout of cellular state, and lay a foundation for live-cell profiling of phenotypic trajectories.