Single-Cell Image Analysis Enables High-Throughput Phenotypic Drug Screen and Elucidates Cell-Fate Decision Principles

Cellular phenotypes encode information that can be used to infer the external signals cells experience. Here we applied quantitative image analysis to enable a one-pass multi-class phenotypic drug screen. Our combined experimental and computational approach can functionally annotate large compound libraries across diverse drug classes in a single-pass screen with high prediction accuracy confirmed via orthogonal, secondary validation assays. We further investigated how heterogeneity arose from an isogenic population. We monitored the dynamics of p21 to understand the proliferation-senescence cell-fate decision in single cells under non-lethal dose of chemotherapy via time-lapse microscopy before, during and days after treatment. Surprisingly, while high p21 is associated with senescence at late times, we find the opposite at early times during drug treatment: most senescence-fated cells have low p21 levels, while proliferation-fated cells have much higher p21 expression. Further, we identify a p21 "Goldilocks zone" for proliferation, in which increasing p21 levels has the undesirable effect of increasing proliferative outcomes. Our study identifies a counter-intuitive role for early p21 dynamics in cell-fate decision and pinpoints the source of proliferative cancer cells that emerge after exposure to non-lethal doses of chemotherapy.