Abstract
Genetic and chemical perturbations impact diverse cellular phenotypes, including multiple indicators of cell health. These readouts reveal toxicity and antitumorigenic effects relevant to drug discovery and personalized medicine. We developed two customized microscopy assays, one using four targeted reagents and the other three targeted reagents, to collectively measure 70 specific cell health phenotypes including proliferation, apoptosis, reactive oxygen species (ROS), DNA damage, and cell cycle stage. We then tested an approach to predict multiple cell health phenotypes using Cell Painting, an inexpensive and scalable image-based morphology assay. In matched CRISPR perturbations of three cancer cell lines, we collected both Cell Painting and cell health data. We found that simple machine learning algorithms can predict many cell health readouts directly from Cell Painting images, at less than half the cost. We hypothesized that these trained models can be applied to accurately predict cell health assay outcomes for any future or existing Cell Painting dataset. For Cell Painting images from a set of 1,500+ compound perturbations across multiple doses, we validated predictions by orthogonal assay readouts, and by confirming mitotic arrest, ROS, and DNA damage phenotypes via PLK, proteasome, and aurora kinase/tubulin inhibition, respectively. We provide an intuitive web app to browse all predictions at http://broad.io/cell-health-app. Our approach can be used to add cell health annotations to Cell Painting perturbation datasets.
Competing Interest Statement
G.P.W. serves on the Scientific Advisory Board for Infixion Biosciences. W.C.H. is a consultant for ThermoFisher, Solasta, MPM Capital, iTeos, Frontier Medicines, and Paraxel and is a Scientific Founder and serves on the Scientific Advisory Board for KSQ Therapeutics. A.E.C. serves on the Scientific and Technical Advisory Board of Recursion. F.V. receives research support from Novo Ventures.
Footnotes
↵+ Co-First Authors
↵^ Co-Senior Authors
This version of the manuscript has been revised after responding to three expert reviewers via Review Commons. The manuscript has updates throughout all sections including: Figure 1, Figure 4, additional machine learning robustness analyses, and additional gH2AX model validation.
