Research Area: Machine Learning
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, 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 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 1500+ compound perturbations across multiple doses, we validated predictions by orthogonal assay readouts.
Keywords:
Cell Health Phenotypes
Genetic
Perturbations
Morphology profiling
DNA damage
Author(s) Name: Gregory P. Way , Maria Kost-Alimova , Tsukasa Shibue , William F. Harrington , Stanley Gill , Federica Piccioni , Tim Becker , Hamdah Shafqat-Abbasi , William C. Hahn , Anne E. Carpenter , Francisca Vazquez , and Shantanu Singh
Journal name: Molecular Biology of the Cell
Conferrence name:
Publisher name: ascb MBoC
DOI: 10.1091/mbc.E20-12-0784
Volume Information: Volume 32
