|Publication Type||Journal Article|
|Year of Publication||2022|
|Authors||Rohban, MH, Fuller, AM, Tan, C, Goldstein, JT, Syangtan, D, Gutnick, A, DeVine, A, Nijsure, MP, Rigby, M, Sacher, JR, Corsello, SM, Peppler, GB, Bogaczynska, M, Boghossian, A, Ciotti, GE, Hands, AT, Mekareeya, A, Doan, M, Gale, JP, Derynck, R, Turbyville, T, Boerckel, JD, Singh, S, Kiessling, LL, Schwarz, TL, Varelas, X, Wagner, FF, Kafri, R, Eisinger-Mathason, TSKarin, Carpenter, AE|
|Date Published||2022 Sep 01|
Identifying the chemical regulators of biological pathways is a time-consuming bottleneck in developing therapeutics and research compounds. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to each disease. Here, our uncustomized, virtual, profile-based screening approach instead identifies compounds that match to pathways based on the phenotypic information in public cell image data, created using the Cell Painting assay. Our straightforward correlation-based computational strategy retrospectively uncovered the expected, known small-molecule regulators for 32% of positive-control gene queries. In prospective, discovery mode, we efficiently identified new compounds related to three query genes and validated them in subsequent gene-relevant assays, including compounds that phenocopy or pheno-oppose YAP1 overexpression and kill a Yap1-dependent sarcoma cell line. This image-profile-based approach could replace many customized labor- and resource-intensive screens and accelerate the discovery of biologically and therapeutically useful compounds.
|Alternate Journal||Cell Syst|