Integrating high-content screening and ligand-target prediction to identify mechanism of action
Department of Cell Biology
Life Sciences | Medicine and Health Sciences
High-content screening is transforming drug discovery by enabling simultaneous measurement of multiple features of cellular phenotype that are relevant to therapeutic and toxic activities of compounds. High-content screening studies typically generate immense datasets of image-based phenotypic information, and how best to mine relevant phenotypic data is an unsolved challenge. Here, we introduce factor analysis as a data-driven tool for defining cell phenotypes and profiling compound activities. This method allows a large data reduction while retaining relevant information, and the data-derived factors used to quantify phenotype have discernable biological meaning. We used factor analysis of cells stained with fluorescent markers of cell cycle state to profile a compound library and cluster the hits into seven phenotypic categories. We then compared phenotypic profiles, chemical similarity and predicted protein binding activities of active compounds. By integrating these different descriptors of measured and potential biological activity, we can effectively draw mechanism-of-action inferences.
DOI of Published Version
Nat Chem Biol. 2008 Jan;4(1):59-68. Epub 2007 Dec 9. Link to article on publisher's site
Nature chemical biology
Young DW, Bender A, Hoyt J, McWhinnie E, Chirn G, Tao CY, Tallarico JA, Labow MA, Jenkins JL, Mitchison TJ, Feng Y. (2007). Integrating high-content screening and ligand-target prediction to identify mechanism of action. GSBS Student Publications. https://doi.org/10.1038/nchembio.2007.53. Retrieved from https://escholarship.umassmed.edu/gsbs_sp/1419