Integrating high-content screening and ligand-target prediction to identify mechanism of action

• Published in: Nature chemical biology Vol. 4; no. 1; pp. 59 - 68
• Main Authors: Jenkins, Jeremy L, Feng, Yan, Young, Daniel W, Bender, Andreas, Hoyt, Jonathan, McWhinnie, Elizabeth, Chirn, Gung-Wei, Tao, Charles Y, Tallarico, John A, Labow, Mark, Mitchison, Timothy J
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.01.2008; Nature Publishing Group
• Subjects: Antineoplastic Agents - chemistry; Antineoplastic Agents - pharmacology; Binding sites; Biochemical Engineering; Biochemistry; Bioorganic Chemistry; Cell Biology; Cell cycle; Cell Cycle - drug effects; Cell Nucleus - drug effects; Cell Nucleus - ultrastructure; Cell Proliferation - drug effects; Cellular biology; Chemical compounds; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Cluster Analysis; Computational Biology - methods; Computational Biology - statistics & numerical data; Data reduction; Discriminant analysis; DNA Replication - drug effects; Dose-Response Relationship, Drug; Drug Design; Factor analysis; Genotype & phenotype; HeLa Cells; Humans; Ligands; Models, Statistical; Molecular Structure; Predictive Value of Tests; Protein Binding; Small Molecule Libraries - chemistry; Small Molecule Libraries - pharmacology; Structure-Activity Relationship
• ISSN: 1552-4450; 1552-4469
• DOI: 10.1038/nchembio.2007.53

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.