Integrating high-content screening and ligand-target prediction to identify mechanism of action
AuthorsYoung, Daniel W.
Tao, Charles Y.
Tallarico, John A.
Labow, Mark A.
Jenkins, Jeremy L.
Mitchison, Timothy J.
UMass Chan AffiliationsDepartment of Cell Biology
Document TypeJournal Article
Keywords*Antineoplastic Agents; Cell Cycle; Cell Nucleus; Cell Proliferation; Cluster Analysis; Computational Biology; DNA Replication; Dose-Response Relationship, Drug; *Drug Design; Hela Cells; Humans; Ligands; Models, Statistical; Molecular Structure; Predictive Value of Tests; Protein Binding; *Small Molecule Libraries; Structure-Activity Relationship
Medicine and Health Sciences
MetadataShow full item record
AbstractHigh-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.
SourceNat Chem Biol. 2008 Jan;4(1):59-68. Epub 2007 Dec 9. Link to article on publisher's site
Permanent Link to this Itemhttp://hdl.handle.net/20.500.14038/32865
Related ResourcesLink to Article in PubMed