Integrating high-content screening and ligand-target prediction to identify mechanism of action.
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.
Young
Daniel W
DW
Developmental and Molecular Pathways, Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.
Bender
Andreas
A
Hoyt
Jonathan
J
McWhinnie
Elizabeth
E
Chirn
Gung-Wei
GW
Tao
Charles Y
CY
Tallarico
John A
JA
Labow
Mark
M
Jenkins
Jeremy L
JL
Mitchison
Timothy J
TJ
Feng
Yan
Y
D000970:Antineoplastic Agents / Q000737:chemistry / Q000494:pharmacology; D002453:Cell Cycle / Q000187:drug effects; D002467:Cell Nucleus / Q000187:drug effects / Q000648:ultrastructure; D049109:Cell Proliferation / Q000187:drug effects; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods* / Q000706:statistics & numerical data; D004261:DNA Replication / Q000187:drug effects; D004305:Dose-Response Relationship, Drug; D015195:Drug Design; D006367:HeLa Cells; D006801:Humans; D008024:Ligands; D015233:Models, Statistical; D015394:Molecular Structure; D011237:Predictive Value of Tests; D011485:Protein Binding; D054852:Small Molecule Libraries / Q000737:chemistry / Q000494:pharmacology; D013329:Structure-Activity Relationship
D000970:Antineoplastic Agents; D008024:Ligands; D054852:Small Molecule Libraries