Image-based multivariate profiling of drug responses from single cells.
Quantitative analytical approaches for discovering new compound mechanisms are required for summarizing high-throughput, image-based drug screening data. Here we present a multivariate method for classifying untreated and treated human cancer cells based on approximately 300 single-cell phenotypic measurements. This classification provides a score, measuring the magnitude of the drug effect, and a vector, indicating the simultaneous phenotypic changes induced by the drug. These two quantities were used to characterize compound activities and identify dose-dependent multiphasic responses. A systematic survey of profiles extracted from a 100-compound compendium of image data revealed that only 10-15% of the original features were required to detect a compound effect. We report the most informative image features for each compound and fluorescence marker set using a method that will be useful for determining minimal collections of readouts for drug screens. Our approach provides human-interpretable profiles and automatic determination of on- and off-target effects.
Loo
Lit-Hsin
LH
Department of Pharmacology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., ND 9.214, Dallas, Texas 75390, USA.
Wu
Lani F
LF
Altschuler
Steven J
SJ
D004353:Drug Evaluation, Preclinical / Q000379:methods*; D006367:HeLa Cells / Q000378:metabolism; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015999:Multivariate Analysis; D010600:Pharmacology / Q000379:methods; D010641:Phenotype