Image-based multivariate profiling of drug responses from single cells
Lit-Hsin Loo, Lani F. Wu, and Steven J. Altschuler
| Similar target, different phenotypes DNA (blue), microtubule (green) and actin (red) of HeLa cells subjected to several microtubule-targeted drugs. The upper row shows distinct phenotypes across different concentrations of the same drug, Taxol (left - low, center - middle, right - high). Another microtubule-stabilizing drug, Epothilone B (lower right), induced similar phenotypes as Taxol at high concentration. However, other microtubule-depolymerizing drugs, such as 105D (lower left) and Nocodazole (lower center), gave very different phenotypes. |
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Abstract: 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 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. Publication link: Nature Methods, April 2007 (full text) |