Image-based multivariate profiling of drug responses from single cells

Nat Methods. 2007 May;4(5):445-53. doi: 10.1038/nmeth1032. Epub 2007 Apr 1.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Drug Evaluation, Preclinical / methods*
  • HeLa Cells / metabolism
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Multivariate Analysis
  • Pharmacology / methods
  • Phenotype