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Image-based multivariate profiling of drug responses from single cells

Nature Methods volume 4, pages 445–453 (2007)Cite this article

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.

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Figure 1: Representing multivariate changes in cellular phenotypes by human-interpretable vectors.
Figure 2: Drugs effects can be detected by a small number of features.
Figure 3: Titration clustering reveals multiphasic drug effects.
Figure 4: Common phenotypic changes are shared by different compounds from the same category.
Figure 5: The category of a new d-profile can be inferred from its closest characterized d-profiles.

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Acknowledgements

We thank S. Johnson from the Texas Advanced Computing Center for high-performance computing technical assistance, and all members of the Altschuler and Wu lab at the UT Southwestern Medical Center for stimulating discussions. This research was supported by the Endowed Scholars program at UT Southwestern Medical Center and the Welch Foundation (I-1619, I-1644).

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Authors and Affiliations

  1. Department of Pharmacology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., ND 9.214, Dallas, 75390, Texas, USA

    Lit-Hsin Loo, Lani F Wu & Steven J Altschuler

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  1. Lit-Hsin Loo
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  2. Lani F Wu
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  3. Steven J Altschuler
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Contributions

L.-H.L. designed, implemented and performed the profiling methods. All authors contributed to the conception of the overall approach, statistical analysis of the methods and writing of the manuscript.

Note: Supplementary information is available on the Nature Methods website.

Corresponding author

Correspondence to Steven J Altschuler.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Distributions of the number of selected features. (PDF 67 kb)

Supplementary Fig. 2

Common phenotypic changes for the DNA-SC35-anillin, DNA-p53-cFos, and DNA-MT-actin marker sets. (PDF 1442 kb)

Supplementary Fig. 3

Some of the common phenotypic changes are cell-cycle independent. (PDF 161 kb)

Supplementary Data (PDF 3334 kb)

Supplementary Methods (PDF 86 kb)

Supplementary Note (PDF 342 kb)

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Loo, LH., Wu, L. & Altschuler, S. Image-based multivariate profiling of drug responses from single cells. Nat Methods 4, 445–453 (2007). https://doi.org/10.1038/nmeth1032

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