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Integrating high-content screening and ligand-target prediction to identify mechanism of action

Nature Chemical Biology volume 4, pages 59–68 (2008)Cite this article

Abstract

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.

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Figure 1: High-content screen.
Figure 2: Common factor model defines a multidimensional biological activity space.
Figure 3: Screen layout and phenotypic compound profiling.
Figure 4: Correlation of biological activity and compound structure similarity.
Figure 5: Factor-based phenotypic profiling elucidates SARs in biological activity space.
Figure 6: Factor-based phenotypic profiling provides biological support to structure-based target predictions.

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Acknowledgements

We thank L. Martell, M. Thoma, J. Nettles, B. Dwyer and M. Pflumm for insightful comments and discussions, G. Paris for assembly of the climax screening collection, A. Salic (Harvard Medical School) for the gift of rhodamine azide, C. Mickanin and S. Zhao for automation support, and Q. Yang for database support. D.W.Y. and A.B. are both Novartis Presidential Postdoctoral Fellows. Work in the T.J.M. lab is supported by US National Institutes of Health grant CA78048.

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  1. Daniel W Young

    Present address: Present address: Wolf, Greenfield & Sacks, P.C., 600 Atlantic Avenue, Boston, Massachusetts 02210, USA.,

Authors and Affiliations

  1. Developmental and Molecular Pathways, Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, 02139, Massachusetts, USA

    Daniel W Young, Jonathan Hoyt, Elizabeth McWhinnie, Gung-Wei Chirn, Charles Y Tao, Mark Labow & Yan Feng

  2. Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, 02115, Massachusetts, USA

    Daniel W Young & Timothy J Mitchison

  3. Lead Discovery Informatics, Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, 02139, Massachusetts, USA

    Andreas Bender & Jeremy L Jenkins

  4. Global Discovery Chemistry, Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, 02139, Massachusetts, USA

    John A Tallarico

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Contributions

D.W.Y., A.B., J.L.J., T.J.M. and Y.F. conceived the work. J.H., D.W.Y. and E.M. performed experiments. D.W.Y. developed and implemented factor analysis of HCS data. A.B. performed ligand-target and compound structure analysis. D.W.Y. and A.B. performed integrated statistical analysis of biological and chemical data. D.W.Y., T.J.M. and Y.F. analyzed phenotypes. C.Y.T. performed cell cycle analysis. J.A.T. and M.L. contributed to experimental design and interpretation. G.-W.C. assisted in data processing and analysis. D.W.Y. and A.B. wrote the paper with assistance from J.L.J., T.J.M. and Y.F.

Corresponding authors

Correspondence to Jeremy L Jenkins or Yan Feng.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6, Supplementary Table 1 and Supplementary Methods (PDF 1158 kb)

Supplementary Data 1

Workbook containing results from the factor analysis. (XLS 86 kb)

Supplementary Data 2

Table of hit data. (XLS 445 kb)

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Young, D., Bender, A., Hoyt, J. et al. Integrating high-content screening and ligand-target prediction to identify mechanism of action. Nat Chem Biol 4, 59–68 (2008). https://doi.org/10.1038/nchembio.2007.53

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