Article | Published:

Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines

Nature Biotechnology volume 34, pages 7077 (2016) | Download Citation

Abstract

High-content, image-based screens enable the identification of compounds that induce cellular responses similar to those of known drugs but through different chemical structures or targets. A central challenge in designing phenotypic screens is choosing suitable imaging biomarkers. Here we present a method for systematically identifying optimal reporter cell lines for annotating compound libraries (ORACLs), whose phenotypic profiles most accurately classify a training set of known drugs. We generate a library of fluorescently tagged reporter cell lines, and let analytical criteria determine which among them—the ORACL—best classifies compounds into multiple, diverse drug classes. We demonstrate that an ORACL can functionally annotate large compound libraries across diverse drug classes in a single-pass screen and confirm high prediction accuracy by means of orthogonal, secondary validation assays. Our approach will increase the efficiency, scale and accuracy of phenotypic screens by maximizing their discriminatory power.

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Acknowledgements

We thank members of the Altschuler and Wu laboratories for critical feedback; U. Alon (Weizmann Institute of Science, Rehovot, Israel), and members of his laboratory for providing the CD tag plasmid and guidance on its use; G. DeMartino (University of Texas Southwestern Medical Center, Dallas) for useful conversations reagents for the proteasome validation and the Ub-R clone of HeLa cells; and S. Wei for help with HTS experiments. This research was partially supported by the US National Institutes of Health grants CA133253 (S.J.A.), R01CA184984 (L.F.W.) and the Institute of Computational Health Sciences at UCSF (S.J.A., L.F.W.).

Author information

Author notes

    • Jungseog Kang
    •  & Chien-Hsiang Hsu

    These authors contributed equally to this work.

Affiliations

  1. Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

    • Jungseog Kang
    • , Chien-Hsiang Hsu
    • , Qi Wu
    • , Shanshan Liu
    • , Adam D Coster
    • , Steven J Altschuler
    •  & Lani F Wu
  2. Department of Arts and Science, New York University-Shanghai, Shanghai, China.

    • Jungseog Kang
  3. Simmons Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

    • Chien-Hsiang Hsu
  4. Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA.

    • Chien-Hsiang Hsu
    • , Steven J Altschuler
    •  & Lani F Wu
  5. Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

    • Bruce A Posner

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Contributions

J.K., Q.W. and S.L. generated the reporter library; A.D.C. built pSEG; J.K. designed the experiments; J.K. and C.-H.H. performed the experiments; B.A.P. helped perform the HTS experiments; C.-H.H. performed the data analysis; J.K., C.-H.H., L.F.W. and S.J.A. wrote the manuscript; and L.F.W. and S.J.A. guided all aspects of this study.

Competing interests

S.J.A. and L.F.W. have submitted a patent application.

Corresponding authors

Correspondence to Steven J Altschuler or Lani F Wu.

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    Supplementary Tables 1–6

    Table 1 Feature list Table 2 Reference drug list Table 3 NCI oncology drug annotation and prediction Table 4 Literature supported compounds used as new reference drugs Table 5 Recall and new predictions Table 6 Validated hits and structure

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DOI

https://doi.org/10.1038/nbt.3419

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