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

Jungseog Kang, Chien Hsiang Hsu, Qi Wu, Shanshan Liu, Adam D. Coster, Bruce A. Posner, Steven J. Altschuler, Lani F. Wu

Research output: Contribution to journalArticle

40 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)70-77
Number of pages8
JournalNature biotechnology
Volume34
Issue number1
DOIs
StatePublished - Jan 1 2016

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology
  • Molecular Medicine
  • Biomedical Engineering

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