title: Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines.
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.
authors.affiliation: Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.|Department of Biology, Arts and Science, New York University-Shanghai, Shanghai, 200122, China.
authors.forename: Jungseog
authors.initials: J
authors.lastname: Kang
authors.affiliation: Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.|Simmons Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.|Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
authors.forename: Chien-Hsiang
authors.initials: CH
authors.lastname: Hsu
authors.affiliation: Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
authors.forename: Qi
authors.initials: Q
authors.lastname: Wu
authors.affiliation: Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
authors.forename: Shanshan
authors.initials: S
authors.lastname: Liu
authors.affiliation: Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
authors.forename: Adam D
authors.initials: AD
authors.lastname: Coster
authors.affiliation: Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
authors.forename: Bruce A
authors.initials: BA
authors.lastname: Posner
authors.affiliation: Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.|Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
authors.forename: Steven J
authors.initials: SJ
authors.lastname: Altschuler
authors.affiliation: Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.|Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
authors.forename: Lani F
authors.initials: LF
authors.lastname: Wu
mesh_terms: D000818:Animals; D002460:Cell Line; D055808:Drug Discovery; D006801:Humans; D010641:Phenotype