ArticleNature biotechnology2016
Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines.
Article in Nature biotechnology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
50 citing papers in PubMed.
- Transitive prediction of small-molecule function through alignment of high-content screening resources.Nature biotechnology · 2026Article
- Identification of BET inhibitors (BETi) against solitary fibrous tumor (SFT) through high-throughput screening (HTS).Neoplasia (New York, N.Y.) · 2025Article
- A human metabolic map of pharmacological perturbations reveals drug modes of action.Nature biotechnology · 2025Article
- Genetically Encoded Cell Fate Reporter System for Multiplex Single-Cell Detection of DNA and Mitochondrial Damage.ACS omega · 2025Article
- A phenopushing platform to identify compounds that alleviate acute hypoxic stress by fast-tracking cellular adaptation.Nature communications · 2025Article
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- Evaluation of image analysis tools for the measurement of cellular morphology.Frontiers in cell and developmental biology · 2025Review
- Small molecule in situ resin capture provides a compound first approach to natural product discovery.Nature communications · 2024Article
- Pooled multicolour tagging for visualizing subcellular protein dynamics.Nature cell biology · 2024Article
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- Metabolomics: A Tool to Envisage Biomarkers in Clinical Interpretation of Cancer.Current drug research reviews · 2024Review
- Genome-wide CRISPR screens identify ILF3 as a mediator of mTORC1-dependent amino acid sensing.Nature cell biology · 2023Article
- Selection of Optimal Cell Lines for High-Content Phenotypic Screening.ACS chemical biology · 2023Article
- Selection of optimal cell lines for high-content phenotypic screening.bioRxiv : the preprint server for biology · 2023Article
- Article
- Early Pharmacological Profiling of Antiproliferative Compounds by Live Cell Imaging.Molecules (Basel, Switzerland) · 2022Article
- Visual barcodes for clonal-multiplexing of live microscopy-based assays.Nature communications · 2022Article
- Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs.PLoS computational biology · 2022Article
- Pharmaceutical therapeutics for articular regeneration and restoration: state-of-the-art technology for screening small molecular drugs.Cellular and molecular life sciences : CMLS · 2021Review
- Multiplexing Methods for Simultaneous Large-Scale Transcriptomic Profiling of Samples at Single-Cell Resolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Review
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Authors and funding
8 authors.
Funding
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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