ArticleACS chemical biology2023
Selection of Optimal Cell Lines for High-Content Phenotypic Screening.
Article in ACS chemical biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
12 citing papers in PubMed.
- Transitive prediction of small-molecule function through alignment of high-content screening resources.Nature biotechnology · 2026Article
- A diffusion model conditioned on compound bioactivity profiles for generating high-content images.Scientific reports · 2026Article
- Genetically Encoded Cell Fate Reporter System for Multiplex Single-Cell Detection of DNA and Mitochondrial Damage.ACS omega · 2025Article
- Exploiting similarity in drug molecular effects for drug repurposing.Human genomics · 2025Article
- Morphological profiling data resource enables prediction of chemical compound properties.iScience · 2025Article
- Cell Painting: a decade of discovery and innovation in cellular imaging.Nature methods · 2025Review
- Small molecule in situ resin capture provides a compound first approach to natural product discovery.Nature communications · 2024Article
- A Decade in a Systematic Review: The Evolution and Impact of Cell Painting.bioRxiv : the preprint server for biology · 2024Article
- Article
- Improving the power of drug toxicity measurements by quantitative nuclei imaging.Cell death discovery · 2024Article
- A semiconductor 96-microplate platform for electrical-imaging based high-throughput phenotypic screening.Nature communications · 2023Article
- Human Pangenomics: Promises and Challenges of a Distributed Genomic Reference.Life (Basel, Switzerland) · 2023Review
Corrections and comments
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5 authors.
Funding
Abstract
High-content microscopy offers a scalable approach to screen against multiple targets in a single pass. Prior work has focused on methods to select "optimal" cellular readouts in microscopy screens. However, methods to select optimal cell line models have garnered much less attention. Here, we provide a roadmap for how to select the cell line or lines that are best suited to identify bioactive compounds and their mechanism of action (MOA). We test our approach on compounds targeting cancer-relevant pathways, ranking cell lines in two tasks: detecting compound activity ("phenoactivity") and grouping compounds with similar MOA by similar phenotype ("phenosimilarity"). Evaluating six cell lines across 3214 well-annotated compounds, we show that optimal cell line selection depends on both the task of interest (e.g., detecting phenoactivity vs inferring phenosimilarity) and distribution of MOAs within the compound library. Given a task of interest and a set of compounds, we provide a systematic framework for choosing optimal cell line(s). Our framework can be used to reduce the number of cell lines required to identify hits within a compound library and help accelerate the pace of early drug discovery.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.