ArticleNature methods2024
Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 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.
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Who cites it
83 citing papers in PubMed.
- Development of a universal imaging "phenome" using shape, appearance and motion (SAM) features and the SAM Phenotype Observation Tool (SPOT).Nature communications · 2026Article
- Identifying phenotype-genotype-function coupling in 3D organoid imaging using Shape, Appearance and Motion Phenotype Observation Tool (SPOT).Nature communications · 2026Article
- Identification of a NUAK1/2 Inhibitor as a Macrophage-Polarizing Compound by a Machine Learning-Based Phenotypic Cell Painting Screen.International journal of molecular sciences · 2026Article
- Cell Painting in activated cells illuminates phenotypic dark space and drug mechanisms of action.Nature communications · 2026Article
- CALIPERS: Cell cycle-aware live imaging for phenotyping experiments and regeneration studies.Nature communications · 2026Article
- Mapping human microglial morphological diversity via handcrafted and deep learning-derived image features.iScience · 2026Article
- Buffering of gene dosage response curves for human complex traits.Cell genomics · 2026Article
- Image-based, pooled phenotyping reveals multidimensional, disease-specific variant effects.Cell · 2026Article
- A vitronectin-functionalized ECM-mimetic hydrogel platform for modelling integrin-dependent tumour responses to cilengitide.Materials today. Bio · 2026Article
- Generative machine learning unlocks the first proteome-wide image of human cells.bioRxiv : the preprint server for biology · 2026Article
- Scalable data harmonization for single-cell image-based profiling with CytoTable.Patterns (New York, N.Y.) · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Autophagy revealed as a targetable vulnerability in senescent cells by cell painting phenotypic profiling: a mechanistic study of MCOPPB and related compounds.GeroScience · 2026Article
- Automated Morphological Profiling via Deep Learning-Based Segmentation for High-Throughput Phenotypic Screening.Journal of imaging · 2026Article
- Single-cell hit calling in high-content imaging screens with Buscar.bioRxiv : the preprint server for biology · 2026Article
- Cell Painting PLUS: An Iterative Staining-Elution Protocol for High-Content Phenotypic Screenings.Current protocols · 2026Article
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
- Self-supervised pretraining with NuSPIRe unlocks nuclear morphology-driven insights in spatial omics.Genome biology · 2026Article
- A self-supervised machine learning pipeline for extracting information from live cell images at multiple doses and timepoints.Scientific reports · 2026Article
- Toward trustworthy virtual cells: a roadmap for perturbation-resolved, context-aware, and experimentally validated cell models.Frontiers in cell and developmental biology · 2026Review
23 more citing papers are in PubMed but not listed here.
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Authors and funding
23 authors.
Funding
Abstract
The identification of genetic and chemical perturbations with similar impacts on cell morphology can elucidate compounds' mechanisms of action or novel regulators of genetic pathways. Research on methods for identifying such similarities has lagged due to a lack of carefully designed and well-annotated image sets of cells treated with chemical and genetic perturbations. Here we create such a Resource dataset, CPJUMP1, in which each perturbed gene's product is a known target of at least two chemical compounds in the dataset. We systematically explore the directionality of correlations among perturbations that target the same protein encoded by a given gene, and we find that identifying matches between chemical and genetic perturbations is a challenging task. Our dataset and baseline analyses provide a benchmark for evaluating methods that measure perturbation similarities and impact, and more generally, learn effective representations of cellular state from microscopy images. Such advancements would accelerate the applications of image-based profiling of cellular states, such as uncovering drug mode of action or probing functional genomics.
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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.