ArticleNature methods2022
High-dimensional gene expression and morphology profiles of cells across 28,000 genetic and chemical perturbations.
Article in Nature methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed, 109 citations in OpenAlex.
- Sparse Cell Labeling Reveals Morphologic Diversity Among Mouse Scleral Fibroblasts.Investigative ophthalmology & visual science · 2026Article
- Cell Painting in activated cells illuminates phenotypic dark space and drug mechanisms of action.Nature communications · 2026Article
- Empowering chemical structures with biological insights for scalable phenotypic virtual screening.Bioinformatics (Oxford, England) · 2026Article
- Data modeling the interplay between single-cell shape, single-cell protein expression, and tissue state.Cell reports methods · 2026Article
- Transitive prediction of small-molecule function through alignment of high-content screening resources.Nature biotechnology · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- A graph-based learning approach to predict the effects of gene perturbations on molecular phenotypes.bioRxiv : the preprint server for biology · 2026Article
- Stability of non-canonical nucleic acid structure as a potential modulator of cell fate.Nucleic acids research · 2026Review
- SHEST: single-cell-level artificial intelligence from haematoxylin and eosin morphology for cell-type prediction and spatial transcriptomics reconstruction.Briefings in bioinformatics · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- MicroRNAs and genes regulating responses to hypoxia and inflammation expression levels in blood leukocytes as potential biomarkers of initial oxygen deficiency tolerance.Frontiers in molecular biosciences · 2026Article
- Quantifying the Single-Cell Morphological Landscape of Cellular Transdifferentiation through Force Field Reconstruction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A pooled Cell Painting CRISPR screening platform enables de novo inference of gene function by self-supervised deep learning.Nature communications · 2025Article
- PhenoProfiler: advancing phenotypic learning for image-based drug discovery.Nature communications · 2025Article
- Joint profiling of cell morphology and gene expression during in vitro neurodevelopment.eLife · 2025Article
- Machine-learning models based on histological images from healthy donors identify imageQTLs and predict chronological age.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Machine Learning Models Based on Histological Images from Healthy Donors Identify ImageQTLs and Predict Chronological Age.bioRxiv : the preprint server for biology · 2025Article
- Prediction of cellular morphology changes under perturbations with a transcriptome-guided diffusion model.Nature communications · 2025Article
- High-throughput profiling of chemical-induced gene expression across 93,644 perturbations.Nature methods · 2025Article
- Article
7 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Cells can be perturbed by various chemical and genetic treatments and the impact on gene expression and morphology can be measured via transcriptomic profiling and image-based assays, respectively. The patterns observed in these high-dimensional profile data can power a dozen applications in drug discovery and basic biology research, but both types of profiles are rarely available for large-scale experiments. Here, we provide a collection of four datasets with both gene expression and morphological profile data useful for developing and testing multimodal methodologies. Roughly a thousand features are measured for each of the two data types, across more than 28,000 chemical and genetic perturbations. We define biological problems that use the shared and complementary information in these two data modalities, provide baseline analysis and evaluation metrics for multi-omic applications, and make the data resource publicly available ( https://broad.io/rosetta/ ).
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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.