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Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes
Open accessPublished version of a preprint
Abstract
In morphological profiling, quantitative data are extracted from microscopy images of cells to identify biologically relevant similarities and differences among samples based on these profiles. This protocol describes the design and execution of experiments using Cell Painting, which is a morphological profiling assay that multiplexes six fluorescent dyes, imaged in five channels, to reveal eight broadly relevant cellular components or organelles. Cells are plated in multiwell plates, perturbed with the treatments to be tested, stained, fixed, and imaged on a high-throughput microscope. Next, an automated image analysis software identifies individual cells and measures ∼1,500 morphological features (various measures of size, shape, texture, intensity, and so on) to produce a rich profile that is suitable for the detection of subtle phenotypes. Profiles of cell populations treated with different experimental perturbations can be compared to suit many goals, such as identifying the phenotypic impact of chemical or genetic perturbations, grouping compounds and/or genes into functional pathways, and identifying signatures of disease. Cell culture and image acquisition takes 2 weeks; feature extraction and data analysis take an additional 1-2 weeks.
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“The concept of PP was pioneered by the laboratory of A.E. Carpenter at the Broad Institute of Harvard and MIT (Gustafsdottir et al. 2013; Bray et al. 2016) and has quickly been successfully implemented, optimised, and standardised for high-throughput phenotypic profiling at various sites worldwide (Cimini et al. 2023; Tromans-Coia et al. 2023).”
From Bridging imaging-based in vitro methods from biomedical research to regulatory toxicology (2025) · cites this paper as Bray et al. 2016
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“These were acquired via fluorescent microscopy using a 6-channel variant of the Cell Painting assay (Bray et al., 2016).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Bray et al., 2016
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CellProfiler: image analysis software for identifying and quantifying cell phenotypes
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Annotated high-throughput microscopy image sets for validation
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CellProfiler 4: improvements in speed, utility and usability
Stirling, D. R., Swain-Bowden, M. J., Lucas, A. M., Carpenter, A. E., Cimini, B. A., & Goodman, A. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04344-9 · Cited by 2,109
Self-citationCellProfiler 3.0: Next-generation image processing for biology
McQuin, C., Goodman, A., Chernyshev, V. S., Kamentsky, L. D., Cimini, B. A., Karhohs, K. W., Doan, M., Ding, L., Rafelski, S. M., Thirstrup, D. J., et al. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2005970 · Cited by 1,809
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Genetic and transcriptional evolution alters cancer cell line drug response
Ben-David, U., Siranosian, B., Ha, G., Tang, H., Oren, Y., Hinohara, K., Strathdee, C. A., Dempster, J., Lyons, N. J., Burns, R., et al. (2018) · Nature · doi:10.1038/s41586-018-0409-3 · Cited by 931
Data-analysis strategies for image-based cell profiling
Caicedo, J. C., Cooper, S., Heigwer, F., Warchal, S., Qiu, P., Molnar, C., Vasilevich, A. S., Barry, J. D., Bansal, H. S., Kraus, O., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4397 · Cited by 739
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Metadata
- Authors
- Bray, M.-A., Singh, S., Han, H., Davis, C. T., Borgeson, B., Hartland, C., Kost-Alimova, M., Gustafsdottir, S. M., Gibson, C. C., & Carpenter, A. E.
- Affiliations
- Broad Institute (US)
- Recursion (United States) (US)
- Corresponding author
- Gibson, Christopher C
- Carpenter, Anne E
- Venue
- Nature Protocols
- Published
- 2016-08-25
- Volume
- 11
- Issue
- 9
- Pages
- 1757-1774
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- PMC
- PMC5223290
- OpenAlex
- W2498090354
- Cited by
- 1,090
- References
- 47
- Citations by year
- 2026: 180 · 2025: 205 · 2024: 191 · 2023: 143 · 2022: 93 · 2021: 98 · 2020: 81 · 2019: 44 · 2018: 30 · 2017: 20 · 2016: 4
- FWCI
- 20.62
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Protocols (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (1119830, 1148823, CAREER)
- University of Utah
- Broad Institute
- National Institutes of Health (NIH-R01, R44TR001197, GM089652, NIH R01 GM089652)
- National Institute of General Medical Sciences (R01GM089652)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Keywords
- Cell Painting, morphological profiling, high-content imaging, cell morphology, automated image analysis, chemical perturbation, gene perturbation, disease signatures, high-throughput imaging
- MeSH terms
- Fluorescent Dyes, Humans, Image Processing, Computer-Assisted, Staining and Labeling, Cell Line, Tumor, Cell Size, Cell Shape, Molecular Imaging
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