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Cell Painting Gallery: an open resource for image-based profiling

Nature Methods (2024)Cite this article

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Image-based or morphological profiling is a rapidly expanding field wherein cells are ‘profiled’ by extracting hundreds to thousands of unbiased, quantitative features from images of cells that have been genetically or chemically perturbed. It is the least expensive high-dimensional profiling technique to date and offers single-cell resolution. The Cell Painting assay is the most popular imaged-based profiling assay; in it, six small-molecule dyes label eight cellular compartments and are imaged in five fluorescence channels (plus brightfield, if desired). Images are segmented and thousands of measurements are then made within the nucleus, cytoplasm and whole cell, describing quantitative traits such as size, shape, stain intensity and stain texture. First published in 2013 (ref. 1), the standard protocol was updated in 2016 and more recently in 2023 (ref. 2).

The field of bioimaging, like most scientific fields, is transitioning toward making data increasingly FAIR (findable, accessible, interoperable and reusable)3. In this vein, we have created the Cell Painting Gallery, a publicly available collection of Cell Painting datasets with granular dataset descriptions and comprehensive documentation, including access instructions, at https://github.com/broadinstitute/cellpainting-gallery. It is hosted by Amazon Web Services (AWS) on the Registry of Open Data, and anyone can access the data without requiring an AWS account. As of May 2024, the Cell Painting Gallery holds 688 TB of image and associated numerical data. It includes the largest publicly available Cell Painting dataset, in terms of perturbations tested (Joint Undertaking for Morphological Profiling, or JUMP), along with many other canonical datasets using Cell Painting and close derivatives (Table 1). Other sources of publicly available Cell Painting datasets include Recursion (https://RxRx.ai) and Image Data Resource (IDR; https://idr.openmicroscopy.org).

Table 1 Complete datasets available in the Cell Painting Gallery as of publication
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Data availability

All data are freely available in the Cell Painting Gallery, hosted by AWS on the Registry of Open Data. An AWS account is not necessary for accessing data. Documentation is provided at https://broadinstitute.github.io/cellpainting-gallery.

Code availability

The Cell Painting Gallery landing page is at https://github.com/broadinstitute/cellpainting-gallery. Cell Painting Gallery documentation is at https://broadinstitute.github.io/cellpainting-gallery. The Cell Painting Gallery data validator and other tools that interact with Cell Painting Gallery are available at https://github.com/broadinstitute/cpg.

References

  1. Gustafsdottir, S. M. et al. PLoS One 8, e80999 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Cimini, B. A. et al. Nat. Protoc. 18, 1981–2013 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Schmied, C. et al. Nat. Methods 21, 170–181 (2024).

    Article  CAS  PubMed  Google Scholar 

  4. Chandrasekaran, S. N., Ceulemans, H., Boyd, J. D. & Carpenter, A. E. Nat. Rev. Drug Discov. 20, 145–159 (2021).

    Article  CAS  PubMed  Google Scholar 

  5. Carreras-Puigvert, J. & Spjuth, O. Curr. Opin. Struct. Biol. 87, 102842 (2024).

    Article  CAS  PubMed  Google Scholar 

  6. Moore, J. et al. Nat. Methods 18, 1496–1498 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Moore, J. et al. Histochem. Cell Biol. 160, 223–251 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Weisbart, E. & Cimini, B. A. Nat. Methods 20, 1120–1121 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank E. Chu and N. Ragusa of AWS for assistance in getting the Cell Painting Gallery set up, along with members of the Cimini and Carpenter-Singh labs for their feedback on this project and manuscript. We also thank T. Treis and F. Theis for publicly sharing their workflow accessing gallery data. This study was supported by Calico Life Sciences LLC; Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc.; NIH P41 GM135019 (to B.A.C. and A.E.C,), NIH R35 GM122547 (to A.E.C.); and CZI grant DAF2020-225720 (grant https://doi.org/10.37921/977328pjvbca) from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation (funder https://doi.org/10.13039/100014989) (to B.A.C.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Imaging Platform Department, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Erin Weisbart, Ankur Kumar, John Arevalo, Anne E. Carpenter, Beth A. Cimini & Shantanu Singh

Authors
  1. Erin Weisbart
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  2. Ankur Kumar
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  3. John Arevalo
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  4. Anne E. Carpenter
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  5. Beth A. Cimini
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  6. Shantanu Singh
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Contributions

S.S., B.A.C. and A.E.C. conceived and supervised the project and revised the manuscript. A.K. and J.A. wrote data validation scripts and revised the manuscript. E.W. curated the data, wrote documentation and wrote the manuscript.

Corresponding authors

Correspondence to Erin Weisbart or Shantanu Singh.

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Competing interests

S.S. and A.E.C. serve as scientific advisors for companies that use image-based profiling and Cell Painting (A.E.C: Recursion, SyzOnc, Quiver Bioscience, S.S.: Waypoint Bio, Dewpoint Therapeutics, Deepcell) and receive honoraria for occasional talks at pharmaceutical and biotechnology companies. The other authors declare no competing interests.

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Nature Methods thanks Isabel Kemmer, Aastha Mathur, Ugis Sarkans and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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Weisbart, E., Kumar, A., Arevalo, J. et al. Cell Painting Gallery: an open resource for image-based profiling. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02399-z

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