A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay

• Published in: Gigascience Vol. 6; no. 12; pp. 1 - 5
• Main Authors: Bray, Mark-Anthony, Gustafsdottir, Sigrun M, Rohban, Mohammad H, Singh, Shantanu, Ljosa, Vebjorn, Sokolnicki, Katherine L, Bittker, Joshua A, Bodycombe, Nicole E, Dančík, Vlado, Hasaka, Thomas P, Hon, Cindy S, Kemp, Melissa M, Li, Kejie, Walpita, Deepika, Wawer, Mathias J, Golub, Todd R, Schreiber, Stuart L, Clemons, Paul A, Shamji, Alykhan F, Carpenter, Anne E
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.12.2017
• Subjects: Algorithms; Annotations; Cell Line; Cells - drug effects; Cells - ultrastructure; Computer applications; Data Note; Datasets; Drug development; Fluorescence; Humans; Image acquisition; Image analysis; Image processing; Image Processing, Computer-Assisted; Libraries; Microscopy; Morphology; Perturbation; Phenotypes; Small Molecule Libraries; Target recognition; image-based screening; small-molecule library; U2OS; cellular morphology; high-content screening; phenotypic profiling
• ISSN: 2047-217X; 2047-217X
• DOI: 10.1093/gigascience/giw014

Abstract Background Large-scale image sets acquired by automated microscopy of perturbed samples enable a detailed comparison of cell states induced by each perturbation, such as a small molecule from a diverse library. Highly multiplexed measurements of cellular morphology can be extracted from each image and subsequently mined for a number of applications. Findings This microscopy dataset includes 919 265 five-channel fields of view, representing 30 616 tested compounds, available at “The Cell Image Library” (CIL) repository. It also includes data files containing morphological features derived from each cell in each image, both at the single-cell level and population-averaged (i.e., per-well) level; the image analysis workflows that generated the morphological features are also provided. Quality-control metrics are provided as metadata, indicating fields of view that are out-of-focus or containing highly fluorescent material or debris. Lastly, chemical annotations are supplied for the compound treatments applied. Conclusions Because computational algorithms and methods for handling single-cell morphological measurements are not yet routine, the dataset serves as a useful resource for the wider scientific community applying morphological (image-based) profiling. The dataset can be mined for many purposes, including small-molecule library enrichment and chemical mechanism-of-action studies, such as target identification. Integration with genetically perturbed datasets could enable identification of small-molecule mimetics of particular disease- or gene-related phenotypes that could be useful as probes or potential starting points for development of future therapeutics.