SPACe: an open-source, single-cell analysis of Cell Painting data

Fabio Stossi (), Pankaj K. Singh, Michela Marini, Kazem Safari, Adam T. Szafran, Alejandra Rivera Tostado, Christopher D. Candler, Maureen G. Mancini, Elina A. Mosa, Michael J. Bolt, Demetrio Labate and Michael A. Mancini ()
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Fabio Stossi: Baylor College of Medicine
Pankaj K. Singh: GCC Center for Advanced Microscopy and Image Informatics
Michela Marini: GCC Center for Advanced Microscopy and Image Informatics
Kazem Safari: GCC Center for Advanced Microscopy and Image Informatics
Adam T. Szafran: Baylor College of Medicine
Alejandra Rivera Tostado: Baylor College of Medicine
Christopher D. Candler: Baylor College of Medicine
Maureen G. Mancini: Baylor College of Medicine
Elina A. Mosa: Baylor College of Medicine
Michael J. Bolt: Baylor College of Medicine
Demetrio Labate: GCC Center for Advanced Microscopy and Image Informatics
Michael A. Mancini: Baylor College of Medicine

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Phenotypic profiling by high throughput microscopy, including Cell Painting, has become a leading tool for screening large sets of perturbations in cellular models. To efficiently analyze this big data, available open-source software requires computational resources usually not available to most laboratories. In addition, the cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused. We introduce SPACe (Swift Phenotypic Analysis of Cells), an open-source platform for analysis of single-cell image-based morphological profiles produced by Cell Painting. We highlight several advantages of SPACe, including processing speed, accuracy in mechanism of action recognition, reproducibility across biological replicates, applicability to multiple models, sensitivity to variable cell-to-cell responses, and biological interpretability to explain image-based features. We illustrate SPACe in a defined screening campaign of cell metabolism small-molecule inhibitors tested in seven cell lines to highlight the importance of analyzing perturbations across models.

Date: 2024
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DOI: 10.1038/s41467-024-54264-4

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