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 Mosa, Michael J. Bolt, Demetrio Labate, Michael A. Mancini, Michael A. Mancini, Michael A. Mancini
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. Phenotypic profiling by high-throughput imaging can aid in the screening of perturbations in cell models, but most studies often overlook cell-to-cell variation of responses within samples/populations. Here, the authors present SPACe, an easy-to-deploy, open-source platform for analysis of single-cell image-based morphological profiles produced by Cell Painting.