SPACe: An Open-Source, Single-Cell Analysis of Cell Painting Data

Authors

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
Michael A Mancini

Publication Date

11-23-2024

Journal

Nature Communications

DOI

10.1038/s41467-024-54264-4

PMID

39580445

PMCID

PMC11585637

PubMedCentral® Posted Date

11-23-2024

PubMedCentral® Full Text Version

Post-Print

Published Open-Access

yes

Keywords

Single-Cell Analysis, Humans, Software, Image Processing, Computer-Assisted, Reproducibility of Results, Cell Line, Phenotype, High-throughput screening, Software

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.

Comments

Associated Data