A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

TL;DR

This review summarizes a decade of methodological and application advancements in Cell Painting, highlighting improvements in protocols, machine learning integration, and its impact on understanding disease mechanisms and drug discovery.

Contribution

It provides a comprehensive overview of the evolution of Cell Painting techniques and their expanding role in biological research and drug development over ten years.

Findings

Machine learning now surpasses classical methods in data analysis.

Cell Painting effectively elucidates mechanisms of action and toxicity.

Methodological improvements have enhanced cellular response detection.

Abstract

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other -omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded the ability of Cell Painting to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.