  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.
