From Pixels to Phenotypes: Integrating Image-Based Profiling with Cell Health Data Improves Interpretability

Abstract

SUMMARYCell Painting assays generate morphological profiles that are versatile descriptors of biological systems and have been used to predictin vitroandin vivodrug effects. However, Cell Painting features are based on image statistics, and are, therefore, often not readily biologically interpretable. In this study, we introduce an approach that maps specific Cell Painting features into the BioMorph space using readouts from comprehensive Cell Health assays. We validated that the resulting BioMorph space effectively connected compounds not only with the morphological features associated with their bioactivity but with deeper insights into phenotypic characteristics and cellular processes associated with the given bioactivity. The BioMorph space revealed the mechanism of action for individual compounds, including dual-acting compounds such as emetine, an inhibitor of both protein synthesis and DNA replication. In summary, BioMorph space offers a more biologically relevant way to interpret cell morphological features from the Cell Painting assays and to generate hypotheses for experimental validation.GRAPHICAL ABSTRACTIN BRIEFSeal et al. used machine learning models and feature selection approaches to group cell morphological features from Cell Painting assays and to describe the shared role of these morphological features in various cell health phenotypes. The resulting BioMorph space improves the ability to understand the mechanism of action and toxicity of compounds and to generate hypotheses to guide future experiments.HIGHLIGHTSCombining Cell Painting and Cell Health imaging data defines the BioMorph space.BioMorph space allows detecting less common mechanisms for bioactive compounds.BioMorph space can generate MOA hypotheses to guide experimental validation.BioMorph space is more biologically relevant and interpretable than Cell Painting features.