Abstract
Inflammasomes are protein complexes that mediate innate immune responses whose dysregulation has been linked to a spectrum of acute and chronic human conditions, which dictates therapeutic development that is aligned with disease variability. We designed a scalable, physiologic high-content imaging assay in human PBMCs that we analyzed using a combination of machine-learning and cell biology methods. This resulted in a set of biologically interpretable readouts that can resolve a spectrum of cellular states associated with inflammasome activation and inhibition. These methods were applied to a phenotypic screen that surfaced mechanistically distinct inflammasome inhibitors from an annotated 12,000 compound library. A set of over 100 inhibitors, including an array of Raf-pathway inhibitors, were validated in downstream functional assays. This approach demonstrates how complementary machine learning-based methods can be used to generate profiles of cellular states associated with different stages of complex biological pathways and yield compound and target discovery.