Abstract

Membraneless organelles (MLOs) are liquid-like compartments that organize cellular functions through liquid-liquid phase separation of proteins and RNA. Their regulation is crucial for RNA metabolism, stress response, and signaling, yet leveraging their full spatial and quantitative diversity for phenotypic screening remains challenging. Here, we present processing body (PB)-scope, an unsupervised deep-learning framework for imaging-based screening of PBs, a representative MLO. The model was trained on >400,000 single-cell confocal images from a colon cancer cell line treated with 280 compounds. PB-scope enabled precise drug classification based on multiple PB features, including number, size, and spatial distribution. This approach uncovered phenotypic patterns that were previously obscured by the subtle and dynamic nature of PBs and highlighted a set of compounds that converge on Janus kinase (JAK) signaling as a regulator of PB dynamics. PB-scope is readily extensible to other MLOs, offering broad applicability in this emerging field of cell biology. Natural sciences; Biological sciences; Biochemistry; Cell biology; Computational Science; Image analysis; Drug Screening