Mapping high resolution, multidimensional phase diagrams of physiological protein condensates

Abstract

Biomolecular condensates are membraneless compartments, crucial for organising and regulating diverse cellular processes. Current approaches to study condensate biology either use simplified recombinant protein systems with limited physiological relevance, or complex live-cell models with restricted experimental control and scalability. Here, we present ExVivo PhaseScan, a droplet microfluidics platform that couples mammalian lysate-based reconstitution with scalable analysis to generate high-resolution phase diagrams of physiological protein condensates. We apply this approach to study two complex multicomponent condensate systems, stress granules and nucleoli, and dissect the physicochemical interactions that influence their stability. We further developed a machine learning pipeline to analyse condensate morphology which we use to reveal how mutations in the ALS-linked protein FUS remodels condensate phase landscapes. We identify liquid-to-solid transitions of mutant FUS within stress granules and nucleoli, and show that these transitions can be reversed by RNA aptamer-based interventions. Together, these findings establish ExVivo PhaseScan as a versatile tool for dissecting the physicochemical and pathological regulation of condensates, with potential to inform therapeutic strategies for diseases driven by aberrant phase transitions.