GelMetrics: An Algorithm for Analyzing the Dynamics of Gel-Like Phase-Separated Condensates

Cellular compartments and organelles are essential for the spatial organization of biological matter. Recently, membraneless organelles like paraspeckles, stress granules, and Cajal bodies have garnered significant scientific interest due to their lack of membrane boundaries and crucial cellular functions. These organelles self-assemble through phase separation, a process in which a homogeneous solution separates into distinct phases. The phases most commonly encountered in cells are liquids and gels. Although various microscopy techniques exist to study phase-separated compartments, they are often inadequate for investigating the dynamics of gel-like condensates, where molecular motion occurs over tens of minutes rather than seconds. Here, we introduce the GelMetrics algorithm to quantitatively measure the dynamics of gel-like phase-separated structures by tracking their fluorescence signals over extended durations. First, we show using simulations that this method can identify molecular motion amidst measurement noise and estimate biophysical parameters such as the number of molecules and the average fluorescence of a single molecule exiting a condensate, providing insights into the dynamics of phase-separated organelles. Second, we validate our approach on data from synthetic RNA-protein (sRNP) granules in E. coli and in vitro , establishing its applicability both in vitro and in vivo . Finally, we demonstrate the biological meaning of our algorithm’s output by quantitatively determining how the presence of an intrinsically disordered region causes sRNP granules to exhibit a more brittle phase-behavior. Thus, not only does the proposed method fill a gap in analysis methods for gel-like condensates, but it also elucidates biophysical mechanisms, improving our understanding of both synthetic and natural systems.