A Computational Framework for Morphology-Based Analysis of Biomolecular Condensate Dynamics

Biomolecular condensation has been widely studied in recent years. However, advanced analytical techniques for characterizing phase-separated systems are limited. We developed a Python-based computational pipeline (DOI: 10.1038/s41598-025-09148-y), integrated into a user-friendly Jupyter notebook, to quantify morphological heterogeneity in biomolecular condensates. Our approach leverages advanced morphological descriptors, such as the Euler characteristic number and fractal dimension, to capture subtle spatiotemporal features of biomolecular condensates. In addition, an intuitive and interactive tool for creating phase diagrams was incorporated for real-time summarizing the quantifications. Following the pipeline, we applied robust statistical analyses, beyond conventional descriptors, to account for asymmetric data distributions, and performed multivariate analysis using interactive principal component analysis (PCA) visualizations complemented by correlation and scree plots. We applied the proposed statistical framework to analyze the condensation behavior of the neurodevelopmental protein DDX3X, which forms spherical droplets in vitro. The R376C mutant, associated with neurodevelopmental disorders, progressively formed elongated aggregates over time. Our pipeline integrates live plotting, phase diagram analysis, and high-throughput automation, enabling detailed investigation of condensate assembly dynamics and promoting the standardization of morphological descriptor analysis in biomolecular condensate research.