A computational pipeline for image-based statistical analysis of biomolecular condensates dynamics using
Ivan Rosa E Silva1, Guilherme Gurian Dariani2, Felipe Zanghelini Benevenutti3
1Brazilian Biosciences National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, Brazil. ivan.silva@lnbio.cnpem.br.
Scientific Reports
|July 29, 2025
Summary
Researchers developed a computational pipeline to analyze biomolecular condensate morphology. This tool quantifies heterogeneity using advanced features and statistical methods, aiding the study of proteins like DDX3X and neurodevelopmental disorders.
Area of Science:
- Biochemistry
- Computational Biology
- Biophysics
Background:
- Biomolecular condensation is crucial for cellular functions.
- Advanced analytical methods for characterizing phase-separated biomolecular systems are limited.
- Morphological heterogeneity in condensates requires sophisticated quantification.
Purpose of the Study:
- To develop a user-friendly computational pipeline for quantifying morphological heterogeneity in biomolecular condensates.
- To implement advanced morphological and statistical analyses for detailed characterization.
- To apply the pipeline to study DDX3X protein condensation and the impact of mutations.
Main Methods:
- Developed a Python-based computational pipeline integrated with Jupyter notebooks.
- Utilized advanced morphological features like Euler characteristic number and fractal dimension.
- Incorporated statistical analyses including skewness, kurtosis, and principal component analysis (PCA).
Main Results:
- The pipeline successfully quantified morphological heterogeneity in biomolecular condensates.
- PEG3350 altered condensate morphology, while the DDX3X R376C mutant formed elongated aggregates.
- Demonstrated live plotting, phase diagram analysis, and high-throughput automation capabilities.
Conclusions:
- The developed pipeline offers a robust and user-friendly platform for analyzing biomolecular condensate morphology.
- Advanced statistical and morphological descriptors provide deeper insights into condensate dynamics.
- This tool advances the standardization of morphological analysis for biomolecular condensates and aids in studying disease-related mutations.
