An Image Analysis Pipeline for Quantifying the Features of Fluorescently-Labeled Biomolecular Condensates in Cells

Biomolecular condensates are cellular organelles formed through liquid-liquid phase separation (LLPS) that play critical roles in cellular functions including signaling, transcription, translation, and stress response. Importantly, condensate misregulation is associated with human diseases, including neurodegeneration and cancer among others. When condensate-forming biomolecules are fluorescently-labeled and examined with fluorescence microscopy they appear as illuminated foci, or puncta, in cells. Puncta features such as number, volume, shape, location, and concentration of biomolecular species within them are influenced by the thermodynamics of biomolecular interactions that underlie LLPS. Quantification of puncta features enables evaluation of the thermodynamic driving force for LLPS and facilitates quantitative comparisons of puncta formed under different cellular conditions or by different biomolecules. Our work on nucleoporin 98 (NUP98) fusion oncoproteins (FOs) associated with pediatric leukemia inspired us to develop an objective and reliable computational approach for such analyses. The NUP98-HOXA9 FO forms hundreds of punctate transcriptional condensates in cells, leading to hematopoietic cell transformation and leukemogenesis. To quantify the features of these puncta and derive the associated thermodynamic parameters, we developed a live-cell fluorescence microscopy image processing pipeline based on existing methodologies and open-source tools. The pipeline quantifies the numbers and volumes of puncta and fluorescence intensities of the fluorescently-labeled biomolecule(s) within them and generates reports of their features for hundreds of cells. Using a standard curve of fluorescence intensity versus protein concentration, the pipeline determines the apparent molar concentration of fluorescently-labeled biomolecules within and outside of puncta and calculates the partition coefficient (K_p) and Gibbs free energy of transfer (ΔG_Tr), which quantify the favorability of a labeled biomolecule partitioning into puncta. In addition, we provide a library of R functions for statistical analysis of the extracted measurements for certain experimental designs. The source code, analysis notebooks, and test data for the Punctatools pipeline are available on GitHub: https://github.com/stjude/punctatools. Here, we provide a protocol for applying our Punctatools pipeline to extract puncta features from fluorescence microscopy images of cells.