PARSHO: A Pipeline for Aggregate and Regional Segmentation using High-throughput Optical analysis (Supplementary Material)

Fluorescent puncta analysis is a central experimental approach for investigating protein quality control, autophagy, and protein aggregation in cells, with broad relevance to disease mechanisms including neurodegeneration and myopathies. However, its widespread use is hindered by a reliance on manual quantification, which introduces observer bias, limits reproducibility, and prevents scalable, high-throughput analysis. Existing automated solutions only partially address these challenges, often lacking robust cell segmentation, flexibility in integrating external masks, and ease of use. Furthermore, many of these tools are proprietary, poorly maintained, or provide limited quantitative outputs, restricting their adoption across diverse experimental settings. To address these limitations, we present PARSHO, a user-friendly and versatile computational resource for reproducible, high-throughput analysis of fluorescent puncta. PARSHO integrates state-of-the-art segmentation approaches with flexible input options, including externally generated masks and reference-guided segmentation, enabling reliable and adaptable analysis across a wide range of imaging conditions. By providing a comprehensive set of quantitative metrics and a plug-and-play interface accessible to non-expert users, PARSHO meets a critical need within the community for standardized, scalable, and accessible puncta analysis.