A semi-automated pipeline for morphological analysis of myonuclei along single muscle fibers

Manual quantitation of skeletal muscle myonuclear number, spatial orientation, and morphology is time-consuming and subject to error and bias. To overcome these limitations, we developed and validated a semi-automated, quantitative, and reproducible image-analysis pipeline. The workflow combines FIJI-based preprocessing with custom Python scripts to process immunohistological images of individual muscle fibers, enabling high resolution and scalable quantification of nuclei. The analyses incorporate morphometric parameters including nuclear position, shape, and three-dimensional orientation, as well as centroid-to-skeleton distance and nearest-neighbor relationships to capture spatial patterns of myonuclear organization along the fiber. Outputs include per-fiber and biopsy-level summaries integrated with IMARIS metrics. This semi-automated approach provides a robust and efficient platform for high-throughput analysis of myonuclear number and structural features across large single fiber datasets.