Megowan, H. G. | bioRxiv Science

bioRxiv2026

A semi-automated pipeline for quantitation of Pax7+, myonuclei, and cross-sectional area by fiber type

Manual analysis of skeletal muscle cross-sections is time-consuming and subject to error and user bias. To overcome these limitations, we developed and validated a semi-automated, quantitative, and reproducible image-analysis pipeline specifically tailored to quantify Pax7+ satellite cells, myonuclei, and cross-sectional area by fiber type. The workflow combines FIJI/ImageJ-based image preprocessing with CellProfiler, Cellpose, and a custom Python script to process and analyze immunohistological images of muscle tissue cross-sections. Outcomes include Pax7+ satellite cells and myonuclei quantified per fiber by fiber type, along with cross-sectional area, perimeter, and fiber type classification. This semi-automated approach provides a robust and efficient platform for high-throughput analysis of muscle tissue cross-sections from large datasets. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/729866v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@e85bfdorg.highwire.dtl.DTLVardef@ef75e0org.highwire.dtl.DTLVardef@123e461org.highwire.dtl.DTLVardef@166a304_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2025

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. 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.

cell biology↗