Abstract
Skeletal muscle metabolism and meat quality are usually interpreted through myofiber type composition, fiber size, and histochemical state. Whether the spatial topology of myofibers constitutes an independent phenotype layer remains poorly quantified. Here, using succinate dehydrogenase (SDH)-stained cross-sectional images from multiple pig breeds and more than 40 skeletal muscle sites, we established a multiscale spatial phenotyping framework for type I myofibers. To enable scalable instance-level quantification, we developed an improved Mask R-CNN segmentation workflow, trained with SAM-assisted and manually corrected annotations, and optimized for dense, small, irregular, and variably stained muscle fibers. This framework captures single-fiber morphology and staining, local contact, connected-cluster architecture, diffusion-ring neighborhoods, and global tissue configuration. Type I myofiber abundance was separable from spatial organization capacity: some muscles formed stronger-than-expected local connectivity and clustering despite modest type I fiber abundance. Spatial transcriptomic integration further showed that slow-oxidative genes and functional modules were non-randomly organized and formed local high-expression domains in type I fiber-enriched, high-contact neighborhoods. Across muscle sites and breeds, skeletal muscles displayed distinguishable type I fiber spatial topology identities, which were remodeled by breed background in a muscle-specific manner. In samples with matched meat-quality measurements, spatial topology features improved the explanation of pH, meat color, conductivity, and intramuscular fat. These results suggest that skeletal muscle should be modeled as a spatially organized tissue system rather than a simple mixture of fiber types.