FiNuTyper: an automated deep learning-based platform for simultaneous fiber and nucleus type analysis in human skeletal muscle

Summary While manual quantification is still considered the gold standard for skeletal muscle histological analysis, it is time-consuming and prone to investigator bias. We assembled an automated image analysis pipeline, FiNuTyper (Fiber and Nucleus Typer), from recently developed deep learning-based image segmentation methods, optimized for unbiased evaluation of fresh and postmortem human skeletal muscle. We validated and utilized SERCA1 and SERCA2 as type-specific myonucleus and myofiber markers. Parameters including myonuclei per fiber, myonuclear domain, central myonuclei per fiber, and grouped myofiber ratio were determined in a fiber type-specific manner, revealing a large degree of gender- and muscle-related heterogeneity. Our platform was also tested on pathological muscle tissue (ALS) and adapted for the detection of other resident cell types (leukocytes, satellite cells, capillary endothelium). In summary, we present an automated image analysis tool for the simultaneous quantification of myofiber and myonuclear types, to characterize the composition of healthy and diseased human skeletal muscle. Highlights A deep learning-based automated platform for skeletal muscle microscopic analysis High-fidelity identification and characterization of myonuclei and myofibers Validation of SERCA1 and SERCA2 as markers for myofiber and myonuclear subtypes Characterization of healthy and pathological human skeletal muscle tissue features Adaptations provided for studies on other resident cell types like satellite cells eTOC Blurb An automated platform for unbiased analysis of skeletal muscle immunohistochemical images, focusing on type-specific myofiber-myonucleus relationships, facilitating high-throughput studies of healthy and diseased tissues.