Duchenne muscular dystrophy is caused by mutations in the DMD gene, leading to lack of dystrophin. Chronic muscle damage eventually leads to histological alterations in skeletal muscles. The identification of genes and cell types driving tissue remodeling is a key step to developing effective therapies. Here we use spatial transcriptomics in two Duchenne muscular dystrophy mouse models differing in disease severity to identify gene expression signatures underlying skeletal muscle pathology and to directly link gene expression to muscle histology. We perform deconvolution analysis to identify cell types contributing to histological alterations. We show increased expression of specific genes in areas of muscle regeneration (Myl4, Sparc, Hspg2), fibrosis (Vim, Fn1, Thbs4) and calcification (Bgn, Ctsk, Spp1). These findings are confirmed by smFISH. Finally, we use differentiation dynamic analysis in the D2-mdx muscle to identify muscle fibers in the present state that are predicted to become affected in the future state.

Contractile properties of myofibers are dictated by the abundance of myosin heavy chain (MyHC) isoforms. MyHC composition designates muscle function, and its alterations could unravel differential muscle involvement in muscular dystrophies and aging. Current analyses are limited to visual assessments in which myofibers expressing multiple MyHC isoforms are prone to misclassification. As a result, complex patterns and subtle alterations are unidentified. We developed a high-throughput, data-driven myofiber analysis to quantitatively describe the variations in myofibers across the muscle. We investigated alterations in myofiber composition between genotypes, 2 muscles, and 2 age groups. We show that this analysis facilitates the discovery of complex myofiber compositions and its dependency on age, muscle type, and genetic conditions.—Raz, V., Raz, Y., van de Vijver, D., Bindellini, D., van Putten, M., van den Akker, E. B. High-throughput data-driven analysis of myofiber composition reveals muscle-specific disease and age-associated patterns. FASEB J. 33, 4046–4053 (2019). www.fasebj.org.