Dystrophinopathies are X-linked, recessively inherited diseases, caused by pathogenic variants in the DMD gene leading to a partial or complete loss of the dystrophin protein. Duchenne and Becker muscular dystrophy (DMD and BMD) are the most prevalent forms. Dystrophin provides a crucial link between the intracellular cytoskeleton and the extracellular matrix. In DMD, the absence of dystrophin results in severe disruption of this linkage, leading to progressive muscle wasting, fatty replacement, fibrosis, and progressive loss of function. Conversely, in BMD, dystrophin is partially functional and expression is reduced leading to slower muscle degeneration and an overall milder phenotype. To better understand the cell types and genes contributing to the histopathological tissue changes, we applied Visium spatial transcriptomics (ST) on skeletal muscle biopsies of DMD and BMD patients and healthy controls (n=4 per condition). We directly linked whole transcriptome data to histological images, allowing us to estimate tissue type composition (muscle, fibrotic, fat) based on images and gene expression. We assessed the proportions of various cell types and their spatial distribution across samples using a deconvolution strategy with single-nuclei RNA-sequencing data. We discovered genes enriched in fat patches (e.g., FABP4, PLIN1, LPL, FTL) and identified cell types such as fibroadipogenic progenitor cells (FAPs) in areas of active pathology, such as tissue fibrosis. By analyzing ligand-receptor pair expression data, we highlighted cell-cell communications that contribute to fibrotic and/or adipogenic lesions. Additionally, by examining gene expression gradients in regions where muscle and fat are adjacent, we proposed genes that can identify muscle areas destined to become fat. Overall, this approach enabled us to map molecular changes to tissue alterations in clinical samples while preserving spatial context, which is crucial for better understanding histopathology and developing effective therapeutic interventions.