Bioinformatic Workflow for the Analysis of Single-Nucleus RNA-Sequencing Data from Skeletal Muscle

Understanding cellular diversity within a tissue is essential to elucidate how distinct cell populations cooperate to maintain homeostasis, regenerate, and respond to aging or disease. Unlike bulk methods that measure averaged gene expression, single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling of the transcriptomes of individual cells, providing a powerful tool for analyzing cellular heterogeneity. However, its application in skeletal muscle remains limited to the mononucleated cellular fraction, as standard single-cell approaches cannot efficiently capture muscle fibers due to their large and multinucleated nature. Single-nucleus RNA sequencing (snRNA-seq), which profiles individual nuclei rather than intact cells, offers a more suitable and technically robust approach, enabling comprehensive characterization of the transcriptional landscape across the entire tissue. In this chapter, we present a workflow for the bioinformatic analysis of snRNA-seq data generated from mouse skeletal muscle using the 10x Genomics Chromium platform. The chapter focuses on computational processing using R software and the Seurat package, including quality control, normalization, dimensionality reduction, clustering, and cell-type annotation. By applying this pipeline, researchers can identify distinct muscle-resident cell populations and explore the complexity of skeletal muscle. The described framework also illustrates how to perform downstream analyses such as differential gene expression and cell-cell communication analysis.