Background

Skeletal muscle is the largest metabolic organ in the human body, driving glucose metabolism and insulin response. Many genetic variants that increase the risk of cardiometabolic diseases, such as type 2 diabetes or dyslipidemia, are deeply intertwined with metabolic pathways in skeletal muscle. While Genome-Wide Association Studies (GWAS) have identified thousands of disease-risk loci, the molecular pathways through which non-coding variants lead to disease remain unclear.

Previous transcriptome analyses and genomic studies have relied on bulk analysis methods that grind entire tissue lysates. In muscle tissue, which is a complex mixture of muscle fibers, satellite cells, fibro-adipogenic progenitors, endothelial cells, and immune cells, it was nearly impossible to detect subtle gene regulation abnormalities specific to each cell type or physiological state. The technical difficulty of applying conventional single-cell isolation techniques due to the structural uniqueness of muscle fibers as multinucleated cells also posed a barrier. There was an urgent need for a high-resolution molecular map to reveal in which cell-nuclear states disease-associated variants cause aberrant regulation.

Key Findings

Researchers constructed an integrated multi-omics reference map by simultaneously performing single-nucleus chromatin accessibility analysis (snATAC-seq) and single-nucleus transcriptome analysis (snRNA-seq) based on isolated single nuclei from human skeletal muscle tissue. After precisely isolating myofiber subtypes (Type I, Type IIa, Type IIx) and the stromal cell nuclei constituting the microenvironment, they compared chromatin accessibility and transcriptional activity at the single-nucleus level.

Systematic mapping of single-nucleus expression quantitative trait loci (eQTL) and chromatin accessibility quantitative trait loci (caQTL) revealed a distinct group of regulatory variants that function only in specific cellular states and physiological contexts. When analyzing the entire tissue, thousands of cell-specific QTLs that were obscured by average signals came to light. By combining GWAS statistical signals for metabolic diseases with colocalization analysis of these QTL signals, the causal gene regulatory circuits underlying disease susceptibility were revealed. The mechanism by which specific risk alleles selectively block chromatin accessibility and distort gene expression at distal regulatory element sites involved in insulin signaling and glucose transporter (GLUT4) translocation pathways was directly confirmed.

Significance and Outlook

This study clearly demonstrates that non-coding genetic variants causing cardiometabolic diseases induce pathological transcriptional dysfunction only within specific cell types and functional states inside skeletal muscle. By narrowing the horizon of target discovery from the gene level to cell-state-specific regulatory regions, a foundation has been laid to significantly reduce failure rates in drug candidate selection.

However, since the map was constructed primarily using static tissue biopsy samples, follow-up studies are needed to track how the chromatin environment is restructured during dynamic metabolic stimuli, such as exercise or acute dietary changes. Expanding the ethnic diversity of skeletal muscle biopsy samples and standardizing processes to lower the analysis costs of the single-nucleus multi-omics pipeline are also challenges to be addressed in the future.