Abstract Advances in transcriptomic technologies have progressively transformed the questions we can ask and answer about muscle stem cells (MuSCs) during aging. Early microarray and bulk RNA sequencing studies established foundational population-level signatures of aged MuSCs, including attenuation of myogenic and metabolic programs as well as induction of inflammatory and stress-associated transcription. However, these averaged readouts obscured cell-to-cell variability and rare functional states. The transition to single-cell and single-nucleus RNA sequencing marked a turning point by resolving MuSC heterogeneity and revealing that MuSC aging is not purely stochastic. Instead, aged MuSC pools show reproducible changes in state composition, delayed or altered myogenic lineage progression, and selective vulnerability of specific functional subsets. Emerging spatial transcriptomic approaches, although still limited by sensitivity and cell-type discrimination in muscle, are beginning to place these MuSC states into their native tissue context, directly linking transcriptional states, niche organization, and age-associated remodeling. In parallel, integrative multi-omic designs that pair transcriptomics with chromatin accessibility and metabolic measurements have strengthened mechanistic connections among age-associated gene programs, epigenetic remodeling, and metabolic state shifts. Finally, computational frameworks — including trajectory inference, dynamic modeling, and machine learning — are increasingly applied to high-dimensional transcriptomic data to predict aging trajectories and identify candidate rejuvenation targets. In this Perspective, we trace the evolution of transcriptomic technologies through the lens of MuSC aging and highlight how increasing resolution has reframed core models of MuSC decline and plasticity.
Kim et al. (Wed,) studied this question.