Muscle stem cells (MuSCs) normally exist in a non-cycling quiescent state, undergoing activation to fuel muscle regeneration following injury. It is known that aging is associated with diminished regenerative capacity of the MuSC pool but the mechanisms underlying this reduced regenerative capacity are unclear. Now, Andrew Brack and colleagues demonstrate that aging induces aberrant cell state transition kinetics in murine MuSCs. Using time-lapse imaging and transcriptomic analyses, the authors first demonstrate that young and aged MuSCs exhibit only minor transcriptional differences at a given point during the activation process, suggesting that the overall activation trajectory of aged MuSCs is similar to that of young MuSCs. By contrast, using RNA velocity inference from single cell RNA-seq, the researchers reveal that aged MuSCs exhibit delayed activation kinetics, becoming activated at a slower rate than their younger counterparts. The authors also apply machine learning to classify young and old MuSCs and, in doing so, provide an effective assay to assess the age of individual MuSCs. Moreover, their analyses of MuSC gene expression reveal heterogeneity in the activation process and highlight that MuSCs progress through activation stochastically, with some cells even moving ‘backwards’ through the process. Overall, these findings offer key insights into MuSC behaviour and provide a valuable data-rich resource for the community.
