In Development (Cambridge, England)
Murine muscle stem cells (MuSCs) experience a transition from quiescence to activation that is required for regeneration, but it remains unknown if the trajectory and dynamics of activation change with age. Here, we use timelapse imaging and single cell RNA-seq to measure activation trajectories and rates in young and aged MuSCs. We find that the activation trajectory is conserved in aged cells, and develop effective machine learning classifiers for cell age. Using cell behavior analysis and RNA velocity, we find that activation kinetics are delayed in aged MuSCs, suggesting that changes in stem cell dynamics may contribute to impaired stem cell function with age. Intriguingly, we also find that stem cell activation appears to be a random walk like process, with frequent reversals, rather than a continuous, linear progression. These results support a view of the aged stem cell phenotype as a combination of differences in the location of stable cell states and differences in transition rates between them.
Kimmel Jacob C, Hwang Ara B, Scaramozza Annarita, Marshall Wallace F, Brack Andrew S
Aging, Muscle stem cell, Single cell RNA-seq, State transition, Stem cell activation, Timelapse imaging