Abstract
AbstractMurine muscle stem cells (MuSCs) experience a transition from quiescence to activation that is required for regeneration, but it remains unclear if the transition states and rates of activation are uniform across cells, or how features of this process may 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.Summary StatementWe find that aged muscle stem cells display delayed activation dynamics, but retain a youthful activation trajectory, suggesting that changes to cell state dynamics may contribute to aging pathology.
Publisher
Cold Spring Harbor Laboratory
Cited by
4 articles.
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