Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells

Genome Res. 2015 Dec;25(12):1860-72. doi: 10.1101/gr.192237.115. Epub 2015 Oct 1.

Abstract

Both intrinsic cell state changes and variations in the composition of stem cell populations have been implicated as contributors to aging. We used single-cell RNA-seq to dissect variability in hematopoietic stem cell (HSC) and hematopoietic progenitor cell populations from young and old mice from two strains. We found that cell cycle dominates the variability within each population and that there is a lower frequency of cells in the G1 phase among old compared with young long-term HSCs, suggesting that they traverse through G1 faster. Moreover, transcriptional changes in HSCs during aging are inversely related to those upon HSC differentiation, such that old short-term (ST) HSCs resemble young long-term (LT-HSCs), suggesting that they exist in a less differentiated state. Our results indicate both compositional changes and intrinsic, population-wide changes with age and are consistent with a model where a relationship between cell cycle progression and self-renewal versus differentiation of HSCs is affected by aging and may contribute to the functional decline of old HSCs.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Age Factors
  • Animals
  • Biomarkers
  • Cell Cycle / genetics*
  • Cell Differentiation / genetics*
  • Cellular Senescence / genetics*
  • Cluster Analysis
  • Computational Biology / methods
  • Female
  • Gene Expression Profiling
  • Gene Expression Regulation*
  • Hematopoietic Stem Cells / cytology*
  • Hematopoietic Stem Cells / metabolism*
  • High-Throughput Nucleotide Sequencing
  • Mice
  • Models, Biological
  • Multipotent Stem Cells / cytology
  • Multipotent Stem Cells / metabolism
  • Organ Specificity / genetics
  • Phenotype
  • Sequence Analysis, RNA
  • Single-Cell Analysis
  • Transcription, Genetic
  • Transcriptome

Substances

  • Biomarkers

Associated data

  • GEO/GSE59114