Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells
Kowalczyk MS, Tirosh I, Heckl D, Rao TN, Dixit A, Haas BJ, Schneider RK, Wagers AJ, Ebert BL, Regev A. 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.
Contact: monika@broadinstitute.org
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.
Single-cell RNA-seq of young and old HSCs. (A) Overview of experimental design. (B,C) Sorting strategy for isolating LT-HSCs (LSK CD48−CD150+), ST-HSCs (LSK CD48−CD150−), and MPPs (LSK CD48+CD150−) from young (B) and old (C) C57BL/6 mice. (D,E) LT-HSC compartment expands during aging. Shown are frequencies of LT-HSC, ST-HSC, and MPPs (x-axis) in young (black) and old (white) C57BL/6 mice as a percentage of bone marrow (BM; D) or stem cell compartment (lineage− SCA1+KIT+, LSK; E). Statistically significant differences are as follows: (**) P < 0.01, (*) P < 0.05; n = 8–10. (F) Single-cell RNA-seq recapitulates population RNA-seq. Shown are expression levels for all genes calculated from RNA-seq of a population of young LT-HSCs (x-axis) and by averaging expression levels from approximately 200 single young LT-HSCs (y-axis). The Pearson correlation coefficient (r = 0.9) is denoted. Gray scale bar indicates gene density. (G) Heatmap of Pearson correlation coefficients (r; color bar) between pairs of RNA-seq profiles of populations (columns) and matching averaged single-cell data (rows) from C57BL/6. (H) RNA-seq coverage of known cell surface markers in representative cells from young C57BL/6 mice (plot generated by the Integrative Genome Viewer 2.3) (Robinson et al. 2011; Thorvaldsdottir et al. 2013).
