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

By Monika S. Kowalczyk, I. Tirosh, D. Heckl, T. N. Rao, Atray C Dixit, Brian J. Haas, R. Schneider, A. Wagers, Benjamin L. Ebert, Aviv Regev. Genome Research, 2015

666 citations recorded by the source corpus.

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.

Subfields: Hematology, Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b798053a5c1b4c1a5733072d829c2d9bdffec136

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Immunology

In Molecular Biology

Citations in the corpus

The Human Cell Atlas (2017, positive): “…studies have shown that cyclical processes and transient responses—from the cell cycle (Buettner et al., 2015; Gut et al., 2015; Kafri et al., 2013; Kowalczyk et al., 2015; Macosko et al., 2015; Proserpio et al., 2016; Tirosh et al., 2016a) to the response of immune cells to pathogen components…”
Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq (2017, background): “S8A) (11, 12, 14) and found that in both IDH-A and IDH-O, only a small proportion of cells are proliferating (~4% on average in our cohort) and that there is an inverse correlation between proliferation and differentiation (Fig.”
Revealing the vectors of cellular identity with single-cell genomics (2016, methodology): “In another example, whereas a cell cycle phase may have invariant characteristics across systems 30,35,102 , the ability of a cell to enter the cell cycle and the duration of the phase can depend on cell type and can influence other temporal processes like differentiation.”
SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species (2018, background): “, 2017), and to determine how perturbations such as age, pathology, or genetic variation impact cell composition and state (Haber et al., 2017; Kowalczyk et al., 2015; Park et al., 2018; Patel et al., 2014).”