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

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 selfrenewal versus differentiation of HSCs is affected by aging and may contribute to the functional decline of old HSCs.

Global delay in nascent strand DNA methylation

“…Detectable transcript were defined as transcripts with (TPM > 1). After removing apoptotic cells and background differentiation, cells were ordered according progress in the cell cycle as done previously 41 . Expression values displayed in Supplementary Fig.…”

A single-cell survey of the small intestinal epithelium

“…1b). We identified proliferating cells using a cell-cycle signature 12 . The enteroendocrine, Paneth, goblet, stem and tuft cells were each represented by a single distinct cluster (Fig.…”

“…To obtain a score for a specific set of n genes in a given cell, a ‘background’ gene set was defined to control for differences in sequencing coverage and library complexity between cells in a manner similar to 12 . The background gene set was selected to be similar to the genes of interest in terms of expression level.…”

Single-cell transcriptomics reveals specific RNA editing signatures in the human brain

“…The absence of significant correlation between the expression levels of ADARs and RNA editing levels or AEI values in single cells may be due to specific regulation mechanisms acting on ADAR enzymes or to a biased detection of gene expression levels. Indeed, many factors, such as cell cycle or cell size, may affect the correct quantification of gene expression levels, especially in cases of low expressed genes (Wu et al 2014;Kowalczyk et al 2015;Padovan-Merhar et al 2015). Additional experimental evidence will be required to clarify this aspect.…”