Single-Cell RNAseq Reveals That Pancreatic β-Cells From Very Old Male Mice Have a Young Gene Signature

Abstract

Aging improves pancreatic β-cell function in mice. This is a surprising finding because aging is typically associated with functional decline. We performed single-cell RNA sequencing of β-cells from 3- and 26-month-old mice to explore how changes in gene expression contribute to improved function with age. The old mice were healthy and had reduced blood glucose levels and increased β-cell mass, which correlated to their body weight. β-Cells from young and old mice had similar transcriptome profiles. In fact, only 193 genes (0.89% of all detected genes) were significantly regulated (≥2-fold; false discovery rate < 0.01; normalized counts > 5). Of these, 183 were down-regulated and mainly associated with pathways regulating gene expression, cell cycle, cell death, and survival as well as cellular movement, function, and maintenance. Collectively our data show that β-cells from very old mice have transcriptome profiles similar to those of young mice. These data support previous findings that aging is not associated with reduced β-cell mass or functional β-cell decline in mice.

Pseudotemporal Ordering of Single Cells Reveals Metabolic Control of Postnatal β Cell Proliferation

“…This could indicate differences between rodent and human beta-cells, but could also be due to the specific time window covered in our study. We found that 34 genes with significantly lower expression in beta-cells from very old mice compared to 3-month-old mice (Xin et al, 2016) also decreased in expression in our maturation time course. Among these shared genes were the TFs Srf , Jun , Fos, Nr4a1, Fosl2 , and Fosb , suggesting that expression of these TFs continues to decrease as beta-cells age.…”

“…2B). To further validate the time ordering method, we projected published RNA-seq data from single beta-cells of 3-month-old mice (Xin et al, 2016) onto our pseudotemporal trajectory and found that the median of these cells correctly projected at the end of the pseudotime spectrum (see Suppl. Information).…”

“…To place our data into the context of findings from recently published single-cell data of beta-cells from juvenile and adult humans (Wang et al, 2016) as well as young and aged mice (Xin et al, 2016), we compared gene signatures identified in these studies to genes regulated in our pseudotemporal trajectory. Wang et al reported higher expression of alpha-cell lineage markers in juvenile compared to adult human beta-cells.…”

Heterogeneity of the Pancreatic Beta Cell

“…Three research groups have described heterogeneity in the beta cell population with particular gene signatures driving such differences (Baron et al, 2016; Muraro et al, 2016; Segerstolpe et al, 2016). However, Xin et al (2016b) were not able to detect such diverse cell types. Several reasons could account for these discrepancies.…”

“…To begin to lay the foundation for studying beta cells at the molecular level, two approaches have been undertaken: an unbiased analysis of gene expression through single cell RNA sequencing, and the separation of beta cells into groups that differ in their protein expression. Although there are caveats associated with single cell RNA sequencing approaches (Xin et al, 2016a), several studies have provided important new insights into beta cell biology (Bader et al, 2016; Baron et al, 2016; Dorrell et al, 2016; Li et al, 2016; Muraro et al, 2016; Segerstolpe et al, 2016; Wang et al, 2016; Xin et al, 2016b). The separation of cells based on protein expression has also resulted in the discovery of several molecular markers ranging from cell-surface proteins to intracellular molecules involved in metabolism that are differentially expressed in a beta cell subpopulation or denote a particular metabolic state ( Figure 1 ) (Pipeleers, 1987; Jetton and Magnuson, 1992; Kiekens et al, 1992; Giordano et al, 1993; Heimberg et al, 1993; Jorns et al, 1999; Guz et al, 2001; Johnson et al, 2006; Bosco et al, 2007; Hermann et al, 2007; Saisho et al, 2008; Wang et al, 2008, 2016; Karaca et al, 2009; Szabat et al, 2009; Smukler et al, 2011; Katsuta et al, 2012; Bader et al, 2016; Beamish et al, 2016; Dorrell et al, 2016).…”