GSE194386
Single-cell RNA-Seq of young, old and metformin-treated mouse muscle and adipose
Here we investigate the nature of cellular heterogeneity in the gene expression of aging and characterize the changes that metformin causes in mouse adipose and muscle at the single-cell resolution. The single cell transcriptomes of ~13,000 cells were captured using single-cell RNA-sequencing, resulting in gene expression data for 13 cell types from the visceral adipose tissue stromal-vascular fraction and ~5,000 cells from 12 cell types from the gastrocnemius muscle. The study design featured three groups of male C57BL/6J mice that corresponded to three-month-old controls (young), 18-month-old controls (old), and 18-month-olds treated with 1000 ppm (0.1% w/w) metformin for 6 weeks (treated). Our analyses demonstrate that metformin's age-associated changes results in the modulation of an old to young expression profile via a cell type-specific manner. This reversion is demonstrated by significant changes observed in cell-type proportions and the degree of cell-cell heterogeneity. Additionally, metformin is known to restore the dysregulation due to age in autophagy and immune response in adipose, hypoxia in muscle, and inflammatory responses in both tissues. In our data, we detect evidence of these processes at play where endothelial cells, macrophages, and stem/progenitor cells respond most effectively to metformin's gerotherapeutic response compared to other cell types. We further characterize metformin's role in reverting age-associated cell-type-specific shifts in the transcriptional space by investigating changes in gene expression distributions and gene regulatory network dynamics. Overall design: Gastrocnemius muscle and visceral adipose tissues were collected from 12 C57BL/6J mice belonging to 3 groups: young, old and metformin-treated (4 mice/group). Single-cells were captured from each of the tissues and scRNA-Seq was conducted in 13 cell types from adipose stromal vascular fraction and 12 cell types from muscle using the 10x Genomics Chromium platform.