Dataset Information

Predicting age in single cells and low coverage DNA methylation data [RRBS]

ABSTRACT: Ageing is the accumulation of changes and overall decline of the function of cells, organs and organisms over time. At the molecular and cellular level, the concept of biological age has been established and novel biomarkers of biological age have been identified, notably epigenetic DNA-methylation based clocks. With the emergence of single-cell DNA methylation profiling methods, the possibility to study biological age of individual cells has been proposed, and a first proof-of-concept study, based on limited single cell datasets mostly from early developmental origin, indicated the feasibility and relevance of this approach to better understand organismal changes and cellular ageing heterogeneity. Here, we generated a large single-cell DNA methylation and matched transcriptome dataset from mouse peripheral blood samples, spanning a broad range of ages (10-101 weeks of age), and developed a robust single-cell DNA methylation age prediction model (scEpiAge-blood and also scEpiAge-liver). We find that our new scEpiAge can accurately predict age in a broad range of publicly available datasets, including very sparse data and it also predicts age in single cells. Interestingly, the epigenetic age distribution is wider than technically expected in 19% of single cells, suggesting that epigenetic age heterogeneity is present in vivo and may relate to functional differences between cells. In addition, we observe differences in epigenetic ageing between the major blood cell types. Our work provides a foundation for better single-cell and sparse data epigenetic age predictors and highlights the significance of cellular heterogeneity during ageing.

ORGANISM(S): Mus musculus

PROVIDER: GSE225166 | GEO | 2024/06/03

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Beneficial effects of SIRT1 on healthspan are likely to be pleiotropic and may include effects on DNA methylation. We demonstrated recently that manipulating SIRT1 in human cells affected DNA methylation of a panel of test genes, and that genes with expression modified by dietary restriction corresponded with genes that underwent changes in DNA methylation during ageing. Here we tested the hypothesis that genes particularly susceptible to SIRT1-induced effects on DNA methylation across the genome map to genes for which DNA methylation changes during ageing. We increased or reduced SIRT1 expression in human intestinal (Caco-2) and vascular endothelial (HuVEC) cells by transient transfection with an expression construct or with siRNA, respectively. Effects on DNA methylation were measured by enriching for the methylated fraction then either sequencing (HuVEC) or hybridising to a human promoter microarray (Caco-2). Effects using these two different cell lines and techniques for analysis were remarkably consistent. Genes with a DNA methylation status affected by SIRT1 manipulation were enriched for those that undergo age-dependent changes in DNA methylation, thus supporting our hypothesis. Polycomb group protein target genes (PCGTs), which are suppressed by epigenetic mechanisms in stem cells and have been shown previously to correspond with loci particularly susceptible to age-related changes in DNA methylation, were over-represented within the set of genes showing altered DNA methylation in response to SIRT1 manipulation in both cell lines. We thus propose that effects of SIRT1 to extend healthspan include influences on the DNA methylation status of genes affected during ageing, in particular PCGTs. Analysis of methylation profiles of Caco-2 cells meeting the following conditions: SIRT1 overexpressed by transfection with pCMV6-ENTRY-SIRT1 (2 replicates), corresponding vector control (2 replicates), each of the siRNAs that target SIRT1 (1 & 2 replicates, respectively) and control siRNA (2 replicates).

Dataset Information

Predicting age in single cells and low coverage DNA methylation data [RRBS]

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets