Single cell DNA methylation ageing in mouse blood
Bonder, M. J.; Clark, S.; Krueger, F.; Luo, S.; Agostinho de Sousa, J.; Hashtroud, A. M.; Stubbs, T. M.; Stark, A.-K.; Rulands, S.; Stegle, O.; Reik, W.; von Meyenn, F.
Show 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 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). We observed that the number of genes expressed increased at older ages, but gene specific changes were small. We next developed a robust single cell DNA methylation age predictor (scEpiAge), which 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 DNA methylation 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. Highlights- Model to estimate DNA methylation age in single cells - Large multi-omics dataset of single cells from murine blood - Epigenetic age deviations from chronological age are greater than technical expected from technical variability - Number of genes expressed increases with chronological and epigenetic age
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.