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Series GSE225166 Query DataSets for GSE225166
Status Public on Jun 03, 2024
Title Predicting age in single cells and low coverage DNA methylation data [RRBS]
Organism Mus musculus
Experiment type Methylation profiling by high throughput sequencing
Summary 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.
Overall design DNA was extracted from various tissue of mice spanning a range of ages and DNA methylation analysed using RRBS.
Contributor(s) Bonder MJ, Clark SJ, Krueger F, Luo S, Agostinho de Sousa J, Hashtroud AM, Stubbs TM, Rulands S, Stegle O, Reik W, von Meyenn F
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Submission date Feb 13, 2023
Last update date Jun 04, 2024
Contact name Laura Biggins
Organization name The Babraham Institute
Department Bioinformatics
Street address Babraham Research Campus
City Cambridge
ZIP/Postal code CB22 3AT
Country United Kingdom
Platforms (1)
GPL17021 Illumina HiSeq 2500 (Mus musculus)
Samples (232)
GSM7040480 M04-20525_3M_Liver_iPCRtag1
GSM7040481 M00017913_15M_Heart_iPCRtag82
GSM7040482 M04-20526_3M_Liver_iPCRtag9
This SubSeries is part of SuperSeries:
GSE225173 Predicting age in single cells and low coverage DNA methylation data
BioProject PRJNA934476

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Supplementary file Size Download File type/resource
GSE225166_RAW.tar 4.7 Gb (http)(custom) TAR (of COV)
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Raw data are available in SRA
Processed data provided as supplementary file

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