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GEO help: Mouse over screen elements for information. |
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| Status |
Public on Jun 03, 2024 |
| Title |
Predicting age in single cells and low coverage DNA methylation data [scRNA] |
| Organism |
Mus musculus |
| Experiment type |
Expression profiling by high throughput sequencing
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| 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.
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| Overall design |
Whole blood taken from wild type black 6 mice spanning a range of ages is processed using scM&T-seq to produce paired single-cell transcriptomes and single-cell methylomes.
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| 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 |
| Citation missing |
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| Submission date |
Feb 13, 2023 |
| Last update date |
Jun 03, 2024 |
| Contact name |
Laura Biggins |
| E-mail(s) |
laura.biggins@babraham.ac.uk
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| Organization name |
The Babraham Institute
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| Department |
Bioinformatics
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| Street address |
Babraham Research Campus
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| City |
Cambridge |
| ZIP/Postal code |
CB22 3AT |
| Country |
United Kingdom |
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| Platforms (1) |
| GPL21103 |
Illumina HiSeq 4000 (Mus musculus) |
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| Samples (2) |
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| This SubSeries is part of SuperSeries: |
| GSE225173 |
Predicting age in single cells and low coverage DNA methylation data |
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| Relations |
| BioProject |
PRJNA934480 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE225172_SRRs_rawfilenames.txt.gz |
16.4 Kb |
(ftp)(http) |
TXT |
| GSE225172_sc_MT_Babraham_Blood_aging.txt.gz |
5.2 Mb |
(ftp)(http) |
TXT |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
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