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A unifying model of stem cell dynamics explains age-related methylation patterns across mammals

Nature Aging (2026) Cite this article

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Abstract

DNA methylation changes are reliable biomarkers of aging, but the driving mechanisms remain poorly understood. Here we present SCARLET (Stem Cells and Age-ReLated Epigenetic Trajectories), a parsimonious mathematical model that describes how methylation changes in blood arise and propagate through hematopoietic stem cell divisions. Using a large human cohort, we demonstrate that seemingly distinct age-related methylation patterns can be explained by a unifying mechanistic model. We show that SCARLET captures known drivers of epigenetic aging, with accelerated individuals showing reduced ratios of stem cell pool size to division rate (N/s). Applying SCARLET to methylation data from 11 mammalian species reveals that N/s scales with maximum lifespan, suggesting that evolutionary adjustments to stem cell dynamics, rather than epigenetic maintenance efficiency, drive the previously observed relationship between methylation rates and lifespan. Our findings provide a quantitative framework for understanding epigenetic aging and suggest that stem cell dynamics may be a key driver of aging across mammals.

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Fig. 1: Study overview.
The alternative text for this image may have been generated using AI.
Fig. 2: A single model (SCARLET) recapitulates distinct age-related trajectories and captures biological aging.
The alternative text for this image may have been generated using AI.
Fig. 3: N/s scales with maximum lifespan across mammals.
The alternative text for this image may have been generated using AI.

Data availability

The mammal methylation dataset used was created by the Mammalian Methylation Consortium37 and is publicly available via the Gene Expression Omnibus under accession number GSE223748. Data on maximum lifespan were taken from the AnAge database (https://genomics.senescence.info/species/index.html)59. Human data were from the Generation Scotland cohort. According to the terms of consent for Generation Scotland participants, access to data must be reviewed by the Generation Scotland Access Committee. Applications should be made to access@generationscotland.org.

Code availability

All code used for the analysis (conducted in Python v.3.11.4) is available via GitFront at https://gitfront.io/r/scrofts/znVKdYUPtz66/SCARLET/.

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Acknowledgements

We thank all the Generation Scotland (GS) study participants and research team members who have contributed, and continue to contribute, to ongoing studies. This research was funded in whole, or in part, by the Wellcome Trust grant 218492/Z/19/Z. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. S.J.C.C., C.M.G. and T.C. were supported by the Mayo Clinic Robert and Arlene Kogod Center on Aging. C.M.G. was supported by National Institutes of Health (NIH) Training grant T32 GM145408/GM/NIGMS, awarded to Mayo Clinic. GS received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). E.L.-C. was partially supported by funding from the University of Edinburgh and the Medical Research Council (MC_UU_00009/2) and by an EHA Bilateral grant to K. Kirschner (BCG-202209–02649). E.L.C. has been funded by the Spanish Research Agency (AEI), through the Severo Ochoa (CEX2020-001049-S, MCIN/AEI/10.13039/501100011033) and Maria de Maeztu Program for Centers and Units of Excellence in R&D (CEX2020–001084-M). E.L.-C. thanks CERCA Program/Generalitat de Catalunya for institutional support. Research into this publication has been partially carried out in the Barcelona Collaboratorium for Modelling and Predictive Biology. DNA methylation profiling of the GS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, UK, and was funded by the Medical Research Council UK, the Brain & Behavior Research Foundation (ref. 27404) and the Wellcome Trust (Wellcome Trust Strategic Award ‘STratifying Resilience and Depression Longitudinally’ ((STRADL) ref. 104036/Z/14/Z)). R.E.M. was supported by BBSRC grant (UKRI/BB/C001941/1).

Author information

Author notes

  1. These authors contributed equally: Eric Latorre-Crespo, Tamir Chandra.

Authors and Affiliations

  1. Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA

    Samuel J. C. Crofts, Caleb M. Grenko & Tamir Chandra

  2. Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK

    Samuel J. C. Crofts, Riccardo E. Marioni, Eric Latorre-Crespo & Tamir Chandra

  3. School of Biological Sciences, University of Edinburgh, Edinburgh, UK

    Samuel J. C. Crofts

  4. Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, USA

    Samuel J. C. Crofts & Caleb M. Grenko

  5. Centre de Recerca Matemàtica, Bellaterra, Spain

    Eric Latorre-Crespo

  6. Barcelona Collaboratorium for Modelling and Predictive Biology, Barcelona, Spain

    Eric Latorre-Crespo

  7. Centre de Regulacio Genomica, Barcelona, Spain

    Eric Latorre-Crespo

  8. Cancer Research UK Scotland Institute, Glasgow, UK

    Eric Latorre-Crespo

  9. School of Informatics, University of Edinburgh, Edinburgh, UK

    Eric Latorre-Crespo

  10. Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA

    Tamir Chandra

  11. Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA

    Tamir Chandra

Authors
  1. Samuel J. C. Crofts
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  2. Caleb M. Grenko
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  3. Riccardo E. Marioni
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Contributions

E.L.-C. and T.C. conceived and supervised the study. S.J.C.C., C.M.G., R.E.M., E.L.-C. and T.C. wrote the paper. S.J.C.C., E.L.-C. and T.C. conducted data analysis.

Corresponding authors

Correspondence to Eric Latorre-Crespo or Tamir Chandra.

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The authors declare no competing interests.

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Nature Aging thanks Yifan Yang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Site parameters derived from SCARLET across CpG categories.

a, Comparisons of model fits across site categories, across varying sample sizes and numbers of selected sites. Models compared using expected log pointwise predictive density using leave-one-out cross-validation. Points represent means and error bars indicate standard deviations. Estimates use the approximately optimal value of \(N/s\)=10,000, while \(s\) is fixed at a plausible value of 1. b, The absolute difference between the inferred starting methylation value (at 0 years post sexual maturity, p) and steady-state methylation level approached as age increases (η) against the sum of epigenetic error probabilities, \({\,P}_{M\to U}\) and \({\,P}_{U\to M}\). Estimates use the approximately optimal value of \(N/s\)=10,000, while \(s\) is fixed at a plausible value of 1. c, Boxplots (showing median and interquartile range) of epigenetic maintenance efficiencies when \(N/s\) is the optimal value shown in Fig. 2b (10,000) and the rate of stem cell division, \(s\), is fixed at a realistic value of 1 division per stem cell per year. Sites are split into those that increase or decrease in mean methylation with age (note that for VMPs, this categorization will be essentially arbitrary). Estimates taken from 200 sites and 200 samples for each category.

Source Data

Extended Data Fig. 2 Sensitivity analyses and further model validation for Fig. 2.

a, Sensitivity analysis for human \(N/s\) estimates by group (Fig. 2c), with age-related sites selected based on variance change (White test) rather than mean change. Each group comprises 500 participants individually age-matched to the other cohorts within each plot. Each estimate is derived from the top 500 age-related sites. Smoking and non-smoking defined by a binarised ‘weighted smoking’ variable (see Methods for details). High epigenetic age acceleration defined as the top 500 accelerated participants as measured according to Dabrowski et al. (2024)29 (see Methods for further details). Estimates displayed as mean of the posterior, with error bars indicating the 95% credible interval. b, Sensitivity analysis of the effect of number of methylation sites used in the inference of \(N/s\). Sample size fixed at 200 participants. Sites selected based on the top age-related sites (Spearman’s rank correlation coefficient). Estimates displayed as mean of the posterior, with error bars indicating the 95% credible interval. c, Sensitivity analysis of the effect of number of participants analysed in the inference of \(N/s\). Number of CpGs fixed at 200. Estimates displayed as mean of the posterior, with error bars indicating the 95% credible interval.

Source Data

Extended Data Fig. 3 Sensitivity analyses for Fig. 3.

a, Sensitivity analysis on the effect of different analysed timespans (a proxy for lifespans) on estimates of \(N/s\). For each estimate, the human cohort was trimmed to the stated maximum age after adulthood. The top 100 age-related sites (Spearman’s rank correlation coefficient) were then recalculated on this restricted dataset, and the model was then fit. Each estimate was calculated from a sample of 150 participants (approximately the maximum sample size in the smallest age range), uniformly sampled within the given age range. Estimates displayed as mean of the posterior, with error bars indicating the 95% credible interval (log scale). Included in red is the regression line we find from our scaling analysis, i.e., what we would expect if our results were due solely to the fact that animals of longer lifespan are generally observed over longer timespans. b, As in a), except with sites selected based on variance change (White test) rather than mean change. c, Sensitivity analysis on the effect of site selection on the scaling of \(N/s\) with maximum lifespan across mammals. Each inference based on the top 500 sites that change in variance (White test) instead of mean change, and using the maximum sample size available for each mammal (minimum 110, capped at 1000 for humans). d, Example in mice of a CpG fit using a single \(N/s\) value across all mammals. e, The same data shown in d, fit using an \(N/s\) value that scales with lifespan across mammals. f, The same data shown in d, fit using the \(N/s\) values determined independently for each mammal. g, Example in humans of a CpG fit using a single \(N/s\) value across all mammals. h, The same data shown in g), fit using an \(N/s\) value that scales with lifespan across mammals. i, The same data shown in g), fit using the \(N/s\) values determined independently for each mammal.

Source Data

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Crofts, S.J.C., Grenko, C.M., Marioni, R.E. et al. A unifying model of stem cell dynamics explains age-related methylation patterns across mammals. Nat Aging (2026). https://doi.org/10.1038/s43587-026-01125-y

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