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Profiling epigenetic age in single cells

Nature Aging volume 1, pages 1189–1201 (2021)Cite this article

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Abstract

DNA methylation dynamics have emerged as a promising biomarker of mammalian aging, with multivariate machine learning models (‘epigenetic clocks’) enabling measurement of biological age in bulk tissue samples. However, intrinsically sparse and binarized methylation profiles of individual cells have so far precluded the assessment of aging in single-cell data. Here we introduce scAge, a statistical framework for epigenetic age profiling at single-cell resolution, and validate our approach in mice. Our method recapitulates the chronological age of tissues while uncovering heterogeneity among cells. We show accurate tracking of the aging process in hepatocytes, demonstrate attenuated epigenetic aging in muscle stem cells and track age dynamics in embryonic stem cells. We also use scAge to reveal, at the single-cell level, a natural and stratified rejuvenation event occurring during early embryogenesis. We provide our framework as a resource to enable exploration of epigenetic aging trajectories at single-cell resolution.

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Fig. 1: Designing the scAge framework.
Fig. 2: scAge tracks aging in hepatocytes and embryonic fibroblasts.
Fig. 3: MuSCs display attenuated epigenetic aging.
Fig. 4: Culture conditions influence epigenetic age in single ESCs.
Fig. 5: An epigenetic rejuvenation event during mouse embryogenesis.
Fig. 6: Lineage-specific resolution reveals stratification in the epigenetic rejuvenation event.

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Data availability

All data used in this work were obtained from publicly available repositories. Processed single-cell coverage matrices were downloaded from GEO under the following accession nos.: GSE68642 (ref. 27), GSE121436 (ref. 38), GSE56879 (ref. 25) and GSE121690 (ref. 28). Trimmed sequencing files for the hepatocyte/MEF study were downloaded from the SRA under accession no. SRA344045 (ref. 23). Bulk processed methylation data used for model training were downloaded from GEO under accession no. GSE120132 (ref. 34).

Code availability

The scAge framework is publicly available at https://github.com/alex-trapp/scAge.

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Acknowledgements

We thank T. Fox and A. Ganguly for help with schematic figures. We thank M. Mariotti, A. Shindyapina, S. H. Yim, S.-G. Lee, D. Santesmasses, P. Griffin and Y. Hu for helpful discussions. This work was supported by NIA grants to V.N.G. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Some figures were created with BioRender.com.

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Authors and Affiliations

  1. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Alexandre Trapp, Csaba Kerepesi & Vadim N. Gladyshev

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  1. Alexandre Trapp
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  2. Csaba Kerepesi
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  3. Vadim N. Gladyshev
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Contributions

A.T. and C.K. conceptualized the single-cell age prediction algorithm and processed the training data. A.T. handled all single-cell data, developed the scAge framework and performed all analyses in the manuscript. A.T. and V.N.G. wrote and revised the manuscript with help from C.K. A.T. and V.N.G. conceived the study and V.N.G. supervised the work.

Corresponding author

Correspondence to Vadim N. Gladyshev.

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Competing interests

Brigham and Women’s Hospital is the sole owner of a provisional patent application directed at this invention in which all authors are named inventors.

Additional information

Peer review information Nature Aging thanks Alex Zhavoronkov, Danica Chen and Lenhard Rudolph for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Bulk training data characteristics and dimensionality reduction.

a) Age distributions for bulk training data in liver (n = 29), blood (n = 50), muscle (n = 24), and multi-tissue (n = 196) datasets, stratified by gender (female, blue; male, orange). b) Principal component analyses (PCA) across 748,955 CpG sites in liver, blood, muscle, and multi-tissue methylation matrices. For single-tissue datasets, black circles encompass samples that were retained for linear model training to exclude outliers and improve model accuracy. The number of samples before and after filtration is shown in the bottom right of each panel. Color scales depict the age in months of the animal (from young, yellow to old, purple). c) Age distribution in the multi-tissue dataset (n = 196), stratified by tissue type (blood, red; liver, orange; muscle, green; kidney, brown; adipose, yellow; lung, pink).

Extended Data Fig. 2 Relationship between age and bulk methylation level in age-associated CpG sites in liver.

a) Kernel density estimation plots for the top 5 positively and negatively age-correlated CpG sites in the bulk liver data (based on n = 29 samples). CpG genomic positions are shown above each panel, along with the Pearson correlation coefficient (r) between methylation level and age. Colors correspond to the ages of mice (2 m, blue; 10 m, orange; 20 m, red). b) Representative scatterplots showing the relationship between age and methylation level in strongly positively (left) and negatively (right) age-associated CpG sites. Jitter was applied to the x-axis (age) purely for visualization purposes. Regression lines (grey) with 95% confidence intervals (light grey) are shown. Pearson correlation coefficients (r) and associated p-values (p) are shown. Two-tailed Pearson correlation analysis was employed for statistical testing, with statistics for each model treated independently without correction. Colors correspond to the ages of mice (2 m, blue; 10 m, orange; 20 m, red).

Extended Data Fig. 3 Global methylation, coverage, and scDNAm predictions in embryonic fibroblasts and hepatocytes with outliers.

a) Bar plot of mean global methylation (top) and CpG coverage (bottom) in single mouse embryonic fibroblasts and hepatocytes. Each bar represents one cell. MEFs are shown in green, young hepatocytes in blue, and old hepatocytes in red. b) Predicted epigenetic age versus chronological age (top) in all young hepatocytes (blue, n = 11) and old hepatocytes (red, n = 10) across liver (left), multi-tissue (middle) and blood (right) models. Jitter was applied to x-axis (chronological age) strictly for visualization purposes. Pearson correlation (r), the associated p-value (p) and the median absolute error (MedAE) are shown. Two-tailed Pearson correlation analysis was employed for statistical testing with statistics for each model treated independently without correction. Violin plots show kernel density estimation of the data, with the median displayed by a black line. Further analysis of outliers is shown in Extended Data Fig. 5. Dots depict individual cells. c) Predicted epigenetic age, grouped by cell type, across liver (left), multi-tissue (middle) and blood (right) models for MEFs (n = 5, green), young hepatocytes (n = 11, blue), and old hepatocytes (n = 10, red). Two-tailed Welch’s t-test was used for statistical testing, and Bonferroni correction was applied to correct for multiple testing. Box plots show median levels and the first and third quartile, whiskers show up to 1.5× the interquartile range. Dots depict individual cells.

Extended Data Fig. 4 Likelihood distributions in young and old hepatocytes.

Likelihood distributions for all young (blue, n = 11) and old (red, n = 10) hepatocytes, based on scDNAm results from the liver model sampling the top 1% age-associated CpGs per cell. Black lines indicate age of maximum likelihood (predicted epigenetic age), which is depicted numerically in the top right or left corners of each panel. Labels indicate cell identifier, as given in the study metadata on the SRA. Likelihood was calculated by taking the exponential of the log-likelihood profiles, which was subsequently scaled between 0 and 1 to normalize distributions between cells.

Extended Data Fig. 5 Outlier analysis and lack of relationship between scDNAm age and technical covariates in hepatocytes and fibroblasts.

a) Scatterplot depicting the strongly linear relationship between CpG coverage in a single cell (x-axis) and the number of CpGs intersecting with the liver training dataset (y-axis) for embryonic fibroblasts (green, n = 5), young hepatocytes (blue, n = 11) and old hepatocytes (red, n = 10). Regression line (grey) is shown with a 95% confidence interval (light grey). Outlier samples based on scDNAm predictions and PCA analysis in the original study are within the black circle, highlighting these cells are not outliers in regard to CpG coverage. Pearson correlation coefficient (r) and the associated two-tailed p-value (p) are shown. b) Mean global methylation of embryonic fibroblasts (green, n = 5), young (blue, n = 11) and old hepatocytes (red, n = 10). Outlier samples detected during dimensionality reduction and age predictions are circled in black. c, d) Scatterplots depicting the relationship of mean global methylation (left) and CpG coverage (right) with predicted epigenetic age (scDNAm age) for single embryonic fibroblasts (n = 5, green) and hepatocytes (young, n = 11, blue; old, n = 10, red) across liver and multi-tissue datasets with the two outliers included (c) and with the two outliers excluded (d). Regression lines (grey) are shown with a 95% confidence interval (light grey). Two-tailed Pearson correlation analysis was used for statistical testing, with each analysis treated independently without correction. Pearson correlation coefficients (r) and associated two-tailed p-values (p) are shown. No significant relationship is observed in any comparison. The legend in panel (a) applies to all of the panels in this figure.

Extended Data Fig. 6 Pearson correlation distributions and predicted ages in single cells based on various CpG selection parameters.

a) Violin plots depicting the distribution of the Pearson correlation coefficient of scAge-chosen CpGs in embryonic fibroblasts (n = 5, green) and hepatocytes (young, n = 11, blue; old, n = 10, red) based on the selection method. On the left, a percentile-based method is employed, wherein the top x% absolute age-associated CpGs are chosen in every cell. On the right, a defined number of CpGs is chosen across every cell, leading to more uneven distributions due to differential cell CpG coverage. Various parameters for both methods (grey boxes) and their effects on the distributions are shown. Violin plots depict kernel density estimations of the data. Inner boxplots depict median levels (white dot) and first and third quartiles, with whiskers extending up to 1.5x the interquartile range. The central legend applies to all subpanels in this panel. b) Predicted epigenetic ages using the liver model for all embryonic fibroblasts (n = 5) and hepatocytes (young, n = 11; old, n = 10), based on the selection method (left, percentile; right, defined number of CpGs) and parameter. Colors depict the % or number of CpGs chosen for scAge computations (top 0.5% or 100 CpGs, blue; top 1% or 500 CpGs, orange; top 5% or 1,000 CpGs, green; top 10% or 5,000 CpGs, red).

Extended Data Fig. 7 Single-cell epigenetic age predictions differ based on selection mode and training dataset.

Predicted epigenetic ages in all embryonic fibroblasts (green, n = 5), young hepatocytes (blue, n = 11) and old hepatocytes (red, n = 10) using the liver model (left two columns) and multi-tissue models (right two columns) across different CpG selection modes and parameters. Parameters are labeled in grey boxes above the plots. Bonferroni corrections were applied to account for multiple testing. Pearson correlation (r), its associated p-value (p), and the median absolute error (MedAE) are shown for each panel. Two-tailed Pearson correlation analysis was employed for statistical testing. Dashed lines represent the chronological age of animals from which hepatocytes were obtained (4-months-old, dark blue; 26-months-old, dark red). Boxplots depict median levels and the first/third quartile, with whiskers extending up to 1.5x the interquartile range. Individual cells are depicted as points.

Extended Data Fig. 8 Distribution of Pearson correlation coefficients and linear association metrics across training datasets.

a, b) Kernel density estimation plots for (a) Pearson correlation coefficients and (b) linear regression coefficients in processed training reference data for liver (orange), blood (red), muscle (green), and multi-tissue (magenta) datasets. Individual distributions are labeled on the upper right side to indicate which tissue is depicted.

Extended Data Fig. 9 Single-cell coverage distributions and the effect of coverage on scAge predictions.

a) Distributions of single-cell CpG coverage across all 5 datasets analyzed in this study. Dotted lines represent the cutoff value that was used for downstream analysis (at least 500,000 CpGs per cell), in line with previous work38. Given the low sample size and relatively high coverage, no filtration was applied to cells from the Gravina et al. study23. The number of cells passing the filtration cutoff in each dataset is further detailed in Supplementary Table 1. Violin plots depict the kernel density estimation of the data. Inner boxplots depict the median (white dot), as well as the first/third quartile (grey box), with whiskers extending up to 1.5x the interquartile range. Individual dots depict single cells. Colors align with those presented in main figures (Gravina et al: MEFs in green; young hepatocytes in blue; old hepatocytes in red; Hernando-Herraez et al: young MuSCs in blue, old MuSCs in red; Angermueller et al and Smallwood et al: 2i ESCs in yellow, serum ESCs in pink; Argelaguet et al: E4.5 cells in purple, E5.5 cells in dark blue, E6.5 cells in dark green, E7.5 cells in light green). b) Scatterplot depicting the relationship between CpG coverage and predicted epigenetic ages in all unfiltered muscle stem cells38 (n = 275). Dotted black line represents the cutoff of 500,000 CpGs per cell, after which predictions greatly stabilize. MuSCs from young animals are shown in blue, and those from old animals are shown in red.

Extended Data Fig. 10 Single-cell profile simulations and epigenetic age predictions.

a) Heatmap of methylation values in bulk and simulated single-cell profiles. 100 CpGs were randomly selected from a bulk liver sample34, and random Bernoulli distributions were used to generate 10 simulated binary profiles per bulk sample. As the bulk methylation level (top) of CpGs increases from left to right, more simulated single-cell profiles are methylated as opposed to unmethylated. Color scale depicts methylation level from unmethylated (0, black) to methylated (1, white). b) Mean global methylation of bulk samples (blue) and 10 simulated full binary profiles per sample (orange) across 29 bulk liver RRBS samples, arranged from young (left) to old (right). Simulated binary profiles cluster with their bulk source, despite shifting from a fractional to a binary data modality. c, d) Predicted epigenetic age for each simulated binary profile with (c) full coverage of 748,955 CpGs per simulated profile and (d) randomly 10x downsampled coverage of 74,896 distinct CpGs per simulated profile. Profiles are arranged from young (left) to old (right). Age of the animals is denoted by the color of the points (2 m, light green; 10 m, dark green; 20 m, dark blue). Two-tailed Pearson correlation analysis was employed for statistical testing, with statistics for each simulation treated independently without correction. The Pearson correlation coefficient (r), the associated two-tailed p-value (p), median absolute error (MedAE) and mean of the standard deviations for each sample (μ(σ)) are shown. Violin plots in (c) and (d) depict the kernel density estimation of the data. Inner boxplots depict the median (white dot), as well as the first/third quartile (grey box), with whiskers extending up to 1.5x the interquartile range. Individual dots depict simulated single cell profiles.

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Trapp, A., Kerepesi, C. & Gladyshev, V.N. Profiling epigenetic age in single cells. Nat Aging 1, 1189–1201 (2021). https://doi.org/10.1038/s43587-021-00134-3

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