CoChat
Sign Up
Cited by 245Epigenetics and DNA MethylationSingle-cell and spatial transcriptomicsGenomics and Chromatin Dynamics

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

Ageing affects DNA methylation drift and transcriptional cell-to-cell variability in mouse muscle stem cells

Open access
Hernando-Herraez, Irene, Evano, Brendan, Stubbs, Thomas, Commere, Pierre-Henri, Jan Bonder, Marc, Clark, Stephen, Andrews, Simon, Tajbakhsh, Shahragim, Reik, Wolf·2019·Nature Communications
DOI 10.1038/s41467-019-12293-4PMC PMC6761124

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Age-related tissue alterations have been associated with a decline in stem cell number and function. Although increased cell-to-cell variability in transcription or epigenetic marks has been proposed to be a major hallmark of ageing, little is known about the molecular diversity of stem cells during ageing. Here we present a single cell multi-omics study of mouse muscle stem cells, combining single-cell transcriptome and DNA methylome profiling. Aged cells show a global increase of uncoordinated transcriptional heterogeneity biased towards genes regulating cell-niche interactions. We find context-dependent alterations of DNA methylation in aged stem cells. Importantly, promoters with increased methylation heterogeneity are associated with increased transcriptional heterogeneity of the genes they drive. These results indicate that epigenetic drift, by accumulation of stochastic DNA methylation changes in promoters, is associated with the degradation of coherent transcriptional networks during stem cell ageing. Furthermore, our observations also shed light on the mechanisms underlying the DNA methylation clock.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-019-12293-4

Cites

45 of 45 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Aged muscle stem cells have increased cell-to-cell transcriptional variability. a Experimental scheme. Single cells were isolated from Tg:Pax7-nGFP young and old mice and subjected to parallel single-cell methylation and RNA sequencing. b Principal Component Analysis (PCA) of a total of 377 cells from young (n = 4) and old (n = 2) individuals. c Selected markers and differentially expressed genes between young and old cells. Source data are provided in Supplementary Data 1. d Cumulative distribution of cell-to-cell Spearman correlation values per individual (left) showing that transcriptional heterogeneity dramatically increases with age. Heatmap showing cell-to-cell Spearman correlation values from a young and an old mouse (right). e Distance to the median of the top 500 most variable genes among all genes (left) and of the top 500 most variable genes among the 5127 common genes expressed in the six individuals (right). For all boxplots, the box represents the interquartile range and the horizontal line in the box represents the median (n = 500). f Frequency of gene expression in young and old cells. Note that the proportion of cells expressing a given gene is reduced in old cells. g Independence between frequency of gene expression differences and gene expression level
Fig. 1 Aged muscle stem cells have increased cell-to-cell transcriptional variability. a Experimental scheme. Single cells were isolated from Tg:Pax7-nGFP young and old mice and subjected to parallel single-cell methylation and RNA sequencing. b Principal Component Analysis (PCA) of a total of 377 cells from young (n = 4) and old (n = 2) individuals. c Selected markers and differentially expressed genes between young and old cells. Source data are provided in Supplementary Data 1. d Cumulative distribution of cell-to-cell Spearman correlation values per individual (left) showing that transcriptional heterogeneity dramatically increases with age. Heatmap showing cell-to-cell Spearman correlation values from a young and an old mouse (right). e Distance to the median of the top 500 most variable genes among all genes (left) and of the top 500 most variable genes among the 5127 common genes expressed in the six individuals (right). For all boxplots, the box represents the interquartile range and the horizontal line in the box represents the median (n = 500). f Frequency of gene expression in young and old cells. Note that the proportion of cells expressing a given gene is reduced in old cells. g Independence between frequency of gene expression differences and gene expression levelCC BY · PubMed Central
Fig. 2 Variability within aged muscle stem cells and cell-niche interactions. a Dag1 and Itgb1 expression in young and old cells. Each dot represents a cell. Vertical lines on the x-axis indicate cells that do not express the gene. Expression frequencies, Dag1: young 0.96, old 0.65; Itgb1: young 0.79, old 0.59. b P-values of the GO terms associated with the top 200 anticorrelated (top) and correlated (bottom) genes with the similarity score to young cells. c Similarity between old and young cells. Each dot represents an old cell; on the x-axis old cells are ordered according to their similarity (Spearman correlation coefficient) to young cells (young mean expression). Colours indicate the normalised levels of expression of selected genes correlated across ordered cells. Source data are provided in Supplementary Data 3. d Model: aged muscle stem cells that have the most different transcriptomes from young stem cells are likely to have impaired stem cell-niche interactions and are more prone to exit quiescence
Fig. 2 Variability within aged muscle stem cells and cell-niche interactions. a Dag1 and Itgb1 expression in young and old cells. Each dot represents a cell. Vertical lines on the x-axis indicate cells that do not express the gene. Expression frequencies, Dag1: young 0.96, old 0.65; Itgb1: young 0.79, old 0.59. b P-values of the GO terms associated with the top 200 anticorrelated (top) and correlated (bottom) genes with the similarity score to young cells. c Similarity between old and young cells. Each dot represents an old cell; on the x-axis old cells are ordered according to their similarity (Spearman correlation coefficient) to young cells (young mean expression). Colours indicate the normalised levels of expression of selected genes correlated across ordered cells. Source data are provided in Supplementary Data 3. d Model: aged muscle stem cells that have the most different transcriptomes from young stem cells are likely to have impaired stem cell-niche interactions and are more prone to exit quiescenceCC BY · PubMed Central
Fig. 3 Changes in methylation levels and methylation heterogeneity. a Prediction of chronological age from a DNA methylation clock25. Left: x-axis shows the chronological age and y-axis shows the predicted epigenetic age. Grey circles indicate the training data sets. Test samples are represented in different colours: Muscle samples from Reizel et al.42 are represented in purple (3 weeks-old) and brown (20 weeks-old). Pseudo bulk muscle stem cells from young animals (6–7 weeks-old) in red and from old animals (115 weeks-old) in blue. Right: Predicted epigenetic age of different combinations of single cells, permutations = 100. For all boxplots, the box represents the interquartile range and the horizontal line in the box represents the median (n = 100). b Levels of DNA methylation per cell across different genomic regions (Chip-seq data from 2-months-old mice20,27, Ac: activated muscle stem cells). Circles inside the violin plots represent the median of the data and the boxes indicate the interquartile range. c Genome-wide mean methylation values in old and young cells. Each dot represents a genomic region. d Increase of DNA methylation with age across LINE-1 elements (P < 0.05). DNA methylation values computed per cell and individual. e Mean methylation difference between old and young cells across different genomic elements. For all boxplots, the box represents the interquartile range and the horizontal line in the box represents the median. f Examples of different distributions of DNA methylation heterogeneity at loci with similar average methylation. Empty circles represent unmethylated CpG sites and filled circles methylated CpG sites. g DNA methylation levels and DNA methylation heterogeneity. Each dot represents a genomic region from young or old cells. Colour scale represents the methylation-level normalised measure of DNA methylation heterogeneity. h Boxplot showing the normalised DNA methylation heterogeneity across different genomic elements in young cells (top). Normalised methylation heterogeneity and methylation levels across all the different genomic elements (grey) and across CpG Islands (purple) or enhancer regions (purple) in young cells (bottom). For all boxplots, the box represents the interquartile range and the horizontal line in the box represents the median
Fig. 3 Changes in methylation levels and methylation heterogeneity. a Prediction of chronological age from a DNA methylation clock25. Left: x-axis shows the chronological age and y-axis shows the predicted epigenetic age. Grey circles indicate the training data sets. Test samples are represented in different colours: Muscle samples from Reizel et al.42 are represented in purple (3 weeks-old) and brown (20 weeks-old). Pseudo bulk muscle stem cells from young animals (6–7 weeks-old) in red and from old animals (115 weeks-old) in blue. Right: Predicted epigenetic age of different combinations of single cells, permutations = 100. For all boxplots, the box represents the interquartile range and the horizontal line in the box represents the median (n = 100). b Levels of DNA methylation per cell across different genomic regions (Chip-seq data from 2-months-old mice20,27, Ac: activated muscle stem cells). Circles inside the violin plots represent the median of the data and the boxes indicate the interquartile range. c Genome-wide mean methylation values in old and young cells. Each dot represents a genomic region. d Increase of DNA methylation with age across LINE-1 elements (P < 0.05). DNA methylation values computed per cell and individual. e Mean methylation difference between old and young cells across different genomic elements. For all boxplots, the box represents the interquartile range and the horizontal line in the box represents the median. f Examples of different distributions of DNA methylation heterogeneity at loci with similar average methylation. Empty circles represent unmethylated CpG sites and filled circles methylated CpG sites. g DNA methylation levels and DNA methylation heterogeneity. Each dot represents a genomic region from young or old cells. Colour scale represents the methylation-level normalised measure of DNA methylation heterogeneity. h Boxplot showing the normalised DNA methylation heterogeneity across different genomic elements in young cells (top). Normalised methylation heterogeneity and methylation levels across all the different genomic elements (grey) and across CpG Islands (purple) or enhancer regions (purple) in young cells (bottom). For all boxplots, the box represents the interquartile range and the horizontal line in the box represents the medianCC BY · PubMed Central
Fig. 4 Changes in cell-to-cell methylation heterogeneity during ageing. a Normalised methylation heterogeneity changes with age (Δ methylation heterogeneity: old–young) across different genomic features (Chip-seq data from 2-months-old mice20,27, Ac: activated muscle stem cells). For all boxplots, the box represents the interquartile range and the horizontal line in the box represents the median. b Genome-wide normalised methylation heterogeneity difference with ages (Δ methylation heterogeneity: old–young) binned by 0.1 methylation level differences (left). Changes in promoter methylation heterogeneity (y-axis) and methylation levels (x-axis) with age (right). For all boxplots, the box represents the interquartile range and the horizontal line in the box represents the median. Source data are provided in Supplementary Data 4 (n = 2,355). c Distribution of Pearson’s correlation coefficients between promoter DNA methylation and gene expression (one association test per cell, number of cells: young = 64, old more similar to young = 30, old less similar to young = 20, *P < 0.05). For all boxplots, the box represents the interquartile range and the horizontal line in the box represents the median. d Increase of transcriptional heterogeneity with age across all promoters (n = 394) and promoters with increased DNA methylation heterogeneity (Δ methylation heterogeneity > 0.3, n = 113) (P < 0.001). Source data are provided in Supplementary Data 4. e Global increase of transcriptional cell-to-cell variability with age with enhanced heterogeneity in the multiple extracellular matrix-related genes (top). Relationship between transcriptional and DNA methylation heterogeneity in aged muscle stem cells (bottom). Empty circles represent unmethylated CpG sites and filled circles methylated CpG sites. Repeat elements become more homogeneous with age by increasing their methylation levels in a coordinated manner. In contrast, promoter regions become more heterogeneous by randomly losing DNA methylation, and this is coupled with an increase in transcriptional variability of the genes they drive
Fig. 4 Changes in cell-to-cell methylation heterogeneity during ageing. a Normalised methylation heterogeneity changes with age (Δ methylation heterogeneity: old–young) across different genomic features (Chip-seq data from 2-months-old mice20,27, Ac: activated muscle stem cells). For all boxplots, the box represents the interquartile range and the horizontal line in the box represents the median. b Genome-wide normalised methylation heterogeneity difference with ages (Δ methylation heterogeneity: old–young) binned by 0.1 methylation level differences (left). Changes in promoter methylation heterogeneity (y-axis) and methylation levels (x-axis) with age (right). For all boxplots, the box represents the interquartile range and the horizontal line in the box represents the median. Source data are provided in Supplementary Data 4 (n = 2,355). c Distribution of Pearson’s correlation coefficients between promoter DNA methylation and gene expression (one association test per cell, number of cells: young = 64, old more similar to young = 30, old less similar to young = 20, *P < 0.05). For all boxplots, the box represents the interquartile range and the horizontal line in the box represents the median. d Increase of transcriptional heterogeneity with age across all promoters (n = 394) and promoters with increased DNA methylation heterogeneity (Δ methylation heterogeneity > 0.3, n = 113) (P < 0.001). Source data are provided in Supplementary Data 4. e Global increase of transcriptional cell-to-cell variability with age with enhanced heterogeneity in the multiple extracellular matrix-related genes (top). Relationship between transcriptional and DNA methylation heterogeneity in aged muscle stem cells (bottom). Empty circles represent unmethylated CpG sites and filled circles methylated CpG sites. Repeat elements become more homogeneous with age by increasing their methylation levels in a coordinated manner. In contrast, promoter regions become more heterogeneous by randomly losing DNA methylation, and this is coupled with an increase in transcriptional variability of the genes they driveCC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 45 references

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Hernando-Herraez, I., Evano, B., Stubbs, T., Commere, P.-H., Jan Bonder, M., Clark, S., Andrews, S., Tajbakhsh, S., & Reik, W.
Affiliations
  • Babraham Institute (GB)
  • Centre National de la Recherche Scientifique (FR)
Corresponding author
  • Hernando-Herraez, Irene
  • Tajbakhsh, Shahragim
  • Reik, Wolf
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2019-09-25
Volume
10
Issue
1
Pages
4361-4361
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-019-12293-4
PMC
PMC6761124
OpenAlex
W2976009347
Cited by
236
References
45
Citations by year
2026: 31 · 2025: 25 · 2024: 35 · 2023: 40 · 2022: 37 · 2021: 29 · 2020: 33 · 2019: 5
FWCI
8.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
  • HAL (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • Apollo (cc-by)
Funders
  • Agence Nationale de la Recherche (ANR-10-LABX-73)
  • Association Française contre les Myopathies (332893)
  • RCUK | Biotechnology and Biological Sciences Research Council (CBBS/E/B/000C0425)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords
aging, muscle stem cells, epigenetic drift, transcriptional heterogeneity, cell-to-cell variability, single-cell multi-omics, promoter methylation, DNA methylation clocks, transcriptional regulatory networks, stem cell heterogeneity
MeSH terms
Aging, Animals, Cells, Cultured, Humans, Mice, Inbred C57BL, Mice, Inbred DBA, Mice, Transgenic, Muscles, Promoter Regions, Genetic, Stem Cells, Cellular Senescence, DNA Methylation, Gene Expression Profiling, Epigenesis, Genetic, Epigenomics, Single-Cell Analysis, Transcriptome, Gene Ontology

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review