A single-cell transcriptomic atlas characterizes ageing tissues in the mouse

Abstract

Ageing is characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death1. Despite rapid advances over recent years, many of the molecular and cellular processes that underlie the progressive loss of healthy physiology are poorly understood2. To gain a better insight into these processes, here we generate a single-cell transcriptomic atlas across the lifespan of Mus musculus that includes data from 23 tissues and organs. We found cell-specific changes occurring across multiple cell types and organs, as well as age-related changes in the cellular composition of different organs. Using single-cell transcriptomic data, we assessed cell-type-specific manifestations of different hallmarks of ageing—such as senescence3, genomic instability4 and changes in the immune system2. This transcriptomic atlas—which we denote Tabula Muris Senis, or ‘Mouse Ageing Cell Atlas’—provides molecular information about how the most important hallmarks of ageing are reflected in a broad range of tissues and cell types.

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Fig. 1: Overview of the Tabula Muris Senis.
Fig. 2: Cellular changes during ageing.
Fig. 3: Mutational burden across tissues in ageing mice.
Fig. 4: The ageing immune system.

Data availability

The entire dataset can be explored interactively at http://tabula-muris-senis.ds.czbiohub.org/. Gene counts and metadata are available from figshare (https://doi.org/10.6084/m9.figshare.8273102.v2) and the Gene Expression Omnibus under accession code GSE132042; the raw data files are available from a public AWS S3 bucket (https://registry.opendata.aws/tabula-muris-senis/).

Code availability

The code used for the analysis is available from GitHub at https://github.com/czbiohub/tabula-muris-senis.

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Acknowledgements

We thank Sony Biotechnology for making an SH800S instrument available for this project. Some of the cell sorting and flow cytometry analysis for this project was done on a Sony SH800S instrument in the Stanford Shared FACS Facility. Some FACS experiments were performed with instruments in the VA Flow Cytometry Core, which is supported by the US Department of Veterans Affairs (VA), Palo Alto Veterans Institute for Research (PAVIR), and the National Institutes of Health (NIH). This work was supported by the Chan Zuckerberg Biohub, Department of Veterans Affairs grant IK6 BX004599 (T.W.-C.) and NIH/NIA DP1 grant AG053015 (T.W.-C.). We thank B. Tojo for the artwork, and C. Xu and J. Batson for discussions.

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Contributions

A full list of author contributions can be found in the Supplementary Information.

Corresponding authors

Correspondence to Spyros Darmanis or Stephen R. Quake or Tony Wyss-Coray.

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The authors declare no competing interests.

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Peer review information Nature thanks Fan Zhang and the other, anonymous, reviewer(s) 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 UMAP visualizations of the whole Tabula Muris Senis.

a, b, UMAP plot of all cells collected for FACS coloured by tissue (a) or age (b). c, UMAP plot of all cells collected by FACS, coloured by organ (Extended Data Fig. 4c), overlaid with the Louvain cluster numbers. n = 110,824 individual cells for FACS. d, e, UMAP plot of all cells collected for droplet coloured by tissue (d) or age (e). f, UMAP plot of all cells collected by droplet, coloured by organ (Extended Data Fig. 4c), overlaid with the Louvain cluster numbers. n = 245,389 individual cells for droplet. g, B cells (left) and endothelial cells (right) in FACS independently annotated for each organ cluster together by unbiased whole-transcriptome Louvain clustering, irrespective of the organ from which they originated. h, B cells (left) and endothelial cells (right) in droplet independently annotated for each organ cluster together by unbiased whole-transcriptome Louvain clustering, irrespective of the organ from which they originated. i, j, UMAP plot of all cells collected coloured by method (i) or tissue (j). n = 356,213 individual cells for FACS and droplet combined. k, l, B cells (k) and endothelial cells (l) cluster together by unbiased whole-transcriptome Louvain clustering, irrespective of the technology by which they were found.

Extended Data Fig. 2 Tabula Muris Senis quality control statistics overall summary and detailed for the FACS dataset.

a, Pie chart with the summary statistics for FACS. b, Pie chart with the summary statistics for droplet. c, Box plot of the number of genes detected per cell for each organ and age for FACS. d, Box plot of the number of reads per cell (log-scale) for each organ and age for FACS. For c, d, all data are expressed as mean ± s.d. Individual data points (black diamonds) correspond to outliers outside of the quantile distribution. The sample size (number of cells for each tissue and age) is available in Supplementary Table 1.

Extended Data Fig. 3 Tabula Muris Senis quality control statistics detailed for the droplet dataset.

a, Box plot of the number of genes detected per cell for each organ and age for droplet. b, Box plot of the number of UMIs per cell (log scale) for each organ and age for droplet. All data are expressed as mean ± s.d. Individual data points (black diamonds) correspond to outliers outside of the quantile distribution. The sample size (number of cells for each tissue and age) is available in Supplementary Table 2.

Extended Data Fig. 4 Number of cells in Tabula Muris Senis across age, sex, tissue and technology and schematic the of data processing.

a, Balloon plot showing the number of sequenced cells per sequencing method per organ per sex per age. b, Schematic of the analysis workflow. c, d, Tabula Muris Senis colour dictionary for organs and tissues (c) and ages (d).

Extended Data Fig. 5 Comparison of bulk and single-cell datasets and tissue cell compositions.

a, b, Ageing patterns from bulk and single-cell data are consistent. Strong changes in bulk gene expression with ageing can be either explained by cell or read count-based changes in single-cell data FACS (a) and droplet (b). Two-sided Wilcoxon–Mann–Whitney indicates that single-cell data based log2 fold-changes of cell or read counts distinguish between up and down regulated genes in bulk data. n = 110,824 individual cells for FACS and n = 245,389 individual cells for droplet. c, Mammary gland T cell relative abundances change significantly with age (P < 0.05 and r2 > 0.7 for a hypothesis test with the null hypothesis that the slope is zero, using two-sided Wald test with t-distribution of the test statistic). d, Top 20 upregulated and downregulated genes in mammary gland computed using MAST51, treating age as a continuous covariate while controlling for sex and technology. Genes were classified as significant under an FDR threshold of 0.01 and an age coefficient threshold of 0.005 (corresponding to an approximately 10% fold change). n = 6,393, 3,635 and 5,549 individual cells for mammary gland at 3, 18 and 21 months, respectively. e, Relative abundances of marrow precursor B cells change significantly with age (P < 0.05 and r2 > 0.7 for a hypothesis test with the null hypothesis that the slope is zero, using two-sided Wald test with t-distribution of the test statistic). f, Top 20 upregulated and downregulated genes in marrow computed using MAST51, treating age as a continuous covariate while controlling for sex and technology. Genes were classified as significant under an FDR threshold of 0.01 and an age coefficient threshold of 0.005 (corresponding to an approximately 10% fold change). n = 3,027, 8,559, 11,496, 5,216, 12,943 and 13,496 individual cells for marrow at 1, 3, 18, 21, 24 and 30 months, respectively. g, Relative abundances of skin keratinocyte stem cells change significantly with age (P < 0.05 and r2 > 0.7 for a hypothesis test with the null hypothesis that the slope is zero, using two-sided Wald test with t-distribution of the test statistic). h, Top 20 upregulated and downregulated genes in skin computed using MAST51, treating age as a continuous covariate while controlling for sex and technology. Genes were classified as significant under an FDR threshold of 0.01 and an age coefficient threshold of 0.005 (corresponding to an approximately 10% fold change). n = 2,346, 1,494, 4,352 and 1,122 individual cells for skin at 3, 18, 21 and 24 months, respectively. The P values for the cell type compositional changes are shown in Supplementary Table 5.

Extended Data Fig. 6 Cellular changes during ageing in the liver.

a, Relative abundances of liver hepatocytes change significantly with age (P < 0.05 and r2 > 0.7 for a hypothesis test with the null hypothesis that the slope is zero, using two-sided Wald test with t-distribution of the test statistic). n = 2,791, 2,832, 3,806, 2,257, 6,384 and 5,713 individual cells for liver 1, 3, 18, 21, 24 and 30 months, respectively. The P values for the cell type compositional changes are shown in Supplementary Table 5. bd, Bright-field imaging of hepatocytes at different ages (b) and respective quantification (c, d). e, Top 10 upregulated and downregulated genes in liver computed using MAST51, treating age as a continuous covariate while controlling for sex and technology. Genes were classified as significant under an FDR threshold of 0.01 and an age coefficient threshold of 0.005 (corresponding to an approximately 10% fold change). The sample size is the same as for a. f, k, Gene expression of Il1b and Clec4f (f) and Pecam1 and Mrc1 (k) in the liver droplet dataset for the six ages. gj, Staining of Kupffer cells at different ages (g) and respective quantification (hj). lo, Staining of liver endothelial cells at different ages (l) and respective quantification (mo). Scale bars, 100 μm. For c, d, hj and mo, all data are expressed as mean ± s.d. and P values were obtained using a Welch’s test. The sample size for each group is available in Supplementary Table 7.

Extended Data Fig. 7 Mean number of somatic mutations with age.

a–f, Mean number of somatic mutations in genes (a, c, e) and ERCC spike-in controls (b, d, f) across all tissues per age group (3 months and 24 months (a, b), 3 months and 18 months (c, d), 18 months and 24 months (e, f)). Mutations are presented as the mean number of mutations per gene or ERCC spike-in per cell.

Extended Data Fig. 8 Gene (raw) expression with age.

af, Gene raw expression (a, c, e) and ERCC spike-in control raw expression (b, d, f) across all tissues per age group (3 months and 24 months (a, b), 3 months and 18 months (b, d), 18 months and 24 months (c, e)). Raw expression are presented as the mean number of counts per gene or ERCC spike-inn control per cell.

Extended Data Fig. 9 Immune repertoire clonality analysis.

a, B-cell clonal families. For each time point, the clonal families are represented in a tree structure for which the central node is age. Connected to the age node there is an additional node (dark grey) that represents each mouse and the clonal families are depicted for each mouse. For each clonal family, cells that are part of that family are coloured by the organ of origin. b, T-cell clonal families. For each time point, clonal families are represented in a tree structure for which the central node is age. Connected to the age node there is an additional node (dark grey) that represents each mouse and the clonal families are depicted for each mouse. For each clonal family, cells that are part of that family are coloured by the organ of origin.

Extended Data Fig. 10 Diversity score summary.

a, b, Heat map summary of the overall tissue diversity score for FACS (a) and droplet (b). c, d, Heat map summary of the tissue cell-type diversity score for FACS (c) and droplet (d).

Extended Data Fig. 11 Diversity score and differential gene expression analysis for brain myeloid and kidney.

a, b, Diversity score at different cluster resolutions for FACS brain myeloid microglia cell (a) and droplet kidney macrophage (b). n = 14 mice for a and n = 16 mice for b. All data are expressed as quantiles. The P values were obtained using a linear regression and two-sided F-test, adjusted for multiple comparison using the Benjamini–Hochberg procedure (that is, bh-p value). c, d, Diversity score correlation with the number of genes expressed per tissue (c) or tissue cell-type (d). The red line corresponds to the linear regression curve. e, Trajectory analysis for a brain myeloid microglia cell. f, Heat map showing differential gene expression analysis of cluster 10 (mostly young macrophages) compared with cluster 13 (mostly old macrophages). For the complete gene list, see Supplementary Table 10.

Supplementary information

Supplementary Information

This file contains a fill list of authors in the Tabula Muris Consortium

Reporting Summary

Supplementary Table 1

Summary of the FACS dataset. a, Number of cells grouped by age, sex, mouse id and tissue. b, Number of cells grouped by tissue, cell ontology class and age. c, Number of cells grouped by Louvain cluster number, cell ontology class, tissue and age. d, Number of cells grouped by cell ontology class, Louvain cluster number, tissue and age. e, Fraction of cells in each Louvain cluster per cell ontology class and tissue. f, Fraction of cells in each Louvain cluster per tissue. g, Fraction of cells in each Louvain cluster per cell ontology class.

Supplementary Table 2

Summary of the droplet dataset. a, Number of cells grouped by age, sex, mouse id and tissue. b, Number of cells grouped by tissue, cell ontology class and age. c, Number of cells grouped by Louvain cluster number, cell ontology class, tissue and age. d, Number of cells grouped by cell ontology class, Louvain cluster number, tissue and age. e, Fraction of cells in each Louvain cluster per cell ontology class and tissue. f, Fraction of cells in each Louvain cluster per tissue. g, Fraction of cells in each Louvain cluster per cell ontology class.

Supplementary Table 3

Summary of the batch corrected dataset (FACS and droplet). a, Number of cells grouped by age, sex, mouse id and tissue. b, Number of cells grouped by tissue, cell ontology class and age. c, Number of cells grouped by Louvain cluster number, cell ontology class, tissue and age. d, Number of cells grouped by cell ontology class, Louvain cluster number, tissue and age. e, Fraction of cells in each Louvain cluster per cell ontology class and tissue. f, Fraction of cells in each Louvain cluster per tissue. g, Fraction of cells in each Louvain cluster per cell ontology class. h, Adjusted rand score before and after bbknn batch correction for method (droplet, FACS).

Supplementary Table 4

Cellular fraction changes for senescence markers. This Supplementary Table supports Figure 2a-d. We performed linear regression analysis to identify for which senescence marker genes the proportion of cells expressing that marker significantly (p-value<0.05) changed with age for a hypothesis test whose null hypothesis is that the slope is zero, using two-sided Wald Test with t-distribution of the test statistic. Correlation coefficients were computed using the Pearson’s correlation metrics.

Supplementary Table 5

Cellular fraction changes. This Supplementary Table supports Figure 2e,g,i; Extended Data Figure 5c,e,g and Extended Data Figure 6a. We performed linear regression analysis to identify which cellular relative abundances change significantly with age (p-value<0.05 and r2>0.7) for a hypothesis test whose null hypothesis is that the slope is zero, using two-sided Wald Test with t-distribution of the test statistic. Correlation coefficients were computed using the Pearson’s correlation metrics.

Supplementary Table 6

Differential gene expression analysis. This Supplementary Table supports Figure 2f,h,j; Extended Data Figure 5d,f,h and Extended Data Figure 6e. The differential gene expression was performed using MAST57. For a more detailed explanation please refer to the Methods section.

Supplementary Table 7

Quantification of Liver in-situ staining. This Supplementary Table supports Figure 6b-d,g-j and l-o. fov stands for field of view.

Supplementary Table 8

Summary statistics for the GATK analysis. This Supplementary Table supports Figure 3 and Extended Data Figures 7,8. Cell is the unique cell identifier; ercc is the average number of mutations per cell found in the ERCC spike-in, adata is the average number of mutations per cell in the gene set of the tissue; ercc_raw_counts are the average number of ERCC spike-in counts per cell and ercc_counts are the log(ercc_raw_counts+1); adata_raw_counts are the average number of gene counts per cell and adata_counts are the log(adata_raw_counts+1); tissue, age and cell_ontology_class are the metadata of the respective cell id and agenum is the age as a numerical variable; functional_annotations is a categorical variable binning each cell type as endothelial, immune, parenchymal, stem cell/progenitor or stromal.

Supplementary Table 9

B-cell and T-cell repertoire analysis raw data. This table supports Figure 4a, b and Extended Data Figure 9.

Supplementary Table 10

Differential gene expression for the tissue cell type whose diversity significantly changes with age. a, FACS brain myeloid microglia differentially upregulated genes between clusters 10, 12 and 14 versus clusters 1 and 6. b, FACS brain myeloid microglia differentially upregulated genes between clusters 1 and 6 versus clusters 10, 12 and 14. c, Droplet kidney macrophage differentially upregulated genes between cluster 13 and cluster 10. d, Droplet kidney macrophage differentially upregulated genes between cluster 10 and cluster 13. e, Alzheimer’s disease microglia signature from47. This Supplementary Table supports Figure 4d-f and Extended Data Figure 11f. Differential gene expression was computed using Wilcoxon rank-sum adjusted for multiple comparison using the Benjamini-Hochberg procedure.

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Almanzar, N., Antony, J., Baghel, A.S. et al. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020). https://doi.org/10.1038/s41586-020-2496-1

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