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A single-cell transcriptomic atlas characterizes ageing tissues in the mouse
Open accessAbstract
Ageing is characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death 1. Despite rapid advances over recent years, many of the molecular and cellular processes that underlie the progressive loss of healthy physiology are poorly understood 2. 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 senescence 3, genomic instability 4 and changes in the immune system 2. 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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“All analyses were repeated in independent datasets from the same tissue, resulting in similar observations across tissues (muscle, bone, and liver were taken from the Tabula Muris Senis (Almanzar et al, 2020)) (Figs. EV3–EV5).”
From Distinct senotypes in p16- and p21-positive cells across human and mouse aging tissues (2025) · cites this paper as Almanzar et al, 2020
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“genes upregulated with age in neurons, in particular in inhibitory neurons, were enriched in pathways associated with neurodegenerative diseases, oxidative response, and mitochondria function, whereas genes upregulated with age in non-neuronal cells tended to be associated with inflammatory and immune response”
From Molecular and spatial signatures of mouse brain aging at single-cell resolution (2023) · cites this paper as [19]
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“However, as tissues age at different rates41,42,75,76, there is likely a limit to how well ageing of blood cells can measure and explain mechanisms of ageing of the rest of the body; perhaps this is evidenced by the small (albeit statistically significant) associations between second-generation ageing clocks and organ-specific measures of age-related dysfunction.”
From Measuring biological age using omics data (2022) · cites this paper as [41]
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In the citing paper
“Consistent with previous research13, at a very old age (26 months), mice showed an inflammatory and type I IFN signature in the kidneys and the liver (Fig. 1d and Extended Data Fig. 4a,b).”
From cGAS–STING drives ageing-related inflammation and neurodegeneration (2023) · cites this paper as [13]
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“The presence of a type I IFN signature in brain cells, in particular in microglia, is increasingly being associated with ageing and neurodegenerative disease in various species13,15,39,44–50.”
From cGAS–STING drives ageing-related inflammation and neurodegeneration (2023) · cites this paper as [13]
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“These changes specially affect certain biological processes, such as inflammation, protein folding, extracellular matrix regulation and mitochondrial function, which are widely deregulated in aging 78 .”
From Hallmarks of aging: An expanding universe (2023) · cites this paper as [78]
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“using murine bone marrow from the tabula muris senis (a murine single cell transcriptome atlas of young vs. aged tissues36), the applicability of SenMayo in identifying senescent cells associated with aging was confirmed by GSEA (Fig. 2d).”
From A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues (2022) · cites this paper as [36]
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“Previous transcriptomic studies have revealed widespread changes in cell state in specific cell types with age, with many of these studies highlighting a role for increased inflammation as a key aspect of brain aging.17–20,25–30”
From Molecular and spatial signatures of mouse brain aging at single-cell resolution (2023) · cites this paper as [19]
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“Large mouse studies have observed that the transcriptomes of different organs and cell types have starkly different ageing trajectories41,42,156.”
From Measuring biological age using omics data (2022) · cites this paper as [41]
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In the citing paper
“In total, 33 exhibited continued expression in 2-year-old mice16”
From Active repression of cell fate plasticity by PROX1 safeguards hepatocyte identity and prevents liver tumorigenesis (2025) · cites this paper as [16]
Experimental — accuracy not yet measuredIn the citing paper
“In particular, leukocytes in advanced age acquire a proinflammatory profile (IL-1β, CD14, TNFRSF12A) and show decreased expression of antiinflammatory markers (e.g., CD9, CD88) (110).”
From Connecting aging biology and inflammation in the omics era (2022) · cites this paper as [110]
In the citing paper
“Single-cell RNA sequencing studies have revealed complex patterns of common and organ-specific immune cell changes with aging in mice and humans (109–111).”
From Connecting aging biology and inflammation in the omics era (2022) · cites this paper as [110]
In the citing paper
“One component of this large database was an examination of senescence in older mouse populations which showed that p16 expression increased significantly with aging as did E2f2, Lmnb1, Tnf, and Itgaf [42].”
From The heterogeneity of cellular senescence: insights at the single-cell level (2023) · cites this paper as [42]
In the citing paper
“Tabula Muris Senis, or Mouse Ageing Cell Atlas, is a single-cell RNA-seq database, completed in 2020, that contains sequencing data from 23 mouse tissues across the lifespan [42].”
From The heterogeneity of cellular senescence: insights at the single-cell level (2023) · cites this paper as [42]
In the citing paper
“High-throughput genomic, proteomic and metabolomic methods are enabling the characterization and quantification of thousands of epigenetic marks, transcripts, proteins and metabolites, and are beginning to reveal how complex organisms change globally with age at the molecular level41,42.”
From Measuring biological age using omics data (2022) · cites this paper as [41]
In the citing paper
“In addition, the atlas, as well as the related tissue RNA bulk study,32 demonstrate a change of composition of the cellular constituents.”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [49]
In the citing paper
“A recent mouse aging cell atlas (Tabula muris senis) further underlines this finding across 23 tissues and organs.49”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [49]
In the citing paper
“Indeed, based on scRNA-Seq data, the proportion of cells expressing the senescence marker p16 more than doubled in a number of tissues of old mice compared to young mice in multiple tissues of mouse.49”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [49]
In the citing paper
“Indeed, in the scRNA-Seq study mentioned above, parenchymal cells in multiple tissues exhibited progressive changes in gene expression with age.49”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [49]
In the citing paper
“The infiltration of clonally expanded T cells and B cells is also observed in the brain and other tissues with ageing in mice and humans69,71–73, and it is accentuated in age-related diseases74,75.”
From Ageing and rejuvenation of tissue stem cells and their niches (2023) · cites this paper as [69]
In the citing paper
“Niche cells in many tissues also experience drastic transcriptomic changes with ageing69, which could indirectly impair stem cell function.”
From Ageing and rejuvenation of tissue stem cells and their niches (2023) · cites this paper as [69]
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Self-citation
Metadata
- Authors
- The Tabula Muris Consortium, Almanzar, N., Antony, J., Baghel, A. S., Bakerman, I., Bansal, I., Barres, B. A., Beachy, P. A., Berdnik, D., Bilen, B., Brownfield, D., Cain, C., Chan, C. K. F., Chen, M. B., Clarke, M. F., Conley, S. D., Darmanis, S., Demers, A., Demir, K., de Morree, A., Divita, T., du Bois, H., Ebadi, H., Espinoza, F. H., Fish, M., Gan, Q., George, B. M., Gillich, A., Gòmez-Sjöberg, R., Green, F., Genetiano, G., Gu, X., Gulati, G. S., Hahn, O., Haney, M. S., Hang, Y., Harris, L., He, M., Hosseinzadeh, S., Huang, A., Huang, K. C., Iram, T., Isobe, T., Ives, F., Jones, R. C., Kao, K. S., Karkanias, J., Karnam, G., Keller, A., Kershner, A. M., Khoury, N., Kim, S. K., Kiss, B. M., Kong, W., Krasnow, M. A., Kumar, M. E., Kuo, C. S., Lam, J., Lee, D. P., Lee, S. E., Lehallier, B., Leventhal, O., Li, G., Li, Q., Liu, L., Lo, A., Lu, W.-J., Lugo-Fagundo, M. F., Manjunath, A., May, A. P., Maynard, A., McGeever, A., McKay, M., McNerney, M. W., Merrill, B., Metzger, R. J., Mignardi, M., Min, D., Nabhan, A. N., Neff, N. F., Ng, K. M., Nguyen, P. K., Noh, J., Nusse, R., Pálovics, R., Patkar, R., Peng, W. C., Penland, L., Pisco, A. O., Pollard, K., Puccinelli, R., Qi, Z., Quake, S. R., Rando, T. A., Rulifson, E. J., Schaum, N., Segal, J. M., Sikandar, S. S., Sinha, R., Sit, R. V., Sonnenburg, J., Staehli, D., Szade, K., Tan, M., Tan, W., Tato, C., Tellez, K., Dulgeroff, L. B. T., Travaglini, K. J., Tropini, C., Tsui, M., Waldburger, L., Wang, B. M., van Weele, L. J., Weinberg, K., Weissman, I. L., Wosczyna, M. N., Wu, S. M., Wyss-Coray, T., Xiang, J., Xue, S., Yamauchi, K. A., Yang, A. C., Yerra, L. P., Youngyunpipatkul, J., Yu, B., Zanini, F., Zardeneta, M. E., Zee, A., Zhao, C., Zhang, F., Zhang, H., Zhang, M. J., Zhou, L., & Zou, J.
- Affiliations
- Stanford Medicine (US)
- Howard Hughes Medical Institute (US)
- VA Palo Alto Health Care System (US)
- Stanford University (US)
- Chan Zuckerberg Biohub San Francisco (US)
- University of California, San Francisco (US)
- University of Pittsburgh (US)
- Princess Máxima Center (NL)
- Jagiellonian University (PL)
- Canadian Institute for Advanced Research (CA)
- Harvard University (US)
- Corresponding author
- Darmanis, Spyros
- Quake, Stephen R.
- ORCID
- Almanzar, Nicole
- Antony, Jane
- Baghel, Ankit S.
- Barres, Ben A.
- Beachy, Philip A.
- Bilen, Biter
- Brownfield, Douglas
- Chan, Charles K. F.
- Clarke, Michael F.
- Darmanis, Spyros
- Espinoza, F. Hernán
- Fish, Matt
- Gan, Qiang
- George, Benson M.
- Gillich, Astrid
- Gu, Xueying
- Gulati, Gunsagar S.
- Hahn, Oliver
- Haney, Michael Seamus
- Hang, Yan
- Harris, Lincoln
- He, Mu
- Hosseinzadeh, Shayan
- Huang, Albin
- Huang, Kerwyn Casey
- Iram, Tal
- Isobe, Taichi
- Jones, Robert C.
- Kao, Kevin S.
- Karkanias, Jim
- Keller, Andreas
- Kershner, Aaron M.
- Kim, Seung K.
- Kiss, Bernhard M.
- Krasnow, Mark A.
- Kumar, Maya E.
- Kuo, Christin S.
- Lam, Jonathan
- Lee, Song E.
- Lehallier, Benoit
- Leventhal, Olivia
- Li, Guang
- Li, Qingyun
- Liu, Ling
- Lu, Wan-Jin
- Manjunath, Anoop
- May, Andrew P.
- Maynard, Ashley
- McGeever, Aaron
- McNerney, M. Windy
- Merrill, Bryan
- Metzger, Ross J.
- Mignardi, Marco
- Nabhan, Ahmad N.
- Neff, Norma F.
- Ng, Katharine M.
- Nguyen, Patricia K.
- Noh, Joseph
- Pálovics, Róbert
- Patkar, Rasika
- Peng, Weng Chuan
- Penland, Lolita
- Pisco, Angela Oliveira
- Pollard, Katherine
- Puccinelli, Robert
- Qi, Zhen
- Quake, Stephen R.
- Rando, Thomas A.
- Segal, Joe M.
- Sikandar, Shaheen S.
- Sinha, Rahul
- Sonnenburg, Justin
- Szade, Krzysztof
- Tan, Michelle
- Tato, Cristina
- Tellez, Krissie
- Travaglini, Kyle J.
- Tropini, Carolina
- Tsui, Margaret
- Waldburger, Lucas
- Wang, Bruce M.
- Weinberg, Kenneth
- Weissman, Irving L.
- Wosczyna, Michael N.
- Wu, Sean M.
- Yamauchi, Kevin A.
- Yang, Andrew C.
- Youngyunpipatkul, Justin
- Zanini, Fabio
- Zardeneta, Macy E.
- Zee, Alexander
- Zhao, Chunyu
- Zhang, Fan
- Zhang, Hui
- Zhang, Martin Jinye
- Zhou, Lu
- Zou, James
- Venue
- Nature
- Published
- 2020-07-15
- Volume
- 583
- Issue
- 7817
- Pages
- 590-595
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC8240505
- OpenAlex
- W3179857518
- Cited by
- 1,247
- References
- 63
- Citations by year
- 2026: 195 · 2025: 223 · 2024: 213 · 2023: 222 · 2022: 188 · 2021: 155 · 2020: 48
- FWCI
- 49.57
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Homo Politicus (Academy of Humanities and Economics in Lodz) (submittedVersion, cc-by-nc-nd)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- U.S. Department of Veterans Affairs (IK6 BX004599)
- National Institutes of Health
- Japan Society for the Promotion of Science (19K17831)
- National Institute on Aging (AG053015, DP1AG053015)
- National Heart, Lung, and Blood Institute (K99HL127267, R00HL127267)
- National Institute on Deafness and Other Communication Disorders (R01DC016892)
- National Institute of Diabetes and Digestive and Kidney Diseases (K08DK101603, P30DK026743)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Telomeres, Telomerase, and Senescence
- Keywords
- aging, hallmarks of aging, single-cell transcriptomics, mice, cell composition, cellular senescence, genomic instability, cell type-specific gene expression, lifespan
- MeSH terms
- Aging, Animals, DNA Mutational Analysis, Female, Immunity, Liver, Male, Organ Specificity, T-Lymphocytes, Cellular Senescence, Gene Expression Regulation, Developmental, Models, Animal, Genomic Instability, Mice, Single-Cell Analysis, Transcriptome
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