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.
“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
The citation identifies Tabula Muris Senis as the source of the datasets, not as evidence that the citing authors repeated their analyses or obtained similar observations. The paper lists limb muscle and liver among its sampled tissues, but lists bone marrow—not bone tissue (Abstract; Experimental Procedures, “Mice and organ collection”; pages unavailable). (LLM, conf=0.85)
Experimental — accuracy not yet measuredGenes upregulated with age in neurons, especially inhibitory neurons, were enriched in neurodegenerative disease, oxidative response, and mitochondria pathways, whereas age-upregulated genes in non-neuronal cells tended to be associated with inflammatory and immune response.
“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]
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredAt very old age (26 months), mice show an inflammatory and type I IFN signature in kidneys and liver.
“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]
The main text (indexed under Abstract; page unavailable) reports an increased immune signature in ageing liver and a pro-inflammatory macrophage state in older kidneys. It does not report a type I IFN signature in both organs or a finding specifically at 26 months; its interferon-responsive gene finding concerns brain microglia. (LLM, conf=0.87)
Experimental — accuracy not yet measuredPresence of a type I IFN signature in brain cells, in particular microglia, is increasingly associated with ageing and neurodegenerative disease in various species.
“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]
The passage on brain myeloid microglia (section labelled Abstract; page unavailable) reports enrichment of interferon-responsive genes in older mice and overlap with a previously reported Alzheimer’s disease microglial signature. It does not identify the signature specifically as type I IFN or establish the association across various species. (LLM, conf=0.85)
Experimental — accuracy not yet measuredUsing murine bone marrow from tabula muris senis, the applicability of SenMayo in identifying senescent cells associated with aging was confirmed by GSEA.
“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]
Previous transcriptomic studies have revealed widespread changes in cell state in specific cell types with age, with many highlighting increased inflammation as a key aspect of brain aging.
“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]
33 candidates exhibited continued expression in 2-year-old mice.
“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]
The study description (section labelled Abstract; page unavailable) confirms that the atlas includes 24-month-old mice, and the Differential gene expression methods describe analyses of older age groups. Neither passage identifies 33 candidates with continued expression. Searches for “33,” “33 candidates expression 24m,” and “continued expression in 2-year-old mice” found no passage establishing that count. (LLM, conf=0.76)
Experimental — accuracy not yet measuredLeukocytes in advanced age acquire a proinflammatory profile (IL-1β, CD14, TNFRSF12A) and show decreased expression of antiinflammatory markers (e.g., CD9, CD88).
“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]
Single-cell RNA sequencing studies have revealed complex patterns of common and organ-specific immune cell changes with aging in mice and humans.
“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 older mouse populations, p16 expression increased significantly with aging, as did E2f2, Lmnb1, Tnf, and Itgaf.
“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]
Tabula Muris Senis is a single-cell RNA-seq database containing sequencing data from 23 mouse tissues across the lifespan.
“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]
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 level.
“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]
As tissues age at different rates, 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.
“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]
Large mouse studies have observed that the transcriptomes of different organs and cell types have starkly different ageing trajectories.
“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]
The atlas, as well as the related tissue RNA bulk study, demonstrate a change of composition of the cellular constituents.
“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]
A recent mouse aging cell atlas (Tabula muris senis) underlines this finding across 23 tissues and organs.
“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]
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.
“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]
Parenchymal cells in multiple tissues exhibited progressive changes in gene expression with age.
“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]
The infiltration of clonally expanded T cells and B cells is also observed in the brain and other tissues with ageing in mice and humans, and it is accentuated in age-related diseases.
“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]
Niche cells in many tissues experience drastic transcriptomic changes with ageing, which could indirectly impair stem cell function.
“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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cGAS–STING drives ageing-related inflammation and neurodegeneration
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“29 and from the tabula muris senis.(35) Sequencing data were aligned to the human reference genome”
MethodAdipose-tissue plasticity in health and disease
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The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans
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Ageing hallmarks exhibit organ-specific temporal signatures
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Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma
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Organ aging signatures in the plasma proteome track health and disease
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Capillary cell-type specialization in the alveolus
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Self-citationSenolysis by glutaminolysis inhibition ameliorates various age-associated disorders
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“19, Imputed genome-wide expression profiles of the cells measured by MERFISH allowed us the determine the spatial distributions of all genes across different ages.”
Senescent cells promote tissue NAD+ decline during ageing via the activation of CD38+ macrophages
Covarrubias, A. J., Kale, A., Perrone, R., Lopez-Dominguez, J. A., Pisco, A. O., Kasler, H. G., Schmidt, M. S., Heckenbach, I., Kwok, R., Wiley, C. D., et al. (2020) · Nature Metabolism · doi:10.1038/s42255-020-00305-3 · Cited by 420 · see citing works
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A Single-Cell Transcriptomic Atlas of Human Skin Aging
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Ageing and rejuvenation of tissue stem cells and their niches
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Self-citationFrom geroscience to precision geromedicine: Understanding and managing aging
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YAP/TAZ activity in stromal cells prevents ageing by controlling cGAS–STING
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Spatial components of molecular tissue biology
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Aging Atlas: a multi-omics database for aging biology
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Self-citationPredicting cellular responses to complex perturbations in high‐throughput screens
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Self-citationThe heterogeneity of cellular senescence: insights at the single-cell level
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Predicting cell-to-cell communication networks using NATMI
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Self-citationHallmarks of cardiovascular ageing
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DestVI identifies continuums of cell types in spatial transcriptomics data
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Regulation of adult stem cell quiescence and its functions in the maintenance of tissue integrity
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Inhibition of IL-11 signalling extends mammalian healthspan and lifespan
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Efficient and precise single-cell reference atlas mapping with Symphony
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Human skeletal muscle aging atlas
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Self-citationCD8+ T cells induce interferon-responsive oligodendrocytes and microglia in white matter aging
Kaya, T., Mattugini, N., Liu, L., Ji, H., Cantuti-Castelvetri, L., Wu, J., Schifferer, M., Groh, J., Martini, R., Besson-Girard, S., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01183-6 · Cited by 224 · see citing works
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Aging-Associated Alterations in Mammary Epithelia and Stroma Revealed by Single-Cell RNA Sequencing
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Self-citationTransformer for one stop interpretable cell type annotation
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APOE4 derived from astrocytes leads to blood–brain barrier impairment
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Single-cell analyses of aging, inflammation and senescence
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Generation of a p16 Reporter Mouse and Its Use to Characterize and Target p16high Cells In Vivo
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Connecting aging biology and inflammation in the omics era
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Self-citationHepatic stellate cell senescence in liver fibrosis: Characteristics, mechanisms and perspectives
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Self-citationTransformers in single-cell omics: a review and new perspectives
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Self-citationDistinct longevity mechanisms across and within species and their association with aging
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Self-citationA single-cell tumor immune atlas for precision oncologyPreprint
Nieto, P., Elosua-Bayés, M., Trincado, J. L., Marchese, D., Massoni-Badosa, R., Salvany, M., Henriques, A. R., Nieto, J. C., Aguilar-Fernández, S., Mereu, E., et al. (2021) · Genome Research · doi:10.1101/gr.273300.120 · Cited by 169 · see citing works
Self-citationSimilarities and interplay between senescent cells and macrophages
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Accurate and fast cell marker gene identification with COSG
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The bright side of fibroblasts: molecular signature and regenerative cues in major organs
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The Senescence Markers p16INK4A, p14ARF/p19ARF, and p21 in Organ Development and Homeostasis
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Targeted tissue delivery of RNA therapeutics using antibody–oligonucleotide conjugates (AOCs)
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Profiling epigenetic age in single cells
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Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging
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Self-citationMolecular hallmarks of heterochronic parabiosis at single-cell resolution
Pálovics, R., Keller, A., Schaum, N., Tan, W., Fehlmann, T., Borja, M., Kern, F., Bonanno, L., Calcuttawala, K., Webber, J., et al. (2022) · Nature · doi:10.1038/s41586-022-04461-2 · Cited by 153 · see citing works
Self-citationSpatiotemporal transcriptomic changes of human ovarian aging and the regulatory role of FOXP1
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Self-citationSpatial metatranscriptomics resolves host–bacteria–fungi interactomes
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Construction of a cross-species cell landscape at single-cell level
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Eliminating Senescent Cells Can Promote Pulmonary Hypertension Development and Progression
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- 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 · 2019: 2
- 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
- Health Sciences › Medicine › Physiology › 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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