Ageing hallmarks exhibit organ-specific temporal signatures
Open accessAbstract
Ageing is the single greatest cause of disease and death worldwide, and understanding the associated processes could vastly improve quality of life. Although major categories of ageing damage have been identified-such as altered intercellular communication, loss of proteostasis and eroded mitochondrial function 1-these deleterious processes interact with extraordinary complexity within and between organs, and a comprehensive, whole-organism analysis of ageing dynamics has been lacking. Here we performed bulk RNA sequencing of 17 organs and plasma proteomics at 10 ages across the lifespan of Mus musculus, and integrated these findings with data from the accompanying Tabula Muris Senis 2-or 'Mouse Ageing Cell Atlas'-which follows on from the original Tabula Muris 3. We reveal linear and nonlinear shifts in gene expression during ageing, with the associated genes clustered in consistent trajectory groups with coherent biological functions-including extracellular matrix regulation, unfolded protein binding, mitochondrial function, and inflammatory and immune response. Notably, these gene sets show similar expression across tissues, differing only in the amplitude and the age of onset of expression. Widespread activation of immune cells is especially pronounced, and is first detectable in white adipose depots during middle age. Single-cell RNA sequencing confirms the accumulation of T cells and B cells in adipose tissue-including plasma cells that express immunoglobulin J-which also accrue concurrently across diverse organs. Finally, we show how gene expression shifts in distinct tissues are highly correlated with corresponding protein levels in plasma, thus potentially contributing to the ageing of the systemic circulation. Together, these data demonstrate a similar yet asynchronous inter- and intra-organ progression of ageing, providing a foundation from which to track systemic sources of declining health at old age.
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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 [20]
The Gene expression dynamics with age section reports age-related expression changes enriched for inflammation, and the scRNA-seq section identifies inflammatory genes specific to kidney macrophages. Brain was among the organs sequenced, but the cited passages do not establish increased inflammation in specific brain cell types; the paper emphasizes organism-wide patterns and adipose immune-cell accumulation instead. Page numbers are unavailable in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe findings are broadly consistent with prior aging atlas studies, including Tabula Muris Senis and a recent human liver single-cell aging atlas, which similarly suggest that aging-associated and senescence-related cell states are distributed across multiple parenchymal and non-parenchymal liver populations and are accompanied by inflammatory and stress-associated transcriptional programs.
“Our findings are broadly consistent with prior aging atlas studies, including Tabula Muris Senis and a recent human liver single-cell aging atlas, which similarly suggest that aging-associated and senescence-related cell states are distributed across multiple parenchymal and non-parenchymal liver populations and are accompanied by inflammatory and stress-associated transcriptional programs [10,44,”
From Fluorescence-Guided Spatial Mapping of p21-Expressing Senescent Cells in Aged Liver at Single-Cell Resolution (2026) · cites this paper as [44]
The section “scRNA-seq confirms plasma B cell infiltration” (page not specified) reports an age-related increase in liver B cells that contributes to a whole-organ inflammatory signal. It does not establish senescence-related states across multiple parenchymal and non-parenchymal liver populations; the stress-associated pathways described there concern plasma B cells across organs, not those liver populations. (LLM, conf=0.86)
Experimental — accuracy not yet measuredGenes linked to immune response pathways were among the most differentially expressed with age across organ systems in a mouse lifespan bulk RNA sequencing analysis of 17 tissues at ten time points.
“A recent analysis that used bulk RNA sequencing at ten time points in 17 tissues across the mouse lifespan found that genes linked to immune response pathways were among the most differentially expressed with age across organ systems.”
From Connecting aging biology and inflammation in the omics era (2022) · cites this paper as [107]
Two clusters of immune response genes, including immunoglobulin J chain, β2-microglobulin, and complement C1q A chain (C1QA), were upregulated well before other gene clusters during the transition from middle to late life in mice.
“Two clusters of immune response genes, which included immunoglobulin J chain, β2-microglobulin, and complement C1q A chain (C1QA), were upregulated well before other gene clusters during the transition from middle to late life.”
From Connecting aging biology and inflammation in the omics era (2022) · cites this paper as [107]
Recent investigations propose that ageing constitutes a nonlinear dynamic procedure demarcated by heterogeneity among diverse organs and systems.
“Recent investigations propose that ageing constitutes a nonlinear dynamic procedure demarcated by heterogeneity among diverse organs and systems.86,202”
From Antiageing strategy for neurodegenerative diseases: from mechanisms to clinical advances (2025) · cites this paper as [202]
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 [42]
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 [42]
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 [42]
Transcriptomic studies strongly agree that tissues, organs, and even sub-regions within an organ such as the brain age at different rates.
“As mentioned above, transcriptomic studies strongly agree that tissues, organs, and even sub-regions within an organ such as the brain age at different rates.32,33”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [32]
Transcriptional changes in 17 organs across 10 ages from neonatal to 30-month-old mice show very different trajectories of aging with varied onset and amplitudes of changes.
“Transcriptional changes in 17 organs across 10 ages from neonatal to 30-month-old-mice show very different trajectories of aging with varied onset and amplitudes of changes.32”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [32]
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 [32]
Changes in gene expression can be a readout of cellular deterioration and molecular processes accompanying aging, permitting quantitative comparisons of aging rates between tissues and cell types.
“In contrast, changes in gene expression can be a readout of cellular deterioration and molecular processes accompanying aging, permitting quantitative comparisons of aging rates between tissues (Schaum et al., 2020) and cell types (Buckley et al., 2022).”
From Atlas of the aging mouse brain reveals white matter as vulnerable foci (2023) · cites this paper as Schaum et al., 2020
RNA-seq permits quantitative comparisons of aging rates between organs and cell types based on timing and effect size of gene expression shifts.
“RNA-seq permits quantitative comparisons of aging rates between organs and cell types (Schaum et al., 2020; Tabula Muris Consortium, 2020) based on timing and effect size of gene expression shifts.”
From Atlas of the aging mouse brain reveals white matter as vulnerable foci (2023) · cites this paper as Schaum et al., 2020
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“…human liver single-cell aging atlas, which similarly suggest that aging-associated and senescence-related cell states are distributed across multiple parenchymal and non-parenchymal liver populations and are accompanied by inflammatory and stress-associated transcriptional programs [10,44,45].”
- Authors
- Schaum, N., Lehallier, B., Hahn, O., Pálovics, R., Hosseinzadeh, S., Lee, S. E., Sit, R., Lee, D. P., Losada, P. M., Zardeneta, M. E., Fehlmann, T., Webber, J. T., McGeever, A., Calcuttawala, K., Zhang, H., Berdnik, D., Mathur, V., Tan, W., Zee, A., Tan, M., 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., 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., Y. 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., Pisco, A. O., Karkanias, J., Neff, N. F., Keller, A., Darmanis, S., Quake, S. R., & Wyss-Coray, T.
- Affiliations
- Stanford Medicine (US)
- Saarland University (DE)
- Chan Zuckerberg Biohub San Francisco (US)
- VA Palo Alto Health Care System (US)
- Stanford University (US)
- Howard Hughes Medical Institute (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)
- ORCID
- Losada, Patricia Morán
- Fehlmann, Tobias
- Webber, James T.
- Almanzar, Nicole
- Antony, Jane
- Baghel, Ankit S.
- Barres, Ben A.
- Beachy, Philip A.
- Bilen, Biter
- Brownfield, Douglas
- Chan, Charles K. F.
- Clarke, Michael F.
- Espinoza, F. Hernán
- Fish, Matt
- Gan, Qiang
- George, Benson M.
- Gillich, Astrid
- Gu, Xueying
- Gulati, Gunsagar S.
- Haney, Michael Seamus
- Hang, Yan
- Harris, Lincoln
- He, Mu
- Huang, Albin
- Huang, Kerwyn Casey
- Iram, Tal
- Isobe, Taichi
- Jones, Robert
- Kao, Kevin S.
- Kershner, Aaron M.
- Kim, Seung K.
- Kiss, Bernhard M.
- Krasnow, Mark A.
- Kumar, Maya E.
- Kuo, Christin S.
- Leventhal, Olivia
- Li, Guang
- Li, Qingyun
- Liu, Ling
- Lu, Wan-Jin
- Manjunath, Anoop
- May, Andrew P.
- Maynard, Ashley
- McNerney, M. Windy
- Merrill, Bryan
- Metzger, Ross J.
- Mignardi, Marco
- Nabhan, Ahmad N.
- Ng, Katharine M.
- Nguyen, Patricia K.
- Noh, Joseph
- Patkar, Rasika
- Peng, Weng Chuan
- Penland, Lolita
- Pollard, Katherine
- Puccinelli, Robert
- Qi, Zhen
- Rando, Thomas A.
- Segal, Joe M.
- Sikandar, Shaheen S.
- Sinha, Rahul
- Sonnenburg, Justin
- Szade, Krzysztof
- 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
- Zhao, Chunyu
- Zhang, Fan
- Zhang, Martin Jinye
- Zhou, Lu
- Zou, James
- Venue
- Nature
- Published
- 2020-07-15
- Volume
- 583
- Issue
- 7817
- Pages
- 596-602
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC7757734
- OpenAlex
- W3042406747
- Cited by
- 671
- References
- 36
- Citations by year
- 2026: 116 · 2025: 144 · 2024: 117 · 2023: 111 · 2022: 105 · 2021: 60 · 2020: 18
- FWCI
- 24.89
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Jagiellonian University Repository (Jagiellonian University) (submittedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- U.S. Department of Veterans Affairs (IK6 BX004599, I01 BX004544)
- Japan Society for the Promotion of Science (19K17831)
- National Institute on Aging (R01AG045034, DP1AG053015, T32AG047126, K99AG053438)
- National Heart, Lung, and Blood Institute (R00HL127267)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK026743)
- U.S. National Library of Medicine (DP1LM012179)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
- Keywords
- aging, gene expression trajectories, plasma proteomics, immune cell activation, adipose tissue, T cells, B cells, plasma cells, extracellular matrix, unfolded protein response, mitochondrial function, inflammation, mouse lifespan
- MeSH terms
- RNA-Seq, Aging, Animals, Blood Proteins, Female, Gene Expression Regulation, Immunoglobulin J-Chains, Male, Organ Specificity, Plasma Cells, RNA, Messenger, T-Lymphocytes, Time Factors, Mice, Single-Cell Analysis, Transcriptome
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