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Ageing hallmarks exhibit organ-specific temporal signatures

Nature volume 583, pages 596–602 (2020)Cite this article

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Abstract

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 function1—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 Senis2—or ‘Mouse Ageing Cell Atlas’—which follows on from the original Tabula Muris3. 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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Fig. 1: Pairwise differential expression across organs.
Fig. 2: Ageing gene expression dynamics across organs.
Fig. 3: Integration of bulk and single-cell transcriptomic data identifies cross-tissue infiltration of Igjhigh plasma B cells.
Fig. 4: Correlation of plasma proteins with organ-specific gene expression.

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Data availability

Raw data are available from the Gene Expression Omnibus under accession code GSE132040. Source data are provided with this paper.

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Acknowledgements

We thank members of the Wyss-Coray laboratory for volunteering help during organ collection and processing: E. Berber, K. Brewer, B. Chang, J. Marschallinger, L. Bonanno, J. Sun, M. F. Lugo-Fagundo, A. Yang and M. W. McNerny. We also thank the members of the Wyss-Coray laboratory and the Chan-Zuckerberg Biohub for feedback and support, and H. Zhang and K. Dickey for laboratory management. This work was funded by the Department of Veterans Affairs (BX004599 to T.W.-C.), the National Institute on Aging (R01-AG045034 and DP1-AG053015 to T.W.-C.), the NOMIS Foundation (T.W.-C.), The Glenn Foundation for Medical Research (T.W.-C.) and the Wu Tsai Neurosciences Institute (T.W.-C.).

Author information

Author notes

  1. These authors contributed equally: Nicholas Schaum, Benoit Lehallier, Oliver Hahn

Authors and Affiliations

  1. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Nicholas Schaum, Jane Antony, Ankit S. Baghel, Isaac Bakerman, Ishita Bansal, Philip A. Beachy, Michael F. Clarke, Kubilay Demir, Matt Fish, Benson M. George, Gunsagar S. Gulati, Taichi Isobe, Kevin S. Kao, Aaron M. Kershner, Bernhard M. Kiss, William Kong, Wan-Jin Lu, Anoop Manjunath, Patricia K. Nguyen, Joseph Noh, Nicholas Schaum, Shaheen S. Sikandar, Rahul Sinha, Krzysztof Szade, Laughing Bear Torrez Dulgeroff, Linda J. van Weele, Irving L. Weissman, Sean M. Wu & Jinyi Xiang

  2. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA

    Nicholas Schaum, Benoit Lehallier, Oliver Hahn, Róbert Pálovics, Song E. Lee, Davis P. Lee, Patricia Morán Losada, Macy E. Zardeneta, Kruti Calcuttawala, Hui Zhang, Daniela Berdnik, Vidhu Mathur, Daniela Berdnik, Biter Bilen, Antoine de Morree, Qiang Gan, Oliver Hahn, Michael Seamus Haney, Albin Huang, Tal Iram, Andreas Keller, Nathalie Khoury, Davis P. Lee, Song E. Lee, Benoit Lehallier, Olivia Leventhal, Ling Liu, Maria F. Lugo-Fagundo, Róbert Pálovics, Thomas A. Rando, Nicholas Schaum, Daniel Staehli, Michael N. Wosczyna, Tony Wyss-Coray, Lakshmi P. Yerra, Macy E. Zardeneta, Hui Zhang, Andreas Keller & Tony Wyss-Coray

  3. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Shayan Hosseinzadeh, Rene Sit, James T. Webber, Aaron McGeever, Weilun Tan, Alexander Zee, Michelle Tan, Stephanie D. Conley, Spyros Darmanis, Aaron Demers, Tessa Divita, Hamid Ebadi, Rafael Gòmez-Sjöberg, Foad Green, Geraldine Genetiano, Lincoln Harris, Shayan Hosseinzadeh, Kerwyn Casey Huang, Feather Ives, Robert Jones, Jim Karkanias, Annie Lo, Andrew P. May, Ashley Maynard, Aaron McGeever, Marina McKay, Norma F. Neff, Lolita Penland, Angela Oliveira Pisco, Robert Puccinelli, Zhen Qi, Stephen R. Quake, Rene V. Sit, Justin Sonnenburg, Michelle Tan, Weilun Tan, Cristina Tato, Lucas Waldburger, Kevin A. Yamauchi, Justin Youngyunpipatkul, Brian Yu, Alexander Zee, Chunyu Zhao, James Zou, Angela Oliveira Pisco, Jim Karkanias, Norma F. Neff, Spyros Darmanis & Stephen R. Quake

  4. Veterans Administration Palo Alto Healthcare System, Palo Alto, CA, USA

    Davis P. Lee, Macy E. Zardeneta, Haley du Bois, Davis P. Lee, Thomas A. Rando, Tony Wyss-Coray, Macy E. Zardeneta & Tony Wyss-Coray

  5. Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany

    Tobias Fehlmann, Andreas Keller & Andreas Keller

  6. Department of Bioengineering, Stanford University, Stanford, CA, USA

    Michelle B. Chen, Kerwyn Casey Huang, Marco Mignardi, Stephen R. Quake, Soso Xue, Andrew C. Yang, Fabio Zanini & Stephen R. Quake

  7. Paul F. Glenn Center for the Biology of Aging, Stanford University School of Medicine, Stanford, CA, USA

    Thomas A. Rando, Tony Wyss-Coray & Tony Wyss-Coray

  8. Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA, USA

    Tony Wyss-Coray & Tony Wyss-Coray

  9. Department of Pediatrics, Pulmonary Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Nicole Almanzar, Christin S. Kuo, Dullei Min & Kenneth Weinberg

  10. Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA

    Isaac Bakerman

  11. Department of Medicine, Division of Cardiology, Stanford University School of Medicine, Stanford, CA, USA

    Isaac Bakerman

  12. Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA

    Ben A. Barres, Qingyun Li & Lu Zhou

  13. Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA

    Douglas Brownfield, F. Hernán Espinoza, Astrid Gillich, Mark A. Krasnow, Christin S. Kuo, Ahmad N. Nabhan, Katharine M. Ng, Roel Nusse & Kyle J. Travaglini

  14. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Kubilay Demir, Matt Fish, Mark A. Krasnow, Christin S. Kuo & Roel Nusse

  15. Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA

    Philip A. Beachy, Matt Fish, Xueying Gu, Yan Hang, Seung K. Kim, Jonathan Y. Lam, Roel Nusse, Weng Chuan Peng, Eric J. Rulifson & Krissie Tellez

  16. Flow Cytometry Core, Veterans Administration Palo Alto Healthcare System, Palo Alto, CA, USA

    Corey Cain

  17. Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University, Stanford, CA, USA

    Charles K. F. Chan

  18. Department of Physiology, University of California, San Francisco, CA, USA

    Mu He

  19. Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA

    Kerwyn Casey Huang, Bryan Merrill, Justin Sonnenburg & Carolina Tropini

  20. Department of Medicine and Liver Center, University of California San Francisco, San Francisco, CA, USA

    Guruswamy Karnam, Rasika Patkar, Joe M. Segal, Margaret Tsui & Bruce M. Wang

  21. Department of Medicine and Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA

    Seung K. Kim

  22. Department of Urology, Stanford University School of Medicine, Stanford, CA, USA

    Philip A. Beachy & Bernhard M. Kiss

  23. Sean N. Parker Center for Asthma and Allergy Research, Stanford University School of Medicine, Stanford, CA, USA

    Maya E. Kumar

  24. Department of Medicine, Division of Pulmonary and Critical Care, Stanford University School of Medicine, Stanford, CA, USA

    Maya E. Kumar

  25. Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA

    Guang Li & Sean M. Wu

  26. Mental Illness Research Education and Clinical Center, Veterans Administration Palo Alto Healthcare System, Palo Alto, CA, USA

    M. Windy McNerney

  27. Department of Psychiatry, Stanford University School of Medicine, Stanford, CA, USA

    M. Windy McNerney

  28. Vera Moulton Wall Center for Pulmonary and Vascular Disease, Stanford University School of Medicine, Stanford, CA, USA

    Ross J. Metzger & Fan Zhang

  29. Department of Pediatrics, Division of Cardiology, Stanford University School of Medicine, Stanford, CA, USA

    Ross J. Metzger & Fan Zhang

  30. Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA

    Katherine Pollard

  31. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    Rahul Sinha & Irving L. Weissman

  32. Ludwig Center for Cancer Stem Cell Research and Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Rahul Sinha & Irving L. Weissman

  33. Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA

    Rahul Sinha & Irving L. Weissman

  34. Department of Medical Biotechnology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Krakow, Poland

    Krzysztof Szade

  35. Department of Electrical Engineering, Stanford University, Palo Alto, CA, USA

    Martin Jinye Zhang & James Zou

  36. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Martin Jinye Zhang

  37. Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA

    James Zou

  38. Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Guang Li

  39. Cardiology Section, Veterans Administration Palo Alto Healthcare System, Palo Alto, CA, USA

    Patricia K. Nguyen

  40. Princess Máxima Center for Pediatric, Oncology, Utrecht, The Netherlands

    Weng Chuan Peng

  41. School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada

    Carolina Tropini

  42. Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada

    Carolina Tropini

  43. Humans and the Microbiome Program, Canadian Institute for Advanced Research, Toronto, Ontario, Canada

    Carolina Tropini

Authors
  1. Nicholas Schaum
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  2. Benoit Lehallier
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  3. Oliver Hahn
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  4. Róbert Pálovics
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  5. Shayan Hosseinzadeh
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  11. Tobias Fehlmann
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  14. Kruti Calcuttawala
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  15. Hui Zhang
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  27. Tony Wyss-Coray
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Consortia

The Tabula Muris Consortium

  • Nicole Almanzar
  • , Jane Antony
  • , Ankit S. Baghel
  • , Isaac Bakerman
  • , Ishita Bansal
  • , Ben A. Barres
  • , Philip A. Beachy
  • , Daniela Berdnik
  • , Biter Bilen
  • , Douglas Brownfield
  • , Corey Cain
  • , Charles K. F. Chan
  • , Michelle B. Chen
  • , Michael F. Clarke
  • , Stephanie D. Conley
  • , Spyros Darmanis
  • , Aaron Demers
  • , Kubilay Demir
  • , Antoine de Morree
  • , Tessa Divita
  • , Haley du Bois
  • , Hamid Ebadi
  • , F. Hernán Espinoza
  • , Matt Fish
  • , Qiang Gan
  • , Benson M. George
  • , Astrid Gillich
  • , Rafael Gòmez-Sjöberg
  • , Foad Green
  • , Geraldine Genetiano
  • , Xueying Gu
  • , Gunsagar S. Gulati
  • , Oliver Hahn
  • , Michael Seamus Haney
  • , Yan Hang
  • , Lincoln Harris
  • , Mu He
  • , Shayan Hosseinzadeh
  • , Albin Huang
  • , Kerwyn Casey Huang
  • , Tal Iram
  • , Taichi Isobe
  • , Feather Ives
  • , Robert Jones
  • , Kevin S. Kao
  • , Jim Karkanias
  • , Guruswamy Karnam
  • , Andreas Keller
  • , Aaron M. Kershner
  • , Nathalie Khoury
  • , Seung K. Kim
  • , Bernhard M. Kiss
  • , William Kong
  • , Mark A. Krasnow
  • , Maya E. Kumar
  • , Christin S. Kuo
  • , Jonathan Y. Lam
  • , Davis P. Lee
  • , Song E. Lee
  • , Benoit Lehallier
  • , Olivia Leventhal
  • , Guang Li
  • , Qingyun Li
  • , Ling Liu
  • , Annie Lo
  • , Wan-Jin Lu
  • , Maria F. Lugo-Fagundo
  • , Anoop Manjunath
  • , Andrew P. May
  • , Ashley Maynard
  • , Aaron McGeever
  • , Marina McKay
  • , M. Windy McNerney
  • , Bryan Merrill
  • , Ross J. Metzger
  • , Marco Mignardi
  • , Dullei Min
  • , Ahmad N. Nabhan
  • , Norma F. Neff
  • , Katharine M. Ng
  • , Patricia K. Nguyen
  • , Joseph Noh
  • , Roel Nusse
  • , Róbert Pálovics
  • , Rasika Patkar
  • , Weng Chuan Peng
  • , Lolita Penland
  • , Angela Oliveira Pisco
  • , Katherine Pollard
  • , Robert Puccinelli
  • , Zhen Qi
  • , Stephen R. Quake
  • , Thomas A. Rando
  • , Eric J. Rulifson
  • , Nicholas Schaum
  • , Joe M. Segal
  • , Shaheen S. Sikandar
  • , Rahul Sinha
  • , Rene V. Sit
  • , Justin Sonnenburg
  • , Daniel Staehli
  • , Krzysztof Szade
  • , Michelle Tan
  • , Weilun Tan
  • , Cristina Tato
  • , Krissie Tellez
  • , Laughing Bear Torrez Dulgeroff
  • , Kyle J. Travaglini
  • , Carolina Tropini
  • , Margaret Tsui
  • , Lucas Waldburger
  • , Bruce M. Wang
  • , Linda J. van Weele
  • , Kenneth Weinberg
  • , Irving L. Weissman
  • , Michael N. Wosczyna
  • , Sean M. Wu
  • , Tony Wyss-Coray
  • , Jinyi Xiang
  • , Soso Xue
  • , Kevin A. Yamauchi
  • , Andrew C. Yang
  • , Lakshmi P. Yerra
  • , Justin Youngyunpipatkul
  • , Brian Yu
  • , Fabio Zanini
  • , Macy E. Zardeneta
  • , Alexander Zee
  • , Chunyu Zhao
  • , Fan Zhang
  • , Hui Zhang
  • , Martin Jinye Zhang
  • , Lu Zhou
  •  & James Zou

Contributions

N.S., B.L., S.R.Q. and T.W.-C. conceptualized the study. N.S., O.H., B.L., R.P. and T.W.-C. conceptualized the analysis. O.H., B.L. and R.P., with contributions from A.K. and T.F., conducted the transcriptomic analyses. N.S., S.E.L., D.P.L., M.E.Z., H.Z. and D.B. collected samples and extracted RNA. S.H., A.Z. and W.T. conducted cDNA and library preparation. P.M.L. created the Shiny web interface. R.S. and M.T. performed sequencing and library quality control. S.H., A.O.P., J.T.W. and A.M. processed raw sequencing data. The Tabula Muris Consortium generated the single-cell sequencing database. N.S. and K.C. performed and analysed FACS and RNAscope experiments. N.S., B.L., O.H., T.W.-C. and S.R.Q. wrote and edited the manuscript. T.W.-C., S.R.Q., S.D., N.F.N. and J.K. supervised the work. A full list of author contributions for The Tabula Muris Consortium can be found in the Supplementary Information.

Corresponding authors

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

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Fan Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Gene expression variance analysis.

a, Visualization of the principal variance component analysis, displaying the gene expression variance explained by residuals (that is, biological and technical noise) or experimental factors such as tissue, age, sex and respective combinations. n = 904 total samples. b, c, t-SNE visualization of all samples, based on the first six principal components coloured by age (b) and sex (c). d, Hierarchical clustering of all samples using Ward’s algorithm. Samples are annotated by tissue, sex and age. Highlighted are samples clustering by sex in selected tissues. Non-specific clustering of samples derived from white adipose tissues is further highlighted.

Extended Data Fig. 2 Validation of differential gene expression analysis.

a, Heat map displaying the number of DEGs per tissue for pairwise analysis on adjacent time points. b, Heat map displaying the number of DEGs per tissue for pairwise comparisons, referenced to tissues from 1-month-old mice. c, Heat map displaying the number of DEGs per tissue for pairwise comparisons, referenced to 6-month tissues. d, Box plot displaying the number of DEGs per tissue (n = 17 tissues) for pairwise comparisons with a 3-month reference. In box plots, the centre line denotes the mean, box boundaries denote the first and third quartiles. Outliers show tissues undergoing exceptionally strong expression shifts at a given age. e, Enrichment for functional categories in the top 100 genes that are differentially expressed in the most tissues (ranked using pairwise comparisons with a 3-month reference). Pathway enrichment with GO, Reactome and KEGG databases. Enrichment was tested using Fisher’s exact test (GO) and the hypergeometric test (Reactome and KEGG). To estimate the contribution of each tissue, we used the number of genes per pathway in the top 100 DEGs and estimated the percentage of significant genes per tissue. q-values were estimated using the Benjamini–Hochberg procedure for each database separately, and for GO classes (molecular function, cellular component, biological process) independently. Values of n are as in d. f, Cumulative sum of DEGs per tissue in the ranked top 100 genes. g, Number of DEGs per tissue in the top 100 genes. n = 54 (MAT), 52 (kidney), 52 (GAT), 54 (spleen), 50 (liver), 54 (lung), 50 (intestine), 55 (SCAT), 51 (skin), 53 (BAT), 52 (heart), 52 (muscle), 53 (brain), 52 (WBC), 54 (bone), 51 (marrow), 46 (pancreas). q-values are as in e. h, STRING analysis of the top 30 genes in Fig. 1g.

Extended Data Fig. 3 SOMs of gene correlation with age and sex.

a, b, SOMs were generated from transcriptome-wide gene expression correlation (Spearman’s rank correlation coefficient) of each gene (n = 12,462 genes) with age (a) and sex (b). Genes with similar correlation are mapped to the same cell, and cells are grouped by similarity. The SOM cell layout is common across organs, with the average across all organs at the bottom.

Extended Data Fig. 4 Sex-specific expression changes across organs.

a, Smoothed line plot displaying the number of DEGs between female and male mice at each age. Positive (negative) values represent upregulated (downregulated) genes. Grey lines represent all other tissues. b, Heat map representation of a. c, Expression of Apoe mRNA in GAT and Axin2 mRNA in spleen. The black line indicates LOESS regression. n = 45 (GAT) and n = 47 (spleen) independent samples. d, Venn diagrams depicting the overlap of DEGs in female and male mice detected at 3 months and 18 months of age in GAT, SCAT, liver and kidney. One-sided Fisher’s exact test, ***P < 0.0001. e–h, Top 10 GO terms enriched among the DEGs between female and male mice at 18 months of age in GAT (e), SCAT (f), liver (g) and kidney (h). Data are mean ± s.e.m. n = 2 (female) and n = 4 (male) independent mice for each organ. q values were estimated using the Benjamini–Hochberg procedure for each database separately, and for GO classes (molecular function, cellular component, biological process) independently.

Extended Data Fig. 5 Organ-specific gene expression dynamics.

For each of the 17 organs (rows), the average trajectory of the 15,000 most highly expressed genes is represented in column 1. Five clusters were used (columns 2–6) for further analysis. Average trajectories for each cluster ± s.d. are represented.

Extended Data Fig. 6 Pathway enrichment analysis of organ-specific clusters.

Clusters from Extended Data Fig. 5 show enrichment for genes in functional categories. Pathway enrichment was tested using GO, Reactome and KEGG databases. Enrichment was tested using Fisher’s exact test (GO) and the hypergeometric test (Reactome and KEGG). The top five pathways for each cluster are shown. q values were estimated with the Benjamini–Hochberg procedure for each database separately, and for GO classes (molecular function, cellular component, biological process) independently. Sample size per cluster or tissue is indicated in Extended Data Fig. 5.

Extended Data Fig. 7 Analysis of cytokines and transcription factors.

a, Age-related changes in inflammatory cytokine and chemokines (cytokine-mediated signalling pathways GO:0019221; n = 501 genes), and transcription factors (TRANSFAC database; n = 334 genes). Thicker lines outlined with white represent the average trajectory for each cluster ± s.d. b, c, Spearman correlation coefficient for ageing genes in a.

Extended Data Fig. 8 Integration of bulk and single-cell transcriptomic data.

a, b, Representative GO terms enriched among the genes with highly disperse (a) and cell-specific (b) expression patterns. n = 1,108 cells. q values were estimated using the Benjamini–Hochberg procedure for each database separately, and for GO classes (molecular function, cellular component, biological process) independently. c, Expression of Aco2 mRNA in the kidney. Black line shows LOESS regression; ρ, Spearman’s rank correlation coefficient. n = 52 independent samples. Data are mean ± s.e.m. d, e, t-SNE visualization of scRNA-seq data (FACS) from the kidney, coloured by expression of Aco2 (d) and Cs (e). n = 1,108 cells. f, Violin plot representing expression of Aco1 and Aco2 across all profiled cell types in the kidney. Points indicate cell-wise expression levels and the violin indicates average distribution split by age. Significance was assessed using a Student’s t-test. n = 325 cells (3 months) and 783 cells (24 months). g, Spearman’s rank correlation for cell type fractions significantly (P < 0.05) changing with age, based on deconvolution with FACS or droplet scRNA-seq expression signatures. n = 38 (FACS, BAT), n = 37 (droplet, GAT), n = 37 (FACS, GAT), n = 34 (droplet, kidney), n = 35 (FACS, kidney), n = 35 (droplet, liver), n = 35 (FACS, liver), n = 37 (droplet, lung), n = 37 (FACS, lung), n = 38 (droplet, marrow), n = 36 (FACS, marrow), n = 38 (droplet, MAT), n = 39 (FACS, MAT), n = 34 (droplet, pancreas), n = 32 (FACS, pancreas), n = 37 (droplet, SCAT), n = 38 (FACS, SCAT), n = 35 (droplet, skin), n = 33 (FACS, skin), n = 36 (droplet, spleen), n = 37 (FACS, spleen) independent samples. h, Pairwise comparisons cell fractions between scRNA-seq (FACS), scRNA-seq (droplet), FACS-based bulk RNA-seq deconvolution and droplet-based bulk RNA-seq deconvolution. Each point represents an individual cell type in an individual tissue type.

Extended Data Fig. 9 Identifying Igjhigh B cells with FACS and droplet scRNA-seq.

a, t-SNE visualization of all Cd79a-expressing cells present in the Tabula Muris Senis FACS dataset (17 tissues). Coloured clusters as identified with the Seurat software toolkit. Igjhigh B cell cluster 11 is highlighted. n = 10,867 cells. b, t-SNE in a coloured by the Igjhigh B cell markers Igj, Xbp1 and Derl3. c, GO terms enriched among the top 300 marker genes of Igjhigh (n = 129 cells) versus Igjlow(n = 10,738 cells) (FACS). q values were estimated using the Benjamini–Hochberg procedure for each database separately, and for GO classes (molecular function, cellular component, biological process) independently. d, Distribution of Igjhigh as percentages of Cd79a-expressing cells per tissue. e, Percentage of Igjhigh B cells of all Cd79a-expressing cells across all tissues. n = 5 (3 months) and n = 4 (24 months) independent mice. Significance was assessed using a Student’s t-test. Data are mean ± s.e.m. f, t-SNE visualization of all Cd79a-expressing cells present in the Tabula Muris Senis droplet dataset (17 tissues). Coloured clusters are as identified with the Seurat software toolkit. IgJhigh B cell cluster 5 is highlighted. n = 23,796 cells. g, t-SNE in f coloured by the B cell marker Cd79a and Igjhigh B cell marker Derl3. h, Percentage of Igjhigh B cells of all Cd79a-expressing cells across all tissues. i, Heat map of the z-transformed Igj expression trajectories across bone (n = 54), marrow (n = 51), spleen (n = 54), liver (n = 50), GAT (n = 52), kidney (n = 52), heart (n = 52) and muscle (n = 52). j, Change in Igj mRNA expression in human visceral fat (aged 20–29, n = 25; aged 50–59, n = 124; aged 70–79, n = 12) and subcutaneous fat (aged 20–29, n = 32; aged 50–59, n = 149; aged 70–79, n = 13) (data from GTEx consortium). In the box plot, the median is represented by the centre line and the box boundaries represent the first and third quartiles. k, Number of Igjhigh B cells with a successfully assembled B cell receptor locus, split by mouse and immunoglobulin class. l, Clonally amplified Igjhigh B cells as detected in mouse 1 and mouse 3, grouped by tissue of origin (colour) and immunoglobulin class (shape).

Extended Data Fig. 10 STRING analysis of top correlating plasma proteins.

a, The top seven plasma proteins correlated with gene expression in muscle, coloured by pathway. b, The top 25 plasma proteins correlated with gene expression in any organ.

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Supplementary Figure

Supplementary Figure 1. Example gating strategy for detecting plasma cells. Gating strategy for detecting plasma cells as in Figure 3j.

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This file contains Supplementary Tables 1-15.

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Schaum, N., Lehallier, B., Hahn, O. et al. Ageing hallmarks exhibit organ-specific temporal signatures. Nature 583, 596–602 (2020). https://doi.org/10.1038/s41586-020-2499-y

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