Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Molecular hallmarks of heterochronic parabiosis at single-cell resolution

Nature volume 603, pages 309–314 (2022)Cite this article

Subjects

Abstract

The ability to slow or reverse biological ageing would have major implications for mitigating disease risk and maintaining vitality1. Although an increasing number of interventions show promise for rejuvenation2, their effectiveness on disparate cell types across the body and the molecular pathways susceptible to rejuvenation remain largely unexplored. Here we performed single-cell RNA sequencing on 20 organs to reveal cell-type-specific responses to young and aged blood in heterochronic parabiosis. Adipose mesenchymal stromal cells, haematopoietic stem cells and hepatocytes are among those cell types that are especially responsive. On the pathway level, young blood invokes new gene sets in addition to reversing established ageing patterns, with the global rescue of genes encoding electron transport chain subunits pinpointing a prominent role of mitochondrial function in parabiosis-mediated rejuvenation. We observed an almost universal loss of gene expression with age that is largely mimicked by parabiosis: aged blood reduces global gene expression, and young blood restores it in select cell types. Together, these data lay the groundwork for a systemic understanding of the interplay between blood-borne factors and cellular integrity.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Cell-type-specific DGE.
Fig. 2: Young blood reverses mitochondrial and global gene expression loss.
Fig. 3: Structured responses to parabiosis.
Fig. 4: Coordinated, organism-wide cellular responses to ageing and parabiosis.

Code availability

Code used for downstream analyses of the raw count matrices is available at GitHub:https://github.com/rpalovics/parabiosis/releases/tag/0.1.0 (https://doi.org/10.5281/zenodo.5762659).

Data availability

The dataset can be explored interactively at https://ccb-web.cs.uni-saarland.de/parabiosis/. Gene counts and metadata are available on Figshare: https://figshare.com/projects/Molecular_hallmarks_of_heterochronic_parabiosis_at_single_cell_resolution/127628. Raw sequencing data are available in the Gene Expression Omnibus under accession code GSE132042 and via the following public AWS S3 bucket: https://registry.opendata.aws/tabula-muris-senis/. The versions of the noted databases used are available at the following URLs: STRING database, https://version-11-0.string-db.org/; MitoCarta 3.0, https://www.broadinstitute.org/files/shared/metabolism/mitocarta/mouse.mitocarta3.0.html; GENCODE vM19, https://www.gencodegenes.org/mouse/release_M19.html.

References

  1. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Mahmoudi, S., Xu, L. & Brunet, A. Turning back time with emerging rejuvenation strategies. Nat. Cell Biol. 21, 32–43 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Schaum, N. et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    Article  ADS  PubMed Central  Google Scholar 

  4. Schaum, N. et al. Ageing hallmarks exhibit organ-specific temporal signatures. Nature 583, 596–602 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. Almanzar, N. et al. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020).

    Article  CAS  Google Scholar 

  6. Castellano, J. M., Kirby, E. D. & Wyss-Coray, T. Blood-borne revitalization of the aged brain. JAMA Neurol. 72, 1191–1194 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Villeda, S. A. et al. The ageing systemic milieu negatively regulates neurogenesis and cognitive function. Nature 477, 90–96 (2011).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. Katsimpardi, L. et al. Vascular and neurogenic rejuvenation of the aging mouse brain by young systemic factors. Science 344, 630–634 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Smith, L. K. et al. β2-microglobulin is a systemic pro-aging factor that impairs cognitive function and neurogenesis. Nat. Med. 21, 932–937 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Khrimian, L. et al. Gpr158 mediates osteocalcin’s regulation of cognition. J. Exp. Med. 214, 2859–2873 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Conboy, I. M. et al. Rejuvenation of aged progenitor cells by exposure to a young systemic environment. Nature 433, 760–764 (2005).

    Article  ADS  CAS  PubMed  Google Scholar 

  12. Ma, S. et al. Caloric restriction reprograms the single-cell transcriptional landscape of Rattus norvegicus aging. Cell 180, 984–1001 (2020).

    Article  CAS  PubMed  Google Scholar 

  13. Das, M. M. et al. Young bone marrow transplantation preserves learning and memory in old mice. Commun. Biol. 2, 73 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Baht, G. S. et al. Exposure to a youthful circulation rejuvenates bone repair through modulation of β-catenin. Nat. Commun. 6, 7131 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  15. Kovina, M. V., Zuev, V. A., Kagarlitskiy, G. O. & Khodarovich, Y. M. Effect on lifespan of high yield non-myeloablating transplantation of bone marrow from young to old mice. Front. Genet. https://doi.org/10.3389/fgene.2013.00144 (2013).

  16. Wang, C.-H. et al. Bone marrow rejuvenation accelerates re-endothelialization and attenuates intimal hyperplasia after vascular injury in aging mice. Circ. J. 77, 3045–3053 (2013).

    Article  CAS  PubMed  Google Scholar 

  17. Smith, L. K. et al. The aged hematopoietic system promotes hippocampal-dependent cognitive decline. Aging Cell 19, e13192 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Lee, S. S. et al. A systematic RNAi screen identifies a critical role for mitochondria in C. elegans longevity. Nat. Genet. 33, 40–48 (2003).

    Article  CAS  PubMed  Google Scholar 

  19. Zhang, M. J., Pisco, A. O., Darmanis, S. & Zou, J. Mouse Aging Cell Atlas analysis reveals global and cell type specific aging signatures revision 1. Elife 10, e62293 (2021).

  20. Kular, J. K., Basu, S. & Sharma, R. I. The extracellular matrix: structure, composition, age-related differences, tools for analysis and applications for tissue engineering. J. Tissue Eng. 5, https://doi.org/10.1177/2041731414557112 (2014).

  21. Pinti, M. et al. Aging of the immune system: focus on inflammation and vaccination. Eur. J. Immunol. 46, 2286–2301 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Villeda, S. A. et al. Young blood reverses age-related impairments in cognitive function and synaptic plasticity in mice. Nat. Med. 20, 659–663 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    Article  CAS  PubMed  Google Scholar 

  24. Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  25. Picelli, S. et al. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res. 24, 2033–2040 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hennig, B. P. et al. Large-scale low-cost NGS library preparation using a robust Tn5 purification and tagmentation protocol. G3 (Bethesda) 8, 79–89 (2018).

    Article  CAS  Google Scholar 

  27. Luecken, M. D. & Theis, F. J. Current best practices in single‐cell RNA‐seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Polański, K. et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36, 964–965 (2020).

    PubMed  Google Scholar 

  30. Traag, V. A., Waltman, L. & Van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. McInnes, L., Healy, J. & Melville, J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018) .

  32. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Pedregosa, F. et al. Scikit-learn: machine Learning in Python. J. Machine Learn. Res. 12, 2825–2830 (2011).

    MathSciNet  MATH  Google Scholar 

  34. Mann, H. B. & Whitney, D. R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18, 50–60 (1947).

    Article  MathSciNet  MATH  Google Scholar 

  35. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    MathSciNet  MATH  Google Scholar 

  36. Enge, M. et al. Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns. Cell 171, 321–330 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kimmel, J. C. et al. Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging. Genome Res. 29, 2088–2103 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Gerstner, N. et al. GeneTrail 3: advanced high-throughput enrichment analysis. Nucleic Acids Res. 48, W515–W520 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Carbon, S. et al. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2019).

    Article  CAS  Google Scholar 

  40. Rath, S. et al. MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations. Nucleic Acids Res. 49, D1541–D1547 (2021).

    Article  CAS  PubMed  Google Scholar 

  41. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

    Article  CAS  PubMed  Google Scholar 

  42. Wickham, H. ggplot2. Wiley Interdiscip. Rev. Comput. Stat. 3, 180–185 (2011).

    Article  Google Scholar 

  43. Hagberg, A., Swart, P. & Schult, D. Exploring Network Structure, Dynamics, and Function Using Networkx (Los Alamos National Laboratory, 2008); https://www.osti.gov/servlets/purl/960616.

  44. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    Article  CAS  PubMed  Google Scholar 

  45. Ewels, P. A. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. 38, 276–278 (2020).

    Article  CAS  PubMed  Google Scholar 

  46. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  47. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 12, 323 (2011).

    Article  CAS  Google Scholar 

  48. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences [version 1; peer review: 2 approved]. F1000Research 4, 1521 (2015).

    Article  PubMed  Google Scholar 

  49. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinform. 14, 128 (2013).

    Article  Google Scholar 

Download references

Acknowledgements

We thank the members of the laboratory of T.W.-C. and the Chan Zuckerberg Biohub for feedback and support. Financial support for library preparation, sequencing and AWS time was provided by the Chan Zuckerberg Biohub. Further financial support was provided by the Department of Veterans Affairs (BX004599 to T.W.-C.), the National Institute on Aging (R01-AG045034, AG064928, AG072255, and DP1-AG053015 to T.W.-C.), the NOMIS Foundation (T.W.-C.), The Glenn Foundation for Medical Research (T.W.-C.), Nan Fung Life Sciences (T.W.-C.), and the Wu Tsai Neurosciences Institute and Bertarelli Foundation (T.W.-C.). This work was supported by the National Institute of Aging and the National Institutes of Health under award number P30AG059307.

Author information

Author notes

  1. These authors contributed equally: Róbert Pálovics, Andreas Keller, Nicholas Schaum, Weilun Tan

Authors and Affiliations

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

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

  2. Clinical Bioinformatics, Saarland University, Saarbrücken, Germany

    Andreas Keller, Tobias Fehlmann & Fabian Kern

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

    Weilun Tan, Michael Borja, James Webber, Aaron McGeever, Stephanie D. Conley, Aaron Demers, Tessa Divita, Hamid Ebadi, Rafael Gòmez-Sjöberg, Foad Green, Geraldine Genetiano, Lincoln Harris, Shayan Hosseinzadeh, Kerwyn Casey Huang, Feather Ives, Robert C. Jones, Annie Lo, Andrew P. May, Ashley Maynard, Marina McKay, Lolita Penland, Robert Puccinelli, Rene V. Sit, Justin Sonnenburg, Michelle 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

    Haley du Bois, Olivia Leventhal, Thomas A. Rando & Jian Luo

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

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

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

    Thomas A. Rando, Macy E. Zardeneta & Tony Wyss-Coray

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

    Philip A. Beachy & Tony Wyss-Coray

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

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

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

    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, Zhen Qi, Shaheen S. Sikandar, Rahul Sinha, Krzysztof Szade, Laughing Bear Torrez Dulgeroff, Linda J. van Weele, Irving L. Weissman & Jinyi Xiang

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

    Isaac Bakerman, Patricia K. Nguyen & Sean M. Wu

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

    Isaac Bakerman, Guang Li, Patricia K. Nguyen & Sean M. Wu

  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

    Philip A. Beachy, Douglas Brownfield, F. Hernán Espinoza, Astrid Gillich, Mark A. Krasnow, Wan-Jin Lu, Ahmad N. Nabhan, Katharine M. Ng, Roel Nusse & Kyle J. Travaglini

  14. Maternal & Child Health Research Institute, Stanford University School of Medicine, Stanford, CA, USA

    Philip A. Beachy, Kubilay Demir, Matt Fish, Mark A. Krasnow, Roel Nusse & Sean M. Wu

  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 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 Biomedicine, Aarhus University, Aarhus, Denmark

    Antoine de Morree

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

    Mu He

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

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

  21. 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

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

    Seung K. Kim

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

    Bernhard M. Kiss

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

    Maya E. Kumar

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

    Maya E. Kumar

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

    Guang Li

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

    M. Windy McNerney

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

    M. Windy McNerney

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

    Ross J. Metzger & Fan Zhang

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

    Ross J. Metzger & Fan Zhang

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

    Weng Chuan Peng

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

    Katherine Pollard

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

    Rahul Sinha & Irving L. Weissman

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

    Rahul Sinha & Irving L. Weissman

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

    Rahul Sinha & Irving L. Weissman

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

    Krzysztof Szade

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

    Carolina Tropini

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

    Carolina Tropini

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

    Carolina Tropini

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

    Martin Jinye Zhang & James Zou

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

    Martin Jinye Zhang

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

    James Zou

Authors
  1. Róbert Pálovics
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andreas Keller
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nicholas Schaum
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weilun Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tobias Fehlmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Michael Borja
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Fabian Kern
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Liana Bonanno
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kruti Calcuttawala
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. James Webber
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Aaron McGeever
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jian Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Angela Oliveira Pisco
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Jim Karkanias
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Norma F. Neff
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Spyros Darmanis
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Stephen R. Quake
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Tony Wyss-Coray
    View author publications

    You can also search for this author in PubMed Google Scholar

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 C. 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 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

R.P., A.K., N.S. and W.T. contributed equally. N.S., S.R.Q. and T.W.-C. conceptualized the study. R.P., A.K., N.S., T.F. and T.W.-C. conceptualized the analysis. R.P., A.K., T.F. and F.K. conducted the analysis. N.S., L.B. and J.L. performed parabiosis surgeries. N.S. and K.C. collected and processed bulk organ samples for RNA-seq. The Tabula Muris Consortium processed organs and captured cells for scRNA-seq. W.T. and M.B. conducted cDNA and library preparation. R.P. created the web browser. W.T. and M.B. performed sequencing and library quality control. W.T., M.B., A.O.P., J.W. and A.M. processed raw sequencing data. R.P., A.K., N.S., S.R.Q. and T.W.-C. wrote and edited the manuscript. T.W.-C., S.R.Q., S.D., N.F.N., J.K. and A.O.P supervised the work.

Corresponding authors

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

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Johan Auwerx, Yousin Suh, Fan Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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 Outline of data analyses and tissue specific data overview.

a, Outline of computational analyses. Single-cell count data are processed per tissue, see Methods ‘Quality control’-’Cell type annotation’. Differential gene expression is then conducted per cell type and comparison (AGE, ACC, REJ) within each tissue, see Methods ‘Differential gene expression’. All of the next panels present data after quality control. b, Number of cells per tissue and replicate. Replicates are colored by their condition. c, Number of replicates per tissue. Replicates are colored by their condition. d, Total number of cells per tissue. e, Fraction of cells within each condition per tissue. f-i, For each experimental condition within each tissue: total read counts (f), the percent of reads mapped to ribosomal genes (g), mitochondrial genes (h), and ERCC spike-ins (i) plotted against the mean number of genes expressed. j, Average LISI scores of mouse replicates calculated over the batch corrected tissue specific UMAP embeddings plotted against the mean LISI scores of tissue specific UMAP embeddings calculated from neighborhood graphs without batch correction. k, Mean entropy batch mixing of mouse replicates calculated over the tissue specific batch-corrected neighborhood graph plotted against the mean entropy batch mixing calculated from neighborhood graphs without batch correction. l, Result of saturation analyses shown per condition (Y, A, IY, HY, IA, HA). Downsampling was carried out per condition within each tissue separately. Results indicate the number of detected genes as the function of the downsampled total counts.

Extended Data Fig. 2 Cell type specific data overview.

All panels present data after quality control. a, Number of cells per cell type and replicate. Replicates are colored by their condition. b, Number of replicates per cell type. Replicates are colored by their condition. c, Total number of cells per cell type. d, Fraction of cells within each condition per cell type. e-h, For each experimental condition within each cell type, total read counts (e), the percent of reads mapped to ribosomal genes (f), mitochondrial genes (g), and ERCC spike-ins (h) plotted against the mean number of genes expressed.

Extended Data Fig. 3 Validation of differential gene expression analysis.

a, Number of DEGs plotted against the total number of cells within the control and treatment groups. Each dot represents a DGE comparison of a cell type. b-d, Cumulative distributions of the calculated effect size (b), -log10(adj. p-value) (c) and log2 fold change values (d). Distributions are shown separately for ACC, REJ and AGE DGE. Vertical lines indicate the cutoffs applied throughout the study. e, Summary of ACC DGE results. Each cell type that has at least 50 cells in IY and HY is studied in the context of ACC and hence shown. From top to bottom: control and treatment sample sizes indicated separately for AGE and ACC, the number of genes differentially expressed in AGE and ACC, overlaps between AGE and ACC. Overlaps are normalized by the number of DEGs in the union of ACC and AGE DEGs. f, Summary of REJ DGE results. Each cell type that has at least 50 cells in IA and HA is studied in the context of REJ and hence shown. From top to bottom: control and treatment sample sizes indicated separately for AGE and REJ, the number of genes differentially expressed in AGE and REJ, overlaps between AGE and REJ. Overlaps are normalized by the number of DEGs in the union of REJ and AGE DEGs. g, Percent of DEGs that change in the same direction with AGE and ACC are plotted against the total number of DEGs within AGE and ACC for each comparison. Percentages are based on the union of DEGs as defined in (e) h, Percent of DEGs that change in the opposite direction with AGE and REJ are plotted against the total number of DEGs within AGE and REJ for each comparison. Percentages are based on the union of DEGs as defined in (f). i, Fraction of DEGs changing in the same direction with AGE and ACC plotted against the fraction of DEGs changing in the opposite direction with AGE and ACC. Each dot represents a cell type of the study. Colored area indicates where more DEGs change in the same direction than in the opposite direction. j, Fraction of DEGs changing in the same direction with AGE and REJ plotted against the fraction of DEGs changing in the opposite direction with AGE and REJ. Each dot represents a cell type of the study. Colored area indicates where more DEGs change in the opposite direction than in the same direction.

Extended Data Fig. 4 Validation of differential gene expression analysis.

a, Violin plots showing the number of differentially expressed genes as the function of the number of replicates per comparison. The number of replicates are defined as the minimum number of replicates within the control and treatment groups. Results are shown separately for TMS (left) and parabiosis (right). b, Comparison of differential gene expression results with and without subsampling in case of each cell type specific comparison. Spearman correlation values indicate (dis)similarities between: p-values derived from the original and subsampled data (left), effect sizes calculated from the original and subsampled data (middle), and effect sizes calculated on the original data and p-values derived from the subsampled datasets. c, Number of DEGs identified at different p-value and effect size cutoffs per comparison in AGE (left), ACC (middle), and REJ (right).

Extended Data Fig. 5 Differential gene expression results.

a, Top list of the 50 most frequent DEGs identified for ACC and REJ. Results are shown separately for up and downregulation. Columns with darker bars indicate top lists where only changes consistent with AGE are shown. These include genes changing in the same direction with ACC and AGE, as well as genes changing in the opposite direction with REJ and AGE. b, DGE results for marrow HSCs for ACC (left) and for REJ (right). from top to bottom: volcano plots (top) show top DEGs. Comparisons of log2-fold changes (middle) show changes with parabiosis on the x-axis and with normal ageing on the y-axis. DEGs with adj. p-value < 0.05, eff. size > 0.6 are shown. Areas where ACC and AGE change in the same direction as well as where REJ and AGE change in the opposite direction are highlighted. Top pathways (GO Biological Process) with highest ‘Combined scores’ defined as in Enrichr50 are shown at the bottom. c, Most enriched pathways (GO Biological Process) among the 100 most frequent DEGs shared across multiple cell types. Results shown for ACC and ACC-AGE same direction (top), and REJ and REJ-AGE opposite direction (bottom). Combined scores are defined as in Enrichr50. d, Gene expression violin plots for liver hepatocytes, GAT MSCs and marrow HSCs of select genes encoding proteins of the electron transport chain. Significance values show the adj. p-values of the Wilcoxon–Mann–Whitney test (two-sided) based differential gene expression, see Methods: ‘Differential gene expression’.

Extended Data Fig. 6 Analyses of genes associated with the 5 OXPHOS complexes.

a, Log2-fold changes with AGE, ACC and REJ of genes associated with the 5 OXPHOS complexes. Changes with adj. p-val.<0.05 and eff. size>0.6 are shown. Each column corresponds to one complex and the three separate colors distinguish between AGE, ACC and REJ. b, Spearman correlation of gene expression values with age in case of genes associated with the 5 OXPHOS complexes in the Tabula Muris Senis bulk dataset. Data has been analyzed as in2. Correlation values with adj. p-value < 0.05 are shown. c, Log2-fold changes with ACC and REJ of genes associated with the 5 OXPHOS complexes in the bulk parabiosis dataset.

Extended Data Fig. 7 Analysis of transcriptional noise.

a–c, Mean number of genes expressed within each cell type, x and y axes indicate Y and A (a), IY and HY (b) and IA and HA (c), each dot represents a cell type. d–f, Cell-cell variability within each cell type in Y and A (d), IY and HY (e) and IA and HA (f), each dot represents a cell type. g–i, Overdispersion within each cell type in Y and A (g), IY and HY (h) and IA and HA (i), each dot represents a cell type.

Extended Data Fig. 8 Pathway analysis.

a, Top 10 most differently affected pathways over all ACC tissues and cell types (top, largest difference at the top) and top 10 most differently affected pathways over all AGE (bottom, largest difference at the bottom) tissues and cell types. b, Same as for a, comparing REJ and AGE pathways. c, Heatmap showing the top 30 most strongly affected pathways in AGE, ACC and REJ in hematopoietic stem cells (HSCs) of the marrow. Pathways related to mitochondria are highlighted in green on the left. Each entry of the heatmap shows the significance level and the number of genes associated with the pathway. d, Same as for c, showing the top 30 most strongly affected pathways for beta cells of the pancreas.

Extended Data Fig. 9 Ageing and rejuvenation similarity analysis.

a–c, AGE (a), ACC (b), and REJ (c) DGE based cosine similarity matrices of the cell types studied, see Methods section ‘Ageing and rejuvenation similarity analysis’. All matrices are clustered with complete link hierarchical clustering. d, Force-directed network visualization of the STRING links between DEGs common to MSCs from GAT, MAT, SCAT, bladder, limb muscle, and diaphragm. e, Force-directed network visualization of the STRING links between DEGs common to MSCs (GAT and MAT), hepatocytes, basal and epidermal cells (skin), and HSCs and macrophages (marrow). All links with >0.9 STRING confidence score (scale from 0-1) are queried and shown. f, g Most enriched pathways (GO Biological Process) among the nodes of the networks shown in (d, e), combined scores are defined as in Enrichr50.

Supplementary information

Supplementary Fig. 1

Statistics related to quality control, batch correction and cell type annotation for the data used after quality control throughout the study. Results shown separately, for each tissue analysed.

Supplementary Fig. 2

AGE, ACC and REJ DGE results shown for each cell type separately. Blank plots indicate insufficient data (that is, fewer than 50 cells per control or treatment group of the specific comparison).

Supplementary Fig. 3

Heatmaps showing the top 30 most strongly affected pathways in AGE, ACC and REJ for each tissue and cell type. Pathways related to mitochondria are highlighted in green. Heatmaps show the significance level and the number of genes associated with the corresponding pathway.

Supplementary Information

This file contains the legends for Supplementary Tables 1–8.

Supplementary Tables

This file contains Supplementary Tables 1–8; see separate PDF for table legends.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pálovics, R., Keller, A., Schaum, N. et al. Molecular hallmarks of heterochronic parabiosis at single-cell resolution. Nature 603, 309–314 (2022). https://doi.org/10.1038/s41586-022-04461-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-022-04461-2

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing