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Molecular hallmarks of heterochronic parabiosis at single-cell resolution
Open accessPublished version of a preprint
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. A transcriptomics study demonstrates cell-type-specific responses to differentially aged blood and shows young blood to have restorative and rejuvenating effects that may be invoked through enhanced mitochondrial function.
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“By contrast, established geroprotective strategies such as long-term exercise and exposure to youthful bodily fluids,17”
From Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging (2024) · cites this paper as [48]
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“Heterochronic parabiosis experiments followed by extensive single-cell transcriptomics have confirmed the capacity of young blood to rejuvenate multiple tissues 237 and to restore age-associated reduction in general gene expression, in particular that of mitochondrial genes involved in the electron transport chain 238 .”
From Hallmarks of aging: An expanding universe (2023) · cites this paper as [238]
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“| Altered intercellular communication | Mouse | Heterochronic parabiosis | Rejuvenation in multiple tissues | 237 238”
From Hallmarks of aging: An expanding universe (2023) · cites this paper as [238]
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1131 · Cited by 17,749
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013) · Cell · doi:10.1016/j.cell.2013.05.039 · Cited by 14,982
On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
Mann, H. B., & Whitney, D. R. (1947) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177730491 · Cited by 14,320
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488
Self-citationComplex heatmaps reveal patterns and correlations in multidimensional genomic data
Gu, Z., Eils, R., & Schlesner, M. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw313 · Cited by 10,037
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Metadata
- Authors
- Pálovics, R., Keller, A., Schaum, N., Tan, W., Fehlmann, T., Borja, M., Kern, F., Bonanno, L., Calcuttawala, K., Webber, J., McGeever, A., 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., 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., Karnam, G., 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., McKay, M., McNerney, M. W., Merrill, B., Metzger, R. J., Mignardi, M., Min, D., Nabhan, A. N., Ng, K. M., Nguyen, P. K., Noh, J., Nusse, R., Patkar, R., Peng, W. C., Penland, L., Pollard, K., Puccinelli, R., Qi, Z., Rando, T. A., Rulifson, E. J., Segal, J. M., Sikandar, S. S., Sinha, R., Sit, R. V., Sonnenburg, J., Staehli, D., Szade, K., Tan, M., Tato, C., Tellez, K., Torrez Dulgeroff, L. B., 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., 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., Luo, J., Pisco, A. O., Karkanias, J., Neff, N. F., Darmanis, S., Quake, S. R., & Wyss-Coray, T.
- Affiliations
- Stanford Medicine (US)
- Chan Zuckerberg Biohub San Francisco (US)
- Saarland University (DE)
- VA Palo Alto Health Care System (US)
- Stanford University (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
- Keller, Andreas
- Darmanis, Spyros
- Quake, Stephen R.
- ORCID
- Pálovics, Róbert
- Keller, Andreas
- Fehlmann, Tobias
- Borja, Michael
- Kern, Fabian
- Webber, James
- McGeever, Aaron
- Almanzar, Nicole
- Antony, Jane
- Baghel, Ankit S.
- Barres, Ben A.
- Beachy, Philip A.
- Bilen, Biter
- Brownfield, Douglas
- Chan, Charles K. F.
- Clarke, Michael F.
- du Bois, Haley
- 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.
- 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
- 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
- Tan, Michelle
- Tato, Cristina
- Tellez, Krissie
- Travaglini, Kyle J.
- Tropini, Carolina
- Tsui, Margaret
- Waldburger, Lucas
- Wang, Bruce M.
- van Weele, Linda J.
- 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
- Luo, Jian
- Pisco, Angela Oliveira
- Karkanias, Jim
- Neff, Norma F.
- Darmanis, Spyros
- Quake, Stephen R.
- Wyss-Coray, Tony
- Venue
- Nature
- Published
- 2022-03-02
- Volume
- 603
- Issue
- 7900
- Pages
- 309-314
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC9387403
- OpenAlex
- W4220972523
- Cited by
- 153
- References
- 52
- Citations by year
- 2026: 32 · 2025: 36 · 2024: 39 · 2023: 29 · 2022: 17
- FWCI
- 10.14
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Jagiellonian University Repository (Jagiellonian University) (submittedVersion, cc-by-nc-nd)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Funders
- U.S. Department of Veterans Affairs (IK6 BX004599)
- Glenn Foundation for Medical Research
- Fondation pour la Recherche Médicale
- NOMIS Stiftung
- National Institutes of Health (AG045034)
- Wu Tsai Neurosciences Institute, Stanford University
- Japan Society for the Promotion of Science (21K07152)
- National Institute on Aging (RF1AG064928, R01AG045034, DP1AG053015, P30AG059307, R01AG072255, P30AG066515)
- National Heart, Lung, and Blood Institute (R00HL127267, R01HL163013)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Telomeres, Telomerase, and Senescence
- Keywords
- heterochronic parabiosis, biological aging, rejuvenation, young blood, cell type-specific gene expression, hematopoietic stem cells, hepatocytes, mitochondrial function, electron transport chain, blood-borne factors
- MeSH terms
- RNA-Seq, Aging, Electron Transport, Hematopoietic Stem Cells, Mitochondria, Organ Specificity, Parabiosis, Rejuvenation, Adipocytes, Hepatocytes, Single-Cell Analysis, Mesenchymal Stem Cells
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