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Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain

The diversity of cell types is a challenge for quantifying aging and its reversal. Here we develop ‘aging clocks’ based on single-cell transcriptomics to characterize cell-type-specific aging and rejuvenation. We generated single-cell transcriptomes from the subventricular zone neurogenic region of...

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Autores principales: Buckley, Matthew T., Sun, Eric D., George, Benson M., Liu, Ling, Schaum, Nicholas, Xu, Lucy, Reyes, Jaime M., Goodell, Margaret A., Weissman, Irving L., Wyss-Coray, Tony, Rando, Thomas A., Brunet, Anne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154228/
https://www.ncbi.nlm.nih.gov/pubmed/37118510
http://dx.doi.org/10.1038/s43587-022-00335-4
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author Buckley, Matthew T.
Sun, Eric D.
George, Benson M.
Liu, Ling
Schaum, Nicholas
Xu, Lucy
Reyes, Jaime M.
Goodell, Margaret A.
Weissman, Irving L.
Wyss-Coray, Tony
Rando, Thomas A.
Brunet, Anne
author_facet Buckley, Matthew T.
Sun, Eric D.
George, Benson M.
Liu, Ling
Schaum, Nicholas
Xu, Lucy
Reyes, Jaime M.
Goodell, Margaret A.
Weissman, Irving L.
Wyss-Coray, Tony
Rando, Thomas A.
Brunet, Anne
author_sort Buckley, Matthew T.
collection PubMed
description The diversity of cell types is a challenge for quantifying aging and its reversal. Here we develop ‘aging clocks’ based on single-cell transcriptomics to characterize cell-type-specific aging and rejuvenation. We generated single-cell transcriptomes from the subventricular zone neurogenic region of 28 mice, tiling ages from young to old. We trained single-cell-based regression models to predict chronological age and biological age (neural stem cell proliferation capacity). These aging clocks are generalizable to independent cohorts of mice, other regions of the brains, and other species. To determine if these aging clocks could quantify transcriptomic rejuvenation, we generated single-cell transcriptomic datasets of neurogenic regions for two interventions—heterochronic parabiosis and exercise. Aging clocks revealed that heterochronic parabiosis and exercise reverse transcriptomic aging in neurogenic regions, but in different ways. This study represents the first development of high-resolution aging clocks from single-cell transcriptomic data and demonstrates their application to quantify transcriptomic rejuvenation.
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spelling pubmed-101542282023-05-04 Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain Buckley, Matthew T. Sun, Eric D. George, Benson M. Liu, Ling Schaum, Nicholas Xu, Lucy Reyes, Jaime M. Goodell, Margaret A. Weissman, Irving L. Wyss-Coray, Tony Rando, Thomas A. Brunet, Anne Nat Aging Resource The diversity of cell types is a challenge for quantifying aging and its reversal. Here we develop ‘aging clocks’ based on single-cell transcriptomics to characterize cell-type-specific aging and rejuvenation. We generated single-cell transcriptomes from the subventricular zone neurogenic region of 28 mice, tiling ages from young to old. We trained single-cell-based regression models to predict chronological age and biological age (neural stem cell proliferation capacity). These aging clocks are generalizable to independent cohorts of mice, other regions of the brains, and other species. To determine if these aging clocks could quantify transcriptomic rejuvenation, we generated single-cell transcriptomic datasets of neurogenic regions for two interventions—heterochronic parabiosis and exercise. Aging clocks revealed that heterochronic parabiosis and exercise reverse transcriptomic aging in neurogenic regions, but in different ways. This study represents the first development of high-resolution aging clocks from single-cell transcriptomic data and demonstrates their application to quantify transcriptomic rejuvenation. Nature Publishing Group US 2022-12-19 2023 /pmc/articles/PMC10154228/ /pubmed/37118510 http://dx.doi.org/10.1038/s43587-022-00335-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Resource
Buckley, Matthew T.
Sun, Eric D.
George, Benson M.
Liu, Ling
Schaum, Nicholas
Xu, Lucy
Reyes, Jaime M.
Goodell, Margaret A.
Weissman, Irving L.
Wyss-Coray, Tony
Rando, Thomas A.
Brunet, Anne
Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain
title Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain
title_full Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain
title_fullStr Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain
title_full_unstemmed Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain
title_short Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain
title_sort cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain
topic Resource
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154228/
https://www.ncbi.nlm.nih.gov/pubmed/37118510
http://dx.doi.org/10.1038/s43587-022-00335-4
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