Cell-Type-Specific Aging Clocks To Quantify Aging and Rejuvenation in Neurogenic Regions of the Brain

Authors

Matthew T Buckley
Eric D Sun
Benson M George
Ling Liu
Nicholas Schaum
Lucy Xu
Jaime M Reyes
Margaret A Goodell
Irving L Weissman
Tony Wyss-Coray
Thomas A Rando
Anne Brunet

Publication Date

1-1-2023

Journal

Nature Aging

DOI

10.1038/s43587-022-00335-4

PMID

37118510

PMCID

PMC10154228

PubMedCentral® Posted Date

12-19-2022

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Mice, Animals, Rejuvenation, Aging, Cellular Senescence, Brain, Neurogenesis, Gene expression, Machine learning

Abstract

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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