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

TLDR

Cell‑type diversity complicates quantifying aging and its reversal in tissues. The study develops single‑cell transcriptomic aging clocks to characterize cell‑type‑specific aging and to quantify transcriptomic rejuvenation after interventions. Single‑cell RNA‑seq from the subventricular zone of 28 mice across ages was used to train regression models that predict chronological age and neural stem cell proliferation capacity, and the clocks were applied to datasets from heterochronic parabiosis and exercise. The clocks generalize to independent mouse cohorts, other brain regions, and species, and they show that heterochronic parabiosis and exercise reverse transcriptomic aging in neurogenic regions in distinct ways, marking the first high‑resolution single‑cell aging clocks.

Abstract

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.