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

Abstract

Aging manifests as progressive dysfunction culminating in death. The diversity of cell types is a challenge to the precise quantification of aging and its reversal. Here we develop a suite of 'aging clocks' based on single cell transcriptomic data to characterize cell type-specific aging and rejuvenation strategies. The subventricular zone (SVZ) neurogenic region contains many cell types and provides an excellent system to study cell-level tissue aging and regeneration. We generated 21,458 single-cell transcriptomes from the neurogenic regions of 28 mice, tiling ages from young to old. With these data, we trained a suite of single cell-based regression models (aging clocks) to predict both chronological age (passage of time) and biological age (fitness, in this case the proliferative capacity of the neurogenic region). Both types of clocks perform well on independent cohorts of mice. Genes underlying the single cell-based aging clocks are mostly cell-type specific, but also include a few shared genes in the interferon and lipid metabolism pathways. We used these single cell-based aging clocks to measure transcriptomic rejuvenation, by generating single cell RNA-seq datasets of SVZ neurogenic regions for two interventions - heterochronic parabiosis (young blood) and exercise. Interestingly, the use of aging clocks reveals that both heterochronic parabiosis and exercise reverse transcriptomic aging in the niche, but in different ways across cell types and genes. This study represents the first development of high-resolution aging clocks from single cell transcriptomic data and demonstrates their application to quantify transcriptomic rejuvenation.

Heterochronic parabiosis reprograms the mouse brain transcriptome by shifting aging signatures in multiple cell types

“…Collectively, our gene expression matrices provide unprecedented data that can be further explored to form working hypotheses for follow-up studies. Similar to other recent reports 118,123,133 , our study advances fundamental understanding of the mechanisms underlying the aging process and potential interventions that go beyond descriptive studies of cell states. Our work extends current knowledge regarding the effects of heterochronic parabiosis on the aging process and supports the key role of the brain vasculature in mediating these effects.…”

Dynamic visualization of high-dimensional data

“…Dynamic visualizations can help to discriminate robust bridging connections that appear across most bootstrap visualizations from incidental or artificial bridging connections that only appear in one or a small minority of bootstrap visualizations. For example, interactive UMAP visualization of single-cell transcriptomes of the mouse subventricular zone [27], a neurogenic region in the brain, highlights a robust bridging connection between the activated neural stem cell / neural progenitor cell (aNSC-NPC) cluster and neuroblast cluster, and a much less stable connection between the aNSC-NPC and astrocyte cluster (see Figure 1B). From a single visualization, the relative stability of these connections is not apparent (see leftmost frame of Figure 1B).…”

“…For the mouse subventricular zone data, we selected a random sample of 1,000 cells drawn from the single-cell RNA sequencing dataset of the subventricular zone, a neurogenic niche, from 28 male mice aged 3 months to 28 months old [27]. We used the 11 cell type labels identified in the original study [27]. The data is currently under embargo pending initial publication.…”

A Spectral Method for Assessing and Combining Multiple Data Visualizations

“…Computational Cost. We considered the single-cell transcriptomic dataset of Buckley et al (2022) that contains more than 20,000 cells of different cell types from the neurogenic regions of 28 mice. For each n ∈ {1000, 2000, 4000, 8000, 14000}, we randomly select n cells of nine different cell types, and selected subsets of p ∈ {500, 1000, 2000} genes to obtain an n × p count matrix.…”