Cell type-specific aging clocks to quantify aging and rejuvenation in regenerative regions of the brain
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (PREreview)
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.
Article activity feed
-
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/5860860.
This study addresses an essential question of the contribution of different cell types and rejuvenation strategies to the process of aging. Since many of the models used in this field are based on bulk tissue input or purified cell populations, the contribution of specific cell types is difficult to discern. The authors use single-cell transcriptomic data to define 'aging clocks' to predict chronological and biological age using the subventricular zone (SVZ) neurogenic region of young and old mouse models (This region has many different cell populations). They also use these models to study the effect of different rejuvenation strategies (exercise and parabiosis) on the process of aging.
In…
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/5860860.
This study addresses an essential question of the contribution of different cell types and rejuvenation strategies to the process of aging. Since many of the models used in this field are based on bulk tissue input or purified cell populations, the contribution of specific cell types is difficult to discern. The authors use single-cell transcriptomic data to define 'aging clocks' to predict chronological and biological age using the subventricular zone (SVZ) neurogenic region of young and old mouse models (This region has many different cell populations). They also use these models to study the effect of different rejuvenation strategies (exercise and parabiosis) on the process of aging.
In my review of this article, I am limited by the lack of proficiency in machine learning models; however, I enjoyed the challenge of reading this article. The study is robust and well designed, and the article follows a pattern that makes the data easy to understand. The following comments are geared towards including points to help with the layout and discussion points to help with understanding.
Comments:
1. The introduction provides a clear and concise summary of the process of aging, the current state of the machine learning approaches to quantify aging, and the challenges associated with it. The authors highlight the current need for single cell-based systems and provide the proper introduction to help get acquainted with the subject matter. However, it would help the readers if the introduction had a summary paragraph of the study's aims, methods, and main findings.
2. In the experiments related to the heterochronic parabiosis experiments, there is a difference in the results obtained between the two cohorts. It would be helpful for the readers if the authors could explain why the cohort with the 21m difference between the young and old mice had a better rejuvenation effect than the cohort with the 15.5m difference?
3. Since the mice in this study are male, It would be helpful if the authors could include in their discussion their speculation on if they would anticipate sex-based differences in their observations.
4. The current version of the preprint does not have a complete discussion, and it appears to be missing a page (page 11 when converted to a pdf). Adding this information will help the readers better understand the points discussed.
5. There are two references, 1-94 and then again 1-11 after the methods section. It wasn't easy to find some of the references in the text due to this reason.
-
-