Mouse aging cell atlas analysis reveals global and cell type specific aging signatures

Abstract
Data availability
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Abstract

Aging is associated with complex molecular and cellular processes that are poorly understood. Here we leveraged the Tabula Muris Senis single-cell RNA-seq dataset to systematically characterize gene expression changes during aging across diverse cell types in the mouse. We identified aging-dependent genes in 76 tissue-cell types from 23 tissues and characterized both shared and tissue-cell-specific aging behaviors. We found that the aging-related genes shared by multiple tissue-cell types also change their expression congruently in the same direction during aging in most tissue-cell types, suggesting a coordinated global aging behavior at the organismal level. Scoring cells based on these shared aging genes allowed us to contrast the aging status of different tissues and cell types from a transcriptomic perspective. In addition, we identified genes that exhibit age-related expression changes specific to each functional category of tissue-cell types. Altogether, our analyses provide one of the most comprehensive and systematic characterizations of the molecular signatures of aging across diverse tissue-cell types in a mammalian system.

Data availability

All data can be downloaded at https://figshare.com/articles/dataset/tms_gene_data_rv1/12827615.

The following previously published data sets were used

1. The Tabula Muris Consortium
(2020) Tabula Muris
https://www.nature.com/articles/s41586-020-2496-1.

https://figshare.com/articles/dataset/tms_gene_data_rv1/12827615

Article and author information

Author details

Martin Jinye Zhang

Epidemiology, Harvard University, Boston, United States

For correspondence
jinyezhang@hsph.harvard.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-0006-2466
Angela O Pisco

Data Science, Chan-Zuckerberg Biohub, San Francisco, United States

For correspondence
angela.pisco@czbiohub.org

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-0142-2355
Spyros Darmanis

Data Science, Chan-Zuckerberg Biohub, San Francisco, United States

Competing interests
The authors declare that no competing interests exist.
James Zou

Biomedical Data Science, Stanford University, Stanford, United States

For correspondence
jamesyzou@gmail.com

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-8880-4764

Funding

Chan-Zuckberg Biohub

James Zou

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.