1. Computational and Systems Biology
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Mouse aging cell atlas analysis reveals global and cell type specific aging signatures

  1. Martin Jinye Zhang  Is a corresponding author
  2. Angela O Pisco  Is a corresponding author
  3. Spyros Darmanis
  4. James Zou  Is a corresponding author
  1. Harvard University, United States
  2. Chan-Zuckerberg Biohub, United States
  3. Stanford University, United States
Research Article
  • Cited 3
  • Views 5,384
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Cite this article as: eLife 2021;10:e62293 doi: 10.7554/eLife.62293

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

Article and author information

Author details

  1. 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.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0006-2466
  2. 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.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0142-2355
  3. Spyros Darmanis

    Data Science, Chan-Zuckerberg Biohub, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. 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.
    ORCID icon "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.

Reviewing Editor

  1. Jing-Dong Jackie Han, Chinese Academy of Sciences, China

Publication history

  1. Received: August 20, 2020
  2. Accepted: March 29, 2021
  3. Accepted Manuscript published: April 13, 2021 (version 1)
  4. Version of Record published: April 14, 2021 (version 2)

Copyright

© 2021, Zhang et al.

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.

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    Funding:

    NIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB); DOE: DE-AC02-05CH11231 (DM).