Evidence map›Paper›PMID 33847263›Full record

ArticleeLife2021

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

Martin Jinye Zhang, Angela Oliveira Pisco, Spyros Darmanis, James Zou

Open access · goldAbstract read
In one paragraph

Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
78citing papers in PubMed, 1 pooled it
7.5field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

78 citing papers in PubMed, 1 synthesis or guideline pooled it, 121 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Determining the age of single cells using scMLEAge.bioRxiv : the preprint server for biology · 2026
    Article
  8. Article
  9. Review
  10. Article
  11. A novel information-theoretic approach reveals loss of effective biomolecular communication in aging muscle cells.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025
    Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
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  18. Article
  19. Multiorgan transcriptomics in mice identifies immunoglobulin heavy constant mu (Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  20. Article

18 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Martin Jinye ZhangDepartment of Electrical Engineering, Stanford University, Palo Alto, United States.ORCID 0000-0003-0006-2466
Angela Oliveira PiscoChan-Zuckerberg Biohub, San Francisco, United States.ORCID 0000-0003-0142-2355
Spyros DarmanisChan-Zuckerberg Biohub, San Francisco, United States.
James ZouDepartment of Electrical Engineering, Stanford University, Palo Alto, United States.ORCID 0000-0001-8880-4764
Chan Zuckerberg Initiative (United States) · USBroad Institute · USPalo Alto University · US

Funding

Joint genomic and statistical analyses of schizophrenia and bipolar to decipher genetic susceptibilityR01MH115676 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Roel A Ophoff, Bogdan Pasaniuc · 2018 to 2026
$5.9M
Stanford Medicine Center for Longevity and Healthy Aging Research Education CoreP30AG059307 · NIA · STANFORD UNIVERSITY · PI PERIYAKOIL, VYJEYANTHI S, YESAVAGE, JEROME A · 2018 to 2025
$4.9M
National Science Foundation CCF 1763191NIA NIH HHS P30 AG059307NIH HHS R01 MH115676NIMH NIH HHS R01 MH115676
6 · The paper itself

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 data set 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.

Indexed as

Gene Expression ProfilingSingle-Cell AnalysisAnimalsCellular SenescenceFemaleMaleMiceMice, Inbred C57BLagingcomputationcomputational biologymousesingle cellsystems biology

Identifiers

PMID33847263
PMCPMC8046488
OpenAlexW3154288382

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.