Cited by 114Telomeres, Telomerase, and SenescenceGenetics, Aging, and Longevity in Model OrganismsSingle-cell and spatial transcriptomics

The landscape of human tissue and cell type specific expression and co-regulation of senescence genes

Open access
Xu, Peng, Wang, Minghui, Song, Won-min, Wang, Qian, Yuan, Guo-Cheng, Sudmant, Peter H., Zare, Habil, Tu, Zhidong +2 more·2022·Molecular Neurodegeneration

Abstract

Background: Cellular senescence is a complex stress response that impacts cellular function and organismal health. Multiple developmental and environmental factors, such as intrinsic cellular cues, radiation, oxidative stress, oncogenes, and protein accumulation, activate genes and pathways that can lead to senescence. Enormous efforts have been made to identify and characterize senescence genes (SnGs) in stress and disease systems. However, the prevalence of senescent cells in healthy human tissues and the global SnG expression signature in different cell types are poorly understood. Methods: This study performed an integrative gene network analysis of bulk and single-cell RNA-seq data in non-diseased human tissues to investigate SnG co-expression signatures and their cell-type specificity. Results: Through a comprehensive transcriptomic network analysis of 50 human tissues in the Genotype-Tissue Expression Project (GTEx) cohort, we identified SnG-enriched gene modules, characterized SnG co-expression patterns, and constructed aggregated SnG networks across primary tissues of the human body. Our network approaches identified 51 SnGs highly conserved across the human tissues, including CDKN1A (p21)-centered regulators that control cell cycle progression and the senescence-associated secretory phenotype (SASP). The SnG-enriched modules showed remarkable cell-type specificity, especially in fibroblasts, endothelial cells, and immune cells. Further analyses of single-cell RNA-seq and spatial transcriptomic data independently validated the cell-type specific SnG signatures predicted by the network analysis. Conclusions: This study systematically revealed the co-regulated organizations and cell type specificity of SnGs in major human tissues, which can serve as a blueprint for future studies to map senescent cells and their cellular interactions in human tissues.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s13024-021-00507-7

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Visium has been used to investigate the spatial characteristics of cellular senescence in mouse and human brains and to decipher age-dependent tissue-regeneration capabilities.

    “Already there are reports of Visium being used to investigate the spatial characteristics of cellular senescence in mouse52 and human brains53 as well as to decipher age-dependent tissue-regeneration capabilities54.”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 80 · Cited by 114

Research works printed but not linked: 1

  1. [46]Korotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A. Fast gene set enrichment analysis. bioRxiv. 2021:060012.

Authors
Xu, P., Wang, M., Song, W.-M., Wang, Q., Yuan, G.-C., Sudmant, P. H., Zare, H., Tu, Z., Orr, M. E., & Zhang, B.
Affiliations
  • Icahn School of Medicine at Mount Sinai (US)
  • University of California, Berkeley (US)
  • The University of Texas at San Antonio Health Science Center (US)
  • Wake Forest University Health Sciences (US)
Corresponding author
Zhang, Bin
Venue
Molecular Neurodegeneration
Published
2022-01-09
Volume
17
Issue
1
Pages
5-5
ISSN
1750-1326 (ISSN-L)
OpenAlex
W4206526603
Cited by
114
References
80
Citations by year
2026: 19 · 2025: 29 · 2024: 39 · 2023: 23 · 2022: 4
FWCI
14.69
Type
journal article
Language
en
Open access
Locations
  • Molecular Neurodegeneration (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • national institute on aging (U01AG04617, RF1AG054014, RF1AG057440, R01AG057907, U01AG052411, R01AG062355, R01AG068293, U01AG058635, R01AG068030, RF1AG074010)
  • u.s. department of veterans affairs (IK2BX003804)
Topics
  • Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords
cellular senescence, senescence genes, gene co-expression network, cell type specificity, human tissue, p21, senescence-associated secretory phenotype, fibroblasts, endothelial cells, immune cells, senescent cells, GTEx, spatial transcriptomics, single-cell RNA sequencing
MeSH terms
Fibroblasts, Humans, Cellular Senescence, Gene Expression Profiling, Endothelial Cells, Transcriptome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review