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Cellular Senescence

Molecular Biology · NLM MeSH · updated 2026-10-29 · version 1

Citations verified against PubMedNot yet clinician-reviewed9 referencesApproximately 3 min read

Process by which cells irreversibly stop dividing and enter a state of permanent growth arrest without undergoing cell death.

Table of Contents

1. Definition2. Research landscape3. Reading the evidence4. Open-access studies5. Key reviews6. References7. About this article

Definition and research guide

This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.

Content available: 3 complete abstracts (553 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.

Definition

Process by which cells irreversibly stop dividing and enter a state of permanent growth arrest without undergoing cell death. Senescence can be induced by dna damage or other cellular stresses, such as oxidative stress.

Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.

Also known as: Senescence, Cellular; Cell Senescence; Senescence, Cell; Cell Aging; Cellular Ageing; Ageing, Cellular; Aging, Cell; Cell Ageing.

Where it fits in the MeSH classification

Broader topics
Cell Physiological Phenomena; Aging
Narrower topics
Erythrocyte Aging; Senescence-Associated Secretory Phenotype; T-Cell Senescence; Telomere Shortening
See also
Cell Death; Plant Senescence

Related concepts

  • Cell Death
  • DNA Damage
  • Oxidative Stress

Research landscape

PubMed indexes 33,908 records tagged with the MeSH heading “Cellular Senescence” (counts retrieved 2026-10-29). 11,925 of them (35.2%) were published from 2020 onward, and the busiest period so far is 2020 onward with 11,925 records.

Reviews account for 6,774 records (20.0%) and meta-analyses for 96. Randomized controlled trials number 111 (0.3%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.

PubMed records by publication periodthrough 1999: 5,399 records; 2000-2009: 5,258 records; 2010-2019: 11,326 records; 2020 onward: 11,925 records5,399through 19995,2582000-200911,3262010-201911,9252020 onward
Records in PubMed indexed with this heading, by publication period. Real counts from NCBI E-utilities; the final period covers only part of the current year. Volume reflects how much has been published, not how strong the evidence is.
Show the data
PeriodRecords
through 19995,399
2000-20095,258
2010-201911,326
2020 onward11,925
Share of records by publication typeReviews: 20.0 percent; Meta-analyses: 0.3 percent; Randomized controlled trials: 0.3 percentReviews20.0% (6,774)Meta-analyses0.3% (96)Randomized controlled trials0.3% (111)
Share of all PubMed records on this heading that carry each publication type. Types overlap with other records, so the shares do not sum to 100%.
Publication-type chart data
Publication types among 33,908 PubMed records
TypeRecordsShare
Reviews6,77420.0%
Meta-analyses960.3%
Randomized controlled trials1110.3%

Method: PubMed query "Cellular Senescence"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.

Reading the evidence in molecular biology

Molecular biology establishes mechanisms through controlled experiments, often in simplified systems such as purified proteins, cultured cells or model organisms. Such work can show what is possible in a defined system, but whether a pathway matters in a whole organism or in human disease needs further evidence. Check the experimental system, whether findings were reproduced by independent groups, and whether the authors tested the mechanism directly or inferred it from correlation.

Evidence from open-access studies

These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.

Study comparison — selected abstracts, not a systematic review
Study / authorsYearSource label / designEurope PMC citationsLicence / source
Plasma proteomic signatures of cellular aging predict human disease
Ding DY, Bot VA, Chen KL, Groves JW, Pálovics R, Masuda D, et al.
2026Research article
Design not recorded in structured metadata
12cc by
PMID 42297981 · DOI · PMC13279268
Mitochondrial metabolism and epigenetic crosstalk drive SASP
Martini H, Birch J, Marques FDM, Victorelli S, Lagnado AB, Pirius N, et al.
2026Research article
Design not recorded in structured metadata
7cc by
PMID 42527602 · DOI · PMC13518223
Mitochondria-lysosome coupling contributes to lysosome acidification and aging
Liu Q, Yoo S, Zhang ZA, Li L, Su H, Vannur L, et al.
2026Research article
Design not recorded in structured metadata
5cc by
PMID 42214330 · DOI · PMC13263124

Plasma proteomic signatures of cellular aging predict human disease [1]

Research article Ding DY, Bot VA, Chen KL, Groves JW, Pálovics R, Masuda D, et al. · · 2026 · cited 12 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
Aging is asynchronous across cells and organs.
Closing excerpt
These findings establish a framework for quantifying human physiology at cellular resolution, revealing heterogeneous aging trajectories and their impact on disease susceptibility and resilience.
Interpretation limits
Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (243 words)

Aging is asynchronous across cells and organs. Here we tested whether plasma proteomics can be used to analyze cell type-specific aging. From analyses of over 7,000 plasma proteins measured in 60,542 individuals, we developed machine learning models to estimate the biological age of over 40 cell types spanning neuronal, immune, glial, endocrine, epithelial and musculoskeletal origins. We observed that 20-25% of individuals exhibited accelerated aging in a single cell type and 1-3% in 10 or more cell types. Cellular aging signatures were associated with disease status and predicted incident disease and mortality over 15 years of follow-up. Individuals with the APOE4 genotype showed older astrocytes but younger macrophages compared to APOE3 carriers, whereas the APOE2 genotype had inverse associations. Moreover, extreme astrocyte aging tripled the risk of incident Alzheimer's Disease in individuals with two APOE4 alleles, while youthful astrocytes reduced risk. Individuals with extremely aged compared to youthful skeletal myocytes exhibited a 12.7-fold higher risk of developing amyotrophic lateral sclerosis. In individuals who smoked, extreme respiratory epithelial cell aging was associated with a 58% higher lung cancer risk compared to smoking alone. Specific cellular vulnerabilities and cumulative cellular aging burden influenced survival, with youthful immune and neuronal cell types conferring protective effects. Finally, we developed a polycellular aging risk score that stratified mortality risk across cohorts and proteomics platforms. These findings establish a framework for quantifying human physiology at cellular resolution, revealing heterogeneous aging trajectories and their impact on disease susceptibility and resilience.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42297981 · DOI · PMC13279268

Mitochondrial metabolism and epigenetic crosstalk drive SASP [2]

Research article Martini H, Birch J, Marques FDM, Victorelli S, Lagnado AB, Pirius N, et al. · · 2026 · cited 7 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
Senescent cells promote tissue dysfunction in part through the senescence-associated secretory phenotype (SASP)1.
Closing excerpt
Together, these findings identify a mitochondrial metabolic checkpoint that enables the epigenetic execution of innate immune signalling, revealing a mechanism that selectively controls the inflammatory output of senescent cells.
Interpretation limits
Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (159 words)

Senescent cells promote tissue dysfunction in part through the senescence-associated secretory phenotype (SASP)1. Cytosolic mitochondrial nucleic acids activate innate immune signalling to initiate this inflammatory programme2,3. Here we show that mitochondrial metabolism provides a second layer of control that enables execution of the inflammatory programme. In senescent cells, the mitochondrial pyruvate-citrate-acetyl-CoA axis is upregulated, increasing the availability of acetyl-CoA to support histone acetylation at SASP genes. Whereas mitochondrial DNA-driven signalling activates inflammatory transcription factors, acetyl-CoA availability is required for robust transcription of SASP genes. Accordingly, enhancing acetyl-CoA levels promotes SASP gene expression, whereas inhibition of SLC25A1, the mitochondrial citrate exporter, reduces histone acetylation at SASP loci, limiting activity of this programme. In vivo, inhibition of SLC25A1 reduces chromatin accessibility at SASP loci, dampens inflammation and improves healthspan in aged mice. Together, these findings identify a mitochondrial metabolic checkpoint that enables the epigenetic execution of innate immune signalling, revealing a mechanism that selectively controls the inflammatory output of senescent cells.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42527602 · DOI · PMC13518223

Mitochondria-lysosome coupling contributes to lysosome acidification and aging [3]

Research article Liu Q, Yoo S, Zhang ZA, Li L, Su H, Vannur L, et al. · · 2026 · cited 5 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
Nearly all cellular processes are pH dependent.
Closing excerpt
These findings reshape our current understanding of the mechanisms underlying lysosomal/vacuolar (de-)acidification in both young and aged/senescent cells.
Interpretation limits
Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (151 words)

Nearly all cellular processes are pH dependent. The acidic pH inside the lysosome (vacuole in yeast) is essential for cellular content degradation, signaling, and autophagy. Defects in lysosome/vacuole acidification are a conserved hallmark of aging and age-related diseases. Traditionally, the lysosome/vacuole is thought to import free protons (H⁺) from the surrounding neutral cytosol. Here, we uncovered a conserved lysosome/vacuole acidification mechanism from yeast to human involving lysosomal/vacuolar uptake of H+ pumped out by mitochondrial electron transport chain through mitochondria-lysosomes/vacuoles membrane contacts. Aging/senescence-associated disruption of mitochondria-lysosome/vacuole contacts causes lysosomal/vacuolar de-acidification, which can be reversed by either expressing an engineered linker to connect these two organelles or through an asymmetry-dependent rejuvenation process in daughter cells. Preserving lysosomal acidification in senescent human cells prevents the induction of major senescence-associated secretory phenotype factors and restores autophagic flux. These findings reshape our current understanding of the mechanisms underlying lysosomal/vacuolar (de-)acidification in both young and aged/senescent cells.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42214330 · DOI · PMC13263124

Key reviews and meta-analyses

The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.

  • Research article Leucocyte telomere length and risk of cardiovascular disease: systematic review and meta-analysis [4]
    Haycock PC, Heydon EE, Kaptoge S, Butterworth AS, Thompson A, Willeit P · BMJ · 2014 · cited 700 times (Europe PMC)
  • Research article Cigarette smoking and telomere length: A systematic review of 84 studies and meta-analysis [5]
    Astuti Y, Wardhana A, Watkins J, Wulaningsih W, PILAR Research Network · Environ Res · 2017 · cited 272 times (Europe PMC)
  • Research article Telomere Length and All-Cause Mortality: A Meta-analysis [6]
    Wang Q, Zhan Y, Pedersen NL, Fang F, Hägg S · Ageing Res Rev · 2018 · cited 254 times (Europe PMC)
  • Research article Early life adversity and telomere length: a meta-analysis [7]
    Ridout KK, Levandowski M, Ridout SJ, Gantz L, Goonan K, Palermo D, et al. · Mol Psychiatry · 2018 · cited 216 times (Europe PMC)
  • Research article Perceived stress and telomere length: A systematic review, meta-analysis, and methodologic considerations for advancing the field [8]
    Mathur MB, Epel E, Kind S, Desai M, Parks CG, Sandler DP, et al. · Brain Behav Immun · 2016 · cited 191 times (Europe PMC)
  • Research article Meta-analysis of Telomere Length in Alzheimer's Disease [9]
    Forero DA, González-Giraldo Y, López-Quintero C, Castro-Vega LJ, Barreto GE, Perry G · J Gerontol A Biol Sci Med Sci · 2016 · cited 184 times (Europe PMC)

References

  1. Ding DY, Bot VA, Chen KL, Groves JW, Pálovics R, Masuda D, et al.. Plasma proteomic signatures of cellular aging predict human disease. . 2026. PMID 42297981 · DOI · PMC13279268
  2. Martini H, Birch J, Marques FDM, Victorelli S, Lagnado AB, Pirius N, et al.. Mitochondrial metabolism and epigenetic crosstalk drive SASP. . 2026. PMID 42527602 · DOI · PMC13518223
  3. Liu Q, Yoo S, Zhang ZA, Li L, Su H, Vannur L, et al.. Mitochondria-lysosome coupling contributes to lysosome acidification and aging. . 2026. PMID 42214330 · DOI · PMC13263124
  4. Haycock PC, Heydon EE, Kaptoge S, Butterworth AS, Thompson A, Willeit P. Leucocyte telomere length and risk of cardiovascular disease: systematic review and meta-analysis. BMJ. 2014. PMID 25006006 · DOI · PMC4086028
  5. Astuti Y, Wardhana A, Watkins J, Wulaningsih W, PILAR Research Network. Cigarette smoking and telomere length: A systematic review of 84 studies and meta-analysis. Environ Res. 2017. PMID 28704792 · DOI · PMC5562268
  6. Wang Q, Zhan Y, Pedersen NL, Fang F, Hägg S. Telomere Length and All-Cause Mortality: A Meta-analysis. Ageing Res Rev. 2018. PMID 30254001 · DOI
  7. Ridout KK, Levandowski M, Ridout SJ, Gantz L, Goonan K, Palermo D, et al.. Early life adversity and telomere length: a meta-analysis. Mol Psychiatry. 2018. PMID 28322278 · DOI · PMC5608639
  8. Mathur MB, Epel E, Kind S, Desai M, Parks CG, Sandler DP, et al.. Perceived stress and telomere length: A systematic review, meta-analysis, and methodologic considerations for advancing the field. Brain Behav Immun. 2016. PMID 26853993 · DOI · PMC5590630
  9. Forero DA, González-Giraldo Y, López-Quintero C, Castro-Vega LJ, Barreto GE, Perry G. Meta-analysis of Telomere Length in Alzheimer's Disease. J Gerontol A Biol Sci Med Sci. 2016. PMID 27091133 · DOI · PMC4945891

About this article

Sources
NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
Verification
Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
Review state
automated source verified; no human expert review has been recorded yet.
Version
1, last updated 2026-10-29.

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EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.

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Related research topics

  • AgingAnatomy and Physiology
  • Cell DeathMolecular Biology
  • Immunogenic Cell DeathMolecular Biology
  • FerroptosisMolecular Biology
  • ApoptosisMolecular Biology
  • DNA RepairMolecular Biology

All 100 articles in Molecular Biology →

At a glance

33,908
PubMed records on this heading
35%
published since 2020
6,774
review articles
111
randomized trials
9
verified references below

Counts come from PubMed on 2026-10-29; they show how much has been written, not how strong the evidence is.

Related topics See all →

  • AgingAnatomy and Physiology
  • Cell DeathMolecular Biology
  • Immunogenic Cell DeathMolecular Biology
  • FerroptosisMolecular Biology
  • ApoptosisMolecular Biology

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