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Cited by 304Telomeres, Telomerase, and SenescenceSingle-cell and spatial transcriptomicsGenetics, Aging, and Longevity in Model Organisms

The heterogeneity of cellular senescence: insights at the single-cell level

Open access
Cohn, Rachel L., Gasek, Nathan S., Kuchel, George A., Xu, Ming·2023·Trends in Cell Biology
DOI 10.1016/j.tcb.2022.04.011PMC PMC9812642
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Abstract

Senescent cells are highly associated with aging and pathological conditions and could be targeted to slow the aging process. One commonly used marker to examine senescent cells in vivo is p16, which has led to important discoveries. Recent studies have also described new senescence markers beyond p16 and have highlighted the importance of investigating senescence heterogeneity in cell types and tissues. With the development of high-throughput technologies, such as single-cell RNA-seq and single-nucleus RNA-seq, we can examine senescent cells at the single-cell level and potentially uncover new markers. This review emphasizes that there is an urgent need to investigate senescence heterogeneity and discuss how this could be accomplished by using advanced technologies and sequencing datasets.

AI snapshot

Written from the paper’s full text.

TL;DR

This review argues that cellular senescence is highly heterogeneous and that p16 alone might be neither specific nor sensitive enough to identify senescent cells in vivo. It highlights single-cell/nucleus RNA-seq and other high-throughput methods as key tools for uncovering new markers, tissue-specific senescence populations, and precision-medicine targets.

Key findings

  • p16 is not a universal senescence marker: not all p16high cells are senescent, and some senescent cells do not express p16; p21, p19, uPAR, and GPNMB are among emerging alternative markers.
  • A p21-Cre model targets p21high cells comprising 1–10% of cells in various tissues of aged mice; these cells are distinct from p16high cells, and their clearance reduces senescence markers, improves physical function in aged mice, and alleviates metabolic dysfunction in obese mice.
  • Single-cell/nucleus RNA-seq studies show senescent cells distributed across various cell populations rather than forming specific clusters; examples include p19/CDKN2D as the most significant marker in postmortem Alzheimer’s disease brains, p16high cells in liver/kidney varying by cell type and disease, and a Col1a1-high senescent endothelial subset in mouse retina.
  • Current technologies have limits and strengths: spatial transcriptomics resolution is ~55 μm (one to ten cells per section), sequencing depth captures only up to 50–60% of transcripts per cell, multiplexed antibody-based imaging detects 20–50 genes, and mFISH detects more than 100 genes.

Methods

  • Narrative review; no primary experimental data are presented.
  • Synthesizes current literature on senescence biology, markers, mouse models, high-throughput technologies, and single-cell/nucleus RNA-seq studies across adipose, brain, liver, kidney, eye, and other tissues.
  • Discusses analytic approaches and resources including Tabula Muris Senis and the NIH SenNet program.

Limitations

  • Stated: p16 and p21 might not be specific or sensitive markers; some p16high cells are not senescent, and some senescent cells do not express p16.
  • Stated: single-cell RNA-seq may miss large or dissociation-sensitive cells, single-nucleus RNA-seq may have lower sensitivity, sequencing depth is relatively low, and spatial transcriptomics resolution remains limited; enrichment of senescent cells requires robust markers.
  • Evident from the text: as a review, it does not provide new experimental validation or a systematic meta-analysis, so its conclusions depend on the cited studies and their marker limitations.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.tcb.2022.04.011

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 63 cited sources checked · 0 supported · 0 flagged

  • Interpretationreasoning

    There is a general lack of knowledge about exactly which cell populations are targeted by senolytics in vivo at the single-cell level, limiting their utility in a broader pathological context.

    “Even so, there remains a general lack of knowledge about exactly which cell populations are being targeted by senolytics in vivo at the single-cell level, limiting their utility in a broader pathological context.”
    Quote in the paperSubstantial claim
  • Premisecitation

    Multiple p16-based mouse models might not target the same p16high cells in vivo due to different transgene designs.

    “Moreover, multiple p16-based mouse models [15,26–28] might not target the same p16high cells in vivo due to different transgene designs (such as different p16 promoters or transgene locations).”
    Quote in the paperSubstantial claim0 of 4 sources checked
    • cites [15] Baker DJ (2011) Clearance of p16Ink4a-positive senescent cells delays ageing-associated disorders. Nature 479, 232– 236 22048312 PMC3468323 source

      Not checked in the free run.

    • cites [26] Grosse L (2020) Defined p16(high) senescent cell types are indispensable for mouse healthspan. Cell Metab. 32, 87– 99 e6 32485135 source

      Not checked in the free run.

    • cites [27] Demaria M (2014) An essential role for senescent cells in optimal wound healing through secretion of PDGF-AA. Dev. Cell 31, 722– 733 25499914 PMC4349629 source

      Not checked in the free run.

    • cites [28] Omori S (2020) Generation of a p16 reporter mouse and its use to characterize and target p16(high) cells in vivo. Cell Metab. 32, 814– 828 e6 32949498 source

      Not checked in the free run.

  • Premisecitation

    All current senolytics have distinct efficacy on diverse senescent cell populations.

    “It is now evident that all current senolytics have distinct efficacy on diverse senescent cell populations.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [19] Zhu Y (2015) The Achilles’ heel of senescent cells: from transcriptome to senolytic drugs. Aging Cell 14, 644– 658 25754370 PMC4531078 source

      Not checked in the free run.

    • cites [20] Yousefzadeh MJ (2018) Fisetin is a senotherapeutic that extends health and lifespan. EBioMedicine 36, 18– 28 30279143 PMC6197652 source

      Not checked in the free run.

    • cites [21] Chang J (2016) Clearance of senescent cells by ABT263 rejuvenates aged hematopoietic stem cells in mice. Nat. Med 22, 78– 83 26657143 PMC4762215 source

      Not checked in the free run.

  • Premisecitation

    Bulk RNA-seq data from human and mouse fibroblasts demonstrate differences in transcriptomic signatures and SASPs based on senescence inducers, cell types, and stages of the senescence process.

    “Analysis of bulk RNA sequencing (RNA-seq) data from fibroblasts from both humans (foreskin fibroblasts, epidermal melanocytes) and mice (embryonic fibroblasts, skin microvascular endothelial cells) demonstrate differences in transcriptomic signatures and SASPs based on senescence inducers, cell types, and stages of the senescence process [22], which may contribute to differences in function.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [22] Hernandez-Segura A Unmasking transcriptional heterogeneity in senescent cells. Curr. Biol 27, 2652– 2660 e4 PMC5788810 28844647 source

      Not checked in the free run.

  • Premisecitation

    GPNMB is a marker for senescent cells, enriched in senescent vascular endothelial cells compared with non-senescent cells in both human patients and mice with atherosclerosis.

    “Additionally, a separate study investigated the transcriptomes of vascular endothelial cells and identified GPNMB, a cell-surface protein, as a marker for senescent cells, which was evidenced by its enrichment in senescent vascular endothelial cells compared with non-senescent cells in both human patients and mice with atherosclerosis.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [13] Suda M (2021) Senolytic vaccination improves normal and pathological age-related phenotypes and increases lifespan in progeroid mice. Nature Aging 1, 1117– 1126 37117524 source

      Not checked in the free run.

  • May combine two claims. 29
  • Classified as probably not a substantial claim. 11

  • May combine two claims.

    Premisecitation

    Senescent cells are associated with aging and pathological conditions and can be targeted to slow the aging process.

    “Senescent cells are highly associated with aging and pathological conditions and could be targeted to slow the aging process.”
    • Also stated in: § The importance of understanding cellular senescence heterogeneity · hedged
      “Cellular senescence has emerged as a mechanism that could be targeted to delay the aging process.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [3] Gasek NS (2021) Strategies for targeting senescent cells in human disease. Nat Aging. 1, 870– 879 34841261 PMC8612694 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    At the transcript level, p16 and p21 are the two most commonly used markers for senescent cells.

    “At the transcript level, p16 and p21 are the two most commonly used markers for senescent cells [3,16].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [3] Gasek NS (2021) Strategies for targeting senescent cells in human disease. Nat Aging. 1, 870– 879 34841261 PMC8612694 source

      Not checked in the free run.

    • cites [16] Gorgoulis V (2019) Cellular senescence: defining a path forward. Cell 179, 813– 827 31675495 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Senescence markers beyond p16 include p21, p19, uPAR, and GPNMB.

    “Studies have begun to reveal the remarkable heterogeneity of cellular senescence in different tissues, highlighting the importance of cellular senescence markers beyond p16 – including Cdkn1a (p21) [7–10], Cdkn2d (p19) [11], uPAR [12], and glycoprotein non-metastatic melanoma protein B (GPNMB) [13] – and emphasizing the urgent need for a better understanding of senescent cell heterogeneity.”
    Quote in the paperSubstantial claim0 of 6 sources checked
    • cites [7] Sturmlechner I (2021) p21 produces a bioactive secretome that places stressed cells under immunosurveillance. Science 374 PMC8985214 34709885 source

      Not checked in the free run.

    • cites [8] Wang B (2020) Transplanting cells from old but not young donors causes physical dysfunction in older recipients. Aging Cell 19, e13106 31971661 PMC7059132 source

      Not checked in the free run.

    • cites [9] Wang B (2021) An inducible p21-Cre mouse model to monitor and manipulate p21-highly-expressing senescent cells in vivo. Nat Aging 1, 962– 973 35024619 PMC8746571 source

      Not checked in the free run.

    • cites [10] Wang L (2022) Targeting p21(Cip1) highly expressing cells in adipose tissue alleviates insulin resistance in obesity. Cell Metab. 34, 75– 89 e8 34813734 PMC8732323 source

      Not checked in the free run.

    • cites [11] Dehkordi SK (2021) Profiling senescent cells in human brains reveals neurons with CDKN2D/p19 and tau neuropathology. Nature Aging 1, 1107– 1116 35531351 PMC9075501 source

      Not checked in the free run.

    • cites [12] Amor C (2020) Senolytic CAR T cells reverse senescence-associated pathologies. Nature 583, 127– 132 32555459 PMC7583560 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Senescent cells produce a characteristic proinflammatory secretome termed the senescence-associated secretory phenotype (SASP).

    “Also, senescent cells produce a characteristic proinflammatory secretome, collectively termed the senescence-associated secretory phenotype (SASP) [16].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [16] Gorgoulis V (2019) Cellular senescence: defining a path forward. Cell 179, 813– 827 31675495 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    p16 is not a specific or sensitive marker for cellular senescence because some p16high cells are not senescent and some senescent cells do not express p16.

    “However, p16 might not be a specific or sensitive marker for cellular senescence since not all p16 highly expressing (p16high) cells are necessarily senescent [4] and some senescent cells do not express p16 [5,6].”
    • Also stated in: § Models for understanding senescent cell heterogeneity in vivo · hedged
      “Emerging evidence has indicated that p16 might be neither a sensitive nor a specific marker for senescence because some p16high cells are not senescent (e.g., pancreatic β-cells [24], macrophages [4], mesenchymal stem cells [25], endothelial cells [26]), while not all senescent cells express high levels of p16 [16].”
    Quote in the paperSubstantial claim0 of 4 sources checked
    • cites [4] Hall BM (2017) p16(Ink4a) and senescence-associated beta-galactosidase can be induced in macrophages as part of a reversible response to physiological stimuli. Aging 9, 1867– 1884 28768895 PMC5611982 source

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    • cites [5] Yosef R (2017) p21 maintains senescent cell viability under persistent DNA damage response by restraining JNK and caspase signaling. EMBO J. 36, 2280– 2295 28607003 PMC5538795 source

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    • cites [6] Wang L (2021) Targeting p21Cip1 highly expressing cells in adipose tissue alleviates insulin resistance in obesity. Cell Metab. 34, 75– 89.e8 34813734 PMC8732323 source

      Not checked in the free run.

    • cites [16] Gorgoulis V (2019) Cellular senescence: defining a path forward. Cell 179, 813– 827 31675495 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Elimination of uPAR-expressing cells with CAR T cells specifically kills senescent cells both in vivo and in vitro, and extends the lifespan of mice with lung adenocarcinoma.

    “Elimination of uPAR-expressing cells with chimeric antigen receptor (CAR) T cells specifically kills senescent cells both in vivo and in vitro, and extends the lifespan of mice with lung adenocarcinoma [12].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [12] Amor C (2020) Senolytic CAR T cells reverse senescence-associated pathologies. Nature 583, 127– 132 32555459 PMC7583560 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Dasatinib is most effective at eliminating senescent preadipocytes, while quercetin better targets endothelial cells.

    “For example, dasatinib is most effective at eliminating senescent preadipocytes, while quercetin better targets endothelial cells [19].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [19] Zhu Y (2015) The Achilles’ heel of senescent cells: from transcriptome to senolytic drugs. Aging Cell 14, 644– 658 25754370 PMC4531078 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    A p21-Cre transgenic mouse model targets p21high cells in vivo, which comprise 1-10% of cells in various tissues in aged mice, are distinct from p16high cells, and both display key characteristics of senescence.

    “In 2021, a p21-Cre transgenic mouse model was generated, containing a p21 promoter driving an inducible Cre. This model targets p21 highly expressing (p21high) cells in vivo, which comprise 1–10% of cells in various tissues in aged mice, and are distinct from p16high cells, with both displaying a number of key characteristics of senescence [9].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [9] Wang B (2021) An inducible p21-Cre mouse model to monitor and manipulate p21-highly-expressing senescent cells in vivo. Nat Aging 1, 962– 973 35024619 PMC8746571 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Clearance of p21high cells reduces senescence markers, improves physical function in aged mice, and alleviates metabolic dysfunction in obese mice.

    “Remarkably, clearance of p21high cells reduces senescence markers, improves physical function in aged mice [9], and alleviates metabolic dysfunction in obese mice [10].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [9] Wang B (2021) An inducible p21-Cre mouse model to monitor and manipulate p21-highly-expressing senescent cells in vivo. Nat Aging 1, 962– 973 35024619 PMC8746571 source

      Not checked in the free run.

    • cites [10] Wang L (2022) Targeting p21(Cip1) highly expressing cells in adipose tissue alleviates insulin resistance in obesity. Cell Metab. 34, 75– 89 e8 34813734 PMC8732323 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    uPAR was identified as a unique senescence marker by comparing differentially expressed genes in bulk RNA-seq datasets of three known mouse models of senescence, and was validated by flow cytometry.

    “In another study, urokinase plasminogen activator receptor (uPAR), was identified as a unique senescence marker by comparing differentially expressed genes in bulk RNA-seq datasets of three known mouse models of senescence, and was validated by flow cytometry [12].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [12] Amor C (2020) Senolytic CAR T cells reverse senescence-associated pathologies. Nature 583, 127– 132 32555459 PMC7583560 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Fisetin and navitoclax kill senescent fibroblasts and endothelial cells but have limited effect on senescent preadipocytes.

    “Both fisetin and navitoclax kill senescent fibroblasts and endothelial cells, yet have limited effect on senescent preadipocytes [20,21].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [20] Yousefzadeh MJ (2018) Fisetin is a senotherapeutic that extends health and lifespan. EBioMedicine 36, 18– 28 30279143 PMC6197652 source

      Not checked in the free run.

    • cites [21] Chang J (2016) Clearance of senescent cells by ABT263 rejuvenates aged hematopoietic stem cells in mice. Nat. Med 22, 78– 83 26657143 PMC4762215 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    One marker may not be enough to define senescent cells as a whole, and a combination of many markers and features will ensure that cells are in a senescent state.

    “In addition, as evidenced by the different senescence markers discovered earlier, it is possible that one marker may not be enough to define senescent cells as a whole, and that a combination of many markers and features will ensure that cells are in a senescent state.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Individual senescent cells within the same culture of human fibroblasts demonstrate cell-to-cell transcriptomic variability, with a lack of correlation of gene expression among several senescence-associated genes.

    “Furthermore, individual senescent cells within the same culture of human fibroblasts also demonstrate cell-to-cell transcriptomic variability, as evidenced by single-cell isolation and nanofluidic PCR experiments showing a lack of correlation of gene expression among several senescence-associated genes [23].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [23] Wiley CD (2017) Analysis of individual cells identifies cell-to-cell variability following induction of cellular senescence. Aging Cell 16, 1043– 1050 28699239 PMC5595671 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    Single-nucleus RNA-seq can potentially capture all cell types while sequencing sensitivity might be lower due to lower mRNA abundance in nuclei.

    “Conversely, single-nucleus RNA-seq can potentially capture all cell types while the sequencing sensitivity might be lower due to lower mRNA abundance in the nuclei.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Genetic elimination of GPNMB-positive cells reduces senescent cell burden and alleviates metabolic irregularities in obese mice, and vaccination against GPNMB improves conditions associated with aging.

    “Genetic elimination of GPNMB-positive cells reduces the senescent cell burden and alleviates metabolic irregularities in obese mice. Vaccination against GPNMB also improves a number of conditions associated with aging [13].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [13] Suda M (2021) Senolytic vaccination improves normal and pathological age-related phenotypes and increases lifespan in progeroid mice. Nature Aging 1, 1117– 1126 37117524 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationnone given

    The consensus senescence features might not be conserved in all senescent cells and might not be sufficient or accessible to examine senescence heterogeneity in vivo.

    “However, these features might not be conserved in all senescent cells and might not be sufficient or accessible to examine senescence heterogeneity in vivo.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    Senescent cells in vivo are likely heterogeneous, with heterogeneity arising from age, sex, pathological states, tissue locations, microenvironment, and accumulation kinetics.

    “Based on these in vitro findings, senescent cells in vivo are likely heterogeneous as well, which can come from diversity in terms of age and sex as well as varied pathological states, locations of the tissues, microenvironment, and accumulation kinetics.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Single-cell RNA-seq remains the most comprehensive method for capturing the transcriptome of individual cells due to high sequencing depth, but it may miss large or dissociation-sensitive cells, likely including some senescent cells due to their enlarged size and physical fragility.

    “Although single-cell RNA-seq remains the most comprehensive method for capturing the transcriptome of individual cells, due to high sequencing depth, it may miss capturing cells that are large or sensitive to dissociation, which likely include some senescent cells due to their enlarged size and physical fragility [18].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [18] Di Micco R (2021) Cellular senescence in ageing: from mechanisms to therapeutic opportunities. Nat. Rev. Mol. Cell Biol 22, 75– 95 33328614 PMC8344376 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    In healthy and young mouse livers, p16high cells are mostly endothelial cells and a few Kupffer cells; in mouse livers with non-alcoholic steatohepatitis, p16high cells are mainly macrophages along with other cell types; in healthy and young kidneys, p16high cells are mainly epithelial cells.

    “In healthy and young mouse livers, p16high cells are mostly endothelial cells and a few Kupffer cells. In mouse livers with non-alcoholic steatohepatitis (NASH), p16high cells are mainly macrophages along with other cell types. In healthy and young kidneys, p16high cells are mainly epithelial cells.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [28] Omori S (2020) Generation of a p16 reporter mouse and its use to characterize and target p16(high) cells in vivo. Cell Metab. 32, 814– 828 e6 32949498 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    p19 upregulation was validated by mFISH and mABI in human Alzheimer's disease brains, and p19-expressing cells had larger nuclei and more lipofuscin-positive cells than non-p19-expressing cells.

    “p19 upregulation was further validated by mFISH and mABI in human AD brains and additional experiments confirmed that p19-expressing cells had several key features, including larger nuclei and more lipofuscin-positive cells than non-p19-expressing cells.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [11] Dehkordi SK (2021) Profiling senescent cells in human brains reveals neurons with CDKN2D/p19 and tau neuropathology. Nature Aging 1, 1107– 1116 35531351 PMC9075501 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Multiplexed antibody-based imaging technologies such as imaging mass cytometry and CODEX can detect 20-50 selected genes simultaneously in each cell at the protein level, complementing transcript-based detection especially given the potential for poor correlation of transcriptomic and proteomic data sets in senescent cells.

    “For example, multiplexed antibody-based imaging (mABI) technologies such as imaging mass cytometry (IMC) [31] and codetection by indexing (CODEX) [32] can detect 20–50 selected genes simultaneously in each cell at the protein level, which is a useful complement to transcript-based detection – especially given the potential for poor correlation of transcriptomic and proteomic data sets implicated”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [31] Giesen C (2014) Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417– 422 24584193 source

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    • cites [32] Goltsev Y (2018) Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968– 981 e15 30078711 PMC6086938 source

      Not checked in the free run.

    • cites [33] Sullivan DI (2021) Transcriptional and proteomic characterization of telomere-induced senescence in a human alveolar epithelial cell line. Front Med. 8, 600626 PMC7902064 33634147 source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Tabula Muris Senis is a single-cell RNA-seq database containing sequencing data from 23 mouse tissues across the lifespan.

    “Tabula Muris Senis, or Mouse Ageing Cell Atlas, is a single-cell RNA-seq database, completed in 2020, that contains sequencing data from 23 mouse tissues across the lifespan [42].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [42] Tabula Muris C. (2020) A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590– 595 32669714 PMC8240505 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The senescent cells found in the human Alzheimer's disease brain dataset were mostly excitatory neurons with tau pathology commonly seen with Alzheimer's disease.

    “The senescent cells found in this dataset were mostly excitatory neurons with tau pathology commonly seen with AD.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [11] Dehkordi SK (2021) Profiling senescent cells in human brains reveals neurons with CDKN2D/p19 and tau neuropathology. Nature Aging 1, 1107– 1116 35531351 PMC9075501 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisenone given

    RNA-seq-based methods can detect thousands of transcripts at the same time, a feature essential for discovering new senescent cell markers.

    “These powerful RNA-seq-based methods can detect thousands of transcripts at the same time, a feature that is essential for discovering new senescent cell markers.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    In human Alzheimer's disease brains, p19/CDKN2D was the most significant marker of senescent cells in postmortem brains and defined a unique group of senescent cells.

    “In human brains, a single-nucleus RNA sequencing study using an eigengene approach found that p19/CDKN2D was the most significant marker of senescent cells in postmortem brains from patients with Alzheimer’s disease (AD), and also defined a unique group of senescent cells in human AD brains [11].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [11] Dehkordi SK (2021) Profiling senescent cells in human brains reveals neurons with CDKN2D/p19 and tau neuropathology. Nature Aging 1, 1107– 1116 35531351 PMC9075501 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    E2f2 and Lmnb1 are typically downregulated in senescent cells.

    “To note, E2f2 and Lmnb1 are typically downregulated in senescent cells [43].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [43] Shimi T (2011) The role of nuclear lamin B1 in cell proliferation and senescence. Genes Dev. 25, 2579– 2593 22155925 PMC3248680 source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Spatial transcriptomics allows transcriptomic detection in small tissue sections by RNA-seq while maintaining the histological information of those sections.

    “Spatial transcriptomics is another newly developed method which allows for transcriptomic detection in small tissue sections by RNA-seq while maintaining the histological information of these sections [30].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [30] Stahl PL (2016) Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78– 82 27365449 source

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  • May combine two claims.

    Premisecitation

    In older mouse populations, p16 expression increased significantly with aging, as did E2f2, Lmnb1, Tnf, and Itgaf.

    “One component of this large database was an examination of senescence in older mouse populations which showed that p16 expression increased significantly with aging as did E2f2, Lmnb1, Tnf, and Itgaf [42].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [42] Tabula Muris C. (2020) A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590– 595 32669714 PMC8240505 source

      Not checked in the free run.

  • May combine two claims.

    Premisereasoning

    Features measurable by imaging flow cytometry include SA-β-gal staining, cell size, proliferation, DNA damage foci, and cytosolic DNA presence, all currently not measurable by sequencing-based methods.

    “These features include SA-β-gal staining, cell size, proliferation, DNA damage foci, and cytosolic DNA presence, all of which are currently not measurable by sequencing-based methods.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Single-cell mass spectrometry, including single-cell proteomics and single-cell metabolomics, provides protein and metabolite identification on a large scale.

    “One method that has yet to be used to study senescence is single-cell mass spectrometry, including single-cell proteomics and single-cell metabolomics which provide protein (including post-translational modification) and metabolite identification on a large scale [37] and could have enormous implications for studying senescent cells.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [37] Perkel JM (2021) Single-cell proteomics takes centre stage. Nature 597, 580– 582 34545225 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    A subset of senescent endothelial cells in the retina highly express Col1a1 and can be specifically eliminated by a senolytic drug.

    “They identified a subset of senescent endothelial cells in the retina, which highly express Col1a1, that can be specifically eliminated by a senolytic drug [41].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [41] Crespo-Garcia S (2021) Pathological angiogenesis in retinopathy engages cellular senescence and is amenable to therapeutic elimination via BCL-xL inhibition. Cell Metab. 33, 818– 832 e7 33548171 source

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  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Premisenone given

    The sequencing depth of current methods remains relatively low, and only up to 50-60% of transcripts can be sequenced and detected in each cell.

    “However, the sequencing depth of current methods remains relatively low, and only up to 50–60% of transcripts can be sequenced and detected in each cell.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    p21high cells accumulate more with aging and exhibit altered pathways commonly seen in senescent cells induced in vitro, including upregulation of SCAPs, SASP, NF-κB, HMGB1, and FOXO pathways.

    “Moreover, by performing single-cell RNA-seq on adipose-derived mesenchymal stem cells isolated from young and aged mice, another study demonstrated that p21high cells accumulate more with aging and exhibit a number of altered pathways commonly seen in senescent cells induced in vitro, including upregulation of SCAPs, SASP, NF-κB, HMGB1, and FOXO pathways [8].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [8] Wang B (2020) Transplanting cells from old but not young donors causes physical dysfunction in older recipients. Aging Cell 19, e13106 31971661 PMC7059132 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Single-cell RNA-seq comparison of kidney, lung, and spleen between aged and young mice found that various cell types express unique genes with aging, similar cell types exhibit similar aging trajectories in gene enrichment, and dissimilar cell types have distinct trajectories.

    “Another study used single-cell RNA-seq to compare transcriptomic differences in the kidney, lung, and spleen between aged and young mice [44] and found that various cell types express unique genes with aging, and similar cell types exhibit similar aging trajectories in terms of gene enrichment, while dissimilar cell types have distinct trajectories.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [44] Kimmel JC Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging. Genome Res. 29, 2088– 2103 PMC6886498 31754020 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Understanding the heterogeneity of senescence will play a major role in improving precision medicine approaches for older populations.

    “Due to the high therapeutic value of targeting senescent cells, understanding the heterogeneity of senescence will also play a major role in improving precision medicine approaches for older populations.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Tissue-specific differences in senescent cells suggest that a combination of many markers, rather than just one marker, could more accurately detect senescent cells.

    “Additionally, the tissue-specific differences in senescent cells described in this review suggest that a combination of many markers, rather than just one marker, could more accurately detect senescent cells.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Common genes differentially expressed with age among kidney, lung, and spleen include decreased expression of genes in SRP-dependent protein translation and upregulated expression of genes related to inflammation.

    “The study did show common genes differentially expressed with age among the three tissues studied, including decreased expression of genes in SRP-dependent protein translation and upregulated expression of genes related to inflammation [44].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [44] Kimmel JC Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging. Genome Res. 29, 2088– 2103 PMC6886498 31754020 source

      Not checked in the free run.

37 of 37

  • The paper relies on this without support.

    Senescent cells are of low abundance in vivo.

    “This could be a particular challenge for mapping senescent cells due to their low abundance in vivo.”
1 of 1

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?

  • Aging is extremely heterogeneous, especially from a transcriptome perspective.

    “Aging is extremely heterogeneous, especially from a transcriptome perspective.198”

    From Inflammation and aging: signaling pathways and intervention therapies (2023) · cites this paper as [198]

    The 'Other tissues' section reports that different cell types in aged versus young mice express different genes and follow distinct aging trajectories. This supports heterogeneity in age-related transcriptomic changes, but the review focuses on cellular senescence and does not establish that aging as a whole is 'extremely' heterogeneous. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The transcriptional heterogeneity and dynamic progression of primary and secondary senescent cells at the single-cell level remain largely underexplored.

    “However, the transcriptional heterogeneity and dynamic progression of primary and secondary SnCs at the single‐cell level remain largely underexplored (Cohn et al. 2023).”

    From Transcriptional Profiling at Single-Cell Resolution Reveals Diversity and Regulatory Networks of Primary and Secondary Senescent Cells (2026) · cites this paper as Cohn et al. 2023

    The “Senescence heterogeneity at a glance” section describes transcriptomic differences across cells and stages of senescence, and “Senescent heterogeneity in tissues (single-cell/nucleus level)” says current knowledge is insufficient. The passages do not specifically address the transcriptional heterogeneity or dynamic progression of primary versus secondary senescent cells at the single-cell level. Page numbers were not provided in our copy. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • A central challenge in senescence research is the inherent heterogeneity within senescent cell populations.

    “A central challenge in senescence research is the inherent heterogeneity within SnC populations (Cohn et al. 2023; Hernandez‐Segura et al. 2017; Wechter et al. 2023).”

    From Transcriptional Profiling at Single-Cell Resolution Reveals Diversity and Regulatory Networks of Primary and Secondary Senescent Cells (2026) · cites this paper as Cohn et al. 2023

Self-check

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

Each contradiction between two passages listed here was confirmed by an independent check.

  • Same cited paper has conflicting publication years

    Minor

    References 6 and 10 give the same title, journal volume, pages, and identifiers but list its publication year as 2021 and 2022, respectively.

1 of 1

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Wang, 2022, Targeting p21(Cip1) highly expressing cells in adipose tissue alleviates insulin resistance in obesity, Cell Metab., 34, 75, 10.1016/j.cmet.2021.11.002 · DOI · Paper page

Cited by

Sorted by citations

Related

Authors
Cohn, R. L., Gasek, N. S., Kuchel, G. A., & Xu, M.
Affiliations
University of Connecticut (US)
Corresponding author
Xu, Ming
ORCID
Venue
Trends in Cell Biology
Publisher
Elsevier BV
Published
2023-01
Volume
33
Issue
1
Pages
9-17
ISSN
0962-8924 (ISSN-L)
DOI
10.1016/j.tcb.2022.04.011
PMC
PMC9812642
OpenAlex
W4280558960
Semantic Scholar
b30edd548bc3e9c62d4b7fdb2af6d580692770e2
Cited by
294
References
45
Citations by year
2026: 67 · 2025: 93 · 2024: 83 · 2023: 42 · 2022: 9
FWCI
8.19
Type
journal article
Language
en
Open access
Licence
Locations
  • Trends in Cell Biology (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health (AG063528, AG066679, AG068860, R33AG061456, T32HG010463, U54AG075941)
  • Mayo Clinic
Topics
  • Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
Keywords
cellular senescence, senescence heterogeneity, senescence markers, p16, aging, single-cell RNA sequencing, single-nucleus RNA sequencing
MeSH terms
Aging, Humans, Cellular Senescence

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.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.

Statistics and arithmetic are recomputed, not judged.

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