The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index; 3 of the 63 sources its claims cite were checked for support.
The heterogeneity of cellular senescence: insights at the single-cell level
Open accessAbstract
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.
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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.
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Claims the paper makes
40 Not checked yet · 10 Own interpretation · 3 No basis given · 1 Partly supported · accuracy not yet measured
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In the paper
“Another useful technology for examining senescent cells is imaging flow cytometry, which has been used to examine senescent cells in vivo [9,10,36]. The major advantage of this method is that it allows for protein-level detection of several senescence markers or reporters along with assessment of several senescence features at the single-cell level in a relatively high-throughput manner.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Current high-throughput technologies for examining senescent cells at the single-cell level
What the sources say
- Partly supportedcites [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 sourceExperimental — accuracy not yet measured
The Methods section, “Imaging flow cytometry analysis” (page unavailable), describes measuring GFP and tdTomato reporters, p21 protein, SA-β-gal, EdU incorporation, and cell area in individual cells. The Results section, “p21high cells exhibit features of cellular senescence” (page unavailable), explicitly describes single-cell characterization. Our copy does not establish the claimed relative high-throughput advantage. (LLM, conf=0.82)
- Could not verifycites [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 sourceExperimental — accuracy not yet measured
The STAR METHODS ‘Flow cytometry analysis’ section describes antibody-based detection of p21, p16 and NF-κB with a BD LSR II flow cytometer, but does not identify the method as imaging flow cytometry. Searches for imaging flow cytometry, ImageStream and high-throughput single-cell assessment found no passage in our copy describing the claimed capabilities. (LLM, conf=0.75)
- Supportedcites [36] Biran A (2017) Quantitative identification of senescent cells in aging and disease. Aging Cell 16, 661– 671 28455874 PMC5506427 source
The Introduction describes imaging flow cytometry as producing multiple images of each cell, and the Discussion reports simultaneous analysis of SA-β-gal activity and additional senescence biomarkers. The ImageStreamX analysis section describes single-cell measurements at up to 5,000 cells per second, supporting the relatively high-throughput characterization. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Partly supported
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescence heterogeneity at a glance
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None of the sources cited for this claim have been checked yet.
In the paper
“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).”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Models for understanding senescent cell heterogeneity in vivo
What the sources say
- Not checked yetcites [15] Baker DJ (2011) Clearance of p16Ink4a-positive senescent cells delays ageing-associated disorders. Nature 479, 232– 236 22048312 PMC3468323 source
- Not checked yetcites [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 yetcites [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 yetcites [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
hedgedconditional- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“It is now evident that all current senolytics have distinct efficacy on diverse senescent cell populations.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescence heterogeneity at a glance
What the sources say
- Not checked yetcites [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 yetcites [20] Yousefzadeh MJ (2018) Fisetin is a senotherapeutic that extends health and lifespan. EBioMedicine 36, 18– 28 30279143 PMC6197652 source
- Not checked yetcites [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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescence heterogeneity at a glance
What the sources say
- Not checked yetcites [22] Hernandez-Segura A Unmasking transcriptional heterogeneity in senescent cells. Curr. Biol 27, 2652– 2660 e4 PMC5788810 28844647 source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Models for understanding senescent cell heterogeneity in vivo
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“They showed that p21high and p16high cells are two distinct cell populations in terms of cell types, tissue location, accumulation kinetics, and physiological roles.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Adipose tissue
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“In aged mice, single-nucleus RNA-seq and single-cell RNA-seq have shown that both p16high and p21high cells accumulate more in microglia, oligodendrocyte progenitor cells (OPCs), and oligodendrocytes in aged mouse hippocampus with certain degrees of heterogeneity [38].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Brain
What the sources say
- Not checked yetcites [38] Ogrodnik M (2021) Whole-body senescent cell clearance alleviates age-related brain inflammation and cognitive impairment in mice. Aging Cell 20, e13296 33470505 PMC7884042 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Similar discrepancies were recapitulated in aged mice [9].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Adipose tissue
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This could be a particular challenge for mapping senescent cells due to their low abundance in vivo.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Current high-throughput technologies for examining senescent cells at the single-cell level
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- Rests on an assumption: Senescent cells are of low abundance in vivo.
In the paper
“Notably, adipose p21high cells showed a number of key senescence features (assessed by imaging flow cytometry) in the contexts of both aging and obesity.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Adipose tissue
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Gene ontology (GO) analysis on p16high cells showed highly heterogeneous pathway changes among different cell types and conditions.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Liver and kidney
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While these studies showcased a novel population of senescent cells, it is important to recognize that – like p16 – p21 might not be a specific marker for senescent cells.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Adipose tissue
hedged- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Another study leveraged gene set variation analysis (GSVA) to analyze single-cell RNA-seq datasets of mouse retinas with proliferative retinopathies; it revealed that certain cell types, including astrocytes, pericytes, endothelial cells, and glial cells, showed expression of senescence-related genes from two published senescence-related gene-sets [39,40].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Eye
What the sources say
- Not checked yetcites [39] Fridman AL and Tainsky MA (2008) Critical pathways in cellular senescence and immortalization revealed by gene expression profiling. Oncogene 27, 5975– 5987 18711403 PMC3843241 source
- Not checked yetcites [40] Avelar RA (2020) A multidimensional systems biology analysis of cellular senescence in aging and disease. Genome Biol. 21, 91 32264951 PMC7333371 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“For example, some SASP genes were used to identify senescent cells, but their expression levels were also upregulated in cells showing no common senescent hallmarks.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Brain
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Multiplex fluorescence in situ hybridization (mFISH) [34,35] is another effective approach to detect senescent cells, which can detect more than 100 selected genes at single-molecule resolution.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Current high-throughput technologies for examining senescent cells at the single-cell level
What the sources say
- Not checked yetcites [34] Eng CL (2019) Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235– 239 30911168 PMC6544023 source
- Not checked yetcites [35] Moffitt JR (2018) Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 30385464 PMC6482113 source
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In summary, in most of these studies, senescent cells are distributed in various cell populations rather than forming specific clusters, further demonstrating heterogeneity.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Other tissues
plain- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 29
- Probably not a substantial claim. 11
In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Abstract
What the sources say
- Not checked yetcites [3] Gasek NS (2021) Strategies for targeting senescent cells in human disease. Nat Aging. 1, 870– 879 34841261 PMC8612694 source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Also stated in: § The importance of understanding cellular senescence heterogeneity · hedged
None of the sources cited for this claim have been checked yet.
In the paper
“At the transcript level, p16 and p21 are the two most commonly used markers for senescent cells [3,16].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > The current consensus on the biology of senescent cells
What the sources say
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > The importance of understanding cellular senescence heterogeneity
What the sources say
- Not checked yetcites [7] Sturmlechner I (2021) p21 produces a bioactive secretome that places stressed cells under immunosurveillance. Science 374 PMC8985214 34709885 source
- Not checked yetcites [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 yetcites [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 yetcites [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 yetcites [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 yetcites [12] Amor C (2020) Senolytic CAR T cells reverse senescence-associated pathologies. Nature 583, 127– 132 32555459 PMC7583560 source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Also, senescent cells produce a characteristic proinflammatory secretome, collectively termed the senescence-associated secretory phenotype (SASP) [16].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > The current consensus on the biology of senescent cells
What the sources say
- Not checked yetcites [16] Gorgoulis V (2019) Cellular senescence: defining a path forward. Cell 179, 813– 827 31675495 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“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].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > The importance of understanding cellular senescence heterogeneity
What the sources say
- Not checked yetcites [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
- Not checked yetcites [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
- Not checked yetcites [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 yetcites [16] Gorgoulis V (2019) Cellular senescence: defining a path forward. Cell 179, 813– 827 31675495 source
hedged- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Also stated in: § Models for understanding senescent cell heterogeneity in vivo · hedged
In the paper
“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].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Models for understanding senescent cell heterogeneity in vivo
What the sources say
- Not checked yetcites [12] Amor C (2020) Senolytic CAR T cells reverse senescence-associated pathologies. Nature 583, 127– 132 32555459 PMC7583560 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“For example, dasatinib is most effective at eliminating senescent preadipocytes, while quercetin better targets endothelial cells [19].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescence heterogeneity at a glance
What the sources say
- Not checked yetcites [19] Zhu Y (2015) The Achilles’ heel of senescent cells: from transcriptome to senolytic drugs. Aging Cell 14, 644– 658 25754370 PMC4531078 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Models for understanding senescent cell heterogeneity in vivo
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Remarkably, clearance of p21high cells reduces senescence markers, improves physical function in aged mice [9], and alleviates metabolic dysfunction in obese mice [10].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Models for understanding senescent cell heterogeneity in vivo
What the sources say
- Not checked yetcites [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 yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Models for understanding senescent cell heterogeneity in vivo
What the sources say
- Not checked yetcites [12] Amor C (2020) Senolytic CAR T cells reverse senescence-associated pathologies. Nature 583, 127– 132 32555459 PMC7583560 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Both fisetin and navitoclax kill senescent fibroblasts and endothelial cells, yet have limited effect on senescent preadipocytes [20,21].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescence heterogeneity at a glance
What the sources say
- Not checked yetcites [20] Yousefzadeh MJ (2018) Fisetin is a senotherapeutic that extends health and lifespan. EBioMedicine 36, 18– 28 30279143 PMC6197652 source
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Models for understanding senescent cell heterogeneity in vivo
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In the paper
“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].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescence heterogeneity at a glance
What the sources say
- Not checked yetcites [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
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In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Current high-throughput technologies for examining senescent cells at the single-cell level
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“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].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Models for understanding senescent cell heterogeneity in vivo
What the sources say
- Not checked yetcites [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
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In the paper
“However, these features might not be conserved in all senescent cells and might not be sufficient or accessible to examine senescence heterogeneity in vivo.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > The current consensus on the biology of senescent cells
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In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescence heterogeneity at a glance
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In the paper
“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].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Current high-throughput technologies for examining senescent cells at the single-cell level
What the sources say
- Not checked yetcites [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
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In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Liver and kidney
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Brain
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“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”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Current high-throughput technologies for examining senescent cells at the single-cell level
What the sources say
- Not checked yetcites [31] Giesen C (2014) Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417– 422 24584193 source
- Not checked yetcites [32] Goltsev Y (2018) Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968– 981 e15 30078711 PMC6086938 source
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Other tissues
What the sources say
- Not checked yetcites [42] Tabula Muris C. (2020) A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590– 595 32669714 PMC8240505 source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The senescent cells found in this dataset were mostly excitatory neurons with tau pathology commonly seen with AD.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Brain
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Current high-throughput technologies for examining senescent cells at the single-cell level
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“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].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Brain
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“To note, E2f2 and Lmnb1 are typically downregulated in senescent cells [43].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Other tissues
What the sources say
- Not checked yetcites [43] Shimi T (2011) The role of nuclear lamin B1 in cell proliferation and senescence. Genes Dev. 25, 2579– 2593 22155925 PMC3248680 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Current high-throughput technologies for examining senescent cells at the single-cell level
What the sources say
- Not checked yetcites [30] Stahl PL (2016) Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78– 82 27365449 source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Other tissues
What the sources say
- Not checked yetcites [42] Tabula Muris C. (2020) A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590– 595 32669714 PMC8240505 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Current high-throughput technologies for examining senescent cells at the single-cell level
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Current high-throughput technologies for examining senescent cells at the single-cell level
What the sources say
- Not checked yetcites [37] Perkel JM (2021) Single-cell proteomics takes centre stage. Nature 597, 580– 582 34545225 source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Eye
What the sources say
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Current high-throughput technologies for examining senescent cells at the single-cell level
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“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].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Adipose tissue
What the sources say
- Not checked yetcites [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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Other tissues
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Concluding remarks
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Concluding remarks
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“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].”
§ The heterogeneity of cellular senescence: insights at the single-cell level > Senescent heterogeneity in tissues (single-cell/nucleus level) > Other tissues
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
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.”
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The heterogeneity of cellular senescence: insights at the single-cell level > References
“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”
§ The heterogeneity of cellular senescence: insights at the single-cell level > References
“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”
Citations
Cites
The serial cultivation of human diploid cell strains
Hayflick, L., & Moorhead, P. S. (1961) · Experimental Cell Research · doi:10.1016/0014-4827(61)90192-6 · Cited by 8,508
Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834
Clearance of p16Ink4a-positive senescent cells delays ageing-associated disorders
Baker, D. J., Wijshake, T., Tchkonia, T., LeBrasseur, N. K., Childs, B. G., van de Sluis, B., Kirkland, J. L., & van Deursen, J. M. (2011) · Nature · doi:10.1038/nature10600 · Cited by 3,670
Cellular Senescence: Defining a Path Forward
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The Achilles' heel of senescent cells: from transcriptome to senolytic drugs
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Cellular senescence in ageing: from mechanisms to therapeutic opportunities
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An Essential Role for Senescent Cells in Optimal Wound Healing through Secretion of PDGF-AA
Demaria, M., Ohtani, N., Youssef, S. A., Rodier, F., Toussaint, W., Mitchell, J. R., Laberge, R.-M., Vijg, J., Van Steeg, H., Dollé, M. E. T., et al. (2014) · Developmental Cell · doi:10.1016/j.devcel.2014.11.012 · Cited by 2,045
Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry
Giesen, C., Wang, H. A. O., Schapiro, D., Zivanovic, N., Jacobs, A., Hattendorf, B., Schüffler, P. J., Grolimund, D., Buhmann, J. M., Brandt, S., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2869 · Cited by 1,951
Clearance of senescent cells by ABT263 rejuvenates aged hematopoietic stem cells in mice
Chang, J., Wang, Y., Shao, L., Laberge, R.-M., Demaria, M., Campisi, J., Janakiraman, K., Sharpless, N. E., Ding, S., Feng, W., et al. (2016) · Nature Medicine · doi:10.1038/nm.4010 · Cited by 1,819
Research works printed but not linked: 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
Inflammation and aging: signaling pathways and intervention therapies
Li, X., Li, C., Zhang, W., Wang, Y., Qian, P., & Huang, H. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01502-8 · Cited by 1,443
“Aging is extremely heterogeneous, especially from a transcriptome perspective.(198) The aging transcriptome landscape in mouse, rat, and cynomolgus monkey is presented in Table 3, including samples, time, platform, cell numbers, and main conclusions.”
BackgroundMeta-hallmarks of aging and cancer
López-Otín, C., Pietrocola, F., Roiz-Valle, D., Galluzzi, L., & Kroemer, G. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2022.11.001 · Cited by 529
Redox regulation: mechanisms, biology and therapeutic targets in diseases
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“426,427 The causes of the initial senescence process include oncogenic signaling, genotoxic harm, extremely short telomeres, malfunctioning mitochondria, infections, oxidative stress, nutrient imbalances, and physical stress.”
The role of p21 in cellular senescence and aging-related diseases
Yan, J., Chen, S., Yi, Z., Zhao, R., Zhu, J., Shuwen, D., & Wu, J. (2024) · Molecules and Cells · doi:10.1016/j.mocell.2024.100113 · Cited by 188
“Most studies have shown that senescent cells are dispersed among various cell populations rather than forming distinct clusters (Cohn et al., 2023).”
Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators
Tao, W., Yu, Z., & Han, J.-D. J. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.03.009 · Cited by 147
Metadata
- Authors
- Cohn, R. L., Gasek, N. S., Kuchel, G. A., & Xu, M.
- Affiliations
- University of Connecticut (US)
- Corresponding author
- Xu, Ming
- Venue
- Trends in Cell Biology
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 33
- Issue
- 1
- Pages
- 9-17
- ISSN
- 0962-8924 (ISSN-L)
- PMC
- PMC9812642
- OpenAlex
- W4280558960
- Semantic Scholar
- b30edd548bc3e9c62d4b7fdb2af6d580692770e2
- Cited by
- 294
- References
- 45
- Citations by year
- 2026: 67 · 2025: 93 · 2024: 83 · 2023: 42
- FWCI
- 8.19
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › 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.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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.
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