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Targeting p21Cip1 highly expressing cells in adipose tissue alleviates insulin resistance in obesity
Open accessAbstract
Insulin resistance is a pathological state often associated with obesity, representing a major risk factor for type 2 diabetes. Limited mechanism-based strategies exist to alleviate insulin resistance. Here, using single-cell transcriptomics, we identify a small, critically important, but previously unexamined cell population, p21 Cip1 highly expressing (p21 high) cells, which accumulate in adipose tissue with obesity. By leveraging a p21-Cre mouse model, we demonstrate that intermittent clearance of p21 high cells can both prevent and alleviate insulin resistance in obese mice. Exclusive inactivation of the NF-κB pathway within p21 high cells, without killing them, attenuates insulin resistance. Moreover, fat transplantation experiments establish that p21 high cells within fat are sufficient to cause insulin resistance in vivo. Importantly, a senolytic cocktail, dasatinib plus quercetin, eliminates p21 high cells in human fat ex vivo and mitigates insulin resistance following xenotransplantation into immuno-deficient mice. Our findings lay the foundation for pursuing the targeting of p21 high cells as a new therapy to alleviate insulin resistance.
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“Senescent cells in both human and mouse adipose tissue have been characterized by expression of CDKN2A and CDKN1A, higher SA-β-gal activity, DNA damage (γH2AX) foci and arrest of cell proliferation (MKI67), and by expression of SASP factors including IL-6, CXCL8 (also known as IL-8), IL-1β, TNF, CCL2, SERPINE1, MMP3 and MMP12 (refs. 119–122).”
From SenNet recommendations for detecting senescent cells in different tissues (2024) · cites this paper as [122]
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
“Importantly, Cdkn1ahigh and Cdkn2ahigh cells exist as two distinct cell populations122,125, indicating the heterogeneity and complexity of senescent cell populations in adipose tissue.”
From SenNet recommendations for detecting senescent cells in different tissues (2024) · cites this paper as [122]
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
“The accumulation of p21 positive (p21+) cells has been identified in aged tissues in vivo, and clearance of this population in rodents has been shown to both extend lifespan and improve healthspan [7,8,9].”
From Fluorescence-Guided Spatial Mapping of p21-Expressing Senescent Cells in Aged Liver at Single-Cell Resolution (2026) · cites this paper as [9]
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
“These include the p21CIP1 pathway125, novel SnC surface markers revealed through transcriptomic profiling, and distinctive metabolic dependencies such as glutaminolysis126.”
From Emerging strategies in senotherapeutics: from broad-spectrum senolysis to precision reprogramming (2026) · cites this paper as [125]
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“Moreover, while decondensation occurs in essentially all centromeric satellites in vitro24, only a fraction of centromeric satellites undergo unwinding in vivo25,26 (Fig. 1 F).”
From Guidelines for minimal information on cellular senescence experimentation in vivo (2024) · cites this paper as [26]
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In the citing 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.”
From The heterogeneity of cellular senescence: insights at the single-cell level (2023) · cites this paper as [10]
In the citing 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].”
From The heterogeneity of cellular senescence: insights at the single-cell level (2023) · cites this paper as [6]
In the citing paper
“Remarkably, clearance of p21high cells reduces senescence markers, improves physical function in aged mice [9], and alleviates metabolic dysfunction in obese mice [10].”
From The heterogeneity of cellular senescence: insights at the single-cell level (2023) · cites this paper as [10]
In the citing 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.”
From The heterogeneity of cellular senescence: insights at the single-cell level (2023) · cites this paper as [10]
In the citing 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.”
From The heterogeneity of cellular senescence: insights at the single-cell level (2023) · cites this paper as [10]
In the citing 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.”
From The heterogeneity of cellular senescence: insights at the single-cell level (2023) · cites this paper as [10]
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Metadata
- Authors
- Wang, L., Wang, B., Gasek, N. S., Zhou, Y., Cohn, R. L., Martin, D. E., Zuo, W., Flynn, W. F., Guo, C., Jellison, E. R., Kim, T., Prata, L. G. P. L., Palmer, A. K., Li, M., Inman, C. L., Barber, L. S., Al-Naggar, I. M. A., Zhou, Y., Du, W., Kshitiz, Kuchel, G. A., Meves, A., Tchkonia, T., Kirkland, J. L., Robson, P., & Xu, M.
- Affiliations
- University of Connecticut (US)
- Central South University (CN)
- The Jackson Laboratory for Genomic Medicine (US)
- UConn Health (US)
- Mayo Clinic (US)
- Corresponding author
- Xu, Ming
- ORCID
- Venue
- Cell Metabolism
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 34
- Issue
- 1
- Pages
- 75-89.e8
- ISSN
- 1550-4131 (ISSN-L)
- PMC
- PMC8732323
- OpenAlex
- W3216561293
- Cited by
- 163
- References
- 41
- Citations by year
- 2026: 22 · 2025: 31 · 2024: 42 · 2023: 41 · 2022: 27
- FWCI
- 19.79
- Type
- journal article
- Language
- en
- Update notices
- erratum (2022-01-04)
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 368 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell Metabolism (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion)
- Funders
- American Foundation for Aging Research
- University of Connecticut (T32HG010463)
- National Institutes of Health (K08CA215105, P01AG062413, P30AG067988, R33AG061456, R37AG013925)
- Noaber Foundation
- NIA (AG063528, AG066679, AG068860)
- Mayo Clinic
- Glenn Foundation for Medical Research
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Adipokines, Inflammation, and Metabolic Diseases
- Health Sciences › Medicine › Physiology (medical) › Adipose Tissue and Metabolism
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Peroxisome Proliferator-Activated Receptors
- Keywords
- p21, insulin resistance, obesity, adipose tissue, senolytic therapy, dasatinib, quercetin, NF-κB signaling, type 2 diabetes, fat transplantation, single-cell transcriptomics
- MeSH terms
- Adipose Tissue, Animals, Diabetes Mellitus, Type 2, Insulin Resistance, Mice, Inbred C57BL, Obesity, Cellular Senescence, Mice, Diet, High-Fat
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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