A single-cell atlas of human and mouse white adipose tissue
Open accessPublished version of a preprint
Abstract
White adipose tissue, once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic and heterogenous, and is involved in a wide array of biological processes including energy homeostasis, glucose and lipid handling, blood pressure control and host defence 1. High-fat feeding and other metabolic stressors cause marked changes in adipose morphology, physiology and cellular composition 1, and alterations in adiposity are associated with insulin resistance, dyslipidemia and type 2 diabetes 2. Here we provide detailed cellular atlases of human and mouse subcutaneous and visceral white fat at single-cell resolution across a range of body weight. We identify subpopulations of adipocytes, adipose stem and progenitor cells, vascular and immune cells and demonstrate commonalities and differences across species and dietary conditions. We link specific cell types to increased risk of metabolic disease and provide an initial blueprint for a comprehensive set of interactions between individual cell types in the adipose niche in leanness and obesity. These data comprise an extensive resource for the exploration of genes, traits and cell types in the function of white adipose tissue across species, depots and nutritional conditions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
More recently, snRNA-seq has revealed evidence of cellular heterogeneity within adipocyte populations.
“More recently, snRNA-seq has been employed, revealing evidence of the cellular heterogeneity within adipocyte populations (Sárvári et al., 2021; Emont et al., 2022; Holman et al., 2023).”
From Robust single-nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity (2025) · cites this paper as Emont et al., 2022
Integration with Emont et al., 2022 and re-clustering all cells together consistently showed similar overall cellular composition with a substantially higher proportion of vascular cells in the authors' dataset.
“For a more robust comparison, we integrated our data with the previous study by Emont et al., 2022 and re-clustered all the cells together. The results consistently showed the similar overall cellular composition with a substantially higher proportion of vascular cells in our dataset (Figure 2—figure supplement 2C).”
From Robust single-nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity (2025) · cites this paper as Emont et al., 2022
snRNA-seq has recently begun to reveal the heterogeneity of adipocytes within white adipose tissue.
“snRNA-seq has recently begun to reveal the heterogeneity of adipocytes within white adipose tissue (Sárvári et al., 2021; Emont et al., 2022; Holman et al., 2023).”
From Robust single-nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity (2025) · cites this paper as Emont et al., 2022
In integration with previous datasets, they observed a comparable adipocyte population identified in the previous study (mAd3 in Emont et al., 2022), with similar marker gene expression and lower transcript abundance.
“In the integration of our data with previous datasets, we observed a comparable adipocyte population identified in the previous study (mAd3 in Emont et al., 2022Emont et al., 2022; Figure 6—figure supplement 1A), with similar marker gene expression (Figure 6—figure supplement 1B) and lower transcript abundance (Figure 6—figure supplement 1C).”
From Robust single-nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity (2025) · cites this paper as Emont et al., 2022
Their data integration analysis found that Ad6 adipocytes are comparable to mAd3 adipocytes identified in the study by Emont et al., 2022.
“Our data integration analysis found that Ad6 adipocytes are comparable to mAd3 adipocytes identified in the study by Emont et al., 2022.”
From Robust single-nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity (2025) · cites this paper as Emont et al., 2022
Ex vivo differentiated human adipocytes derived from various donors displayed distinct cellular phenotypes, consistent with the heterogeneity in adipocyte subpopulations identified by snRNA-seq.
“Interestingly, ex vivo differentiated human adipocytes derived from various donors displayed distinct cellular phenotypes, consistent with the heterogeneity in adipocyte subpopulations identified by snRNA-seq (Emont et al., 2022).”
From Robust single-nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity (2025) · cites this paper as Emont et al., 2022
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C. (1985) · Diabetologia · doi:10.1007/bf00280883 · Cited by 31,502 · see citing works
Integrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508 · see citing works
Obesity is associated with macrophage accumulation in adipose tissue
Weisberg, S. P., McCann, D., Desai, M., Rosenbaum, M., Leibel, R. L., & Ferrante, A. W. (2003) · Journal of Clinical Investigation · doi:10.1172/jci200319246 · Cited by 10,041 · see citing works
Xu, H., Barnes, G. T., Yang, Q., Tan, G., Yang, D., Chou, C. J., Sole, J., Nichols, A., Ross, J. S., Tartaglia, L. A., et al. (2003) · Journal of Clinical Investigation · doi:10.1172/jci200319451 · Cited by 6,760 · see citing works
Hafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479 · see citing works
Katz, A., Nambi, S. S., Mather, K., Baron, A. D., Follmann, D. A., Sullivan, G., & Quon, M. J. (2000) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.85.7.6661 · Cited by 3,831 · see citing works
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2018) · doi:10.1101/357368 · Cited by 2,758 · see citing works
Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors
Villani, A.-C., Satija, R., Reynolds, G., Sarkizova, S., Shekhar, K., Fletcher, J., Griesbeck, M., Butler, A., Zheng, S., Lazo, S., et al. (2017) · Science · doi:10.1126/science.aah4573 · Cited by 2,413 · see citing works
Self-citationNew genetic loci link adipose and insulin biology to body fat distribution
The ADIPOGen Consortium, Shungin, D., The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The GEFOS Consortium, The GENIE Consortium, The, G. L. G. C., The, I. C. B. P., The International Endogene Consortium, Study, T. L. C., et al. (2015) · Nature · doi:10.1038/nature14132 · Cited by 1,580 · see citing works
Self-citationLipid-associated macrophages control metabolic homeostasis in a Trem2-dependent manner
Jaitin, D. A., Adlung, L., Thaiss, C. A., Weiner, A., Li, B., Descamps, H., Lundgren, P., Bleriot, C., Liu, Z., Deczkowska, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.05.054 · Cited by 1,377 · see citing works
Identification of a mesenchymal progenitor cell hierarchy in adipose tissue
Merrick, D., Sakers, A., Irgebay, Z., Okada, C., Calvert, C., Morley, M. P., Percec, I., & Seale, P. (2019) · Science · doi:10.1126/science.aav2501 · Cited by 664 · see citing works
A stromal cell population that inhibits adipogenesis in mammalian fat depots
Schwalie, P. C., Dong, H., Zachara, M., Russeil, J., Alpern, D., Akchiche, N., Caprara, C., Sun, W., Schlaudraff, K.-U., Soldati, G., et al. (2018) · Nature · doi:10.1038/s41586-018-0226-8 · Cited by 463 · see citing works
Rapid depot-specific activation of adipocyte precursor cells at the onset of obesity
Jeffery, E., Church, C. D., Holtrup, B., Colman, L., & Rodeheffer, M. S. (2015) · Nature Cell Biology · doi:10.1038/ncb3122 · Cited by 404 · see citing works
Inter-organ cross-talk in metabolic syndrome
Priest, C., & Tontonoz, P. (2019) · Nature Metabolism · doi:10.1038/s42255-019-0145-5 · Cited by 279 · see citing works
Selective Inhibition of Hypoxia-Inducible Factor 1α Ameliorates Adipose Tissue Dysfunction
Sun, K., Halberg, N., Khan, M., Magalang, U. J., & Scherer, P. E. (2013) · Molecular and Cellular Biology · doi:10.1128/mcb.00951-12 · Cited by 231 · see citing works
Flagged first, then by citations
Research works printed but not linked: 68
- [1]Rosen ED & Spiegelman BM What We Talk About When We Talk About Fat. Cell 156, 20– 44 (2014). 24439368 PMC3934003
- [2]Kahn SE, Hull RL & Utzschneider KM Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444, 840– 846 (2006). 17167471
- [4]Burl RB Deconstructing Adipogenesis Induced by β3-Adrenergic Receptor Activation with Single-Cell Expression Profiling. Cell Metab. 28, 300– 309.e4 (2018). 29937373 PMC6082711
- [6]Hepler C Identification of functionally distinct fibro-inflammatory and adipogenic stromal subpopulations in visceral adipose tissue of adult mice. eLife 7, e39636 (2018). 30265241 PMC6167054
- [7]Vijay J Single-cell analysis of human adipose tissue identifies depot- and disease-specific cell types. Nat. Metab 2, 97– 109 (2020). 32066997 PMC7025882
- [8]Rajbhandari P Single cell analysis reveals immune cell–adipocyte crosstalk regulating the transcription of thermogenic adipocytes. eLife 8, e49501 (2019). 31644425 PMC6837845
- [9]Sárvári AK Plasticity of Epididymal Adipose Tissue in Response to Diet-Induced Obesity at Single-Nucleus Resolution. Cell Metab. 33, 437– 453.e5 (2021). 33378646
- [10]Sun W snRNA-seq reveals a subpopulation of adipocytes that regulates thermogenesis. Nature 587, 98– 102 (2020). 33116305
- [11]Benites-Zapata VA High waist-to-hip ratio levels are associated with insulin resistance markers in normal-weight women. Diabetes Metab. Syndr. Clin. Res. Rev 13, 636– 642 (2019). 30641781
- [12]Wang X, Park J, Susztak K, Zhang NR & Li M Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun 10, 380 (2019). 30670690 PMC6342984
- [13]Raulerson CK Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits. Am. J. Hum. Genet 105, 773– 787 (2019). 31564431 PMC6817527
- [14]Blüher M Transgenic animal models for the study of adipose tissue biology. Best Pract. Res. Clin. Endocrinol. Metab 19, 605– 623 (2005). 16311220
- [15]An atlas of cell types in the mouse epididymis and vas deferens ∣ eLife. https://elifesciences.org/articles/55474. PMC7426093 32729827
- [17]Hildreth AD Single-cell sequencing of human white adipose tissue identifies new cell states in health and obesity. Nat. Immunol 1– 15 (2021) doi: 10.1038/s41590-021-00922-4. 33907320 PMC8102391 · DOI
- [18]Suganami T & Ogawa Y Adipose tissue macrophages: their role in adipose tissue remodeling. J. Leukoc. Biol 88, 33– 39 (2010). 20360405
- [20]Reilly SM & Saltiel AR Adapting to obesity with adipose tissue inflammation. Nat. Rev. Endocrinol 13, 633– 643 (2017). 28799554
- [21]Shi M & Shi G-P Different Roles of Mast Cells in Obesity and Diabetes: Lessons from Experimental Animals and Humans. Front. Immunol 3, 7 (2012). 22566893 PMC3341969
- [23]Ferrero R, Rainer P & Deplancke B Toward a Consensus View of Mammalian Adipocyte Stem and Progenitor Cell Heterogeneity. Trends Cell Biol. 30, 937 (2020). 33148396
- [24]Wang QA, Tao C, Gupta RK & Scherer PE Tracking adipogenesis during white adipose tissue development, expansion and regeneration. Nat. Med 19, 1338– 1344 (2013). 23995282 PMC4075943
- [26]Bäckdahl J Spatial mapping reveals human adipocyte subpopulations with distinct sensitivities to insulin. Cell Metab. 33, 1869– 1882.e6 (2021). 34380013
- [27]Stefan N Circulating Palmitoleate Strongly and Independently Predicts Insulin Sensitivity in Humans. Diabetes Care 33, 405– 407 (2010). 19889804 PMC2809292
- [28]Laber S Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler. 2021.07.17.452050 https://www.biorxiv.org/content/10.1101/2021.07.17.452050v1 (2021) doi: 10.1101/2021.07.17.452050. PMC10363917 37492099 · DOI
- [29]Rajakumari S EBF2 determines and maintains brown adipocyte identity. Cell Metab. 17, 562– 574 (2013). 23499423 PMC3622114
- [30]Pulit SL Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum. Mol. Genet 28, 166– 174 (2019). 30239722 PMC6298238
- [31]Agrawal S Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots. 2021.08.24.21262564 https://www.medrxiv.org/content/10.1101/2021.08.24.21262564v1 (2021) doi: 10.1101/2021.08.24.21262564. PMC9247093 35773277 · DOI
- [32]Willows JW Visualization and analysis of whole depot adipose tissue neural innervation. iScience 24, 103127 (2021). 34622172 PMC8479257
- [34]Roh HC Adipocytes fail to maintain cellular identity during obesity due to reduced PPARγ activity and elevated TGFβ-SMAD signaling. Mol. Metab 42, 101086 (2020). 32992037 PMC7559520
- [36]Park J Progenitor-like characteristics in a subgroup of UCP1+ cells within white adipose tissue. Dev. Cell 56, 985– 999.e4 (2021). 33711247 PMC8026751
- [38]Schling P & Löffler G Cross talk between adipose tissue cells: impact on pathophysiology. News Physiol. Sci. Int. J. Physiol. Prod. Jointly Int. Union Physiol. Sci. Am. Physiol. Soc 17, 99– 104 (2002). 12021379
- [39]Kane H & Lynch L Innate Immune Control of Adipose Tissue Homeostasis. Trends Immunol. 40, 857– 872 (2019). 31399336
- [40]Efremova M, Vento-Tormo M, Teichmann SA & Vento-Tormo R CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat. Protoc 15, 1484– 1506 (2020). 32103204
- [41]Cao Y Angiogenesis and vascular functions in modulation of obesity, adipose metabolism, and insulin sensitivity. Cell Metab. 18, 478– 489 (2013). 24035587
- [42]Hubert A Selective Deletion of Leptin Signaling in Endothelial Cells Enhances Neointima Formation and Phenocopies the Vascular Effects of Diet-Induced Obesity in Mice. Arterioscler. Thromb. Vasc. Biol 37, 1683– 1697 (2017). 28705795
- [43]Scott RA An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans. Diabetes 66, 2888– 2902 (2017). 28566273 PMC5652602
- [45]Huang LO Genome-wide discovery of genetic loci that uncouple excess adiposity from its comorbidities. Nat. Metab 3, 228– 243 (2021). 33619380
- [46]Timshel PN, Thompson JJ & Pers TH Genetic mapping of etiologic brain cell types for obesity. eLife 9, e55851 (2020). 32955435 PMC7505664
- [47]O’Rahilly S & Farooqi IS Human obesity as a heritable disorder of the central control of energy balance ∣ International Journal of Obesity. https://www.nature.com/articles/ijo2008239. 19136992
- [48]Sailer S, Keller MA, Werner ER & Watschinger K The Emerging Physiological Role of AGMO 10 Years after Its Gene Identification. Life Basel Switz. 11, (2021). PMC7911779 33530536
- [49]Dupuis J New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet 42, 105– 116 (2010). 20081858 PMC3018764
- [50]Loh NY RSPO3 impacts body fat distribution and regulates adipose cell biology in vitro. Nat. Commun 11, 2797 (2020). 32493999 PMC7271210
- [51]Chi J Three-Dimensional Adipose Tissue Imaging Reveals Regional Variation in Beige Fat Biogenesis and PRDM16-Dependent Sympathetic Neurite Density. Cell Metab. 27, 226– 236.e3 (2018). 29320703
- [54]Macosko EZ Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell 161, 1202– 1214 (2015). 26000488 PMC4481139
- [55]Drokhlyansky E The Human and Mouse Enteric Nervous System at Single-Cell Resolution. Cell 182, 1606– 1622.e23 (2020). 32888429 PMC8358727
- [56]Slyper M A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat. Med 26, 792– 802 (2020). 32405060 PMC7220853
- [57]Delorey TM A single-cell and spatial atlas of autopsy tissues reveals pathology and cellular targets of SARS-CoV-2. bioRxiv (2021) doi: 10.1101/2021.02.25.430130. · DOI
- [59]CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets ∣ bioRxiv. https://www.biorxiv.org/content/10.1101/791699v1. · DOI
- [60]Lun ATL EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019). 30902100 PMC6431044
- [62]Patro R, Duggal G, Love MI, Irizarry RA & Kingsford C Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417– 419 (2017). 28263959 PMC5600148
- [63]Srivastava A Alignment and mapping methodology influence transcript abundance estimation. Genome Biol. 21, 239 (2020). 32894187 PMC7487471
- [64]Soneson C, Love MI & Robinson MD Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4, 1521 (2015). 26925227 PMC4712774
- [65]Stuart T Comprehensive integration of single-cell data. Cell 177, 1888– 1902.e21 (2019). 31178118 PMC6687398
- [68]Yu G, Wang L-G, Han Y & He Q-Y clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. OMICS J. Integr. Biol 16, 284– 287 (2012). PMC3339379 22455463
- [69]Littlejohns TJ The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat. Commun 11, 2624 (2020). 32457287 PMC7250878
- [70]Sudlow C UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLOS Med. 12, e1001779 (2015). 25826379 PMC4380465
- [71]Agrawal S Association of machine learning-derived measures of body fat distribution in >40,000 individuals with cardiometabolic diseases. 2021.05.07.21256854 https://www.medrxiv.org/content/10.1101/2021.05.07.21256854v1 (2021) doi: 10.1101/2021.05.07.21256854. · DOI
- [72]Kichaev G Leveraging Polygenic Functional Enrichment to Improve GWAS Power. Am. J. Hum. Genet 104, 65– 75 (2019). 30595370 PMC6323418
- [73]Pruim RJ LocusZoom: regional visualization of genome-wide association scan results. Bioinforma. Oxf. Engl 26, 2336– 2337 (2010). PMC2935401 20634204
- [74]Timshel PN, Thompson JJ & Pers TH Genetic mapping of etiologic brain cell types for obesity. eLife 9, e55851 (2020). 32955435 PMC7505664
- [75]Mahajan A Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet 50, 1505– 1513 (2018). 30297969 PMC6287706
- [76]Loh P-R, Kichaev G, Gazal S, Schoech AP & Price AL Mixed-model association for biobank-scale datasets. Nat. Genet 50, 906– 908 (2018). 29892013 PMC6309610
- [77]Finucane HK Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet 47, 1228– 1235 (2015). 26414678 PMC4626285
- [78]Teslovich TM Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707– 713 (2010). 20686565 PMC3039276
- [79]Bradfield JP A Genome-Wide Meta-Analysis of Six Type 1 Diabetes Cohorts Identifies Multiple Associated Loci. PLOS Genet. 7, e1002293 (2011). 21980299 PMC3183083
- [80]Loh P-R Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet 48, 1443– 1448 (2016). 27694958 PMC5096458
- [81]Das S Next-generation genotype imputation service and methods. Nat. Genet 48, 1284– 1287 (2016). 27571263 PMC5157836
- [82]Robinson MD, McCarthy DJ & Smyth GK edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139– 140 (2010). 19910308 PMC2796818
- [83]Büttner M, Ostner J, Müller C, Theis F & Schubert B scCODA: A Bayesian model for compositional single-cell data analysis. bioRxiv 2020.12.14.422688 (2020) doi: 10.1101/2020.12.14.422688. PMC8616929 34824236 · DOI
- [84]Cawthorn WP, Scheller EL & MacDougald OA Adipose tissue stem cells meet preadipocyte commitment: going back to the future[S]. J. Lipid Res 53, 227– 246 (2012). 22140268 PMC3269153
Cited by
Sorted by citations
Exercise Induces Tissue-Specific Adaptations to Enhance Cardiometabolic Health
Ashcroft, S. P., Stocks, B., Egan, B., & Zierath, J. R. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2023.12.008 · Cited by 149 · see citing works
Ghesmati, Z., Rashid, M., Fayezi, S., Gieseler, F., Alizadeh, E., & Darabi, M. (2024) · Reviews in Endocrine and Metabolic Disorders · doi:10.1007/s11154-023-09850-0 · Cited by 139 · see citing works
A panoramic view of cell population dynamics in mammalian aging
Zhang, Z., Schaefer, C., Jiang, W., Lu, Z., Lee, J., Sziraki, A., Abdulraouf, A., Wick, B., Haeussler, M., Li, Z., et al. (2025) · Science · doi:10.1126/science.adn3949 · Cited by 95 · see citing works
Deciphering the decline of metabolic elasticity in aging and obesity
Zhou, Q., Yu, L., Cook, J. R., Qiang, L., & Sun, L. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2023.08.001 · Cited by 80 · see citing works
Sympathetic neuropeptide Y protects from obesity by sustaining thermogenic fat
Zhu, Y., Yao, L., Gallo-Ferraz, A. L., Bombassaro, B., Simões, M. R., Abe, I., Chen, J., Sarker, G., Ciccarelli, A., Zhou, L., et al. (2024) · Nature · doi:10.1038/s41586-024-07863-6 · Cited by 78 · see citing works
Advancing biological understanding of cellular senescence with computational multiomics
Li, S., Agudelo Garcia, P. A., Aliferis, C., Becich, M. J., Calyeca, J., Cosgrove, B. D., Elisseeff, J., Farzad, N., Fertig, E. J., Glass, C., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02314-y · Cited by 40 · see citing works
So, J., Strobel, O., Wann, J., Kim, K., Paul, A., Acri, D. J., Dabin, L. C., Kim, J., Peng, G., & Roh, H. C. (2025) · eLife · doi:10.7554/elife.97981 · Cited by 18 · see citing works
Taurine lowers inflammaging by regulating the NLRP3 inflammasome in macrophages
Guan, C., Ryu, S., Dong, M., Zhu, Y., Maeda, R., Youm, Y.-H., Mohanty, S., Kim, H.-H., Orliaguet, L., Dlugos, T., et al. (2026) · Cell · doi:10.1016/j.cell.2026.09.061
- Authors
- Emont, M. P., Jacobs, C., Essene, A. L., Pant, D., Tenen, D., Colleluori, G., Di Vincenzo, A., Jørgensen, A. M., Dashti, H., Stefek, A., McGonagle, E., Strobel, S., Laber, S., Agrawal, S., Westcott, G. P., Kar, A., Veregge, M. L., Gulko, A., Srinivasan, H., Kramer, Z., De Filippis, E., Merkel, E., Ducie, J., Boyd, C. G., Gourash, W., Courcoulas, A., Lin, S. J., Lee, B. T., Morris, D., Tobias, A., Khera, A. V., Claussnitzer, M., Pers, T. H., Giordano, A., Ashenberg, O., Regev, A., Tsai, L. T., & Rosen, E. D.
- Affiliations
- Broad Institute (US)
- Beth Israel Deaconess Medical Center (US)
- Marche Polytechnic University (IT)
- University of Copenhagen (DK)
- University of Pittsburgh Medical Center (US)
- Corresponding author
- Rosen, Evan D.
- ORCID
- Emont, Margo P.
- Jacobs, Christopher
- Pant, Deepti
- Colleluori, Georgia
- Jørgensen, Anja M.
- Dashti, Hesam
- McGonagle, Elizabeth
- Strobel, Sophie
- Laber, Samantha
- Agrawal, Saaket
- Westcott, Gregory P.
- Veregge, Molly L.
- Gulko, Anton
- Srinivasan, Harini
- Kramer, Zachary
- De Filippis, Eleanna
- Gourash, William
- Courcoulas, Anita
- Lin, Samuel J.
- Lee, Bernard T.
- Tobias, Adam
- Khera, Amit V.
- Claussnitzer, Melina
- Pers, Tune H.
- Giordano, Antonio
- Ashenberg, Orr
- Regev, Aviv
- Tsai, Linus T.
- Rosen, Evan D.
- Venue
- Nature
- Published
- 2022-03-16
- Volume
- 603
- Issue
- 7903
- Pages
- 926-933
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC9504827
- OpenAlex
- W4221010612
- Cited by
- 840
- References
- 16
- Citations by year
- 2026: 193 · 2025: 218 · 2024: 202 · 2023: 161 · 2022: 65
- FWCI
- 100.17
- Type
- journal article
- Language
- en
- Update notices
- correction (2023-07-26)
- Open access
- green
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Università Politecnica delle Marche (Università Politecnica delle Marche) (submittedVersion, other-oa)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- Funders
- Foundation for the National Institutes of Health (RC2 DK116691)
- American Heart Association
- Boston Area Diabetes Endocrinology Research Center (5P30DK057521)
- Broad Institute
- Sarnoff Cardiovascular Research Foundation
- Lundbeckfonden (R190 2014-3904)
- Novo Nordisk (NNF18CC0034900)
- Novo Nordisk Fonden (NNF 18 CC0034900)
- Novo Nordisk Foundation Center for Basic Metabolic Research (NNF 18 CC0034900)
- H. Lundbeck A/S
- National Institutes of Health
- Boston Nutrition Obesity Research Center, Boston University
- National Human Genome Research Institute (K08HG010155, U01HG011719)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK057521, UM1DK126185, P30DK046200, F32DK124914, R01DK087092, DK116691, R01DK102173, T32DK007516, RC2DK116691)
- Topics
- Health Sciences › Medicine › Physiology › Adipose Tissue and Metabolism
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Epidemiology › Adipokines, Inflammation, and Metabolic Diseases
- Keywords
- white adipose tissue, single-cell atlas, adipocytes, subcutaneous adipose tissue, visceral fat, obesity, leanness, high-fat diet, metabolic disorders, type 2 diabetes, insulin resistance, human-mouse comparison
- MeSH terms
- Adipose Tissue, Animals, Atlases as Topic, Diabetes Mellitus, Type 2, Humans, Insulin Resistance, Metabolic Diseases, Obesity, Adiposity, Mice, Adipose Tissue, White
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review