CoChat
Sign Up
Cited by 195Single-cell and spatial transcriptomicsAdipose Tissue and MetabolismAdipokines, Inflammation, and Metabolic Diseases

An integrated single cell and spatial transcriptomic map of human white adipose tissue

Open access
Massier, Lucas, Jalkanen, Jutta, Elmastas, Merve, Zhong, Jiawei, Wang, Tongtong, Nono Nankam, Pamela A., Frendo-Cumbo, Scott, Bäckdahl, Jesper +20 more·2023·Nature Communications
DOI 10.1038/s41467-023-36983-2PMC PMC10017705
Open this paper in CoChat — free accountOpen PDF

Abstract

To date, single-cell studies of human white adipose tissue (WAT) have been based on small cohort sizes and no cellular consensus nomenclature exists. Herein, we performed a comprehensive meta-analysis of publicly available and newly generated single-cell, single-nucleus, and spatial transcriptomic results from human subcutaneous, omental, and perivascular WAT. Our high-resolution map is built on data from ten studies and allowed us to robustly identify >60 subpopulations of adipocytes, fibroblast and adipogenic progenitors, vascular, and immune cells. Using these results, we deconvolved spatial and bulk transcriptomic data from nine additional cohorts to provide spatial and clinical dimensions to the map. This identified cell-cell interactions as well as relationships between specific cell subtypes and insulin resistance, dyslipidemia, adipocyte volume, and lipolysis upon long-term weight changes. Altogether, our meta-map provides a rich resource defining the cellular and microarchitectural landscape of human WAT and describes the associations between specific cell types and metabolic states.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-023-36983-2

Cites

13 of 13 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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?

  • This inconsistency is unique to adipocytes, as other cell types in adipose tissues display consistent sub cell types across species and studies.

    “Interestingly, this inconsistency is unique to adipocytes, as other cell types in adipose tissues display consistent sub cell types across species and studies (Massier 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 Massier et al., 2023

    The Results section, “Adipocytes display inconsistent heterogeneity between studies” (page unavailable), reports limited reproducibility of adipocyte marker genes across studies, compared with clearer overlap for other cell classes. That supports the comparison across studies, but the cited passages do not establish that other cell subtypes are consistent across species or that adipocyte inconsistency is unique across species. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • This finding contrasts with the heterogeneity of human adipocytes, where insulin sensitivity is shown to be a primary factor in adipocyte classification rather than cell size, as demonstrated by spatial transcriptomics mapping.

    “This finding contrasts with the heterogeneity of human adipocytes, where insulin sensitivity is shown to be a primary factor in adipocyte classification rather than cell size, as demonstrated by spatial transcriptomics mapping (Massier 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 Massier et al., 2023

    The Results section, “Two clusters of cells are reciprocally associated with metabolic health” (page unavailable), reports that spatial-transcriptomic adipocyte markers have different associations with insulin resistance. But it does not show that insulin sensitivity is the primary basis for classifying adipocytes or compare its importance with cell size. The Discussion also cautions that adipocyte heterogeneity was less reproducible across studies. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Human adipocyte data show poor reproducibility between different studies.

    “Furthermore, human adipocyte data show poor reproducibility between different studies (Massier 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 Massier et al., 2023

  • Human adipocyte heterogeneity does not correspond well to that of mouse adipocytes.

    “Notably, human adipocyte heterogeneity does not correspond well to that of mouse adipocytes (Massier 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 Massier et al., 2023

    The Results section, “Adipocytes display inconsistent heterogeneity between studies” (page unavailable), reports limited reproducibility of adipocyte subtypes across human datasets, not a comparison with mouse adipocytes. A separate Results passage compares human and mouse adipocyte progenitors (FAPs), but does not assess whether mature adipocyte heterogeneity corresponds between species. Searches for human–mouse adipocyte heterogeneity and subtype correspondence found no passage addressing the claim. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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

Flagged first, then by citations

Research works printed but not linked: 67

  1. [1]Sakers A De Siqueira MK Seale P Villanueva CJ Adipose-tissue plasticity in health and disease Cell 2022 185 419 446 10.1016/j.cell.2021.12.016 35120662 PMC11152570 · DOI
  2. [2]Shao M De novo adipocyte differentiation from Pdgfrbeta(+) preadipocytes protects against pathologic visceral adipose expansion in obesity Nat. Commun. 2018 9 890 10.1038/s41467-018-03196-x 29497032 PMC5832777 · DOI
  3. [4]Hepler C Identification of functionally distinct fibro-inflammatory and adipogenic stromal subpopulations in visceral adipose tissue of adult mice Elife 2018 7 e39636 10.7554/eLife.39636 30265241 PMC6167054 · DOI · Paper page
  4. [5]Dong H Identification of a regulatory pathway inhibiting adipogenesis via RSPO2 Nat. Metab. 2022 4 90 105 10.1038/s42255-021-00509-1 35027768 PMC8803606 · DOI
  5. [7]Backdahl J Spatial mapping reveals human adipocyte subpopulations with distinct sensitivities to insulin Cell Metab. 2021 33 1869 1882.e1866 10.1016/j.cmet.2021.07.018 34380013 · DOI
  6. [8]Sun W snRNA-seq reveals a subpopulation of adipocytes that regulates thermogenesis Nature 2020 587 98 102 10.1038/s41586-020-2856-x 33116305 · DOI
  7. [9]Emont MP A single-cell atlas of human and mouse white adipose tissue Nature 2022 603 926 933 10.1038/s41586-022-04518-2 35296864 PMC9504827 · DOI · Paper page
  8. [10]Vijay J Single-cell analysis of human adipose tissue identifies depot and disease specific cell types Nat. Metab. 2020 2 97 109 10.1038/s42255-019-0152-6 32066997 PMC7025882 · DOI
  9. [11]Hildreth AD Single-cell sequencing of human white adipose tissue identifies new cell states in health and obesity Nat. Immunol. 2021 22 639 653 10.1038/s41590-021-00922-4 33907320 PMC8102391 · DOI
  10. [12]Acosta JR Single cell transcriptomics suggest that human adipocyte progenitor cells constitute a homogeneous cell population Stem Cell Res. Ther. 2017 8 250 10.1186/s13287-017-0701-4 29116032 PMC5678572 · DOI
  11. [14]Karunakaran D RIPK1 gene variants associate with obesity in humans and can be therapeutically silenced to reduce obesity in mice Nat. Metab. 2020 2 1113 1125 10.1038/s42255-020-00279-2 32989316 PMC8362891 · DOI
  12. [15]Angueira AR Defining the lineage of thermogenic perivascular adipose tissue Nat. Metab. 2021 3 469 484 10.1038/s42255-021-00380-0 33846639 PMC8136151 · DOI
  13. [17]Li B Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution Nat. Methods 2022 19 662 670 10.1038/s41592-022-01480-9 35577954 · DOI · Paper page
  14. [18]Dominguez Conde C Cross-tissue immune cell analysis reveals tissue-specific features in humans Science 2022 376 eabl5197 10.1126/science.abl5197 35549406 PMC7612735 · DOI
  15. [19]Eto H Characterization of structure and cellular components of aspirated and excised adipose tissue Plast. Reconstr. Surg. 2009 124 1087 1097 10.1097/PRS.0b013e3181b5a3f1 19935292 · DOI
  16. [21]Forcato M Romano O Bicciato S Computational methods for the integrative analysis of single-cell data Brief Bioinform. 2021 22 20 29 10.1093/bib/bbaa042 32363378 PMC7820847 · DOI · Paper page
  17. [22]Peng M Li Y Wamsley B Wei Y Roeder K Integration and transfer learning of single-cell transcriptomes via cFIT Proc. Natl. Acad. Sci. USA 2021 118 e2024383118 10.1073/pnas.2024383118 33658382 PMC7958425 · DOI
  18. [24]Polanski K BBKNN: fast batch alignment of single cell transcriptomes Bioinformatics 2020 36 964 965 10.1093/bioinformatics/btz625 31400197 PMC9883685 · DOI · Paper page
  19. [25]Korsunsky I Fast, sensitive and accurate integration of single-cell data with Harmony Nat. Methods 2019 16 1289 1296 10.1038/s41592-019-0619-0 31740819 PMC6884693 · DOI · Paper page
  20. [28]Tran HTN A benchmark of batch-effect correction methods for single-cell RNA sequencing data Genome Biol. 2020 21 12 10.1186/s13059-019-1850-9 31948481 PMC6964114 · DOI · Paper page
  21. [29]Hubert L Arabie P Comparing partitions J. Classif. 1985 2 193 218 10.1007/BF01908075 · DOI · Paper page
  22. [30]Buttner M Miao Z Wolf FA Teichmann SA Theis FJ A test metric for assessing single-cell RNA-seq batch correction Nat. Methods 2019 16 43 49 10.1038/s41592-018-0254-1 30573817 · DOI · Paper page
  23. [31]Simpson EH Measurement of diversity Nature 1949 163 688 688 10.1038/163688a0 · DOI
  24. [32]Arner P The epigenetic signature of systemic insulin resistance in obese women Diabetologia 2016 59 2393 2405 10.1007/s00125-016-4074-5 27535281 PMC5506095 · DOI
  25. [33]Krieg L Multiomics reveal unique signatures of human epiploic adipose tissue related to systemic insulin resistance Gut 2021 71 2179 2193 10.1136/gutjnl-2021-324603 34598978 PMC9554031 · DOI
  26. [34]Chen L ITLN1 inhibits tumor neovascularization and myeloid derived suppressor cells accumulation in colorectal carcinoma Oncogene 2021 40 5925 5937 10.1038/s41388-021-01965-5 34363021 · DOI
  27. [35]Johnson MD Inhibition of angiogenesis by tissue inhibitor of metalloproteinase J. Cell Physiol. 1994 160 194 202 10.1002/jcp.1041600122 7517405 · DOI
  28. [36]Medina RJ Molecular analysis of endothelial progenitor cell (EPC) subtypes reveals two distinct cell populations with different identities BMC Med. Genomics 2010 3 18 10.1186/1755-8794-3-18 20465783 PMC2881111 · DOI
  29. [38]Wang QA Tao C Gupta RK Scherer PE Tracking adipogenesis during white adipose tissue development, expansion and regeneration Nat. Med. 2013 19 1338 1344 10.1038/nm.3324 23995282 PMC4075943 · DOI · Paper page
  30. [39]Borrelli MR The antifibrotic adipose-derived stromal cell: grafted fat enriched with CD74+ adipose-derived stromal cells reduces chronic radiation-induced skin fibrosis Stem Cells Transl. Med. 2020 9 1401 1413 10.1002/sctm.19-0317 32563212 PMC7581454 · DOI
  31. [40]Buechler MB Cross-tissue organization of the fibroblast lineage Nature 2021 593 575 579 10.1038/s41586-021-03549-5 33981032 · DOI
  32. [41]Ehrlund A Transcriptional dynamics during human adipogenesis and its link to adipose morphology and distribution Diabetes 2017 66 218 230 10.2337/db16-0631 27803022 PMC5860264 · DOI
  33. [42]Khan A SNEV(hPrp19/hPso4) regulates adipogenesis of human adipose stromal cells Stem Cell Rep. 2017 8 21 29 10.1016/j.stemcr.2016.12.001 PMC5233435 28041875 · DOI
  34. [43]Tini G DNA methylation during human adipogenesis and the impact of fructose Genes Nutr. 2020 15 21 10.1186/s12263-020-00680-2 33243154 PMC7691080 · DOI
  35. [44]Shao M Pathologic HIF1alpha signaling drives adipose progenitor dysfunction in obesity Cell Stem Cell 2021 28 685 701.e687 10.1016/j.stem.2020.12.008 33539723 PMC8026607 · DOI
  36. [45]Harms MJ Mature human white adipocytes cultured under membranes maintain identity, function, and can transdifferentiate into brown-like adipocytes Cell Rep. 2019 27 213 225.e215 10.1016/j.celrep.2019.03.026 30943403 · DOI
  37. [46]Forrest AR A promoter-level mammalian expression atlas Nature 2014 507 462 470 10.1038/nature13182 24670764 PMC4529748 · DOI · Paper page
  38. [47]Gupta A Characterization of transcript enrichment and detection bias in single-nucleus RNA-seq for mapping of distinct human adipocyte lineages Genome Res. 2022 32 242 257 10.1101/gr.275509.121 35042723 PMC8805720 · DOI
  39. [48]Jin S Inference and analysis of cell-cell communication using CellChat Nat. Commun 2021 12 1088 10.1038/s41467-021-21246-9 33597522 PMC7889871 · DOI · Paper page
  40. [49]Timokhina I Kissel H Stella G Besmer P Kit signaling through PI 3-kinase and Src kinase pathways: an essential role for Rac1 and JNK activation in mast cell proliferation EMBO J. 1998 17 6250 6262 10.1093/emboj/17.21.6250 9799234 PMC1170951 · DOI
  41. [50]Huizer K Periostin is expressed by pericytes and is crucial for angiogenesis in glioma J. Neuropathol. Exp. Neurol. 2020 79 863 872 10.1093/jnen/nlaa067 32647861 · DOI
  42. [51]Rosen ED Spiegelman BM What we talk about when we talk about fat Cell 2014 156 20 44 10.1016/j.cell.2013.12.012 24439368 PMC3934003 · DOI · Paper page
  43. [53]Kerr AG Andersson DP Ryden M Arner P Dahlman I Long-term changes in adipose tissue gene expression following bariatric surgery J. Intern. Med. 2020 288 219 233 10.1111/joim.13066 32406570 · DOI
  44. [54]Petrus P Transforming growth factor-beta3 regulates adipocyte number in subcutaneous white adipose tissue Cell Rep. 2018 25 551 560.e555 10.1016/j.celrep.2018.09.069 30332637 · DOI
  45. [55]Lenz M Arts ICW Peeters RLM de Kok TM Ertaylan G Adipose tissue in health and disease through the lens of its building blocks Sci. Rep. 2020 10 10433 10.1038/s41598-020-67177-1 32591560 PMC7319996 · DOI · Paper page
  46. [56]Norreen-Thorsen M A human adipose tissue cell-type transcriptome atlas Cell Rep. 2022 40 111046 10.1016/j.celrep.2022.111046 35830816 · DOI
  47. [57]Hoffstedt J Long-term protective changes in adipose tissue after gastric bypass Diabetes Care 2017 40 77 84 10.2337/dc16-1072 27852664 · DOI
  48. [58]Mileti E Human white adipose tissue displays selective insulin resistance in the obese state Diabetes 2021 70 1486 1497 10.2337/db21-0001 33863803 · DOI
  49. [59]Ryden M The adipose transcriptional response to insulin is determined by obesity, not insulin sensitivity Cell Rep. 2016 16 2317 2326 10.1016/j.celrep.2016.07.070 27545890 · DOI
  50. [60]Slyper M A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors Nat. Med. 2020 26 792 802 10.1038/s41591-020-0844-1 32405060 PMC7220853 · DOI
  51. [61]Fleming, S. J., Marioni, J. C. & Babadi, M. 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 (2019). · DOI
  52. [62]Germain PL Lun A Garcia Meixide C Macnair W Robinson MD Doublet identification in single-cell sequencing data using scDblFinder F1000Research 2022 10 979 10.12688/f1000research.73600.2 PMC9204188 35814628 · DOI · Paper page
  53. [65]Hyvarinen A Oja E Independent component analysis: algorithms and applications Neural Netw. 2000 13 411 430 10.1016/S0893-6080(00)00026-5 10946390 · DOI
  54. [66]Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol.37, 38–44 (2018). 30531897
  55. [67]Gustavsen JA Pai S Isserlin R Demchak B Pico AR RCy3: network biology using cytoscape from within R F1000Res 2019 8 1774 10.12688/f1000research.20887.2 31819800 PMC6880260 · DOI
  56. [68]Jew B Accurate estimation of cell composition in bulk expression through robust integration of single-cell information Nat. Commun 2020 11 1971 10.1038/s41467-020-15816-6 32332754 PMC7181686 · DOI
  57. [69]Arner E Adipose tissue microRNAs as regulators of CCL2 production in human obesity Diabetes 2012 61 1986 1993 10.2337/db11-1508 22688341 PMC3402332 · DOI
  58. [70]Arner P Andersson DP Backdahl J Dahlman I Ryden M Weight gain and impaired glucose metabolism in women are predicted by inefficient subcutaneous fat cell lipolysis Cell Metab. 2018 28 45 54.e43 10.1016/j.cmet.2018.05.004 29861390 · DOI
  59. [71]Imbert A Network analyses reveal negative link between changes in adipose tissue GDF15 and BMI during dietary-induced weight loss J. Clin. Endocrinol. Metab. 2022 107 e130 e142 10.1210/clinem/dgab621 34415992 · DOI
  60. [72]Armenise C Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects Am. J. Clin. Nutr. 2017 106 736 746 10.3945/ajcn.117.156216 28793995 · DOI
  61. [74]Acosta JR Increased fat cell size: a major phenotype of subcutaneous white adipose tissue in non-obese individuals with type 2 diabetes Diabetologia 2016 59 560 570 10.1007/s00125-015-3810-6 26607638 · DOI
  62. [75]Kleshchevnikov V Cell2location maps fine-grained cell types in spatial transcriptomics Nat. Biotechnol. 2022 40 661 671 10.1038/s41587-021-01139-4 35027729 · DOI · Paper page
  63. [76]Andersson A Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography Commun. Biol. 2020 3 565 10.1038/s42003-020-01247-y 33037292 PMC7547664 · DOI
  64. [77]Elosua-Bayes M Nieto P Mereu E Gut I Heyn H SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes Nucleic Acids Res. 2021 49 e50 10.1093/nar/gkab043 33544846 PMC8136778 · DOI · Paper page
  65. [78]Cable DM Robust decomposition of cell type mixtures in spatial transcriptomics Nat. Biotechnol. 2022 40 517 526 10.1038/s41587-021-00830-w 33603203 PMC8606190 · DOI · Paper page
  66. [79]Biancalani T Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram Nat. Methods 2021 18 1352 1362 10.1038/s41592-021-01264-7 34711971 PMC8566243 · DOI · Paper page
  67. [81]Noguchi S FANTOM5 CAGE profiles of human and mouse samples Sci. Data 2017 4 170112 10.1038/sdata.2017.112 28850106 PMC5574368 · DOI · Paper page

Cited by

Sorted by citations

Authors
Massier, L., Jalkanen, J., Elmastas, M., Zhong, J., Wang, T., Nono Nankam, P. A., Frendo-Cumbo, S., Bäckdahl, J., Subramanian, N., Sekine, T., Kerr, A. G., Tseng, B. T. P., Laurencikiene, J., Buggert, M., Lourda, M., Kublickiene, K., Bhalla, N., Andersson, A., Valsesia, A., Astrup, A., Blaak, E. E., Ståhl, P. L., Viguerie, N., Langin, D., Wolfrum, C., Blüher, M., Rydén, M., & Mejhert, N.
Affiliations
  • Karolinska University Hospital (SE)
  • ETH Zurich (CH)
  • Science for Life Laboratory (SE)
  • Nestlé (Switzerland) (CH)
  • Novo Nordisk Foundation (DK)
  • Maastricht University Medical Centre (NL)
  • Université Toulouse III - Paul Sabatier (FR)
  • Helmholtz Munich (DE)
Corresponding author
  • Rydén, Mikael
  • Mejhert, Niklas
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2023-03-15
Volume
14
Issue
1
Pages
1438-1438
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-023-36983-2
PMC
PMC10017705
OpenAlex
W4327676110
Cited by
195
References
13
Citations by year
2026: 50 · 2025: 72 · 2024: 52 · 2023: 21
FWCI
17.64
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Research Publications (Maastricht University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
  • ETH Zürich Research Collection
Funders
  • European Commission
  • Svenska Sällskapet för Medicinsk Forskning
  • Karolinska Institutet
  • Novo Nordisk
  • Stockholms Läns Landsting
  • Vetenskapsrådet
  • Novo Nordisk Fonden (NNF15CC0018486, NNF22SA0081233, NNF15SA0018346, 0064142, NNF20SA0064144, NNF20SA0064145, NNF20SA0064142, NNF20OC0061149)
  • Uppsala Multidisciplinary Center for Advanced Computational Science
  • Center for Innovative Medicine
  • H2020 European Research Council (856404)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Health Sciences › Medicine › Physiology › Adipose Tissue and Metabolism
  • Health Sciences › Medicine › Epidemiology › Adipokines, Inflammation, and Metabolic Diseases
Keywords
white adipose tissue, adipocytes, adipose tissue immune cells, cell-cell interactions, insulin resistance, dyslipidemia, adipocyte size, lipolysis, weight change, subcutaneous adipose tissue, omental adipose tissue, perivascular adipose tissue, single-cell transcriptomics, spatial transcriptomics, single-nucleus transcriptomics, meta-analysis
MeSH terms
Adipose Tissue, Humans, Adipocytes, Gene Expression Profiling, Adipogenesis, Adipose Tissue, White, Transcriptome

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