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A single-cell atlas of human and mouse white adipose tissue

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 defence1. High-fat feeding and other metabolic stressors cause marked changes in adipose morphology, physiology and cellular composition1, and alterations in adiposity are associated with insulin resistance, dyslipidemia and type 2 diabetes2. 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.

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Fig. 1: A single-cell atlas of human white adipose tissue.
Fig. 2: A single cell atlas of mouse white adipose tissue.
Fig. 3: Subclustering of human and mouse adipocytes reveals multiple distinct populations that vary across depot and diet.
Fig. 4: Extensive cell–cell interactions in WAT and associations with human disease traits.

Data availability

Single-cell RNA expression and count data are deposited in the Single Cell Portal (study no. SCP1376). Processed count data for bulk RNA-seq and differential gene expression matrices for single-cell and single-nucleus RNA-seq have been deposited in the Gene Expression Omnibus (bulk-sequencing accession GSE174475, scRNA-seq accession GSE176067, sNuc-seq accession GSE176171); raw sequencing reads for mouse data are available in the Sequence Read Archive under study no. SRP322736. FASTQ and SNP array files for human samples are deposited in dbGaP under accession phs002766.v1.p1Source data are provided with this paper.

Code availability

Data analysis pipelines used in this study for processing of raw sequencing data, integration and clustering can be obtained from https://gitlab.com/rosen-lab/white-adipose-atlas.

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Acknowledgements

This work was supported by NIH grants RC2 DK116691 to E.D.R., L.T.T., A.C., O.A. and A.R., AHA POST14540015 and DoD PRMRP-DAW81XWH to L.T.T., Broad-BADERC Collaboration Initiative Award (NIH 5P30DK057521) to L.T.T. and E.D.R., and R01 DK102173 to E.D.R. M.P.E. is supported by NIH grant F32DK124914. Additional support includes PRIN 2017 (Italian Ministry of University, no. 2017L8Z2EM) to A. Giordano, T.H.P. acknowledges the Novo Nordisk Foundation (unconditional donation to the Novo Nordisk Foundation Center for Basic Metabolic Research; grant number NNF18CC0034900) and the Lundbeck Foundation  (grant number R190-2014-3904), grants AMP-T2D RFB8b (FNIH) and UM1DK126185 (NIDDK) to M.C., Sarnoff Cardiovascular Research Foundation Fellowship to S.A., grants 1K08HG010155 and 1U01HG011719  to A.V.K. from the National Human Genome Research Institute, and a sponsored research agreement from IBM Research to the Broad Institute of MIT and Harvard to A.V.K. All single cell library construction and sequencing was performed through the Boston Nutrition Obesity Research Center Functional Genomics and Bioinformatics Core (NIH P30DK046200). We thank C. Usher for artistic support and M. Udler for helpful discussions.

Author information

Authors and Affiliations

Authors

Contributions

M.P.E., L.T.T. and E.D.R. conceived of the project. M.P.E. and E.D.R. wrote the manuscript with assistance from L.T.T., C.J., O.A. and A.R. M.P.E., A.L.E., D.P., D.T., G.C., A.D.V., A.S., E. McGonagle, S.S., S.L., G.P.W., M.L.V., A. Gulko and E. Merkel performed experiments. G.P.W., A. Gulko, Z.K., E.D.F., J.D., C.G.B., W.G., A.C., S.J.L., B.T.L., D.M. and A.T. collected samples. M.P.E., C.J., A.M.J., H.D., S.A., A.K. and H.S. performed computational analysis. A.V.K., M.C., T.H.P., A. Giordano, O.A. and A.R. provided additional intellectual input.

Corresponding author

Correspondence to Evan D. Rosen.

Ethics declarations

Competing interests

S.A. has served as a scientific consultant to Third Rock Ventures. A.V.K. has served as a scientific advisor to Sanofi, Amgen, Maze Therapeutics, Navitor Pharmaceuticals, Sarepta Therapeutics, Novartis, Verve Therapeutics, Silence Therapeutics, Veritas International, Color Health, Third Rock Ventures and Columbia University (NIH); received speaking fees from Illumina, MedGenome, Amgen, and the Novartis Institute for Biomedical Research; and received a sponsored research agreement from the Novartis Institute for Biomedical Research. M.C. holds equity in Waypoint Bio and is a member of the Nestle Scientific Advisory Board. A.R. is a co-founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas Therapeutics and a scientific advisory board member of Thermo Fisher Scientific, Syros Pharmaceuticals, Asimov and Neogene Therapeutics. A.R. is also an employee of Genentech. All other authors declare no competing interests.

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Nature thanks Bart Deplancke and the other, anonymous, reviewers for their contribution to the peer review of this work. Peer review reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Additional analysis of the effects of depot and BMI on human WAT populations.

a, UMAP projections of cells from the lowest and highest BMI ranges in the dataset, split by depot. To facilitate comparison, samples were randomly subset to contain the same number of cells in each plot (n = 20,339). b, Graph showing the proportion of sNuc-seq cells in each cluster per sample, split by depot and BMI, n = 4 SAT < 30, 6 SAT > 40, 3 VAT < 30, 5 VAT > 40. C, Estimated cell type proportions in bulk RNA sequencing data of subcutaneous adipose tissue from 331 individuals from the METSIM cohort calculated using sNuc-seq data as reference. Vascular cells include endothelial, lymphatic endothelial, pericytes, and smooth muscle cells. Myeloid immune includes macrophages, monocytes, dendritic cells, mast cells and neutrophils, and lymphoid immune includes B cells, NK cells, and T cells. For lines of best fit: Adipocytes R2 = 0.031, ASPCs R2 = 0.034, Vascular R2 = 0.076, Myeloid Immune R2 = 0.13, Lymphoid Immune R2 = 0.0049. For scatterplots, error bands represent a confidence level of 0.95 and P values were calculated using an F-test with the null hypothesis that the slope = 0. For bar graphs, error bars represent SEM, * indicates credible depot effect and # indicates credible BMI effect, calculated using dendritic cells as reference.

Source data

Extended Data Fig. 2 Additional analysis of the effects of depot and diet on mouse WAT populations and association with human WAT populations.

a, UMAP projection of all mouse WAT cells split by depot. b, Proportion of cells in each cluster per sample, split by sex as well as by depot and diet, for male mice n = 4 ING Chow, 4 ING HFD, 3 EPI Chow, and 5 EPI HFD. For female mice, n = 2 per condition. c, Riverplot showing the relationship between mouse and human clusters. Mouse cells were mapped onto human sNuc-seq cells using multimodal reference mapping. The riverplot represents the relationship between manually assigned mouse cluster and mapped human cluster for every mouse cell. For bar graphs, error bars represent SEM, * indicates credible depot effect and # indicates credible diet effect, calculated using dendritic cells as reference.

Source data

Extended Data Fig. 3 Highly similar vascular cells in human and mouse WAT.

a, UMAP projection of 22,734 human vascular cells. b, Marker genes for 11 distinct clusters of human WAT vascular cells. c, UMAP projection of 7,632 mouse vascular cells. d, Marker genes for 9 distinct clusters of mouse WAT vascular cells. e, Riverplot showing the correlation between annotated mouse and human vascular clusters based on multimodal reference mapping for each mouse cell. f, g, Bar graphs showing the proportion of cells in each cluster per sample split by depot and BMI for human (f) and depot, diet, and sex for mouse (g). For humans, n = 9 SAT < 30, 6 SAT > 40, 3 VAT < 30, and 5 VAT > 40. For male mice n = 4 ING Chow, 4 ING HFD, 3 EPI Chow, and 5 EPI HFD. For female mice, n = 2 per condition. For bar graphs, error bars represent SEM, * indicates credible depot effect and # indicates credible BMI/diet effect, calculated using hEndoA2 (human) and mEndoA2 (mouse) as reference.

Source data

Extended Data Fig. 4 Comparison of immune cells in human and mouse WAT.

a, UMAP projection of 34,268 immune cells from human WAT. b, Marker genes for human immune cell clusters. c, UMAP projection of 70,547 immune cells from mouse WAT. d, Marker genes for mouse immune cell clusters. e-f, Riverplots showing the correlation between annotated mouse cluster and mapped human cluster for mouse (e) dendritic cells, mast cells, neutrophils, B cells, NK cells, and T cells and (f) monocytes and macrophages.

Extended Data Fig. 5 Human and mouse immune cells are differentially regulated by depot and BMI/diet.

a, b, UMAP projections of human (a) and mouse (b) WAT immune cells split by depot. c, d, UMAP projections of human (c) and mouse (d) WAT immune cells split by BMI (c) and diet (d). e-f, Bar graphs showing the proportion of cells in each cluster per sample split by depot and BMI for human (e) and depot, diet, and sex for mouse (f). For humans, n = 10 SAT < 30, 6 SAT > 40, 3 VAT < 30, and 5 VAT > 40. For male mice n = 4 ING Chow, 4 ING HFD, 3 EPI Chow, and 5 EPI HFD. For female mice, n = 2 per condition. For bar graphs, error bars represent SEM, * indicates credible depot effect and # indicates credible BMI/diet effect, calculated using hMono2 (human) and mcDC1 (mouse) as reference.

Source data

Extended Data Fig. 6 Subpopulations of human and mouse mesothelial cells.

a, UMAP projection of 30,482 human mesothelial cells. b, Marker genes for distinct human mesothelial populations. c, UMAP projection of 14,947 mouse mesothelial cells. d Marker genes for distinct mouse mesothelial populations. e, Riverplots showing relationship of mouse and human mesothelial clusters. f, g, Proportion of cells in each cluster per sample, split by BMI for human (f) and diet and sex for mouse (g). For humans, n = 3 VAT < 30, and 5 VAT > 40. For male mice n = 3 EPI Chow, and 5 EPI HFD. For female mice, n = 2 per condition. Error bars represent SEM, # indicates credible BMI/diet effect, calculated using hMes3 (human) and mMes1 (mouse) as reference.

Source data

Extended Data Fig. 7 Human and mouse ASPCs share commonalities with previously reported subtypes.

a, UMAP projection of 52,482 human ASPCs. b, Marker genes for distinct ASPC subpopulations. c, UMAP projection of 51,227 mouse ASPCs. d, Marker genes for distinct ASPC subpopulations. e, Riverplot depicting the relationship between mouse and human ASPC clusters. f, Integration of ASPCs from this paper with ASPCs from other groups.

Extended Data Fig. 8 Human ASPCs exhibit strong depot dependency while mouse ASPCs are dependent on both depot and diet.

a, b, UMAP projections of human (a) and mouse (b) ASPCs split by depot. c-d, UMAP projections of human (c) and mouse (d) ASPCs split by BMI/diet. e, f, Proportion of ASPC cells in each cluster per sample split by depot and BMI for human (e) and depot, diet, and sex for mouse (f). For humans, n = 11 SAT < 30, 6 SAT > 40, 3 VAT < 30, and 5 VAT > 40. For male mice n = 4 ING Chow, 4 ING HFD, 3 EPI Chow, and 5 EPI HFD. For female mice, n = 2 per condition. For bar graphs, error bars represent SEM, * indicates credible depot effect and # indicates credible BMI/diet effect, calculated using hASPC2 (human) and mASPC4 (mouse) as reference.

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Extended Data Fig. 9 Human adipocyte subtypes are highly dependent on depot and may be responsible for distinct functions.

a, b, UMAP projections of human white adipocytes split by depot (a) and BMI (b). c, Proportion of cells in each human cluster by sample split by depot and BMI, n = 4 SAT < 30, 6 SAT > 40, 3 VAT < 30, and 5 VAT > 40. D, Quantification of immunofluorescence analysis of GRIA4+ cells in mature human adipocytes from two individuals. Each dot represents an image, n = 12 images from individual 1 and 9 images from individual 2 with a total of 704 counted cells. Only cells with visible nuclei were included in the quantification. e, Representative image of GRIA4+ cells, white arrows represent positive cells, grey represent negative, scale bar = 100 μm. In total, there were 21 images from samples taken from two individuals. f, Expression of genes associated with adipokine secretion, insulin signaling, lipid handling, and thermogenesis across human adipocyte subclusters. g–m, Expression of genes associated with GO or KEGG pathways indicative of individual human adipocyte subclusters. For bar graph, error bars represent SEM, * indicates credible depot effect and # indicates credible BMI effect, calculated using hAd5 as reference.

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Extended Data Fig. 10 Human adipocytes differentiated ex vivo recapitulate many of the adipocyte subclusters found in vivo.

a, Plot of estimated cell type proportion in ex vivo adipocyte cultures differentiated from subcutaneous or visceral preadipocytes for 14 days, ordered by estimated proportion. b, c, Scatterplots showing the relationship between estimated cell type proportion and the LipocyteProfiler-calculated features Large BODIPY objects (b) and Median BODIPY Intensity (c). p values were calculated using an F-test with the null hypothesis that the slope = 0. d, Representative images of hAd3 low/hAd5 or hAd3 high hAd5 low ex vivo differentiated cultures. Green represents BODIPY staining, blue represents Hoechst staining. Scale bars are 100 μm, in total, 3 randomly selected images/sample were analyzed from 3 SAT samples and 3 VAT samples with the lowest and highest predicted proportions of hAd3 and hAd5.

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Extended Data Fig. 11 Visceral-specific adipocyte subpopulation hAd6 is associated with thermogenic traits.

a, Regional visualization of associations of common genetic variants near EBF2 with VATadj. b, Effect size of association of rs4872393 with VATadj, ASATadj, GFATadj, and BMI per minor allele A; n = 37,641. Error bars reflect a 95% confidence interval around the effect size estimate from regression. c, VATadj raw data plotted according to rs4872393 carrier status; n = 36,185. For box plots, horizontal line = median, lower and upper bounds of the box = 1st and 3rd quartile respectively, lower and upper whisker = 1st quartile – 1.5 x interquartile range (IQR) and 3rd quartile + 1.5 x IQR respectively, outliers are plotted as points. d, Scatterplot showing the relationship between estimated cell type proportion and the LipocyteProfiler calculated feature Mitochondrial Intensity in visceral samples. e, Expression of mitochondrial and thermogenic genes in visceral ex vivo differentiated adipocytes stratified by estimated hAd6 proportion and matched for amount of differentiation using PPARG expression, n = 7 mAd6 low and 5 mAd6 high. Error bars represent SEM, P values were calculated using two tailed t-tests with no adjustments for multiple comparison, *, P < .05, **, P < .01. Exact P values: EBF2 = 0.027, TFAM = 0.019, CKMT1A = 0.049, CKMT1B = 0.005. f, Representative images of hAd6 low and high visceral in vitro differentiated cultures. Green represents BODIPY staining, red represents MitoTracker staining, and blue represents Hoechst staining. Scale bars are 100 μm, in total 3 random images/sample were analyzed from 5 hAd6 low and 5 hAd6 high samples. g, Violin plot of sNuc-seq data showing axon guidance genes in adipocyte subclusters. h, Scatterplots showing the relationship between calculated proportion of visceral subpopulations hAd2 and hAd6 and expression of pan-neuronal markers on the ambient RNA of individual visceral sNuc-seq samples. For scatterplots, P values were calculated using an F-test with the null hypothesis that the slope = 0.

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Extended Data Fig. 12 Mouse adipocytes appear to have distinct functionality but are not analogous to human adipocyte subpopulations.

a, b, UMAP projections of mouse adipocytes split by depot (a) and diet (b). c, Proportion of cells in each mouse cluster per sample split by depot, diet, and sex. For male mice n = 4 ING Chow, 4 ING HFD, 3 EPI Chow, and 5 EPI HFD. For female mice, n = 2 per condition. d, Expression of genes associated with known adipocyte functions in mouse adipocyte subclusters. e–k, Expression of genes associated with GO or KEGG pathways indicative of individual mouse adipocyte subclusters. l–n, Riverplots of mouse cells showing the association between mouse and human adipocyte clusters from both subcutaneous and visceral depots (l), subcutaneous (ING and SAT) adipocytes only (m) or visceral (PG and VAT) adipocytes only (n). For depot comparisons, both mouse query objects and human reference objects were subset to the respective depot before mapping. For bar graph, error bars represent SEM, * indicates credible depot effect and # indicates credible diet effect, calculated using mAd6 as reference.

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Extended Data Fig. 13 CellphoneDB identifies increasing numbers of cell-cell interactions within WAT during obesity.

a, Heatmap showing number of significant interactions identified between cell types in SAT of low (<30) and high (>40) BMI individuals as determined by CellphoneDB. b, Expression of ligand and receptor genes from Fig. 4b in human adipocyte subclusters. c, Heatmaps showing number of significant interactions identified between cell types in ING and PG WAT of chow and HFD fed mice. d, Venn diagrams showing the overlap of significant interactions between adipocytes and endothelial cells, ASPCs, and macrophages between depot, BMI/diet, and species. e, Jitter plots of the relationship between number of WAT cell types expressing a ligand (y axis) vs. the number of cell types expressing the receptor (x axis) for all significant interactions in high BMI human VAT (left) and mouse HFD PG (right).

Extended Data Fig. 14 Association with GWAS data provides further insight into the contribution of white adipocytes to human traits.

a-c, Expression of PPARG in human adipocyte subclusters (a), and in METSIM SAT bulk RNA-seq plotted against WHR (b) or HOMA-IR (c). d, Expression of PPARG in isolated subcutaneous adipocyte bulk RNA-seq plotted against HOMA-IR. e-h, SNPs in the PPARG gene identified by DEPICT as associated with BMI-adjusted WHR plotted against PPARG gene expression (e, g) and HOMA-IR (f, h) in isolated subcutaneous adipocyte bulk RNA-seq data and cohort. For rs17819328 n = 7 for T/T, 30 for T/G, and 6 for G/G. For rs1797912 n = 7 for A/A, 31 for A/C, and 5 for C/C. For box plots, horizontal line = median, lower and upper bounds of the box = 1st and 3rd quartile respectively, lower and upper whisker = 1st quartile – 1.5 x interquartile range (IQR) and 3rd quartile + 1.5 x IQR respectively. P values were calculated using a Wilcoxin test. i-j, Expression of genes in human adipocyte subtypes from sNuc-seq data (i) and from isolated subcutaneous adipocyte bulk RNA-seq plotted against LDL (j). k, p values of the association between mouse cell types and GWAS studies. l–m, p values of the association between mouse adipocyte (l) or ASPC (m) subclusters with GWAS studies. For all graphs, the grey line represents P = 0.05 and the orange line represents significant P value after Bonferroni adjustment (P = 0.003 for all cell, P = 0.001 for subclusters), calculated based on number of cell types queried. For scatterplots, P values were calculated using an F-test with the null hypothesis that the slope = 0.

Extended Data Table 1 Subject information for Drop-Seq, sNuc-seq, and bulk RNA-seq of isolated subcutaneous human adipocytes
Extended Data Table 2 Numbers of cells in human and mouse single cell experiments broken down by cluster, depot, BMI/diet, and technology
Extended Data Table 3 GWAS studies used for CELLECT analysis

Supplementary information

Supplementary Information This file contains Supplementary Figs. 1–3 and Supplementary Note 1.

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Supplementary Table 1

Markers for human clusters and subclusters

Supplementary Table 2

Markers for mouse clusters and subclusters

Supplementary Table 3

GO and KEGG analysis of markers of human and mouse adipocyte subtypes

Supplementary Table 4

Significant interactions identified by CellphoneDB in human and mouse adipose tissue

Supplementary Table 5

Average expression of genes in clusters split by high or low BMI (human) or diet (mouse) for genes in interactions identified by CellphoneDB

Supplementary Table 6

Interactions between adipocytes and endothelial cells, ASPCs, and macrophages in human and mouse adipose tissue. TRUE refers to an interaction that is statistically significant under the given condition

Supplementary Table 7

CELLECT output for human and mouse clusters and subclusters

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Emont, M.P., Jacobs, C., Essene, A.L. et al. A single-cell atlas of human and mouse white adipose tissue. Nature 603, 926–933 (2022). https://doi.org/10.1038/s41586-022-04518-2

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