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Single-cell sequencing of human white adipose tissue identifies new cell states in health and obesity

Nature Immunology volume 22, pages 639–653 (2021)Cite this article

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Abstract

White adipose tissue (WAT) is an essential regulator of energy storage and systemic metabolic homeostasis. Regulatory networks consisting of immune and structural cells are necessary to maintain WAT metabolism, which can become impaired during obesity in mammals. Using single-cell transcriptomics and flow cytometry, we unveil a large-scale comprehensive cellular census of the stromal vascular fraction of healthy lean and obese human WAT. We report new subsets and developmental trajectories of adipose-resident innate lymphoid cells, dendritic cells and monocyte-derived macrophage populations that accumulate in obese WAT. Analysis of cell–cell ligand–receptor interactions and obesity-enriched signaling pathways revealed a switch from immunoregulatory mechanisms in lean WAT to inflammatory networks in obese WAT. These results provide a detailed and unbiased cellular landscape of homeostatic and inflammatory circuits in healthy human WAT.

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Fig. 1: Single-cell sequencing reveals the cellular heterogeneity of the SVF of human WAT.
Fig. 2: Single-cell analysis unveils unique human WAT-resident ILC subsets.
Fig. 3: RNA-velocity and CytoTRACE analyses identify a shared ILC precursor to mature adipose ILC1s and ILC3s.
Fig. 4: Single-cell analysis identifies unique cell lineages within human WAT myeloid populations.
Fig. 5: RNA-velocity analysis uncovers a distinct monocyte state upstream of adipose IMs in obese individuals.
Fig. 6: CellphoneDB analysis reveals the lean human WAT interactome.
Fig. 7: CellphoneDB analysis predicts a dramatic remodeling of the human WAT interactome in obesity.
Fig. 8: Analysis of putative upstream regulators uncovers a distinct obese human WAT-enriched signalome.

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Data availability

The scRNA-seq datasets are accessible from the Gene Expression Omnibus with accession nos. GSE155960 and GSE156110.

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Acknowledgements

We thank members of the following labs for helpful discussions: A. Freud, T.E.O., M.P. and M. Su. We thank D. Barrett for discarded surgical subcutaneous adipose tissue samples. We thank R. Modlin for assistance with IPA. A.D.H was supported by the Ruth L. Kirschstein National Research Service (award no. AI007323). T.E.O. was supported by the National Institutes of Health (grant nos. P30DK063491 and AI145997). F.M. was supported by the Whitcome Fellowship at UCLA.

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Author notes

  1. These authors contributed equally: Andrew D. Hildreth, Feiyang Ma.

Authors and Affiliations

  1. Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Andrew D. Hildreth, Yung Yu Wong, Ryan Sun & Timothy E. O’Sullivan

  2. Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA, USA

    Andrew D. Hildreth & Timothy E. O’Sullivan

  3. Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA

    Feiyang Ma & Matteo Pellegrini

  4. Institute for Genomics and Proteomics, University of California, Los Angeles, Los Angeles, CA, USA

    Feiyang Ma & Matteo Pellegrini

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Contributions

A.D.H. and T.E.O. designed the study. F.M. and M.P. performed scRNA-seq bioinformatics. A.D.H, YY.W. and R.S. performed all other experiments. A.D.H. and T.E.O. wrote the manuscript.

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Correspondence to Timothy E. O’Sullivan.

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Peer review information Nature Immunology thanks Jorge Moscat, Sarah Teichmann, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Zoltan Fehervari was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Lineage-associated gene signatures of CD45+ and CD45– SVF cells from healthy human WAT.

a, Dot plot showing selected top differentially expressed marker genes for each cluster, supporting assignment of clusters to compartments shown in Fig. 1b. Color saturation indicates the strength of expression in positive cells, while dot size reflects the percentage of each cell cluster expressing the gene. b, Violin plots showing expression levels of additional cluster markers for the indicated structural cell populations. c, Representative gating strategy for scRNAseq-defined human WAT non-immune cell populations (CD45–): Endothelial cell: CD31+, Smooth muscle cell (SMC): CD31–CD34–CD29–, Adipocyte precursor cell (APC): CD31–CD34–CD29+, Preadipocyte: CD31–CD34+CD29intICAM-1+CD26–, Interstitial progenitor cell: CD31–CD34+CD29intICAM-1+CD26+. d, Density correlation analysis of the indicated non-immune subsets with patient BMI. Line of best fit and 95% confidence intervals are shown for each plot. Each point represents an individual patient. Linear regression and two-tailed Pearson Correlation analysis with 95% confidence intervals were conducted. p < 0.05 was considered significant.

Extended Data Fig. 2 Single cell analysis reveals heterogeneous adaptive lymphocyte populations in healthy human WAT.

a, UMAP plot of 36,601 subclustered human adipose effector lymphocytes from Fig. 1. Cluster analysis yields 9 distinct clusters comprising of T cell subsets, ILCs and NK cells. b, Unbiased heatmap of gene expression of the top 6 unique cluster marker genes for each T cell cluster. Cluster identities are shown above the heatmap. Color saturation indicates the strength of expression. c, Violin plots showing RNA expression of additional cluster markers for the indicated T cell populations. d, Representative gating strategy for scRNAseq-defined human WAT T cell populations (CD45+Lin–(CD34+CD19+CD14+): γδT cell (γδ T): TCRγδ+Vα24-Jα18–Vα7.2–, NKT cell (NKT): TCRγδ–Vα24-Jα18+Vα7.2–, MAIT cell (MAIT): TCRγδ–Vα24-Jα18–Vα7.2+TCRαβ+, CD8+ T cell (CD8 T): TCRγδ–Vα24-Jα18–Vα7.2–TCRαβ+CD8α+CD4–, CD4+ T cell (CD4 T): TCRγδ–Vα24-Jα18–Vα7.2–TCRαβ+CD8α–CD4+, Regulatory T cell (Treg): TCRγδ–Vα24-Jα18–Vα7.2–TCRαβ+CD8α–CD4+FoxP3+. e, Representative histogram of KLRB1 (CD161) expression on human WAT T cell subsets. f, Representative flow cytometry plot of CD8α and CD4 expression on human WAT γδ T cells. e,f, Data is representative of 4 individual patient samples. g, Density correlation analysis of the indicated T cell subsets with patient BMI. Line of best fit and 95% confidence intervals are shown for each plot. Each point represents an individual patient. Linear regression and two-tailed Pearson Correlation analysis with 95% confidence intervals were conducted. p < 0.05 was considered significant.

Extended Data Fig. 3 Single cell analysis identifies unique human WAT-resident ILCs.

a, Representative sorting strategy for Lin–CD7+ cell populations based on expression of CD200R1; used in Fig. 2. b, Unsupervised heatmap of the top 5 differentially expressed cluster marker genes for each indicated innate lymphoid cell cluster. Cluster identities are shown above the heatmap. Color saturation indicates the strength of expression. c, Selected UMAP feature plots showing RNA expression of additional cluster markers, based on the UMAP shown in Fig. 2a. d, UMAP of sorted innate lymphoid cell populations denoted by the source of the sorted sample (CD200R1+ vs CD200R1–) and patient source classification as lean (red, green) or obese (blue, purple); based on the UMAP shown in Fig. 2a. e, Bar plots showing the proportion of innate lymphoid cells derived from 7 lean (blue) and 5 obese (red) patients.

Extended Data Fig. 4 Flow cytometry analysis of scRNAseq-identified human WAT ILCs.

a,b, Representative gating strategies for the scRNAseq-defined human WAT (a) NK cell and (b) ILC populations identified in Fig. 2. Human WAT ILC populations are defined as CD45+Lin–(CD3+TCRαβ+CD19+CD34+CD14+CD5+TCRγδ+EOMES+)CD7+CD200R1+); ILC1: TBET+, ILC2: TBET–CRTH2+NKp44–, ILC3: TBET–CRTH2–NKp44+, ILCP-like: TBET–CRTH2–NKp44–CD62L+/–. c, Representative histogram of IFN-γ by human WAT NK cell subsets. Unstim refers to CD200R1– cells cultured without PMA and Ionomycin. d, Representative histograms of TBET, CRTH2, NKp44, and RORC expression on human WAT ILC subsets. e, Analysis of RORC MFI values from human WAT ILC subsets. Each point represents an individual patient (n = 3). ILCP-like: p = 0.0198, ILC1: p = 0.0313, ILC2: p = 0.0194. f, Representative flow cytometry plots of CD7+CD200R1+ cells isolated from human PBMC. g, Density correlation analysis of ILC2 with patient BMI. Each point represents an individual patient. Line of best fit and 95% confidence intervals are shown for the plot. h,i, Density of indicated ILCs by BMI classification. Each point represents an individual patient; (h) n = 8 lean and n = 7 obese patients. i, ILC1: n = 10 lean and n = 7 obese patients; ILC2, ILC3, ILCP-like: n = 5 lean and n = 3 obese patients. ILC2: p = 0.0293, ILC3: p = 0.0094, ILCP-like: p = 0.022. c,d,f, Data is representative of 3 individual patient samples. Samples were compared using two-tailed Student’s t test with Welch’s correction, assuming unequal SD, and data are presented as individual points with the mean ± SEM (*p < 0.05, **p < 0.01). Linear regression and two-tailed Pearson Correlation analysis with 95% confidence intervals were conducted. p < 0.05 was considered significant.

Extended Data Fig. 5 Analysis of ILC1 and ILC3 fate DEGs suggests a clear developmental bifurcation.

Bifurcation heatmap of enriched genes for ILC1 (left), ILCP-like (middle) and ILC3 (right). Color indicates increased (red) or decreased (blue) expression.

Extended Data Fig. 6 Single cell analysis identifies unique myeloid populations within healthy human WAT.

a, Representative sorting strategies for CD11b+CD14+ macrophage and HLA-DR+CD11c+ dendritic cell populations indicated in Fig. 4. b, Heatmap shows the top 4 differentially expressed cluster marker genes for each indicated myeloid cell cluster. Cluster identities are shown above the heatmap. Color saturation indicates the strength of expression. c,d, Violin plots showing RNA expression levels of cluster markers for (c) myeloid (d) and monocyte populations. e, Classical, Intermediate, and Nonclassical Monocyte gene module score analysis for the indicated monocyte populations based on comparison of signature genes for each cell type from previously defined datasets to DEGs within each cluster. f, Neutrophil gene module score analysis for the indicated myeloid populations based on comparison of signature genes for each cell type from previously defined datasets to DEGs within each cluster. g, Bar plots showing the proportion of myeloid cells derived from 7 lean (blue) and 5 obese (red) patients.

Extended Data Fig. 7 Flow cytometry analysis of scRNAseq-identified human WAT macrophage and dendritic cell populations.

a, Representative gating strategy for scRNAseq-defined human WAT macrophage and DC populations identified in Fig. 4. b, Representative histograms of CD64, CD68, CD88, and CD15 expression on human WAT macrophage cell subsets, CD14+ monocytes and CD15+ neutrophils isolated from human PBMC. c, Flow cytometry analysis of endogenous TNF-α production by human WAT macrophage subsets from n = 4 lean, n = 2 overweight, and n = 4 obese patients. Each point represents an individual patient. d, Representative flow cytometry histograms of CLEC10A and FCER1A expression on human WAT DC subsets. e, Density of the indicated DC populations by BMI classification, n = 9 lean and n = 7 obese patients. Each point represents an individual patient (f) Representative flow cytometry plots of human WAT macrophage populations from lean (left) and obese (right) patients. g, Density of the indicated macrophage populations by BMI classification, n = 7 lean and n = 7 obese patients. Each point represents an individual patient. b,d, Data is representative of 3 individual patient samples. Samples were compared using two-tailed Student’s t test with Welch’s correction, assuming unequal SD, and data are presented as individual points with the mean ± SEM (*p < 0.05, **p < 0.01).

Extended Data Fig. 8 Analysis of the DEGs between PVM and IM fates shows a clear bifurcation in gene expression programs.

Bifurcation heatmap of enriched genes for PVM (left), Mo-1 (middle) and IM (right). Color indicates increased (red) or decreased (blue) expression.

Extended Data Fig. 9 Novel human WAT cell types contribute to obesity-associated inflammatory networks.

a, Dot plots showing expression of ligands (left) and receptors (right) in novel human WAT cells; only ligands and receptors from cell types with detected expression (>25%) are shown. Implicated chemokines can be found in the lower panel. Color saturation indicates the strength of expression in positive cells, while dot size reflects the percentage of each cell cluster expressing the gene. b-e, GOSt analysis of obese-enriched differentially expressed genes for the indicated novel human WAT cell types. b, GOSt analysis of trNK. c, GOSt analysis of ILC3. d, GOSt analysis of IM. e, GOSt analysis of cDC2B. Terms were considered statistically significantly enriched if -log10(Padj)<0.05.

Extended Data Fig. 10 Proposed model of cell-cell interactions in healthy lean and obese human WAT.

In lean WAT, IL-15 expression from cDC2A coupled with IL-15RA expression from APCs may support the viability of IL-15RB-expressing ILCs and NK cell populations. CSF1 (M-CSF) expression from APCs and IL-13 from ILC2s likely drives the Mo-1 transition to PVMs, while CSF2 from APCs may support dendritic cell homeostasis. cDC2A-derived CD200 could suppress ILC activation at steady-state. TGFβ1, PDGF, AREG, and GAS6 signaling from dendritic cells and PVM to APCs may promote tissue homeostasis. During obesity, IL-23 from an unknown source could drive the differentiation and accumulation of WAT ILC3s from ILCP-like cells. cDC2B-derived IL-18 and potentially IL-12 might stimulate the production of IFNγ by trNK and ILC1 subsets and contribute to the development of LAM from PVM. Increased CCL2 production from hypoxia-sensing PVM and SMC could recruit circulating Mo-1 into the WAT where MIF, LIF, and IFN-γ signaling from ILCs and NKs, as well as IL-1β, OSM and TNF-α signaling from IM, LAM, and cDC2B could polarize Mo-1 to the IM fate. trNK production of TNFSF14 (LIGHT) may further promote activation of cDC2B and IM. Together, these interactions suggest a cell type specific positive feedback loop whereby accumulation and polarization of WAT-resident lymphoid and myeloid cell types potentiate inflammation during human obesity.

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Supplementary Tables 1–18

Supplementary tables 1–18, containing patient data, gene lists, DEGs, IPA and gene set enrichment analyses.

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Hildreth, A.D., Ma, F., Wong, Y.Y. et al. Single-cell sequencing of human white adipose tissue identifies new cell states in health and obesity. Nat Immunol 22, 639–653 (2021). https://doi.org/10.1038/s41590-021-00922-4

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