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Defining the lineage of thermogenic perivascular adipose tissue

Nature Metabolism volume 3, pages 469–484 (2021)Cite this article

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Abstract

Brown adipose tissue can expend large amounts of energy, and therefore increasing its size or activity is a promising therapeutic approach to combat metabolic disease. In humans, major deposits of brown fat cells are found intimately associated with large blood vessels, corresponding to perivascular adipose tissue (PVAT). However, the cellular origins of PVAT are poorly understood. Here, we determine the identity of perivascular adipocyte progenitors in mice and humans. In mice, thoracic PVAT develops from a fibroblastic lineage, consisting of progenitor cells (Pdgfra+, Ly6a+ and Pparg−) and preadipocytes (Pdgfra+, Ly6a+ and Pparg+), which share transcriptional similarity with analogous cell types in white adipose tissue. Interestingly, the aortic adventitia of adult animals contains a population of adipogenic smooth muscle cells (Myh11+, Pdgfra− and Pparg+) that contribute to perivascular adipocyte formation. Similarly, human PVAT contains presumptive fibroblastic and smooth muscle-like adipocyte progenitor cells, as revealed by single-nucleus RNA sequencing. Together, these studies define distinct populations of progenitor cells for thermogenic PVAT, providing a foundation for developing strategies to augment brown fat activity.

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Fig. 1: Aortic PVAT expresses an EBF2-regulated classical brown fat programme.
Fig. 2: Identification of multiple fibroblast populations in developing aortic PVAT.
Fig. 3: Isolation of adipogenic fibroblasts from perinatal thoracic aorta.
Fig. 4: Adipogenic fibroblasts are the major source of aortic adipocytes in neonates.
Fig. 5: Comparative gene expression profiling of white and brown adipogenic cells.
Fig. 6: Identification of adipogenic smooth muscle cells in adult PVAT.
Fig. 7: Adipogenic activity of smooth muscle cells in adult PVAT.

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Data availability

Further information and requests for resources and reagents, including unique/stable reagents generated in this study, are available from the corresponding author without restriction. Sequencing data and code are available on the Gene Expression Omnibus under accession GSE164528.

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Acknowledgements

We thank X. Bi, D. Rader, M. Lazar, M. Kahn, J.-H. Mejia, and members of the Seale laboratory for thoughtful discussion. We thank the University of Pennsylvania Diabetes Research Center for use of the Functional Genomics Core (P30-DK19525). We are grateful to J. Smiler, R. P. Da Silva, F. A. Mafra and J. P. Garifallou in the Center for Applied Genomics Sequencing Core (Children’s Hospital of Philadelphia) for the single-cell and single-nucleus sequencing. This work was supported by National Institute of Health grants DK123356 and DK120982 (to P.S.); HL141149 (to L.L.); R01DK077097, R01DK102898 and R01DK122808 (to Y.-H.T.); DK120062 (to A.R.A.); T32GM008216 (to A.P.S.); K01DK125608 (to F.S.); K01DK111714 (to M.D.L.); P30-DK19525 (to Penn Diabetes Research Center); and P30DK036836 (to Joslin Diabetes Center’s Diabetes Research Center).

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Author notes

  1. These authors contributed equally: Anthony R. Angueira, Alexander P. Sakers.

Authors and Affiliations

  1. Institute for Diabetes, Obesity & Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    Anthony R. Angueira, Alexander P. Sakers, Corey D. Holman, Lan Cheng, Michelangella N. Arbocco, Rojesh Shrestha, Chihiro Okada, Kirill Batmanov, Katalin Susztak & Patrick Seale

  2. Department of Cell and Developmental Biology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    Anthony R. Angueira, Alexander P. Sakers, Corey D. Holman, Michelangella N. Arbocco, Chihiro Okada & Patrick Seale

  3. Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA

    Farnaz Shamsi, Matthew D. Lynes & Yu-Hua Tseng

  4. Renal Electrolyte and Hypertension Division, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    Rojesh Shrestha & Katalin Susztak

  5. Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    Rojesh Shrestha

  6. Center for Molecular Medicine, Maine Medical Center Research Institute, Scarborough, ME, USA

    Lucy Liaw

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Contributions

A.R.A., A.P.S. and P.S. were responsible for conceptualization, data analysis and writing. A.R.A. and A.P.S. contributed equally and conducted the majority of the experiments and carried out the bioinformatics analyses. C.D.H. and M.N.A. prepared the human aortic PVAT for single-nucleus RNA-seq analysis. L.C. processed tissue sections and performed staining. F.S., M.D.L. and Y.-H.T. performed the analysis of Trpv1 (SMC)-reporter mice. C.O. assisted with experimental procedures. R.S. performed cell capture and library preparation for perinatal single-cell datasets. K.S. provided sequencing reagents and key experimental insight. K.B. assisted with data analysis. L.L. obtained and provided human peri-aortic PVAT samples.

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Correspondence to Patrick Seale.

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Peer review information Nature Metabolism thanks Kosaku Shinoda, Christian Wolfrum and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: George Caputa.

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Extended data

Extended Data Fig. 1 Single cell transcriptional profiling of aortas from E18 and P3 mice.

a, UMAPs of gene expression in cells from E18 and P3 thoracic aorta of pooled CD1 mice. (b–f) UMAPs showing expression of indicated genes for: smooth muscle cells (SMC) (b); Intermediate cells (c); Progenitors (d); Preadipocytes (e); and Adipocytes (f). g, Expression dotplot of indicated genes for cell clusters from P3 aorta.

Extended Data Fig. 2 Purification of smooth muscle and fibroblast cells from aorta (Related to Fig. 3).

a, FACS isolation of fibroblastic and smooth muscle cell (SMC) populations. Dissociated cells were gated on: (1) SSC-A and FSC-A to exclude debris; (2) FSC-H vs. FSC-W then SSC-H vs. SSC-W to isolate single cells; and (3) live (FVS510-) Lin- (CD45-,CD31-,Ter119-) cells. Depicted sort gates were used to isolate the following populations: Progenitors [LY6A high], Preadipocytes (PreAd) [LY6A(-), CD142 mid; CD200(-)]; SMCs [LY6A(-); CD142(-); CD200+], Intermediate Cells (Int) [LY6A(-); CD142+; CD200+, CD317(-)], Meso (Mesothelium) [LY6A(-); CD142+; CD200+, CD317+] (Representative images from n=10 expts). Related to experiments shown in Figs. 3a and 5. b, Violin plots showing expression of genes used in the sorting strategy. c, Staining of CD200 (red), MYH11 (green), and DAPI (blue) in P3 thoracic aorta. White arrowhead shows an Intermediate cell. Yellow arrowhead shows an SMC (scale bar, 65 μm; Lu: lumen). Representative of n=1 experiment. (d) Expression of Seurat cluster-defining genes in the sorted cell bulk RNAseq datasets. Gene levels in sorted cell populations were compared to the row mean and annotated as “enriched” or “de-enriched” (Log2 FC>0.25).

Extended Data Fig. 3 Expression profiles for Cre-driver mouse lines (Related to Fig. 4).

a, UMAPs showing expression of genes used for Cre and CreER driver mouse strains. b, Vipr2 expression levels in indicated cell types from sorted cell bulk RNAseq analysis. (One way ANOVA followed by two sided pairwise comparisons with Holm Sidak correction. n=3 biologically independent samples per group; mean +/- SEM). Statistical Testing: ****p≤0.0001.

Extended Data Fig. 4 Gene profiling analyses of iWAT vs. PVAT progenitors and preadipocytes (Related to Fig. 5).

a, mRNA levels of indicated genes in differentiated primary adipocytes from: iWAT preadipocytes, aortic PVAT progenitors, aortic PVAT preadipocyte cells (all from P3 CD1 mice). Second experimental replicate performed on different day (n=2 PVAT Pread; n=3 PVAT Progenitor; n=4 iWAT Pread independent wells per group from pooled FACS samples; mean+/- SEM). b, Z-Score split heatmap of genes from overlaps depicted in Fig. 5c,d. Gene expression levels are calculated between cell types (that is PVAT Progenitors vs PVAT PreAd) within a tissue of origin (n=3 biological replicates). c, Expression heatmap of all cell cycle genes (used by Seurat) in sorted progenitor and preadipocyte cells from iWAT and thoracic aortic PVAT. Data comes from DESeq2 normalized count data (bulk RNAseq). d, Pathway analysis of genes enriched in preadipocytes from PVAT vs. iWAT (significantly differentially expressed and LFC>0). Graph plots -log10 unadjusted p values. P values were calculated using clusterProlifer with WikiPathways2019 annotations using the hypergeometric distribution. Padjusted for multiple comparisons was calculated with FDR. Padjusted in order (2.48E-34, 1.07E-20, 1.31E-15, 2.97E-13, 1.37E-12, 2.01E-10, 1.71E-07, 2.66E-07, 1.84E-05).

Extended Data Fig. 5 Purification and analysis of aorta-associated cells from adult animals (Related to Fig. 6).

a, Expression dot plot of indicated genes in cell clusters from adult aorta. b, mRNA in situ hybridization of Ly6a (green) in adult aorta. Arrowhead in inset shows progenitor cell. DAPI (blue) stains nuclei. (scale bar, 362.3 μm). Lu: lumen. Representative of n=2 experiments. c, mRNA in situ hybridization of Pi16 (green) and Bace2 (red). White arrowhead shows progenitor cell. Yellow arrowhead shows intermediate cell (scale bar, 145 μm). Representative of n=2 experiments. d, UMAPs showing Cd81 and Prdm16 expression. e, Violin plots showing expression of indicated genes used for sorting. f, FACS isolation of fibroblasts and smooth muscle cells (SMCs). To exclude debris and isolate single live cells, we gated on: (1) SSC-A and FSC-A ; (2) FSC-H vs FSC-W; and (3) SSC-H vs SSC-W. We then gated on Live (DAPI-); Lin- (CD45-,CD31-,TER119-) cells. Further selection was used to isolate: Intermediate Cells [PDGFRa+, MCAM(-), CD200+], Progenitors [PDGFRa+, MCAM(-), CD200(-)], SMC1 [PDGFRa(-), MCAM+, CD200+], SMC2 [PDGFRa(-), MCAM+, CD200(-)]. (Representative image of 6 separate experiments). Related to experiments shown in Figs. 6d and 7a,c. g, Expression of Seurat cluster-defining genes in the sorted cell bulk RNAseq datasets. Gene levels in sorted cell populations were compared to the row mean and annotated as “enriched” or “de-enriched” (Log2 FC>0.25). h,i, Expression heatmap of Seurat cluster-defining genes mapped on to sorted-cell RNASeq results for fibroblast (H) and SMC populations (I) (n=3).

Extended Data Fig. 6 Analysis of aortic PVAT in adult mice (Related to Fig. 7).

a, Staining for MYH11 (red) and PLIN (green) in aortas of Rosiglitazone (Rosi)-treated mice, scale bar 200 μm. Representative of n=1 experiment. Scale bar, 200 μm. b, mRNA levels of Trpv1 and Gli1 in sorted cell populations. c, Staining of GFP (green) and either PLIN1 or UCP1 (red) in aortas of male Trpv1-Cre; mTmG mice housed at thermoneutrality or following 1 week of 4C cold exposure. Scale bar, 100 μm. d, Staining of TAGLN (red) in adult aorta. White arrowheads show adventitial SMCs. Yellow arrowhead indicates parenchymal SMCs of the aorta. Representative of n=2 experiments. Scale bar, 271.8 μm. e, H&E staining of adult aorta. Arrowheads show adventitial blood vessel. f, Staining of GFP (green) in aortas of Pdgfra-CreER+; mTmG+ mice following a 5-day pulse of Tamoxifen and a 2.5 week treatment with DMSO or Rosi. Arrowheads=GFP+ adipocytes (n=1 Cre-; n=4 DMSO; n=5 Rosi; scale bar, 543.5 μm). g, Staining of GFP (green) in aortas of Pdgfra-CreER+; mTmG+ mice following a 5-day pulse of Tamoxifen and 2-week chase at room temperature or 4C (cold). Representative of n=1 experiment. Scale bar, 100 μm. h, Staining of GFP (green) in aortas of CreER- Ctl and Gli1-CreER+; mTmG+ mice following a 5-day pulse with Tamoxifen and 2.5-week treatment with DMSO or Rosi. (n=1 Cre-; n=3 Cre+; DMSO; n=3 Cre+; Rosi scale bar, 543.5 μm). DAPI (blue) was used to stain nuclei.

Extended Data Fig. 7 Single nucleus transcriptomic analyses of human PVAT (Related to Fig. 7).

a, UMAP of gene expression in 18,758 nuclei from adult human peri-aortic PVAT (Full dataset, n=3 humans, integrated analysis). b, Expression dotplot of indicated genes in dataset from (A). c,d, Violin plots (c) and UMAPs (d) showing expression of select genes, corresponding to the subclustered dataset in Fig. 7d. e, Pathway analysis of cluster defining genes (average logFC>0.5) in SMC and PPARg+ SMC-like cells compared to all other clusters in the dataset from Fig. 7d. Graph plots -log10 unadjusted p values. P values were calculated using clusterProlifer with WikiPathways2019 annotations using the hypergeometric distribution. Padjusted for multiple comparisons was calculated with FDR. Padjusted in order SMC (1.59E-07, 2.50E-06, 2.75E-06, 0.000106107, 0.000968522, 0.000968522, 0.001899498); PPARg+ SMC Like Cells (5.75E-05, 0.000135397, 0.003396457, 0.00971142, 0.00971142, 0.010636022, 0.010636022). f, Violin plots showing expression of select marker genes in the fibroblastic populations from Fig. 7d. g, UMAP of gene expression in 7285 nuclei from adult human deep neck BAT (subclustered to remove immune and endothelial cells, n=16 humans, integrated analysis, re-analyzed data from: Sun et al 40). h-j, UMAPs (h), expression dotplot (i) and violin plots (j) showing expression of select genes in cells from the dataset above (g).

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Angueira, A.R., Sakers, A.P., Holman, C.D. et al. Defining the lineage of thermogenic perivascular adipose tissue. Nat Metab 3, 469–484 (2021). https://doi.org/10.1038/s42255-021-00380-0

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