Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Defining the lineage of thermogenic perivascular adipose tissue

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

Subjects

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

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.

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.

References

  1. Cypess, A. M. et al. Activation of human brown adipose tissue by a β3-adrenergic receptor agonist. Cell Metab. 21, 33–38 (2015).

  2. Harms, M. & Seale, P. Brown and beige fat: development, function and therapeutic potential. Nat. Med. 19, 1252–1263 (2013).

    Article  CAS  PubMed  Google Scholar 

  3. O’Mara, A. E. et al. Chronic mirabegron treatment increases human brown fat, HDL cholesterol and insulin sensitivity. J. Clin. Invest. 130, 2209–2219 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Yoneshiro, T. et al. Recruited brown adipose tissue as an antiobesity agent in humans. J. Clin. Invest. 123, 3404–3408 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Cannon, B. & Nedergaard, J. Brown adipose tissue: function and physiological significance. Physiol. Rev. 84, 277–359 (2004).

    Article  CAS  PubMed  Google Scholar 

  6. Kazak, L. et al. Genetic depletion of adipocyte creatine metabolism inhibits diet-induced thermogenesis and drives obesity. Cell Metab. 26, 660–671 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kazak, L. et al. A creatine-driven substrate cycle enhances energy expenditure and thermogenesis in beige fat. Cell https://doi.org/10.1016/j.cell.2015.09.035 (2015).

  8. Ikeda, K. et al. UCP1-independent signaling involving SERCA2b-mediated calcium cycling regulates beige fat thermogenesis and systemic glucose homeostasis. Nat. Med. 23, 1454–1465 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sacks, H. & Symonds, M. E. Anatomical locations of human brown adipose tissue: functional relevance and implications in obesity and type 2 diabetes. Diabetes 62, 1783–1790 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cypess, A. M. et al. Identification and importance of brown adipose tissue in adult humans. N. Engl. J. Med. 360, 1509–1517 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. van Marken Lichtenbelt, W. D. et al. Cold-activated brown adipose tissue in healthy men. N. Engl. J. Med. 360, 1500–1508 (2009).

    Article  PubMed  Google Scholar 

  12. Virtanen, K. A. et al. Functional brown adipose tissue in healthy adults. N. Engl. J. Med. 360, 1518–1525 (2009).

    Article  CAS  PubMed  Google Scholar 

  13. Aherne, W. & Hull, D. The site of heat production in the newborn infant. Proc. R. Soc. Med. 57, 1172–1173 (1964).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Heaton, J. M. The distribution of brown adipose tissue in the human. J. Anat. 112, 35–39 (1972).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Wang, W. & Seale, P. Control of brown and beige fat development. Nat. Rev. Mol. Cell Biol. 17, 691–702 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Orava, J. et al. Different metabolic responses of human brown adipose tissue to activation by cold and insulin. Cell Metab. 14, 272–279 (2011).

    Article  CAS  PubMed  Google Scholar 

  17. Lidell, M. E. et al. Evidence for two types of brown adipose tissue in humans. Nat. Med. 19, 631–634 (2013).

    Article  CAS  PubMed  Google Scholar 

  18. Cypess, A. M. et al. Anatomical localization, gene expression profiling and functional characterization of adult human neck brown fat. Nat. Med. 19, 635–639 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Huttunen, P., Hirvonen, J. & Kinnula, V. The occurrence of brown adipose tissue in outdoor workers. Eur. J. Appl. Physiol. Occup. Physiol. 46, 339–345 (1981).

    Article  CAS  PubMed  Google Scholar 

  20. Cheung, L. et al. Human mediastinal adipose tissue displays certain characteristics of brown fat. Nutr. Diabetes 3, e66 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Rajakumari, S. et al. EBF2 determines and maintains brown adipocyte identity. Cell Metab. https://doi.org/10.1016/j.cmet.2013.01.015 (2013).

  22. Angueira, A. R. et al. Early B cell factor activity controls developmental and adaptive thermogenic gene programming in adipocytes. Cell Rep. 30, 2869–2878 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Stine, R. R. et al. EBF2 promotes the recruitment of beige adipocytes in white adipose tissue. Mol. Metab. 5, 57–65 (2016).

    Article  CAS  PubMed  Google Scholar 

  24. Shapira, S. N. et al. EBF2 transcriptionally regulates brown adipogenesis via the histone reader DPF3 and the BAF chromatin remodeling complex. Genes Dev. 31, 660–673 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Dowal, L. et al. Intrinsic properties of brown and white adipocytes have differential effects on macrophage inflammatory responses. Mediators Inflamm. 2017, 9067049 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Lumeng, C. N., Bodzin, J. L. & Saltiel, A. R. Obesity induces a phenotypic switch in adipose tissue macrophage polarization. J. Clin. Invest. 117, 175–184 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Tian, X. Y. et al. Thermoneutral housing accelerates metabolic inflammation to potentiate atherosclerosis but not insulin resistance. Cell Metab. 23, 165–178 (2016).

    Article  CAS  PubMed  Google Scholar 

  28. Weisberg, S. P. et al. CCR2 modulates inflammatory and metabolic effects of high-fat feeding. J. Clin. Invest. 116, 115–124 (2006).

    Article  CAS  PubMed  Google Scholar 

  29. Boucher, J. M. et al. Pathological conversion of mouse perivascular adipose tissue by notch activation. Arterioscler. Thromb. Vasc. Biol. 40, 2227–2243 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Miano, J. M., Cserjesi, P., Ligon, K. L., Periasamy, M. & Olson, E. N. Smooth muscle myosin heavy chain exclusively marks the smooth muscle lineage during mouse embryogenesis. Circ. Res. 75, 803–812 (1994).

    Article  CAS  PubMed  Google Scholar 

  32. Merrick, D. et al. Identification of a mesenchymal progenitor cell hierarchy in adipose tissue. Science 364, eaav2501 (2019).

  33. Domenga, V. et al. Notch3 is required for arterial identity and maturation of vascular smooth muscle cells. Genes Dev. 18, 2730–2735 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Cristancho, A. G. & Lazar, M. A. Forming functional fat: a growing understanding of adipocyte differentiation. Nat. Rev. Mol. Cell Biol. 12, 722–734 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ross, S. E. et al. Inhibition of adipogenesis by Wnt signaling. Science 289, 950–953 (2000).

    Article  CAS  PubMed  Google Scholar 

  36. Ignotz, R. A. & Massagué, J. Type beta transforming growth factor controls the adipogenic differentiation of 3T3 fibroblasts. Proc. Natl Acad. Sci. USA 82, 8530–8534 (1985).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Oguri, Y. et al. CD81 controls beige fat progenitor cell growth and energy balance via FAK signaling. Cell 182, 563–577 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Tang, W., Zeve, D., Seo, J., Jo, A.-Y. & Graff, J. M. Thiazolidinediones regulate adipose lineage dynamics. Cell Metab. 14, 116–122 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Dietrich, A. et al. Increased vascular smooth muscle contractility in TRPC6−/− mice. Mol. Cell. Biol. 25, 6980–6989 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Sun, W. et al. snRNA-seq reveals a subpopulation of adipocytes that regulates thermogenesis. Nature 587, 98–102 (2020).

    Article  CAS  PubMed  Google Scholar 

  41. Chang, L. et al. Loss of perivascular adipose tissue on peroxisome proliferator-activated receptor-gamma deletion in smooth muscle cells impairs intravascular thermoregulation and enhances atherosclerosis. Circulation 126, 1067–1078 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Ye, M. et al. Developmental and functional characteristics of the thoracic aorta perivascular adipocyte. Cell. Mol. Life Sci. 76, 777–789 (2019).

    Article  CAS  PubMed  Google Scholar 

  43. Longo, K. A. et al. Wnt10b inhibits development of white and brown adipose tissues. J. Biol. Chem. 279, 35503–35509 (2004).

    Article  CAS  PubMed  Google Scholar 

  44. Kennell, J. A. & MacDougald, O. A. Wnt signaling inhibits adipogenesis through beta-catenin-dependent and -independent mechanisms. J. Biol. Chem. 280, 24004–24010 (2005).

    Article  CAS  PubMed  Google Scholar 

  45. MacDougald, O. A. & Mandrup, S. Adipogenesis: forces that tip the scales. Trends Endocrinol. Metab. 13, 5–11 (2002).

    Article  CAS  PubMed  Google Scholar 

  46. Tang, Q. -Q., Otto, T. C. & Lane, M. D. Mitotic clonal expansion: a synchronous process required for adipogenesis. Proc. Natl Acad. Sci. USA 100, 44–49 (2003).

    Article  CAS  PubMed  Google Scholar 

  47. Tang, Q. Q. & Lane, M. D. Adipogenesis: from stem cell to adipocyte. Annu. Rev. Biochem. 81, 715–736 (2012).

    Article  CAS  PubMed  Google Scholar 

  48. Passman, J. N. et al. A Sonic Hedgehog signaling domain in the arterial adventitia supports resident Sca1+ smooth muscle progenitor cells. Proc. Natl Acad. Sci. USA 105, 9349–9354 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Kramann, R. et al. Perivascular Gli1+ progenitors are key contributors to injury-induced organ fibrosis. Cell Stem Cell 16, 51–66 (2015).

    Article  CAS  PubMed  Google Scholar 

  50. Tang, J. et al. Arterial Sca1+ vascular stem cells generate de novo smooth muscle for artery repair and regeneration. Cell Stem Cell 26, 81–96 (2020).

    Article  CAS  PubMed  Google Scholar 

  51. Jiang, Y., Berry, D. C., Tang, W. & Graff, J. M. Independent stem cell lineages regulate adipose organogenesis and adipose homeostasis. Cell Rep. 9, 1007–1022 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Lee, Y. -H., Petkova, A. P., Konkar, A. A. & Granneman, J. G. Cellular origins of cold-induced brown adipocytes in adult mice. FASEB J. 29, 286–299 (2015).

    Article  CAS  PubMed  Google Scholar 

  53. Shao, M. et al. Cellular origins of beige fat cells revisited. Diabetes 68, 1874–1885 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Wang, Q. A., Tao, C., Gupta, R. K. & Scherer, P. E. Tracking adipogenesis during white adipose tissue development, expansion and regeneration. Nat. Med. 19, 1338–1344 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Yu, G., Wang, L. -G., Han, Y. & He, Q. -Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Martens, M. et al. WikiPathways: connecting communities. Nucleic Acids Res. 49, D613–D621 (2021).

    Article  CAS  PubMed  Google Scholar 

  57. Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Kuleshov, M. V. et al. Enrichr: a comprehensive gene-set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Hu, P. et al. Dissecting cell-type composition and activity-dependent transcriptional state in mammalian brains by massively parallel single-nucleus RNA-seq. Mol. Cell 68, 1006–1015 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

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).

Author information

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

Authors
  1. Anthony R. Angueira
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander P. Sakers
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Corey D. Holman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lan Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michelangella N. Arbocco
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Farnaz Shamsi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Matthew D. Lynes
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Rojesh Shrestha
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Chihiro Okada
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kirill Batmanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Katalin Susztak
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yu-Hua Tseng
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Lucy Liaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Patrick Seale
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Corresponding author

Correspondence to Patrick Seale.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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).

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42255-021-00380-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing