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Vascular smooth muscle-derived Trpv1+ progenitors are a source of cold-induced thermogenic adipocytes

Nature Metabolism volume 3, pages 485–495 (2021)Cite this article

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Abstract

Brown adipose tissue (BAT) and beige fat function in energy expenditure in part due to their role in thermoregulation, making these tissues attractive targets for treating obesity and metabolic disorders. While prolonged cold exposure promotes de novo recruitment of brown adipocytes, the exact sources of cold-induced thermogenic adipocytes are not completely understood. Here, we identify transient receptor potential cation channel subfamily V member 1 (Trpv1)+ vascular smooth muscle (VSM) cells as previously unidentified thermogenic adipocyte progenitors. Single-cell RNA sequencing analysis of interscapular brown adipose depots reveals, in addition to the previously known platelet-derived growth factor receptor (Pdgfr)α-expressing mesenchymal progenitors, a population of VSM-derived adipocyte progenitor cells (VSM-APC) expressing the temperature-sensitive cation channel Trpv1. We demonstrate that cold exposure induces the proliferation of Trpv1+ VSM-APCs and enahnces their differentiation to highly thermogenic adipocytes. Together, these findings illustrate the landscape of the thermogenic adipose niche at single-cell resolution and identify a new cellular origin for the development of brown and beige adipocytes.

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Fig. 1: scRNA-seq reveals remodelling of the brown adipocyte niche by cold.
Fig. 2: Lineage trajectory analysis predicts the contribution of Trpv1+ VSMs to brown adipogenesis.
Fig. 3: Genetic lineage tracing confirms that Trpv1+ cells give rise to brown and white adipocytes.
Fig. 4: Cold exposure induces the proliferation and differentiation of Trpv1+ adipocyte progenitors to highly thermogenic adipocytes.

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Data availability

The authors declare that the data supporting the findings of this study are available within the paper and the Supplementary Information. scRNA-seq data were deposited at the Gene Expression Omnibus (GSE160585).

References

  1. Kajimura, S. & Saito, M. A new era in brown adipose tissue biology: molecular control of brown fat development and energy homeostasis. Annu. Rev. Physiol. 76, 225–249 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. Nedergaard, J., Wang, Y. & Cannon, B. Cell proliferation and apoptosis inhibition: essential processes for recruitment of the full thermogenic capacity of brown adipose tissue. Biochim Biophys. Acta Mol. Cell Biol. Lipids 1864, 51–58 (2019).

    Article  CAS  PubMed  Google Scholar 

  3. Hepler, C., Vishvanath, L. & Gupta, R. K. Sorting out adipocyte precursors and their role in physiology and disease. Genes Dev. 31, 127–140 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Berry, D. C., Stenesen, D., Zeve, D. & Graff, J. M. The developmental origins of adipose tissue. Development 140, 3939–3949 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Seale, P. et al. PRDM16 controls a brown fat/skeletal muscle switch. Nature 454, 961–967 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sebo, Z. L., Jeffery, E., Holtrup, B. & Rodeheffer, M. S. A mesodermal fate map for adipose tissue. Development 145, dev166801 (2018).

  7. Lang, D. et al. Pax3 functions at a nodal point in melanocyte stem cell differentiation. Nature 433, 884–887 (2005).

    Article  CAS  PubMed  Google Scholar 

  8. Sanchez-Gurmaches, J. et al. PTEN loss in the Myf5 lineage redistributes body fat and reveals subsets of white adipocytes that arise from Myf5 precursors. Cell Metab. 16, 348–362 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  10. Chouchani, E. T. & Kajimura, S. Metabolic adaptation and maladaptation in adipose tissue. Nat. Metab. 1, 189–200 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Geloen, A., Collet, A. J., Guay, G. & Bukowiecki, L. J. β-adrenergic stimulation of brown adipocyte proliferation. Am. J. Physiol. 254, C175–C182 (1988).

    Article  CAS  PubMed  Google Scholar 

  12. Bukowiecki, L. J., Geloen, A. & Collet, A. J. Proliferation and differentiation of brown adipocytes from interstitial cells during cold acclimation. Am. J. Physiol. 250, C880–C887 (1986).

    Article  CAS  PubMed  Google Scholar 

  13. 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 

  14. Berry, D. C., Jiang, Y. & Graff, J. M. Mouse strains to study cold-inducible beige progenitors and beige adipocyte formation and function. Nat. Commun. 7, 10184 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Long, J. Z. et al. A smooth muscle-like origin for beige adipocytes. Cell Metab. 19, 810–820 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Menigoz, A. & Boudes, M. The expression pattern of TRPV1 in brain. J. Neurosci. 31, 13025–13027 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Caterina, M. J. et al. The capsaicin receptor: a heat-activated ion channel in the pain pathway. Nature 389, 816–824 (1997).

    Article  CAS  PubMed  Google Scholar 

  18. Storozhuk, M. V., Moroz, O. F. & Zholos, A. V. Multifunctional TRPV1 ion channels in physiology and pathology with focus on the brain, vasculature, and some visceral systems. Biomed. Res. Int. 2019, 5806321 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2018).

  21. Lun, A. T. L. & Marioni, J. C. Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data. Biostatistics 18, 451–464 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  23. Liu, J. et al. Changes in integrin expression during adipocyte differentiation. Cell Metab. 2, 165–177 (2005).

    Article  PubMed  Google Scholar 

  24. Xue, Y. et al. Hypoxia-independent angiogenesis in adipose tissues during cold acclimation. Cell Metab. 9, 99–109 (2009).

    Article  CAS  PubMed  Google Scholar 

  25. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  26. DeTomaso, D. et al. Functional interpretation of single cell similarity maps. Nat. Commun. 10, 4376 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  27. 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 

  28. Tang, W. et al. White fat progenitor cells reside in the adipose vasculature. Science 322, 583–586 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Vishvanath, L. et al. Pdgfrβ+ mural preadipocytes contribute to adipocyte hyperplasia induced by high-fat-diet feeding and prolonged cold exposure in adult mice. Cell Metab. 23, 350–359 (2016).

    Article  CAS  PubMed  Google Scholar 

  30. Rodeheffer, M. S., Birsoy, K. & Friedman, J. M. Identification of white adipocyte progenitor cells in vivo. Cell 135, 240–249 (2008).

    Article  CAS  PubMed  Google Scholar 

  31. Gupta, R. K. et al. Zfp423 expression identifies committed preadipocytes and localizes to adipose endothelial and perivascular cells. Cell Metab. 15, 230–239 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 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 

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

  34. Cavanaugh, D. J. et al. Trpv1 reporter mice reveal highly restricted brain distribution and functional expression in arteriolar smooth muscle cells. J. Neurosci. 31, 5067–5077 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lee, Y. H., Petkova, A. P., Mottillo, E. P. & Granneman, J. G. In vivo identification of bipotential adipocyte progenitors recruited by β3-adrenoceptor activation and high-fat feeding. Cell Metab. 15, 480–491 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Szallasi, A., Cortright, D. N., Blum, C. A. & Eid, S. R. The vanilloid receptor TRPV1: 10 years from channel cloning to antagonist proof-of-concept. Nat. Rev. Drug Discov. 6, 357–372 (2007).

    Article  CAS  PubMed  Google Scholar 

  38. Yoshioka, M. et al. Effects of red pepper on appetite and energy intake. Br. J. Nutr. 82, 115–123 (1999).

    Article  CAS  PubMed  Google Scholar 

  39. Westerterp-Plantenga, M. S., Smeets, A. & Lejeune, M. P. Sensory and gastrointestinal satiety effects of capsaicin on food intake. Int. J. Obes. 29, 682–688 (2005).

    Article  CAS  Google Scholar 

  40. Masuda, Y. et al. Upregulation of uncoupling proteins by oral administration of capsiate, a nonpungent capsaicin analog. J. Appl. Physiol. 95, 2408–2415 (2003).

    Article  CAS  PubMed  Google Scholar 

  41. Zhang, L. L. et al. Activation of transient receptor potential vanilloid type-1 channel prevents adipogenesis and obesity. Circ. Res. 100, 1063–1070 (2007).

    Article  CAS  PubMed  Google Scholar 

  42. Motter, A. L. & Ahern, G. P. TRPV1-null mice are protected from diet-induced obesity. FEBS Lett. 582, 2257–2262 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the US National Institutes of Health (grants R01DK077097, R01DK102898, R01DK122808 (to Y.-H.T.), K01DK125608 (to F.S.) and K01DK111714 (to M.D.L)), P30DK036836 (to Joslin Diabetes Center’s Diabetes Research Center) from the National Institute of Diabetes and Digestive and Kidney Diseases, grant 1-18-PDF-169 (to F.S.) from the American Diabetes Association and CZF2019-002454 from the Chan Zuckerberg Foundation (to Y.-H.T. and A.S.). Work by M.P. at the Harvard Chan Bioinformatics Core was funded by the Harvard Stem Cell Institute’s Center for Stem Cell Bioinformatics. We thank A. Marotta for technical assistance.

Author information

Authors and Affiliations

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

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

  2. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Mary Piper

  3. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA

    Li-Lun Ho

  4. Graduate Program in Bioengineering, UC Berkeley–UC San Francisco, Berkeley, CA, USA

    Anushka Gupta

  5. Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA

    Aaron Streets

  6. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Aaron Streets

  7. Harvard Stem Cell Institute, Harvard University, Cambridge, MA, USA

    Yu-Hua Tseng

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Contributions

F.S. and Y.-H.T. designed and conducted the scRNA-seq study. F.S., M.D.L. and Y.-H.T. designed and conducted the lineage-tracing experiments and wrote the manuscript. M.P. conducted the scRNA-seq analyses. L.-L.H. assisted in single-cell isolation and library preparation. T.L.H. provided technical assistance. A.G. and A.S. analysed human single-nuclei RNA-seq data. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Matthew D. Lynes or Yu-Hua Tseng.

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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 Characterization of cell types present in the BAT-SVF, related to Fig. 1.

Individual gene UMAP plots showing the expression levels and distribution of representative marker genes for each cell type.

Extended Data Fig. 2 Cold-induced transcriptional changes in BAT endothelial and schwann cells, related to Fig. 1.

a, Gene ontology (GO) enrichment analysis of the transcripts significantly upregulated in capillary endothelial cells by cold. b, Violin plots showing the expression levels and distribution of representative upregulated transcripts for the selected GO terms. c, Gene ontology (GO) enrichment analysis of the transcripts significantly downregulated in capillary endothelial cells by cold. d, Violin plots showing the expression levels and distribution of representative downregulated transcripts for the selected GO terms. e, Gene ontology (GO) enrichment analysis of the transcripts significantly upregulated in schwann cells by cold. f, Violin plots showing the expression levels and distribution of representative upregulated transcripts for the selected GO terms. g, Gene ontology (GO) enrichment analysis of the transcripts significantly downregulated in schwann cells by cold. h, Violin plots showing the expression levels and distribution of representative downregulated transcripts for the selected GO terms.

Extended Data Fig. 3 Cold-induced transcriptional changes in vascular smooth muscles of BAT, related to Fig. 1.

a, Gene ontology (GO) enrichment analysis of the transcripts significantly upregulated in vascular smooth muscles by cold. b, Violin plots showing the expression levels and distribution of representative upregulated transcripts for the selected GO terms. c, Gene ontology (GO) enrichment analysis of the transcripts significantly downregulated in vascular smooth muscles by cold. d, Violin plots showing the expression levels and distribution of representative downregulated transcripts for the selected GO terms. e, Adipogenesis and Fatty acid metabolism gene signatures visualized on UMAP plots using VISION. (f) Pathway enrichment analysis of the top 100 genes driving the trajectory.

Extended Data Fig. 4 Trpv1 expression is restricted to adipose tissue vasculature, related to Fig. 2.

Immunohistochemistry for Trpv1 in adipose tissue from mice housed at RT or cold for 7 days. Scale bars=10 μm. N = 4 biologically independent animals examined in 1 experiment.

Extended Data Fig. 5 Flow cytometry analysis of adipose tissue SVF, related to Fig. 2.

Representative flow cytometry analysis of BAT-SVF derived from Trpv1cre Rosa26mTmG mice. a, Gating strategy for isolating single SVF cells from BAT-SVF based on side scatter (SSC) and forward scatter (FSC). b, Unstained, single channel and isotype control gates for GFP and Pdgfrα. c-f, Unstained and single channel control gates for GFP and Sca-1 (c), Pdgfrβ (d), CD81 (e) and αSMA (f) antibodies.

Extended Data Fig. 6 TRPV1 and PDGFRA expression in human adipose progenitor cells, related to Fig. 2.

a, Unsupervised clustering of adipocytes and adipocyte progenitors in the single nuclei RNA-sequencing of human deep neck adipose tissue with 5 separate clusters indicated in each color. b, TRPV1 and (c) PDGFRA expression visualized on UMAP plots. d, Violin plots showing the expression levels and distribution of TRPV1, PDGFRA, and adiponectin (ADIPOQ) expression in each cluster identified in panel a.

Extended Data Fig. 7 Cold recruits adipocytes from the Trpv1pos lineage in BAT, related to Fig. 4.

a, Violin plot showing the Trpv1 expression in BAT-SVF at different housing conditions. b, Percentage of cells in brown adipocyte cluster at different housing conditions in the scRNA-seq experiment. N = 4 per group. Data are presented as Means ± SEM. One-Way ANOVA with Dunnett’s multiple comparisons test. c, EdU detection followed by immunohistochemistry staining for Plin1 and GFP in BAT from mice raised from birth to 6 weeks of age in thermoneutrality and transferred to room temperature (1 week) and cold (1 week). EdU was administered for 1 week at cold. Scale bar=50 μm. N = 6 biologically independent animals examined in one experiment.

Extended Data Fig. 8 Cold increases the number of GFPpos adipocytes in ingWAT, related to Fig. 4.

a, Left: Immunohistochemistry staining for GFP and Plin1 in ingWAT from mice housed at room temperature or cold for 7 days. Right: Percentage of GFPpos adipocytes (Plinpos) in ingWAT of Trpv1cre Rosa26mTmG mice housed at RT or cold (5 °C) for 7 days. N = 5 mice per group, 4–11 images/mouse. Data are presented as Means ± SEM. Two-Way ANOVA with Sidak’s multiple comparisons test. b, Top: Immunohistochemistry staining for GFP and Plin1 in pgWAT from mice housed at room temperature or cold for 7 days. Bottom: Percentage of GFPpos adipocytes (Plinpos) in pgWAT of Trpv1cre Rosa26mTmG mice housed at RT or cold (5 °C) for 7 days. N = 5 mice per group, 4–6 images/mouse. Data are presented as Means ± SEM. Scale bar=50 μm.

Extended Data Fig. 9 Cold recruits GFPpos beige adipocytes in WAT, related to Fig. 4.

a, Schematic presentation of housing conditions utilized in this study. To measure the contribution of the Trpv1 lineage to newly recruited beige adipocytes, Trpv1cre Rosa26mTmG mice were raised from birth in thermoneutrality to minimize beige adipogenesis, after which a cohort of animals was first moved to room temperature for one week followed by one week of cold exposure (TN → Cold). The control group was kept at TN the whole time (TN → TN). b, Left: Immunohistochemistry staining for UCP1 and GFP in ingWAT. Right: Percentage of UCP1neg (unilocular) and UCP1pos (multilocular) GFPpos adipocytes in each group. N = 5 mice per group, 6–11 images/mouse. Data are presented as Means ± SEM. Scale bar=50 μm. c, EdU detection followed by immunohistochemistry staining for Plin1 and GFP in ingWAT from mice housed in the TN → Cold condition. EdU was administered for 1 week at cold. Scale bar=100 μm. N = 2 biologically independent animals examined in one experiment. d, Left: Immunohistochemistry staining for Plin1 and GFP in pgWAT. Right: Percentage of GFPpos adipocytes in each group. N = 5 mice per group, 4–5 images/mouse. Data are presented as Means ± SEM. Scale bar=100 μm.

Extended Data Fig. 10 Brown adipocytes derived from the Trpv1pos progenitors are highly thermogenic, related to Fig. 4.

a-d, Expression of the indicated transcripts in tdTomatopos and GFPpos adipocytes isolated from BAT and ingWAT of Trpv1cre Rosa26mTmGmice housed at cold for 7 days. N = 6 mice per group. Data are presented as Means ± SEM. Two-Way ANOVA with Sidak’s multiple comparisons test.

Supplementary information

Supplementary Information

Supplementary Fig. 1

Reporting Summary

Supplementary Tables 1 and 2

Supplementary Table 1. qPCR primer sequences. Supplementary Table 2. List of the antibodies.

Supplementary Data 1

Marker genes for clusters of cells in BAT-SVF identified using Seurat. As a default, Seurat performs differential expression based on the non-parametric Wilcoxon rank sum test.

Supplementary Data 2

List of differentially expressed transcripts in Pdgfrα+ APCs (AP_8 and AP_9), capillary endothelial cells (EC_4), VSMs (all VSM clusters) and Schwann cells (Schwann cells_14) identified using the likelihood-ratio test.

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Shamsi, F., Piper, M., Ho, LL. et al. Vascular smooth muscle-derived Trpv1+ progenitors are a source of cold-induced thermogenic adipocytes. Nat Metab 3, 485–495 (2021). https://doi.org/10.1038/s42255-021-00373-z

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