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Plasticity and heterogeneity of thermogenic adipose tissue

Abstract

The perception of adipose tissue, both in the scientific community and in the general population, has changed dramatically in the past 20 years. While adipose tissue was thought for a long time to be a rather simple lipid storage entity, it is now recognized as a highly heterogeneous organ and a critical regulator of systemic metabolism, composed of many different subtypes of cells, with important endocrine functions. Additionally, adipose tissue is nowadays recognized to contribute to energy turnover, due to the presence of specialized thermogenic adipocytes, which can be found in many adipose depots. This review discusses the unprecedented insights that we have gained into the heterogeneity of thermogenic adipocytes and their respective precursors due to the technical developments in single-cell and nucleus technologies. These methodological advances have increased our understanding of how adipose tissue catabolic function is influenced by developmental and intercellular communication events.

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Fig. 1: Technologies for transcriptomic analysis of adipose tissue at single-cell resolution.
Fig. 2: BAT heterogeneity revealed by scRNA-seq and snRNA-seq.
Fig. 3: Inguinal WAT heterogeneity revealed by scRNA-seq and snRNA-seq.

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References

  1. Gesner, K. Historiae Animalium (Apud Christ. Froschouerum, 1551).

  2. Hatai, S. On the presence in human embryos of an interscapular gland corresponding to the so-called hibernating gland of lower mammals. Anat. Anz. 21, 369–373 (1902).

    Google Scholar 

  3. Young, P., Arch, J. R. S. & Ashwell, M. Brown adipose tissue in the parametrial fat pad of the mouse. FEBS Lett. 167, 10–14 (1984).

    Article  CAS  PubMed  Google Scholar 

  4. Rondini, E. A. & Granneman, J. G. Single cell approaches to address adipose tissue stromal cell heterogeneity. Biochem. J. 477, 583–600 (2020).

    Article  CAS  PubMed  Google Scholar 

  5. Deutsch, A., Feng, D., Pessin, J. E. & Shinoda, K. The impact of single-cell genomics on adipose tissue research. Int. J. Mol. Sci. 21, 4773 (2020).

    Article  CAS  PubMed Central  Google Scholar 

  6. Hull, D. & Segall, M. M. Distinction of brown from white adipose tissue. Nature 212, 469–472 (1966).

    Article  CAS  PubMed  Google Scholar 

  7. Cadrin, M. et al. Immunohistochemical identification of the uncoupling protein in rat brown adipose tissue. J. Histochem. Cytochem. 33, 150–154 (1985).

    Article  CAS  PubMed  Google Scholar 

  8. Petrovic, N. et al. Chronic peroxisome proliferator-activated receptor γ (PPARγ) activation of epididymally derived white adipocyte cultures reveals a population of thermogenically competent, UCP1-containing adipocytes molecularly distinct from classic brown adipocytes. J. Biol. Chem. 285, 7153–7164 (2010).

    Article  CAS  PubMed  Google Scholar 

  9. Wu, J. et al. Beige adipocytes are a distinct type of thermogenic fat cell in mouse and human. Cell 150, 366–376 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zingaretti, M. C. et al. The presence of UCP1 demonstrates that metabolically active adipose tissue in the neck of adult humans truly represents brown adipose tissue. FASEB J. 23, 3113–3120 (2009).

    Article  CAS  PubMed  Google Scholar 

  11. Lee, Y.-H., Kim, S.-N., Kwon, H.-J. & Granneman, J. G.Metabolic heterogeneity of activated beige/brite adipocytes in inguinal adipose tissue. Sci. Rep. 7, 39794 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Shinoda, K. et al. Genetic and functional characterization of clonally derived adult human brown adipocytes. Nat. Med. 21, 389–394 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Xue, R. et al. Clonal analyses and gene profiling identify genetic biomarkers of the thermogenic potential of human brown and white preadipocytes. Nat. Med. 21, 760–768 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lee, K. Y. et al. Developmental and functional heterogeneity of white adipocytes within a single fat depot. EMBO J. 38, e99291 (2019).

    PubMed  Google Scholar 

  15. Min, S. Y. et al. Diverse repertoire of human adipocyte subtypes develops from transcriptionally distinct mesenchymal progenitor cells. Proc. Natl Acad. Sci. USA 116, 17970–17979 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Rosenwald, M., Perdikari, A., Rülicke, T. & Wolfrum, C. Bi-directional interconversion of brite and white adipocytes. Nat. Cell Biol. 15, 659–667 (2013).

    Article  CAS  PubMed  Google Scholar 

  17. 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).

    CAS  PubMed  Google Scholar 

  18. 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  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Chen, Y. et al. Thermal stress induces glycolytic beige fat formation via a myogenic state. Nature 565, 180–185 (2019).

    Article  CAS  PubMed  Google Scholar 

  21. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  CAS  PubMed  Google Scholar 

  22. Cao, J. et al. A human cell atlas of fetal gene expression. Science 370, eaba7721 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ngara, M. et al. Exploring parasite heterogeneity using single-cell RNA-seq reveals a gene signature among sexual stage Plasmodium falciparum parasites. Exp. Cell Res. 371, 130–138 (2018).

    Article  CAS  PubMed  Google Scholar 

  24. Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013).

    Article  CAS  PubMed  Google Scholar 

  25. Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zheng, G. X. Y. et al. Haplotyping germline and cancer genomes with high-throughput linked-read sequencing. Nat. Biotechnol. 34, 303–311 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Rajbhandari, P. et al. Single cell analysis reveals immune cell–adipocyte crosstalk regulating the transcription of thermogenic adipocytes. eLife 8, e49501 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  32. Schwalie, P. C. et al. A stromal cell population that inhibits adipogenesis in mammalian fat depots. Nature 559, 103–108 (2018).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Song, A. et al. Low- and high-thermogenic brown adipocyte subpopulations coexist in murine adipose tissue. J. Clin. Invest. 130, 247–257 (2020).

    Article  CAS  PubMed  Google Scholar 

  35. Karlina, R. et al. Identification and characterization of distinct brown adipocyte subtypes in C57BL/6J mice. Life Sci. Alliance 4, e202000924 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Henriques, F. et al. Single-cell RNA profiling reveals adipocyte to macrophage signaling sufficient to enhance thermogenesis. Cell Rep. 32, 107998 (2020).

    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.e20 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ramirez, A. K. et al. Single-cell transcriptional networks in differentiating preadipocytes suggest drivers associated with tissue heterogeneity. Nat. Commun. 11, 2117 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hepler, C. et al. Identification of functionally distinct fibro-inflammatory and adipogenic stromal subpopulations in visceral adipose tissue of adult mice. eLife 7, e39636 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Vijay, J. et al. Single-cell analysis of human adipose tissue identifies depot and disease specific cell types. Nat. Metab. 2, 97–109 (2020).

    Article  PubMed  Google Scholar 

  42. Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Spallanzani, R. G. et al. Distinct immunocyte-promoting and adipocyte-generating stromal components coordinate adipose tissue immune and metabolic tenors. Sci. Immunol. 4, eaaw3658 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Burl, R. B. et al. Deconstructing adipogenesis induced by β3-adrenergic receptor activation with single-cell expression profiling. Cell Metab. 28, 300–309.e4 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Lacar, B. et al. Nuclear RNA-seq of single neurons reveals molecular signatures of activation. Nat. Commun. 7, 11022 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Habib, N. et al. Massively-parallel single nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Habib, N. et al. Div-Seq: single-nucleus RNA-seq reveals dynamics of rare adult newborn neurons. Science 353, 925–928 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Krishnaswami, S. R. et al. Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons. Nat. Protoc. 11, 499–524 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Dos Santos, M. et al. Single-nucleus RNA-seq and FISH identify coordinated transcriptional activity in mammalian myofibers. Nat. Commun. 11, 5102 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Petrany, M. J. et al. Single-nucleus RNA-seq identifies transcriptional heterogeneity in multinucleated skeletal myofibers. Nat. Commun. 11, 6374 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Sárvári, A. K. et al. Plasticity of epididymal adipose tissue in response to diet-induced obesity at single-nucleus resolution. Cell Metab. 33, 437–453.e5 (2020).

    Article  PubMed  CAS  Google Scholar 

  52. Slyper, M. et al. A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumors. Nat. Med. 26, 792–802 (2020). Slyper et al. describe multiple practical workflows for performing single-cell and single-nucleus transcriptome analyses.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Bakken, T. E. et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLoS ONE 13, e0209648 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Grindberg, R. V. et al. RNA-sequencing from single nuclei. Proc. Natl Acad. Sci. USA 110, 19802–19807 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Lake, B. B. et al. A comparative strategy for single-nucleus and single-cell transcriptomes confirms accuracy in predicted cell-type expression from nuclear RNA. Sci. Rep. 7, 6031 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Bagchi, M. et al. Vascular endothelial growth factor is important for brown adipose tissue development and maintenance. FASEB J. 27, 3257–3271 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Drokhlyansky, E. et al. The human and mouse enteric nervous system at single-cell resolution. Cell 182, 1606–1622.e23 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    Article  CAS  PubMed  Google Scholar 

  59. Hagemann-Jensen, M. et al. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat. Biotechnol. 38, 708–714 (2020).

    Article  CAS  PubMed  Google Scholar 

  60. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    Article  CAS  PubMed  Google Scholar 

  61. Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17, 77 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Keren-Shaul, H. et al. MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing. Nat. Protoc. 14, 1841–1862 (2019).

    Article  CAS  PubMed  Google Scholar 

  63. Carninci, P. et al. High-efficiency full-length cDNA cloning by biotinylated CAP trapper. Genomics 37, 327–336 (1996).

    Article  CAS  PubMed  Google Scholar 

  64. Kouno, T. et al. C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution. Nat. Commun. 10, 360 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Murata, M. et al. Detecting expressed genes using CAGE. Methods Mol. Biol. 1164, 67–85 (2014).

    Article  PubMed  CAS  Google Scholar 

  66. Batut, P. & Gingeras, T. R. RAMPAGE: promoter activity profiling by paired-end sequencing of 5′-complete cDNAs. Curr. Protoc. Mol. Biol. 104, 25B.11 (2013).

    Article  Google Scholar 

  67. Policastro, R. A., Raborn, R. T., Brendel, V. P. & Zentner, G. E. Simple and efficient profiling of transcription initiation and transcript levels with STRIPE-seq. Genome Res. 30, 910–923 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Yamashita, R. et al. Genome-wide characterization of transcriptional start sites in humans by integrative transcriptome analysis. Genome Res. 21, 775–789 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Core, L. J. et al. Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers. Nat. Genet. 46, 1311–1320 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Svensson, V. et al. Power analysis of single-cell RNA-sequencing experiments. Nat. Methods 14, 381–387 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Natarajan, K. N. et al. Comparative analysis of sequencing technologies for single-cell transcriptomics. Genome Biol. 20, 70 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Wang, X., He, Y., Zhang, Q., Ren, X. & Zhang, Z. Direct comparative analyses of 10X Genomics Chromium and smart-seq2. Genomics Proteomics Bioinformatics https://doi.org/10.1016/j.gpb.2020.02.005 (2021).

  74. Xi, N. M. & Li, J. J.Benchmarking computational doublet-detection methods for single-cell RNA sequencing data. Cell Syst. 12, 176–194.e6 (2020).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  75. McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337.e4 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291.e9 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. DePasquale, E. A. K. et al. DoubletDecon: deconvoluting doublets from single-cell RNA-sequencing data. Cell Rep. 29, 1718–1727.e8 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Bernstein, N. J. et al. Solo: doublet identification in single-cell RNA-seq via semi-supervised deep learning. Cell Syst. 11, 95–101.e5 (2020).

    Article  CAS  PubMed  Google Scholar 

  79. Goldstein, L. D. et al. Massively parallel nanowell-based single-cell gene expression profiling. BMC Genomics 18, 519 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887.e17 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685–691 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. 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 

  85. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Chazarra-Gil, R., van Dongen, S., Kiselev, V. Y. & Hemberg, M. Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench. Nucleic Acids Res. 49, e42 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Ding, J. & Regev, A. Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces. Nat. Commun. 12, 2554 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Jolliffe, I. T. Principal Component Analysis (Springer, 2002).

  89. Hinton, G. E. & Roweis, S. Stochastic neighbor embedding. Adv. Neural Inf. Process. Syst. 15, 857–864 (2002).

    Google Scholar 

  90. McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2020).

  91. Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Fleming, S. J., Marioni, J. C. & Babadi, M. CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets. Preprint at bioRxiv https://doi.org/10.1101/791699 (2019). Fleming et al. develop a machine-learning algorithm to remove ambient RNA and exclude empty droplets in scRNA-seq datasets.

  93. Spaethling, J. M. et al. Single-cell transcriptomics and functional target validation of brown adipocytes show their complex roles in metabolic homeostasis. FASEB J. 30, 81–92 (2016).

    Article  CAS  PubMed  Google Scholar 

  94. Hagberg, C. E. et al. Flow cytometry of mouse and human adipocytes for the analysis of browning and cellular heterogeneity. Cell Rep. 24, 2746–2756.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Tran, K.-V. et al. The vascular endothelium of the adipose tissue gives rise to both white and brown fat cells. Cell Metab. 15, 222–229 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Cattaneo, P. et al. Parallel lineage-tracing studies establish fibroblasts as the prevailing in vivo adipocyte progenitor. Cell Rep. 30, 571–582.e2 (2020).

    Article  CAS  PubMed  Google Scholar 

  97. Roh, H. C. et al. Warming induces significant reprogramming of beige, but not brown, adipocyte cellular identity. Cell Metab. 27, 1121–1137.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Sun, W. et al. Cold-induced epigenetic programming of the sperm enhances brown adipose tissue activity in the offspring. Nat. Med. 24, 1372–1383 (2018).

    Article  CAS  PubMed  Google Scholar 

  99. Park, J. et al. Progenitor-like characteristics in a subgroup of UCP1+ cells within white adipose tissue. Dev. Cell 56, 985–999.e4 (2021). Park et al. identify that mature brite/beige adipocytes are proliferative and contribute to WAT browning.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Betz, M. J. & Enerbäck, S. Targeting thermogenesis in brown fat and muscle to treat obesity and metabolic disease. Nat. Rev. Endocrinol. 14, 77–87 (2018).

    Article  CAS  PubMed  Google Scholar 

  101. Müller, S. et al. Proteomic analysis of human brown adipose tissue reveals utilization of coupled and uncoupled energy expenditure pathways. Sci. Rep. 6, 30030 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  102. Shamsi, F. et al. Vascular smooth muscle-derived Trpv1+ progenitors are a source of cold-induced thermogenic adipocytes. Nat. Metab. 3, 485–495 (2021). Shamsi et al. describe a novel type of thermogenic adipocyte progenitor cell.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  103. Angueira, A. R. et al. Defining the lineage of thermogenic perivascular adipose tissue. Nat. Metab. 3, 469–484 (2021). Angueira et al. identify a novel type of adipogenic SMC.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  104. 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 

  105. Chabowska-Kita, A. & Kozak, L. P. The critical period for brown adipocyte development: genetic and environmental influences. Obesity 24, 283–290 (2016).

    Article  PubMed  Google Scholar 

  106. Xue, B., Coulter, A., Rim, J. S., Koza, R. A. & Kozak, L. P. Transcriptional synergy and the regulation of Ucp1 during brown adipocyte induction in white fat depots. Mol. Cell Biol. 25, 8311–8322 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. 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 

  108. Berry, R. & Rodeheffer, M. S. Characterization of the adipocyte cellular lineage in vivo. Nat. Cell Biol. 15, 302–308 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Sanchez-Gurmaches, J. & Guertin, D. A. Adipocytes arise from multiple lineages that are heterogeneously and dynamically distributed. Nat. Commun. 5, 4099 (2014).

    Article  CAS  PubMed  Google Scholar 

  112. Sebo, Z. L. & Rodeheffer, M. S.Assembling the adipose organ: adipocyte lineage segregation and adipogenesis in vivo. Development 146, dev172098 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Xia, C., Fan, J., Emanuel, G., Hao, J. & Zhuang, X. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl Acad. Sci. USA 116, 19490–19499 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Stickels, R. R. et al. Sensitive spatial genome wide expression profiling at cellular resolution. Preprint at bioRxiv https://doi.org/10.1101/2020.03.12.989806 (2020).

  116. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    Article  PubMed  CAS  Google Scholar 

  117. Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020).

    Article  CAS  PubMed  Google Scholar 

  119. Zhou, Y. et al. Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease. Nat. Med. 26, 131–142 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Ferrante, A. W. Macrophages, fat, and the emergence of immunometabolism. J. Clin. Invest. 123, 4992–4993 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Fischer, K. et al. Alternatively activated macrophages do not synthesize catecholamines or contribute to adipose tissue adaptive thermogenesis. Nat. Med. 23, 623–630 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Pirzgalska, R. M. et al. Sympathetic neuron-associated macrophages contribute to obesity by importing and metabolizing norepinephrine. Nat. Med. 23, 1309–1318 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Camell, C. D. et al. Inflammasome-driven catecholamine catabolism in macrophages blunts lipolysis during ageing. Nature 550, 119–123 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Hill, D. A. et al. Distinct macrophage populations direct inflammatory versus physiological changes in adipose tissue. Proc. Natl Acad. Sci. USA 115, E5096–E5105 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Winer, S. et al. Normalization of obesity-associated insulin resistance through immunotherapy. Nat. Med. 15, 921–929 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Feuerer, M. et al. Lean, but not obese, fat is enriched for a unique population of regulatory T cells that affect metabolic parameters. Nat. Med. 15, 930–939 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Bapat, S. P. et al. Depletion of fat-resident Treg cells prevents age-associated insulin resistance. Nature 528, 137–141 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Vasanthakumar, A. et al. Sex-specific adipose tissue imprinting of regulatory T cells. Nature 579, 581–585 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. LaMarche, N. M. et al. Distinct iNKT cell populations use IFNγ or ER stress-induced IL-10 to control adipose tissue homeostasis. Cell Metab. 32, 243–258.e6 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Cryer, A., Riley, S. E., Williams, E. R. & Robinson, D. S. Effect of nutritional status on rat adipose tissue, muscle and post-heparin plasma clearing factor lipase activities: their relationship to triglyceride fatty acid uptake by fat-cells and to plasma insulin concentrations. Clin. Sci. Mol. Med. 50, 213–221 (1976).

    CAS  PubMed  Google Scholar 

  132. Davies, B. S. J. et al. GPIHBP1 is responsible for the entry of lipoprotein lipase into capillaries. Cell Metab. 12, 42–52 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Fischer, A. W. et al. Lysosomal lipoprotein processing in endothelial cells stimulates adipose tissue thermogenic adaptation. Cell Metab. 33, 547–564.e7 (2021).

    Article  CAS  PubMed  Google Scholar 

  134. Hagberg, C. E. et al. Vascular endothelial growth factor B controls endothelial fatty acid uptake. Nature 464, 917–921 (2010).

    Article  CAS  PubMed  Google Scholar 

  135. Crewe, C. et al. An endothelial-to-adipocyte extracellular vesicle axis governed by metabolic state. Cell 175, 695–708.e13 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Lamalice, L., Le. Boeuf, F. & Huot, J. Endothelial cell migration during angiogenesis. Circ. Res. 100, 782–794 (2007).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  138. Cao, Y. Angiogenesis and vascular functions in modulation of obesity, adipose metabolism, and insulin sensitivity. Cell Metab. 18, 478–489 (2013).

    Article  CAS  PubMed  Google Scholar 

  139. 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 

  140. Seki, T. et al. Endothelial PDGF-CC regulates angiogenesis-dependent thermogenesis in beige fat. Nat. Commun. 7, 12152 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Bae, C.-R. et al. Overexpression of C-type natriuretic peptide in endothelial cells protects against insulin resistance and inflammation during diet-induced obesity. Sci. Rep. 7, 9807 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank members of the laboratory of C.W. and Ian Mitchell for helpful discussions. Research in the laboratory of C.W. was supported by the Swiss National Science Foundation (grant 185011).

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W.S. and C.W. wrote the main text. S.M. contributed to illustrating the figures. H.D. contributed Fig. 3.

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Correspondence to Christian Wolfrum.

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Peer review information Nature Metabolism thanks James G. Granneman and Patrick Seale for their contribution to the peer review of this work. Primary Handling Editors: Christoph Schmitt; Isabella Samuelson.

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Sun, W., Modica, S., Dong, H. et al. Plasticity and heterogeneity of thermogenic adipose tissue. Nat Metab 3, 751–761 (2021). https://doi.org/10.1038/s42255-021-00417-4

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