4.8 Article

Single-cell analysis of human adipose tissue identifies depot- and disease-specific cell types

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NATURE METABOLISM
卷 2, 期 1, 页码 97-+

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NATURE RESEARCH
DOI: 10.1038/s42255-019-0152-6

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资金

  1. Canadian Institute of Health Research (CIHR) [EGM141898, EP1-120608]
  2. Canada Research Chair in Genomics Applied to Nutrition and Metabolic Health (tier 1)
  3. Wellcome Trust [081917/Z/07/Z]
  4. Wellcome Trust Centre for Human Genetics [090532]
  5. European Community [FP7/2007-2013]
  6. National Institute for Health Research (NIHR)
  7. King's College London
  8. Common Fund of the Office of the Director of the NIH
  9. National Cancer Institute (NCI)
  10. National Human Genome Research Institute (NHGRI)
  11. National Heart, Lung and Blood Institute (NHLBI)
  12. National Institute of Drug Abuse (NIDA)
  13. National Institute of Mental Health (NIMH)
  14. National Institue of Neurological Disorders and Stroke (NINDS)

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The complex relationship between metabolic disease risk and body fat distribution in humans involves cellular characteristics that are specific to body fat compartments. Here we show depot-specific differences in the stromal vascular fraction of visceral and subcutaneous adipose tissue by performing single-cell RNA sequencing of tissue specimens from obese individuals. We characterize multiple immune cells, endothelial cells, fibroblasts, adipose and haematopoietic stem cell progenitors. Subpopulations of adipose-resident immune cells are metabolically active and associated with metabolic disease status, including a population of potential dysfunctional CD8(+) T cells that express metallothioneins. We identify multiple types of adipocyte progenitors that are common across depots, including a subtype enriched in individuals with type 2 diabetes. Depot-specific analysis reveals a class of adipocyte progenitors unique to visceral adipose tissue, which shares common features with beige pre-adipocytes. Our human single-cell transcriptome atlas across fat depots provides a resource to dissect the functional genomics of metabolic disease.

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