Single-cell analysis reveals a subpopulation of adipose progenitor cells that impairs glucose homeostasis.
Hongdong WangYanhua DuShanshan HuangXitai SunYouqiong YeHaixiang SunXuehui ChuXiaodong ShanYue YuanLei ShenYan BiPublished in: Nature communications (2024)
Adipose progenitor cells (APCs) are heterogeneous stromal cells and help to maintain metabolic homeostasis. However, the influence of obesity on human APC heterogeneity and the role of APC subpopulations on regulating glucose homeostasis remain unknown. Here, we find that APCs in human visceral adipose tissue contain four subsets. The composition and functionality of APCs are altered in patients with type 2 diabetes (T2D). CD9 + CD55 low APCs are the subset which is significantly increased in T2D patients. Transplantation of these cells from T2D patients into adipose tissue causes glycemic disturbance. Mechanistically, CD9 + CD55 low APCs promote T2D development through producing bioactive proteins to form a detrimental niche, leading to upregulation of adipocyte lipolysis. Depletion of pathogenic APCs by inducing intracellular diphtheria toxin A expression or using a hunter-killer peptide improves obesity-related glycemic disturbance. Collectively, our data provide deeper insights in human APC functionality and highlights APCs as a potential therapeutic target to combat T2D. All mice utilized in this study are male.
Keyphrases
- adipose tissue
- insulin resistance
- end stage renal disease
- endothelial cells
- type diabetes
- high fat diet induced
- single cell
- ejection fraction
- chronic kidney disease
- newly diagnosed
- high fat diet
- metabolic syndrome
- poor prognosis
- induced pluripotent stem cells
- weight loss
- peritoneal dialysis
- weight gain
- machine learning
- physical activity
- skeletal muscle
- stem cells
- prognostic factors
- signaling pathway
- blood pressure
- electronic health record
- long non coding rna
- cell proliferation
- patient reported outcomes
- big data
- high throughput
- binding protein
- artificial intelligence
- deep learning
- climate change
- drug induced
- wild type