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Single-cell RNA sequencing combined with single-cell proteomics identifies the metabolic adaptation of islet cell subpopulations to high-fat diet in mice
Diabetologia ( IF 8.2 ) Pub Date : 2022-12-20 , DOI: 10.1007/s00125-022-05849-5
Qi Fu 1 , Hemin Jiang 1 , Yu Qian 1 , Hui Lv 1 , Hao Dai 1 , Yuncai Zhou 1 , Yang Chen 1 , Yunqiang He 1 , Rui Gao 1 , Shuai Zheng 1 , Yucheng Liang 1 , Siqi Li 2, 3 , Xinyu Xu 1 , Kuanfeng Xu 1 , Tao Yang 1
Affiliation  

Aims/hypothesis

Islets have complex heterogeneity and subpopulations. Cell surface markers representing alpha, beta and delta cell subpopulations are urgently needed for investigations to explore the compositional changes of each subpopulation in obesity progress and diabetes onset, and the adaptation mechanism of islet metabolism induced by a high-fat diet (HFD).

Methods

Single-cell RNA sequencing (scRNA-seq) was applied to identify alpha, beta and delta cell subpopulation markers in an HFD-induced mouse model of glucose intolerance. Flow cytometry and immunostaining were used to sort and assess the proportion of each subpopulation. Single-cell proteomics was performed on sorted cells, and the functional status of each alpha, beta and delta cell subpopulation in glucose intolerance was deeply elucidated based on protein expression.

Results

A total of 33,999 cells were analysed by scRNA-seq and clustered into eight populations, including alpha, beta and delta cells. For alpha cells, scRNA-seq revealed that the Ace2low subpopulation had downregulated expression of genes related to alpha cell function and upregulated expression of genes associated with beta cell characteristics in comparison with the Ace2high subpopulation. The impaired function and increased fragility of ACE2low alpha cells exposure to HFD was further suggested by single-cell proteomics. As for beta cells, the CD81high subpopulation may indicate an immature signature of beta cells compared with the CD81low subpopulation, which had robust function. We also found differential expression of Slc2a2 in delta cells and a potentially stronger cellular function and metabolism in GLUT2low delta cells than GLUT2high delta cells. Moreover, an increased proportion of ACE2low alpha cells and CD81low beta cells, with a constant proportion of GLUT2low delta cells, were observed in HFD-induced glucose intolerance.

Conclusions/interpretation

We identified ACE2, CD81 and GLUT2 as surface markers to distinguish, respectively, alpha, beta and delta cell subpopulations with heterogeneous maturation and function. The changes in the proportion and functional status of islet endocrine subpopulations reflect the metabolic adaptation of islets to high-fat stress, which weakened the function of alpha cells and enhanced the function of beta and delta cells to bring about glycaemic homeostasis. Our findings provide a fundamental resource for exploring the mechanisms maintaining each islet endocrine subpopulation’s fate and function in health and disease.

Data availability

The scRNA-seq analysis datasets from the current study are available in the Gene Expression Omnibus (GEO) repository under the accession number GSE203376.

Graphical abstract



中文翻译:

单细胞 RNA 测序结合单细胞蛋白质组学鉴定小鼠胰岛细胞亚群对高脂饮食的代谢适应

目标/假设

胰岛具有复杂的异质性和亚群。迫切需要代表 α、β 和 δ 细胞亚群的细胞表面标志物,以探索每个亚群在肥胖进展和糖尿病发病过程中的组成变化,以及高脂饮食 (HFD) 诱导的胰岛代谢适应机制。

方法

应用单细胞 RNA 测序 (scRNA-seq) 来鉴定 HFD 诱导的葡萄糖不耐受小鼠模型中的 alpha、beta 和 delta 细胞亚群标记。流式细胞术和免疫染色用于分类和评估每个亚群的比例。对分选的细胞进行单细胞蛋白质组学分析,并根据蛋白质表达深入阐明葡萄糖不耐受中每个 α、β 和 δ 细胞亚群的功能状态。

结果

通过 scRNA-seq 分析了总共 33,999 个细胞,并将其分为八个群体,包括 alpha、beta 和 delta 细胞。对于 α 细胞,scRNA-seq 显示与Ace2高亚群相比, Ace2亚群下调了与 α 细胞功能相关的基因表达,并上调了与 β 细胞特征相关的基因表达。单细胞蛋白质组学进一步表明,暴露于 HFD的 ACE2低α 细胞功能受损和脆弱性增加。对于 β 细胞,与 CD81亚群相比, CD81高亚群可能表明 β 细胞的特征不成熟亚群,具有强大的功能。我们还发现Slc2a2在 delta 细胞中的差异表达,以及 GLUT2delta 细胞比 GLUT2delta 细胞可能更强的细胞功能和代谢。此外,在 HFD 诱导的葡萄糖不耐受中观察到ACE2α 细胞和 CD81β 细胞比例增加,而 GLUT2δ 细胞比例恒定。

结论/解释

我们将 ACE2、CD81 和 GLUT2 鉴定为表面标记,以分别区分具有异质成熟和功能的 alpha、beta 和 delta 细胞亚群。胰岛内分泌亚群比例和功能状态的变化反映了胰岛对高脂应激的代谢适应,减弱了α细胞的功能,增强了β和δ细胞的功能,从而实现血糖稳态。我们的研究结果为探索维持每个胰岛内分泌亚群在健康和疾病中的命运和功能的机制提供了基础资源。

数据可用性

当前研究的 scRNA-seq 分析数据集可在 Gene Expression Omnibus (GEO) 存储库中获得,登录号为 GSE203376。

图形概要

更新日期:2022-12-21
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