Verschueren van Rees Nicolás, Ashwin Peter, McMullan Conor, Krogvold Lars, Dahl-Jørgensen Knut, Morgan Noel G, Leete Pia, Wedgwood Kyle C A
Department of Mathematics and Statistics, University of Exeter, Exeter, UK.
EPSRC Hub for Quantitative Modelling in Healthcare, University of Exeter, Exeter, UK.
Diabetologia. 2025 May;68(5):1031-1043. doi: 10.1007/s00125-025-06376-9. Epub 2025 Feb 26.
AIMS/HYPOTHESIS: The organisation and cellular architecture of islets of Langerhans are critical to the physiological regulation of hormone secretion but it is debated whether human islets adhere to the characteristic mantle-core (M-C) structure seen in rodents. It is also unclear whether inherent architectural changes contribute to islet dysfunction in type 1 diabetes, aside from the loss of beta cells. Therefore, we have exploited advances in immunostaining, spatial biology and machine learning to undertake a detailed, systematic analysis of adult human islet architecture in health and type 1 diabetes, by a quantitative analysis of a dataset of >250,000 endocrine cells in >3500 islets from ten individuals.
Formalin-fixed paraffin-embedded pancreatic sections (4 μm) from organ donors without diabetes and living donors with recent-onset type 1 diabetes were stained for all five islet hormones and imaged prior to analysis, which employed a novel automated pipeline using QuPath software, capable of running on a standard laptop. Whole-slide image analysis involved segmentation classifiers, cell detection and phenotyping algorithms to identify islets, specific cell types and their locations as (x,y)-coordinates in regions of interest. Each endocrine cell was categorised into binary variables for cell type (i.e. beta or non-beta) and position (mantle or core). A χ test for independence of these properties was performed and the OR was considered to estimate the effect size of the potential association between position and cell type. A quantification of the M-C structure at islet level was performed by computing the probability, r, that the observed number of non-beta cells in the mantle is due to a random arrangement. The distribution of the r values for the islets in the study was contrasted against the r values of a digital population of equivalent randomly arranged islets, termed digital siblings. Both distributions of r values were compared using the earth mover's distance (EMD), a mathematical tool employed to describe differences in distribution patterns. The EMD was also used to contrast the distribution of islet size and beta cell fraction between type 1 diabetes and control islets.
The χ test supports the existence of a significant (p<0.001) relationship between cell position and type. The effect size was measured via the OR <0.8, showing that non-beta cells are more likely to be found at the mantle (and vice versa). At the islet level, the EMD between the distributions of r values of the observed islets and the digital siblings was emd-1d=0.10951 (0<emd-1d<1). The transport plan showed a substantial group of islets with a small r value, thus supporting the M-C hypothesis. The bidimensional distribution (beta cell fraction vs size) of islets showed a distance emd-2d=0.285 (0<emd-2d<2) between the control and type 1 diabetes islets. The suffixes '-1d' and '-2d' are used to distinguish the comparison between the distribution of one and two variables.
CONCLUSIONS/INTERPRETATION: Using a novel analysis pipeline, statistical evidence supports the existence of an M-C structure in human adult islets, irrespective of type 1 diabetes status. The methods presented in the current study offer potential applications in spatial biology, islet immunopathology, transplantation and organoid research, and developmental research.
目的/假设:胰岛的组织结构和细胞结构对于激素分泌的生理调节至关重要,但人类胰岛是否遵循在啮齿动物中所见的特征性被膜-核心(M-C)结构仍存在争议。除了β细胞的丧失外,内在的结构变化是否导致1型糖尿病中的胰岛功能障碍也尚不清楚。因此,我们利用免疫染色、空间生物学和机器学习的进展,通过对来自10名个体的3500多个胰岛中超过250,000个内分泌细胞的数据集进行定量分析,对健康和1型糖尿病状态下的成人人类胰岛结构进行了详细、系统的分析。
对来自非糖尿病器官供体和近期发病的1型糖尿病活体供体的福尔马林固定石蜡包埋胰腺切片(4μm)进行所有五种胰岛激素染色,并在分析前成像,分析采用了一种使用QuPath软件的新型自动化流程,该流程能够在标准笔记本电脑上运行。全玻片图像分析涉及分割分类器、细胞检测和表型算法,以识别胰岛、特定细胞类型及其在感兴趣区域中的位置(x,y坐标)。每个内分泌细胞被分类为细胞类型(即β细胞或非β细胞)和位置(被膜或核心)的二元变量。对这些属性的独立性进行χ检验,并考虑OR来估计位置与细胞类型之间潜在关联的效应大小。通过计算被膜中非β细胞的观察数量是由于随机排列的概率r,对胰岛水平的M-C结构进行量化。将研究中胰岛的r值分布与等效随机排列胰岛的数字群体(称为数字同胞)的r值进行对比。使用推土机距离(EMD)比较r值的两种分布,EMD是一种用于描述分布模式差异的数学工具。EMD还用于对比1型糖尿病胰岛和对照胰岛之间的胰岛大小和β细胞分数分布。
χ检验支持细胞位置与类型之间存在显著关系(p<0.001)。通过OR<0.8测量效应大小,表明非β细胞更可能出现在被膜中(反之亦然)。在胰岛水平,观察到的胰岛与数字同胞的r值分布之间的EMD为emd-1d = 0.10951(0<emd-1d<1)。运输计划显示有大量r值较小的胰岛,从而支持了M-C假说。胰岛的二维分布(β细胞分数与大小)显示对照胰岛和1型糖尿病胰岛之间的距离为emd-2d = 0.285(0<emd-2d<2)。后缀“-1d”和“-2d”用于区分一个变量和两个变量分布之间的比较。
结论/解读:使用新型分析流程,统计证据支持成人人类胰岛中存在M-C结构,无论1型糖尿病状态如何。本研究中提出的方法在空间生物学、胰岛免疫病理学、移植和类器官研究以及发育研究中具有潜在应用。