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Automatic Detection of Large Dense-Core Vesicles in Secretory Cells and Statistical Analysis of Their Intracellular Distribution

机译:分泌细胞中大密度核囊泡的自动检测及其细胞内分布的统计分析

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Analyzing the morphological appearance and the spatial distribution of large dense-core vesicles (granules) in the cell cytoplasm is central to the understanding of regulated exocytosis. This paper is concerned with the automatic detection of granules and the statistical analysis of their spatial locations in different cell groups. We model the locations of granules of a given cell as a realization of a finite spatial point process and the point patterns associated with the cell groups as replicated point patterns of different spatial point processes. First, an algorithm to segment the granules using electron microscopy images is proposed. Second, the relative locations of the granules with respect to the plasma membrane are characterized by two functional descriptors: the empirical cumulative distribution function of the distances from the granules to the plasma membrane and the density of granules within a given distance to the plasma membrane. The descriptors of the different cells for each group are compared using bootstrap procedures. Our results show that these descriptors and the testing procedure allow discriminating between control and treated cells. The application of these novel tools to studies of secretion should help in the analysis of diseases associated with dysfunctional secretion, such as diabetes.
机译:分析细胞胞质中大的密实小囊泡(颗粒)的形态学外观和空间分布,对于了解受调控的胞吐作用至关重要。本文涉及颗粒的自动检测以及不同细胞组中颗粒空间位置的统计分析。我们将给定细胞的颗粒位置建模为有限空间点过程的实现,并将与细胞组关联的点模式建模为不同空间点过程的复制点模式。首先,提出了一种使用电子显微镜图像分割颗粒的算法。其次,颗粒相对于质膜的相对位置由两个功能描述子表征:从颗粒到质膜的距离的经验累积分布函数以及在给定距离内质膜的颗粒密度。使用引导程序比较每个组的不同单元的描述符。我们的结果表明,这些描述符和测试程序可以区分对照细胞和处理过的细胞。这些新颖的工具在分泌研究中的应用应有助于分析与分泌功能障碍有关的疾病,例如糖尿病。

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