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肠道分析工具包——自动化定量分析肠神经元。

Gut Analysis Toolbox - automating quantitative analysis of enteric neurons.

机构信息

Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC 3052, Australia.

Flinders Health and Medical Research Institute , College of Medicine and Public Health, Flinders University, Bedford Park, SA 5042, Australia.

出版信息

J Cell Sci. 2024 Oct 15;137(20). doi: 10.1242/jcs.261950. Epub 2024 Oct 30.

Abstract

The enteric nervous system (ENS) consists of an extensive network of neurons and glial cells embedded within the wall of the gastrointestinal (GI) tract. Alterations in neuronal distribution and function are strongly associated with GI dysfunction. Current methods for assessing neuronal distribution suffer from undersampling, partly due to challenges associated with imaging and analyzing large tissue areas, and operator bias due to manual analysis. We present the Gut Analysis Toolbox (GAT), an image analysis tool designed for characterization of enteric neurons and their neurochemical coding using two-dimensional images of GI wholemount preparations. GAT is developed in Fiji, has a user-friendly interface, and offers rapid and accurate segmentation via custom deep learning (DL)-based cell segmentation models developed using StarDist, as well as a ganglia segmentation model in deepImageJ. We apply proximal neighbor-based spatial analysis to reveal differences in cellular distribution across gut regions using a public dataset. In summary, GAT provides an easy-to-use toolbox to streamline routine image analysis tasks in ENS research. GAT enhances throughput, allowing rapid unbiased analysis of larger tissue areas, multiple neuronal markers and numerous samples.

摘要

肠神经系统(ENS)由嵌入胃肠道(GI)壁内的神经元和神经胶质细胞广泛网络组成。神经元分布和功能的改变与 GI 功能障碍密切相关。目前评估神经元分布的方法存在采样不足的问题,部分原因是与成像和分析大组织区域相关的挑战,以及由于手动分析导致的操作人员偏差。我们提出了 Gut Analysis Toolbox(GAT),这是一种图像分析工具,用于使用 GI 全层准备的二维图像对肠神经元及其神经化学编码进行特征描述。GAT 是在 Fiji 中开发的,具有用户友好的界面,并通过使用 StarDist 开发的自定义基于深度学习(DL)的细胞分割模型以及 deepImageJ 中的神经节分割模型提供快速准确的分割。我们应用基于最近邻的空间分析来使用公共数据集揭示肠区域细胞分布的差异。总之,GAT 提供了一个易于使用的工具箱,可简化 ENS 研究中的常规图像分析任务。GAT 提高了通量,允许对更大的组织区域、多个神经元标记和多个样本进行快速、无偏的分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bc2/11698042/ab9bef24df68/joces-137-261950-g1.jpg

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