肠道分析工具箱:肠道神经元定量分析自动化

IF 3.3 3区 生物学 Q3 CELL BIOLOGY Journal of cell science Pub Date : 2024-10-15 Epub Date: 2024-10-30 DOI:10.1242/jcs.261950
Luke Sorensen, Adam Humenick, Sabrina S B Poon, Myat Noe Han, Narges S Mahdavian, Matthew C Rowe, Ryan Hamnett, Estibaliz Gómez-de-Mariscal, Peter H Neckel, Ayame Saito, Keith Mutunduwe, Christie Glennan, Robert Haase, Rachel M McQuade, Jaime P P Foong, Simon J H Brookes, Julia A Kaltschmidt, Arrate Muñoz-Barrutia, Sebastian K King, Nicholas A Veldhuis, Simona E Carbone, Daniel P Poole, Pradeep Rajasekhar
{"title":"肠道分析工具箱:肠道神经元定量分析自动化","authors":"Luke Sorensen, Adam Humenick, Sabrina S B Poon, Myat Noe Han, Narges S Mahdavian, Matthew C Rowe, Ryan Hamnett, Estibaliz Gómez-de-Mariscal, Peter H Neckel, Ayame Saito, Keith Mutunduwe, Christie Glennan, Robert Haase, Rachel M McQuade, Jaime P P Foong, Simon J H Brookes, Julia A Kaltschmidt, Arrate Muñoz-Barrutia, Sebastian K King, Nicholas A Veldhuis, Simona E Carbone, Daniel P Poole, Pradeep Rajasekhar","doi":"10.1242/jcs.261950","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":15227,"journal":{"name":"Journal of cell science","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Gut Analysis Toolbox - automating quantitative analysis of enteric neurons.\",\"authors\":\"Luke Sorensen, Adam Humenick, Sabrina S B Poon, Myat Noe Han, Narges S Mahdavian, Matthew C Rowe, Ryan Hamnett, Estibaliz Gómez-de-Mariscal, Peter H Neckel, Ayame Saito, Keith Mutunduwe, Christie Glennan, Robert Haase, Rachel M McQuade, Jaime P P Foong, Simon J H Brookes, Julia A Kaltschmidt, Arrate Muñoz-Barrutia, Sebastian K King, Nicholas A Veldhuis, Simona E Carbone, Daniel P Poole, Pradeep Rajasekhar\",\"doi\":\"10.1242/jcs.261950\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":15227,\"journal\":{\"name\":\"Journal of cell science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of cell science\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1242/jcs.261950\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/30 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of cell science","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1242/jcs.261950","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/30 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

肠道神经系统(ENS)由嵌入胃肠道(GI)壁内的神经元和神经胶质细胞组成。神经元分布和功能的改变与胃肠道功能障碍密切相关。目前评估神经元分布的方法存在取样不足的问题,部分原因是与成像和分析大面积组织相关的挑战,以及手动分析导致的操作员偏差。我们介绍了肠道分析工具箱(GAT),这是一种图像分析工具,设计用于利用消化道整块制备物的二维图像描述肠道神经元及其神经化学编码。该工具箱采用斐济语言开发,具有用户友好的界面,并通过使用 StarDist 开发的基于深度学习(DL)的自定义细胞分割模型和 deepImageJ 中的神经节分割模型提供快速准确的分割。我们使用基于近邻的空间分析,利用公共数据集揭示了肠道各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 提高了吞吐量,可对更大的组织区域、多种神经元标记物和大量样本进行快速、无偏见的分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Gut Analysis Toolbox - automating quantitative analysis of enteric neurons.

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of cell science
Journal of cell science 生物-细胞生物学
CiteScore
7.30
自引率
2.50%
发文量
393
审稿时长
1.4 months
期刊介绍: Journal of Cell Science publishes cutting-edge science, encompassing all aspects of cell biology.
期刊最新文献
Shear stress-stimulated AMPK couples endothelial cell mechanics, metabolism, and vasodilation. Topology surveillance of the lanosterol demethylase CYP51A1 by Signal Peptide Peptidase. Borg5/Cdc42EP1 restricts contractility and motility in epithelial MDCK cells. Decreased DNA density is a better indicator of a nuclear bleb than lamin B loss. A microtubule stability switch alters isolated vascular smooth muscle Ca2+ flux in response to matrix rigidity.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1