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}
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.