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Gut Analysis Toolbox - automating quantitative analysis of enteric neurons.

Authors :
Sorensen L
Humenick A
Poon SSB
Han MN
Mahdavian NS
Rowe MC
Hamnett R
Gómez-de-Mariscal E
Neckel PH
Saito A
Mutunduwe K
Glennan C
Haase R
McQuade RM
Foong JPP
Brookes SJH
Kaltschmidt JA
Muñoz-Barrutia A
King SK
Veldhuis NA
Carbone SE
Poole DP
Rajasekhar P
Source :
Journal of cell science [J Cell Sci] 2024 Oct 15; Vol. 137 (20). Date of Electronic Publication: 2024 Oct 30.
Publication Year :
2024

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.<br />Competing Interests: Competing interests The authors declare no competing or financial interests.<br /> (© 2024. Published by The Company of Biologists Ltd.)

Details

Language :
English
ISSN :
1477-9137
Volume :
137
Issue :
20
Database :
MEDLINE
Journal :
Journal of cell science
Publication Type :
Academic Journal
Accession number :
39219476
Full Text :
https://doi.org/10.1242/jcs.261950