TY - JOUR
T1 - Gut Analysis Toolbox–automating quantitative analysis of enteric neurons
AU - Sorensen, Luke
AU - Humenick, Adam
AU - Poon, Sabrina S.B.
AU - Han, Myat Noe
AU - Mahdavian, Narges S.
AU - Rowe, Matthew C.
AU - Hamnett, Ryan
AU - Gómez-De-Mariscal, Estibaliz
AU - Neckel, Peter H.
AU - Saito, Ayame
AU - Mutunduwe, Keith
AU - Glennan, Christie
AU - Haase, Robert
AU - McQuade, Rachel M.
AU - Foong, Jaime P.P.
AU - Brookes, Simon J.H.
AU - Kaltschmidt, Julia A.
AU - Muñoz-Barrutia, Arrate
AU - King, Sebastian K.
AU - Veldhuis, Nicholas A.
AU - Carbone, Simona E.
AU - Poole, Daniel P.
AU - Rajasekhar, Pradeep
PY - 2024/10
Y1 - 2024/10
N2 - 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.
AB - 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.
KW - Enteric nervous system
KW - Fiji
KW - Gut analysis toolbox
KW - Image analysis
KW - Machine learning
KW - Spatial analysis
UR - http://www.scopus.com/inward/record.url?scp=85208128396&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/ARC/DE200100825
U2 - 10.1242/jcs.261950
DO - 10.1242/jcs.261950
M3 - Article
C2 - 39219476
AN - SCOPUS:85208128396
SN - 0021-9533
VL - 137
JO - Journal of Cell Science
JF - Journal of Cell Science
IS - 20
M1 - jcs261950
ER -