Gut Analysis Toolbox - automating quantitative analysis of enteric neurons.

Area of Science:

• Neuroscience
• Gastroenterology
• Computational Biology

Background:

• The enteric nervous system (ENS) regulates gastrointestinal function, and its alterations are linked to GI disorders.
• Current methods for analyzing ENS neuronal distribution are limited by undersampling and manual analysis bias.
• Efficient tools are needed to analyze large tissue areas and complex neuronal networks within the GI tract.

Purpose of the Study:

• To develop and validate the Gut Analysis Toolbox (GAT), an automated image analysis tool for characterizing enteric neurons.
• To improve the speed, accuracy, and objectivity of ENS research using 2D wholemount preparations.
• To facilitate the analysis of neuronal distribution and neurochemical coding in the GI tract.

Main Methods:

• GAT is developed in Fiji, integrating custom deep learning models (StarDist, deepImageJ) for cell and ganglia segmentation.
• The toolbox utilizes a user-friendly interface for processing 2D images of GI wholemounts.
• Proximal neighbor-based spatial analysis is employed to assess cellular distribution patterns.

Main Results:

• GAT provides rapid and accurate segmentation of enteric neurons and ganglia.
• The tool enables unbiased analysis of larger tissue areas, multiple neuronal markers, and numerous samples.
• Spatial analysis using GAT revealed distinct cellular distribution differences across various gut regions.

Conclusions:

• GAT offers a streamlined, user-friendly solution for routine ENS image analysis.
• The toolbox significantly enhances research throughput and analytical objectivity.
• GAT empowers researchers to conduct more comprehensive studies on ENS structure and function.