Gut Analysis Toolbox (GAT)

Gut Analysis Toolbox (GAT) automates quantitative analysis of enteric neurons and their neurochemical coding from two-dimensional images of gastrointestinal wholemount preparations.

Key Features:

• Deep learning-based cell segmentation: Custom segmentation models developed using StarDist enable rapid and accurate cell detection.
• Ganglia segmentation: Ganglia segmentation model implemented in deepImageJ.
• Spatial analysis: Proximal neighbor-based spatial analysis reveals differences in cellular distribution across gut regions.
• Multi-marker analysis: Supports analysis of multiple neuronal markers and numerous samples.
• Large tissue area processing: Enables analysis of larger tissue areas with reduced undersampling.

Scientific Applications:

• Enteric nervous system research: Characterization of neuronal distribution and neurochemical coding in gastrointestinal tissue.
• GI dysfunction studies: Assessment of alterations in neuronal distribution and function associated with gastrointestinal dysfunction.
• Regional comparison: Analysis of cellular distribution differences across gut regions using public datasets.

Methodology:

GAT is developed in Fiji and uses custom deep learning-based cell segmentation models developed using StarDist, a ganglia segmentation model in deepImageJ, and proximal neighbor-based spatial analysis.