Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons

Sorensen, L.; Humenick, A.; Poon, S. S. B.; Han, M. N.; Mahdavian, N. S.; Hamnett, R.; Gomez-de-Mariscal, E.; Neckel, P. H.; Saito, A.; Mutunduwe, K.; Glennan, C.; Haase, R.; McQuade, R. M.; Foong, J. P. P.; Brookes, S. J. H.; Kaltschmidt, J. A.; Munoz-Barrutia, A.; King, S. K.; Veldhuis, N. A.; Carbone, S. E.; Poole, D. P.; Rajasekhar, P.

doi:10.1101/2024.01.17.576140

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bioRxiv · 10.1101/2024.01.17.576140

Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons

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Abstract

The enteric nervous system (ENS) plays an important role in coordinating gut function. The ENS consists of an extensive network of neurons and glial cells within the wall of the gastrointestinal tract. Alterations in neuronal distribution, function, and type are strongly associated with enteric neuropathies and gastrointestinal (GI) dysfunction and can serve as biomarkers for disease. However, current methods for assessing neuronal counts and distribution suffer from undersampling. This is partly due to challenges associated with imaging and analyzing large tissue areas, and operator bias due to manual analysis. Here, we present the Gut Analysis Toolbox (GAT), an image analysis tool designed for characterization of enteric neurons and their neurochemical coding using 2D images of GI wholemount preparations. GAT is developed for the Fiji distribution of ImageJ. It has a user-friendly interface and offers rapid and accurate cell segmentation. Custom deep learning (DL) based cell segmentation models were developed using StarDist. GAT also includes a ganglion segmentation model which was developed using deepImageJ. In addition, GAT allows importing of segmentation generated by other software. DL models have been trained using ZeroCostDL4Mic on diverse datasets sourced from different laboratories. This captures the variability associated with differences in animal species, image acquisition parameters, and sample preparation across research groups. We demonstrate the robustness of the cell segmentation DL models by comparing them against the state-of-the-art cell segmentation software, Cellpose. To quantify neuronal distribution GAT applies proximal neighbor-based spatial analysis. We demonstrate how the proximal neighbor analysis can reveal differences in cellular distribution across gut regions using a published dataset. In summary, GAT provides an easy-to-use toolbox to streamline routine image analysis tasks in ENS research. GAT enhances throughput allowing unbiased analysis of larger tissue areas, multiple neuronal markers and numerous samples rapidly.

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Sorensen, L., Humenick, A., Poon, S. S. B., Han, M. N., Mahdavian, N. S., Hamnett, R., Gomez-de-Mariscal, E., Neckel, P. H., Saito, A., Mutunduwe, K., Glennan, C., Haase, R., McQuade, R. M., Foong, J. P. P., Brookes, S. J. H., Kaltschmidt, J. A., Munoz-Barrutia, A., King, S. K., Veldhuis, N. A., Carbone, S. E., Poole, D. P., Rajasekhar, P.. 2024-01-20. Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. https://doi.org/10.1101/2024.01.17.576140

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