Apr 26, 2024
Version 2
A protocol for computerized quantitative analysis of nerve fibers, mast cells, enteric glial cells and the proximity of mast cells to the nerve fibers in 3D Images of human sigmoid mucosal biopsies V.2
- Tao Li1,
- Pu-Qing Yuan1,
- Yvette Taché1
- 1University of California at Los Angeles (UCLA)
- Pu-Qing Yuan: Corresponding author
Protocol Citation: Tao Li, Pu-Qing Yuan, Yvette Taché 2024. A protocol for computerized quantitative analysis of nerve fibers, mast cells, enteric glial cells and the proximity of mast cells to the nerve fibers in 3D Images of human sigmoid mucosal biopsies. protocols.io https://dx.doi.org/10.17504/protocols.io.rm7vzj3qrlx1/v2Version created by Pu-Qing Yuan
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: April 26, 2024
Last Modified: April 26, 2024
Protocol Integer ID: 98876
Keywords: 3D images, quantitative analysis, nerve fibers, mast cells, human sigmoid biopsy, Imaris
Funders Acknowledgement:
NIH/SPARC
Grant ID: 1OT2OD024899-01
Abstract
This protocol describes a step-by-step computational workflow that we developed by adapting Imaris 9.7-9.9 Surfaces Rendering Technology (https://imaris.oxinst.com/products/imaris-for-neuroscientists) to perform the quantitative analysis of nerve fibers, enteric glial and immune cells in 3D images of human sigmoid mucosal biopsies, as well as to assess the proximity of mast cells to the nerve fibers which cannot be easily portrayed and precisely measured with 2D images. The volumes of surface-masked nerve fibers and enteric glial cells, the numbers of spotted mast cells and the shortest distances of the centers of each individual spot to the surfaces of nerve fibers in 3D images were automatically computed and plotted correspondently with Imaris 9.7-9.9. This computerized protocol not only reduces the biases due to observer/examiner judgment and overcomes limitations of 2D images, but also much faster than measuring manually and allows us to quantitate a larger number of samples, increasing statistical accuracy. The created parameters also provide efficient reference for us to apply the New AI Machine Learning Segmentation implemented into Imaris 10.1 recently for our quantitative analysis of large datasets. This protocol has been using in out study relevant to the underlying peripheral mechanisms of visceral pain in irritable bowel syndrome.
Attachments
protocol.docx
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