ArticlebioRxiv : the preprint server for biology2026
Quantifying the Localization of Histological Staining Markers within the GI Epithelial Unit Axis: A Gastrointestinal Spatial Pathology Plugin for ImageJ.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Histological analysis is crucial for understanding gastrointestinal (GI) tract homeostasis and disease pathophysiology. Various histological stains are commonly used in research settings for assessing development, disease pathogenesis, and therapeutic impacts. Specifically in the ordered architecture of the GI epithelium, current semi-quantitative analysis of histological staining relies heavily on manual scoring rubrics and often lacks robust spatial assessment. To address this gap, we developed an open-source ImageJ plugin, building on a closed-source predecessor, aimed at analyzing the spatial localization pattern of user-defined points-of-interest, such as positively stained cells, along the GI epithelial units. The plugin, developed using ImageJ 1.53.0 and Java programming language in Eclipse, interfaces with ImageJ and leverages Java libraries for data processing. The workflow involves uploading a microscopy image of the GI tissue of interest, determining base and top orientation landmarks of the GI epithelial units through manual identification, and annotating point-of-interest coordinates using ImageJ. The output includes centroid coordinates for each point-of-interest, the absolute distances of the points from the base and top landmarks, and the normalized distances of the points relative to the total height of the GI unit. The plugin generates histograms for displaying average point-of-interest distances along the GI unit axis. This information facilitates quantification of total points-of interest counts, analysis of height localization within the GI unit, and determination of average GI unit heights. The plugin serves as a crucial tool for robustly assessing various biological mechanisms within the GI tract, including EdU localization, migration distances, changes in cell type localization, and identification of new expression patterns along the GI unit axis. Overall, this open-source ImageJ plugin provides a semi-automated, user-friendly solution for leveraging important insights into spatial localization of tissue histology expression patterns within the GI structured architecture, with streamlined post-processing pipelines for robust large-scale analysis.
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