Image Tracing of Inflammatory Intestinal Organoids via Computational Clearing.

Area of Science:

• Biomedical Imaging
• Cell Biology
• Microscopy

Background:

• Widefield (WF) fluorescence microscopy suffers from out-of-focus haze and autofluorescence.
• Computational clearing (CC) enhances WF microscopy, producing semi-confocal images for better analysis.
• Mouse intestinal organoids (mIOs) are valuable models for studying intestinal biology and disease.

Purpose of the Study:

• To develop an "image tracing" workflow using CC and WF microscopy for analyzing inflammatory mIOs.
• To quantify epithelial disruption and identify inflammatory phenotypes in mIOs.
• To correlate microscopy findings with transcriptomic data.

Main Methods:

• Generating mIOs from Lgr5-EGFP stem cell reporter mice and inducing inflammation with dextran sulfate sodium (DSS).
• Applying CC to WF fluorescence images to enhance signal-to-noise ratio and enable segmentation.
• Utilizing a differential signal (CC - WF) and 3D morphometric plots (area-perimeter-circularity) for analysis.
• Performing RNA-sequencing (RNA-seq) on DSS-treated mIOs.

Main Results:

• CC processing improved the visualization of F-actin and EGFP signals in mIOs.
• The CC-WF workflow effectively captured DSS-induced epithelial disruption, mimicking a leaky-epithelium phenotype.
• RNA-seq confirmed upregulation of inflammatory pathways (TNF-α, IL-6/JAK/STAT3) in DSS-treated mIOs.
• ROC analysis demonstrated high accuracy (AUC=0.95) for the CC-WF workflow in distinguishing intact from injured mIOs.

Conclusions:

• The proposed "image tracing" workflow enhances CC and WF microscopy for sensitive detection of epithelial damage in mIOs.
• This method provides quantitative morphometric data that correlates with inflammatory responses.
• The workflow shows promise for image-based phenotyping and disease modeling in intestinal organoids.