Q-VAT: Quantitative Vascular Analysis Tool.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Microscopy Image Analysis

Background:

• Increasing imaging capabilities necessitate advanced tools for quantitative analysis of biological samples.
• Manual quantification of vascular networks in large tissue sections is labor-intensive and prone to limitations.
• Distinguishing between macrovasculature and microvasculature is crucial for comprehensive biological studies.

Purpose of the Study:

• To introduce Quantitative Vascular Analysis Tool (Q-VAT), an open-source software for automated analysis of vascular networks in large tissue sections.
• To enable separate quantification of macro- and microvasculature based on vessel diameter.
• To demonstrate the software's utility in analyzing multi-stained slides and various tissue types.

Main Methods:

• Development of Q-VAT as an open-source plugin for Fiji (ImageJ).
• Implementation of a tile-wise analysis approach for handling large 2D images on standard laboratory computers.
• Adaptation for analyzing double or triple-stained slides to quantify staining overlap in vessels.

Main Results:

• Q-VAT performs automated analysis and quantification of vascular networks in whole tissue sections.
• The software successfully separates and quantifies macro- and microvasculature.
• Demonstrated versatility by applying Q-VAT to various mouse tissues with different staining protocols.

Conclusions:

• Q-VAT provides an efficient and automated solution for quantitative vascular analysis of large tissue sections.
• The tool overcomes limitations of manual quantification and enables detailed morphological read-outs.
• Q-VAT is a valuable resource for researchers studying vascular biology across different tissues and experimental conditions.