Contrast optimization of mass spectrometry imaging (MSI) data visualization by threshold intensity quantization...

Area of Science:

• Analytical Chemistry
• Biotechnology
• Data Science

Background:

• Mass spectrometry imaging (MSI) reveals chemical composition on surfaces.
• Identifying "regions of interest" (ROI) is crucial for biological MSI analysis.
• High background noise and artifacts often hinder ROI discovery, especially in ambient ionization MSI.

Purpose of the Study:

• To develop a novel algorithm, Threshold Intensity Quantization (TrIQ), to enhance contrast in MSI data visualizations.
• To improve the efficiency and accuracy of identifying biologically relevant regions in MSI datasets.
• To make MSI datasets more comparable by adjusting the dynamic signal intensity range.

Main Methods:

• Implementation of the Threshold Intensity Quantization (TrIQ) algorithm.
• Development of an R script for post-processing MSI data in the imzML format.
• Integration of TrIQ into the open-source imaging software RmsiGUI.

Main Results:

• The TrIQ algorithm effectively reduces the impact of extreme values and rescales mass signal dynamic ranges.
• Demonstrated universal applicability of TrIQ for improving contrast across diverse biological MSI datasets.
• TrIQ significantly enhances subsequent ROI detection through sectioning.

Conclusions:

• TrIQ is a valuable tool for improving MSI data visualization and analysis.
• The algorithm facilitates more reliable identification of biologically significant areas.
• TrIQ contributes to the standardization and comparability of MSI data.