MassExplorer: a computational tool for analyzing desorption electrospray ionization mass spectrometry data

Area of Science:

• Biochemistry
• Computational Biology
• Analytical Chemistry

Background:

• Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) enables simultaneous detection of thousands of metabolites and lipids.
• Analyzing complex DESI-MSI data requires robust computational and statistical methods for accurate interpretation.

Purpose of the Study:

• To introduce MassExplorer, a novel computational tool designed for the analysis of DESI-MSI data.
• To provide a flexible platform for pre-processing, visualization, and statistical analysis of mass spectrometry imaging data.

Main Methods:

• MassExplorer utilizes statistical lasso for predictive modeling and selection of significant molecular features.
• The tool offers modules for raw data visualization, normalization, and differential metabolite analysis.
• It supports both online (R-Shiny) and offline (Mac OS X) applications.

Main Results:

• MassExplorer facilitates the identification of statistically significant metabolites differentially expressed between sample groups.
• The tool enables visualization of DESI-MSI data structures and interpretation of selected metabolites.
• It provides a streamlined workflow for analyzing complex mass spectrometry datasets.

Conclusions:

• MassExplorer is a versatile and user-friendly tool for chemists, biologists, and statisticians.
• The application is expected to be highly valuable for diverse biological and chemical applications in mass spectrometry.
• It simplifies the complex process of identifying significant molecular changes in DESI-MSI data.