Signal preprocessing, multivariate analysis and software tools for MA(LDI)-TOF mass spectrometry imaging for...

Area of Science:

• Analytical Chemistry
• Biotechnology
• Medical Imaging

Background:

• Mass spectrometry imaging (MSI) is a label-free technique for molecular characterization of biological samples.
• Advancements in instrumentation have established MSI as a key tool in clinical research.
• Current MSI studies often focus on targeted analysis or visualization, not fully leveraging its potential for untargeted biomarker discovery and tissue segmentation.

Purpose of the Study:

• To provide an overview of data processing steps and tools for mass spectrometry imaging (MSI) experiments.
• To guide researchers in improving computational strategies for MSI data analysis.
• To highlight advanced techniques for MSI image visualization and segmentation.

Main Methods:

• Review of existing literature on MSI data processing.
• Discussion of computational strategies for handling large MSI datasets.
• Exploration of preprocessing, visualization, and segmentation tools for MSI data.

Main Results:

• MSI data processing presents challenges due to large data volumes and pixel-to-pixel variations.
• A comprehensive overview of data processing workflows, from raw data preprocessing to advanced segmentation, is presented.
• Various computational tools and strategies are available to address MSI data challenges.

Conclusions:

• Effective data processing is critical for unlocking the full potential of MSI.
• This review equips researchers with knowledge of current MSI data processing tools and strategies.
• Implementing advanced computational approaches can enhance tissue classification and biomarker identification using MSI.