Maximizing Data Coverage through Eight Sequential Mass Spectrometry Images of a Single Tissue Section

Area of Science:

• Analytical Chemistry
• Biomedical Imaging
• Molecular Pathology

Background:

• Traditional mass spectrometry imaging (MSI) typically analyzes one molecular class per tissue section.
• Using serial sections for multiple analyte classes can introduce biological variability and registration errors.
• There is a need for methods that allow comprehensive molecular profiling from a single tissue section.

Purpose of the Study:

• To develop and present a sequential MSI method for analyzing multiple molecular classes from the same tissue section.
• To enable direct co-registration and comparison of diverse molecular distributions within a single biopsy.
• To overcome limitations associated with serial sectioning in MSI experiments.

Main Methods:

• Sequential acquisition of 8 mass spectrometry images from a single tissue section.
• Analysis of metabolites (positive/negative mode), lipids (positive/negative mode), N-linked glycans, O-linked N-acetylglucosamine, small intact proteins, and tryptic peptides.
• Utilized a washing step between acquisitions to remove residual analytes and enhance subsequent signals.

Main Results:

• Demonstrated the feasibility of collecting multiple, diverse molecular datasets from one tissue section.
• The sequential approach allows for effective analyte removal and signal enhancement.
• Facilitated facile co-registration of multiple datasets for spatial analysis of molecular localization.

Conclusions:

• The presented method allows for comprehensive, multi-class molecular imaging from a single tissue section.
• This approach enhances the accuracy of co- and differential localization studies across various molecular classes.
• It offers a powerful tool for detailed spatial molecular profiling in biomedical research.