Toolbox Accelerating Glycomics (TAG): Improving Large-Scale Serum Glycomics and Refinement to Identify SALSA-Modified...

Area of Science:

• Glycoscience
• Mass Spectrometry
• Bioinformatics

Background:

• Glycans play crucial roles in cellular functions but their structural complexity hinders analysis.
• Existing glycomics informatics tools struggle to keep pace with advanced mass spectrometry techniques.
• Manual glycan list design and sialic acid modification analysis presented limitations in previous methods.

Purpose of the Study:

• To enhance the Toolbox Accelerating Glycomics (TAG) software for large-scale glycomics analysis, particularly for cohort studies.
• To integrate novel glycan structures and modifications, including those identified by the SALSA method, into TAG.
• To implement quantitative capabilities through calibration curve generation for improved glycan analysis.

Main Methods:

• Improved TAG software by redefining residues and glycans to include previously uncharacterized structures.
• Integrated the sialic acid linkage-specific alkylamidation (SALSA) method to differentiate sialic acid linkages by mass.
• Developed a routine for generating calibration curves to enable quantitative glycan analysis.

Main Results:

• Successfully analyzed purchased serum samples and 74 spectra, identifying 81 glycan structures with high linearity (R2 > 0.8).
• Included novel glycan structures and rare glycans, such as those with N,N’-diacetyllactosediamine, in the analysis.
• Demonstrated the software's capability for large-scale glycomics analysis through successful verification.

Conclusions:

• The enhanced TAG software, with SALSA integration and expanded glycan library, is suitable for large-scale glycomics studies.
• The improvements facilitate more comprehensive and accurate analysis of complex glycan profiles in biological samples.
• The developed quantitative features pave the way for robust glycomics cohort analysis and biomarker discovery.