Toward Automatic and Comprehensive Glycan Characterization by Online PGC-LC-EED MS/MS

Area of Science:

• Glycomics
• Analytical Chemistry
• Bioinformatics

Background:

• Glycan structural analysis via mass spectrometry (MS) faces challenges due to isomeric structures and complex data interpretation.
• A shortage of bioinformatics tools hinders the widespread adoption of MS methods for glycomics.
• Accurate glycan sequencing requires specialized expertise and laborious data analysis.

Purpose of the Study:

• To develop an automated and comprehensive method for glycan structural characterization using mass spectrometry.
• To improve glycan sequencing algorithms for accurate and rapid analysis of glycan mixtures.
• To overcome the limitations of existing methods in resolving glycan isomers and determining complex structures.

Main Methods:

• Development of an online porous graphitic carbon liquid chromatography (PGC-LC) coupled with electronic excitation dissociation (EED) MS/MS.
• Improvement of the GlycoDeNovo algorithm for automated de novo glycan sequencing from EED MS/MS data.
• Application of the method to analyze N-glycans released from ribonuclease B.

Main Results:

• The PGC-LC-EED MS/MS method demonstrated superior isomer resolving power for glycan mixtures.
• The enhanced GlycoDeNovo algorithm accurately identified glycan topologies and a majority of linkages de novo.
• Analysis of ribonuclease B N-glycans revealed 18 high-mannose structures, including novel isomers, with relative quantification.

Conclusions:

• The integrated PGC-LC-EED MS/MS approach with improved GlycoDeNovo offers automated and comprehensive glycan characterization.
• This method significantly advances the field of glycomics by enabling rapid and accurate analysis of complex glycan structures.
• The developed approach has the potential to broaden the application of MS-based glycomics within and beyond the glycoscience community.