6-Plex mdSUGAR Isobaric-Labeling Guide Fingerprint Embedding for Glycomics Analysis.

Area of Science:

• Glycobiology and Mass Spectrometry
• Biomolecular analysis and characterization
• Cancer biomarker discovery

Background:

• Glycans are crucial biomolecules involved in numerous biological processes.
• Mass spectrometry (MS) is a primary tool for glycomics, but traditional methods limit the detection of low-abundance glycans.
• Data-dependent acquisition in MS reduces reproducibility and misses low-abundance glycan species.

Purpose of the Study:

• To develop a novel method for multiplexed, quantitative, and targeted glycan analysis.
• To overcome the limitations of traditional MS methods in detecting low-abundance glycans.
• To enable sensitive characterization of N-glycans and identify differential glycosylation patterns.

Main Methods:

• Introduction of 6-plex isobaric-labeling guide fingerprint embedding (MAGNI) for glycan analysis.
• Utilizing a triplicate-labeling strategy with a 6-plex mdSUGAR tag to embed glycan peak signatures.
• Employing ultrahigh-resolution MS and a custom software tool (MAGNIFinder) to recognize low-abundance glycans via mass fingerprints in MS1 spectra.

Main Results:

• MAGNI demonstrated accuracy and precision in quantitative analysis of standard glycoproteins.
• Identification of 304 N-glycans in two ovarian cancer cell lines, with 65 differentially expressed N-glycans.
• Quantification of 31 N-glycans in as few as 1 × 10^3 cells, highlighting the method's high sensitivity.

Conclusions:

• The MAGNI method provides a valuable tool for characterizing low-abundance N-glycans with high sensitivity.
• MAGNI enables the determination of small quantitative differences in N-glycan profiling, advancing glycobiology research.
• This approach enhances understanding of glycosylation patterns and their role in biological processes, including cancer.