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

Min Ma1, Miyang Li2, Yinlong Zhu3,4

  • 1School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin 53705, United States.

Analytical Chemistry
|November 20, 2023
PubMed
Summary
This summary is machine-generated.

A new method called 6-plex isobaric-labeling guide fingerprint embedding (MAGNI) enables sensitive, quantitative analysis of low-abundance glycans. This technique improves glycomics reproducibility and reveals distinct glycosylation patterns in ovarian cancer cells.

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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.