Twoplex 12/13 C6 aniline stable isotope and linkage-specific sialic acid labeling 2D-LC-MS workflow for quantitative N-glycomics

Quantitative glycomics represents an actively expanding research field ranging from the discovery of disease-associated glycan alterations to the quantitative characterization of N-glycans on therapeutic proteins. Commonly used analytical platforms for comparative relative quantitation of complex gl...

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Veröffentlicht in:Proteomics (Weinheim) 2017-01, Vol.17 (1-2)
Hauptverfasser: Albrecht, Simone, Mittermayr, Stefan, Smith, Josh, Martín, Silvia Millán, Doherty, Margaret, Bones, Jonathan
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Sprache:eng
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Zusammenfassung:Quantitative glycomics represents an actively expanding research field ranging from the discovery of disease-associated glycan alterations to the quantitative characterization of N-glycans on therapeutic proteins. Commonly used analytical platforms for comparative relative quantitation of complex glycan samples include MALDI-TOF-MS or chromatographic glycan profiling with subsequent data alignment and statistical evaluation. Limitations of such approaches include run-to-run technical variation and the potential introduction of subjectivity during data processing. Here, we introduce an offline 2D LC-MSE workflow for the fractionation and relative quantitation of twoplex isotopically labeled N-linked oligosaccharides using neutral 12 C6 and 13 C6 aniline (Δmass = 6 Da). Additional linkage-specific derivatization of sialic acids using 4-(4,6-dimethoxy-1,3,5-trizain-2-yl)-4-methylmorpholinium chloride offered simultaneous and advanced in-depth structural characterization. The potential of the method was demonstrated for the differential analysis of structurally defined N-glycans released from serum proteins of patients diagnosed with various stages of colorectal cancer. The described twoplex 12 C6 /13 C6 aniline 2D LC-MS platform is ideally suited for differential glycomic analysis of structurally complex N-glycan pools due to combination and analysis of samples in a single LC-MS injection and the associated minimization in technical variation.
ISSN:1615-9861
DOI:10.1002/pmic.201600304