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Site-Specific Glycosylation Quantitation of 50 Serum Glycoproteins Enhanced by
Predictive Glycopeptidomics for Improved Disease Biomarker Discovery.

Authors	Li Q, Kailemia MJ, Merleev AA, Xu G, Serie D, Danan LM, Haj FG, Maverakis E, Lebrilla CB
Submitted By	Submitted Externally on 4/6/2020
Status	Published
Journal	Analytical chemistry
Year	2019
Date Published	4/1/2019
Volume : Pages	91 : 5433 - 5445
PubMed Reference	30882205
Abstract	Analysis of serum protein glycovariants has the potential to identify new biomarkers of human disease. However, the inability to rapidly quantify glycans in a site-specific fashion remains the major barrier to applying such biomarkers clinically. Advancements in sample preparation and glycopeptide quantification are thus needed to better bridge glycoscience with biomarker discovery research. We present here the successful utilization of several sample preparation techniques, including multienzyme digestion and glycopeptide enrichment, to increase the repertoire of glycopeptides that can be generated from serum glycoproteins. These techniques combined with glycopeptide retention time prediction and UHPLC-QqQ conditions optimization were then used to develop a dynamic multiple-reaction monitoring (dMRM)-based strategy to simultaneously monitor over 100 glycosylation sites across 50 serum glycoproteins. In total, the abundances of over 600 glycopeptides were simultaneously monitored, some of which were identified by utilizing theoretically predicted ion products and presumed m/ z values. The dMRM method was found to have good sensitivity. In the targeted dMRM mode, the limit of quantitation (LOQ) of nine standard glycoproteins reached femtomole levels with dynamic ranges spanning 3-4 orders of magnitude. The dMRM-based strategy also showed high reproducibility with regards to both instrument and sample preparation performance. The high coverage of the serum glycoproteins that can be quantitated to the glycopeptide level makes this method especially suitable for the biomarker discovery from large sample sets. We predict that, in the near future, biomarkers, such as these, will be deployed clinically, especially in the fields of cancer and autoimmunity.

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