An improved strategy for deep and quantitative N-glycomics

Abstract

In this study, an improved strategy for deep N-glycomics that employs an optimized glycan sample preparation involving enhanced (glyco)protein recovery, purification and permethylation efficiency, newly-developed R-scripts matching experimental high-accuracy MS1 data to theoretical monosaccharide compositions in order to enhance the coverage and identification accuracy of protein N-glycome with data quality control and a novel bundled sequencing algorithm characterizing the N-glycan structures at MS2 level was developed. By this strategy, 57 monosaccharide compositions (133 N-glycans) from chicken ovalbumin, 90 monosaccharide compositions (162 N-glycans) from etanercept, 133 monosaccharide compositions (230 N-glycans) from erythropoietin, 245 monosaccharide compositions (398 N-glycans) from human acute promyelocytic leukemia cells and 343 monosaccharide compositions (832 N-glycans) from corpus callosum of an adult mouse are identified. The identified N-glycans are verified by pGlyco software. This strategy is also applicable to O-glycomics. Besides, stable isotopic labeling and relative quantification are performed for N-glycome. Finally, this study provides a novel pathway for N-glycomics to realize deep identification and biomarker discovery.