Multistage mass spectrometry with intelligent precursor selection for N-glycan branching pattern analysis

Abstract

Biological functions of N-glycans are frequently related to their unique branching patterns. Multistage mass spectrometry (MSn) has become the primary method for glycan structural analysis. However, selection of the best fragment as the precursor for the next round of product-ion scanning is important but difficult. We have previously proposed the concept and designed the approach of glycan intelligent precursor selection (GIPS) to guide MSn experiments, but its use in N-glycans is not straightforward as some N-glycans are of high similarity in branching patterns. In the present work we introduced new elements to GIPS to improve its performance in N-glycan branching pattern analysis. These include a hypothesis and significance test, based on Bayes factor, and DPbiased as a new precursor selection strategy. The improved GIPS was successfully applied to identification of individual N-glycans, and incorporated into MALDI-MS N-glycan profiling for assignment of N-glycans obtained from glycoproteins and complex human serum.