Being one of common posttranslational modifications (PTMs) of proteins, glycosylation is involved in important biological processes and associated with diseases. Glycan profiling aims at identifying glycan compositions and determining their abundances within a complex sample by using mass spectrometry. Traditionally, identification of glycans is achieved using MALDI-MS.  Here, we present several bioinformatics approaches to assisting automatic glycan profiling from LC-MS experiments by inducing multiple adducts into the sample. As Liquid Chromatography (LC) provides a highly reproducible separation of glycans, we devise algorithmic techniques to utilize the elution order of glycans to eliminate false glycan annotation. We will also present a glycan sequencing algorithm that incorporates glycan fragment ions to further improve glycan identification. Glycans released from both standard and pooled serum glycoproteins are reduced and permethylated prior to the analysis on a LTQ Orbitrap Velos hybrid FT-MS. A pre-defined library of glycan compositions and adducts masses are used to annotate putative glycan ions observed in the experiments. We used the abduct ion profile of each glycan, consisting of ions formed by the glycan with different adducts, to enhance the confidence of our annotation. We also implemented a longest common subsequence algorithm to match the observed elution order of annotated glycans with their expected order to further eliminate false glycan annotations. We combine these approaches with a glycan sequencing algorithm (when MS/MS data is available) to achieve a high confident glycan annotation in LC-MS data.

Identification and quantification of glycans in glycomics: Towards biomarker discovery for human diseases

246th National Meeting (2013)

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