U.S. Department of Energy

Pacific Northwest National Laboratory

GlyQ-IQ: glycomics quintavariate-informed quantification with high-performance computing and GlycoGrid 4D visualization.

Publication TypeJournal Article
Year of Publication2014
AuthorsKronewitter SR, Slysz GW, Marginean I, Hagler CD, LaMarche BL, Zhao R, Harris MY, Monroe ME, Polyukh CA, Crowell KL, Fillmore TL, Carlson TS, Camp DG, Moore RJ, Payne SH, Anderson GA, Smith RD
JournalAnal Chem

Glycomics quintavariate-informed quantification (GlyQ-IQ) is a biologically guided glycomics analysis tool for identifying N-glycans in liquid chromatography-mass spectrometry (LC-MS) data. Glycomics LC-MS data sets have convoluted extracted ion chromatograms that are challenging to deconvolve with existing software tools. LC deconvolution into constituent pieces is critical in glycomics data sets because chromatographic peaks correspond to different intact glycan structural isomers. The biological targeted analysis approach offers several key advantages to traditional LC-MS data processing. A priori glycan information about the individual target's elemental composition allows for improved sensitivity by utilizing the exact isotope profile information to focus chromatogram generation and LC peak fitting on the isotopic species having the highest intensity. Glycan target annotation utilizes glycan family relationships and in source fragmentation in addition to high specificity feature LC-MS detection to improve the specificity of the analysis. The GlyQ-IQ software was developed in this work and evaluated in the context of profiling the N-glycan compositions from human serum LC-MS data sets. A case study is presented to demonstrate how GlyQ-IQ identifies and removes confounding chromatographic peaks from high mannose glycan isomers from human blood serum. In addition, GlyQ-IQ was used to generate a broad human serum N-glycan profile from a high resolution nanoelectrospray-liquid chromatography-tandem mass spectrometry (nESI-LC-MS/MS) data set. A total of 156 glycan compositions and 640 glycan isomers were detected from a single sample. Over 99% of the GlyQ-IQ glycan-feature assignments passed manual validation and are backed with high-resolution mass spectra.

Alternate JournalAnal. Chem.
PubMed ID24881670
PubMed Central IDPMC4082388
Grant ListP41 GM103493 / GM / NIGMS NIH HHS / United States
P41 GM103493-11 / GM / NIGMS NIH HHS / United States
Area of Research: 
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