Separation and identification of permethylated glycan isomers by reversed phase nanoLC-NSI-MSn [preprint]
UMass Chan Affiliations
Department of MedicineDocument Type
PreprintPublication Date
2020-08-04Keywords
biochemistrypermethylated glycan isomers
LC-MS analysis
optimized workflow
Amino Acids, Peptides, and Proteins
Biochemistry
Medicinal-Pharmaceutical Chemistry
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High performance liquid chromatography has been employed for decades to enhance detection sensitivity and quantification of complex analytes within biological mixtures. Among these analytes, glycans released from glycoproteins and glycolipids have been characterized as underivatized or fluorescently tagged derivatives by HPLC coupled to various detection methods. These approaches have proven extremely useful for profiling the structural diversity of glycoprotein and glycolipid glycosylation but require the availability of glycan standards and secondary orthogonal degradation strategies to validate structural assignments. A robust method for HPLC separation of glycans as their permethylated derivatives, coupled with in-line MSn fragmentation to assign structural features independent of standards, would significantly enhance the depth of knowledge obtainable from biological samples. Here, we report an optimized workflow for LC-MS analysis of permethylated glycans that includes sample preparation, mobile phase optimization, and MSn method development to resolve structural isomers on-the-fly. We report baseline separation and MSn fragmentation of isomeric N- and O-glycan structures, aided by supplementing mobile phases with Li+, which simplifies adduct heterogeneity and facilitates cross-ring fragmentation to obtain valuable monosaccharide linkage information. Our workflow has been adapted from standard proteomics-based workflows and, therefore, provides opportunities for laboratories with expertise in proteomics to acquire glycomic data with minimal deviation from existing buffer systems, chromatography media, and instrument configurations. Furthermore, our workflow does not require a mass spectrometer with high-resolution/accurate mass capabilities. The rapidly evolving appreciation of the biological significance of glycans for human health and disease requires the implementation of high-throughput methods to identify and quantify glycans harvested from sample sets of sufficient size to achieve appropriately powered statistical significance. The LC-MSn approach we report generates glycan isomeric separations, robust structural characterization, and is amenable to auto-sampling with associated throughput enhancements.Source
bioRxiv 2020.08.04.236349; doi: https://doi.org/10.1101/2020.08.04.236349. Link to preprint on bioRxiv service.
DOI
10.1101/2020.08.04.236349Permanent Link to this Item
http://hdl.handle.net/20.500.14038/29514Rights
The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It is made available under a CC-BY-ND 4.0 International license.Distribution License
http://creativecommons.org/licenses/by-nd/4.0/ae974a485f413a2113503eed53cd6c53
10.1101/2020.08.04.236349
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Except where otherwise noted, this item's license is described as The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It is made available under a CC-BY-ND 4.0 International license.