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Development and application of GlycanDIA workflow for glycomic analysis
Yixuan Xie1,2, Xingyu Liu1, Chenfeng Zhao3
1Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri, United States.
Biorxiv : the Preprint Server for Biology
|April 1, 2024
Summary
A new GlycanDIA workflow enhances glycan identification and quantification using data-independent acquisition mass spectrometry. This method improves sensitivity for analyzing low-abundance glycans, including those on RNA, revealing their specific forms and tissue-specific differences.
Area of Science:
- Glycomics and Mass Spectrometry
- Cellular Biology and Glycobiology
Background:
- Glycans form the glycocalyx, regulating cellular functions, and altered glycosylation is linked to diseases.
- Characterizing glycans is crucial for understanding their biological roles, but accessible methods are limited.
- Mass spectrometry (MS)-based approaches are common, but data-independent acquisition (DIA) has not been benchmarked for glycan analysis.
Purpose of the Study:
- To develop and validate a DIA-based glycomic workflow (GlycanDIA) for sensitive and accurate glycan identification and quantification.
- To create a user-friendly search engine (GlycanDIA Finder) for processing DIA glycomic data.
- To apply the workflow to profile low-abundance glycans, such as those on RNA, and investigate their biological significance.
Main Methods:
- Development of the GlycanDIA workflow integrating higher-energy collisional dissociation (HCD)-MS/MS and staggered windows for glycomic analysis.
- Implementation of a generic search engine, GlycanDIA Finder, with iterative decoy searching for robust glycan identification and quantification from DIA data.
- Application of the workflow to analyze N-glycans, O-glycans, human milk oligosaccharides (HMOs), and N-glycans on RNA.
Main Results:
- GlycanDIA demonstrated higher sensitivity and accuracy in glycan identification and quantification compared to conventional data-dependent acquisition (DDA).
- The workflow successfully distinguished glycan composition and isomers, including low-abundant modified glycans.
- Analysis of glycoRNA revealed specific glycan forms distinct from protein-glycans and tissue-specific variations, suggesting unique biological functions.
Conclusions:
- GlycanDIA provides a sensitive and accurate method for comprehensive glycan profiling.
- The developed workflow and search engine facilitate in-depth analysis of glycan structures and functions.
- The findings highlight the distinct roles of RNA-glycans in biological processes and disease, opening new avenues for research.
