Abstract
Protein glycosylation is a highly important, yet poorly understood protein post-translational modification. Thousands of possible glycan structures and compositions create potential for tremendous site heterogeneity. A lack of suitable analytical methods for large-scale analyses of intact glycopeptides has limited our abilities both to address the degree of heterogeneity across the glycoproteome and to understand how this contributes biologically to complex systems. Here we show that N-glycoproteome site-specific microheterogeneity can be captured via large-scale glycopeptide profiling methods enabled by activated ion electron transfer dissociation (AI-ETD), ultimately characterizing 1,545 N-glycosites (>5,600 unique N-glycopeptides) from mouse brain tissue. Our data reveal that N-glycosylation profiles can differ between subcellular regions and structural domains and that N-glycosite heterogeneity manifests in several different forms, including dramatic differences in glycosites on the same protein. Moreover, we use this large-scale glycoproteomic dataset to develop several visualizations that will prove useful for analyzing intact glycopeptides in future studies.
Highlights
Protein glycosylation is a highly important, yet poorly understood protein post-translational modification
The combination of vibrational activation and electrondriven dissociation is concurrent in both space and time when performing activated ion electron transfer dissociation (AI-electron transfer dissociation (ETD)), which reduces overhead time in Mass spectrometry (MS)/MS scans compared to other supplemental activation techniques (e.g., ETcaD and EThcD)
EThcD has proven suitable for glycoproteome characterization in a number of recent studies[18,24,25], and future studies will likely focus on more systematic comparisons of multiple supplemental activation strategies that include AI-ETD
Summary
Protein glycosylation is a highly important, yet poorly understood protein post-translational modification. Multiple dissociation strategies (mainly electron-driven dissociation and collision-based methods) are increasingly used to access both glycan and peptide information from intact glycopeptides[11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26] Even with these methods, large-scale analysis of intact glycopeptides remains largely limited to fewer than ~1000 unique glycosite identifications from any one system or tissue (approximately an order of magnitude behind other PTMs)[27,28,29,30,31,32], and few studies assess heterogeneity across the glycoproteome with site-specific resolution. Using an AI-ETD-enabled method for large-scale glycoproteomic analysis, we characterize 5662 unique N-glycopeptides mapping to 1545 unique N-glycosites on 771 glycoproteins and use this dataset to explore profiles of heterogeneity present at multiple levels of proteomic information, from glycosites to subcellular regions
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