Inter-tissue glycan heterogeneity: site-specific glycoform analysis of mouse tissue N-glycoproteomes using MS1-based glycopeptide detection method assisted by lectin microarray.

To understand the biological and pathological functions of protein glycosylation, the glycan heterogeneities for each glycosite in a single glycoprotein need to be elucidated depending on the type and status of cells. For this aim, a reliable strategy is needed to compare site-specific glycoforms of multiple glycoprotein samples in a comprehensive manner. To analyze this "inter-heterogeneity" of samples, we previously introduced an MS1-based glycopeptide detection method, "Glyco-RIDGE." Here, to elucidate inter-tissue glycan heterogeneities, this estimation method was applied to site-specific N-glycoforms of glycoproteins from six normal mouse tissues (liver, kidney, spleen, pancreas, stomach, and testis). The analyses of desialylated glycopeptides estimated 11,325 site-specific N-glycoforms with 239 glycan compositions at 1260 sites (1122 non-redundant core peptides) in 800 glycoproteins, revealing inter-tissue differences in macro-, micro-, and meta-glycan heterogeneities. To obtain detailed information on their glycan features and tissue distribution, the Glyco-RIDGE results were compared with laser microdissection-assisted lectin microarray (LMD-LMA)-based mouse tissue glycome mapping data deposited on LM-GlycomeAtlas, as well as MS-based mouse tissue glycome data deposited on GlycomeAtlas. This integrated approach supported the certainty of Glyco-RIDGE results and suggested that inter-tissue differences exist in glycan motifs, such as alpha-galactose and bisecting N-acetylglucosamine, in both whole tissue glycomes and specific glycoproteins, Anpep and Lamc1. In addition, the comparison with LMD-LMA-based tissue glycome mapping data suggested the possibility of estimating the tissue distribution of the assigned glycans and glycopeptides. Taken together, these findings demonstrate the utility of an integrated approach using MS assisted by LMA for large-scale analyses.