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Identification and quantification of sialylated and core-fucosylated N-glycans in human transferrin by UPLC and LC-MS/MS

Authors
Jin, M.Kim, J.Ha, J.Kim, A.Lee, J.Park, C.S.Kang, M.Kim, J.Mun, C.Kim, J.Kim, H.H.
Issue Date
Jun-2022
Publisher
Academic Press Inc.
Keywords
Column; Core-fucosylated N-glycan; Fluorescent tag; LC-MS/MS; Sialylated N-glycan; UPLC
Citation
Analytical Biochemistry, v.647
Journal Title
Analytical Biochemistry
Volume
647
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/57784
DOI
10.1016/j.ab.2022.114650
ISSN
0003-2697
1096-0309
Abstract
Sialylated and core-fucosylated N-glycans in human transferrin (HTF) are used as glycan biomarkers due to their increased or decreased characteristics in certain diseases. However, their absolute quantities remain unclear. In this study, N-glycans of HTF were identified by UPLC and LC-MS/MS using fluorescence tags [2-aminobenzamide (AB) and procainamide (ProA)] and columns [HILIC and anion exchange chromatography-HILIC (AXH)]. The structures of 14 (including five core-fucosylated) N-glycans in total comprising two non-, six mono-, four di-, and two tri-sialylated N-glycans were identified. The quantities (%) of each N-glycan relative to the total N-glycans (100%) were obtained. HILIC and AXH were better for peak identification and separability except for desialylation, respectively. Specifically, sialylated (in ProA-HILIC and ProA-AXH by UPLC or LC-MS/MS) and core-fucosylated (in AB-HILIC and ProA-AXH by UPLC) N-glycans were efficiently identified. Seven neuraminidase-treated (including three core-fucosylated) N-glycans were efficiently identified in ProA-AXH, even their poor separation. Additionally, ProA-AXH was more efficient for the estimation of the absolute quantities of N-glycans from the results of fluorescence intensity (by UPLC) and relative quantity (by LC-MS/MS). These results first demonstrate that ProA is useful for identifying and quantifying sialylated, core-fucosylated, and neuraminidase-treated desialylated N-glycans in HTF using AXH by UPLC and LC/MS. © 2022 Elsevier Inc.
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