Literature DB >> 31446948

Providing Bionic Glycome as internal standards by glycan reducing and isotope labeling for reliable and simple quantitation of N-glycome based on MALDI- MS.

Wenjun Qin1, Zejian Zhang1, Ruihuan Qin1, Jing Han1, Ran Zhao2, Yong Gu1, Yiqing Pan1, Jianxin Gu3, Shifang Ren4.   

Abstract

Accurate, simple and economical methods for quantifying N-glycans are continuously required for discovering disease biomarkers and quality control of biopharmaceuticals. Quantitative N-glycomics based on MS using exogenous isotopic labeling internal standards is promising as it is simple and accurate. However, it is largely hampered by the lack of available glycan internal standard libraries with good coverage of the natural glycan structural heterogeneity as well as broad dynamic mass and ion abundance range. To overcome this limitation, we developed a novel method, providing 'Bionic Glycome' as internal standards for glycan quantitation by MALDI-MS. Bionic Glycome was produced using N-glycome from pooled samples to be analyzed as substrate by one step of glycan reducing and isotope labeling (Glycan-RAIL). Each bionic glycan has 3 Da mass increment over its corresponding glycan analyte based on hemiacetals/alditols and H/D mass difference. In addition, Bionic Glycome has the same glycome composition and similar glycome profile in abundance with N-glycome to be analyzed from biological sample. Through the investigation of single glycan standard and complex glycans released from model glycoprotein and serum, the results demonstrate that the method has good quantitative accuracy and high reproducibility. Lastly, this method was successfully used for discovery of lung cancer specific glycan markers by comparing the serum glycans from each sample in lung cancer group (n = 16) and healthy controls (n = 16), indicating its potential in clinical applications.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Biomarker; Bionic glycome; Internal standard; N-glycans; Quantitative N-glycomics

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Year:  2019        PMID: 31446948     DOI: 10.1016/j.aca.2019.07.003

Source DB:  PubMed          Journal:  Anal Chim Acta        ISSN: 0003-2670            Impact factor:   6.558


  2 in total

1.  Screening and diagnosis of colorectal cancer and advanced adenoma by Bionic Glycome method and machine learning.

Authors:  Yiqing Pan; Lei Zhang; Rongrong Zhang; Jing Han; Wenjun Qin; Yong Gu; Jichen Sha; Xiaoyan Xu; Yi Feng; Zhipeng Ren; Jiawen Dai; Ben Huang; Shifang Ren; Jianxin Gu
Journal:  Am J Cancer Res       Date:  2021-06-15       Impact factor: 6.166

2.  Serum Protein N-Glycosylation Signatures of Neuroblastoma.

Authors:  Wenjun Qin; Hao Pei; Xiaobing Li; Jia Li; Xuelian Yao; Rufang Zhang
Journal:  Front Oncol       Date:  2021-03-16       Impact factor: 6.244

  2 in total

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