Issue 22, 2022

Facile preparation of a novel chitosan-derived porous graphitized carbon biomaterial for highly efficient capture of N-glycans

Abstract

The comprehensive characterization of N-glycans is of significant importance for the discovery of potential biomarkers and the diagnosis and therapy of diseases. Herein, we designed and fabricated a porous graphitized carbon biomaterial (CS-900-1C) using cheap and available chitosan as the carbon source via a facile pyrolysis process and a post-oxidation strategy for the effective capture of N-glycans. Thanks to its large surface area (2846 m2 g−1), high graphitization degree, suitable oxidation degree and unique porous structure, the CS-900-1C biomaterial exhibits an ultralow detection limit (1 ng μL−1), an excellent size-exclusion effect (OVA digest : BSA protein : OVA protein, 1 : 1000 : 1000, w/w/w) and satisfactory reusability (at least 8 cycles) in the capture of standard N-glycans. Moreover, CS-900-1C has successfully been applied in profiling the difference of N-glycans during diabetes progression (obesity, impaired glucose tolerance, diabetes patients and healthy control) where we discovered that the expression levels of five N-glycans show a gradually increasing trend as diabetes progresses. Remarkably, the five specific N-glycans could be considered as biomarkers to accurately diagnose the progression of diabetes. Our work not only developed a novel porous graphitized carbon biomaterial for the large-scale characterization of N-glycans but also provided new guidance for the precise therapy of diabetes.

Graphical abstract: Facile preparation of a novel chitosan-derived porous graphitized carbon biomaterial for highly efficient capture of N-glycans

Supplementary files

Article information

Article type
Paper
Submitted
15 Aug 2022
Accepted
26 Sep 2022
First published
26 Sep 2022

Analyst, 2022,147, 4954-4961

Facile preparation of a novel chitosan-derived porous graphitized carbon biomaterial for highly efficient capture of N-glycans

J. Wang, L. Weng, W. Liu, H. Zhang, M. Gao, X. Zhang and L. Huang, Analyst, 2022, 147, 4954 DOI: 10.1039/D2AN01342H

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