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Issue 17, 2019
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The efficient profiling of serum N-linked glycans by a highly porous 3D graphene composite

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Abstract

In this work, an enrichment approach for the profiling of N-linked glycans was developed by utilizing a highly porous 3D graphene composite fabricated from graphene oxide nanosheets and a phenol-formaldehyde polymer via graphitization and KOH activation. In tailoring the large surface area (ca. 2213 m2 g−1) and 3D-layered mesoporous structure, the 3D graphene composite demonstrated not only high efficiency in glycan enrichment but also the size-exclusion effect against residual protein interference. For a standard protein ovalbumin digest, 26 N-linked glycans were identified with good repeatability, and the detection limit was as low as 0.25 ng μL−1 with the identification of 13 N-linked glycans (S/N > 10). When the mass ratio of the ovalbumin digest to the interfering proteins, i.e., bovine serum albumin and ovalbumin was 1 : 2000 : 2000, 18 N-linked glycans could still be detected with sufficient signal intensities. From a 60 nL minute complex human serum sample, up to 53 N-linked glycans with S/N > 10 were identified after the 3D graphene enrichment, while only 20 N-linked glycans were identified by the porous graphitized carbon material used for comparison. In addition, the application of the 3D graphene composite in profiling the up-regulated and down-regulated N-linked glycans from the real clinical serum samples of ovarian cancer patients confirmed the potential of the 3D graphene composite for analyzing minute and complicated biological samples.

Graphical abstract: The efficient profiling of serum N-linked glycans by a highly porous 3D graphene composite

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Publication details

The article was received on 18 Jun 2019, accepted on 10 Jul 2019 and first published on 13 Jul 2019


Article type: Paper
DOI: 10.1039/C9AN01119F
Analyst, 2019,144, 5261-5270

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    The efficient profiling of serum N-linked glycans by a highly porous 3D graphene composite

    H. Niu, X. Li, J. Peng, H. Zhang, X. Zhao, X. Zhou, D. Yu, X. Liu and R. Wu, Analyst, 2019, 144, 5261
    DOI: 10.1039/C9AN01119F

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