Issue 13, 2019

An ultrasensitive and simple assay for the Hepatitis C virus using a reduced graphene oxide-assisted hybridization chain reaction

Abstract

Hepatitis C virus (HCV) is a major cause of chronic liver disease, which affects 2–3% of the world population. Until now, the early detection of HCV has been a great challenge, especially for those who live in developing countries. In this study, we developed a novel and ultrasensitive assay for the detection of HCV RNA based on the reduced graphene oxide nanosheets (rGONS) and hybridization chain reaction (HCR) amplification technique. This detection system contains a pair of single fluorophore-labeled hairpin probes that can freely exist in the solution in the absence of target RNA. The introduction of target RNA can robustly trigger a HCR with the two probes and produce long nanowires containing a double-stranded structure. The weak adsorption to rGONS makes the long nanowires emit a strong fluorescence. Using this enzyme-free amplification strategy, we developed a new method for the HCV RNA assay with a detection limit of 10 fM, which is far more sensitive than the common GO-based fluorescence method. Furthermore, the new method exhibits high selectivity for the discrimination of perfectly complementary and mismatched sequences. Finally, the new method was successfully used as a HCV RNA assay in biological samples with a strong anti-interference capability in complicated environments. In summary, these remarkable characteristics of the new method highlight its potential use in a clinical sample primary screening.

Graphical abstract: An ultrasensitive and simple assay for the Hepatitis C virus using a reduced graphene oxide-assisted hybridization chain reaction

Article information

Article type
Paper
Submitted
25 Jan 2019
Accepted
08 May 2019
First published
16 May 2019

Analyst, 2019,144, 3972-3979

An ultrasensitive and simple assay for the Hepatitis C virus using a reduced graphene oxide-assisted hybridization chain reaction

J. Fan, L. Yuan, Q. Liu, C. Tong, W. Wang, F. Xiao, B. Liu and X. Liu, Analyst, 2019, 144, 3972 DOI: 10.1039/C9AN00179D

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