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An enzyme free electrochemical biosensor for sensitive detection of miRNA with high discrimination factor by coupling strand displacement reaction and catalytic hairpin assembly recycling

Abstract

An isothermal, enzyme free, ultra-specific and ultra-sensitive protocol for electrochemical detection of miRNAs is proposed based on toehold-mediated strand displacement reaction (SDR) and non-enzymatic catalytic hairpin reaction (CHA) recycling. SDR was first triggered only in the presence of target miRNA and this process also sinking other miRNA interferences similar in target sequence, thus guaranteed high discrimination factor and could be used in rare content miRNA detection with amounts interferences similar in target sequence. The output protector strand then triggered enzyme free CHA amplification and generates plenty hairpin self-assembly product. This process in turn influences SDR equilibrium moves to the right and generates amounts protector output to ensure analysis sensitivity. Compared with traditional CHA, our proposed method greatly improved signal to noise ratio and shows excellent performance in rare miRNA detection with miRNA analogues interference. Under the optimal experimental conditions and using square wave voltammetry, the established biosensor could detect target miRNA-21 down to 30 fM (S/N=3) with a dynamic range from 100 fM to 2 nM, and discriminate rare target miRNA-21 from mismatched miRNA with high selectivity. This method holds great promise in miRNA detection from human cancer cell lines and would be a versatile and powerful tool for clinical molecular diagnostics.

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Publication details

The article was received on 23 Jul 2017, accepted on 06 Sep 2017 and first published on 07 Sep 2017


Article type: Paper
DOI: 10.1039/C7AN01224A
Citation: Analyst, 2017, Accepted Manuscript
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    An enzyme free electrochemical biosensor for sensitive detection of miRNA with high discrimination factor by coupling strand displacement reaction and catalytic hairpin assembly recycling

    J. Yao, Z. Zhang, Z. Deng, Y. Wang and Y. Guo, Analyst, 2017, Accepted Manuscript , DOI: 10.1039/C7AN01224A

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