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An ultrasensitive microchip electrophoresis assay based on separation assisted double cycling signal amplification strategy for microRNA detection in cell lysate

Abstract

Microchip electrophoresis (MCE) assay is an analysis technique with low consumption and highly automation. It is a useful tool in biomedical research and clinical diagnosis. However, low detection sensitivity limits its application in trace biomarkers analysis because of its extremely small sample size. To address the need for high sensitivity in MCE, we developed an ultrasensitive MCE method based on separation assisted double cycling signal amplification strategy for microRNA (miRNA) detection in cell lysate. In this method, two short single stranded DNAs P1 and P2 complement each other to form a duplex DNA probe (P1/P2). In the presence of target miRNA, P2 in the P1/P2 probe can be displaced to form double-stranded miRNA/P1. Then, the degradation of P1 in the miRNA/P1 by T7 Exo releases the miRNA, and released miRNA participates in a displacement reaction with another P1/P2 probe to complete first cycle. The displaced free P2 hybridizes with the hairpin fluorescence probe (MB) to form P2/MB duplex, which can also be degraded by T7 Exo to release the P2. The released P2 can bind with another MB probe to complete second cycle. By using MCE-laser-induced fluorescence (LIF) as separation and detection platform, miRNA-141 as model analyte, the proposed MCE assay could determine miRNA-141 as low as 8.0 fM, which is the highest sensitivity achieved to date for MCE assay. This method for detecting trace miRNA holds a great potential in biomedical research and clinical diagnosis.

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Publication details

The article was received on 25 Dec 2017, accepted on 07 Feb 2018 and first published on 08 Feb 2018


Article type: Paper
DOI: 10.1039/C7AN02082A
Citation: Analyst, 2018, Accepted Manuscript
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    An ultrasensitive microchip electrophoresis assay based on separation assisted double cycling signal amplification strategy for microRNA detection in cell lysate

    K. Wei, J. Zhao, X. Luo, S. Qiu, F. He, S. Li and S. Zhao, Analyst, 2018, Accepted Manuscript , DOI: 10.1039/C7AN02082A

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