Issue 29, 2024

Ratiometric electrochemical biosensor based on hybridization chain reaction signal amplification for sensitive microRNA-155 detection

Abstract

In this work, a sensitive ratiometric electrochemical biosensor for microRNA-155 (miRNA-155) detection is reported based on a hybridization chain reaction amplifying the electrochemical signal. The biosensor was fabricated using Au NPs as a modified material to assemble capture DNA labeled with ferrocene (Fc) molecules, and a DNA probe labeled with methylene blue (MB) was employed for the signal probe. In the presence of target miRNA-155, it can be dual hybridized with capture and signal probe, especially with signal probe to continuously produce long concatemers containing lots of MB molecules. The electrochemical signal of Fc was used for the internal signal, and the signal from MB was used as an indicator signal. As the concentration of miRNA-155 was altered, the internal reference signal of Fc remained constant, and only the indicator signal changed in a sensitive way. The change in the ratio (IMB/IFc) between the indicator signal of MB and internal reference signal of Fc can be used to monitor the concentration of miRNA-155. Under optimal conditions, the prepared ratiometric biosensor could detect miRNA-155 within a wide linear range from 100 fM to 100 nM with low detection limit of 33 fM (at S/N = 3). Moreover, the biosensor was evaluated with human serum samples, and satisfactory recoveries were obtained, indicating that the ratiometric biosensor can be applied to clinical sample analysis.

Graphical abstract: Ratiometric electrochemical biosensor based on hybridization chain reaction signal amplification for sensitive microRNA-155 detection

Supplementary files

Article information

Article type
Paper
Submitted
10 May 2024
Accepted
25 Jun 2024
First published
27 Jun 2024

Anal. Methods, 2024,16, 5032-5037

Ratiometric electrochemical biosensor based on hybridization chain reaction signal amplification for sensitive microRNA-155 detection

Y. Ma, M. Li and Y. Zhang, Anal. Methods, 2024, 16, 5032 DOI: 10.1039/D4AY00868E

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