Journal of Functional Biomaterials (Mar 2025)

Hybridization Chain Reaction-Enhanced Ultrasensitive Electrochemical Analysis of miRNAs with a Silver Nano-Reporter on a Gold Nanostructured Electrode Array

  • Bin Wang,
  • Huiqiang Ma,
  • Mingxing Zhou,
  • Xian Huang,
  • Ying Gan,
  • Hong Yang

DOI
https://doi.org/10.3390/jfb16030098
Journal volume & issue
Vol. 16, no. 3
p. 98

Abstract

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Abnormal expression of miRNAs is associated with the occurrence and progression of cancer and other diseases, making miRNAs essential biomarkers for disease diagnosis and prognosis. However, the intrinsic properties of miRNAs, such as short length, low abundance, and high sequence homology, represent great challenges for fast and accurate miRNA detection in clinics. Herein, we developed a novel hybridization chain reaction (HCR)-based electrochemical miRNAs chip (e-miRchip), featured with gold nanostructured electrodes (GNEs) and silver nanoparticle reporters (AgNRs), for sensitive and multiplexed miRNA detection. AgNRs were synthesized and applied on the e-miRchip to generate strong redox signals in the presence of miRNA. The stem–loop capture probe was covalently immobilized on the GNEs, and was opened upon miRNA hybridization to consequently trigger the HCR for signal amplification. The multiple long-repeated DNA helix generated by HCR provides the binding sites for the AgNRs, contributing to the amplification of the electrochemical signals of miRNA hybridization. To optimize the detection sensitivity, GNEs with three distinct structures were electroplated, in which flower-like GNEs were found to be the best electrode morphology for miRNAs analysis. Under optimal conditions, the HCR-based e-miRchip showed an excellent detection performance with an LOD of 0.9 fM and a linear detection range from 1 fM to 10 pM. Moreover, this HCR-based e-miRchip platform was able to effectively distinguish miRNAs from the one- or two-base mismatches. This HCR-based e-miRchip holds great potential as a highly efficient and promising miRNA detection platform for the diagnosis and prognosis of cancer and other diseases in the future.

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