DNAzyme walker-driven, electrode-surface label/wash-free ultrasensitive ECL miRNA detection via ssDNA-enhanced peroxidase activity of g-C3N4 nanosheets

Abstract

The value of microRNAs (miRNAs) as clinical biomarkers lies in their differential expression in various diseases, enabling diagnosis and dynamic treatment response assessment. In this study, we developed a label/wash-free, ultrasensitive electrochemiluminescence (ECL) biosensor for miRNA detection based on a Mn2+-dependent DNAzyme walker and ssDNA-enhanced peroxidase-like activity of graphitic carbon nitride nanosheets (g-C3N4 NSs). Therein, the DNAzyme walker performed autonomous movement on Au@Fe3O4 nanocomposites, enabling continuous cleavage of substrate strands and generation of numerous single-stranded DNA (ssDNA) products in the presence of the target miRNA. These ssDNA products enhanced the peroxidase-like activity of g-C3N4 NSs via electrostatic attraction and π–π stacking, significantly amplifying the ECL signal of the luminol–H2O2 system. This strategy achieved a detection limit of 1 fM for miRNA-155, with excellent selectivity and reproducibility. Notably, the method eliminates the need for time-consuming chemical electrode modification and washing steps through label/wash-free ECL signal generation, facilitating relatively rapid and highly convenient operation. The biosensor was successfully applied to detect miRNA-155 in complex biological samples such as cell lysates, demonstrating its great potential for clinical molecular diagnosis.

Graphical abstract: DNAzyme walker-driven, electrode-surface label/wash-free ultrasensitive ECL miRNA detection via ssDNA-enhanced peroxidase activity of g-C3N4 nanosheets

Supplementary files

Article information

Article type
Paper
Submitted
05 Nov 2025
Accepted
26 Nov 2025
First published
03 Dec 2025

Analyst, 2026, Advance Article

DNAzyme walker-driven, electrode-surface label/wash-free ultrasensitive ECL miRNA detection via ssDNA-enhanced peroxidase activity of g-C3N4 nanosheets

J. Wu, C. Pan, L. Wang, Y. Li and G. Liang, Analyst, 2026, Advance Article , DOI: 10.1039/D5AN01164G

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