Issue 45, 2023

A homogeneous label-free electrochemical aptasensor based on an omega-like DNA nanostructure for progesterone detection

Abstract

A novel homogeneous label-free electrochemical aptamer sensor for the detection of progesterone was prepared by combining a well-designed omega (Ω)-like DNA (Ω-DNA) nanostructure, with an isothermal cycling amplification strategy based on the highly efficient exonuclease III (Exo III). The omega-like (Ω) DNA is composed of two oligonucleotide strands: DNA1 and DNA2. The Pro aptamer triggers a chain displacement reaction of Ω-DNA nanostructures, forms a new double-stranded DNA structure (aptamer precursor-DNA2), and releases DNA1. Then, Exo III selectively cleaves the DNA duplex and releases the Pro aptamer to participate in a new displacement reaction. Meanwhile, the released DNA1 strands gain access to the strongly bound hemin, forming a hemin/G-quadruplex (DNAzyme). In the presence of hydrogen peroxide (H2O2), differential pulse voltammetry (DPV) was used to detect the current signal from the oxidation of o-phenylenediamine (OPD) to aminoazobenzene (DAP) catalyzed by DNAzyme. However, the amount of released DNA1 from the Ω-DNA nanostructures is reduced in the presence of the target Pro, and the DPV signal declines because of the small amount of DNAzyme formed. The developed electrochemical aptasensor has a wide dynamic linear relationship in the range of 1 pg mL−1 to 10 ng mL−1 under optimal conditions. Its detection limit is down to 0.3 pg mL−1, providing a potential platform for a sensitive Pro assay among electrochemical assays.

Graphical abstract: A homogeneous label-free electrochemical aptasensor based on an omega-like DNA nanostructure for progesterone detection

Article information

Article type
Paper
Submitted
21 Jūl. 2023
Accepted
31 Okt. 2023
First published
13 Nov. 2023

Anal. Methods, 2023,15, 6159-6164

A homogeneous label-free electrochemical aptasensor based on an omega-like DNA nanostructure for progesterone detection

Z. Wang, W. Shi, Y. Tan and B. Liu, Anal. Methods, 2023, 15, 6159 DOI: 10.1039/D3AY01255G

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