Issue 5, 2021

Target-induced activation of polymerase activity for recycling signal amplification cascades for sensitive aptamer-based detection of biomarkers

Abstract

It is of great importance to develop biosensing methods for the sensitive and selective analysis of biomarkers at very low levels in biological samples. Using a new target-induced activation of the DNA polymerase activity for recycling amplification cascades, we describe an aptamer-based method for highly sensitive detection of platelet-derived growth factor BB (PDGF-BB) in human serums. The polymerase activity is initially inhibited by the binding of the polymerase to the enzyme aptamer sequence. PDGF-BB associates with and switches a PDGF-BB binding aptamer to trigger the release of an active polymerase, which further initiates the simultaneous recycling of the target PDGF-BB molecules and the enzyme aptamer sequence for the subsequent displacement of the fluorescently quenched probes to recover the fluorescence. Due to two recycling cascades, substantial fluorescence magnification is obtained for the highly sensitive detection of PDGF-BB with a low detection limit of 5.1 pM. Moreover, the potential applicability of this method for real samples was verified by determining PDGF-BB in diluted human serums, relying on the excellent specificity and selectivity of the aptamer. The demonstration of the PDGF-BB assay method here thus can be expanded for the construction of diverse sensing platforms for detecting different trace biomarkers with the integration of an elaborate design of the aptamer probes.

Graphical abstract: Target-induced activation of polymerase activity for recycling signal amplification cascades for sensitive aptamer-based detection of biomarkers

Article information

Article type
Paper
Submitted
27 Nov 2020
Accepted
31 Dec 2020
First published
04 Jan 2021

Analyst, 2021,146, 1590-1595

Target-induced activation of polymerase activity for recycling signal amplification cascades for sensitive aptamer-based detection of biomarkers

Y. Li, X. Li, F. Yang, R. Yuan and Y. Xiang, Analyst, 2021, 146, 1590 DOI: 10.1039/D0AN02288H

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