Issue 11, 2021

A polyA DNA probe-based ultra-sensitive and structure-distinguishable electrochemical biosensor for the analysis of RNAi transgenic maize

Abstract

Since the application of RNA interference (RNAi) is rapidly developing in GMO technology, accurate and sensitive detection of functional RNA molecules was urgently needed, for the safety and functional assessment of RNAi crops. In this work, we developed an electrochemical biosensor for transgene-derived long RNA based on a poly-adenine (polyA) DNA capture probe. The polyA self-assembling monolayer (SAM) provided enhanced interface stability and optimized surface density for the subsequent hybridization of the long RNA molecule. A multiple reporter probe system (MRP) containing 12 reporter probes (RPs) and 2 spacers was applied to open the complex molecular secondary structure and hybridize with the long RNA, with the critical assistance of dimethyl sulfoxide (DMSO). By using 3 addressable RPs, structural recognition was performed among long stem-loop RNA, long dsRNA (no loop), and siRNA. Excellent selectivity was achieved when the extracted total RNA samples were directly analyzed. When reverse transcription recombinase polymerase amplification (RT-RPA) technology was combined, the sensitivity was improved to 10 aM. To the best of our knowledge, this is the first electrochemical biosensor with the excellent capability of quantification and structural analysis of the long RNA of the RNAi GMO. Our work shows great potential in a wide range of RNAi GMO samples.

Graphical abstract: A polyA DNA probe-based ultra-sensitive and structure-distinguishable electrochemical biosensor for the analysis of RNAi transgenic maize

Supplementary files

Article information

Article type
Paper
Submitted
20 Feb 2021
Accepted
26 Mar 2021
First published
26 Mar 2021

Analyst, 2021,146, 3526-3533

A polyA DNA probe-based ultra-sensitive and structure-distinguishable electrochemical biosensor for the analysis of RNAi transgenic maize

L. Xu, J. Qi, Y. Wen, W. Liang, L. Wang, Z. Yang, X. Yang, Y. Qi, M. Duan, K. Zhao, J. Gu, Y. Shen, P. Rao, M. Ding, S. Ren, L. Li and G. Liu, Analyst, 2021, 146, 3526 DOI: 10.1039/D1AN00313E

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