Issue 15, 2023

Real-time detection of SARS-CoV-2 in clinical samples via ultrafast ligation-dependent RNA transcription amplification

Abstract

RNA has been recognized as an important biomarker of many infectious pathogens; thus, sensitive, simple and rapid detection of RNA is urgently required for the control of epidemics. Herein, we report an ultrafast ligation-dependent RNA transcription amplification assay with high sensitivity and specificity for real-time detection of SARS-CoV-2 in real clinical samples, termed splint-based cascade transcription amplification (SCAN). Target RNA is first recognized by two DNA probes, which are then ligated together by SplintR, followed by the binding of the T7 promotor and T7 RNA polymerase to the ligated probe and the start of the transcription process. By introducing a vesicular stomatitis virus (VSV) terminator in the ligated probe, large amounts of RNA transcripts are rapidly produced within 10 min, which then directly hybridize with molecular beacons (MBs) and trigger the conformational switch of the MBs to generate a fluorescence signal that can be monitored in real time. The SCAN assay, which can be completed within 30–50 min, has a limit of detection of 104 copies per mL, while exhibiting high specificity to distinguish the target pathogen from those causing similar syndromes. More importantly, the results of SCAN for SARS-CoV-2 detection in clinical samples display great agreement with the most used qRT-PCR and qRT-LAMP, indicating great potential in the diagnosis of pathogens in clinical practice.

Graphical abstract: Real-time detection of SARS-CoV-2 in clinical samples via ultrafast ligation-dependent RNA transcription amplification

Supplementary files

Article information

Article type
Paper
Submitted
17 Jan 2023
Accepted
16 Mar 2023
First published
21 Mar 2023

Anal. Methods, 2023,15, 1915-1922

Real-time detection of SARS-CoV-2 in clinical samples via ultrafast ligation-dependent RNA transcription amplification

P. Zhang, Y. Li, D. Zhang, X. Zhu, J. Guo, C. Ma and C. Shi, Anal. Methods, 2023, 15, 1915 DOI: 10.1039/D3AY00093A

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