Issue 19, 2020

A rapid and sensitive CRISPR/Cas12a based lateral flow biosensor for the detection of Epstein–Barr virus

Abstract

Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumors in the world, and several studies have associated Epstein–Barr virus (EBV) with NPC occurrence and development. EBV-PCR (polymerase chain reaction), in situ hybridization and immunoassays are the most common methods for NPC identification. However, these approaches have drawbacks, which include tedious procedures and false results. Therefore, a rapid, accurate, and sensitive clinical diagnostic method for the prognosis of EBV-related diseases is needed. In this study, we developed a simple and sensitive approach for EBV detection based on the combination of CRISPR-Cas12a and a lateral flow biosensor (LFB). Cas12a exhibits collateral cleavage propensity of both target DNA and any single-stranded(ss) DNA in the vicinity (herein referred to as a reporter). The LFB test line contained an ssDNA probe complementary to the reporter. In the presence of the target, Cas12a trans-cleaved the ssDNA reporter, which resulted in the inability of cleaved sequences to bind the LFB test line. With a PCR pre-amplification of the target (45 min), the assay achieved a sensitivity of 7.1 × 10−14 M (∼42 000 copies per μl) both in plasmid and plasmid-spiked samples. The assay attained a high specificity in the presence of various bacteria and applicability in EBV Burkitt's lymphoma serum samples. This method could be applied for the detection of EBV and other infectious diseases.

Graphical abstract: A rapid and sensitive CRISPR/Cas12a based lateral flow biosensor for the detection of Epstein–Barr virus

Supplementary files

Article information

Article type
Paper
Submitted
04 Apr 2020
Accepted
07 Jun 2020
First published
30 Jul 2020

Analyst, 2020,145, 6388-6394

A rapid and sensitive CRISPR/Cas12a based lateral flow biosensor for the detection of Epstein–Barr virus

T. Yuan, O. Mukama, Z. Li, W. Chen, Y. Zhang, J. de Dieu Habimana, Y. Zhang, R. Zeng, C. Nie, Z. He and L. Zeng, Analyst, 2020, 145, 6388 DOI: 10.1039/D0AN00663G

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