Chain hybridization-based CRISPR-lateral flow assay enables accurate gene visual detection.

Anal Chim Acta

Department of Laboratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China. Electronic address:

Published: August 2023

Visualized gene detection based on the CRISPR-Cas12/CRISPR-Cas13 technology and lateral flow assay device (CRISPR-LFA) has shown great potential in point-of-care testing sector. Current CRISPR-LFA methodology mainly utilizes conventional immuno-based LFA test strips, which could visualize whether the reporter probe is trans-cleaved by Cas protein, indicating the target positive detection. However, conventional CRISPR-LFA usually produces false-positive results in target negative assay. Herein, a nucleic acid Chain Hybridization-based Lateral Flow Assay platform, named CHLFA, has been developed to achieve the CRISPR-CHLFA concept. Different from the conventional CRISPR-LFA, the proposed CRISPR-CHLFA system was established based on the nucleic acid hybridization between the GNP-probe embedded in test strips and ssDNA (or ssRNA) reporter from CRISPR (LbaCas12a or LbuCas13a) reaction, which eliminated the requirement of immunoreaction in conventional immuno-based LFA. The assay realized the detection of 1-10 copy of target gene per reaction within 50 min. The CRISPR-CHLFA system achieved highly accurate visual detection of target negative samples, thus overcoming the false-positive problem that often produced in assays using conventional CRISPR-LFA. The CRISPR-CHLFA platform was further adopted for the visual detection of marker gene from SASR-CoV-2 Omicron variant and Mycobacterium tuberculosis (MTB), respectively, and 100% accuracy for the analysis of clinical specimens (45 SASR-CoV-2 specimens and 20 MTB specimens) was obtained. The proposed CRISPR-CHLFA system could provide an alternative platform for the development of POCT biosensors and can be widely adopted in accurate and visualized gene detection.

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Source
http://dx.doi.org/10.1016/j.aca.2023.341437DOI Listing

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