Aptamer-based colorimetric detection of methicillin-resistant Staphylococcus aureus by using a CRISPR/Cas12a system and recombinase polymerase amplification.

Anal Chim Acta

Key Laboratory of Environment Correlative Dietology, Huazhong Agricultural University, Ministry of Education, Wuhan, 430070, Hubei, China; Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China; Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, Hubei, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, 518120, China. Electronic address:

Published: October 2022

Detection of methicillin-resistant Staphylococcus aureus (MRSA) with superior accuracy, timeliness, and simplicity is highly valuable in clinical diagnosis and food safety. In this study, an aptamer-based colorimetric biosensor was developed to detect MRSA by using a CRISPR/Cas12a system and recombinase polymerase amplification (RPA). The aptamer of silver ion (Ag) pre-coupled to magnetic nanoparticles was employed not only as the substrate of trans-cleavage in the CRISPR/Cas12a system, but also as the modulator of Ag-3,3',5,5'-tetramethylbenzidine (TMB) chromogenic reaction, innovatively integrating the powerful CRISPR/Cas12a system with convenient colorimetry. The utilized aptamer containing consecutive and interrupted cytosine: cytosine mismatched base pairs also served as a signal amplifier because of the one-to-multiple binding of the aptamer to Ag. Using triple amplification of RPA, multiple-turnover nuclease activity of Cas12a, and cytosine-Ag-cytosine coordination chemistry, MRSA was detected as low as 8 CFU mL. Moreover, its satisfactory accuracy in the analysis of real samples, together with visualization and simplicity, revealed the great potential of the proposed biosensor as a robust antibiotic-resistant bacteria detection platform.

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Source
http://dx.doi.org/10.1016/j.aca.2022.340357DOI Listing

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