Detection of Major SARS-CoV-2 Variants of Concern in Clinical Samples via CRISPR-Cas12a-Mediated Mutation-Specific Assay.

Abstract

Objectives: Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants pose a great threat and burden to global public health. Here, we evaluated a clustered regularly interspaced short palindromic repeat-associated enzyme 12a (CRISPR-Cas12a)-based method for detecting major SARS-CoV-2 variants of concern (VOCs) in SARS-CoV-2 positive clinical samples. Methods: Allele-specific CRISPR RNAs (crRNAs) targeting the signature mutations in the spike protein of SARS-CoV-2 are designed. A total of 59 SARS-CoV-2 positive oropharyngeal swab specimens were used to evaluate the performance of the CRISPR-Cas12a-mediated assay to identify major SARS-CoV-2 VOCs. Results: Compared with Sanger sequencing, the eight allele-specific crRNAs analyzed can specifically identify the corresponding mutations with a positive predictive value of 83.3-100% and a negative predictive value of 85.7-100%. Our CRISPR-Cas12a-mediated assay distinguished wild-type and four major VOCs (Alpha, Beta, Delta, and Omicron) of SARS-CoV-2 with a sensitivity of 93.8-100.0% and a specificity of 100.0%. The two methods showed a concordance of 98.3% (58/59) with a κ value of 0.956-1.000, while seven (11.9%) samples were found to be positive for extra mutations by the CRISPR-based assay. Furthermore, neither virus titers nor the sequences adjacent to the signature mutations were associated with the variation of fluorescence intensity detected or the false-positive reaction observed when testing clinical samples. In addition, there was no cross-reaction observed when detecting 33 SARS-CoV-2 negative clinical samples infected with common respiratory pathogens. Conclusions: The CRISPR-Cas12a-based genotyping assay is highly sensitive and specific when detecting both the SARS-CoV-2 wild-type strain and major VOCs. It is a simple and rapid assay that can monitor and track the circulating SARS-CoV-2 variants and the dynamics of the coronavirus disease 2019 (COVID-19) pandemic and can be easily implemented in resource-limited settings.