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RT-RPA-Cas12a-based discrimination of SARS-CoV-2 variants of concern

Zhao, W.; Tang, G.; Zhang, Z.; Tan, W.; Fei, L.; Sun, J.; Li, Y.; Zou, S.; Yang, Y.; Cai, K.; Li, S.; Wang, Z.; Liu, J.; Mao, G.; Ma, Y.; Zhao, G.-P.; Tian, Z.-G.

2022-05-16 infectious diseases
10.1101/2022.05.11.22274884 medRxiv
Show abstract

Timely and accurate detection of SARS-CoV-2 variants of concern (VOCs) is urgently needed for pandemic surveillance and control. However, current methods are limited by the low sensitivity, long turn-around time or high cost. Here, we report a nucleic acid testing-based method aiming to detect and discriminate SARS-CoV-2 VOCs by combining RT-RPA and CRISPR-Cas12a detecting assays (RRCd). With a detection limit of 10 copies RNA/reaction, RRCd was validated in 204 clinical samples, showing 99% positive predictive agreement and 100% negative predictive agreement, respectively. Critically, using specific crRNAs, representatives of single nucleotide polymorphisms and small deletions in SARS-CoV-2 VOCs including N501Y, T478K and {Delta}H69-V70 were discriminated by RRCd, demonstrating 100% accuracy in clinical samples with Ct < 33. The method completes within 65 min and could offer visible results without using any electrical devices, which may facilitate point-of-care testing of SARS-CoV-2 and its variants.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.