Rational design of CRISPR/Cas12a-RPA based one-pot COVID-19 detection with design of experiments

This article was originally published here

ACS Synth Biol. 2022 April 1. doi: 10.1021/acssynbio.1c00617. Online ahead of print.

ABSTRACT

Simple and effective molecular diagnostic methods have gained prominence due to the devastating effects of the COVID-19 pandemic. Various one-pot isothermal COVID-19 detection methods have been proposed as favorable alternatives to standard RT-qPCR methods because they do not require sophisticated and/or expensive devices. However, since one-pot reactions are very complex with a large number of variables, determining the optimal conditions to maximize sensitivity while minimizing diagnostic costs can be cumbersome. Here, statistical design of experiments (DoE) was used to accelerate the development and optimization of a CRISPR/Cas12a-RPA-based one-pot detection method for the first time. Using a definitive screening design, factors having a significant effect on performance were elucidated and optimized, facilitating the detection of two copies/μL of the full genome of SARS-CoV-2 (COVID-19) at using simple instrumentation. Screening revealed that adding reverse transcription buffer and RNase inhibitor, components typically omitted in one-pot reactions, significantly improved performance and optimizing reverse transcription had a critical impact on the sensitivity of the method. This strategic method was also applied in a second approach involving a DNA sequence from the N gene of the COVID-19 genome. The slight differences in optimal conditions for methods using RNA and DNA templates underscore the importance of reaction-specific optimization to ensure robust and efficient diagnostic performance. The proposed detection method is compatible with automation, which makes it suitable for high-throughput testing. This study demonstrated the benefits of DoE for optimizing complex one-pot molecular diagnostic methods to increase detection sensitivity.

PMID:35363475 | DOI:10.1021/acssynbio.1c00617

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