Abstract
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Simple
and effective molecular diagnostic methods have gained importance
due to the devastating effects of the COVID-19 pandemic. Various isothermal
one-pot COVID-19 detection methods have been proposed as favorable
alternatives to standard RT-qPCR methods as they do not require sophisticated
and/or expensive devices. However, as one-pot reactions are highly
complex with a large number of variables, determining the optimum
conditions to maximize sensitivity while minimizing diagnostic cost
can be cumbersome. Here, statistical design of experiments (DoE) was
employed 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 with a significant effect on performance were elucidated
and optimized, facilitating the detection of two copies/μL of
full-length SARS-CoV-2 (COVID-19) genome using simple instrumentation.
The screening revealed that the addition of a reverse transcription
buffer and an RNase inhibitor, components generally omitted in one-pot
reactions, improved performance significantly, and optimization of
reverse transcription had a critical impact on the method’s
sensitivity. This strategic method was also applied in a second approach
involving a DNA sequence of the N gene from the COVID-19 genome. The
slight differences in optimal conditions for the methods using RNA
and DNA templates highlight the importance of reaction-specific optimization
in ensuring robust and efficient diagnostic performance. The proposed
detection method is automation-compatible, rendering it suitable for
high-throughput testing. This study demonstrated the benefits of DoE
for the optimization of complex one-pot molecular diagnostics methods
to increase detection sensitivity.
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