ABSTRACT
Distributed "Point-of-Care" or "at-Home" testing is an important component for a complete suite of testing solutions. This manuscript describes the construction and operation of a platform technology designed to meet this need. The ongoing COVID-19 pandemic will be used as the proof-of-concept for the efficacy and deployment of this platform. The technology outlined consists of a one-pot, reverse-transcription loop-mediated isothermal amplification (RT-LAMP) chemistry coupled with a low-cost and user-assembled reader using saliva as input. This platform is readily adapted to a wide range of pathogens due to the genetic basis of the reaction. A complete guide to the construction of the reader as well as the production of the reaction chemistry are provided here. Additionally, analytical limit of detection data and the results from saliva testing of SARS-CoV-2, are presented. The platform technology outlined here demonstrates a rapid, distributed, molecular point-of-care solution for pathogen detection using crude sample input.
ABSTRACT
Rapid, reliable, and widespread testing is required to curtail the ongoing COVID-19 pandemic. Current gold standard nucleic acid tests are hampered by supply shortages in critical reagents including nasal swabs, RNA extraction kits, personal protective equipment (PPE), instrumentation, and labor. Here we present an approach to overcome these challenges with the development of a rapid colorimetric assay using reverse-transcription loop-mediated isothermal amplification (RT-LAMP) optimized on human saliva samples without an RNA purification step. We describe our optimizations of the LAMP reaction and saliva pretreatment protocols that enabled rapid and sensitive detection of < 102 viral genomes per reaction in contrived saliva controls. Moreover, our saliva pretreatment protocol enabled sensitive viral detection by conventional quantitative reverse transcription polymerase chain reaction (qRT-PCR) without RNA extraction. We validated the high performance of these assays on clinical samples and demonstrate a promising approach to overcome the current bottlenecks limiting widespread testing.
