Development and Validation of a Rapid, Single-Step Reverse Transcriptase Loop-Mediated Isothermal Amplification (RT-LAMP) System Potentially to Be Used for Reliable and High-Throughput Screening of COVID-19
covid-19, SARS-CoV-2, qRT-PCR, RT-LAMP, Diagnostic test
Objectives: Development and validation of a single-step and accurate reverse transcriptase loop-mediated isothermal amplification technique (RT-LAMP) for rapid identification of SARS-CoV-2 relative to commercial quantitative reverse transcriptase real-time PCR (qRT-PCR) assays to allow prompt initiation of proper medical care and containment of virus spread.Methods: Primers showing optimal in-silico features were subjected to analytical sensitivity and specificity to assess the limit of detection (LOD) and cross-reaction with closely- and distantly-related viral species, and clinically prominent bacterial and fungal species. In order to evaluate the clinical utility, our RT-LAMP was subjected to a large number of clinical samples, including 213 negative and 47 positive patients, relative to two commercial quantitative RT-PCR assays.Results: The analytical specificity and sensitivity of our assay was 100% and 500 copies/ml when serial dilution was performed in both water and sputum. Subjecting our RT-LAMP assay to clinical samples showed a high degree of specificity (99.5%), sensitivity (91.4%), positive predictive value (97.7%), and negative predictive value (98.1%) when used relative to qRT-PCR. Our RT-LAMP assay was two times faster than qRT-PCR and is storable at room temperature. A suspected case that later became positive tested positive using both our RT-LAMP and the two qRT-PCR assays, which shows the capability of our assay for screening purposes.Conclusions: We present a rapid RT-LAMP assay that could extend the capacity of laboratories to process two times more clinical samples relative to qRT-PCR and potentially could be used for high-throughput screening purposes when demand is increasing at critical situations.
Cornelia Lass-Flörl, Minghua Jiang, Weihua Pan, Amir Arasthfer, Wenjie Fang, Liyan Ling, Hua Fang, Farnaz Daneshnia, Jian Yu, Wanqing Liao, Hao Pei, Xiaojing Li