首页|Semi-nested RT-PCR enables sensitive and high-throughput detection of SARS-CoV-2 based on melting analysis

Semi-nested RT-PCR enables sensitive and high-throughput detection of SARS-CoV-2 based on melting analysis

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  • NSTL
  • Elsevier
? 2022 Elsevier B.V.Background: Asymptomatic transmission was found to be the Achilles’ heel of the symptom-based screening strategy, necessitating the implementation of mass testing to efficiently contain the transmission of COVID-19 pandemic. However, the global shortage of molecular reagents and the low throughput of available realtime PCR facilities were major limiting factors. Methods: A novel semi-nested and heptaplex (7-plex) RT-PCR assay with melting analysis for detection of SARS-CoV-2 RNA has been established for either individual testing or 96-sample pooled testing. The complex melting spectrum collected from the heptaplex RT-PCR amplicons was interpreted with the support of an artificial intelligence algorithm for the detection of SARS-CoV-2 RNA. The analytical and clinical performance of the semi-nested RT-PCR assay was evaluated using RNAs synthesized in-vitro and those isolated from nasopharyngeal samples. Results: The LOD of the assay for individual testing was estimated to be 7.2 copies/reaction. Clinical performance evaluation indicated a sensitivity of 100% (95% CI: 97.83–100) and a specificity of 99.87% (95% CI: 99.55–99.98). More importantly, the assay supports a breakthrough sample pooling method, which makes possible parallel screening of up to 96 samples in one real-time PCR well without loss of sensitivity. As a result, up to 8,820 individual pre-amplified samples could be screened for SARS-CoV-2 within each 96-well plate of realtime PCR using the pooled testing procedure. Conclusion: The novel semi-nested RT-PCR assay provides a solution for highly multiplex (7-plex) detection of SARS-CoV-2 and enables 96-sample pooled detection for increase of testing capacity.

Artificial intelligentCOVID-19High-throughput PCRMelting analysisPoolingSemi-nested

Thi Nguyen N.A.、Thi Bui H.、Thi-Huong Pham Q.、Thi Thao Hoang L.、Xuan Ta H.、Heikkinen T.、Van Le D.、Dinh Van T.、Quoc Ngo N.、Thi Hong Huynh P.、Thi Huyen Tran T.、Quoc Phan H.、Van Hoang L.、van Doorn H.R.、Thi Ngoc Nguyen D.、Thi Nguyen T.、Sy Vo N.、Viet Vo C.、Khac Trinh S.、The Pham T.、Duc Le Q.、Van Le P.、Thai Nguyen S.、Thi Tran L.、Vu Nguyen Q.A.、Thi Trieu N.、Thi Le T.、Dinh Nguyen U.、Steman J.、Huu Ho T.、Dinh Vu T.

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Department of Genomics & Cytogenetics Institute of Biomedicine and Pharmacy (IBP) Vietnam Military

Top Data Science Ltd

National Hospital of Tropical Diseases

Phusa Biochem Biochemistry Ltd

Department of Molecular Biology

Institute of Biomedicine and Pharmacy (IBP) Vietnam Military Medical University

Oxford University Clinical Research Unit

Department of Computational Biomedicine Vingroup Big Data Institute

Vietnamese- Russian Tropical Center

Department of Systems Engineering and Computer Network National University of Civil Engineering

College of Veterinary Medicine Vietnam National University of Agriculture

103 Military Hospital Vietnam Military Medical University

Department of Women's and Children's Health Karolinska Institutet

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2022

10.1016/j.cca.2022.04.997

Clinica chimica acta

Clinica chimica acta

ISTP
ISSN：0009-8981
年,卷(期)：2022.531
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