Novel automated sample-to-result SARS-CoV-2 laboratory-developed RT-PCR assay for high-throughput testing using LabTurbo AIO 48 system.


Journal

Clinica chimica acta; international journal of clinical chemistry
ISSN: 1873-3492
Titre abrégé: Clin Chim Acta
Pays: Netherlands
ID NLM: 1302422

Informations de publication

Date de publication:
Mar 2021
Historique:
received: 31 08 2020
revised: 05 11 2020
accepted: 02 12 2020
pubmed: 15 12 2020
medline: 29 1 2021
entrez: 14 12 2020
Statut: ppublish

Résumé

Immediate detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical for preventing the spread of coronavirus disease 2019 (COVID-19). The LabTurbo AIO 48 system is an automated platform that allows nucleic acid extraction and sample analysis on the same instrument, producing faster results without affecting their accuracy. We aimed to independently evaluate the LabTurbo AIO 48 (all-in-one system) for SARS-CoV-2 detection. Comparative limit of detection (LOD) was assessed on both the LabTurbo AIO 48 and current standard detection system based on real-time reverse transcriptase polymerase chain reaction (RT-PCR), using SARS-CoV-2 RNA control. Additional 125 primary clinical samples were assessed using both the protocols in parallel. The turnaround time from sample to results for 48 samples analyzed on LabTurbo AIO 48 was approximately 2.5 h, whereas that analyzed using the in-house RT-PCR protocol was 4.8 h. LabTurbo AIO 48 also demonstrated higher sensitivity than our reference RT-PCR assay, with a LOD of 9.4 copies/reaction. The overall percentage agreement between both the methods for 125 samples was 100%. LabTurbo AIO 48 is a robust detection option for SARS-CoV-2, allowing faster results and, consequently, aiding in better control and prevention of COVID-19.

Sections du résumé

BACKGROUND AND AIMS OBJECTIVE
Immediate detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical for preventing the spread of coronavirus disease 2019 (COVID-19). The LabTurbo AIO 48 system is an automated platform that allows nucleic acid extraction and sample analysis on the same instrument, producing faster results without affecting their accuracy. We aimed to independently evaluate the LabTurbo AIO 48 (all-in-one system) for SARS-CoV-2 detection.
MATERIALS AND METHODS METHODS
Comparative limit of detection (LOD) was assessed on both the LabTurbo AIO 48 and current standard detection system based on real-time reverse transcriptase polymerase chain reaction (RT-PCR), using SARS-CoV-2 RNA control. Additional 125 primary clinical samples were assessed using both the protocols in parallel.
RESULTS RESULTS
The turnaround time from sample to results for 48 samples analyzed on LabTurbo AIO 48 was approximately 2.5 h, whereas that analyzed using the in-house RT-PCR protocol was 4.8 h. LabTurbo AIO 48 also demonstrated higher sensitivity than our reference RT-PCR assay, with a LOD of 9.4 copies/reaction. The overall percentage agreement between both the methods for 125 samples was 100%.
CONCLUSION CONCLUSIONS
LabTurbo AIO 48 is a robust detection option for SARS-CoV-2, allowing faster results and, consequently, aiding in better control and prevention of COVID-19.

Identifiants

pubmed: 33316217
pii: S0009-8981(20)30564-7
doi: 10.1016/j.cca.2020.12.003
pmc: PMC7836538
pii:
doi:

Substances chimiques

RNA, Viral 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

54-58

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

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Auteurs

Ming-Jr Jian (MJ)

Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan, Republic of China.

Hsing-Yi Chung (HY)

Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan, Republic of China.

Chih-Kai Chang (CK)

Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan, Republic of China.

Jung-Chung Lin (JC)

Division of Infectious Diseases and Tropical Medicine, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan, Republic of China.

Kuo-Ming Yeh (KM)

Division of Infectious Diseases and Tropical Medicine, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan, Republic of China.

Sheng-Kang Chiu (SK)

Division of Infectious Diseases and Tropical Medicine, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan, Republic of China.

Yi-Hui Wang (YH)

Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan, Republic of China.

Shu-Jung Liao (SJ)

Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan, Republic of China.

Shih-Yi Li (SY)

Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan, Republic of China.

Shan-Shan Hsieh (SS)

Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan, Republic of China.

Cherng-Lih Perng (CL)

Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan, Republic of China.

Feng-Yee Chang (FY)

Division of Infectious Diseases and Tropical Medicine, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan, Republic of China. Electronic address: fychang@ndmctsgh.edu.tw.

Hung-Sheng Shang (HS)

Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan, Republic of China. Electronic address: iamkeith001@gmail.com.

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Classifications MeSH