Performance & Quality Evaluation of Marketed COVID-19 RNA Detection Kits

  1. David Surace Kapitula
  2. Zhuopu Jiang
  3. Jianhao Jiang
  4. Jing Zhu
  5. Xiangyong Chen
  6. Claudia Qiao Lin

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Abstract

Compared to other coronaviruses, COVID-19 has a longer incubation period and features asymptomatic infection at a high rate (>25%) 1,2 . Therefore, early detection of infection is the key to early isolation and treatment. Direct detection of the virus itself has advantages over indirect detection. Currently, the most sensitive and commercially validated method for COVID-19 testing is RT-qPCR, designed to detect amplified virus-specific RNA. Reliable testing has proven to be a bottleneck in early diagnosis of virus infection in all countries dealing with the pandemic. Significant performance and quality issues with available testing kits have caused confusion and serious health risks. In order to provide better understanding of the Quality and performance of COVID-19 RNA detection kits on the market, we designed a system to evaluate the specificity (quantitation), sensitivity (LOD) and robustness of the kits using positive RNA and pseudovirus controls based on COVID-19 genomic sequence 3,4 . We evaluated 8 Nucleic Acid qPCR Kits approved in China, some of which are also approved in the US and EU. Our study showed that half of these 8 kits lack 1:1 linear relationship for virus RNA copy: qPCR signal. Of the 4 with linear response, 2 demonstrated sensitivity at 1 Copy viral RNA/Reaction, suitable for early detection of virus infection. Furthermore, we established the best RNA extraction, handling and qPCR procedures allowing highly sensitive and consistent performance using BGI qPCR kits. Our study provides an effective method to assess and compare performance quality of all COVID-19 nucleic acid testing kits, globally .

Significance Statement

Testing for COVID-19 has been a critical topic in the pandemic management since the first outbreak reported in China, and now globally. Despite of focused efforts from global biomedical industries and regulatory authorities, testing tools currently available on the market are not satisfying the huge and most important needs for virus control, which is specific, sensitive, affordable, and commercially viable early diagnosis of infected populations. We have designed an experimental system to assess and compare all nucleic acid-based COVID-19 testing kits from quality control perspectives. The results reported here demonstrate the suitability of using our system as an objective QC system for all commercial kits, including any future kits. We also identified the best testing method using commercially available reagents.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.25.20080002: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    psPAX2 (Addgene, Cambridge, MA) and pVSV-G (Addgene, Cambridge, MA) were co-transfected by RNAi-Mate (GenePharma, Suzhou) into HEK293T cells.
    HEK293T
    suggested: NCBI_Iran Cat# C498, RRID:CVCL_0063)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a protocol registration statement.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.04.25.20080002v1 on medRxiv